maximo <- function(a, b)
{
if (a > b)
{
max_ <- a
}
else
{
max_ <- b
}
max_
}
maximo(4, 2)[1] 4y <- 15
maximo(17, y + 5)[1] 202 Manipulación de dataframe
2.1 Funciones, Módulos y Librerías principales en R
2.2 Funciones en R
Una función permite escribir un fragmento de código parametrizado. De esta forma, es posible escribir un bloque de código y ejecutarlo para distintos datos. Una función puede considerarse un subprograma que resuelve una subtarea. Un motivo para utilizar funciones es que permiten estructurar u organizar el código de un programa. Cuando hay que resolver un problema complejo, en lugar de intentar solucionarlo mediante un programa muy extenso es mejor descomponerlo en subproblemas. Los subproblemas deben tener una complejidad moderada, de forma que sean resueltos por subprogramas (como las funciones) sencillos. Así, en lugar de utilizar un programa muy grande para resolver un problema complejo se emplean distintos subprogramas que resuelven tareas sencillas y que se combinan para producir una solución final más simple.
2.2.1 Creación de funciones en R
Vamos a empezar a estudiar las funciones creando una función que calcula el máximo de sus dos argumentos:
Para crear una función se usa la palabra reservada function. A continuación se enumeran los parámetros formales de la función separados por comas y encerrados entre paréntesis (en el ejemplo los parámetros formales son a y b). Después viene el código de la función. Puesto que el código del ejemplo tiene más de una instrucción, hay que encerrarlo entre llaves. Con esto se crea un objeto función que se asigna a la variable maximo.
Una vez creado el objeto función y haber sido asignado a maximo, en el ejemplo aparecen dos llamadas a la función. En la primera se usan los parámetros reales 4 y 2, y en la segunda 17 y la expresión y + 5. Al colocar una función se evalúan las expresiones que conforman los parámetros reales y se emparejan con los parámetros formales, es decir, a cada parámetro formal se le asigna el resultado de evaluar la expresión usada como parámetro real. Los parámetros formales son variables que reciben la información de entrada de la función (en el ejemplo, a y b). Una vez emparejados parámetros reales con formales se ejecutan las instrucciones de la función. Una función devuelve como salida el resultado de evaluar la última instrucción que ejecuta. En el ejemplo, la última instrucción ejecutada es m, que es una expresión que produce el máximo de los dos valores de entrada de la función (almacenados en a y b).
2.2.2 La función return
Una función devuelve el resultado de evaluar la última instrucción que ejecuta. Esto es cierto si el código de la función no invoca a la función return. La función return tiene un parámetro y produce el siguiente efecto: termina la ejecución de la función en la que se ejecuta y el valor devuelto por la función es el parámetro con que es invocada return. Por ejemplo, la función maximo previa puede codificarse así:
maximo <- function(a, b)
{
if (a > b)
{
max_ <- a
}
else
{
max_ <- b
}
return(max_)
}
maximo(10, 9)[1] 10Otra forma es la siguiente:
maximo <- function(a, b)
{
if (a > b)
{
return(a)
}
else
{
return(b)
}
}
maximo(1, 17)[1] 17Incluso se puede hacer de la siguiente forma:
maximo <- function(a, b)
{
if (a > b)
{
return(a)
}
return(b)
}
maximo(12, 18)[1] 182.3 Módulos en R
2.3.1 Operadores aritméticos
Los operadores aritméticos son necesarios para realizar operaciones aritméticas. La siguiente tabla es la lista de operadores aritméticos en R:
| Nombre del operador | Simbolo del operador | Descripción | Ejemplo |
|---|---|---|---|
| suma | ‘+’ | Sirve para sumar dos valores. | a+b |
| resta | ‘-’ | Sirve para restar un valor de otro. | a-b |
| multiplicación | ‘*’ | Sirve para multiplicar dos valores. | a*b |
| división | ‘/’ | Sirve para dividir un valor entre otro. | a/b |
| división entera | ‘%/%’ | Sirve para realizar una división entera, que devuelve el cociente de la división sin el residuo. | a%/%b |
| módulo | ‘%%’ | Siirve para obtener el residuo de una división. | a%%b |
| potenciación | ‘^ o **’ | Sirve para elevar un número a una potencia. | a^b o a**b |
| raíz cuadrada | ‘sqrt()’ | Sirve para calcular la raíz cuadrada de un número. | sqrt(a) |
| valor absoluto | ‘abs()’ | Sirve para obtener el valor absoluto de un número. | abs(b) |
| redondeo | ‘round()’ | Sirve para redondear un número al entero más cercano. | round(a) |
2.3.1.1 Ejemplos
a<-4
b<-2# Suma
a+b[1] 6# Resta
a-b[1] 2# División
a/b[1] 2# Redondeo
round(b)[1] 2# Valor absoluto
abs(a)[1] 42.3.2 Operadores relacionales
Los operadores relacionales se utilizan para comprobar la relación entre operandos y comparar los valores. Según la condición, estos operadores devuelven como resultado “Verdadero” o “Falso”:
| Nombre del operador | Simbolo del operador | Descripción | Ejemplo |
|---|---|---|---|
| igualdad | ‘==’ | Comprueba si dos valores son iguales. | a==b |
| desigualdad | ‘!= ó <>’ | Comprueba si dos valores no son iguales. | a!=b |
| mayor que | ‘>’ | Comprueba si un valor es mayor que otro. | a>b |
| menor que | ‘<’ | Comprueba si un valor es menor que otro. | a<b |
| mayor o igual que | ‘>=’ | Comprueba si un valor es mayor o igual que otro. | a>=b |
| menor o igual que | ‘<=’ | Comprueba si un valor es menor o igual que otro. | a<=b |
2.3.2.1 Ejemplos
a<-2
b<-4# Igualdad
a==b[1] FALSE# Mayor que
a>b[1] FALSE# Menor o igual que
a<=b[1] TRUE2.3.3 Operadores de asignación
Para asignar el valor a una variable, utilizamos operadores de asignación. La siguiente es una lista de operadores de asignación en R:
| Nombre del operador | Simbolo del operador | Descripción | Ejemplo |
|---|---|---|---|
| asignación normal | ‘<- ó =’ | Estos operadores se utilizan para asignar un valor a una variable. | a<-1 b<-2 |
| asignación múltiple | ‘->’ | Este operador se utiliza para asignar un valor a una variable de derecha a izquierda. | 3->c |
| asignación en lugar | ‘<<-’ | Este operador se utiliza para asignar un valor a una variable que no está en el entorno actual, sino en un entorno superior (global). | d<<-4 |
2.3.3.1 Ejemplos
# Asignación normal
a<-1
b<-2# Asignación múltiple
3->c# Asignación en lugar
d<<-42.3.4 Operadores lógicos
En R, los operadores lógicos se utilizan para realizar operaciones de lógica booleana en expresiones que devuelven valores TRUE (verdadero) o FALSE (falso).
La siguiente tabla tiene la información completa sobre los operadores lógicos en R.
| Nombre del operador | Simbolo del operador | Descripción |
|---|---|---|
| AND (y) lógico | ‘| ó ||’ | Da como resultado ‘TRUE’ si ambos operandos son ‘VERDADEROS’ y ‘FALSE’ en caso contrario. |
| OR (o) lógico | ‘| ó ||’ | Da como resultado ‘TRUE’ si alguno de los operandos es ‘VERDADERO’ y ‘FALSE’ en caso contrario. |
| NOT (No) lógico | ‘!’ | Da como resultado ‘TRUE’ si un operando es ‘FALSO’ y ‘FALSE’ si un operando es ‘VERDADERO’. |
2.3.4.1 Ejemplos
a<-TRUE
b<-FALSE# AND (y) lógico
a&b[1] FALSE# OR (o) lógico
a|b[1] TRUE# NOT (No) lógico
!a[1] FALSE2.4 Librerias principales en R
Las librerías de programación hacen referencia a los conjuntos de archivos de código que han sido creados para desarrollar software de manera sencilla. Gracias a ellas, los desarrolladores pueden evitar la duplicidad de código y minimizar errores con mayor agilidad y menor coste.
2.4.1 ggplot2
El paquete ggplot2 permite personalizar los gráficos con temas. Es posible personalizar cada detalle de un gráfico, como los colores, tipos de líneas, fuentes o alineación, entre otros, usando los componentes de la función theme. Además, la librería proporciona otras funciones que permiten añadir títulos, subtítulos, líneas, flechas o textos.
2.4.1.1 Instalación
# Instalación: install.packages("ggplot2")
library(ggplot2)Warning: package 'ggplot2' was built under R version 4.3.22.4.2 dplyr
El paquete dplyr fue desarrollado por Hadley Wickham de RStudio y es un versión optimizada de su paquete plyr. El paquete dplyr no proporciona ninguna nueva funcionalidad a R per se, en el sentido que todo aquello que podemos hacer con dplyr lo podríamos hacer con la sintaxis básica de R.
Una importante contribución del paquete dplyr es que proporciona una “gramática” (particularmente verbos) para la manipulación y operaciones con data frames. Con esta gramática podemos comunicar mediante nuestro código que es lo que estamos haciendo en los data frames a otras personas (asumiendo que conozcan la gramática). Esto es muy útil, ya que proporciona una abstracción que anteriormente no existía.
2.4.2.1 Instalación
# Instalación: install.packages("dplyr")
library(dplyr)Warning: package 'dplyr' was built under R version 4.3.2
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union2.4.3 tidyr
Tidyr es un paquete de R que permite ordenar datos “sucios” para obtener objetos de datos en R (tbl_df, muy parecidos a data.frame) en formato ordenado (tidy). La idea de los datos ordenados es que queden organizados del siguiente modo (Wickham, 2014):
Cada variable está en una columna
Cada observación está en una fila
2.4.3.1 Instalación
# Instalación: install.packages("tidyr")
library(tidyr)Warning: package 'tidyr' was built under R version 4.3.22.4.4 readr
readr es una componente del denominado tidyverso (tidyverse en inglés, que parece que siempre suena mejor), un conjunto de librerías que todo usuario de R debería si no dominar, al menos conocer, para así resolver ciertas situaciones de la manera más sencilla posible (basta imaginar tener que hacer a mano algunas de las tareas que paquetes como dplyr o tidyr llevan a cabo para darse cuenta de este hecho).
2.4.4.1 Instalación
# Instalación: install.packages("readr")
library(readr)Warning: package 'readr' was built under R version 4.3.22.4.5 tidyverse
Tidyverse es una colección de paquetes disponibles en R y orientados a la manipulación, importación, exploración y visualización de datos y que se utiliza exhaustivamente en ciencia de datos. El uso de Tidyverse permite facilitar el trabajo estadístico y la generación de trabajos reproducibles.
2.4.5.1 Instalación
# Instalación: install.packages("tidyverse")
library(tidyverse)Warning: package 'tidyverse' was built under R version 4.3.2Warning: package 'tibble' was built under R version 4.3.1Warning: package 'purrr' was built under R version 4.3.2Warning: package 'stringr' was built under R version 4.3.2Warning: package 'forcats' was built under R version 4.3.2Warning: package 'lubridate' was built under R version 4.3.2── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ lubridate 1.9.3 ✔ tibble 3.2.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors2.5 Dataframe, Manipulación e Introducción al análisis de datos
2.6 Dataframe en R
Los data frames (marcos de datos) son el objeto más habitual para almacenar datos en R. En este tipo de objeto, cada individuo o fecha corresponde a una fila y cada columna corresponde a una variable. Dentro de este tipo de estructura puedes almacenar diferentes tipos de datos.
2.6.1 ¿Data frame o matriz?
Una cuestión habitual es preguntarse en qué casos debes usar un data frame o una matriz en R. Los data frames son estructuras de datos muy similares a las matrices, pero en el caso de los data frames puedes tener diferentes tipos de datos dentro de las columnas. En consecuencia, la diferencia es que las matrices almacenan tipos de datos homogéneos mientras que los data frames almacenan tipos de datos heterogéneos. Como ejemplo, supón que tienes los siguientes datos:
Pokemons <- c("pikachu", "Sylveon", "Cleffa")
Comida <- c("Pokélito", "Pokéhaba", "Pokécubo")
Generación <- c(1, 6, 2)Se puede almacenar esas variables como matriz utilizando la función cbind:
x <- cbind(Pokemons, Comida, Generación)Ahora imprimendo la nueva variable se obtendrá el siguiente resultado:
x Pokemons Comida Generación
[1,] "pikachu" "Pokélito" "1"
[2,] "Sylveon" "Pokéhaba" "6"
[3,] "Cleffa" "Pokécubo" "2" Sin embargo, puedes notar que el resultado no es satisfactorio, ya que todas las variables se han transformado a la clase caracter. En cambio, si usas la función data.frame, mantendrás el tipo original de las variables.
2.6.2 ¿Cómo crear un data frame en R?
En R es muy sencillo crear un nuevo data frame. Se Puede unir las variables haciendo uso de la función data.frame para convertir los datos a la estructura de datos de tipo data frame. Primero, se necesita tener algunas variables almacenadas para crear el marco de datos en R. En este ejemplo vamos a definir algunas variables de número de alumnos en tres aulas durante un año. Tener en cuenta que los vectores tienen la misma longitud.
A_4 <- c(20, 23, 30, 19, 25, 27,
31, 22, 21, 36, 20, 28)
B_1 <- c(17, 20, 18, 19, 22, 23,
22, 24, 17, 19, 25, 29)
C_3 <- c(30, 28, 25, 31, 21, 30,
25, 26, 32, 20, 36, 33)
mes <- c("enero", "febrero", "marzo", "abril", "mayo", "junio",
"julio", "agosto", "septiembre", "octubre", "noviembre", "diciembre")Para unir los datos puedes usar la función data.frame. En este ejemplo, vamos a almacenar el data frame en una variable llamada data1:
data1 <- data.frame(mes = mes, Aula4toA = A_4, Aula3roB = B_1, Aula3roC = C_3)
names(data1) # Nombres de las variables (columnas)[1] "mes" "Aula4toA" "Aula3roB" "Aula3roC"Primero, es habitual mostrar los primeros valores para hacer algunas comprobaciones. Para ello, puedes hacer uso de la función head en R, que por defecto mostrará las 6 primeras filas del data frame.
# Primeras filas de nuestro conjunto de datos
head(data1) mes Aula4toA Aula3roB Aula3roC
1 enero 20 17 30
2 febrero 23 20 28
3 marzo 30 18 25
4 abril 19 19 31
5 mayo 25 22 21
6 junio 27 23 30En segundo lugar, puedes utilizar la función summary que devolverá un resumen estadístico de las variables (columnas) del conjunto de datos.
summary(data1) mes Aula4toA Aula3roB Aula3roC
Length:12 Min. :19.00 Min. :17.00 Min. :20.00
Class :character 1st Qu.:20.75 1st Qu.:18.75 1st Qu.:25.00
Mode :character Median :24.00 Median :21.00 Median :29.00
Mean :25.17 Mean :21.25 Mean :28.08
3rd Qu.:28.50 3rd Qu.:23.25 3rd Qu.:31.25
Max. :36.00 Max. :29.00 Max. :36.00 2.7 Manipulación e Introducción al análisis de datos
2.8 Manipulación de Datos
2.8.1 Operaciones con variables
2.8.1.1 Creación (y eliminación) de variables
Consideremos la base de datos cars incluida en el paquete datasets:
data(cars)
head(cars) speed dist
1 4 2
2 4 10
3 7 4
4 7 22
5 8 16
6 9 10Utilizando el comando help(cars) se obtiene que cars es un data.frame con 50 observaciones y dos variables: • speed: Velocidad (millas por hora) • dist: tiempo hasta detenerse (pies)
Recordemos que, para acceder a la variable speed se puede hacer directamente con su nombre o bien utilizando notación “matricial”.
cars$speed [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
[26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25o de la siguiente forma:
cars[, 1] # Equivalente [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
[26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25Supongamos ahora que queremos transformar la variable original speed (millas por hora) en una nueva variable velocidad (kilómetros por hora) y añadir esta nueva variable al data.frame cars. La transformación que permite pasar millas a kilómetros es kilómetros=millas/0.62137 que en R se hace directamente con:
cars$speed/0.62137 [1] 6.437388 6.437388 11.265430 11.265430 12.874777 14.484124 16.093471
[8] 16.093471 16.093471 17.702818 17.702818 19.312165 19.312165 19.312165
[15] 19.312165 20.921512 20.921512 20.921512 20.921512 22.530859 22.530859
[22] 22.530859 22.530859 24.140206 24.140206 24.140206 25.749553 25.749553
[29] 27.358900 27.358900 27.358900 28.968248 28.968248 28.968248 28.968248
[36] 30.577595 30.577595 30.577595 32.186942 32.186942 32.186942 32.186942
[43] 32.186942 35.405636 37.014983 38.624330 38.624330 38.624330 38.624330
[50] 40.233677Finalmente, incluimos la nueva variable que llamaremos velocidad en cars:
cars$velocidad <- cars$speed / 0.62137
head(cars) speed dist velocidad
1 4 2 6.437388
2 4 10 6.437388
3 7 4 11.265430
4 7 22 11.265430
5 8 16 12.874777
6 9 10 14.484124También transformaremos la variable dist (en pies) en una nueva variable distancia (en metros). Ahora la transformación deseada es metros=pies/3.2808:
cars$distancia <- cars$dis / 3.2808
head(cars) speed dist velocidad distancia
1 4 2 6.437388 0.6096074
2 4 10 6.437388 3.0480371
3 7 4 11.265430 1.2192148
4 7 22 11.265430 6.7056815
5 8 16 12.874777 4.8768593
6 9 10 14.484124 3.0480371Ahora, eliminaremos las variables originales speed y dist, y guardaremos el data.frame resultante con el nombre coches. En primer lugar, veamos varias formas de acceder a las variables de interés:
cars[, c(3, 4)] velocidad distancia
1 6.437388 0.6096074
2 6.437388 3.0480371
3 11.265430 1.2192148
4 11.265430 6.7056815
5 12.874777 4.8768593
6 14.484124 3.0480371
7 16.093471 5.4864667
8 16.093471 7.9248964
9 16.093471 10.3633260
10 17.702818 5.1816630
11 17.702818 8.5345038
12 19.312165 4.2672519
13 19.312165 6.0960741
14 19.312165 7.3152890
15 19.312165 8.5345038
16 20.921512 7.9248964
17 20.921512 10.3633260
18 20.921512 10.3633260
19 20.921512 14.0209705
20 22.530859 7.9248964
21 22.530859 10.9729334
22 22.530859 18.2882224
23 22.530859 24.3842965
24 24.140206 6.0960741
25 24.140206 7.9248964
26 24.140206 16.4594001
27 25.749553 9.7537186
28 25.749553 12.1921483
29 27.358900 9.7537186
30 27.358900 12.1921483
31 27.358900 15.2401853
32 28.968248 12.8017557
33 28.968248 17.0690076
34 28.968248 23.1650817
35 28.968248 25.6035113
36 30.577595 10.9729334
37 30.577595 14.0209705
38 30.577595 20.7266520
39 32.186942 9.7537186
40 32.186942 14.6305779
41 32.186942 15.8497927
42 32.186942 17.0690076
43 32.186942 19.5074372
44 35.405636 20.1170446
45 37.014983 16.4594001
46 38.624330 21.3362594
47 38.624330 28.0419410
48 38.624330 28.3467447
49 38.624330 36.5764448
50 40.233677 25.9083150cars[, c("velocidad", "distancia")] velocidad distancia
1 6.437388 0.6096074
2 6.437388 3.0480371
3 11.265430 1.2192148
4 11.265430 6.7056815
5 12.874777 4.8768593
6 14.484124 3.0480371
7 16.093471 5.4864667
8 16.093471 7.9248964
9 16.093471 10.3633260
10 17.702818 5.1816630
11 17.702818 8.5345038
12 19.312165 4.2672519
13 19.312165 6.0960741
14 19.312165 7.3152890
15 19.312165 8.5345038
16 20.921512 7.9248964
17 20.921512 10.3633260
18 20.921512 10.3633260
19 20.921512 14.0209705
20 22.530859 7.9248964
21 22.530859 10.9729334
22 22.530859 18.2882224
23 22.530859 24.3842965
24 24.140206 6.0960741
25 24.140206 7.9248964
26 24.140206 16.4594001
27 25.749553 9.7537186
28 25.749553 12.1921483
29 27.358900 9.7537186
30 27.358900 12.1921483
31 27.358900 15.2401853
32 28.968248 12.8017557
33 28.968248 17.0690076
34 28.968248 23.1650817
35 28.968248 25.6035113
36 30.577595 10.9729334
37 30.577595 14.0209705
38 30.577595 20.7266520
39 32.186942 9.7537186
40 32.186942 14.6305779
41 32.186942 15.8497927
42 32.186942 17.0690076
43 32.186942 19.5074372
44 35.405636 20.1170446
45 37.014983 16.4594001
46 38.624330 21.3362594
47 38.624330 28.0419410
48 38.624330 28.3467447
49 38.624330 36.5764448
50 40.233677 25.9083150cars[, -c(1, 2)] velocidad distancia
1 6.437388 0.6096074
2 6.437388 3.0480371
3 11.265430 1.2192148
4 11.265430 6.7056815
5 12.874777 4.8768593
6 14.484124 3.0480371
7 16.093471 5.4864667
8 16.093471 7.9248964
9 16.093471 10.3633260
10 17.702818 5.1816630
11 17.702818 8.5345038
12 19.312165 4.2672519
13 19.312165 6.0960741
14 19.312165 7.3152890
15 19.312165 8.5345038
16 20.921512 7.9248964
17 20.921512 10.3633260
18 20.921512 10.3633260
19 20.921512 14.0209705
20 22.530859 7.9248964
21 22.530859 10.9729334
22 22.530859 18.2882224
23 22.530859 24.3842965
24 24.140206 6.0960741
25 24.140206 7.9248964
26 24.140206 16.4594001
27 25.749553 9.7537186
28 25.749553 12.1921483
29 27.358900 9.7537186
30 27.358900 12.1921483
31 27.358900 15.2401853
32 28.968248 12.8017557
33 28.968248 17.0690076
34 28.968248 23.1650817
35 28.968248 25.6035113
36 30.577595 10.9729334
37 30.577595 14.0209705
38 30.577595 20.7266520
39 32.186942 9.7537186
40 32.186942 14.6305779
41 32.186942 15.8497927
42 32.186942 17.0690076
43 32.186942 19.5074372
44 35.405636 20.1170446
45 37.014983 16.4594001
46 38.624330 21.3362594
47 38.624330 28.0419410
48 38.624330 28.3467447
49 38.624330 36.5764448
50 40.233677 25.9083150Utilizando alguna de las opciones anteriores se obtiene el data.frame deseado:
coches <- cars[, c("velocidad", "distancia")]
# head(coches)
str(coches)'data.frame': 50 obs. of 2 variables:
$ velocidad: num 6.44 6.44 11.27 11.27 12.87 ...
$ distancia: num 0.61 3.05 1.22 6.71 4.88 ...Finalmente los datos anteriores podrían ser guardados en un fichero exportable a Excel con el siguiente comando:
write.csv2(coches, file = "coches.csv")2.8.1.2 Recodificación de variables
Con el comando cut() podemos crear variables categóricas a partir de variables numéricas. El parámetro breaks permite especificar los intervalos para la discretización, puede ser un vector con los extremos de los intervalos o un entero con el número de intervalos. Por ejemplo, para categorizar la variable cars$speed en tres intervalos equidistantes podemos emplear:
fspeed <- cut(cars$speed, 3, labels = c("Baja", "Media", "Alta"))
table(fspeed)fspeed
Baja Media Alta
11 24 15 Para categorizar esta variable en tres niveles con aproximadamente el mismo número de observaciones podríamos combinar esta función con quantile():
breaks <- quantile(cars$speed, probs = seq(0, 1, len = 4))
fspeed <- cut(cars$speed, breaks, labels = c("Baja", "Media", "Alta"))
table(fspeed)fspeed
Baja Media Alta
17 16 15 Para otro tipo de recodificaciones podríamos emplear la función ifelse() vectorial:
fspeed <- ifelse(cars$speed < 15, "Baja", "Alta")
fspeed <- factor(fspeed, levels = c("Baja", "Alta"))
table(fspeed)fspeed
Baja Alta
23 27 Alternativamente en el caso de dos niveles podríamos emplear directamente la función factor():
fspeed <- factor(cars$speed >= 15, labels = c("Baja", "Alta")) # levels = c("FALSE", "TRUE")
table(fspeed)fspeed
Baja Alta
23 27 En el caso de múltiples niveles se podría emplear ifelse() anidados o la función recode() del paquete car. Para acceder directamente a las variables de un data.frame podríamos emplear la función attach() para añadirlo a la ruta de búsqueda y detach() al finalizar. Sin embargo esta forma de proceder puede causar numerosos inconvenientes, especialmente al modificar la base de datos, por lo que la recomendación sería emplear with(). Por ejemplo:
fspeed <- with(cars, ifelse(speed < 10, "Baja",
ifelse(speed < 20, "Media", "Alta")))
fspeed <- factor(fspeed, levels = c("Baja", "Media", "Alta"))
table(fspeed)fspeed
Baja Media Alta
6 32 12 2.8.2 Operaciones con casos
2.8.2.1 Ordenación
Continuemos con el data.frame cars. Se puede comprobar que los datos disponibles están ordenados por los valores de speed. A continuación haremos la ordenación utilizando los valores de dist. Para ello utilizaremos el conocido como vector de índices de ordenación. Este vector establece el orden en que tienen que ser elegidos los elementos para obtener la ordenación deseada. Veamos un ejemplo sencillo:
x <- c(2.5, 4.3, 1.2, 3.1, 5.0) # valores originales
ii <- order(x)
ii # vector de ordenación[1] 3 1 4 2 5x[ii] # valores ordenados[1] 1.2 2.5 3.1 4.3 5.0En el caso de vectores, el procedimiento anterior se podría hacer directamente con:
sort(x)[1] 1.2 2.5 3.1 4.3 5.0Sin embargo, para ordenar data.frames será necesario la utilización del vector de índices de ordenación. A continuación, los datos de cars ordenados por dist:
ii <- order(cars$dist) # Vector de índices de ordenación
cars2 <- cars[ii, ] # Datos ordenados por dist
head(cars2) speed dist velocidad distancia
1 4 2 6.437388 0.6096074
3 7 4 11.265430 1.2192148
2 4 10 6.437388 3.0480371
6 9 10 14.484124 3.0480371
12 12 14 19.312165 4.2672519
5 8 16 12.874777 4.87685932.8.2.2 Filtrado
El filtrado de datos consiste en elegir un subconjunto que cumpla determinadas condiciones. Para ello se puede utilizar la función subset() (que además permite seleccionar variables). Para una mejor comprensión se mostrará unos ejemplos:
subset(cars, dist > 85) # datos con dis>85 speed dist velocidad distancia
47 24 92 38.62433 28.04194
48 24 93 38.62433 28.34674
49 24 120 38.62433 36.57644subset(cars, speed > 10 & speed < 15 & dist > 45) # speed en (10,15) y dist>45 speed dist velocidad distancia
19 13 46 20.92151 14.02097
22 14 60 22.53086 18.28822
23 14 80 22.53086 24.38430También se pueden hacer el filtrado empleando directamente los correspondientes vectores de índices:
ii <- cars$dist > 85
cars[ii, ] # dis>85 speed dist velocidad distancia
47 24 92 38.62433 28.04194
48 24 93 38.62433 28.34674
49 24 120 38.62433 36.57644ii <- cars$speed > 10 & cars$speed < 15 & cars$dist > 45
cars[ii, ] # speed en (10,15) y dist>45 speed dist velocidad distancia
19 13 46 20.92151 14.02097
22 14 60 22.53086 18.28822
23 14 80 22.53086 24.38430En este caso también puede ser de utilidad la función which():
it <- which(ii)
str(it) int [1:3] 19 22 23cars[it, 1:2] speed dist
19 13 46
22 14 60
23 14 80# rownames(cars[it, 1:2])
id <- which(!ii)
str(cars[id, 1:2])'data.frame': 47 obs. of 2 variables:
$ speed: num 4 4 7 7 8 9 10 10 10 11 ...
$ dist : num 2 10 4 22 16 10 18 26 34 17 ...2.9 Introducción al análisis de datos
El análisis de datos en R involucra la manipulación, visualización y modelado de datos para extraer información significativa. A continuación, se presenta una introducción básica al proceso de análisis de datos en R:
2.9.1 Entorno de trabajo
Instala y carga R y RStudio.
Aprende a instalar y cargar paquetes.
# Instalar un paquete
# install.packages("nombre_del_paquete")
# Cargar un paquete
# library(nombre_del_paquete)2.9.2 Carga de datos
Importa datos desde archivos (CSV, Excel, etc.) o bases de datos.
# Ejemplo: cargar datos desde un archivo CSV
# install.packages("readr")
library(readr)
datos <- read.csv("C:/Users/Sebastian H/Documents/Trabajo Final PLE/ozone.csv")2.9.3 Exploración Inicial de Datos
Acoplarse con la estructura y el contenido de los datos.
# Ver las primeras filas
head(datos) Month Day_of_month Day_of_week ozone_reading pressure_height Wind_speed
1 1 5 1 5.34 5760 3
2 1 6 2 5.77 5720 4
3 1 7 3 3.69 5790 6
4 1 8 4 3.89 5790 3
5 1 9 5 5.76 5700 3
6 1 12 1 6.39 5720 3
Humidity Temperature_Sandburg Temperature_ElMonte Inversion_base_height
1 51 54 45.32 1450
2 69 35 49.64 1568
3 19 45 46.40 2631
4 25 55 52.70 554
5 73 41 48.02 2083
6 44 51 54.32 111
Pressure_gradient Inversion_temperature Visibility
1 25 57.02 60
2 15 53.78 60
3 -33 54.14 100
4 -28 64.76 250
5 23 52.52 120
6 9 63.14 150# Obtener información general
summary(datos) Month Day_of_month Day_of_week ozone_reading
Min. : 1.000 Min. : 1.0 Min. :1.000 Min. : 0.72
1st Qu.: 3.000 1st Qu.: 9.0 1st Qu.:2.000 1st Qu.: 4.77
Median : 6.000 Median :15.0 Median :3.000 Median : 8.90
Mean : 6.522 Mean :15.7 Mean :3.005 Mean :11.37
3rd Qu.:10.000 3rd Qu.:23.0 3rd Qu.:4.000 3rd Qu.:16.07
Max. :12.000 Max. :31.0 Max. :5.000 Max. :37.98
pressure_height Wind_speed Humidity Temperature_Sandburg
Min. :5320 Min. : 0.000 Min. :19.00 Min. :25.00
1st Qu.:5690 1st Qu.: 3.000 1st Qu.:46.00 1st Qu.:51.50
Median :5760 Median : 5.000 Median :64.00 Median :61.00
Mean :5746 Mean : 4.867 Mean :57.61 Mean :61.11
3rd Qu.:5830 3rd Qu.: 6.000 3rd Qu.:73.00 3rd Qu.:71.00
Max. :5950 Max. :11.000 Max. :93.00 Max. :93.00
Temperature_ElMonte Inversion_base_height Pressure_gradient
Min. :27.68 Min. : 111 Min. :-69.00
1st Qu.:49.64 1st Qu.: 869 1st Qu.:-14.00
Median :56.48 Median :2083 Median : 18.00
Mean :56.54 Mean :2602 Mean : 14.43
3rd Qu.:66.20 3rd Qu.:5000 3rd Qu.: 43.00
Max. :82.58 Max. :5000 Max. :107.00
Inversion_temperature Visibility
Min. :27.50 Min. : 0.0
1st Qu.:51.26 1st Qu.: 60.0
Median :60.98 Median :100.0
Mean :60.69 Mean :122.2
3rd Qu.:70.88 3rd Qu.:150.0
Max. :90.68 Max. :350.0 # Visualizaciones iniciales
plot(datos$ozone_reading)
2.9.4 Análisis Exploratorio de Datos
Realizar análisis descriptivos y visualizaciones para entender la distribución y relaciones.
# Ejemplo de un histograma
hist(datos$ozone_reading, col = "skyblue", main = "Histograma de Variable")
2.9.5 Visualización de Datos:
Utiliza paquetes como ggplot2 para crear gráficos informativos.
# Ejemplo de un gráfico de dispersión
# install.packages("ggplot2")
library(ggplot2)
ggplot(datos, aes(x = ozone_reading, y = pressure_height)) +
geom_point() +
labs(title = "Gráfico de Dispersión")
2.10 Flujo del proceso de análisis de datos
Ahora veremos el flujo del proceso de análisis de datos en el lenguaje R y se vera de la siguiente forma:
cargar datos y recursos;
explorar y familiarizarnos con los datos y su estructura;
transformar los datos para limpiarlos y que tengan la estructura óptima para nuestros análisis;
hacer nuestros primeros gráficos;
generar nuestras propias funciones.
Trabajaremos con un dataset de asociaciones libres a la palabra “Big data”. Este es un dataset construido a partir de una breve encuesta a participantes.
2.10.1 Cargar datos y recursos
Lo primero que haremos es cargar algunas librerias (package) que van a poner a nuestra disposición un conjunto de funciones que utilizaremos a lo largo de esta sección. Recordá que una función es una secuencia de comandos que se aplican a un objeto que se le pasa a la función, referenciándolo entre sus paréntesis. Por ejemplo, utilizaremos la función library() y el nombre de la librería para habilitar la función de tidyverse para manipular y visualizar.
# install.packages("tidyverse") # la usamos para manipular datos# una vez instalada hay que cargarla en memoria
library(tidyverse) Luego importaremos los datos con la función read.csv().
asociaciones <- read.csv(file = "https://raw.githubusercontent.com/gastonbecerra/curso-intro-r/main/data/asociaciones.csv")2.10.2 Explorar los datos
El objetivo del momento de exploración de los datos es familiarizarnos con la estructura de los datos, y transformarlos para poder realizar nuestros análisis. Por lo general, aquí se empiezan a decidir las tareas de limpieza.
Lo primero que vamos a hacer es ver el tamaño de la tabla con dim(), y los primeros registros con head().
Esto nos va a permitir saber:
la cantidad de registros y columnas;
los nombres de las columnas y su tipo de dato;
el contenido de los primeros registros.
dim(asociaciones)[1] 1707 4head(asociaciones, n = 10) id palabra orden valoracion
1 -M0-9OQkabuGoSmceB5E información 1 5
2 -M0-9OQkabuGoSmceB5E análisis 2 5
3 -M0-9OQkabuGoSmceB5E investigación 3 5
4 -M0-9OQkabuGoSmceB5E comercial 4 4
5 -M0-9OQkabuGoSmceB5E filtración 5 -5
6 -M0U7_pJAU9Ehga0LIWq información 1 5
7 -M0U7_pJAU9Ehga0LIWq tecnología 2 5
8 -M0U7_pJAU9Ehga0LIWq sistemas 3 5
9 -M0U7_pJAU9Ehga0LIWq computadora 4 0
10 -M0U7_pJAU9Ehga0LIWq freaks 5 -3Luego, es útil tener una idea de los valores posibles de las distintas columnas o variables. summary nos devuelve un resumen de estadísticas descriptivas, que nos permite saber, por ejemplo, en qué rango se registran las valoraciones, o cómo se distribuyen los ordenes de evocación.
summary(asociaciones) id palabra orden valoracion
Length:1707 Length:1707 Min. : 1.00 Min. :-5.000
Class :character Class :character 1st Qu.: 2.00 1st Qu.:-1.000
Mode :character Mode :character Median : 3.00 Median : 1.000
Mean : 3.06 Mean : 1.053
3rd Qu.: 4.00 3rd Qu.: 5.000
Max. :14.00 Max. : 5.000 2.10.3 Transformar, visualizar y limpiar
Ahora vamos a ver de qué manera podemos transformar los datos para obtener respuestas a las siguientes preguntas:
¿cuáles son las palabras más frecuentes?
¿cuáles son las palabras con valoraciones más extremas?
¿cuáles son las palabras que se evocaron más rápido?
Para responder esto vamos a utilizar verbos de manipulación (de dplyr, una librería incluida en `tidyverse’) sobre nuestra tabla. Algunos de estos verbos son:
filter()para filtrar los registos/observaciones por alguna condición de sus valores;mutate()para agregar una columna/variable con el resultado de alguna operación sobre otras columnas;group_by()ysummarise()para realizar alguna operación sobre los datos de distintos registros, reduciendolos en uno sólo por grupos;n()que nos devuelve el conjunto de registros de un grupo (y que por ello se hace luego degroup_by()); algo que podemos simplificar concount();arrange()ordena los datos ascendente o descendentemente;
Antes que nada, empecemos por chusmear (glimpse) la estructura de la base para recordar las columnas/variables con las que podemos trabajar. Los nombres de las columnas aparecen precedidos con un $, una debajo de la otra, para que sea más facil de observar tablas largas. Luego, nos informa el tipo de variable y nos permite ver los primeros valores (que en entran en pantalla)
Nuestra tabla tiene las palabras que los participantes respondieron en la columna asociaciones$palabra, el orden en que dichas palabras fueron ingresadas por el participante en asociaciones$orden, y una valoración sobre esa palabra expresada en forma númerica en asociaciones$valoracion.
Tenemos todos los elementos para responder las preguntas. Sólo falta diseñar un camino de operaciones para visibilizar la respuesta:
(Paso1) vamos a tomar la tabla que nos interesa asociaciones, y la vamos a pasar, para…
(Paso2) …armar grupos de registros que compartan el valor en asociaciones$palabra, y para cada cual vamos a:
(Paso3) contar la cantidad de registros (dandonos la frecuencia con que dicha palabra fue asociada a big data);
(Paso4) y calcular la media de las valoraciones en asociaciones$valoracion,
(Paso5) así como la media del orden en que fue evocado (asociaciones$orden)
(Paso0) … el resultado de esta operación la vamos a guardar en una tabla nueva, que luego operaremos para responder nuestras 2 preguntas. En tanto esta tabla describe la información de la anterior, notaremos que tiene muchos menos registros.
Para estas operaciones vamos a usar los comandos recién vistos:
glimpse(asociaciones) # muestra la estructura de la tabla Rows: 1,707
Columns: 4
$ id <chr> "-M0-9OQkabuGoSmceB5E", "-M0-9OQkabuGoSmceB5E", "-M0-9OQkab…
$ palabra <chr> "información", "análisis", "investigación", "comercial", "f…
$ orden <int> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,…
$ valoracion <int> 5, 5, 5, 4, -5, 5, 5, 5, 0, -3, 2, 4, 4, 3, 2, 5, 4, 0, 4, …asoc_frecuentes <- asociaciones %>% # (Paso0) y (Paso1)
group_by(palabra) %>% # (Paso2)
summarize(
freq = n(), # (Paso3)
valoracion_media = mean(valoracion), # (Paso4)
orden_media = mean(orden) #(Paso5)
)
glimpse(asoc_frecuentes) Rows: 867
Columns: 4
$ palabra <chr> ".", "...", "0-1-2", "1984", "aa", "abiertos", "abund…
$ freq <int> 3, 2, 1, 1, 1, 1, 1, 1, 1, 4, 5, 1, 1, 1, 1, 1, 1, 1,…
$ valoracion_media <dbl> 0.00, 0.00, 0.00, -5.00, 0.00, 3.00, 3.00, 5.00, 0.00…
$ orden_media <dbl> 4.0, 4.5, 5.0, 2.0, 5.0, 2.0, 1.0, 2.0, 5.0, 2.5, 3.6…Más sintéticamente podríamos usar tally() o count(). Recordá que podés obtener ayuda sobre estos comando ejecutando ?tally().
Si ordenamos esta tabla ya estamos en condiciones de indicar cuáles son las palabras más/menos frecuentes. Para esto vamos a usar slice_max(), que ordena los datos y los corta en alguna posición.
asoc_frecuentes %>% # no guardamos el resultado, solo lo imprimimos
slice_max(order_by = freq, n=10) # ordenamos por freq y cortamos en 10 registros# A tibble: 10 × 4
palabra freq valoracion_media orden_media
<chr> <int> <dbl> <dbl>
1 información 102 2.58 2.08
2 datos 81 2.15 1.96
3 control 35 -2.31 2.57
4 internet 34 2.94 2.91
5 tecnología 29 2.45 2.97
6 informacion 28 2.29 2.54
7 grande 20 2.7 2
8 análisis 19 3.11 2.89
9 manipulación 18 -3.44 2.5
10 conocimiento 16 4.44 2.75La palabra más frecuente fue “información”, junto con un conjunto de otras palabras que podemos decir que remiten al manejo de los datos mediados por la tecnología, con diversos productos, como puede ser el análisis de información y la generación de conocimiento, o la manipulación y el control (las únicas palabras que tienen una valoración negativa).
Para conocer las palabras más/menos valoradas deberemos generar otros cortes.
asoc_frecuentes %>%
slice_max(order_by = valoracion_media, n=10) # las de valor mas alto# A tibble: 176 × 4
palabra freq valoracion_media orden_media
<chr> <int> <dbl> <dbl>
1 abundante 1 5 2
2 accionar 1 5 3
3 actualizaciones 1 5 3
4 algo superior 1 5 3
5 almacenamiento de datos 2 5 2
6 amigos 1 5 1
7 amor 1 5 4
8 amplitud de usos 1 5 5
9 análisis datos 1 5 2
10 análisis de grandes masas de datos 1 5 1
# ℹ 166 more rowsasoc_frecuentes %>%
slice_min(order_by = valoracion_media, n=10) # las de valor mas bajo# A tibble: 108 × 4
palabra freq valoracion_media orden_media
<chr> <int> <dbl> <dbl>
1 1984 1 -5 2
2 acción 1 -5 4
3 acoso mediático 1 -5 2
4 altgorismo 1 -5 4
5 atraso 1 -5 4
6 ausencia de intimidad 1 -5 4
7 avismo 1 -5 4
8 buenas noticias 1 -5 3
9 camdbridge analitycs 1 -5 2
10 campaña politica 1 -5 2
# ℹ 98 more rowsMiremos entonces las frecuencias. Otra vez, diseñemos un camino de operaciones:
(Paso1) vamos a tomar la tabla que nos interesa asoc_frecuentes, y la vamos a pasar, para…
(Paso2) …armar grupos de registros que compartan el valor en asociaciones$freq, y para cada cual vamos a:
(Paso3) contar la cantidad de registros (dandonos la cantidad de palabras que fueron dicha la misma cantidad de veces);
(Paso4) vamos a visualizarlo en un gráfico de puntos que cruce la frecuencia y la cantidad de palabras.
Por su parte, el diseño del gráfico va a requerir 3 componentes (mínimos): que insertemos datos (en este caso, vamos a estar pasando la tabla hecha hasta el paso 3 con un pipe, de modo que no necesitamos declararla de nuevo), que mapeemos algunos de esos datos a propiedades visuales del gráfico (como por ejemplo, un cierta columna/variable para un eje del gráfico), y que elijamos un sistema de representación o “geometry” (puntos, barras, áreas, etc.)
asoc_frecuentes %>% # Paso1
group_by(freq) %>% # Paso2
summarize(cant_palabras = n()) %>% # Paso3
ggplot( # Paso 4: usamos la librería ggplot2 y la función ggplot
aes(x=cant_palabras,y=freq) # mapeeamos los datos en los ejes x e y
) + # los componentes de ggplot se concatenan con +, ya que no son pasos secuenciales
geom_point()
Por lo visto, tenemos una distribución que sigue la ley de Zipf: muy poca cantidad de palabras tienen valores que se repiten muchas veces (los sentidos más comúnes), y hay casi 700 palabras que se repiten 1 sola vez. En vistas de esto podemos quedarnos con las palabras que se repiten más de 1 vez, criterio que utilizaremos para establecer un filtro (filter()). En esta ocasión, por simpleza, vamos a “pisar” nuestra tabla de datos con un fragmento de ella misma.
asoc_frecuentes <- asoc_frecuentes %>% # vamos a pisar la tabla con el resultado
filter(freq > 1)
glimpse(asoc_frecuentes)Rows: 188
Columns: 4
$ palabra <chr> ".", "...", "accesibilidad", "acceso", "algoritmos", …
$ freq <int> 3, 2, 4, 5, 4, 7, 2, 2, 6, 19, 2, 2, 2, 3, 3, 2, 4, 1…
$ valoracion_media <dbl> 0.0000000, 0.0000000, 1.7500000, 0.6000000, 1.2500000…
$ orden_media <dbl> 4.000000, 4.500000, 2.500000, 3.600000, 3.500000, 3.1…Por último, nos queda indagar cuáles son las palabras que más rápidamente fueron evocadas en la encuesta, es decir, las que tiene un orden de evocación más cercano a 1. Estas son las ideas que probablemente sean las más accesibles y memorables. Pero ahora en lugar de una lista, veamos un gráfico en el que podamos incluir otra información: la valoración media de la palabra.
asoc_frecuentes %>%
slice_min(order_by = orden_media, n=10) %>% # las de valor mas bajo
ggplot( # vamos a usar ggplot para graficar
aes( # dentro de aes indicamos las variables a vincular con las partes del gráfico
y = palabra, # en el eje y vamos a poner la palabra
x = orden_media, # en el eje x el orden de evocación
fill = valoracion_media # en el color vamos a poner la valoración (-5 y 5)
)
) +
geom_col()
Estos primeros análisis ya nos dan una idea de la estructura de los datos, y algunas pistas sobre las respuestas…
2.10.4 Funciones
Para crear una funcion vamos a utilizar function() incluyendo los parametros que esperamos entre los paréntesis. Vamos a guardar la función en un objeto (y así le vamos a dar un nombre que podamos invocar después) con <-.
analisis_proto <- function (tabla_evocaciones) {
# las operaciones van aca...
# por ahora no vamos a hacer nada mas que asignar nuestro input a un nuevo objeto...
objeto_a_devolver <- tabla_evocaciones
return(objeto_a_devolver) # ... y lo devolvemos
}Podemos llamar a la función con analisis_proto(asociaciones). Dada las operaciones que le definimos dentro, no nos debe sorprender que la función no haga más que llamar al objeto.
analisis_proto(asociaciones) id palabra
1 -M0-9OQkabuGoSmceB5E información
2 -M0-9OQkabuGoSmceB5E análisis
3 -M0-9OQkabuGoSmceB5E investigación
4 -M0-9OQkabuGoSmceB5E comercial
5 -M0-9OQkabuGoSmceB5E filtración
6 -M0U7_pJAU9Ehga0LIWq información
7 -M0U7_pJAU9Ehga0LIWq tecnología
8 -M0U7_pJAU9Ehga0LIWq sistemas
9 -M0U7_pJAU9Ehga0LIWq computadora
10 -M0U7_pJAU9Ehga0LIWq freaks
11 -M2M9R-DBBKvCzlYoWR0 homovidens
12 -M2M9R-DBBKvCzlYoWR0 revolución
13 -M2M9R-DBBKvCzlYoWR0 virtual
14 -M2M9R-DBBKvCzlYoWR0 analogico
15 -M2M9R-DBBKvCzlYoWR0 digital
16 -M2MhH9dvNq3RzShEV1- información
17 -M2MhH9dvNq3RzShEV1- accionables
18 -M2MhH9dvNq3RzShEV1- tecnología
19 -M2MhH9dvNq3RzShEV1- ciencia
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23 -M2OlvD1JZjfdTGZsbHw futuro
24 -M2OlvD1JZjfdTGZsbHw joven
25 -M2OlvD1JZjfdTGZsbHw noticiero
26 -M2OunC4N1HIrIdCXw7z información
27 -M2OunC4N1HIrIdCXw7z tecnología
28 -M2OunC4N1HIrIdCXw7z avance
29 -M2OunC4N1HIrIdCXw7z datos
30 -M2OunC4N1HIrIdCXw7z velocidad
31 -M2UNscfItR-VKSKId1m internet
32 -M2UNscfItR-VKSKId1m control
33 -M2UNscfItR-VKSKId1m gobiernos
34 -M2UNscfItR-VKSKId1m facebook
35 -M2UNscfItR-VKSKId1m estadisticas
36 -M2WVsZ-_IUoEbxBpR26 dataso
37 -M2WVsZ-_IUoEbxBpR26 ayuda
38 -M2WVsZ-_IUoEbxBpR26 importante
39 -M2WVsZ-_IUoEbxBpR26 ...
40 -M2WVsZ-_IUoEbxBpR26 ...
41 -M2YdUBFrDxYvSG9E7X- manipulación
42 -M2YdUBFrDxYvSG9E7X- control
43 -M2YdUBFrDxYvSG9E7X- resolución de problemas
44 -M2YdUBFrDxYvSG9E7X- múltiples estímulos
45 -M2YdUBFrDxYvSG9E7X- eficacia
46 -M2_T-DMepuBXF4aDdvC grande
47 -M2_T-DMepuBXF4aDdvC datos
48 -M2_T-DMepuBXF4aDdvC informativos
49 -M2_T-DMepuBXF4aDdvC engloba
50 -M2_T-DMepuBXF4aDdvC todo
51 -M2_T-DMepuBXF4aDdvC beneficio
52 -M2e6O_WHZM0MXYY-thH avance
53 -M2e6O_WHZM0MXYY-thH tecnológico
54 -M2e6O_WHZM0MXYY-thH conexión
55 -M2e6O_WHZM0MXYY-thH mundial
56 -M2e6O_WHZM0MXYY-thH utilidad
57 -M2fELkpj6exAMlFMdKd información
58 -M2fELkpj6exAMlFMdKd base de datos
59 -M2fELkpj6exAMlFMdKd accesibilidad
60 -M2fELkpj6exAMlFMdKd comunidad
61 -M2fELkpj6exAMlFMdKd gran archivo
62 -M2fpOVv1Q7R8KPgXPKb exceso de información
63 -M2fpOVv1Q7R8KPgXPKb acoso mediático
64 -M2fpOVv1Q7R8KPgXPKb manipulación de información
65 -M2fpOVv1Q7R8KPgXPKb posmodernidad
66 -M2fpOVv1Q7R8KPgXPKb libre acceso
67 -M2hqEpur6zagMpnQfmf información
68 -M2hqEpur6zagMpnQfmf global
69 -M2hqEpur6zagMpnQfmf buscador
70 -M2hqEpur6zagMpnQfmf ordenador
71 -M2hqEpur6zagMpnQfmf gratis
72 -M2i0rFGM9ErVXck8vtf sobreinformación
73 -M2i0rFGM9ErVXck8vtf invasión de la privacidad
74 -M2i0rFGM9ErVXck8vtf globalización
75 -M2i0rFGM9ErVXck8vtf acceso a información
76 -M2i0rFGM9ErVXck8vtf manipulación de datos sensibles
77 -M2i3IK6BZvZimYl7-KA muchos datos
78 -M2i3IK6BZvZimYl7-KA desborde
79 -M2i3IK6BZvZimYl7-KA virtualidad
80 -M2i3IK6BZvZimYl7-KA usos de datos
81 -M2i3IK6BZvZimYl7-KA análisis
82 -M2j8TRe1A9HzDQOz-Dl datos
83 -M2j8TRe1A9HzDQOz-Dl internet
84 -M2j8TRe1A9HzDQOz-Dl web
85 -M2j8TRe1A9HzDQOz-Dl aplicaciones
86 -M2j8TRe1A9HzDQOz-Dl información
87 -M2j_dvQIpocfeq_ONWP banco
88 -M2j_dvQIpocfeq_ONWP datos
89 -M2j_dvQIpocfeq_ONWP códigos
90 -M2j_dvQIpocfeq_ONWP logaritmos
91 -M2j_dvQIpocfeq_ONWP información
92 -M2kRT_Ls0HPsuxuN9ev información
93 -M2kRT_Ls0HPsuxuN9ev globalización
94 -M2kRT_Ls0HPsuxuN9ev cadena
95 -M2kRT_Ls0HPsuxuN9ev masivo
96 -M2kRT_Ls0HPsuxuN9ev empresa
97 -M2n45K5ko32iExLG1_O información
98 -M2n45K5ko32iExLG1_O masivo
99 -M2n45K5ko32iExLG1_O invasivo
100 -M2n45K5ko32iExLG1_O sistemático
101 -M2n45K5ko32iExLG1_O estadística
102 -M2nvwPnmHoGrla4CCtb información
103 -M2nvwPnmHoGrla4CCtb descifrar
104 -M2nvwPnmHoGrla4CCtb interacción
105 -M2nvwPnmHoGrla4CCtb construcción
106 -M2nvwPnmHoGrla4CCtb socialización
107 -M2oCta_znnbqDsXi_is datos
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109 -M2oCta_znnbqDsXi_is complejos
110 -M2oCta_znnbqDsXi_is requiere procesar
111 -M2oCta_znnbqDsXi_is por otros programas
112 -M2oma_G7HRxToHw-cGT internet
113 -M2oma_G7HRxToHw-cGT datos
114 -M2oma_G7HRxToHw-cGT programacion
115 -M2oma_G7HRxToHw-cGT probabilidad
116 -M2oma_G7HRxToHw-cGT procesamiento
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119 -M2p0ylnKukKQXwe_hNM recopilación
120 -M2p0ylnKukKQXwe_hNM procesamiento
121 -M2p0ylnKukKQXwe_hNM masivo
122 -M2sF500LfduXuQ9IwZj grandes datos
123 -M2sF500LfduXuQ9IwZj conocimiento
124 -M2sF500LfduXuQ9IwZj exploracion
125 -M2sF500LfduXuQ9IwZj tecnología
126 -M2sF500LfduXuQ9IwZj información
127 -M2uUDxoOXAOC5Nadm5Q tremendo
128 -M2uUDxoOXAOC5Nadm5Q dato
129 -M2uUDxoOXAOC5Nadm5Q poder
130 -M2uUDxoOXAOC5Nadm5Q dimensión
131 -M2uUDxoOXAOC5Nadm5Q grande
132 -M2v0ec4SiiPOGUqFGUb corporacion
133 -M2v0ec4SiiPOGUqFGUb privacidad
134 -M2v0ec4SiiPOGUqFGUb vender
135 -M2v0ec4SiiPOGUqFGUb informacion
136 -M2v0ec4SiiPOGUqFGUb plata
137 -M2xF57dopHK9BpRx5Hh manipulación
138 -M2xF57dopHK9BpRx5Hh informacion
139 -M2xF57dopHK9BpRx5Hh politica
140 -M2xF57dopHK9BpRx5Hh dominio
141 -M2xF57dopHK9BpRx5Hh ayuda
142 -M2xGoFFBmmgTKpYvfhY conexiones infinitas
143 -M2xGoFFBmmgTKpYvfhY internet
144 -M2xGoFFBmmgTKpYvfhY inteligencia artificial
145 -M2xGoFFBmmgTKpYvfhY nuevos softwares
146 -M2xGoFFBmmgTKpYvfhY respuestas
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148 -M2xPtAsXnj6573wthIF escala
149 -M2xPtAsXnj6573wthIF datos
150 -M2xPtAsXnj6573wthIF vigilancia
151 -M2xPtAsXnj6573wthIF ciencia
152 -M2xtkaLTAGxORvpcqnr gran base de datos
153 -M2xtkaLTAGxORvpcqnr nube
154 -M2xtkaLTAGxORvpcqnr imformatica
155 -M2xtkaLTAGxORvpcqnr datos personales
156 -M2xtkaLTAGxORvpcqnr búsqueda
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161 -M2yAbCja-NoP1Q9Mq22 sistemas
162 -M2yHEk4O2PrIpJ7RFKp datos
163 -M2yHEk4O2PrIpJ7RFKp sistema binario
164 -M2yHEk4O2PrIpJ7RFKp numero
165 -M2yHEk4O2PrIpJ7RFKp informacion codificada
166 -M2yHEk4O2PrIpJ7RFKp 0-1-2
167 -M2zO90WdpFEVPalRjSM informacion
168 -M2zO90WdpFEVPalRjSM privacidad
169 -M2zO90WdpFEVPalRjSM libertad
170 -M2zO90WdpFEVPalRjSM publicidad
171 -M2zO90WdpFEVPalRjSM tecnologia
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173 -M2zTbUMqEycnjuOnRdU capacidad velocidad
174 -M2zTbUMqEycnjuOnRdU procesamiento de datos
175 -M2zTbUMqEycnjuOnRdU tecnologia de la información
176 -M2zTbUMqEycnjuOnRdU capacidad para analizar y relacionar d
177 -M3-EyU4jcQEgYNAOe3s r studio
178 -M3-EyU4jcQEgYNAOe3s estadística
179 -M3-EyU4jcQEgYNAOe3s internet
180 -M3-EyU4jcQEgYNAOe3s control
181 -M3-EyU4jcQEgYNAOe3s modelos
182 -M3-QXl0-gKlc0UmpyNn datos
183 -M3-QXl0-gKlc0UmpyNn conocimiento
184 -M3-QXl0-gKlc0UmpyNn control
185 -M3-QXl0-gKlc0UmpyNn nada
186 -M3-QXl0-gKlc0UmpyNn nada
187 -M32POeX9RW1rFTkIb6d grandes datos
188 -M32POeX9RW1rFTkIb6d volúmenes de datos
189 -M32POeX9RW1rFTkIb6d datos estructurados
190 -M32POeX9RW1rFTkIb6d datos de gran envergadura
191 -M32POeX9RW1rFTkIb6d nuevos servicios comerciales
192 -M32VykWfiNM5m0P3U9V corporaciones
193 -M32VykWfiNM5m0P3U9V información
194 -M32VykWfiNM5m0P3U9V espionaje
195 -M32VykWfiNM5m0P3U9V robo
196 -M32VykWfiNM5m0P3U9V encubierto
197 -M3SNRpAxiy-4pTlRfxi información
198 -M3SNRpAxiy-4pTlRfxi disponibilidad
199 -M3SNRpAxiy-4pTlRfxi riesgo
200 -M3SNRpAxiy-4pTlRfxi intimidad
201 -M3SNRpAxiy-4pTlRfxi computación
202 -M3lt2Bhf7uv-k52J35p tecnología
203 -M3lt2Bhf7uv-k52J35p velocidad
204 -M3lt2Bhf7uv-k52J35p información
205 -M3lt2Bhf7uv-k52J35p intimidad
206 -M3lt2Bhf7uv-k52J35p conexión
207 -M3sxPk0nUoYrZGkm-ja grande
208 -M3sxPk0nUoYrZGkm-ja datos
209 -M3sxPk0nUoYrZGkm-ja comunicación
210 -M3sxPk0nUoYrZGkm-ja comercialización
211 -M3sxPk0nUoYrZGkm-ja globalización
212 -M3vrm1-PnnPcRZt85pD datos
213 -M3vrm1-PnnPcRZt85pD internet
214 -M3vrm1-PnnPcRZt85pD navegacióm
215 -M3vrm1-PnnPcRZt85pD privacidad
216 -M3vrm1-PnnPcRZt85pD derechos
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218 -M3vrtaCfmnZHt109At7 online
219 -M3vrtaCfmnZHt109At7 promos
220 -M3vrtaCfmnZHt109At7 datos
221 -M3vrtaCfmnZHt109At7 email
222 -M3vs7oqT4KboKP9xjC5 compilar información
223 -M3vs7oqT4KboKP9xjC5 banco de datos
224 -M3vs7oqT4KboKP9xjC5 informática
225 -M3vs7oqT4KboKP9xjC5 información virtual
226 -M3vs7oqT4KboKP9xjC5 internet
227 -M3w2JpiZIw8kpUsskzv internet
228 -M3w2JpiZIw8kpUsskzv almacenamiento
229 -M3w2JpiZIw8kpUsskzv base de datos
230 -M3w2JpiZIw8kpUsskzv capacidad
231 -M3w2JpiZIw8kpUsskzv velocidad
232 -M3wGsVZGTr6H_MIHABL información
233 -M3wGsVZGTr6H_MIHABL excesiva
234 -M3wGsVZGTr6H_MIHABL canales
235 -M3wGsVZGTr6H_MIHABL inclusión
236 -M3wGsVZGTr6H_MIHABL útil
237 -M40GnKqZDUDVKuHZeN1 infomación
238 -M40GnKqZDUDVKuHZeN1 tecnología
239 -M40GnKqZDUDVKuHZeN1 programadores
240 -M40GnKqZDUDVKuHZeN1 análisis de mercado/consumidores
241 -M40GnKqZDUDVKuHZeN1 segmentación
242 -M4605TVwYB7Jqvyxfwz control
243 -M4605TVwYB7Jqvyxfwz información
244 -M4605TVwYB7Jqvyxfwz marketing
245 -M4605TVwYB7Jqvyxfwz infinito
246 -M4605TVwYB7Jqvyxfwz conexión
247 -M46ULzYYzYfjd15rq9l memoria artificial
248 -M46ULzYYzYfjd15rq9l muchos datos
249 -M46ULzYYzYfjd15rq9l muchos datos cargados
250 -M46ULzYYzYfjd15rq9l gran información guardada
251 -M46ULzYYzYfjd15rq9l máquina guarda datos
252 -M46fJznS6rvED7w4-Lv privacidad (pérdida de)
253 -M46fJznS6rvED7w4-Lv análisis
254 -M46fJznS6rvED7w4-Lv recurso
255 -M46fJznS6rvED7w4-Lv computación
256 -M46fJznS6rvED7w4-Lv lugar común
257 -M46zXQYg6QKgZVX_pQv universo
258 -M46zXQYg6QKgZVX_pQv control
259 -M46zXQYg6QKgZVX_pQv manipulación
260 -M46zXQYg6QKgZVX_pQv acceso
261 -M46zXQYg6QKgZVX_pQv democratización
262 -M4710w3FVeTgjdkpJPX información
263 -M4710w3FVeTgjdkpJPX intromisión
264 -M4710w3FVeTgjdkpJPX instanteneidad
265 -M4710w3FVeTgjdkpJPX marketing
266 -M4710w3FVeTgjdkpJPX redes
267 -M47ExPq-lbDnXrT8wtS datos
268 -M47ExPq-lbDnXrT8wtS información
269 -M47ExPq-lbDnXrT8wtS noticias
270 -M47ExPq-lbDnXrT8wtS llamar atención
271 -M47ExPq-lbDnXrT8wtS accedé ahora
272 -M47M8rn69ZaP2OdOy7R información
273 -M47M8rn69ZaP2OdOy7R grande
274 -M47M8rn69ZaP2OdOy7R confiable
275 -M47M8rn69ZaP2OdOy7R interesante
276 -M47M8rn69ZaP2OdOy7R contenido
277 -M47Wjv9dk5p4e5a6Iq3 internet
278 -M47Wjv9dk5p4e5a6Iq3 redes sociales
279 -M47Wjv9dk5p4e5a6Iq3 informacion
280 -M47Wjv9dk5p4e5a6Iq3 trabajo
281 -M47Wjv9dk5p4e5a6Iq3 intercambio
282 -M4822FBV7CV-ctwFc9e grande
283 -M4822FBV7CV-ctwFc9e datos
284 -M4822FBV7CV-ctwFc9e numerosos
285 -M4822FBV7CV-ctwFc9e muchos
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287 -M48q9mNBBmUMXpAydKS base de datos
288 -M48q9mNBBmUMXpAydKS privacidad
289 -M48q9mNBBmUMXpAydKS marketing
290 -M48q9mNBBmUMXpAydKS estrategia
291 -M48q9mNBBmUMXpAydKS monetización
292 -M48qPT14N4x4tX0sMf7 base de datos
293 -M48qPT14N4x4tX0sMf7 servidor web
294 -M48qPT14N4x4tX0sMf7 programación
295 -M48qPT14N4x4tX0sMf7 mantenimiento
296 -M48qPT14N4x4tX0sMf7 informática física
297 -M49y46ZZ2pm7i3UcNLu información
298 -M49y46ZZ2pm7i3UcNLu comunicación
299 -M49y46ZZ2pm7i3UcNLu intercambio
300 -M49y46ZZ2pm7i3UcNLu global
301 -M49y46ZZ2pm7i3UcNLu ideas
302 -M4A03asbnw6nzk8I4vR manipulación
303 -M4A03asbnw6nzk8I4vR ampliar
304 -M4A03asbnw6nzk8I4vR intereses
305 -M4A03asbnw6nzk8I4vR trascendente
306 -M4A03asbnw6nzk8I4vR mejorar
307 -M4AJd3BtrZ3xduq4osn datos
308 -M4AJd3BtrZ3xduq4osn analítica
309 -M4AJd3BtrZ3xduq4osn información
310 -M4AJd3BtrZ3xduq4osn decisiones
311 -M4AJd3BtrZ3xduq4osn negocios
312 -M4AcPbQpTC-METSfP5k informática
313 -M4AcPbQpTC-METSfP5k computación
314 -M4AcPbQpTC-METSfP5k tecnología
315 -M4AcPbQpTC-METSfP5k datos
316 -M4AcPbQpTC-METSfP5k matemáticas
317 -M4Cf3KvtccpvZrsub2l datos
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319 -M4Cf3KvtccpvZrsub2l flujo
320 -M4Cf3KvtccpvZrsub2l internet
321 -M4Cf3KvtccpvZrsub2l estadística
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324 -M4CoQCiGFJjbORvBsQH evolución
325 -M4CoQCiGFJjbORvBsQH educación
326 -M4CoQCiGFJjbORvBsQH cambio
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330 -M4DTrFzmy3pxm-A7w_U humano
331 -M4DTrFzmy3pxm-A7w_U variables
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342 -M4EyfqsuQyVAJ0-vi3k marketing
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345 -M4EyfqsuQyVAJ0-vi3k software
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348 -M4FCXhNtqZ5TQk3f0Hr sobre información
349 -M4FCXhNtqZ5TQk3f0Hr accesibilidad
350 -M4FCXhNtqZ5TQk3f0Hr falsas noticias
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353 -M4FE_8DpDP33JDJhyFR control
354 -M4FE_8DpDP33JDJhyFR tecnologia
355 -M4FE_8DpDP33JDJhyFR gobierno
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359 -M4FgHjOrh1WWkiE4Jpq agenda
360 -M4FgHjOrh1WWkiE4Jpq tecnicismo
361 -M4FgHjOrh1WWkiE4Jpq información
362 -M4G0lbgtzXG3Ztts47C grande
363 -M4G0lbgtzXG3Ztts47C informacion
364 -M4G0lbgtzXG3Ztts47C datos
365 -M4G0lbgtzXG3Ztts47C apple
366 -M4G0lbgtzXG3Ztts47C informatica
367 -M4G4quSr9ib-mdfngz3 big mac
368 -M4G4quSr9ib-mdfngz3 microsoft
369 -M4G4quSr9ib-mdfngz3 apple
370 -M4G4quSr9ib-mdfngz3 silicon valle
371 -M4G4quSr9ib-mdfngz3 steve jobs
372 -M4GKqHNosx4NqQ_VwpH gran información
373 -M4GKqHNosx4NqQ_VwpH hackeo de datos
374 -M4GKqHNosx4NqQ_VwpH manipulación de datos
375 -M4GKqHNosx4NqQ_VwpH proximidad para controlar
376 -M4GKqHNosx4NqQ_VwpH gran red de datos
377 -M4GrXMfSAzmuoaAbilF gran conocimiento
378 -M4GrXMfSAzmuoaAbilF grandes fechas
379 -M4GrXMfSAzmuoaAbilF mucho saber
380 -M4GrXMfSAzmuoaAbilF entender
381 -M4GrXMfSAzmuoaAbilF saber
382 -M4H9YevJdxGiEBB66V4 control
383 -M4H9YevJdxGiEBB66V4 negocio
384 -M4H9YevJdxGiEBB66V4 accesibilidad
385 -M4H9YevJdxGiEBB66V4 poder
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395 -M4Lss3W7OI-gRAUdPgq datos
396 -M4Lss3W7OI-gRAUdPgq macrodatos
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399 -M4MHrfa2YMotVOJuC1A chimento
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401 -M4MHrfa2YMotVOJuC1A gato
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404 -M4NCeINm8i0fO1dJpbV informacion
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407 -M4NCeINm8i0fO1dJpbV procesos
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409 -M4PU7s6fDvmB0Njhlf3 dato importante
410 -M4PU7s6fDvmB0Njhlf3 blog de datos
411 -M4PU7s6fDvmB0Njhlf3 página de pdf
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415 -M4Q-V4UIKaX5yHM4Rsg información
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420 -M4Q6TpnDmxgCSCoWP0G recopilación
421 -M4Q6TpnDmxgCSCoWP0G cantidad
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426 -M4Q8Bl_ud6vYf339lER interacción
427 -M4Q8Bl_ud6vYf339lER datos
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431 -M4QshkOdvab21p23HF- memoria
432 -M4QshkOdvab21p23HF- redes
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434 -M4Rk4cOQHm7b0PDarQ- información
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439 -M4UJC5l12tA3E8TzHsO gestion de la información
440 -M4UJC5l12tA3E8TzHsO equipos de trabajo
441 -M4UJC5l12tA3E8TzHsO oferta laboral
442 -M4UJC5l12tA3E8TzHsO saber tecnico y disciplinas
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446 -M4Uabiv1313P9-nn070 datos
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450 -M4VqamiGawLCRXqbZfQ estimaciones
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452 -M4VqamiGawLCRXqbZfQ desarrollo
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455 -M4X1PJ5I-WZklAzj7B- ayuda
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457 -M4X1PJ5I-WZklAzj7B- consideración
458 -M4XHRyShBOXFftOIuLh datos
459 -M4XHRyShBOXFftOIuLh software
460 -M4XHRyShBOXFftOIuLh global
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462 -M4XHRyShBOXFftOIuLh contexto
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467 -M4ZxrqG2qNNfSO7MmwO union
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470 -M4_aDG7_KQD2W0eXLAP correos, spams
471 -M4_aDG7_KQD2W0eXLAP empresas compran datos
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475 -M4_ydo_7cPCZukHzWO4 falta de privacidad
476 -M4_ydo_7cPCZukHzWO4 marketing
477 -M4_ydo_7cPCZukHzWO4 dios del internet
478 -M4_zs5FE37U3rhlgo-m análisis
479 -M4_zs5FE37U3rhlgo-m datos
480 -M4_zs5FE37U3rhlgo-m matemática
481 -M4_zs5FE37U3rhlgo-m asociaciones
482 -M4_zs5FE37U3rhlgo-m predicción
483 -M4a1JVfdap7afYm1Ag2 conocimiento
484 -M4a1JVfdap7afYm1Ag2 saber
485 -M4a1JVfdap7afYm1Ag2 interrogación
486 -M4a1JVfdap7afYm1Ag2 curiosidad
487 -M4a1JVfdap7afYm1Ag2 cultivarse
488 -M4aDKg_r4yUO_JBbmmE base de datos
489 -M4aDKg_r4yUO_JBbmmE información
490 -M4aDKg_r4yUO_JBbmmE almacenamiento
491 -M4aDKg_r4yUO_JBbmmE datos personales
492 -M4aDKg_r4yUO_JBbmmE indexación
493 -M4aHhBk8C92dEQocCWh el gran dato
494 -M4aHhBk8C92dEQocCWh masificación
495 -M4aHhBk8C92dEQocCWh generalización exesivs
496 -M4aHhBk8C92dEQocCWh que esta de moda
497 -M4aHhBk8C92dEQocCWh pasa tiempo
498 -M52mHmaCqOfYTvTVFnU información
499 -M52mHmaCqOfYTvTVFnU importante
500 -M52mHmaCqOfYTvTVFnU informática
501 -M52mHmaCqOfYTvTVFnU datos
502 -M52mHmaCqOfYTvTVFnU aviso
503 -M534Jolbydy8wzpvDVr grandes datos
504 -M534Jolbydy8wzpvDVr una noticia importante
505 -M534Jolbydy8wzpvDVr conocimiento
506 -M534Jolbydy8wzpvDVr un encabezado
507 -M534Jolbydy8wzpvDVr un tipo de problematica
508 -M5Tl1lBApBD-BoX2J90 redes sociales
509 -M5Tl1lBApBD-BoX2J90 google
510 -M5Tl1lBApBD-BoX2J90 digitalización
511 -M5Tl1lBApBD-BoX2J90 vigilancia
512 -M5Tl1lBApBD-BoX2J90 cibercrimen
513 -M5Wgd914D78sKJY72vu análisis
514 -M5Wgd914D78sKJY72vu masivo
515 -M5Wgd914D78sKJY72vu datos personales
516 -M5Wgd914D78sKJY72vu intereses
517 -M5Wgd914D78sKJY72vu gustos
518 -M5Z06LUW02EYo8a7VVy gran información
519 -M5Z06LUW02EYo8a7VVy trafico web
520 -M5Z06LUW02EYo8a7VVy internet
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523 -M5dfoSqs0IAPY1YICp1 información
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525 -M5dfoSqs0IAPY1YICp1 datos
526 -M5dfoSqs0IAPY1YICp1 herramientas
527 -M5dfoSqs0IAPY1YICp1 tecnologías
528 -M5gxUE3YdFAVh2mywii conjunto
529 -M5gxUE3YdFAVh2mywii datos
530 -M5gxUE3YdFAVh2mywii referencia
531 -M5gxUE3YdFAVh2mywii producto
532 -M5gxUE3YdFAVh2mywii sevicio
533 -M5jE-Z80QkdsSZtTKZy prediccion
534 -M5jE-Z80QkdsSZtTKZy segmentacion
535 -M5jE-Z80QkdsSZtTKZy modelo
536 -M5jE-Z80QkdsSZtTKZy inteligencia artificial
537 -M5jE-Z80QkdsSZtTKZy control
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539 -M5lI6XjDnLU0EUKD8Bx mucha información
540 -M5lI6XjDnLU0EUKD8Bx pdf
541 -M5lI6XjDnLU0EUKD8Bx tarea
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544 -M5oJ8_0KMwGQGQ-pQCB computadoras
545 -M5oJ8_0KMwGQGQ-pQCB almacenamiento de datos
546 -M5oJ8_0KMwGQGQ-pQCB archivos pesados
547 -M5oJ8_0KMwGQGQ-pQCB internet
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549 -M5tUgJTRTuAWG6gAvX7 dato
550 -M5tUgJTRTuAWG6gAvX7 almacenado
551 -M5tUgJTRTuAWG6gAvX7 estadistica
552 -M5tUgJTRTuAWG6gAvX7 resolucion
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555 -M5wGqkAWJAwwvxBy89o herramienta
556 -M5wGqkAWJAwwvxBy89o falsas expectativas
557 -M5wGqkAWJAwwvxBy89o complejo
558 -M5wX1AfmIiskq2EUOrI perfil de destinatario
559 -M5wX1AfmIiskq2EUOrI inereses
560 -M5wX1AfmIiskq2EUOrI consumo
561 -M5wX1AfmIiskq2EUOrI segmentación de la audiencia
562 -M5wX1AfmIiskq2EUOrI control de población
563 -M5ynXsyhPg2CFxcBgpz laboratorio
564 -M5ynXsyhPg2CFxcBgpz encuesta
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566 -M5ynXsyhPg2CFxcBgpz masivos
567 -M5ynXsyhPg2CFxcBgpz situación
568 -M74J5gezg3O9ja87NNc datos
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570 -M74J5gezg3O9ja87NNc comercio
571 -M74J5gezg3O9ja87NNc innovación
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574 -M74Tcj272dAK7B25mYP gran cantidad
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576 -M74Tcj272dAK7B25mYP actualidad
577 -M74Tcj272dAK7B25mYP informacion
578 -M79mxE5pkUdnJTg9Wfe análisis
579 -M79mxE5pkUdnJTg9Wfe insights
580 -M79mxE5pkUdnJTg9Wfe accionar
581 -M79mxE5pkUdnJTg9Wfe control
582 -M79mxE5pkUdnJTg9Wfe información
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584 -M7C2RsadFTrnlndpz_L palabras
585 -M7C2RsadFTrnlndpz_L nodos
586 -M7C2RsadFTrnlndpz_L puntos
587 -M7C2RsadFTrnlndpz_L ecosistema
588 -M7JmPhXypDTXgnQzG7R accesibilidad
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590 -M7JmPhXypDTXgnQzG7R comodidad
591 -M7JmPhXypDTXgnQzG7R red
592 -M7JmPhXypDTXgnQzG7R fiabilidad
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594 -M7L0sptMAxfR-8H1f__ mercado
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596 -M7L0sptMAxfR-8H1f__ noticia
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598 -M7M9SpfDAyFtitageb8 grande
599 -M7M9SpfDAyFtitageb8 información
600 -M7M9SpfDAyFtitageb8 dato
601 -M7M9SpfDAyFtitageb8 conplejidad
602 -M7M9SpfDAyFtitageb8 demasiado
603 -M7OrYtgcZdg9EkhavZW mucha información
604 -M7OrYtgcZdg9EkhavZW base de datos
605 -M7OrYtgcZdg9EkhavZW búsqueda
606 -M7OrYtgcZdg9EkhavZW compartición de información
607 -M7OrYtgcZdg9EkhavZW información pública
608 -M7TkcZIegd82PnnRIbp información
609 -M7TkcZIegd82PnnRIbp manipulación
610 -M7TkcZIegd82PnnRIbp espionaje
611 -M7TkcZIegd82PnnRIbp control
612 -M7TkcZIegd82PnnRIbp encuestas
613 -M7VWRKRJ9RDIsukwiM5 datos
614 -M7VWRKRJ9RDIsukwiM5 control
615 -M7VWRKRJ9RDIsukwiM5 previsión
616 -M7VWRKRJ9RDIsukwiM5 acción
617 -M7VWRKRJ9RDIsukwiM5 replanteo
618 -M7_aGS7jBQHsugFYefA gran data
619 -M7_aGS7jBQHsugFYefA mucha información
620 -M7_aGS7jBQHsugFYefA desinformación
621 -M7_aGS7jBQHsugFYefA pluralidad
622 -M7_aGS7jBQHsugFYefA sobredatos
623 -M7efCTT-6V9kSTDrLru grande
624 -M7efCTT-6V9kSTDrLru datos
625 -M7efCTT-6V9kSTDrLru información
626 -M7efCTT-6V9kSTDrLru evolución
627 -M7efCTT-6V9kSTDrLru tecnología
628 -M7f8IVYgt494Mu5npHC conglomerado de datos
629 -M7f8IVYgt494Mu5npHC conocimiento
630 -M7f8IVYgt494Mu5npHC regresión lineal
631 -M7f8IVYgt494Mu5npHC prediccion
632 -M7f8IVYgt494Mu5npHC futuro
633 -M7fK3INqAfoTPSc2Xf9 datos
634 -M7fK3INqAfoTPSc2Xf9 informacion
635 -M7fK3INqAfoTPSc2Xf9 redes
636 -M7fK3INqAfoTPSc2Xf9 internet
637 -M7fK3INqAfoTPSc2Xf9 informatica
638 -M7i1NYVISNV88VCzm0M tecnología
639 -M7i1NYVISNV88VCzm0M información
640 -M7i1NYVISNV88VCzm0M redes
641 -M7i1NYVISNV88VCzm0M conocimiento
642 -M7i1NYVISNV88VCzm0M intimidad
643 -M7iRMbYszd2USiZADYg recopilación
644 -M7iRMbYszd2USiZADYg datos
645 -M7iRMbYszd2USiZADYg sistematización
646 -M7iRMbYszd2USiZADYg análisis
647 -M7iRMbYszd2USiZADYg respuesta
648 -M7uTFrfzCmWBtGFMvR7 información
649 -M7uTFrfzCmWBtGFMvR7 flujo
650 -M7uTFrfzCmWBtGFMvR7 internet
651 -M7uTFrfzCmWBtGFMvR7 tecnología
652 -M7uTFrfzCmWBtGFMvR7 interconexión
653 -M84vUtyVPBKpnBOwmfj grandes datos.
654 -M84vUtyVPBKpnBOwmfj tecnología
655 -M84vUtyVPBKpnBOwmfj internet
656 -M84vUtyVPBKpnBOwmfj redes sociales
657 -M84vUtyVPBKpnBOwmfj tecnología
658 -M8k5rFgbZkuld-qoXl4 volúmen de datos
659 -M8k5rFgbZkuld-qoXl4 análisis
660 -M8k5rFgbZkuld-qoXl4 patrones
661 -M8k5rFgbZkuld-qoXl4 decisiones
662 -M8k5rFgbZkuld-qoXl4 estrategias
663 -M8kBykCTrGkUL_AVXdS base de datos
664 -M8kBykCTrGkUL_AVXdS gestion
665 -M8kBykCTrGkUL_AVXdS informatizar
666 -M8kBykCTrGkUL_AVXdS globalizacion
667 -M8kBykCTrGkUL_AVXdS unificacion
668 -M8kNBsVW19H-qJq3aJ6 tecnologia
669 -M8kNBsVW19H-qJq3aJ6 superficial
670 -M8kNBsVW19H-qJq3aJ6 numeros
671 -M8kNBsVW19H-qJq3aJ6 orden
672 -M8kNBsVW19H-qJq3aJ6 archivos
673 -M8ko1pc4ZScEYkz2fh0 datos
674 -M8ko1pc4ZScEYkz2fh0 gran cantidad
675 -M8ko1pc4ZScEYkz2fh0 información
676 -M8ko1pc4ZScEYkz2fh0 procesaniento
677 -M8ko1pc4ZScEYkz2fh0 masividad
678 -M8l7u2AWWzRvXeLN0Wq un gran dato
679 -M8l7u2AWWzRvXeLN0Wq datos grandes
680 -M8l7u2AWWzRvXeLN0Wq información importante
681 -M8l7u2AWWzRvXeLN0Wq algo grande
682 -M8l7u2AWWzRvXeLN0Wq información importante
683 -M8lA1JN4l55IBlqjeno tecnología
684 -M8lA1JN4l55IBlqjeno datos virtuales
685 -M8lA1JN4l55IBlqjeno software
686 -M8lA1JN4l55IBlqjeno almacenamiento
687 -M8lA1JN4l55IBlqjeno internet
688 -M8lAlX1ZPLv1tOUPX0e venta de informacion
689 -M8lAlX1ZPLv1tOUPX0e campaña politica
690 -M8lAlX1ZPLv1tOUPX0e datos personales
691 -M8lAlX1ZPLv1tOUPX0e oportunidad
692 -M8lAlX1ZPLv1tOUPX0e mejorar la comunicacion
693 -M8nDZE8TE8OHGsDLzvt control
694 -M8nDZE8TE8OHGsDLzvt gustos
695 -M8nDZE8TE8OHGsDLzvt libertad
696 -M8nDZE8TE8OHGsDLzvt limite
697 -M8nDZE8TE8OHGsDLzvt privacidad
698 -M8nFwolbjBkFTBnODuI datos
699 -M8nFwolbjBkFTBnODuI periodismo
700 -M8nFwolbjBkFTBnODuI facebook
701 -M8nFwolbjBkFTBnODuI cambridge
702 -M8nFwolbjBkFTBnODuI infografía
703 -M8nouvktrBFAh_xp5jW monopolizar
704 -M8nouvktrBFAh_xp5jW causalidades
705 -M8nouvktrBFAh_xp5jW contaminación
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714 -M8qFzQdpFuSbW0Ab5fn toma de decisión rapida
715 -M8qFzQdpFuSbW0Ab5fn datos volátiles
716 -M8qFzQdpFuSbW0Ab5fn falta de estándares
717 -M8qFzQdpFuSbW0Ab5fn organización de datos
718 -M8qx2dG5u1DKpQZVpt2 una esfera contenida de datos e información
719 -M8qx2dG5u1DKpQZVpt2 largas estructuras de redes
720 -M8qx2dG5u1DKpQZVpt2 grandes cantidades de bits
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723 -M8rSeoE-hu3qI41cOIt información
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725 -M8rSeoE-hu3qI41cOIt base de datos
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727 -M8rSeoE-hu3qI41cOIt ventas y marketing
728 -M8rxkI-JcGFnIFESa-m información
729 -M8rxkI-JcGFnIFESa-m vigilancia
730 -M8rxkI-JcGFnIFESa-m dinero
731 -M8rxkI-JcGFnIFESa-m gobierno
732 -M8rxkI-JcGFnIFESa-m ayuda
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735 -M8uHmwbICaHfKm1bDuG empresarios
736 -M8uHmwbICaHfKm1bDuG silicon valley
737 -M8uHmwbICaHfKm1bDuG programación
738 -M8uZIh7v0kN8doX-Ocn espionaje
739 -M8uZIh7v0kN8doX-Ocn información
740 -M8uZIh7v0kN8doX-Ocn elecciones
741 -M8uZIh7v0kN8doX-Ocn no privacidad
742 -M8uZIh7v0kN8doX-Ocn publicidad
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744 -M8xLm6N6StAel-mAbJM numeros
745 -M8xLm6N6StAel-mAbJM matemática
746 -M8xLm6N6StAel-mAbJM trabajo
747 -M8xLm6N6StAel-mAbJM futuro
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749 -M8zLQqtQIyhASnI4gXi publicidad de la privacidad
750 -M8zLQqtQIyhASnI4gXi posibilidades
751 -M8zLQqtQIyhASnI4gXi mal uso
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754 -M9-NSakIfzM-p_HRKLV graficos
755 -M9-NSakIfzM-p_HRKLV bases de datos
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757 -M9-NSakIfzM-p_HRKLV analisis
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762 -M9-ZJ7-HIQPqdpA_QXp análisis
763 -M9-hKCsT3NadlNn5y5i facebook
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765 -M9-hKCsT3NadlNn5y5i perfil
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767 -M9-hKCsT3NadlNn5y5i influir
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770 -M9-pqiKL__J2MzXid2J control
771 -M9-pqiKL__J2MzXid2J falta de privacidad
772 -M9-pqiKL__J2MzXid2J manipulacion
773 -M9-q4CdzKmkxwjQ27Ro información
774 -M9-q4CdzKmkxwjQ27Ro tecnología
775 -M9-q4CdzKmkxwjQ27Ro política
776 -M9-q4CdzKmkxwjQ27Ro comunicación
777 -M9-q4CdzKmkxwjQ27Ro ns
778 -M906J5AoaweaWRDWMCp masivo
779 -M906J5AoaweaWRDWMCp tecnología
780 -M906J5AoaweaWRDWMCp almacenamiento
781 -M906J5AoaweaWRDWMCp datos
782 -M906J5AoaweaWRDWMCp analisis
783 -M90EETwVmcgHX08Syvd datos enormes
784 -M90EETwVmcgHX08Syvd complejidad
785 -M90EETwVmcgHX08Syvd alto volumen
786 -M90EETwVmcgHX08Syvd información
787 -M90EETwVmcgHX08Syvd análisis
788 -M90EETwVmcgHX08Syvd consumidored
789 -M9153SL_gfvUEwZ_DEw macrodatos
790 -M9153SL_gfvUEwZ_DEw oracle
791 -M9153SL_gfvUEwZ_DEw casinos
792 -M9153SL_gfvUEwZ_DEw gobierno
793 -M9153SL_gfvUEwZ_DEw guerra
794 -M915jTNhtuxZTBqd2CD celular (whatsapp)
795 -M915jTNhtuxZTBqd2CD computadora
796 -M915jTNhtuxZTBqd2CD sobreinformación
797 -M915jTNhtuxZTBqd2CD ausencia de intimidad
798 -M915jTNhtuxZTBqd2CD sobreexigencias horarias
799 -M91oUSnjYz4Xd9pfYli información
800 -M91oUSnjYz4Xd9pfYli espionaje
801 -M91oUSnjYz4Xd9pfYli manipulación
802 -M91oUSnjYz4Xd9pfYli vida privada
803 -M91oUSnjYz4Xd9pfYli elecciones
804 -M91xRujFlrevhyoAqN3 internet
805 -M91xRujFlrevhyoAqN3 desigualdad de acceso
806 -M91xRujFlrevhyoAqN3 información
807 -M91xRujFlrevhyoAqN3 empresas
808 -M91xRujFlrevhyoAqN3 redes sociales
809 -M93Ik8w6W_dCmG6nshr muchos datos
810 -M93Ik8w6W_dCmG6nshr mucha informacion
811 -M93Ik8w6W_dCmG6nshr corrupcion
812 -M93Ik8w6W_dCmG6nshr gestion
813 -M93Ik8w6W_dCmG6nshr progreso
814 -M944wYW91C0jem2qwdT espionaje
815 -M944wYW91C0jem2qwdT marketing
816 -M944wYW91C0jem2qwdT empresas
817 -M944wYW91C0jem2qwdT facebook
818 -M944wYW91C0jem2qwdT redes sociales
819 -M94gBLGjCsunFmrUSu_ datos
820 -M94gBLGjCsunFmrUSu_ control
821 -M94gBLGjCsunFmrUSu_ tendencias
822 -M94gBLGjCsunFmrUSu_ e commerce
823 -M94gBLGjCsunFmrUSu_ publicidad
824 -M9597kmiUK74myqKh2P nube
825 -M9597kmiUK74myqKh2P computadora
826 -M9597kmiUK74myqKh2P google
827 -M9597kmiUK74myqKh2P facebook
828 -M9597kmiUK74myqKh2P privacidad
829 -M95DFBWagwPFiqQwIPO datos
830 -M95DFBWagwPFiqQwIPO información
831 -M95DFBWagwPFiqQwIPO informatica
832 -M95DFBWagwPFiqQwIPO cómputos
833 -M95DFBWagwPFiqQwIPO grande
834 -M95Rwwbri8WrXLxj5XF tecnologia
835 -M95Rwwbri8WrXLxj5XF util
836 -M95Rwwbri8WrXLxj5XF peligroso
837 -M95Rwwbri8WrXLxj5XF dificil de entender
838 -M95Rwwbri8WrXLxj5XF consumismo
839 -M96-5sjAB1L3BiIOha5 digital
840 -M96-5sjAB1L3BiIOha5 web
841 -M96-5sjAB1L3BiIOha5 trasmedia
842 -M96-5sjAB1L3BiIOha5 virtual
843 -M96-5sjAB1L3BiIOha5 sistemas
844 -M96R3K8rJ6iQmSyJ3IM información
845 -M96R3K8rJ6iQmSyJ3IM ganancia
846 -M96R3K8rJ6iQmSyJ3IM estadísticas
847 -M96R3K8rJ6iQmSyJ3IM intereses
848 -M96R3K8rJ6iQmSyJ3IM eternidad
849 -M973eBgFPcjKRLX_3BM estadísticas
850 -M973eBgFPcjKRLX_3BM investigación
851 -M973eBgFPcjKRLX_3BM datos
852 -M973eBgFPcjKRLX_3BM informática
853 -M973eBgFPcjKRLX_3BM información
854 -M98y38g85yB4Vi094kx tecnología
855 -M98y38g85yB4Vi094kx programacion
856 -M98y38g85yB4Vi094kx .
857 -M98y38g85yB4Vi094kx .
858 -M98y38g85yB4Vi094kx .
859 -M99R_PVrFtM5vlQXWdl datos sobre los datos
860 -M99R_PVrFtM5vlQXWdl cruce de datos
861 -M99R_PVrFtM5vlQXWdl aprendizaje de las máquinas
862 -M99R_PVrFtM5vlQXWdl centralización de los datos
863 -M99R_PVrFtM5vlQXWdl posibilidad de poner a disposición pública los dat
864 -M99nDRmo0UF6UF8Az3a plataforma virtual
865 -M99nDRmo0UF6UF8Az3a simulador
866 -M99nDRmo0UF6UF8Az3a inteligencia artificial
867 -M99nDRmo0UF6UF8Az3a red social
868 -M99nDRmo0UF6UF8Az3a software/ sistema operativo
869 -M9A4D1YZmmvtUkeHsFJ almacenamiento de datos
870 -M9A4D1YZmmvtUkeHsFJ gran manejo de datos
871 -M9A4D1YZmmvtUkeHsFJ archivos
872 -M9A4D1YZmmvtUkeHsFJ seguridad
873 -M9A4D1YZmmvtUkeHsFJ grande
874 -M9B5kjwS61OsoEKoOxO información
875 -M9B5kjwS61OsoEKoOxO gran cantidad de datos
876 -M9B5kjwS61OsoEKoOxO datos
877 -M9B5kjwS61OsoEKoOxO nube
878 -M9B5kjwS61OsoEKoOxO habitos de las personas
879 -M9BIymT2ozqK0F_HAyd información
880 -M9BIymT2ozqK0F_HAyd util
881 -M9BIymT2ozqK0F_HAyd actualizaciones
882 -M9BIymT2ozqK0F_HAyd confiable
883 -M9BIymT2ozqK0F_HAyd leal
884 -M9CYlNcyNBvC5Z2_fcn grande
885 -M9CYlNcyNBvC5Z2_fcn amplio
886 -M9CYlNcyNBvC5Z2_fcn on line
887 -M9CYlNcyNBvC5Z2_fcn información
888 -M9CYlNcyNBvC5Z2_fcn acceso
889 -M9E8gmxN_yG_ULCW0S0 información
890 -M9E8gmxN_yG_ULCW0S0 exposición
891 -M9E8gmxN_yG_ULCW0S0 personalización
892 -M9E8gmxN_yG_ULCW0S0 especulación
893 -M9E8gmxN_yG_ULCW0S0 customización
894 -M9FJc5oq3wkoSf3tMeR grandes datos
895 -M9FJc5oq3wkoSf3tMeR informática
896 -M9FJc5oq3wkoSf3tMeR redes
897 -M9FJc5oq3wkoSf3tMeR almacenamiento
898 -M9FJc5oq3wkoSf3tMeR información
899 -M9FiEZdj-cr7fnwPpsC información
900 -M9FiEZdj-cr7fnwPpsC complejidad
901 -M9FiEZdj-cr7fnwPpsC procesamiento
902 -M9FiEZdj-cr7fnwPpsC criterios
903 -M9FiEZdj-cr7fnwPpsC análisis
904 -M9GBxdGZWSrDeKo1rQ9 información
905 -M9GBxdGZWSrDeKo1rQ9 internet
906 -M9GBxdGZWSrDeKo1rQ9 datos
907 -M9GBxdGZWSrDeKo1rQ9 mucho
908 -M9GBxdGZWSrDeKo1rQ9 cantidad
909 -M9IVj2bLFYgLX54AGya información
910 -M9IVj2bLFYgLX54AGya abundante
911 -M9IVj2bLFYgLX54AGya algoritmos
912 -M9IVj2bLFYgLX54AGya opinión pública
913 -M9IVj2bLFYgLX54AGya consumo
914 -M9J2S7bG1MLAXg9zZ5L información
915 -M9J2S7bG1MLAXg9zZ5L manejo
916 -M9J2S7bG1MLAXg9zZ5L evolución
917 -M9J2S7bG1MLAXg9zZ5L mercado
918 -M9J2S7bG1MLAXg9zZ5L competencia
919 -M9JITBzSs6evKE5hC-l mucho
920 -M9JITBzSs6evKE5hC-l grande
921 -M9JITBzSs6evKE5hC-l importante
922 -M9JITBzSs6evKE5hC-l leer
923 -M9JITBzSs6evKE5hC-l concentracion
924 -M9JL3Zf1JSkgVqeYSGH data analytics
925 -M9JL3Zf1JSkgVqeYSGH información
926 -M9JL3Zf1JSkgVqeYSGH minería de datos
927 -M9JL3Zf1JSkgVqeYSGH análisis estadístico
928 -M9JL3Zf1JSkgVqeYSGH machine learning
929 -M9JN7xfG3zaAyG6fJB1 internet
930 -M9JN7xfG3zaAyG6fJB1 páginas web
931 -M9JN7xfG3zaAyG6fJB1 trabajo colaborativo
932 -M9JN7xfG3zaAyG6fJB1 plagio
933 -M9JN7xfG3zaAyG6fJB1 análisis de fuentes
934 -M9JN7xfG3zaAyG6fJB1 investigación
935 -M9JN7xfG3zaAyG6fJB1 brecha digital
936 -M9JN7xfG3zaAyG6fJB1 celular
937 -M9JN7xfG3zaAyG6fJB1 aplicaciones
938 -M9JN7xfG3zaAyG6fJB1 habilidades digitales
939 -M9JN7xfG3zaAyG6fJB1 espacio-tiempo
940 -M9JN7xfG3zaAyG6fJB1 formación
941 -M9JN7xfG3zaAyG6fJB1 facebook
942 -M9JN7xfG3zaAyG6fJB1 instagram
943 -M9Jhtr7PZhf8u657mwP información
944 -M9Jhtr7PZhf8u657mwP control
945 -M9Jhtr7PZhf8u657mwP investigación
946 -M9Jhtr7PZhf8u657mwP corporación
947 -M9Jhtr7PZhf8u657mwP estado
948 -M9KGZz9Ev3Fw9pESmV8 hacker
949 -M9KGZz9Ev3Fw9pESmV8 internet
950 -M9KGZz9Ev3Fw9pESmV8 base de datos
951 -M9KGZz9Ev3Fw9pESmV8 información
952 -M9KGZz9Ev3Fw9pESmV8 milenio
953 -M9KnSQurkRx9MBn0NG- marketing
954 -M9KnSQurkRx9MBn0NG- programacion
955 -M9KnSQurkRx9MBn0NG- conocimiento
956 -M9KnSQurkRx9MBn0NG- decisiones
957 -M9KnSQurkRx9MBn0NG- informacion
958 -M9O3ozw7PmJkiRsQReK investigación
959 -M9O3ozw7PmJkiRsQReK análisis
960 -M9O3ozw7PmJkiRsQReK marketing
961 -M9O3ozw7PmJkiRsQReK seguimiento
962 -M9O3ozw7PmJkiRsQReK almacenaje
963 -M9OIV5nFgFaAEEzkr-f poder
964 -M9OIV5nFgFaAEEzkr-f politicas
965 -M9OIV5nFgFaAEEzkr-f potencial
966 -M9OIV5nFgFaAEEzkr-f información
967 -M9OIV5nFgFaAEEzkr-f invasivo
968 -M9OMfjG-MEX7PZ7FKPZ numeros
969 -M9OMfjG-MEX7PZ7FKPZ informacion
970 -M9OMfjG-MEX7PZ7FKPZ investigacion
971 -M9OMfjG-MEX7PZ7FKPZ estadistica
972 -M9OMfjG-MEX7PZ7FKPZ peoyeccion
973 -M9P8cEyiTKn1oVHRZgj respuestas
974 -M9P8cEyiTKn1oVHRZgj información
975 -M9P8cEyiTKn1oVHRZgj gran volumen de datos
976 -M9P8cEyiTKn1oVHRZgj tecnología
977 -M9P8cEyiTKn1oVHRZgj vulnerable
978 -M9PCFIA2SK6NYpI1YqT poder
979 -M9PCFIA2SK6NYpI1YqT información
980 -M9PCFIA2SK6NYpI1YqT control
981 -M9PCFIA2SK6NYpI1YqT sistema
982 -M9PCFIA2SK6NYpI1YqT dinero
983 -M9QE6KTmEE9p-e3QXHC generar información
984 -M9QE6KTmEE9p-e3QXHC análisis datos
985 -M9QE6KTmEE9p-e3QXHC decisión en base a información
986 -M9QE6KTmEE9p-e3QXHC eficiencia
987 -M9QE6KTmEE9p-e3QXHC planificación
988 -M9QE6KTmEE9p-e3QXHC reducción costos
989 -M9Qf1k4_L4Yd01_Y4ai gran cantidad de contenido
990 -M9Qf1k4_L4Yd01_Y4ai información
991 -M9Qf1k4_L4Yd01_Y4ai almacenamiento
992 -M9Qf1k4_L4Yd01_Y4ai capacidad
993 -M9Qf1k4_L4Yd01_Y4ai tecnología
994 -M9RK7oRmnhZsy280u5Y tecnologia
995 -M9RK7oRmnhZsy280u5Y innovacion
996 -M9RK7oRmnhZsy280u5Y sistema de datos
997 -M9RK7oRmnhZsy280u5Y rapidez
998 -M9RK7oRmnhZsy280u5Y eficacia
999 -M9S0lo0Ll60FI_v1roX gran
1000 -M9S0lo0Ll60FI_v1roX valorativo
1001 -M9S0lo0Ll60FI_v1roX importante
1002 -M9S0lo0Ll60FI_v1roX difícil
1003 -M9S0lo0Ll60FI_v1roX no entendi
1004 -M9UPWfhWsEnHbOgssvH software
1005 -M9UPWfhWsEnHbOgssvH hardware
1006 -M9UPWfhWsEnHbOgssvH plataformas
1007 -M9UPWfhWsEnHbOgssvH internet
1008 -M9UPWfhWsEnHbOgssvH base de datos
1009 -M9UnyBP_fIDBDCXAT29 información
1010 -M9UnyBP_fIDBDCXAT29 global
1011 -M9UnyBP_fIDBDCXAT29 archivo
1012 -M9UnyBP_fIDBDCXAT29 comunicación
1013 -M9UnyBP_fIDBDCXAT29 conexión
1014 -M9VnOxy1vMHrL8Gzyhz computadoras
1015 -M9VnOxy1vMHrL8Gzyhz mucha cantidad
1016 -M9VnOxy1vMHrL8Gzyhz desorden
1017 -M9VnOxy1vMHrL8Gzyhz tecnologías
1018 -M9VnOxy1vMHrL8Gzyhz clasificación
1019 -M9Vq68asFdI-Ys_aRyj gran cantidad de datos
1020 -M9Vq68asFdI-Ys_aRyj previsión de sucesos
1021 -M9Vq68asFdI-Ys_aRyj peligrosidad en el uso de esos datos
1022 -M9Vq68asFdI-Ys_aRyj manipulación con fines políticos
1023 -M9Vq68asFdI-Ys_aRyj falta de privacidad
1024 -M9Vq68asFdI-Ys_aRyj venta de datos sin consentimiento
1025 -M9Vq68asFdI-Ys_aRyj categorizaciones sociales erróneas sin grises
1026 -M9WGG4v9N1cvwC3ThsY análisis de datos
1027 -M9WGG4v9N1cvwC3ThsY volumen de datos
1028 -M9WGG4v9N1cvwC3ThsY comercio de datos
1029 -M9WGG4v9N1cvwC3ThsY digitalización
1030 -M9WGG4v9N1cvwC3ThsY estructura
1031 -M9WGd1GBwvXyXkw7B-b información
1032 -M9WGd1GBwvXyXkw7B-b intereses
1033 -M9WGd1GBwvXyXkw7B-b habitos
1034 -M9WGd1GBwvXyXkw7B-b consumo
1035 -M9WGd1GBwvXyXkw7B-b tic's
1036 -M9WNG8Up41wap7orj47 información
1037 -M9WNG8Up41wap7orj47 mucho
1038 -M9WNG8Up41wap7orj47 inglés
1039 -M9WNG8Up41wap7orj47 publicidad
1040 -M9WNG8Up41wap7orj47 estudio
1041 -M9WNOC5QR71VdppPym7 datos
1042 -M9WNOC5QR71VdppPym7 métricas
1043 -M9WNOC5QR71VdppPym7 complejidad
1044 -M9WNOC5QR71VdppPym7 cantidad
1045 -M9WNOC5QR71VdppPym7 categorización
1046 -M9WO4jDYdki02VpANnC datos personales
1047 -M9WO4jDYdki02VpANnC espionaje
1048 -M9WO4jDYdki02VpANnC mercado
1049 -M9WO4jDYdki02VpANnC manejo de las relaciones sociales
1050 -M9WO4jDYdki02VpANnC orden mundial
1051 -M9WOiBb4U5IXdnqiGB8 datos
1052 -M9WOiBb4U5IXdnqiGB8 estadística
1053 -M9WOiBb4U5IXdnqiGB8 mejora
1054 -M9WOiBb4U5IXdnqiGB8 control
1055 -M9WOiBb4U5IXdnqiGB8 evaluación
1056 -M9WeXwoKiVw3aRAG9_O posibilidades
1057 -M9WeXwoKiVw3aRAG9_O camdbridge analitycs
1058 -M9WeXwoKiVw3aRAG9_O campañas de macri
1059 -M9WeXwoKiVw3aRAG9_O genética y patentes de monsanto
1060 -M9WeXwoKiVw3aRAG9_O guerra psicopolitica
1061 -M9WeXwoKiVw3aRAG9_O derechos humanos de 4ta generación
1062 -M9YPEZQ-ttuxXhJGs5f hipermediaciones
1063 -M9YPEZQ-ttuxXhJGs5f cultura digital
1064 -M9YPEZQ-ttuxXhJGs5f scolari (autor)
1065 -M9YPEZQ-ttuxXhJGs5f tecnología
1066 -M9YPEZQ-ttuxXhJGs5f comunicación
1067 -M9Z64DJaMWx8MaHNt6y muchos datos
1068 -M9Z64DJaMWx8MaHNt6y complejo
1069 -M9Z64DJaMWx8MaHNt6y análisis
1070 -M9Z64DJaMWx8MaHNt6y volumen
1071 -M9Z64DJaMWx8MaHNt6y información
1072 -M9Zd4cdayURTwJGZqiZ información
1073 -M9Zd4cdayURTwJGZqiZ organización
1074 -M9Zd4cdayURTwJGZqiZ control
1075 -M9Zd4cdayURTwJGZqiZ despersonalización
1076 -M9Zd4cdayURTwJGZqiZ acceso
1077 -M9ZnyLUhv93_A51aj9v datos
1078 -M9ZnyLUhv93_A51aj9v difícil
1079 -M9ZnyLUhv93_A51aj9v computación
1080 -M9ZnyLUhv93_A51aj9v programación
1081 -M9ZnyLUhv93_A51aj9v complejo
1082 -M9_-uaDD0pyk4gmPDbe pesado
1083 -M9_-uaDD0pyk4gmPDbe tráfico
1084 -M9_-uaDD0pyk4gmPDbe datos
1085 -M9_-uaDD0pyk4gmPDbe hardware
1086 -M9_-uaDD0pyk4gmPDbe internet
1087 -M9aPYmq3AHQO7p_IPEO estadística
1088 -M9aPYmq3AHQO7p_IPEO información
1089 -M9aPYmq3AHQO7p_IPEO privacidad
1090 -M9aPYmq3AHQO7p_IPEO comunicación
1091 -M9aPYmq3AHQO7p_IPEO aa
1092 -M9bPEajqZ48NB8XfdgH gran data
1093 -M9bPEajqZ48NB8XfdgH buena información
1094 -M9bPEajqZ48NB8XfdgH información irrelevante
1095 -M9bPEajqZ48NB8XfdgH sarcasmo fake news
1096 -M9bPEajqZ48NB8XfdgH iluminati manipulación de contenidos
1097 -M9eJbiud3d8UgZSkLQX información
1098 -M9eJbiud3d8UgZSkLQX poder
1099 -M9eJbiud3d8UgZSkLQX perfil
1100 -M9eJbiud3d8UgZSkLQX tecnología
1101 -M9eJbiud3d8UgZSkLQX intereses
1102 -M9eMLYoPJpwaiF28JpH bioinformatica
1103 -M9eMLYoPJpwaiF28JpH bases de datos
1104 -M9eMLYoPJpwaiF28JpH informacion
1105 -M9eMLYoPJpwaiF28JpH procesar
1106 -M9eMLYoPJpwaiF28JpH estudio
1107 -M9fIfZbxBQuf_C9ykdU conglomerado de datos
1108 -M9fIfZbxBQuf_C9ykdU base de datos
1109 -M9fIfZbxBQuf_C9ykdU control social
1110 -M9fIfZbxBQuf_C9ykdU altgorismo
1111 -M9fIfZbxBQuf_C9ykdU habeas data
1112 -M9fe1y2pfJPMNDavqwX rastreo
1113 -M9fe1y2pfJPMNDavqwX publicidad
1114 -M9fe1y2pfJPMNDavqwX prediccion
1115 -M9fe1y2pfJPMNDavqwX invasion
1116 -M9fe1y2pfJPMNDavqwX simulacion
1117 -M9ffUSfoDKgHn7g4VHI masividad
1118 -M9ffUSfoDKgHn7g4VHI decisiones
1119 -M9ffUSfoDKgHn7g4VHI predicción
1120 -M9ffUSfoDKgHn7g4VHI aprendizaje
1121 -M9ffUSfoDKgHn7g4VHI futuro
1122 -M9gEVSI0qjc5HlzRTzN sistemas
1123 -M9gEVSI0qjc5HlzRTzN complejidad
1124 -M9gEVSI0qjc5HlzRTzN manipulacion de informacio
1125 -M9gEVSI0qjc5HlzRTzN quebrantar la esfera de lo privado
1126 -M9gEVSI0qjc5HlzRTzN inconmesurable
1127 -M9im7X_vwdllY2L2qcW datos
1128 -M9im7X_vwdllY2L2qcW predictivo
1129 -M9im7X_vwdllY2L2qcW analisis
1130 -M9im7X_vwdllY2L2qcW mineria
1131 -M9im7X_vwdllY2L2qcW aprendizaje
1132 -M9jzjOvfhhnc1HQUw-K tics
1133 -M9jzjOvfhhnc1HQUw-K privacidad
1134 -M9jzjOvfhhnc1HQUw-K información
1135 -M9jzjOvfhhnc1HQUw-K control
1136 -M9jzjOvfhhnc1HQUw-K monitoreo
1137 -M9ktACuVC6ei8uX0_QT ideas
1138 -M9ktACuVC6ei8uX0_QT tecnológica
1139 -M9ktACuVC6ei8uX0_QT avances
1140 -M9ktACuVC6ei8uX0_QT incorporación
1141 -M9ktACuVC6ei8uX0_QT se abren nuevos horizontes
1142 -M9kuexphs2s83feSRIf informático
1143 -M9kuexphs2s83feSRIf desafio
1144 -M9kuexphs2s83feSRIf dificultad
1145 -M9kuexphs2s83feSRIf conocimiento
1146 -M9kuexphs2s83feSRIf velocidad
1147 -M9mWirV6vPcrexA2Ws_ informacion
1148 -M9mWirV6vPcrexA2Ws_ personal
1149 -M9mWirV6vPcrexA2Ws_ privada
1150 -M9mWirV6vPcrexA2Ws_ control
1151 -M9mWirV6vPcrexA2Ws_ dominacion
1152 -M9mWirV6vPcrexA2Ws_ invasion
1153 -M9nqYSkZakrx3Y3WN9S información
1154 -M9nqYSkZakrx3Y3WN9S tecnologia
1155 -M9nqYSkZakrx3Y3WN9S servicios tecnológicos de información
1156 -M9nqYSkZakrx3Y3WN9S tecnología inevitable
1157 -M9nqYSkZakrx3Y3WN9S mercado de información y datos
1158 -M9nqYSkZakrx3Y3WN9S no regulado muchas veces por los estados
1159 -M9nzc4vnb7gkyGluTy9 gran
1160 -M9nzc4vnb7gkyGluTy9 datos
1161 -M9nzc4vnb7gkyGluTy9 información
1162 -M9nzc4vnb7gkyGluTy9 primordial
1163 -M9nzc4vnb7gkyGluTy9 novedad
1164 -M9qLboEZV36oJ_TyVTW base de datos
1165 -M9qLboEZV36oJ_TyVTW informacion
1166 -M9qLboEZV36oJ_TyVTW recopilacion
1167 -M9qLboEZV36oJ_TyVTW inseguro
1168 -M9qLboEZV36oJ_TyVTW investigacion
1169 -M9wcXm0RJpx_JvCsxmp nube
1170 -M9wcXm0RJpx_JvCsxmp información
1171 -M9wcXm0RJpx_JvCsxmp volumen
1172 -M9wcXm0RJpx_JvCsxmp datos
1173 -M9wcXm0RJpx_JvCsxmp tendencia
1174 -M9xMDhgcbyfZt_0h02j muchos datos
1175 -M9xMDhgcbyfZt_0h02j nos escuchan
1176 -M9xMDhgcbyfZt_0h02j base de datos muy grande
1177 -M9xMDhgcbyfZt_0h02j internet
1178 -M9xMDhgcbyfZt_0h02j logaritmos
1179 -M9xUtvCz1CO-fhWhxcW información
1180 -M9xUtvCz1CO-fhWhxcW privada
1181 -M9xUtvCz1CO-fhWhxcW globalización
1182 -M9xUtvCz1CO-fhWhxcW tecnología
1183 -M9xUtvCz1CO-fhWhxcW innovación
1184 -MA1lb7yaEsRL6pfJQIU innovador
1185 -MA1lb7yaEsRL6pfJQIU desafío
1186 -MA1lb7yaEsRL6pfJQIU complejidad
1187 -MA1lb7yaEsRL6pfJQIU tics
1188 -MA1lb7yaEsRL6pfJQIU herramienta
1189 -MA1ofWJV23GYP3n_6CD gran dato
1190 -MA1ofWJV23GYP3n_6CD mucho espacio
1191 -MA1ofWJV23GYP3n_6CD gran espacio
1192 -MA1ofWJV23GYP3n_6CD mucho tiempo
1193 -MA1ofWJV23GYP3n_6CD informatica
1194 -MA2h7BSTc0qWZ3BLU-Y que
1195 -MA2h7BSTc0qWZ3BLU-Y boludes
1196 -MA2h7BSTc0qWZ3BLU-Y que
1197 -MA2h7BSTc0qWZ3BLU-Y están
1198 -MA2h7BSTc0qWZ3BLU-Y haciendo
1199 -MA2sisq3Q0zUncprTLQ poder
1200 -MA2sisq3Q0zUncprTLQ complejidad
1201 -MA2sisq3Q0zUncprTLQ costoso
1202 -MA2sisq3Q0zUncprTLQ ventaja
1203 -MA2sisq3Q0zUncprTLQ privacidad
1204 -MA3n8HEVI12lqBe-nMK sonbreinformación
1205 -MA3n8HEVI12lqBe-nMK vulnerabilidad
1206 -MA3n8HEVI12lqBe-nMK poder
1207 -MA3n8HEVI12lqBe-nMK manipulación
1208 -MA3n8HEVI12lqBe-nMK desconocimiento
1209 -MA4WaBx30V6BCJ1r8h3 individualismo
1210 -MA4WaBx30V6BCJ1r8h3 posmoderno
1211 -MA4WaBx30V6BCJ1r8h3 desinformacion
1212 -MA4WaBx30V6BCJ1r8h3 avismo
1213 -MA4WaBx30V6BCJ1r8h3 perdida
1214 -MA4YbfnliAzhenhPQ2L informacion
1215 -MA4YbfnliAzhenhPQ2L control
1216 -MA4YbfnliAzhenhPQ2L trazabilidad
1217 -MA4YbfnliAzhenhPQ2L decisión
1218 -MA4YbfnliAzhenhPQ2L tecnologia
1219 -MA5drRLHu9kKkVh87Ul opinion publica
1220 -MA5drRLHu9kKkVh87Ul manipulacion
1221 -MA5drRLHu9kKkVh87Ul datos
1222 -MA5drRLHu9kKkVh87Ul publicidad
1223 -MA5drRLHu9kKkVh87Ul redes sociales
1224 -MA6mx_316uOHOgOX_Vq información
1225 -MA6mx_316uOHOgOX_Vq instantánea
1226 -MA6mx_316uOHOgOX_Vq seguimiento
1227 -MA6mx_316uOHOgOX_Vq inmediato
1228 -MA6mx_316uOHOgOX_Vq redes sociales
1229 -MA6xOPVFeWk-2iEI5Ut espionaje
1230 -MA6xOPVFeWk-2iEI5Ut mencancía
1231 -MA6xOPVFeWk-2iEI5Ut inteligencia artificial
1232 -MA6xOPVFeWk-2iEI5Ut concentración de poder
1233 -MA6xOPVFeWk-2iEI5Ut desarrollo intelectual
1234 -MA7I6EGVB6HX0WdjOD3 información
1235 -MA7I6EGVB6HX0WdjOD3 segmentación
1236 -MA7I6EGVB6HX0WdjOD3 control de flujos informativos
1237 -MA7I6EGVB6HX0WdjOD3 acceso a información sensible
1238 -MA7I6EGVB6HX0WdjOD3 manipulación
1239 -MA84bdjTbDNYpW4rVrn grandes datos
1240 -MA84bdjTbDNYpW4rVrn poco abarcativo
1241 -MA84bdjTbDNYpW4rVrn grandes archivos
1242 -MA84bdjTbDNYpW4rVrn mucha información
1243 -MA84bdjTbDNYpW4rVrn datos recopilados
1244 -MA9Ls5s9ZqRh2ad_xRS control
1245 -MA9Ls5s9ZqRh2ad_xRS 1984
1246 -MA9Ls5s9ZqRh2ad_xRS gran hermano
1247 -MA9Ls5s9ZqRh2ad_xRS redes sociales
1248 -MA9Ls5s9ZqRh2ad_xRS china
1249 -MACbOjTBFOUnOTnD_p5 datos
1250 -MACbOjTBFOUnOTnD_p5 cantidad
1251 -MACbOjTBFOUnOTnD_p5 grande
1252 -MACbOjTBFOUnOTnD_p5 tecnologia
1253 -MACbOjTBFOUnOTnD_p5 internet
1254 -MHJlrA1dB1lEQ2dtLwy datos
1255 -MHJlrA1dB1lEQ2dtLwy grabde
1256 -MHJlrA1dB1lEQ2dtLwy personal
1257 -MHJlrA1dB1lEQ2dtLwy global
1258 -MHJlrA1dB1lEQ2dtLwy informacion
1259 -MHvawm4Gby6HjQPqhIF datos grandes
1260 -MHvawm4Gby6HjQPqhIF tecnologia
1261 -MHvawm4Gby6HjQPqhIF avance
1262 -MHvawm4Gby6HjQPqhIF futuro
1263 -MHvawm4Gby6HjQPqhIF globalizacion
1264 -MHwq39ljl8yTbFIUCAt tecnologias
1265 -MHwq39ljl8yTbFIUCAt datos
1266 -MHwq39ljl8yTbFIUCAt investigación
1267 -MHwq39ljl8yTbFIUCAt relaciones
1268 -MHwq39ljl8yTbFIUCAt sustitución
1269 -MI-DBiVXnjO1_xyIqe5 observación
1270 -MI-DBiVXnjO1_xyIqe5 información
1271 -MI-DBiVXnjO1_xyIqe5 superior
1272 -MI-DBiVXnjO1_xyIqe5 amplitud
1273 -MI-DBiVXnjO1_xyIqe5 simplicidad
1274 -MI1Ua2LKwQkRp6L_KL4 sometimiento
1275 -MI1Ua2LKwQkRp6L_KL4 control
1276 -MI1Ua2LKwQkRp6L_KL4 esclavitud
1277 -MI1Ua2LKwQkRp6L_KL4 informacion
1278 -MI1Ua2LKwQkRp6L_KL4 decadencia
1279 -MJiTO5NAjveOIB6uc7G informacion personal
1280 -MJiTO5NAjveOIB6uc7G internet
1281 -MJiTO5NAjveOIB6uc7G redes sociales
1282 -MJiTO5NAjveOIB6uc7G compartir
1283 -MJiTO5NAjveOIB6uc7G macrodatos
1284 -MJiTO5NAjveOIB6uc7G tecnología
1285 -MK66xQT-woKEPPt8iKS información
1286 -MK66xQT-woKEPPt8iKS posibilidades
1287 -MK66xQT-woKEPPt8iKS análisis
1288 -MK66xQT-woKEPPt8iKS trabajo
1289 -MK66xQT-woKEPPt8iKS nuevo
1290 -MKNfl6SdR7h-NBvZ_Jj información a lo grande
1291 -MKNfl6SdR7h-NBvZ_Jj océano de datos
1292 -MKNfl6SdR7h-NBvZ_Jj algo superior
1293 -MKNfl6SdR7h-NBvZ_Jj nube digital
1294 -MKNfl6SdR7h-NBvZ_Jj internet
1295 -MKXOkNDQjknCVeb1YP2 informacion
1296 -MKXOkNDQjknCVeb1YP2 analisis
1297 -MKXOkNDQjknCVeb1YP2 conocimiento
1298 -MKXOkNDQjknCVeb1YP2 investigacion
1299 -MKXOkNDQjknCVeb1YP2 tecnologia
1300 -MKc18qPjUPSYiUegjSz mucha informacion
1301 -MKc18qPjUPSYiUegjSz control de datos
1302 -MKc18qPjUPSYiUegjSz manipulación
1303 -MKc18qPjUPSYiUegjSz almacenamiento global
1304 -MKc18qPjUPSYiUegjSz expansión
1305 -ML7vS4tfqywTm7SVODZ datos cuantitativos
1306 -ML7vS4tfqywTm7SVODZ análisis
1307 -ML7vS4tfqywTm7SVODZ tecnología
1308 -ML7vS4tfqywTm7SVODZ internet
1309 -ML7vS4tfqywTm7SVODZ privacidad
1310 -ML7vS4tfqywTm7SVODZ ciencias sociales
1311 -MLOi-xh9E2OTlK96Wbl hamburguesa
1312 -MLOi-xh9E2OTlK96Wbl grandote
1313 -MLOi-xh9E2OTlK96Wbl latinejo
1314 -MLOi-xh9E2OTlK96Wbl pedantería
1315 -MLOi-xh9E2OTlK96Wbl soberbia
1316 -MLOi-xh9E2OTlK96Wbl mierda
1317 -MLPiOWKMSfxELGJZ-b0 grandes cantidades de datos
1318 -MLPiOWKMSfxELGJZ-b0 control
1319 -MLPiOWKMSfxELGJZ-b0 gran hermano
1320 -MLPiOWKMSfxELGJZ-b0 vida pública expuesta
1321 -MLPiOWKMSfxELGJZ-b0 inclusión
1322 -MLQ-7jgiiIl4wcO5Iod datos
1323 -MLQ-7jgiiIl4wcO5Iod grande
1324 -MLQ-7jgiiIl4wcO5Iod base
1325 -MLQ-7jgiiIl4wcO5Iod muchos
1326 -MLQ-7jgiiIl4wcO5Iod sistema
1327 -MLQq2HSuKCBcMaFvvK9 feedback
1328 -MLQq2HSuKCBcMaFvvK9 redes
1329 -MLQq2HSuKCBcMaFvvK9 postear
1330 -MLQq2HSuKCBcMaFvvK9 instagram
1331 -MLQq2HSuKCBcMaFvvK9 hastag
1332 -MLR-usY5pe5nDXrGI8b ciencia
1333 -MLR-usY5pe5nDXrGI8b datos
1334 -MLR-usY5pe5nDXrGI8b manipulación
1335 -MLR-usY5pe5nDXrGI8b deciciones
1336 -MLR-usY5pe5nDXrGI8b modelos
1337 -MLR7MPhK2XCno_iqlDX temor
1338 -MLR7MPhK2XCno_iqlDX desconfianza
1339 -MLR7MPhK2XCno_iqlDX preocupación
1340 -MLR7MPhK2XCno_iqlDX desigualdad
1341 -MLR7MPhK2XCno_iqlDX elite
1342 -MLSLYhelOqYAdfxsAl1 desconocida
1343 -MLSLYhelOqYAdfxsAl1 desconocida
1344 -MLSLYhelOqYAdfxsAl1 desconocida
1345 -MLSLYhelOqYAdfxsAl1 desconocida
1346 -MLSLYhelOqYAdfxsAl1 desconocida
1347 -MLSXmD3hmOnyus5Ew4T manipulación
1348 -MLSXmD3hmOnyus5Ew4T intromisión
1349 -MLSXmD3hmOnyus5Ew4T violación a la intimidad
1350 -MLSXmD3hmOnyus5Ew4T información
1351 -MLSXmD3hmOnyus5Ew4T velocidad
1352 -MLXt4an9A8nPM2k32Lk análisis de datos
1353 -MLXt4an9A8nPM2k32Lk algoritmo redes sociales
1354 -MLXt4an9A8nPM2k32Lk minería de datos
1355 -MLXt4an9A8nPM2k32Lk data mining
1356 -MLXt4an9A8nPM2k32Lk ingeniería de datos
1357 -MLbm_HvztjTY5SpqHSb sobreabundancia de datos
1358 -MLbm_HvztjTY5SpqHSb exceso de información
1359 -MLbm_HvztjTY5SpqHSb sistemas de algoritmos
1360 -MLbm_HvztjTY5SpqHSb patrones de afinidad
1361 -MLbm_HvztjTY5SpqHSb herramienta para medir comportamientos
1362 -MLepYbouoqwmtJNRRbK internet
1363 -MLepYbouoqwmtJNRRbK google
1364 -MLepYbouoqwmtJNRRbK informacion
1365 -MLepYbouoqwmtJNRRbK conceptos
1366 -MLepYbouoqwmtJNRRbK investigación
1367 -MLjoVz5SmA1mjjGUg-4 gran dato
1368 -MLjoVz5SmA1mjjGUg-4 una hamburguesa
1369 -MLjoVz5SmA1mjjGUg-4 gran noticia
1370 -MLjoVz5SmA1mjjGUg-4 muy grande
1371 -MLjoVz5SmA1mjjGUg-4 gran sorpresa
1372 -MLtIssGbdgViO8EAnsB datos
1373 -MLtIssGbdgViO8EAnsB acceso
1374 -MLtIssGbdgViO8EAnsB calculo
1375 -MLtIssGbdgViO8EAnsB análisis
1376 -MLtIssGbdgViO8EAnsB conocimiento
1377 -MLzJP5OPDMNyjzga20r conocimiento
1378 -MLzJP5OPDMNyjzga20r investigar
1379 -MLzJP5OPDMNyjzga20r evaluar
1380 -MLzJP5OPDMNyjzga20r invertir
1381 -MLzJP5OPDMNyjzga20r desarrollar
1382 -MM1HiQs8bhviZSuYPMN problemas
1383 -MM1HiQs8bhviZSuYPMN distancia
1384 -MM1HiQs8bhviZSuYPMN des humanizar
1385 -MM1HiQs8bhviZSuYPMN robo
1386 -MM1HiQs8bhviZSuYPMN invasión
1387 -MM7Yo-7GxNnXZyKcjKf gran
1388 -MM7Yo-7GxNnXZyKcjKf dato
1389 -MM7Yo-7GxNnXZyKcjKf interés
1390 -MM7Yo-7GxNnXZyKcjKf información
1391 -MM7Yo-7GxNnXZyKcjKf importante
1392 -MM8FtmWeg3r_BMWcbLr telenoche
1393 -MM8FtmWeg3r_BMWcbLr somos lo que escribimos
1394 -MM8FtmWeg3r_BMWcbLr es tendencia
1395 -MM8FtmWeg3r_BMWcbLr ejercitos de cientificos
1396 -MM8FtmWeg3r_BMWcbLr que nos interesa
1397 -MM8aMK1YZ6AiZ9OfLGm tecnologia
1398 -MM8aMK1YZ6AiZ9OfLGm información
1399 -MM8aMK1YZ6AiZ9OfLGm rapidez
1400 -MM8aMK1YZ6AiZ9OfLGm informatica
1401 -MM8aMK1YZ6AiZ9OfLGm amplitud
1402 -MMA3M-WS3ySl6CTG1to ciencia de los datos
1403 -MMA3M-WS3ySl6CTG1to estudio de los datos
1404 -MMA3M-WS3ySl6CTG1to estadisticas de datos
1405 -MMA3M-WS3ySl6CTG1to datos
1406 -MMA3M-WS3ySl6CTG1to evaluacion de los datos
1407 -MMBXdmzqVokHX2B9bMM tecnología
1408 -MMBXdmzqVokHX2B9bMM información
1409 -MMBXdmzqVokHX2B9bMM gates
1410 -MMBXdmzqVokHX2B9bMM computadora
1411 -MMBXdmzqVokHX2B9bMM digital
1412 -MMBqICRyPxt8eTveAcP gran dato
1413 -MMBqICRyPxt8eTveAcP gran fecha
1414 -MMBqICRyPxt8eTveAcP dato interesante
1415 -MMBqICRyPxt8eTveAcP una fecha importante
1416 -MMBqICRyPxt8eTveAcP un gran cambio
1417 -MMCEB6Cs23tAF9gRaT6 estadistica
1418 -MMCEB6Cs23tAF9gRaT6 datos
1419 -MMCEB6Cs23tAF9gRaT6 base de datos
1420 -MMCEB6Cs23tAF9gRaT6 volume
1421 -MMCEB6Cs23tAF9gRaT6 insights
1422 -MMDbwLyHiqYw7KMtGDW amigos
1423 -MMDbwLyHiqYw7KMtGDW familia
1424 -MMDbwLyHiqYw7KMtGDW vecinos
1425 -MMDbwLyHiqYw7KMtGDW amor
1426 -MMDbwLyHiqYw7KMtGDW deportes
1427 -MO400KVthenDRhPgXKp manipulación
1428 -MO400KVthenDRhPgXKp negocio
1429 -MO400KVthenDRhPgXKp capitalismo
1430 -MO400KVthenDRhPgXKp fraudulento
1431 -MO400KVthenDRhPgXKp mentiras
1432 -MO4YBQ3eH4gNQdBzobR futuro
1433 -MO4YBQ3eH4gNQdBzobR conocimiento
1434 -MO4YBQ3eH4gNQdBzobR ciencia
1435 -MO4YBQ3eH4gNQdBzobR apertura
1436 -MO4YBQ3eH4gNQdBzobR creatividad
1437 -MO4jK-s_-051I7hC8cx graficos
1438 -MO4jK-s_-051I7hC8cx funciones
1439 -MO4jK-s_-051I7hC8cx matemática
1440 -MO4jK-s_-051I7hC8cx masivo
1441 -MO4jK-s_-051I7hC8cx cuantitativo
1442 -MO6D6_ijowDF31o8qqE machine learning
1443 -MO6D6_ijowDF31o8qqE base de datos
1444 -MO6D6_ijowDF31o8qqE programación
1445 -MO6D6_ijowDF31o8qqE tecnología
1446 -MO6D6_ijowDF31o8qqE data science
1447 -MO7WBQZMCJiGkty2kSp breve
1448 -MO7WBQZMCJiGkty2kSp parco
1449 -MO7WBQZMCJiGkty2kSp pobre
1450 -MO7WBQZMCJiGkty2kSp dudoso
1451 -MO7WBQZMCJiGkty2kSp peligroso
1452 -MO7ltqjRlPbC6HIssjp importante
1453 -MO7ltqjRlPbC6HIssjp infaltable
1454 -MO7ltqjRlPbC6HIssjp imprescindible
1455 -MO7ltqjRlPbC6HIssjp valioso
1456 -MO7ltqjRlPbC6HIssjp respetable
1457 -MO7qjKMN7eFXgn7orgV informacion
1458 -MO7qjKMN7eFXgn7orgV velocidad
1459 -MO7qjKMN7eFXgn7orgV archivos
1460 -MO7qjKMN7eFXgn7orgV comunicacion
1461 -MO7qjKMN7eFXgn7orgV enlaces
1462 -MOCKpw1sMrvqTcBTPPN datos
1463 -MOCKpw1sMrvqTcBTPPN muchos
1464 -MOCKpw1sMrvqTcBTPPN informatica
1465 -MOCKpw1sMrvqTcBTPPN era digital
1466 -MOCKpw1sMrvqTcBTPPN manipulación
1467 -MODbnI-5VUOlIsPhFVK estadística
1468 -MODbnI-5VUOlIsPhFVK redes sociales
1469 -MODbnI-5VUOlIsPhFVK mercado
1470 -MODbnI-5VUOlIsPhFVK sectorización
1471 -MODbnI-5VUOlIsPhFVK pato donnald
1472 -MOFNcNwtvqZoUYI3rtg integracion
1473 -MOFNcNwtvqZoUYI3rtg confluencia
1474 -MOFNcNwtvqZoUYI3rtg tecnologias
1475 -MOFNcNwtvqZoUYI3rtg valor
1476 -MOFNcNwtvqZoUYI3rtg información
1477 -MOI5QN5ClfIvLo9bBdB gran
1478 -MOI5QN5ClfIvLo9bBdB conocimiento
1479 -MOI5QN5ClfIvLo9bBdB datos
1480 -MOI5QN5ClfIvLo9bBdB conexión
1481 -MOI5QN5ClfIvLo9bBdB democratización
1482 -MOPRrWwutkkwga4-ICU control
1483 -MOPRrWwutkkwga4-ICU almacenamiento datos
1484 -MOPRrWwutkkwga4-ICU deshumanizacion
1485 -MOPRrWwutkkwga4-ICU mercadotecnia
1486 -MOPRrWwutkkwga4-ICU analisis global
1487 -MOQ7IBHLrbgCW4vzd75 grandes cantidades de datos
1488 -MOQ7IBHLrbgCW4vzd75 datos estructurados y no estructurados
1489 -MOQ7IBHLrbgCW4vzd75 las 4 v
1490 -MOQ7IBHLrbgCW4vzd75 predicen
1491 -MOQ7IBHLrbgCW4vzd75 brinda información para ltoma
1492 -MOQb5jH5h7g_RjWllOL gran hermano
1493 -MOQb5jH5h7g_RjWllOL marketing
1494 -MOQb5jH5h7g_RjWllOL gestionar
1495 -MOQb5jH5h7g_RjWllOL desigualdad
1496 -MOQb5jH5h7g_RjWllOL financiarizacion
1497 -MOT9rUexFQzWK9vd9JT gran hermano
1498 -MOT9rUexFQzWK9vd9JT manipulación
1499 -MOT9rUexFQzWK9vd9JT falta de privacidad
1500 -MOT9rUexFQzWK9vd9JT control
1501 -MOT9rUexFQzWK9vd9JT predicción
1502 -MOX6OY28FHFxWiMn_hN grande
1503 -MOX6OY28FHFxWiMn_hN datos
1504 -MOX6OY28FHFxWiMn_hN informacion
1505 -MOX6OY28FHFxWiMn_hN pocos
1506 -MOX6OY28FHFxWiMn_hN util
1507 -MOXQxsRq-1pFi7ICFDx grande
1508 -MOXQxsRq-1pFi7ICFDx información
1509 -MOXQxsRq-1pFi7ICFDx enigma
1510 -MOXQxsRq-1pFi7ICFDx inglés
1511 -MOXQxsRq-1pFi7ICFDx informática
1512 -MOYj-2u1KzgK1Q7WLfR análisis de grandes masas de datos
1513 -MOYj-2u1KzgK1Q7WLfR necesario para el progreso
1514 -MOYj-2u1KzgK1Q7WLfR escases de científicos de datos
1515 -MOYj-2u1KzgK1Q7WLfR fundamental para el desarrollo de una región, país
1516 -MOYj-2u1KzgK1Q7WLfR fundamental la aplicación de la ia en la educación
1517 -MOaYEM7_lQ7HbGHOdC_ datos personales
1518 -MOaYEM7_lQ7HbGHOdC_ información confidencial
1519 -MOaYEM7_lQ7HbGHOdC_ estadísticas
1520 -MOaYEM7_lQ7HbGHOdC_ información ciudadana
1521 -MOaYEM7_lQ7HbGHOdC_ datos abiertos
1522 -MObWOh6k39tV0W8DxWe datos
1523 -MObWOh6k39tV0W8DxWe grandes
1524 -MObWOh6k39tV0W8DxWe control
1525 -MObWOh6k39tV0W8DxWe estadística
1526 -MObWOh6k39tV0W8DxWe cuidado
1527 -MOc5qB5gZ187A94FzCQ inconmensurable
1528 -MOc5qB5gZ187A94FzCQ responsabilidad
1529 -MOc5qB5gZ187A94FzCQ riesgoso
1530 -MOc5qB5gZ187A94FzCQ conocimiento
1531 -MOc5qB5gZ187A94FzCQ conexion
1532 -MOcnYyAE-bzFE0uby1r abundancia de información
1533 -MOcnYyAE-bzFE0uby1r poca privacidad
1534 -MOcnYyAE-bzFE0uby1r desconfianza
1535 -MOcnYyAE-bzFE0uby1r temor
1536 -MOcnYyAE-bzFE0uby1r reglas poco claras
1537 -MOhgrxV2uFhk1tzR7dH datos
1538 -MOhgrxV2uFhk1tzR7dH cantidad
1539 -MOhgrxV2uFhk1tzR7dH investigación
1540 -MOhgrxV2uFhk1tzR7dH grandes
1541 -MOhgrxV2uFhk1tzR7dH información
1542 -MOmSnMKNiXPGsOaXE4v gran base de datos
1543 -MOmSnMKNiXPGsOaXE4v demasiadas datos de la ref
1544 -MOmSnMKNiXPGsOaXE4v descontrol / controlado
1545 -MOmSnMKNiXPGsOaXE4v todo está en la web
1546 -MOmSnMKNiXPGsOaXE4v mucho control sobre los contenidos
1547 -MOpTDtC6o4nieaHi9dn analisis
1548 -MOpTDtC6o4nieaHi9dn manipulacion de personas
1549 -MOpTDtC6o4nieaHi9dn centralización de datos
1550 -MOpTDtC6o4nieaHi9dn inteligencia artificial
1551 -MOpTDtC6o4nieaHi9dn manipulacion de voluntades
1552 -MOpjelbj0HdoS5xs_Iy conocimiento
1553 -MOpjelbj0HdoS5xs_Iy ahorro tiempo
1554 -MOpjelbj0HdoS5xs_Iy maximizar
1555 -MOpjelbj0HdoS5xs_Iy ahorro
1556 -MOpjelbj0HdoS5xs_Iy inteligencia
1557 -MOqIiV_TUfUz5jx7IEu hiperconexión
1558 -MOqIiV_TUfUz5jx7IEu virtualización
1559 -MOqIiV_TUfUz5jx7IEu modelos mentales
1560 -MOqIiV_TUfUz5jx7IEu control
1561 -MOqIiV_TUfUz5jx7IEu recursos
1562 -MOvJrusvVV4uLhlidgH innovacion
1563 -MOvJrusvVV4uLhlidgH oportunidad
1564 -MOvJrusvVV4uLhlidgH privacidad
1565 -MOvJrusvVV4uLhlidgH procesamiento
1566 -MOvJrusvVV4uLhlidgH conocimiento
1567 -MOx2s4RPCCi6muf8Oa6 datos
1568 -MOx2s4RPCCi6muf8Oa6 vida
1569 -MOx2s4RPCCi6muf8Oa6 persona
1570 -MOx2s4RPCCi6muf8Oa6 historia
1571 -MOx2s4RPCCi6muf8Oa6 historial
1572 -MOxAjR19cQYVv8e92I_ informacion
1573 -MOxAjR19cQYVv8e92I_ concentaracion
1574 -MOxAjR19cQYVv8e92I_ manipulación
1575 -MOxAjR19cQYVv8e92I_ targeting
1576 -MOxAjR19cQYVv8e92I_ estatadistica
1577 -MOyi9eXl9hl3BkETQsV control
1578 -MOyi9eXl9hl3BkETQsV posesión
1579 -MOyi9eXl9hl3BkETQsV inseguridad
1580 -MOyi9eXl9hl3BkETQsV atraso
1581 -MOyi9eXl9hl3BkETQsV gatopardismo
1582 -MP1nHo1kV4kVwPY4gwP espía
1583 -MP1nHo1kV4kVwPY4gwP privacidad
1584 -MP1nHo1kV4kVwPY4gwP violento
1585 -MP1nHo1kV4kVwPY4gwP manipulación
1586 -MP1nHo1kV4kVwPY4gwP descolonización
1587 -MP4gH2anX1rrzXAzI5k info
1588 -MP4gH2anX1rrzXAzI5k mundo
1589 -MP4gH2anX1rrzXAzI5k internet
1590 -MP4gH2anX1rrzXAzI5k fotos
1591 -MP4gH2anX1rrzXAzI5k ciencia
1592 -MPm4v8RRhQ9vNcibwPA base de datos
1593 -MPm4v8RRhQ9vNcibwPA computadora
1594 -MPm4v8RRhQ9vNcibwPA memoria ram
1595 -MPm4v8RRhQ9vNcibwPA placa madre
1596 -MPm4v8RRhQ9vNcibwPA virus
1597 -MRWI3eL-7jdzgV1VTY0 informacion
1598 -MRWI3eL-7jdzgV1VTY0 datos
1599 -MRWI3eL-7jdzgV1VTY0 confusión
1600 -MRWI3eL-7jdzgV1VTY0 desorden
1601 -MRWI3eL-7jdzgV1VTY0 soledad
1602 -MRdyLDOe9NYn_V5cEZi computadoras
1603 -MRdyLDOe9NYn_V5cEZi inteligencia artificial
1604 -MRdyLDOe9NYn_V5cEZi informática
1605 -MRdyLDOe9NYn_V5cEZi memoria
1606 -MRdyLDOe9NYn_V5cEZi gran memoria
1607 -MRdyd28LVbYrFrC2Yaf persecución
1608 -MRdyd28LVbYrFrC2Yaf control
1609 -MRdyd28LVbYrFrC2Yaf acoso
1610 -MRdyd28LVbYrFrC2Yaf fraudes
1611 -MRdyd28LVbYrFrC2Yaf orwell
1612 -MRe4V7a_UezuTjSzjTa inteligencia robotica
1613 -MRe4V7a_UezuTjSzjTa solucionar
1614 -MRe4V7a_UezuTjSzjTa rapidez
1615 -MRe4V7a_UezuTjSzjTa facilidad
1616 -MRe4V7a_UezuTjSzjTa desempleo
1617 -MReZBWUKdbBeOLqu7b6 extenso
1618 -MReZBWUKdbBeOLqu7b6 datos
1619 -MReZBWUKdbBeOLqu7b6 múltiple
1620 -MReZBWUKdbBeOLqu7b6 diverso
1621 -MReZBWUKdbBeOLqu7b6 complejo
1622 -MRfHxpmfAaTpPCkIWtS herramientas tecnologica
1623 -MRfHxpmfAaTpPCkIWtS mas datos
1624 -MRfHxpmfAaTpPCkIWtS aplicaciones informaticas
1625 -MRfHxpmfAaTpPCkIWtS datos complejos
1626 -MRfHxpmfAaTpPCkIWtS avances
1627 -MRgd5gqT1sPRxuqRvc6 datazo
1628 -MRgd5gqT1sPRxuqRvc6 secreto
1629 -MRgd5gqT1sPRxuqRvc6 buenas noticias
1630 -MRgd5gqT1sPRxuqRvc6 esperanza
1631 -MRgd5gqT1sPRxuqRvc6 misterio
1632 -MRgd5gqT1sPRxuqRvc6 sorpresa
1633 -MRgeoiYLcV9FerREfAu información
1634 -MRgeoiYLcV9FerREfAu digital
1635 -MRgeoiYLcV9FerREfAu comunicación
1636 -MRgeoiYLcV9FerREfAu conectados
1637 -MRgeoiYLcV9FerREfAu robotizacion
1638 -MRjaXfR_C2Ok75DyXn7 grande
1639 -MRjaXfR_C2Ok75DyXn7 datos
1640 -MRjaXfR_C2Ok75DyXn7 computación
1641 -MRjaXfR_C2Ok75DyXn7 tecnologías
1642 -MRjaXfR_C2Ok75DyXn7 tics
1643 -MRjcLxxcxOLmO3Z6-he masa
1644 -MRjcLxxcxOLmO3Z6-he manipulacion
1645 -MRjcLxxcxOLmO3Z6-he negocio
1646 -MRjcLxxcxOLmO3Z6-he patrones
1647 -MRjcLxxcxOLmO3Z6-he evolución
1648 -MRkEZimIj8bDLw97dNX datos
1649 -MRkEZimIj8bDLw97dNX información
1650 -MRkEZimIj8bDLw97dNX algoritmos
1651 -MRkEZimIj8bDLw97dNX tecnología
1652 -MRkEZimIj8bDLw97dNX futuro
1653 -MRk_WYHDnv0muqmM4Iw python
1654 -MRk_WYHDnv0muqmM4Iw datos
1655 -MRk_WYHDnv0muqmM4Iw volumen
1656 -MRk_WYHDnv0muqmM4Iw informacion
1657 -MRk_WYHDnv0muqmM4Iw internet
1658 -MRl7ZU2McMY5Aqd-B8t manipulación
1659 -MRl7ZU2McMY5Aqd-B8t control
1660 -MRl7ZU2McMY5Aqd-B8t política
1661 -MRl7ZU2McMY5Aqd-B8t marketing
1662 -MRl7ZU2McMY5Aqd-B8t corporaciones
1663 -MRltII4CTK3lHMz6xVa bi
1664 -MRltII4CTK3lHMz6xVa ciencia de datos
1665 -MRltII4CTK3lHMz6xVa power bi
1666 -MRltII4CTK3lHMz6xVa lenguaje r
1667 -MRltII4CTK3lHMz6xVa python
1668 -MRmezoM_UQZ_O8MDjs9 datos
1669 -MRmezoM_UQZ_O8MDjs9 información
1670 -MRmezoM_UQZ_O8MDjs9 estadistica
1671 -MRmezoM_UQZ_O8MDjs9 espionaje
1672 -MRmezoM_UQZ_O8MDjs9 cambio
1673 -MRpz6fn8rwxj_RkhQTF informacion
1674 -MRpz6fn8rwxj_RkhQTF tecnología
1675 -MRpz6fn8rwxj_RkhQTF datos
1676 -MRpz6fn8rwxj_RkhQTF estadistica
1677 -MRpz6fn8rwxj_RkhQTF social
1678 -MRpz9msSJN1PP6KD9cd libres
1679 -MRpz9msSJN1PP6KD9cd abiertos
1680 -MRpz9msSJN1PP6KD9cd compartidos
1681 -MRpz9msSJN1PP6KD9cd resultados
1682 -MRpz9msSJN1PP6KD9cd gestión
1683 -MRqm4K1OHsJ8cTf7Lnx computadora
1684 -MRqm4K1OHsJ8cTf7Lnx memoria
1685 -MRqm4K1OHsJ8cTf7Lnx datos
1686 -MRqm4K1OHsJ8cTf7Lnx carga
1687 -MRqm4K1OHsJ8cTf7Lnx tecnologia
1688 -MRtrH93JCyqS_OdgvhK primicia
1689 -MRtrH93JCyqS_OdgvhK novedad
1690 -MRtrH93JCyqS_OdgvhK increible
1691 -MRtrH93JCyqS_OdgvhK en contexto politico una fraude
1692 -MRtrH93JCyqS_OdgvhK en contexto familiar alegria
1693 -MS0bf_J5MixL6cTfXTc datos
1694 -MS0bf_J5MixL6cTfXTc muchos
1695 -MS0bf_J5MixL6cTfXTc análisis
1696 -MS0bf_J5MixL6cTfXTc computación
1697 -MS0bf_J5MixL6cTfXTc estadística
1698 -MS3vCvqTHVaVeJwTFoG miedo
1699 -MS3vCvqTHVaVeJwTFoG control
1700 -MS3vCvqTHVaVeJwTFoG indefeccion
1701 -MS3vCvqTHVaVeJwTFoG impotencia
1702 -MS3vCvqTHVaVeJwTFoG odio
1703 -MS4IhJXtxWGTAoGZkUI datos
1704 -MS4IhJXtxWGTAoGZkUI manipulación
1705 -MS4IhJXtxWGTAoGZkUI información
1706 -MS4IhJXtxWGTAoGZkUI proyección
1707 -MS4IhJXtxWGTAoGZkUI segmentación
orden valoracion
1 1 5
2 2 5
3 3 5
4 4 4
5 5 -5
6 1 5
7 2 5
8 3 5
9 4 0
10 5 -3
11 1 2
12 2 4
13 3 4
14 4 3
15 5 2
16 1 5
17 2 4
18 3 0
19 4 4
20 5 4
21 1 3
22 2 3
23 3 5
24 4 4
25 5 4
26 1 2
27 2 1
28 3 1
29 4 2
30 5 2
31 1 0
32 2 -4
33 3 -1
34 4 0
35 5 1
36 1 5
37 2 5
38 3 4
39 4 0
40 5 0
41 1 -5
42 2 -5
43 3 0
44 4 -3
45 5 -3
46 1 5
47 2 5
48 3 5
49 4 5
50 5 5
51 6 4
52 1 5
53 2 5
54 3 2
55 4 5
56 5 5
57 1 -5
58 2 -5
59 3 -5
60 4 -5
61 5 -5
62 1 -3
63 2 -5
64 3 -3
65 4 -3
66 5 3
67 1 5
68 2 5
69 3 5
70 4 5
71 5 5
72 1 -4
73 2 -5
74 3 0
75 4 3
76 5 -5
77 1 5
78 2 -3
79 3 3
80 4 -3
81 5 5
82 1 -1
83 2 3
84 3 -2
85 4 2
86 5 -2
87 1 -2
88 2 3
89 3 4
90 4 2
91 5 2
92 1 5
93 2 1
94 3 0
95 4 -1
96 5 -2
97 1 5
98 2 -1
99 3 -5
100 4 0
101 5 2
102 1 5
103 2 3
104 3 5
105 4 4
106 5 4
107 1 0
108 2 4
109 3 0
110 4 1
111 5 0
112 1 5
113 2 5
114 3 5
115 4 0
116 5 0
117 1 0
118 2 0
119 3 0
120 4 0
121 5 0
122 1 5
123 2 5
124 3 5
125 4 5
126 5 5
127 1 3
128 2 4
129 3 3
130 4 3
131 5 3
132 1 -5
133 2 5
134 3 0
135 4 0
136 5 -1
137 1 -5
138 2 5
139 3 -5
140 4 -5
141 5 5
142 1 4
143 2 5
144 3 4
145 4 5
146 5 4
147 1 2
148 2 0
149 3 0
150 4 -1
151 5 4
152 1 -1
153 2 2
154 3 4
155 4 -4
156 5 -1
157 1 4
158 2 4
159 3 4
160 4 2
161 5 3
162 1 0
163 2 0
164 3 0
165 4 0
166 5 0
167 1 0
168 2 -4
169 3 -3
170 4 -4
171 5 0
172 1 5
173 2 5
174 3 5
175 4 5
176 5 5
177 1 5
178 2 5
179 3 5
180 4 -3
181 5 5
182 1 5
183 2 5
184 3 -3
185 4 0
186 5 0
187 1 0
188 2 5
189 3 0
190 4 0
191 5 0
192 1 -3
193 2 5
194 3 -4
195 4 -5
196 5 -5
197 1 3
198 2 4
199 3 -4
200 4 -3
201 5 2
202 1 5
203 2 5
204 3 5
205 4 -2
206 5 5
207 1 2
208 2 2
209 3 2
210 4 -1
211 5 -3
212 1 1
213 2 0
214 3 0
215 4 0
216 5 0
217 1 5
218 2 5
219 3 5
220 4 5
221 5 5
222 1 0
223 2 0
224 3 0
225 4 0
226 5 0
227 1 5
228 2 5
229 3 0
230 4 0
231 5 0
232 1 5
233 2 0
234 3 4
235 4 5
236 5 5
237 1 5
238 2 5
239 3 5
240 4 5
241 5 0
242 1 -4
243 2 0
244 3 -4
245 4 0
246 5 0
247 1 0
248 2 0
249 3 0
250 4 0
251 5 0
252 1 -5
253 2 3
254 3 0
255 4 2
256 5 -3
257 1 3
258 2 -4
259 3 -4
260 4 0
261 5 1
262 1 5
263 2 -3
264 3 2
265 4 2
266 5 2
267 1 5
268 2 5
269 3 5
270 4 -5
271 5 0
272 1 5
273 2 5
274 3 0
275 4 0
276 5 0
277 1 5
278 2 4
279 3 1
280 4 5
281 5 5
282 1 5
283 2 5
284 3 5
285 4 5
286 5 5
287 1 0
288 2 -3
289 3 0
290 4 0
291 5 -2
292 1 -5
293 2 -5
294 3 -5
295 4 -5
296 5 -5
297 1 3
298 2 2
299 3 3
300 4 0
301 5 3
302 1 -3
303 2 2
304 3 -1
305 4 1
306 5 3
307 1 3
308 2 3
309 3 3
310 4 3
311 5 3
312 1 3
313 2 3
314 3 3
315 4 2
316 5 2
317 1 2
318 2 0
319 3 0
320 4 0
321 5 0
322 1 5
323 2 5
324 3 4
325 4 5
326 5 3
327 1 -2
328 2 -2
329 3 1
330 4 4
331 5 0
332 1 0
333 2 0
334 3 0
335 4 -2
336 5 0
337 1 5
338 2 -2
339 3 0
340 4 3
341 5 -4
342 1 -5
343 2 5
344 3 -4
345 4 0
346 5 3
347 1 4
348 2 -2
349 3 5
350 4 -5
351 5 3
352 1 2
353 2 -2
354 3 0
355 4 -1
356 5 -1
357 1 0
358 2 3
359 3 1
360 4 0
361 5 0
362 1 5
363 2 5
364 3 5
365 4 5
366 5 5
367 1 2
368 2 3
369 3 3
370 4 4
371 5 4
372 1 -1
373 2 5
374 3 5
375 4 0
376 5 0
377 1 5
378 2 5
379 3 5
380 4 5
381 5 5
382 1 -2
383 2 -2
384 3 2
385 4 -1
386 5 0
387 1 2
388 2 -3
389 3 0
390 4 -2
391 5 -5
392 1 3
393 2 5
394 3 3
395 4 3
396 5 -1
397 6 0
398 1 -2
399 2 5
400 3 5
401 4 -5
402 5 0
403 1 5
404 2 5
405 3 5
406 4 5
407 5 2
408 1 -1
409 2 0
410 3 0
411 4 0
412 5 0
413 1 5
414 2 5
415 3 5
416 4 5
417 5 5
418 1 0
419 2 -1
420 3 0
421 4 0
422 5 0
423 1 3
424 2 5
425 3 4
426 4 5
427 5 5
428 1 2
429 2 3
430 3 5
431 4 0
432 5 -1
433 1 -5
434 2 4
435 3 -1
436 4 -5
437 5 3
438 1 4
439 2 4
440 3 3
441 4 4
442 5 2
443 1 -5
444 2 5
445 3 0
446 4 2
447 5 -1
448 1 5
449 2 5
450 3 5
451 4 -5
452 5 5
453 1 2
454 2 2
455 3 0
456 4 1
457 5 2
458 1 3
459 2 4
460 3 1
461 4 3
462 5 5
463 1 5
464 2 5
465 3 2
466 4 -4
467 5 5
468 1 2
469 2 3
470 3 -5
471 4 -5
472 5 0
473 1 0
474 2 1
475 3 -3
476 4 -3
477 5 -2
478 1 3
479 2 5
480 3 2
481 4 3
482 5 4
483 1 5
484 2 4
485 3 3
486 4 4
487 5 2
488 1 0
489 2 0
490 3 0
491 4 0
492 5 0
493 1 -5
494 2 -5
495 3 -5
496 4 -5
497 5 5
498 1 4
499 2 4
500 3 0
501 4 3
502 5 2
503 1 5
504 2 5
505 3 5
506 4 0
507 5 5
508 1 -3
509 2 3
510 3 3
511 4 -3
512 5 -3
513 1 5
514 2 3
515 3 3
516 4 0
517 5 0
518 1 -2
519 2 5
520 3 1
521 4 -5
522 5 -5
523 1 2
524 2 4
525 3 3
526 4 2
527 5 3
528 1 3
529 2 3
530 3 3
531 4 3
532 5 3
533 1 5
534 2 0
535 3 2
536 4 2
537 5 -2
538 1 5
539 2 5
540 3 3
541 4 0
542 5 0
543 1 0
544 2 1
545 3 5
546 4 1
547 5 5
548 1 3
549 2 3
550 3 3
551 4 3
552 5 3
553 1 -2
554 2 1
555 3 3
556 4 -3
557 5 2
558 1 -2
559 2 0
560 3 -4
561 4 0
562 5 -4
563 1 5
564 2 5
565 3 1
566 4 -3
567 5 1
568 1 3
569 2 4
570 3 2
571 4 3
572 5 3
573 1 0
574 2 0
575 3 0
576 4 0
577 5 0
578 1 5
579 2 5
580 3 5
581 4 4
582 5 5
583 1 0
584 2 0
585 3 0
586 4 0
587 5 0
588 1 5
589 2 5
590 3 5
591 4 5
592 5 5
593 1 5
594 2 -2
595 3 -1
596 4 -1
597 5 -2
598 1 0
599 2 3
600 3 0
601 4 3
602 5 -3
603 1 0
604 2 5
605 3 5
606 4 5
607 5 0
608 1 -1
609 2 5
610 3 5
611 4 5
612 5 -5
613 1 -5
614 2 -5
615 3 -5
616 4 -5
617 5 -5
618 1 -1
619 2 -2
620 3 -4
621 4 3
622 5 -2
623 1 0
624 2 1
625 3 2
626 4 5
627 5 1
628 1 5
629 2 5
630 3 5
631 4 5
632 5 5
633 1 5
634 2 5
635 3 5
636 4 5
637 5 5
638 1 5
639 2 5
640 3 5
641 4 5
642 5 -5
643 1 0
644 2 0
645 3 0
646 4 0
647 5 0
648 1 5
649 2 5
650 3 2
651 4 3
652 5 5
653 1 -1
654 2 0
655 3 0
656 4 1
657 5 0
658 1 5
659 2 5
660 3 5
661 4 5
662 5 5
663 1 5
664 2 4
665 3 4
666 4 -1
667 5 0
668 1 5
669 2 5
670 3 2
671 4 2
672 5 2
673 1 5
674 2 5
675 3 5
676 4 5
677 5 5
678 1 5
679 2 5
680 3 0
681 4 3
682 5 0
683 1 4
684 2 3
685 3 5
686 4 5
687 5 4
688 1 -5
689 2 -5
690 3 -5
691 4 2
692 5 5
693 1 -5
694 2 0
695 3 -5
696 4 -5
697 5 -5
698 1 2
699 2 2
700 3 -3
701 4 -3
702 5 4
703 1 -5
704 2 0
705 3 -5
706 4 -5
707 5 -5
708 1 0
709 2 -1
710 3 -3
711 4 -4
712 5 5
713 1 -2
714 2 3
715 3 -2
716 4 -3
717 5 3
718 1 5
719 2 1
720 3 4
721 4 1
722 5 3
723 1 5
724 2 -3
725 3 0
726 4 1
727 5 -4
728 1 0
729 2 -5
730 3 -3
731 4 -5
732 5 5
733 1 4
734 2 -2
735 3 -2
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1707 5 -5Es importante notar una cosa. Si prestamos atención al entorno, vamos a ver que el objeto_a_devolver no aparece en nuestra lista de objetos. Esto es porque quedó “encapsulado” en la función, y no está disponible por fuera de ella. Lo mismo puede decirse de tabla_evocaciones, que es la forma en que en la función llamamos a nuestro primer parámetro (los datos).
Aclarada la estructura y el uso de una función, ya sólo resta pensar en cuáles son los pasos a seguir para realizar el análiasis prototípico. Para eso, volvamos a la definición más arriba, y pensemos la secuencia de operaciones que necesitamos:
(paso 1) primero debemos calcular las frecuencias de cada palabra y sus medias de valoracion y orden; (paso 2) deberemos definir una frecuencia mínima; (paso 3) vamos a calcular los criterios de corte para distinguir los mas/menos frecuentes y los evocados mas/menos rápidamente; (paso 4) vamos a utilizar estos criterios de corte para segmentar las asociaciones, indicando qué palabras corresponden a qué segmento;
Como la idea es poder probar distintos criterios y decisiones, algunos de estos pasos suponen que podamos introducir algún parámetro: (paso 2) vamos a introducir una frecuencia mínima; (paso 3) vamos a indicar si queremos usar la media o la mediana para calcular el criterio de corte, es decir si aplicamos mean() o median(), esta operación condicional la haremos con una estructura de control: if( criterio ) { ... } else { ... }. Estos 2 parámetros los vamos a incluir en la función, con un valor por defecto: 2 para la frecuencia mínima, y media para calcular el criterio. Vamos a utilizar el comando message() para que al correr la función se muestren estos parámetros, y también para observar la cantidad de palabras que quedan en cada segmento, dados estos parametros (paso 5).
analisis_proto <- function (tabla_evocaciones, frecuencia_minima = 2, criterio_corte = "media") {
# (paso 1) tabla de frecuencias
asoc_frecuentes <- tabla_evocaciones %>%
group_by(palabra) %>%
summarize(
freq = n(),
valoracion_media = mean(valoracion),
orden_media = mean(orden)
)
# (paso 2) establecemos un umbral de frecuencia minima (usando parametro)
asoc_frecuentes <- asoc_frecuentes %>%
filter(freq > frecuencia_minima)
message("frecuencia minima = ", frecuencia_minima)
# (paso 3) calculamos el corte de frecuencia (usamos mean o median, segun parametro)
if (criterio_corte == "media") {
freq_cut <- mean(asoc_frecuentes$freq)
orden_cut <- mean(asoc_frecuentes$orden_media)
} else {
freq_cut <- median(asoc_frecuentes$freq)
orden_cut <- median(asoc_frecuentes$orden_media)
}
message("valor corte frecuencia = ", freq_cut)
message("valor corte orden = ", orden_cut)
# (paso 4) segmentamos las palabras
prototipico <- asoc_frecuentes %>% mutate( segmento = case_when(
freq >= freq_cut & orden_media < orden_cut ~ 1,
freq >= freq_cut & orden_media >= orden_cut ~ 2,
freq < freq_cut & orden_media < orden_cut ~ 3,
freq < freq_cut & orden_media >= orden_cut ~ 4
)
) %>% arrange(segmento, desc(freq),desc(orden_media))
# (paso 5) vamos a contar las palabras en cada segmento y lo mostramos en pantalla
palabras_por_segmento <- prototipico %>%
count(segmento) %>%
pull(n) # pull extrae una variable, quedando como vector
message("palabras en cada segmento = ",
paste(palabras_por_segmento, collapse = " | "))
# ... y lo devolvemos
return(prototipico)
}Veamos el objeto que nos devuelve, por ahora con los valores por defecto:
analisis_proto(tabla_evocaciones = asociaciones)frecuencia minima = 2valor corte frecuencia = 8.15094339622642valor corte orden = 2.98106020168724palabras en cada segmento = 12 | 5 | 33 | 56# A tibble: 106 × 5
palabra freq valoracion_media orden_media segmento
<chr> <int> <dbl> <dbl> <dbl>
1 información 102 2.58 2.08 1
2 datos 81 2.15 1.96 1
3 control 35 -2.31 2.57 1
4 internet 34 2.94 2.91 1
5 tecnología 29 2.45 2.97 1
6 informacion 28 2.29 2.54 1
7 grande 20 2.7 2 1
8 análisis 19 3.11 2.89 1
9 manipulación 18 -3.44 2.5 1
10 conocimiento 16 4.44 2.75 1
# ℹ 96 more rowsOtra vez, es hora de tomar decisiones. Como vemos, esta última configuración logra quedarse con menos palabras, pero a la vez incrementa el tamaño del 1er segmento, que dado que es donde iremos a buscar los sentidos centrales, conviene que sea más pequeño. Podemos asumir que un umbral de frecuencia mínimas más alto, y una división por medias debería dar un conjnto más chico de palabras en el segmento 1, y menos palabras en total.
analisis_proto(tabla_evocaciones = asociaciones, frecuencia_minima = 5, criterio_corte = "media")frecuencia minima = 5valor corte frecuencia = 15.219512195122valor corte orden = 2.87623694419952palabras en cada segmento = 7 | 3 | 13 | 18# A tibble: 41 × 5
palabra freq valoracion_media orden_media segmento
<chr> <int> <dbl> <dbl> <dbl>
1 información 102 2.58 2.08 1
2 datos 81 2.15 1.96 1
3 control 35 -2.31 2.57 1
4 informacion 28 2.29 2.54 1
5 grande 20 2.7 2 1
6 manipulación 18 -3.44 2.5 1
7 conocimiento 16 4.44 2.75 1
8 internet 34 2.94 2.91 2
9 tecnología 29 2.45 2.97 2
10 análisis 19 3.11 2.89 2
# ℹ 31 more rows2.10.5 Interpretación
Por ejemplo, vamos a dibujar un gráfico que: * permita dibujar puntos en el cruce de frecuencia (X) y orden de evocación (Y), * que incluya las palabras en el gráfico, de modo tal que podamos leerlas por grupitos, * que señale con color la valoración, * que muestre los puntos de corte en frecuencia (X) y orden de evocación (Y), * y que remarque el cuadrante del núcleo central.
prototipico <- analisis_proto(tabla_evocaciones = asociaciones, frecuencia_minima = 5, criterio_corte = "media")frecuencia minima = 5valor corte frecuencia = 15.219512195122valor corte orden = 2.87623694419952palabras en cada segmento = 7 | 3 | 13 | 18prototipico %>%
ggplot(aes(x=freq,y=orden_media,label=palabra)) +
scale_x_continuous(trans='log') +
geom_hline(yintercept = 2.87623694419952, linetype = 2) +
geom_vline(xintercept = 15.219512195122, linetype = 2) +
geom_point(aes(size=freq, colour=valoracion_media), show.legend = TRUE) +
scale_colour_gradient(low = "#76EE00", high = "#CD661D", na.value = NA) +
geom_text( aes(size=20, colour=valoracion_media), fontface = "bold",
show.legend = FALSE, nudge_y = -.1, check_overlap = TRUE) +
labs(y="Orden de evocación", x = "Frecuencia (log)") +
theme_minimal()
2.11 Importación de datos (ejemplificar para diversos tipos de archivos)
2.11.1 Importación de archivos de texto
En R para leer archivos de texto se suele utilizar la función read.table():
# read.table(file, header = FALSE, sep = ",")Los principales argumentos son:
• header: indica si el fichero tiene cabecera (header = TRUE) o no (header = FALSE). Por defecto toma el valor header = FALSE.
• sep: carácter separador de columnas que por defecto es un espacio en blanco (sep = ““). Otras opciones serían: sep =”;” si el separador es un “;”, sep = “\t” si el separador es una tabulación, etc.
Para más detalles y argumentos adicionales ver help(read.table).
2.11.1.1 Ejemplo
Por ejemplo, supongamos que en el subdirectorio datos del directorio actual de trabajo está el fichero user_activity.txt (con valores separados por espacios y con los los nombres de las columnas en la primera línea). Para leer este fichero y almacenarlo en un data.frame podemos emplear el siguiente código:
datos <- read.table("./user_activity.txt", header = TRUE, sep = ",")
datos userId registerStartDate registerFinalDate steps distance runDistance
1 10036 2017/10/09 00:00:00 2017/10/10 00:00:00 1625 1113 36
2 10036 2017/10/10 00:00:00 2017/10/11 00:00:00 1722 1171 0
3 10036 2017/10/11 00:00:00 2017/10/12 00:00:00 203 123 0
4 10036 2017/10/12 00:00:00 2017/10/13 00:00:00 213 129 0
5 10036 2017/10/13 00:00:00 2017/10/14 00:00:00 461 282 16
6 10036 2017/10/14 00:00:00 2017/10/15 00:00:00 305 184 0
7 10036 2017/10/15 00:00:00 2017/10/16 00:00:00 173 105 9
8 10036 2017/10/16 00:00:00 2017/10/17 00:00:00 375 227 29
9 10036 2017/10/17 00:00:00 2017/10/18 00:00:00 623 373 87
10 10036 2017/10/18 00:00:00 2017/10/19 00:00:00 312 179 31
11 10036 2017/10/19 00:00:00 2017/10/20 00:00:00 209 120 6
12 10036 2017/10/20 00:00:00 2017/10/21 00:00:00 160 92 0
13 10036 2017/10/21 00:00:00 2017/10/22 00:00:00 315 181 16
14 10036 2017/10/22 00:00:00 2017/10/23 00:00:00 305 175 0
15 10036 2017/10/23 00:00:00 2017/10/24 00:00:00 59 34 0
16 10036 2017/10/24 00:00:00 2017/10/25 00:00:00 347 199 39
17 10036 2017/10/25 00:00:00 2017/10/26 00:00:00 547 314 22
18 10036 2017/10/26 00:00:00 2017/10/27 00:00:00 348 200 0
19 10036 2017/10/27 00:00:00 2017/10/28 00:00:00 440 253 15
20 10036 2017/10/28 00:00:00 2017/10/29 00:00:00 213 122 0
21 10036 2017/10/29 00:00:00 2017/10/30 00:00:00 471 274 62
22 10036 2017/10/30 00:00:00 2017/10/31 00:00:00 850 488 59
23 10036 2017/10/31 00:00:00 2017/11/01 00:00:00 301 173 8
24 10036 2017/11/01 00:00:00 2017/11/02 00:00:00 262 151 18
25 10036 2017/11/02 00:00:00 2017/11/03 00:00:00 290 167 9
26 10036 2017/11/03 00:00:00 2017/11/04 00:00:00 793 464 35
27 10036 2017/11/04 00:00:00 2017/11/05 00:00:00 229 132 0
28 10036 2017/11/05 00:00:00 2017/11/06 00:00:00 679 409 140
29 10036 2017/11/06 00:00:00 2017/11/07 00:00:00 399 232 21
30 10036 2017/11/07 00:00:00 2017/11/08 00:00:00 297 171 14
31 10036 2017/11/08 00:00:00 2017/11/09 00:00:00 500 287 69
32 10036 2017/11/09 00:00:00 2017/11/10 00:00:00 229 132 7
33 10036 2017/11/10 00:00:00 2017/11/11 00:00:00 590 343 41
34 10036 2017/11/11 00:00:00 2017/11/12 00:00:00 409 235 24
35 10036 2017/11/12 00:00:00 2017/11/13 00:00:00 343 197 46
36 10036 2017/11/13 00:00:00 2017/11/14 00:00:00 707 426 0
37 10036 2017/11/14 00:00:00 2017/11/15 00:00:00 644 391 134
38 10036 2017/11/15 00:00:00 2017/11/16 00:00:00 609 350 86
39 10036 2017/11/16 00:00:00 2017/11/17 00:00:00 3229 1937 162
40 10036 2017/11/17 00:00:00 2017/11/18 00:00:00 609 355 72
41 10036 2017/11/18 00:00:00 2017/11/19 00:00:00 639 384 90
42 10036 2017/11/19 00:00:00 2017/11/20 00:00:00 619 361 58
43 10036 2017/11/20 00:00:00 2017/11/21 00:00:00 513 302 83
44 10036 2017/11/21 00:00:00 2017/11/22 00:00:00 384 221 9
45 10036 2017/11/22 00:00:00 2017/11/23 00:00:00 257 148 0
46 10036 2017/11/23 00:00:00 2017/11/24 00:00:00 546 350 77
47 10036 2017/11/24 00:00:00 2017/11/25 00:00:00 394 235 8
48 10036 2017/11/25 00:00:00 2017/11/26 00:00:00 241 138 9
49 10036 2017/11/26 00:00:00 2017/11/27 00:00:00 415 240 0
50 10036 2017/11/27 00:00:00 2017/11/28 00:00:00 258 148 20
51 10036 2017/11/28 00:00:00 2017/11/29 00:00:00 259 149 7
52 10036 2017/11/29 00:00:00 2017/11/30 00:00:00 184 106 0
53 10036 2017/11/30 00:00:00 2017/12/01 00:00:00 456 262 55
54 10036 2017/12/01 00:00:00 2017/12/02 00:00:00 562 341 82
55 10036 2017/12/02 00:00:00 2017/12/03 00:00:00 352 202 25
56 10036 2017/12/03 00:00:00 2017/12/04 00:00:00 380 236 16
57 10036 2017/12/04 00:00:00 2017/12/05 00:00:00 468 269 0
58 10036 2017/12/05 00:00:00 2017/12/06 00:00:00 458 263 36
59 10036 2017/12/06 00:00:00 2017/12/07 00:00:00 209 120 8
60 10036 2017/12/07 00:00:00 2017/12/08 00:00:00 654 376 0
61 10036 2017/12/08 00:00:00 2017/12/09 00:00:00 603 346 45
62 10036 2017/12/09 00:00:00 2017/12/10 00:00:00 449 264 56
63 10036 2017/12/10 00:00:00 2017/12/11 00:00:00 78 45 0
64 10036 2017/12/11 00:00:00 2017/12/12 00:00:00 558 321 0
65 10036 2017/12/12 00:00:00 2017/12/13 00:00:00 453 260 34
66 10036 2017/12/13 00:00:00 2017/12/14 00:00:00 518 323 102
67 10036 2017/12/14 00:00:00 2017/12/15 00:00:00 274 157 0
68 10036 2017/12/15 00:00:00 2017/12/16 00:00:00 481 276 109
69 10036 2017/12/16 00:00:00 2017/12/17 00:00:00 293 168 74
70 10036 2017/12/17 00:00:00 2017/12/18 00:00:00 323 186 62
71 10036 2017/12/18 00:00:00 2017/12/19 00:00:00 425 245 63
72 10036 2017/12/19 00:00:00 2017/12/20 00:00:00 458 263 98
73 10036 2017/12/20 00:00:00 2017/12/21 00:00:00 793 456 46
74 10036 2017/12/21 00:00:00 2017/12/22 00:00:00 606 350 172
75 10036 2017/12/22 00:00:00 2017/12/23 00:00:00 216 124 35
76 10036 2017/12/23 00:00:00 2017/12/24 00:00:00 337 217 7
77 10036 2017/12/24 00:00:00 2017/12/25 00:00:00 218 125 60
78 10036 2017/12/25 00:00:00 2017/12/26 00:00:00 158 91 32
79 10036 2017/12/26 00:00:00 2017/12/27 00:00:00 190 109 27
80 10036 2017/12/27 00:00:00 2017/12/28 00:00:00 174 100 47
81 10036 2017/12/28 00:00:00 2017/12/29 00:00:00 471 283 147
82 10036 2017/12/29 00:00:00 2017/12/30 00:00:00 377 217 65
83 10036 2017/12/30 00:00:00 2017/12/31 00:00:00 229 132 50
84 10036 2017/12/31 00:00:00 2018/01/01 00:00:00 331 190 56
85 10036 2018/01/01 00:00:00 2018/01/02 00:00:00 299 189 117
86 10036 2018/01/02 00:00:00 2018/01/03 00:00:00 215 124 53
87 10036 2018/01/03 00:00:00 2018/01/04 00:00:00 213 122 29
88 10036 2018/01/04 00:00:00 2018/01/05 00:00:00 359 226 101
89 10036 2018/01/05 00:00:00 2018/01/06 00:00:00 222 128 42
90 10036 2018/01/06 00:00:00 2018/01/07 00:00:00 379 218 73
91 10036 2018/01/07 00:00:00 2018/01/08 00:00:00 126 72 46
92 10036 2018/01/08 00:00:00 2018/01/09 00:00:00 809 488 193
93 10036 2018/01/09 00:00:00 2018/01/10 00:00:00 195 112 39
94 10036 2018/01/10 00:00:00 2018/01/11 00:00:00 825 474 91
95 10036 2018/01/11 00:00:00 2018/01/12 00:00:00 828 501 189
96 10036 2018/01/12 00:00:00 2018/01/13 00:00:00 387 243 167
97 10036 2018/01/13 00:00:00 2018/01/14 00:00:00 394 226 88
98 10036 2018/01/14 00:00:00 2018/01/15 00:00:00 474 282 143
99 10036 2018/01/15 00:00:00 2018/01/16 00:00:00 443 255 80
100 10036 2018/01/16 00:00:00 2018/01/17 00:00:00 134 77 23
101 10036 2018/01/17 00:00:00 2018/01/18 00:00:00 1407 808 66
102 10036 2018/01/18 00:00:00 2018/01/19 00:00:00 618 366 138
103 10036 2018/01/19 00:00:00 2018/01/20 00:00:00 370 225 116
104 10036 2018/01/20 00:00:00 2018/01/21 00:00:00 410 236 68
105 10036 2018/01/21 00:00:00 2018/01/22 00:00:00 202 116 37
106 10036 2018/01/22 00:00:00 2018/01/23 00:00:00 312 179 81
107 10036 2018/01/23 00:00:00 2018/01/24 00:00:00 214 123 35
108 10036 2018/01/24 00:00:00 2018/01/25 00:00:00 58 33 23
109 10036 2018/01/25 00:00:00 2018/01/26 00:00:00 103 59 14
110 10036 2018/01/26 00:00:00 2018/01/27 00:00:00 347 203 83
111 10036 2018/01/27 00:00:00 2018/01/28 00:00:00 461 267 114
112 10036 2018/01/28 00:00:00 2018/01/29 00:00:00 490 293 121
113 10036 2018/01/29 00:00:00 2018/01/30 00:00:00 293 168 62
114 10036 2018/01/30 00:00:00 2018/01/31 00:00:00 227 130 34
115 10036 2018/01/31 00:00:00 2018/02/01 00:00:00 621 357 127
116 10036 2018/02/01 00:00:00 2018/02/02 00:00:00 432 264 90
117 10036 2018/02/02 00:00:00 2018/02/03 00:00:00 217 125 23
118 10036 2018/02/03 00:00:00 2018/02/04 00:00:00 268 154 75
119 10036 2018/02/04 00:00:00 2018/02/05 00:00:00 287 165 52
120 10036 2018/02/05 00:00:00 2018/02/06 00:00:00 617 355 146
121 10036 2018/02/06 00:00:00 2018/02/07 00:00:00 144 83 12
122 10036 2018/02/07 00:00:00 2018/02/08 00:00:00 305 185 101
123 10036 2018/02/08 00:00:00 2018/02/09 00:00:00 190 109 13
124 10036 2018/02/09 00:00:00 2018/02/10 00:00:00 2004 1218 103
125 10036 2018/02/10 00:00:00 2018/02/11 00:00:00 199 114 51
126 10036 2018/02/11 00:00:00 2018/02/12 00:00:00 183 105 20
127 10036 2018/02/12 00:00:00 2018/02/13 00:00:00 231 133 51
128 10036 2018/02/13 00:00:00 2018/02/14 00:00:00 115 66 21
129 10036 2018/02/14 00:00:00 2018/02/15 00:00:00 168 97 43
130 10036 2018/02/15 00:00:00 2018/02/16 00:00:00 447 257 57
131 10036 2018/02/16 00:00:00 2018/02/17 00:00:00 164 94 26
132 10036 2018/02/17 00:00:00 2018/02/18 00:00:00 357 205 54
133 10036 2018/02/18 00:00:00 2018/02/19 00:00:00 257 148 36
134 10036 2018/02/19 00:00:00 2018/02/20 00:00:00 440 253 67
135 10036 2018/02/20 00:00:00 2018/02/21 00:00:00 169 97 35
136 10036 2018/02/21 00:00:00 2018/02/22 00:00:00 102 59 39
137 10036 2018/02/22 00:00:00 2018/02/23 00:00:00 115 66 44
138 10036 2018/02/23 00:00:00 2018/02/24 00:00:00 350 201 60
139 10036 2018/02/24 00:00:00 2018/02/25 00:00:00 163 94 21
140 10036 2018/02/25 00:00:00 2018/02/26 00:00:00 271 156 62
141 10036 2018/02/26 00:00:00 2018/02/27 00:00:00 581 334 61
142 10036 2018/02/27 00:00:00 2018/02/28 00:00:00 138 79 33
143 10036 2018/02/28 00:00:00 2018/03/01 00:00:00 231 133 29
144 10036 2018/03/01 00:00:00 2018/03/02 00:00:00 137 79 16
145 10036 2018/03/02 00:00:00 2018/03/03 00:00:00 52 30 21
146 10036 2018/03/03 00:00:00 2018/03/04 00:00:00 156 90 22
147 10036 2018/03/04 00:00:00 2018/03/05 00:00:00 257 148 41
148 10036 2018/03/05 00:00:00 2018/03/06 00:00:00 910 523 59
149 10036 2018/03/06 00:00:00 2018/03/07 00:00:00 703 424 22
150 10036 2018/03/07 00:00:00 2018/03/08 00:00:00 614 353 73
151 10036 2018/03/08 00:00:00 2018/03/09 00:00:00 138 79 33
152 10036 2018/03/09 00:00:00 2018/03/10 00:00:00 275 160 45
153 10036 2018/03/10 00:00:00 2018/03/11 00:00:00 302 174 64
154 10036 2018/03/11 00:00:00 2018/03/12 00:00:00 406 252 126
155 10036 2018/03/12 00:00:00 2018/03/13 00:00:00 539 310 14
156 10036 2018/03/13 00:00:00 2018/03/14 00:00:00 166 95 48
157 10036 2018/03/14 00:00:00 2018/03/15 00:00:00 88 51 11
158 10036 2018/03/15 00:00:00 2018/03/16 00:00:00 269 155 55
159 10036 2018/03/16 00:00:00 2018/03/17 00:00:00 92 53 23
160 10036 2018/03/17 00:00:00 2018/03/18 00:00:00 269 155 84
161 10036 2018/03/18 00:00:00 2018/03/19 00:00:00 201 115 60
162 10036 2018/03/19 00:00:00 2018/03/20 00:00:00 173 99 32
163 10036 2018/03/20 00:00:00 2018/03/21 00:00:00 192 110 13
164 10036 2018/03/21 00:00:00 2018/03/22 00:00:00 154 88 24
165 10036 2018/03/22 00:00:00 2018/03/23 00:00:00 120 69 30
166 10036 2018/03/23 00:00:00 2018/03/24 00:00:00 129 74 10
167 10036 2018/03/24 00:00:00 2018/03/25 00:00:00 178 102 25
168 10036 2018/03/25 00:00:00 2018/03/26 00:00:00 128 74 27
169 10036 2018/03/26 00:00:00 2018/03/27 00:00:00 412 237 67
170 10036 2018/03/27 00:00:00 2018/03/28 00:00:00 1718 1046 140
171 10036 2018/03/28 00:00:00 2018/03/29 00:00:00 2704 1678 303
172 10036 2018/03/29 00:00:00 2018/03/30 00:00:00 220 126 53
173 10036 2018/03/30 00:00:00 2018/03/31 00:00:00 321 184 69
174 10036 2018/03/31 00:00:00 2018/04/01 00:00:00 887 510 172
175 10036 2018/04/01 00:00:00 2018/04/02 00:00:00 191 110 54
176 10036 2018/04/02 00:00:00 2018/04/03 00:00:00 508 292 130
177 10036 2018/04/03 00:00:00 2018/04/04 00:00:00 177 102 40
178 10036 2018/04/04 00:00:00 2018/04/05 00:00:00 381 219 99
179 10036 2018/04/05 00:00:00 2018/04/06 00:00:00 474 273 50
180 10036 2018/04/06 00:00:00 2018/04/07 00:00:00 199 114 24
181 10036 2018/04/07 00:00:00 2018/04/08 00:00:00 392 225 60
182 10036 2018/04/08 00:00:00 2018/04/09 00:00:00 437 251 98
183 10036 2018/04/09 00:00:00 2018/04/10 00:00:00 372 214 81
184 10036 2018/04/10 00:00:00 2018/04/11 00:00:00 379 218 83
185 10036 2018/04/11 00:00:00 2018/04/12 00:00:00 557 321 101
186 10036 2018/04/12 00:00:00 2018/04/13 00:00:00 369 212 61
187 10036 2018/04/13 00:00:00 2018/04/14 00:00:00 427 245 54
188 10036 2018/04/14 00:00:00 2018/04/15 00:00:00 292 168 59
189 10036 2018/04/15 00:00:00 2018/04/16 00:00:00 897 515 67
190 10036 2018/04/16 00:00:00 2018/04/17 00:00:00 373 214 59
191 10036 2018/04/17 00:00:00 2018/04/18 00:00:00 462 265 72
192 10036 2018/04/18 00:00:00 2018/04/19 00:00:00 292 168 50
193 10036 2018/04/19 00:00:00 2018/04/20 00:00:00 1396 823 170
194 10036 2018/04/20 00:00:00 2018/04/21 00:00:00 270 155 27
195 10036 2018/04/21 00:00:00 2018/04/22 00:00:00 344 198 78
196 10036 2018/04/22 00:00:00 2018/04/23 00:00:00 487 280 120
197 10036 2018/04/23 00:00:00 2018/04/24 00:00:00 336 193 96
198 10036 2018/04/24 00:00:00 2018/04/25 00:00:00 384 221 66
199 10036 2018/04/25 00:00:00 2018/04/26 00:00:00 265 152 29
200 10036 2018/04/26 00:00:00 2018/04/27 00:00:00 410 236 67
201 10036 2018/04/27 00:00:00 2018/04/28 00:00:00 275 158 55
202 10036 2018/04/28 00:00:00 2018/04/29 00:00:00 173 99 40
203 10036 2018/04/29 00:00:00 2018/04/30 00:00:00 272 156 31
204 10036 2018/04/30 00:00:00 2018/05/01 00:00:00 447 257 106
205 10036 2018/05/01 00:00:00 2018/05/02 00:00:00 216 124 47
206 10036 2018/05/02 00:00:00 2018/05/03 00:00:00 377 217 77
207 10036 2018/05/03 00:00:00 2018/05/04 00:00:00 628 361 23
208 10036 2018/05/04 00:00:00 2018/05/05 00:00:00 321 184 38
209 10036 2018/05/05 00:00:00 2018/05/06 00:00:00 268 154 46
210 10036 2018/05/06 00:00:00 2018/05/07 00:00:00 188 108 28
211 10036 2018/05/07 00:00:00 2018/05/08 00:00:00 243 140 53
212 10036 2018/05/08 00:00:00 2018/05/09 00:00:00 171 98 29
213 10036 2018/05/09 00:00:00 2018/05/10 00:00:00 797 458 128
214 10036 2018/05/10 00:00:00 2018/05/11 00:00:00 398 234 110
215 10036 2018/05/11 00:00:00 2018/05/12 00:00:00 3456 2028 165
216 10036 2018/05/12 00:00:00 2018/05/13 00:00:00 155 89 44
217 10036 2018/05/13 00:00:00 2018/05/14 00:00:00 205 118 48
218 10036 2018/05/14 00:00:00 2018/05/15 00:00:00 542 334 147
219 10036 2018/05/15 00:00:00 2018/05/16 00:00:00 1449 858 174
220 10036 2018/05/16 00:00:00 2018/05/17 00:00:00 396 228 48
221 10036 2018/05/17 00:00:00 2018/05/18 00:00:00 504 295 117
222 10036 2018/05/18 00:00:00 2018/05/19 00:00:00 415 238 43
223 10036 2018/05/19 00:00:00 2018/05/20 00:00:00 306 176 68
224 10036 2018/05/20 00:00:00 2018/05/21 00:00:00 479 319 185
225 10036 2018/05/21 00:00:00 2018/05/22 00:00:00 318 194 83
226 10036 2018/05/22 00:00:00 2018/05/23 00:00:00 255 147 48
227 10036 2018/05/23 00:00:00 2018/05/24 00:00:00 546 315 71
228 10036 2018/05/24 00:00:00 2018/05/25 00:00:00 195 112 24
229 10036 2018/05/25 00:00:00 2018/05/26 00:00:00 451 259 77
230 10036 2018/05/26 00:00:00 2018/05/27 00:00:00 446 272 108
231 10036 2018/05/27 00:00:00 2018/05/28 00:00:00 267 153 39
232 10036 2018/05/28 00:00:00 2018/05/29 00:00:00 404 248 113
233 10036 2018/05/29 00:00:00 2018/05/30 00:00:00 300 172 64
234 10036 2018/05/30 00:00:00 2018/05/31 00:00:00 229 132 53
235 10036 2018/05/31 00:00:00 2018/06/01 00:00:00 233 134 53
236 10036 2018/06/01 00:00:00 2018/06/02 00:00:00 489 283 100
237 10036 2018/06/02 00:00:00 2018/06/03 00:00:00 500 297 123
238 10036 2018/06/03 00:00:00 2018/06/04 00:00:00 698 401 115
239 10036 2018/06/04 00:00:00 2018/06/05 00:00:00 258 148 52
240 10036 2018/06/05 00:00:00 2018/06/06 00:00:00 350 201 43
241 10036 2018/06/06 00:00:00 2018/06/07 00:00:00 616 375 150
242 10036 2018/06/07 00:00:00 2018/06/08 00:00:00 472 271 53
243 10036 2018/06/08 00:00:00 2018/06/09 00:00:00 354 203 74
244 10036 2018/06/09 00:00:00 2018/06/10 00:00:00 461 265 97
245 10036 2018/06/10 00:00:00 2018/06/11 00:00:00 429 246 60
246 10036 2018/06/11 00:00:00 2018/06/12 00:00:00 430 247 65
247 10036 2018/06/12 00:00:00 2018/06/13 00:00:00 383 220 71
248 10036 2018/06/13 00:00:00 2018/06/14 00:00:00 205 118 35
249 10036 2018/06/14 00:00:00 2018/06/15 00:00:00 331 190 46
250 10036 2018/06/15 00:00:00 2018/06/16 00:00:00 527 311 89
251 10036 2018/06/16 00:00:00 2018/06/17 00:00:00 301 173 73
252 10036 2018/06/17 00:00:00 2018/06/18 00:00:00 470 270 85
253 10036 2018/06/18 00:00:00 2018/06/19 00:00:00 396 228 75
254 10036 2018/06/19 00:00:00 2018/06/20 00:00:00 229 132 16
255 10036 2018/06/20 00:00:00 2018/06/21 00:00:00 506 291 52
256 10036 2018/06/21 00:00:00 2018/06/22 00:00:00 320 184 49
257 10036 2018/06/22 00:00:00 2018/06/23 00:00:00 2963 1866 117
258 10036 2018/06/23 00:00:00 2018/06/24 00:00:00 691 466 317
259 10036 2018/06/24 00:00:00 2018/06/25 00:00:00 343 197 60
260 10036 2018/06/25 00:00:00 2018/06/26 00:00:00 538 333 142
261 10036 2018/06/26 00:00:00 2018/06/27 00:00:00 385 221 62
262 10036 2018/06/27 00:00:00 2018/06/28 00:00:00 361 248 146
263 10036 2018/06/28 00:00:00 2018/06/29 00:00:00 641 403 166
264 10036 2018/06/29 00:00:00 2018/06/30 00:00:00 436 251 45
265 10036 2018/06/30 00:00:00 2018/07/01 00:00:00 546 319 128
266 10036 2018/07/01 00:00:00 2018/07/02 00:00:00 135 78 26
267 10036 2018/07/02 00:00:00 2018/07/03 00:00:00 555 329 170
268 10036 2018/07/03 00:00:00 2018/08/05 00:00:00 259 149 59
269 10036 2018/08/05 00:00:00 2018/08/06 00:00:00 196 113 1
270 10036 2018/08/06 00:00:00 2018/08/07 00:00:00 163 94 25
271 10036 2018/08/07 00:00:00 2018/08/08 00:00:00 479 276 106
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4617 10062 2018/10/03 00:00:00 2018/10/04 00:00:00 921 557 153
4618 10062 2018/10/04 00:00:00 2018/10/05 00:00:00 2472 1512 212
4619 10062 2018/10/05 00:00:00 2018/10/06 00:00:00 716 433 86
4620 10062 2018/10/18 00:00:00 2018/10/19 00:00:00 545 330 141
4621 10062 2018/10/30 00:00:00 2018/10/31 00:00:00 340 206 88
4622 10062 2018/10/31 00:00:00 2018/11/01 00:00:00 245 148 59
calories deepSleepTime shallowSleepTime wakeTime sleepStartDate
1 43 0 0 0 2017/10/08 22:00:00
2 45 0 0 0 2017/10/09 22:00:00
3 8 0 0 0 2017/10/10 22:00:00
4 10 0 0 0 2017/10/11 22:00:00
5 16 0 0 0 2017/10/12 22:00:00
6 16 0 0 0 2017/10/13 22:00:00
7 10 0 0 0 2017/10/14 22:00:00
8 21 0 0 0 2017/10/15 22:00:00
9 19 0 0 0 2017/10/16 22:00:00
10 15 0 0 0 2017/10/17 22:00:00
11 14 0 0 0 2017/10/18 22:00:00
12 11 0 0 0 2017/10/19 22:00:00
13 13 0 0 0 2017/10/20 22:00:00
14 13 0 0 0 2017/10/21 22:00:00
15 4 0 0 0 2017/10/22 22:00:00
16 18 0 0 0 2017/10/23 22:00:00
17 23 0 0 0 2017/10/24 22:00:00
18 15 0 0 0 2017/10/25 22:00:00
19 17 0 0 0 2017/10/26 22:00:00
20 11 0 0 0 2017/10/27 22:00:00
21 16 0 0 0 2017/10/28 22:00:00
22 28 0 0 0 2017/10/29 23:00:00
23 15 0 0 0 2017/10/30 23:00:00
24 16 0 0 0 2017/10/31 23:00:00
25 13 0 0 0 2017/11/01 23:00:00
26 35 0 0 0 2017/11/02 23:00:00
27 10 0 0 0 2017/11/03 23:00:00
28 20 0 0 0 2017/11/04 23:00:00
29 22 0 0 0 2017/11/05 23:00:00
30 17 0 0 0 2017/11/06 23:00:00
31 17 0 0 0 2017/11/07 23:00:00
32 15 0 0 0 2017/11/08 23:00:00
33 20 0 0 0 2017/11/09 23:00:00
34 18 0 0 0 2017/11/10 23:00:00
35 16 0 0 0 2017/11/11 23:00:00
36 31 0 0 0 2017/11/12 23:00:00
37 26 0 0 0 2017/11/13 23:00:00
38 26 0 0 0 2017/11/14 23:00:00
39 81 0 0 0 2017/11/15 23:00:00
40 24 0 0 0 2017/11/16 23:00:00
41 25 0 0 0 2017/11/17 23:00:00
42 22 0 0 0 2017/11/18 23:00:00
43 21 0 0 0 2017/11/19 23:00:00
44 20 0 0 0 2017/11/20 23:00:00
45 14 0 0 0 2017/11/21 23:00:00
46 19 0 0 0 2017/11/22 23:00:00
47 19 0 0 0 2017/11/23 23:00:00
48 12 0 0 0 2017/11/24 23:00:00
49 18 0 0 0 2017/11/25 23:00:00
50 9 0 0 0 2017/11/26 23:00:00
51 16 0 0 0 2017/11/27 23:00:00
52 11 0 0 0 2017/11/28 23:00:00
53 18 0 0 0 2017/11/29 23:00:00
54 23 0 0 0 2017/11/30 23:00:00
55 17 0 0 0 2017/12/01 23:00:00
56 17 0 0 0 2017/12/02 23:00:00
57 20 0 0 0 2017/12/03 23:00:00
58 21 0 0 0 2017/12/04 23:00:00
59 15 0 0 0 2017/12/05 23:00:00
60 26 0 0 0 2017/12/06 23:00:00
61 26 0 0 0 2017/12/07 23:00:00
62 16 0 0 0 2017/12/08 23:00:00
63 3 0 0 0 2017/12/09 23:00:00
64 22 0 0 0 2017/12/10 23:00:00
65 24 0 0 0 2017/12/11 23:00:00
66 18 0 0 0 2017/12/12 23:00:00
67 13 0 0 0 2017/12/13 23:00:00
68 6 0 119 0 2017/12/15 04:27:00
69 6 0 107 0 2017/12/16 04:40:00
70 4 17 212 0 2017/12/17 03:28:00
71 6 134 372 15 2017/12/17 22:35:00
72 6 31 379 23 2017/12/18 23:54:00
73 10 62 390 70 2017/12/19 22:31:00
74 8 18 184 68 2017/12/20 23:17:00
75 3 58 383 0 2017/12/21 23:33:00
76 5 89 126 0 2017/12/22 23:27:00
77 4 0 0 0 2017/12/22 23:00:00
78 3 0 0 0 2017/12/23 23:00:00
79 3 92 274 0 2017/12/25 22:38:00
80 4 0 0 0 2017/12/25 23:00:00
81 7 27 207 19 2017/12/28 02:57:00
82 5 101 297 38 2017/12/28 22:52:00
83 5 19 360 77 2017/12/29 22:57:00
84 5 37 380 55 2017/12/30 23:14:00
85 5 14 273 0 2017/12/31 23:51:00
86 5 0 149 17 2018/01/01 22:38:00
87 4 21 373 97 2018/01/02 22:50:00
88 5 13 136 0 2018/01/04 01:12:00
89 4 13 93 0 2018/01/05 04:00:00
90 6 16 383 51 2018/01/05 23:32:00
91 3 32 426 28 2018/01/06 23:52:00
92 12 74 203 15 2018/01/07 23:06:00
93 4 29 256 0 2018/01/08 22:46:00
94 11 26 171 77 2018/01/10 03:17:00
95 12 0 95 0 2018/01/10 23:37:00
96 7 45 438 30 2018/01/11 22:38:00
97 6 0 0 0 2018/01/11 23:00:00
98 7 58 378 47 2018/01/13 22:40:00
99 8 63 229 0 2018/01/14 22:25:00
100 2 0 67 0 2018/01/15 22:49:00
101 17 11 314 33 2018/01/16 22:18:00
102 10 11 126 0 2018/01/17 22:37:00
103 5 49 178 0 2018/01/18 23:17:00
104 6 89 263 70 2018/01/20 00:11:00
105 4 24 119 59 2018/01/21 02:34:00
106 5 0 0 0 2018/01/20 23:00:00
107 4 0 0 0 2018/01/21 23:00:00
108 1 0 0 0 2018/01/22 23:00:00
109 1 10 59 0 2018/01/25 03:24:00
110 6 0 0 0 2018/01/24 23:00:00
111 6 0 221 0 2018/01/26 23:11:00
112 8 0 73 0 2018/01/28 01:59:00
113 5 0 257 0 2018/01/28 22:20:00
114 5 15 288 0 2018/01/29 22:16:00
115 9 31 309 15 2018/01/30 23:35:00
116 7 0 203 15 2018/01/31 23:23:00
117 3 29 86 0 2018/02/01 23:52:00
118 6 0 162 0 2018/02/03 03:58:00
119 6 0 207 0 2018/02/04 03:48:00
120 8 26 342 20 2018/02/04 22:43:00
121 3 0 109 74 2018/02/06 02:57:00
122 5 0 294 0 2018/02/07 02:16:00
123 4 0 111 0 2018/02/08 03:22:00
124 26 77 277 0 2018/02/08 23:42:00
125 3 29 341 125 2018/02/09 22:51:00
126 3 22 367 78 2018/02/10 23:21:00
127 4 30 428 50 2018/02/11 22:40:00
128 2 0 175 0 2018/02/13 03:47:00
129 4 14 292 0 2018/02/13 22:59:00
130 7 0 0 0 2018/02/13 23:00:00
131 4 0 113 0 2018/02/16 03:29:00
132 5 0 198 0 2018/02/16 23:29:00
133 3 46 272 0 2018/02/17 23:02:00
134 5 0 0 0 2018/02/17 23:00:00
135 3 41 153 0 2018/02/19 23:12:00
136 3 0 119 0 2018/02/21 04:05:00
137 2 0 152 0 2018/02/22 02:04:00
138 6 15 210 0 2018/02/22 22:52:00
139 3 0 81 0 2018/02/24 02:27:00
140 4 55 277 74 2018/02/24 23:49:00
141 8 13 109 0 2018/02/26 03:29:00
142 3 0 0 0 2018/02/25 23:00:00
143 4 30 232 0 2018/02/28 00:42:00
144 3 0 124 0 2018/02/28 22:49:00
145 2 0 0 0 2018/02/28 23:00:00
146 4 0 117 0 2018/03/03 04:22:00
147 5 0 0 0 2018/03/02 23:00:00
148 13 120 312 19 2018/03/04 23:07:00
149 9 0 113 0 2018/03/05 23:43:00
150 9 32 178 0 2018/03/06 23:11:00
151 3 122 178 68 2018/03/07 23:36:00
152 5 52 271 52 2018/03/08 23:06:00
153 4 57 143 25 2018/03/10 03:31:00
154 7 12 414 80 2018/03/10 22:49:00
155 8 0 157 0 2018/03/12 03:52:00
156 4 0 0 0 2018/03/11 23:00:00
157 2 0 117 0 2018/03/13 23:41:00
158 5 0 148 0 2018/03/15 04:29:00
159 2 46 288 72 2018/03/15 22:58:00
160 4 0 0 0 2018/03/15 23:00:00
161 4 21 412 63 2018/03/17 22:43:00
162 3 0 0 0 2018/03/17 23:00:00
163 3 43 144 0 2018/03/20 01:47:00
164 3 0 0 0 2018/03/19 23:00:00
165 3 82 165 0 2018/03/21 22:36:00
166 2 40 192 0 2018/03/22 23:02:00
167 3 34 88 0 2018/03/24 03:17:00
168 2 48 112 0 2018/03/24 22:20:00
169 6 117 329 32 2018/03/25 21:11:00
170 21 34 173 58 2018/03/27 01:39:00
171 34 0 0 0 2018/03/26 22:00:00
172 3 70 294 30 2018/03/28 21:45:00
173 5 43 412 31 2018/03/29 21:38:00
174 12 20 158 0 2018/03/31 02:29:00
175 4 36 182 0 2018/03/31 22:02:00
176 8 12 136 0 2018/04/02 01:59:00
177 4 57 379 23 2018/04/02 22:27:00
178 5 0 0 0 2018/04/02 22:00:00
179 6 35 113 0 2018/04/05 03:01:00
180 4 0 289 71 2018/04/05 22:28:00
181 6 37 188 0 2018/04/06 21:56:00
182 6 47 118 0 2018/04/08 03:16:00
183 5 135 241 46 2018/04/08 21:55:00
184 6 28 74 0 2018/04/10 00:16:00
185 9 52 245 0 2018/04/10 21:54:00
186 6 0 196 0 2018/04/11 21:46:00
187 7 0 0 0 2018/04/11 22:00:00
188 5 25 153 0 2018/04/14 02:40:00
189 12 50 265 42 2018/04/14 22:18:00
190 7 72 194 0 2018/04/15 22:14:00
191 5 33 181 0 2018/04/16 22:18:00
192 4 0 0 0 2018/04/16 22:00:00
193 17 63 368 68 2018/04/18 21:58:00
194 5 92 281 40 2018/04/19 22:17:00
195 7 13 114 0 2018/04/20 23:52:00
196 8 40 174 29 2018/04/22 01:50:00
197 7 0 0 0 2018/04/21 22:00:00
198 6 102 213 55 2018/04/23 22:12:00
199 5 13 199 14 2018/04/25 02:20:00
200 7 0 0 0 2018/04/24 22:00:00
201 6 24 85 0 2018/04/27 03:11:00
202 4 31 156 0 2018/04/28 02:04:00
203 5 32 196 0 2018/04/28 22:36:00
204 7 28 182 35 2018/04/30 00:51:00
205 4 0 0 0 2018/04/29 22:00:00
206 5 29 149 0 2018/05/02 03:08:00
207 8 13 147 0 2018/05/03 03:29:00
208 4 0 0 0 2018/05/02 22:00:00
209 6 0 185 0 2018/05/05 02:09:00
210 4 87 393 51 2018/05/05 21:23:00
211 4 110 364 37 2018/05/06 21:50:00
212 3 16 50 0 2018/05/08 00:15:00
213 10 122 135 0 2018/05/08 21:39:00
214 6 15 405 34 2018/05/09 22:05:00
215 37 0 0 0 2018/05/09 22:00:00
216 4 70 204 30 2018/05/12 00:54:00
217 3 122 334 22 2018/05/12 22:08:00
218 8 59 404 47 2018/05/13 21:52:00
219 17 53 160 0 2018/05/14 21:54:00
220 7 41 148 0 2018/05/15 21:52:00
221 6 0 0 0 2018/05/15 22:00:00
222 6 0 0 0 2018/05/16 22:00:00
223 5 18 207 0 2018/05/19 02:27:00
224 8 0 62 0 2018/05/20 01:10:00
225 6 97 327 19 2018/05/20 22:01:00
226 4 53 402 48 2018/05/21 21:52:00
227 7 35 394 78 2018/05/22 21:44:00
228 3 69 363 44 2018/05/23 21:58:00
229 7 85 332 63 2018/05/24 21:55:00
230 6 85 354 26 2018/05/25 21:54:00
231 6 34 137 0 2018/05/26 22:07:00
232 7 0 0 0 2018/05/26 22:00:00
233 5 114 143 0 2018/05/28 22:06:00
234 4 206 265 30 2018/05/29 21:47:00
235 4 114 203 0 2018/05/30 21:39:00
236 8 152 320 70 2018/05/31 20:59:00
237 7 62 331 32 2018/06/01 21:51:00
238 8 70 345 0 2018/06/02 22:14:00
239 5 86 379 26 2018/06/03 21:36:00
240 4 80 267 43 2018/06/04 21:59:00
241 8 24 339 68 2018/06/05 21:53:00
242 7 0 335 52 2018/06/06 22:27:00
243 7 0 99 0 2018/06/07 22:00:00
244 7 27 183 0 2018/06/08 22:14:00
245 6 23 120 36 2018/06/10 03:09:00
246 8 27 267 65 2018/06/10 22:15:00
247 5 81 50 0 2018/06/12 03:47:00
248 4 0 158 0 2018/06/13 03:18:00
249 5 42 172 0 2018/06/14 02:10:00
250 8 0 103 0 2018/06/15 03:36:00
251 5 0 0 0 2018/06/14 22:00:00
252 7 56 234 0 2018/06/16 21:53:00
253 7 10 184 0 2018/06/17 22:49:00
254 5 0 179 0 2018/06/19 03:14:00
255 7 117 246 78 2018/06/19 21:24:00
256 5 37 129 0 2018/06/21 02:08:00
257 37 119 116 0 2018/06/21 22:03:00
258 13 62 208 0 2018/06/22 21:39:00
259 5 63 291 43 2018/06/23 23:37:00
260 9 87 135 0 2018/06/25 01:43:00
261 6 163 243 104 2018/06/25 21:34:00
262 8 0 88 0 2018/06/27 00:58:00
263 12 106 277 71 2018/06/27 22:04:00
264 6 30 247 42 2018/06/29 00:54:00
265 7 0 145 0 2018/06/30 03:47:00
266 3 28 395 50 2018/06/30 22:11:00
267 8 64 286 35 2018/07/01 23:36:00
268 3 28 312 61 2018/07/02 21:55:00
269 2 0 0 0 2018/08/03 22:00:00
270 2 61 355 47 2018/08/05 21:18:00
271 6 46 337 47 2018/08/06 22:16:00
272 9 29 123 0 2018/08/08 02:18:00
273 10 66 333 29 2018/08/08 21:41:00
274 1 0 0 0 2018/08/08 22:00:00
275 5 95 358 49 2018/08/10 21:45:00
276 6 43 208 62 2018/08/12 00:12:00
277 11 38 299 85 2018/08/12 21:35:00
278 6 36 158 16 2018/08/14 01:42:00
279 0 0 104 0 2018/08/14 21:58:00
280 43 0 0 0 2018/08/14 22:00:00
281 5 0 0 0 2018/08/15 22:00:00
282 6 15 109 0 2018/08/18 03:18:00
283 5 23 74 0 2018/08/19 03:50:00
284 10 21 131 18 2018/08/19 21:33:00
285 4 0 0 0 2018/08/19 22:00:00
286 4 0 0 0 2018/08/20 22:00:00
287 9 10 108 20 2018/08/23 03:33:00
288 6 0 121 0 2018/08/24 02:54:00
289 7 0 0 0 2018/08/23 22:00:00
290 5 49 149 0 2018/08/25 21:38:00
291 6 37 192 0 2018/08/26 21:01:00
292 1 0 156 0 2018/08/28 01:51:00
293 10 48 356 0 2018/08/28 21:43:00
294 13 33 112 15 2018/08/30 03:16:00
295 4 93 350 35 2018/08/30 21:47:00
296 5 15 217 0 2018/09/01 01:32:00
297 4 150 270 0 2018/09/01 22:16:00
298 7 91 71 0 2018/09/03 02:43:00
299 3 120 363 41 2018/09/03 21:28:00
300 7 30 178 0 2018/09/05 02:42:00
301 6 10 238 0 2018/09/05 22:24:00
302 4 89 137 19 2018/09/06 21:58:00
303 8 90 228 0 2018/09/07 21:50:00
304 9 0 0 0 2018/09/07 22:00:00
305 9 80 374 43 2018/09/09 21:51:00
306 6 82 211 0 2018/09/10 21:44:00
307 4 0 335 102 2018/09/11 22:50:00
308 5 20 136 0 2018/09/13 03:16:00
309 4 0 75 0 2018/09/14 01:33:00
310 55 0 0 0 2017/10/08 22:00:00
311 57 0 0 0 2017/10/09 22:00:00
312 148 0 0 0 2017/10/10 22:00:00
313 154 0 0 0 2017/10/11 22:00:00
314 192 0 0 0 2017/10/12 22:00:00
315 223 0 0 0 2017/10/13 22:00:00
316 199 0 0 0 2017/10/14 22:00:00
317 176 0 0 0 2017/10/15 22:00:00
318 208 0 0 0 2017/10/16 22:00:00
319 262 40 25 0 2017/10/18 03:39:00
320 224 0 0 0 2017/10/18 22:00:00
321 260 0 0 0 2017/10/19 22:00:00
322 235 0 0 0 2017/10/20 22:00:00
323 237 0 0 0 2017/10/21 22:00:00
324 335 0 0 0 2017/10/22 22:00:00
325 306 0 0 0 2017/10/23 22:00:00
326 340 0 0 0 2017/10/24 22:00:00
327 263 0 0 0 2017/10/25 22:00:00
328 254 0 0 0 2017/10/26 22:00:00
329 207 0 0 0 2017/10/27 22:00:00
330 264 0 0 0 2017/10/28 22:00:00
331 267 0 0 0 2017/10/29 23:00:00
332 272 0 0 0 2017/10/30 23:00:00
333 239 0 0 0 2017/10/31 23:00:00
334 315 0 0 0 2017/11/01 23:00:00
335 279 0 0 0 2017/11/02 23:00:00
336 226 0 0 0 2017/11/03 23:00:00
337 265 0 0 0 2017/11/04 23:00:00
338 326 0 0 0 2017/11/05 23:00:00
339 266 0 0 0 2017/11/06 23:00:00
340 258 0 0 0 2017/11/07 23:00:00
341 227 0 0 0 2017/11/08 23:00:00
342 241 0 0 0 2017/11/09 23:00:00
343 208 0 0 0 2017/11/10 23:00:00
344 217 0 0 0 2017/11/11 23:00:00
345 249 0 0 0 2017/11/12 23:00:00
346 288 0 0 0 2017/11/13 23:00:00
347 309 0 0 0 2017/11/14 23:00:00
348 231 0 0 0 2017/11/15 23:00:00
349 260 0 0 0 2017/11/16 23:00:00
350 235 0 0 0 2017/11/17 23:00:00
351 233 0 0 0 2017/11/18 23:00:00
352 241 0 0 0 2017/11/19 23:00:00
353 231 0 0 0 2017/11/20 23:00:00
354 227 0 0 0 2017/11/21 23:00:00
355 218 0 0 0 2017/11/22 23:00:00
356 269 0 0 0 2017/11/23 23:00:00
357 181 0 0 0 2017/11/24 23:00:00
358 231 0 0 0 2017/11/25 23:00:00
359 374 0 0 0 2017/11/26 23:00:00
360 354 0 123 0 2017/11/28 01:00:00
361 299 0 0 0 2017/11/28 23:00:00
362 266 0 0 0 2017/11/29 23:00:00
363 352 40 21 0 2017/12/01 04:27:00
364 283 0 0 0 2017/12/01 23:00:00
365 320 30 52 0 2017/12/03 03:51:00
366 385 0 0 0 2017/12/03 23:00:00
367 281 0 64 0 2017/12/04 22:22:00
368 334 0 0 0 2017/12/05 23:00:00
369 209 0 0 0 2017/12/06 23:00:00
370 277 0 0 0 2017/12/07 23:00:00
371 188 0 0 0 2017/12/08 23:00:00
372 214 0 0 0 2017/12/09 23:00:00
373 271 0 0 0 2017/12/10 23:00:00
374 280 0 0 0 2017/12/11 23:00:00
375 284 0 0 0 2017/12/12 23:00:00
376 208 0 0 0 2017/12/13 23:00:00
377 223 0 0 0 2017/12/14 23:00:00
378 254 0 189 14 2017/12/16 01:01:00
379 294 0 0 0 2017/12/16 23:00:00
380 258 0 80 0 2017/12/18 02:09:00
381 226 0 0 0 2017/12/18 23:00:00
382 292 0 0 0 2017/12/19 23:00:00
383 235 0 0 0 2017/12/20 23:00:00
384 232 0 0 0 2017/12/21 23:00:00
385 285 0 0 0 2017/12/22 23:00:00
386 306 0 0 0 2017/12/23 23:00:00
387 279 0 0 0 2017/12/24 23:00:00
388 276 0 0 0 2017/12/25 23:00:00
389 330 72 37 0 2017/12/27 03:00:00
390 268 0 0 0 2017/12/27 23:00:00
391 99 70 92 0 2017/12/29 03:23:00
392 113 183 99 8 2017/12/29 19:59:00
393 127 324 202 0 2017/12/30 20:19:00
394 97 235 260 23 2017/12/31 21:15:00
395 145 31 76 0 2018/01/02 02:58:00
396 117 149 197 14 2018/01/02 20:07:00
397 104 141 53 0 2018/01/03 20:59:00
398 122 110 186 16 2018/01/05 00:41:00
399 79 147 99 0 2018/01/06 01:21:00
400 59 244 306 18 2018/01/06 20:11:00
401 104 237 273 0 2018/01/07 20:12:00
402 126 267 308 0 2018/01/08 20:16:00
403 116 102 97 0 2018/01/10 02:13:00
404 89 351 231 0 2018/01/10 20:03:00
405 124 145 162 0 2018/01/11 20:04:00
406 78 126 203 2 2018/01/13 00:10:00
407 72 250 310 0 2018/01/13 20:13:00
408 111 282 293 0 2018/01/14 20:01:00
409 96 267 272 0 2018/01/15 20:21:00
410 77 262 207 0 2018/01/16 21:47:00
411 72 139 161 0 2018/01/18 00:40:00
412 105 60 146 0 2018/01/19 02:14:00
413 65 329 245 0 2018/01/19 20:14:00
414 66 273 305 0 2018/01/20 20:09:00
415 126 367 199 0 2018/01/21 20:23:00
416 92 71 337 33 2018/01/22 20:24:00
417 87 161 346 31 2018/01/23 20:28:00
418 66 353 218 0 2018/01/24 20:29:00
419 102 52 131 0 2018/01/26 02:54:00
420 115 147 240 21 2018/01/26 22:46:00
421 102 300 251 0 2018/01/27 20:16:00
422 126 166 158 0 2018/01/28 23:53:00
423 97 28 138 0 2018/01/30 02:45:00
424 112 299 220 0 2018/01/30 20:45:00
425 118 272 293 0 2018/01/31 20:18:00
426 116 327 237 0 2018/02/01 20:27:00
427 63 320 239 0 2018/02/02 20:25:00
428 62 239 343 0 2018/02/03 20:09:00
429 80 278 267 0 2018/02/04 20:38:00
430 80 292 272 0 2018/02/05 20:20:00
431 96 110 178 0 2018/02/07 01:03:00
432 85 255 306 0 2018/02/07 20:23:00
433 95 317 249 0 2018/02/08 20:12:00
434 47 277 287 0 2018/02/09 20:25:00
435 56 183 172 0 2018/02/10 23:52:00
436 74 239 318 0 2018/02/11 20:27:00
437 75 300 359 46 2018/02/12 18:03:00
438 72 123 170 0 2018/02/14 01:05:00
439 53 255 396 81 2018/02/14 17:30:00
440 87 295 282 0 2018/02/15 20:12:00
441 58 253 308 0 2018/02/16 20:20:00
442 48 225 347 0 2018/02/17 20:07:00
443 88 248 309 0 2018/02/18 20:15:00
444 67 87 134 0 2018/02/20 01:57:00
445 76 260 292 11 2018/02/20 20:15:00
446 93 254 313 0 2018/02/21 20:15:00
447 71 174 159 0 2018/02/23 00:13:00
448 69 294 230 0 2018/02/23 20:19:00
449 64 268 302 0 2018/02/24 20:11:00
450 81 243 327 0 2018/02/25 20:10:00
451 74 315 245 0 2018/02/26 20:20:00
452 82 289 262 0 2018/02/27 20:10:00
453 94 200 249 9 2018/02/28 22:01:00
454 97 288 267 0 2018/03/01 20:26:00
455 64 240 334 0 2018/03/02 20:16:00
456 62 296 273 0 2018/03/03 20:17:00
457 77 298 278 0 2018/03/04 20:10:00
458 102 148 228 0 2018/03/05 23:27:00
459 75 324 204 0 2018/03/06 20:35:00
460 66 265 271 0 2018/03/07 20:28:00
461 129 223 225 0 2018/03/08 21:57:00
462 130 246 177 0 2018/03/09 22:18:00
463 72 29 114 0 2018/03/11 02:53:00
464 104 220 318 14 2018/03/11 20:14:00
465 118 312 215 0 2018/03/12 20:53:00
466 155 284 264 0 2018/03/13 20:13:00
467 137 345 209 0 2018/03/14 20:14:00
468 110 245 211 0 2018/03/15 21:45:00
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4573 22 13 129 0 2018/08/20 03:13:00
4574 25 57 101 0 2018/08/21 03:16:00
4575 15 36 83 0 2018/08/22 02:16:00
4576 41 0 0 0 2018/08/21 22:00:00
4577 31 0 202 90 2018/08/23 22:02:00
4578 52 0 0 0 2018/08/23 22:00:00
4579 19 37 144 0 2018/08/26 01:58:00
4580 17 0 80 0 2018/08/27 02:22:00
4581 15 64 376 118 2018/08/27 21:32:00
4582 40 48 168 0 2018/08/29 03:32:00
4583 23 0 171 0 2018/08/29 21:32:00
4584 27 87 93 0 2018/08/31 03:22:00
4585 37 0 0 0 2018/08/30 22:00:00
4586 14 0 80 0 2018/09/02 00:05:00
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4588 26 0 0 0 2018/09/02 22:00:00
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4590 34 32 200 0 2018/09/06 02:19:00
4591 22 0 168 0 2018/09/06 22:05:00
4592 29 55 100 0 2018/09/08 03:54:00
4593 29 92 133 0 2018/09/09 03:28:00
4594 21 0 0 0 2018/09/08 22:00:00
4595 31 0 63 0 2018/09/10 23:08:00
4596 22 48 128 0 2018/09/12 02:38:00
4597 19 47 161 32 2018/09/13 02:24:00
4598 29 71 273 25 2018/09/13 23:59:00
4599 34 32 151 0 2018/09/15 01:46:00
4600 32 28 184 0 2018/09/16 03:47:00
4601 30 97 219 29 2018/09/17 00:42:00
4602 14 21 131 23 2018/09/18 03:46:00
4603 14 39 233 29 2018/09/19 01:45:00
4604 34 0 0 0 2018/09/18 22:00:00
4605 14 69 105 0 2018/09/21 03:29:00
4606 32 33 151 71 2018/09/22 01:23:00
4607 25 0 65 0 2018/09/22 23:35:00
4608 11 28 323 77 2018/09/23 23:33:00
4609 60 20 71 0 2018/09/25 01:03:00
4610 36 90 236 77 2018/09/25 23:52:00
4611 20 24 344 54 2018/09/26 23:25:00
4612 12 11 168 18 2018/09/28 03:17:00
4613 42 68 220 54 2018/09/29 00:41:00
4614 24 0 0 0 2018/09/28 22:00:00
4615 15 94 395 59 2018/09/30 22:07:00
4616 30 76 415 35 2018/10/01 21:27:00
4617 17 10 166 0 2018/10/03 02:54:00
4618 33 0 0 0 2018/10/02 22:00:00
4619 9 10 201 35 2018/10/05 01:37:00
4620 12 143 275 0 2018/10/17 23:35:00
4621 8 15 243 15 2018/10/29 23:08:00
4622 6 84 316 46 2018/10/31 00:13:00
sleepStopDate
1 2017/10/08 22:00:00
2 2017/10/09 22:00:00
3 2017/10/10 22:00:00
4 2017/10/11 22:00:00
5 2017/10/12 22:00:00
6 2017/10/13 22:00:00
7 2017/10/14 22:00:00
8 2017/10/15 22:00:00
9 2017/10/16 22:00:00
10 2017/10/17 22:00:00
11 2017/10/18 22:00:00
12 2017/10/19 22:00:00
13 2017/10/20 22:00:00
14 2017/10/21 22:00:00
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17 2017/10/24 22:00:00
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20 2017/10/27 22:00:00
21 2017/10/28 22:00:00
22 2017/10/29 23:00:00
23 2017/10/30 23:00:00
24 2017/10/31 23:00:00
25 2017/11/01 23:00:00
26 2017/11/02 23:00:00
27 2017/11/03 23:00:00
28 2017/11/04 23:00:00
29 2017/11/05 23:00:00
30 2017/11/06 23:00:00
31 2017/11/07 23:00:00
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34 2017/11/10 23:00:00
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62 2017/12/08 23:00:00
63 2017/12/09 23:00:00
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65 2017/12/11 23:00:00
66 2017/12/12 23:00:00
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68 2017/12/15 06:26:00
69 2017/12/16 06:27:00
70 2017/12/17 07:17:00
71 2017/12/18 07:16:00
72 2017/12/19 07:07:00
73 2017/12/20 07:13:00
74 2017/12/21 03:47:00
75 2017/12/22 06:54:00
76 2017/12/23 03:02:00
77 2017/12/22 23:00:00
78 2017/12/23 23:00:00
79 2017/12/26 04:44:00
80 2017/12/25 23:00:00
81 2017/12/28 07:10:00
82 2017/12/29 06:08:00
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84 2017/12/31 07:06:00
85 2018/01/01 04:38:00
86 2018/01/02 01:24:00
87 2018/01/03 07:01:00
88 2018/01/04 03:41:00
89 2018/01/05 05:46:00
90 2018/01/06 07:02:00
91 2018/01/07 07:58:00
92 2018/01/08 03:58:00
93 2018/01/09 03:31:00
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97 2018/01/11 23:00:00
98 2018/01/14 06:43:00
99 2018/01/15 03:17:00
100 2018/01/15 23:56:00
101 2018/01/17 04:16:00
102 2018/01/18 00:54:00
103 2018/01/19 03:04:00
104 2018/01/20 07:13:00
105 2018/01/21 05:56:00
106 2018/01/20 23:00:00
107 2018/01/21 23:00:00
108 2018/01/22 23:00:00
109 2018/01/25 04:33:00
110 2018/01/24 23:00:00
111 2018/01/27 02:52:00
112 2018/01/28 03:12:00
113 2018/01/29 02:37:00
114 2018/01/30 03:19:00
115 2018/01/31 05:30:00
116 2018/02/01 03:01:00
117 2018/02/02 01:47:00
118 2018/02/03 06:40:00
119 2018/02/04 07:15:00
120 2018/02/05 05:11:00
121 2018/02/06 06:00:00
122 2018/02/07 07:10:00
123 2018/02/08 05:13:00
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125 2018/02/10 07:06:00
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127 2018/02/12 07:08:00
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129 2018/02/14 04:05:00
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140 2018/02/25 06:35:00
141 2018/02/26 05:31:00
142 2018/02/25 23:00:00
143 2018/02/28 05:04:00
144 2018/03/01 00:53:00
145 2018/02/28 23:00:00
146 2018/03/03 06:19:00
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148 2018/03/05 06:38:00
149 2018/03/06 01:36:00
150 2018/03/07 02:41:00
151 2018/03/08 05:44:00
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181 2018/04/07 01:41:00
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186 2018/04/12 01:02:00
187 2018/04/11 22:00:00
188 2018/04/14 05:38:00
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190 2018/04/16 02:40:00
191 2018/04/17 01:52:00
192 2018/04/16 22:00:00
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198 2018/04/24 04:22:00
199 2018/04/25 06:06:00
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201 2018/04/27 05:00:00
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203 2018/04/29 02:24:00
204 2018/04/30 04:56:00
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217 2018/05/13 06:06:00
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230 2018/05/26 05:39:00
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273 2018/08/09 04:49:00
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315 2017/10/13 22:00:00
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318 2017/10/16 22:00:00
319 2017/10/18 04:44:00
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330 2017/10/28 22:00:00
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4087 2018/03/31 05:48:00
4088 2018/04/01 06:40:00
4089 2018/04/02 06:46:00
4090 2018/04/03 05:11:00
4091 2018/04/04 06:39:00
4092 2018/04/05 06:13:00
4093 2018/04/06 06:03:00
4094 2018/04/07 06:30:00
4095 2018/04/08 06:39:00
4096 2018/04/09 05:55:00
4097 2018/04/10 06:29:00
4098 2018/04/11 06:06:00
4099 2018/04/12 06:48:00
4100 2018/04/13 07:10:00
4101 2018/04/14 06:59:00
4102 2018/04/15 06:12:00
4103 2018/04/16 06:29:00
4104 2018/04/17 05:40:00
4105 2018/04/18 06:26:00
4106 2018/04/19 05:31:00
4107 2018/04/20 06:00:00
4108 2018/04/21 06:46:00
4109 2018/04/22 05:29:00
4110 2018/04/23 07:22:00
4111 2018/04/24 04:16:00
4112 2018/04/25 06:56:00
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4114 2018/04/27 06:56:00
4115 2018/04/28 05:38:00
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4119 2018/05/02 05:48:00
4120 2018/05/01 22:00:00
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4124 2018/05/07 06:11:00
4125 2018/05/08 05:25:00
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4128 2018/05/11 06:07:00
4129 2018/05/19 22:00:00
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4135 2018/05/27 06:40:00
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4149 2018/06/10 06:13:00
4150 2018/06/11 06:45:00
4151 2018/06/12 06:15:00
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4153 2018/06/14 06:31:00
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4157 2018/06/18 06:28:00
4158 2018/06/19 03:58:00
4159 2018/06/20 06:11:00
4160 2018/06/21 05:00:00
4161 2018/06/22 04:06:00
4162 2018/06/23 05:09:00
4163 2018/06/24 05:54:00
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4165 2018/06/26 04:36:00
4166 2018/06/27 05:08:00
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4169 2018/06/30 07:22:00
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4179 2018/07/10 05:12:00
4180 2018/07/11 05:45:00
4181 2018/07/12 03:59:00
4182 2018/07/13 03:35:00
4183 2018/07/14 05:29:00
4184 2018/07/15 06:07:00
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4186 2018/07/17 06:00:00
4187 2018/07/18 06:04:00
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4189 2018/07/20 06:15:00
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4191 2018/07/22 05:55:00
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4193 2018/07/24 05:57:00
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4195 2018/07/26 06:05:00
4196 2018/07/27 04:32:00
4197 2018/07/28 06:37:00
4198 2018/07/29 05:45:00
4199 2018/07/30 04:55:00
4200 2018/07/31 06:07:00
4201 2018/08/01 04:54:00
4202 2018/08/02 05:04:00
4203 2018/08/03 04:22:00
4204 2018/08/04 05:11:00
4205 2018/08/05 05:16:00
4206 2018/08/06 04:48:00
4207 2018/08/07 05:57:00
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4209 2018/08/09 05:31:00
4210 2018/08/10 06:34:00
4211 2018/08/11 05:30:00
4212 2018/08/12 06:05:00
4213 2018/08/13 04:46:00
4214 2018/08/14 06:19:00
4215 2018/08/15 02:48:00
4216 2018/08/16 06:07:00
4217 2018/08/17 05:18:00
4218 2018/08/18 05:02:00
4219 2018/08/19 05:51:00
4220 2018/08/20 06:30:00
4221 2018/08/21 05:34:00
4222 2018/08/22 07:01:00
4223 2018/08/23 06:23:00
4224 2018/08/24 05:21:00
4225 2018/08/25 06:03:00
4226 2018/08/26 06:12:00
4227 2018/08/27 06:57:00
4228 2018/08/28 06:34:00
4229 2018/08/29 06:52:00
4230 2018/08/30 07:23:00
4231 2018/08/31 05:22:00
4232 2018/09/01 06:11:00
4233 2018/09/02 05:00:00
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4235 2018/09/04 05:30:00
4236 2018/09/05 06:02:00
4237 2018/09/06 06:03:00
4238 2018/09/23 06:16:00
4239 2018/09/24 06:47:00
4240 2018/09/25 05:45:00
4241 2018/09/26 05:23:00
4242 2018/09/27 06:44:00
4243 2018/09/28 04:18:00
4244 2018/09/29 05:12:00
4245 2018/09/30 06:01:00
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4247 2018/10/02 07:15:00
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4249 2018/10/04 06:49:00
4250 2018/10/05 04:17:00
4251 2018/10/06 06:06:00
4252 2018/10/07 03:56:00
4253 2018/10/08 04:54:00
4254 2018/10/10 04:34:00
4255 2018/10/11 07:29:00
4256 2018/10/12 07:00:00
4257 2018/10/13 06:27:00
4258 2018/10/14 04:51:00
4259 2018/10/15 05:36:00
4260 2018/10/16 05:43:00
4261 2018/10/17 06:43:00
4262 2018/10/18 05:21:00
4263 2018/10/19 06:11:00
4264 2018/10/20 06:08:00
4265 2018/10/21 07:01:00
4266 2018/10/22 05:59:00
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4268 2018/10/24 06:01:00
4269 2018/10/25 06:08:00
4270 2018/10/26 05:49:00
4271 2018/10/27 05:46:00
4272 2018/10/28 05:20:00
4273 2018/10/29 06:50:00
4274 2018/10/30 05:50:00
4275 2018/10/31 06:49:00
4276 2017/10/16 22:00:00
4277 2017/10/19 07:21:00
4278 2017/10/20 06:30:00
4279 2017/10/21 07:33:00
4280 2017/10/22 06:57:00
4281 2017/10/23 06:49:00
4282 2017/10/24 04:45:00
4283 2017/10/25 07:09:00
4284 2017/10/26 06:29:00
4285 2017/10/27 06:30:00
4286 2017/10/28 07:35:00
4287 2017/10/29 07:11:00
4288 2017/10/30 07:09:00
4289 2017/10/31 04:51:00
4290 2017/11/01 06:37:00
4291 2017/11/02 08:10:00
4292 2017/11/03 07:55:00
4293 2017/11/04 05:58:00
4294 2017/11/05 00:23:00
4295 2017/11/06 07:51:00
4296 2017/11/07 07:45:00
4297 2017/11/08 07:45:00
4298 2017/11/09 07:48:00
4299 2017/11/08 23:00:00
4300 2017/11/11 05:41:00
4301 2017/11/12 07:05:00
4302 2017/11/13 07:47:00
4303 2017/11/14 01:08:00
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4305 2017/11/16 07:40:00
4306 2017/11/17 07:40:00
4307 2017/11/18 04:16:00
4308 2017/11/19 08:40:00
4309 2017/11/19 23:48:00
4310 2017/11/21 06:41:00
4311 2017/11/22 00:02:00
4312 2017/11/23 08:15:00
4313 2017/11/24 07:25:00
4314 2017/11/25 07:12:00
4315 2017/11/24 23:00:00
4316 2017/11/27 08:13:00
4317 2017/11/28 04:15:00
4318 2017/11/29 02:50:00
4319 2017/11/30 08:42:00
4320 2017/12/01 03:12:00
4321 2017/12/01 23:04:00
4322 2017/12/03 04:10:00
4323 2017/12/02 23:00:00
4324 2017/12/05 04:25:00
4325 2017/12/04 23:00:00
4326 2017/12/07 07:33:00
4327 2017/12/08 07:45:00
4328 2017/12/09 06:15:00
4329 2017/12/08 23:00:00
4330 2017/12/11 06:53:00
4331 2017/12/12 06:17:00
4332 2017/12/13 07:42:00
4333 2017/12/14 08:03:00
4334 2017/12/14 23:03:00
4335 2017/12/16 07:16:00
4336 2017/12/17 08:16:00
4337 2017/12/18 07:02:00
4338 2017/12/19 07:10:00
4339 2017/12/20 08:29:00
4340 2017/12/21 05:05:00
4341 2017/12/22 06:57:00
4342 2017/12/23 02:19:00
4343 2017/12/24 03:33:00
4344 2017/12/25 08:20:00
4345 2017/12/26 03:27:00
4346 2017/12/27 07:21:00
4347 2017/12/28 07:36:00
4348 2017/12/29 01:22:00
4349 2017/12/30 08:10:00
4350 2017/12/30 23:36:00
4351 2018/01/01 07:57:00
4352 2018/01/02 07:34:00
4353 2018/01/03 07:50:00
4354 2018/01/04 07:49:00
4355 2018/01/05 07:49:00
4356 2018/01/06 06:37:00
4357 2018/01/07 03:31:00
4358 2018/01/08 08:32:00
4359 2018/01/07 23:00:00
4360 2018/01/10 07:35:00
4361 2018/01/11 07:41:00
4362 2018/01/12 07:16:00
4363 2018/01/13 03:58:00
4364 2018/01/14 07:28:00
4365 2018/01/15 07:32:00
4366 2018/01/16 06:24:00
4367 2018/01/17 07:56:00
4368 2018/01/18 02:13:00
4369 2018/01/19 02:16:00
4370 2018/01/20 02:30:00
4371 2018/01/21 02:58:00
4372 2018/01/22 06:24:00
4373 2018/01/23 07:47:00
4374 2018/01/24 07:39:00
4375 2018/01/25 02:14:00
4376 2018/01/26 06:02:00
4377 2018/01/27 05:47:00
4378 2018/01/28 08:00:00
4379 2018/01/29 08:18:00
4380 2018/01/30 07:47:00
4381 2018/01/31 06:45:00
4382 2018/01/30 23:00:00
4383 2018/02/02 02:23:00
4384 2018/02/03 07:25:00
4385 2018/02/02 23:00:00
4386 2018/02/05 07:00:00
4387 2018/02/06 08:31:00
4388 2018/02/07 00:52:00
4389 2018/02/08 07:50:00
4390 2018/02/09 07:29:00
4391 2018/02/10 07:49:00
4392 2018/02/11 06:54:00
4393 2018/02/12 01:39:00
4394 2018/02/13 07:44:00
4395 2018/02/14 07:41:00
4396 2018/02/15 03:20:00
4397 2018/02/16 03:39:00
4398 2018/02/17 07:03:00
4399 2018/02/18 02:29:00
4400 2018/02/19 07:05:00
4401 2018/02/18 23:00:00
4402 2018/02/21 07:15:00
4403 2018/02/22 07:23:00
4404 2018/02/23 05:49:00
4405 2018/02/22 23:00:00
4406 2018/02/23 23:00:00
4407 2018/02/26 07:29:00
4408 2018/02/27 03:02:00
4409 2018/02/28 07:35:00
4410 2018/03/01 07:31:00
4411 2018/03/02 06:43:00
4412 2018/03/03 06:48:00
4413 2018/03/04 00:34:00
4414 2018/03/05 07:13:00
4415 2018/03/06 07:42:00
4416 2018/03/07 06:23:00
4417 2018/03/08 02:32:00
4418 2018/03/09 01:27:00
4419 2018/03/10 07:07:00
4420 2018/03/11 07:01:00
4421 2018/03/12 02:50:00
4422 2018/03/13 06:45:00
4423 2018/03/14 06:45:00
4424 2018/03/15 02:04:00
4425 2018/03/16 07:10:00
4426 2018/03/17 01:53:00
4427 2018/03/18 06:32:00
4428 2018/03/19 07:09:00
4429 2018/03/20 03:27:00
4430 2018/03/21 06:34:00
4431 2018/03/22 07:31:00
4432 2018/03/23 04:01:00
4433 2018/03/24 06:23:00
4434 2018/03/25 06:29:00
4435 2018/03/26 02:22:00
4436 2018/03/27 06:07:00
4437 2018/03/28 06:17:00
4438 2018/03/29 06:32:00
4439 2018/03/30 06:48:00
4440 2018/03/31 06:39:00
4441 2018/04/01 00:24:00
4442 2018/04/02 03:37:00
4443 2018/04/03 01:49:00
4444 2018/04/04 06:57:00
4445 2018/04/05 06:27:00
4446 2018/04/06 01:06:00
4447 2018/04/07 05:47:00
4448 2018/04/06 22:00:00
4449 2018/04/09 05:56:00
4450 2018/04/08 22:00:00
4451 2018/04/11 06:34:00
4452 2018/04/12 02:24:00
4453 2018/04/13 06:13:00
4454 2018/04/14 07:07:00
4455 2018/04/15 06:40:00
4456 2018/04/16 04:54:00
4457 2018/04/17 06:32:00
4458 2018/04/18 06:42:00
4459 2018/04/19 01:16:00
4460 2018/04/20 06:25:00
4461 2018/04/21 02:57:00
4462 2018/04/22 07:12:00
4463 2018/04/23 06:07:00
4464 2018/04/22 22:00:00
4465 2018/04/25 06:05:00
4466 2018/04/26 06:54:00
4467 2018/04/27 04:37:00
4468 2018/04/28 03:32:00
4469 2018/04/29 06:29:00
4470 2018/04/30 07:13:00
4471 2018/05/01 02:56:00
4472 2018/05/02 03:28:00
4473 2018/05/03 07:01:00
4474 2018/05/04 01:08:00
4475 2018/05/05 07:28:00
4476 2018/05/06 06:58:00
4477 2018/05/07 02:02:00
4478 2018/05/08 01:01:00
4479 2018/05/09 05:53:00
4480 2018/05/10 07:01:00
4481 2018/05/11 06:22:00
4482 2018/05/19 22:00:00
4483 2018/05/22 06:56:00
4484 2018/05/23 01:55:00
4485 2018/05/24 02:27:00
4486 2018/05/25 06:41:00
4487 2018/05/26 05:23:00
4488 2018/05/27 07:23:00
4489 2018/05/28 07:12:00
4490 2018/05/29 05:28:00
4491 2018/05/30 07:04:00
4492 2018/05/31 06:57:00
4493 2018/06/01 07:10:00
4494 2018/06/02 07:22:00
4495 2018/06/03 05:47:00
4496 2018/06/04 06:49:00
4497 2018/06/05 07:04:00
4498 2018/06/06 04:12:00
4499 2018/06/07 07:04:00
4500 2018/06/08 02:37:00
4501 2018/06/09 06:40:00
4502 2018/06/10 07:30:00
4503 2018/06/11 05:49:00
4504 2018/06/12 06:54:00
4505 2018/06/13 07:10:00
4506 2018/06/14 07:10:00
4507 2018/06/15 06:18:00
4508 2018/06/14 22:00:00
4509 2018/06/17 07:03:00
4510 2018/06/17 22:55:00
4511 2018/06/19 04:33:00
4512 2018/06/20 06:26:00
4513 2018/06/21 03:34:00
4514 2018/06/22 00:27:00
4515 2018/06/23 05:48:00
4516 2018/06/24 06:55:00
4517 2018/06/23 22:00:00
4518 2018/06/26 02:03:00
4519 2018/06/27 05:03:00
4520 2018/06/28 06:40:00
4521 2018/06/29 06:29:00
4522 2018/06/30 06:40:00
4523 2018/07/01 06:46:00
4524 2018/07/02 03:27:00
4525 2018/07/03 05:43:00
4526 2018/07/04 03:42:00
4527 2018/07/03 22:00:00
4528 2018/07/06 06:36:00
4529 2018/07/07 06:50:00
4530 2018/07/08 06:38:00
4531 2018/07/09 06:55:00
4532 2018/07/10 05:10:00
4533 2018/07/11 06:29:00
4534 2018/07/12 05:26:00
4535 2018/07/13 06:42:00
4536 2018/07/14 06:06:00
4537 2018/07/15 06:07:00
4538 2018/07/16 06:55:00
4539 2018/07/15 22:00:00
4540 2018/07/16 22:00:00
4541 2018/07/19 06:53:00
4542 2018/07/19 22:35:00
4543 2018/07/21 04:46:00
4544 2018/07/22 06:45:00
4545 2018/07/23 04:28:00
4546 2018/07/24 06:30:00
4547 2018/07/25 05:38:00
4548 2018/07/26 06:08:00
4549 2018/07/27 06:22:00
4550 2018/07/28 04:10:00
4551 2018/07/29 01:19:00
4552 2018/07/30 05:57:00
4553 2018/07/30 23:40:00
4554 2018/08/01 04:02:00
4555 2018/08/02 04:43:00
4556 2018/08/02 23:44:00
4557 2018/08/04 07:09:00
4558 2018/08/05 06:31:00
4559 2018/08/04 22:00:00
4560 2018/08/05 22:00:00
4561 2018/08/08 02:44:00
4562 2018/08/09 06:05:00
4563 2018/08/10 07:08:00
4564 2018/08/11 01:16:00
4565 2018/08/12 00:58:00
4566 2018/08/13 06:48:00
4567 2018/08/14 06:56:00
4568 2018/08/15 03:21:00
4569 2018/08/15 23:13:00
4570 2018/08/15 22:00:00
4571 2018/08/18 04:14:00
4572 2018/08/19 02:59:00
4573 2018/08/20 05:35:00
4574 2018/08/21 05:54:00
4575 2018/08/22 04:15:00
4576 2018/08/21 22:00:00
4577 2018/08/24 02:54:00
4578 2018/08/23 22:00:00
4579 2018/08/26 04:59:00
4580 2018/08/27 03:42:00
4581 2018/08/28 06:50:00
4582 2018/08/29 07:08:00
4583 2018/08/30 00:23:00
4584 2018/08/31 06:22:00
4585 2018/08/30 22:00:00
4586 2018/09/02 01:25:00
4587 2018/09/03 07:09:00
4588 2018/09/02 22:00:00
4589 2018/09/05 06:16:00
4590 2018/09/06 06:11:00
4591 2018/09/07 00:53:00
4592 2018/09/08 06:29:00
4593 2018/09/09 07:13:00
4594 2018/09/08 22:00:00
4595 2018/09/11 00:11:00
4596 2018/09/12 05:34:00
4597 2018/09/13 06:24:00
4598 2018/09/14 06:08:00
4599 2018/09/15 04:49:00
4600 2018/09/16 07:19:00
4601 2018/09/17 06:27:00
4602 2018/09/18 06:41:00
4603 2018/09/19 06:46:00
4604 2018/09/18 22:00:00
4605 2018/09/21 06:23:00
4606 2018/09/22 05:38:00
4607 2018/09/23 00:40:00
4608 2018/09/24 06:41:00
4609 2018/09/25 02:34:00
4610 2018/09/26 06:35:00
4611 2018/09/27 06:27:00
4612 2018/09/28 06:34:00
4613 2018/09/29 06:23:00
4614 2018/09/28 22:00:00
4615 2018/10/01 07:15:00
4616 2018/10/02 06:13:00
4617 2018/10/03 05:50:00
4618 2018/10/02 22:00:00
4619 2018/10/05 05:43:00
4620 2018/10/18 06:33:00
4621 2018/10/30 03:41:00
4622 2018/10/31 07:39:00Usamos una data extraida de Kaggle. Ahora en vez de colocar toda la ruta de acceso, se puede colocar ./ que basicamente le dice a R que abra el archivo que se llama user_activity.txt en la carpeta donde se encuentra el script.
2.11.2 Importación de archivos csv
El formato Comma Separated Values (CSV) es un tipo de archivo de texto en el que las variables están separadas por comas y los decimales por punto. En el formato CSV2 las variables se separan por punto y coma y los decimales por coma. Para grabar un marco de datos en estos formatos utilizaremos la siguiente sintaxis:
# read.csv(file, header = FALSE, sep = ",")2.11.2.1 Ejemplo
Para la ejemplificación usaremos un archivo csv extraído de kaggle llamado ozone.csv.
read.csv("./ozone.csv", header = T, sep = ",") Month Day_of_month Day_of_week ozone_reading pressure_height Wind_speed
1 1 5 1 5.34 5760 3
2 1 6 2 5.77 5720 4
3 1 7 3 3.69 5790 6
4 1 8 4 3.89 5790 3
5 1 9 5 5.76 5700 3
6 1 12 1 6.39 5720 3
7 1 13 2 4.73 5760 6
8 1 14 3 4.35 5780 6
9 1 15 4 3.94 5830 3
10 1 16 5 7.00 5870 2
11 1 19 1 4.07 5680 5
12 1 21 3 4.39 5760 3
13 1 22 4 3.69 5730 4
14 1 23 5 4.90 5700 5
15 1 27 2 6.15 5820 5
16 1 29 4 5.68 5810 5
17 1 30 5 11.06 5790 3
18 2 2 1 12.28 5770 8
19 2 3 2 9.29 5670 3
20 2 4 3 2.18 5590 3
21 2 5 4 2.94 5410 6
22 2 6 5 2.74 5350 7
23 2 9 1 2.92 5490 11
24 2 10 2 4.08 5560 10
25 2 12 4 8.32 5680 5
26 2 13 5 5.80 5700 4
27 2 17 2 10.60 5770 5
28 2 18 3 12.77 5770 3
29 2 20 5 5.73 5690 8
30 2 23 1 4.04 5690 7
31 2 24 2 6.04 5640 5
32 2 25 3 10.27 5720 6
33 2 26 4 15.06 5740 3
34 2 27 5 22.89 5740 3
35 3 1 1 2.26 5550 10
36 3 2 2 3.22 5470 7
37 3 3 3 2.79 5320 11
38 3 5 5 4.32 5530 3
39 3 8 1 7.19 5580 5
40 3 9 2 5.65 5510 5
41 3 10 3 3.01 5530 5
42 3 11 4 1.98 5620 9
43 3 12 5 7.63 5690 0
44 3 15 1 15.68 5780 5
45 3 16 2 8.70 5790 2
46 3 17 3 24.29 5760 3
47 3 18 4 12.67 5700 4
48 3 19 5 8.10 5680 4
49 3 22 1 8.93 5730 4
50 3 23 2 10.18 5710 5
51 3 25 4 13.94 5720 4
52 3 26 5 8.90 5710 3
53 3 29 1 8.60 5690 7
54 3 30 2 12.05 5730 6
55 3 31 3 12.33 5710 3
56 4 1 4 7.93 5610 6
57 4 2 5 9.09 5680 5
58 4 5 1 4.33 5540 5
59 4 6 2 9.32 5590 6
60 4 7 3 13.12 5690 6
61 4 8 4 5.09 5550 7
62 4 9 5 10.07 5620 7
63 4 12 1 4.81 5560 4
64 4 13 2 3.65 5440 5
65 4 14 3 6.76 5480 7
66 4 15 4 2.88 5620 5
67 4 19 1 14.89 5760 4
68 4 20 2 21.90 5790 4
69 4 21 3 17.32 5720 5
70 4 22 4 7.26 5660 6
71 4 23 5 9.69 5710 5
72 4 26 1 12.23 5700 5
73 4 27 2 5.73 5620 9
74 4 28 3 8.68 5650 6
75 4 29 4 18.79 5730 5
76 4 30 5 21.12 5810 4
77 5 4 2 11.30 5690 4
78 5 6 4 2.39 5680 6
79 5 7 5 11.79 5650 8
80 5 10 1 19.98 5760 7
81 5 11 2 26.89 5830 6
82 5 12 3 33.04 5880 3
83 5 13 4 24.89 5890 6
84 5 14 5 31.15 5850 4
85 5 25 2 8.68 5730 5
86 5 28 5 4.82 5750 3
87 6 2 3 16.85 5780 8
88 6 3 4 19.16 5740 6
89 6 4 5 16.15 5730 6
90 6 7 1 8.73 5690 8
91 6 8 2 6.68 5650 6
92 6 9 3 5.27 5610 3
93 6 10 4 1.80 5570 9
94 6 11 5 11.90 5690 5
95 6 14 1 25.66 5830 4
96 6 15 2 26.89 5880 6
97 6 16 3 14.31 5860 3
98 6 17 4 10.77 5830 6
99 6 18 5 22.85 5870 4
100 6 21 1 15.27 5800 4
101 6 22 2 19.93 5770 5
102 6 23 3 15.25 5860 4
103 6 24 4 17.95 5870 7
104 7 6 2 34.39 5900 6
105 7 9 5 23.66 5910 4
106 7 12 1 14.27 5830 3
107 7 13 2 13.30 5850 9
108 7 14 3 16.79 5830 6
109 7 16 5 21.87 5810 8
110 7 19 1 24.98 5840 7
111 7 20 2 28.24 5870 6
112 7 21 3 29.21 5870 4
113 7 23 5 22.87 5860 3
114 7 26 1 13.02 5880 4
115 7 27 2 26.10 5890 6
116 7 28 3 21.92 5870 8
117 8 2 1 9.35 5770 3
118 8 3 2 11.60 5790 4
119 8 9 1 13.67 5840 7
120 8 11 3 23.79 5910 5
121 8 12 4 18.77 5890 5
122 8 16 1 4.26 5780 7
123 8 17 2 5.82 5750 7
124 8 18 3 12.16 5760 5
125 8 20 5 14.88 5730 5
126 8 24 2 22.29 5890 7
127 8 25 3 23.62 5880 4
128 8 26 4 25.69 5870 7
129 8 27 5 32.28 5900 6
130 8 30 1 37.98 5950 5
131 8 31 2 23.07 5950 8
132 9 1 3 19.20 5900 5
133 9 2 4 19.08 5890 8
134 9 3 5 14.73 5860 7
135 9 7 2 25.80 5810 6
136 9 8 3 17.06 5850 4
137 9 13 1 29.22 5830 5
138 9 14 2 18.33 5840 5
139 9 15 3 3.35 5800 7
140 9 17 5 8.86 5790 3
141 9 21 2 22.86 5780 6
142 9 22 3 12.81 5800 6
143 9 24 5 7.32 5800 5
144 9 27 1 11.02 5770 5
145 9 28 2 12.25 5750 4
146 9 29 3 4.60 5640 5
147 9 30 4 4.22 5640 7
148 10 1 5 5.14 5650 3
149 10 4 1 16.68 5840 4
150 10 5 2 26.00 5880 3
151 10 6 3 29.79 5890 5
152 10 7 4 18.31 5890 4
153 10 8 5 12.36 5890 5
154 10 12 2 7.00 5830 8
155 10 13 3 28.15 5860 5
156 10 14 4 21.84 5830 5
157 10 15 5 18.28 5800 7
158 10 18 1 10.11 5730 4
159 10 19 2 13.89 5780 5
160 10 20 3 9.14 5740 7
161 10 21 4 11.75 5710 8
162 10 22 5 7.20 5690 6
163 10 27 3 2.61 5760 5
164 10 28 4 7.37 5800 6
165 10 29 5 8.01 5810 7
166 11 3 3 5.62 5920 3
167 11 5 5 4.91 5860 7
168 11 9 2 14.18 5810 2
169 11 10 3 16.00 5770 2
170 11 11 4 4.82 5710 4
171 11 12 5 2.90 5500 9
172 11 15 1 5.33 5810 5
173 11 16 2 4.10 5860 0
174 11 17 3 10.99 5900 0
175 11 18 4 5.91 5850 5
176 11 19 5 8.30 5780 3
177 11 22 1 11.89 5770 2
178 11 23 2 9.09 5750 2
179 11 24 3 7.01 5780 5
180 11 26 5 4.20 5750 6
181 11 29 1 2.82 5770 4
182 11 30 2 3.19 5810 2
183 12 1 3 3.01 5810 2
184 12 2 4 3.04 5870 3
185 12 3 5 2.95 5830 2
186 12 6 1 4.65 5780 4
187 12 7 2 3.29 5810 3
188 12 8 3 4.31 5760 0
189 12 9 4 7.26 5680 0
190 12 10 5 4.98 5750 0
191 12 13 1 3.23 5750 0
192 12 14 2 2.00 5720 0
193 12 15 3 5.23 5760 3
194 12 16 4 2.96 5780 0
195 12 17 5 3.68 5660 4
196 12 20 1 5.71 5680 3
197 12 21 2 3.33 5650 5
198 12 22 3 4.25 5710 4
199 12 23 4 3.21 5680 4
200 12 27 1 1.74 5730 3
201 12 28 2 2.69 5690 3
202 12 29 3 5.05 5650 3
203 12 30 4 0.72 5550 4
Humidity Temperature_Sandburg Temperature_ElMonte Inversion_base_height
1 51 54 45.32 1450
2 69 35 49.64 1568
3 19 45 46.40 2631
4 25 55 52.70 554
5 73 41 48.02 2083
6 44 51 54.32 111
7 33 51 57.56 492
8 19 54 56.12 5000
9 19 58 62.24 1249
10 19 61 64.94 5000
11 73 52 56.48 393
12 19 54 53.60 5000
13 26 58 52.70 5000
14 59 69 51.08 3044
15 19 59 59.36 597
16 19 64 56.66 1791
17 28 63 57.38 793
18 76 63 57.20 816
19 69 54 45.50 3651
20 76 36 37.40 5000
21 64 31 32.18 5000
22 62 30 32.54 1341
23 72 37 38.48 5000
24 72 41 40.46 5000
25 50 51 47.12 5000
26 86 55 49.28 2398
27 68 45 52.88 2778
28 82 55 55.40 442
29 21 41 43.88 5000
30 19 53 50.18 5000
31 68 50 37.40 5000
32 63 60 53.06 1341
33 54 54 56.48 1318
34 47 53 58.82 885
35 74 40 38.84 5000
36 46 30 29.66 5000
37 45 25 27.68 5000
38 43 40 36.14 5000
39 42 48 40.64 3608
40 50 45 36.86 5000
41 61 47 33.80 5000
42 61 43 37.04 5000
43 60 49 46.04 613
44 53 61 57.92 488
45 42 63 57.02 531
46 60 70 58.64 508
47 82 57 50.36 1571
48 57 35 40.10 721
49 32 67 59.54 442
50 77 57 57.38 902
51 71 42 44.96 1381
52 19 55 51.80 5000
53 70 57 46.58 1292
54 45 58 52.52 5000
55 46 62 52.52 472
56 50 51 50.00 1404
57 69 61 51.44 944
58 54 35 33.26 5000
59 51 48 38.12 5000
60 63 59 52.88 2014
61 63 41 37.58 5000
62 57 58 46.76 5000
63 65 40 34.70 5000
64 44 35 33.08 5000
65 51 46 37.40 2490
66 73 39 39.56 5000
67 50 65 56.30 547
68 57 66 63.68 413
69 68 69 60.80 610
70 58 59 42.80 3638
71 65 64 56.30 3848
72 41 52 49.64 869
73 47 56 39.92 5000
74 46 55 40.82 5000
75 61 66 59.72 1148
76 55 74 67.28 856
77 71 67 52.52 314
78 77 41 42.98 5000
79 66 61 51.80 1410
80 78 74 63.14 1184
81 75 74 67.28 898
82 80 80 73.04 436
83 88 84 73.22 774
84 76 78 71.24 1181
85 71 67 49.82 4337
86 76 65 51.08 3644
87 92 68 64.76 1804
88 71 69 64.40 3234
89 64 66 60.44 3441
90 67 64 54.50 2962
91 66 61 53.78 2670
92 61 52 42.08 5000
93 81 48 41.72 5000
94 63 59 51.80 5000
95 71 74 71.78 898
96 67 83 72.50 777
97 64 78 68.72 1279
98 64 75 66.20 1046
99 69 84 74.12 1167
100 69 79 66.20 977
101 64 65 65.12 770
102 33 81 72.68 629
103 38 84 76.10 337
104 86 87 81.68 990
105 73 88 79.88 1204
106 63 79 70.88 2326
107 72 77 69.44 3389
108 82 81 73.22 2818
109 69 76 67.64 2394
110 72 82 68.00 1528
111 73 84 74.12 111
112 90 86 74.48 1899
113 80 80 67.28 1289
114 78 84 70.70 826
115 80 81 69.80 1105
116 74 85 71.42 1023
117 55 68 62.60 4291
118 65 65 56.48 3330
119 65 79 67.10 1233
120 72 81 70.88 1069
121 79 80 69.80 984
122 70 66 51.44 5000
123 58 64 56.48 4212
124 58 62 52.16 5000
125 77 74 64.04 1545
126 70 83 70.88 748
127 73 81 73.76 692
128 73 73 75.20 807
129 71 87 76.46 869
130 62 92 82.40 557
131 61 93 81.68 620
132 71 93 82.58 1404
133 77 86 71.42 898
134 71 76 69.44 377
135 82 80 71.78 895
136 67 81 70.88 721
137 77 72 68.72 1853
138 78 75 69.26 2342
139 72 55 54.32 5000
140 71 61 50.54 4028
141 76 72 66.38 3795
142 73 75 67.10 3120
143 80 65 60.08 2667
144 75 64 55.94 308
145 68 61 59.54 2982
146 93 63 54.32 5000
147 57 62 54.32 5000
148 70 59 50.90 5000
149 73 72 63.14 830
150 77 71 67.64 711
151 80 75 71.06 1049
152 73 71 70.88 511
153 19 71 70.52 5000
154 77 71 67.10 337
155 86 73 69.80 492
156 76 71 69.44 1394
157 66 66 62.96 3146
158 84 64 56.30 5000
159 74 65 63.14 2270
160 48 54 62.96 2191
161 75 62 58.64 3448
162 74 56 52.34 5000
163 23 57 53.42 5000
164 19 60 57.02 5000
165 59 61 55.76 2385
166 22 71 69.08 328
167 19 70 62.78 5000
168 47 69 60.98 469
169 73 59 57.20 1541
170 67 49 44.24 5000
171 56 39 41.36 5000
172 59 54 54.50 5000
173 25 60 61.52 5000
174 24 62 62.60 5000
175 41 65 59.54 2014
176 50 66 59.72 436
177 81 62 60.62 1210
178 85 60 59.72 501
179 76 63 60.44 875
180 58 58 42.62 5000
181 19 44 51.26 2280
182 19 53 55.94 2047
183 19 52 57.74 5000
184 19 53 60.80 3720
185 27 58 59.00 311
186 19 48 54.14 2933
187 19 51 58.28 3064
188 32 62 56.12 826
189 58 40 46.94 5000
190 26 44 52.88 111
191 19 53 51.98 5000
192 19 53 52.70 5000
193 19 55 58.10 948
194 19 51 54.32 5000
195 19 54 49.64 5000
196 52 49 48.38 5000
197 19 48 47.12 5000
198 19 51 51.08 5000
199 57 47 45.32 508
200 53 51 49.28 111
201 23 51 49.28 5000
202 61 50 46.58 3704
203 85 39 41.00 5000
Pressure_gradient Inversion_temperature Visibility
1 25 57.02 60
2 15 53.78 60
3 -33 54.14 100
4 -28 64.76 250
5 23 52.52 120
6 9 63.14 150
7 -44 64.58 40
8 -44 56.30 200
9 -53 75.74 250
10 -67 65.48 200
11 -68 69.80 10
12 -58 51.98 250
13 -26 51.98 200
14 18 52.88 150
15 -52 70.52 70
16 -15 64.76 150
17 -15 65.84 120
18 -7 66.20 6
19 62 49.10 30
20 70 37.94 100
21 28 32.36 200
22 18 45.86 60
23 32 38.12 350
24 -1 37.58 300
25 -8 45.50 300
26 21 53.78 200
27 2 55.76 200
28 26 58.28 40
29 -30 42.26 300
30 7 49.10 300
31 24 42.08 300
32 19 59.18 150
33 2 64.58 150
34 -4 67.10 80
35 73 40.10 80
36 44 29.30 300
37 39 27.50 200
38 -12 33.62 140
39 24 39.38 100
40 38 32.90 140
41 56 35.60 200
42 66 34.34 120
43 -27 59.72 300
44 -20 64.94 2
45 -15 71.06 50
46 7 66.56 70
47 68 56.30 17
48 28 55.40 140
49 -9 75.74 200
50 54 60.44 250
51 4 56.30 60
52 -16 50.00 100
53 -5 53.60 200
54 -14 52.70 100
55 34 62.96 300
56 42 54.50 120
57 35 55.76 100
58 62 33.44 200
59 44 42.08 300
60 31 53.42 300
61 56 37.22 250
62 27 47.66 120
63 59 35.24 140
64 24 32.54 80
65 29 47.48 300
66 107 31.28 100
67 1 66.92 100
68 10 69.62 120
69 46 63.68 60
70 81 51.26 120
71 45 56.84 100
72 0 58.10 40
73 43 38.30 140
74 49 37.94 150
75 31 60.80 100
76 4 75.38 100
77 60 59.00 120
78 75 40.82 120
79 20 55.22 140
80 40 68.72 80
81 24 73.40 70
82 0 86.36 40
83 6 86.00 20
84 50 79.88 17
85 66 59.36 200
86 86 59.36 70
87 56 68.00 70
88 77 62.78 80
89 67 60.98 100
90 80 59.36 120
91 54 55.40 120
92 76 42.08 150
93 57 40.82 140
94 46 51.26 140
95 -24 77.90 60
96 -1 82.58 30
97 75 71.60 17
98 69 68.72 80
99 50 74.30 60
100 60 70.70 150
101 26 75.56 120
102 -11 86.36 140
103 -14 89.78 140
104 22 85.10 40
105 56 79.88 100
106 64 71.24 70
107 56 68.72 80
108 58 71.78 80
109 54 69.62 90
110 42 73.94 100
111 40 78.08 60
112 45 76.46 40
113 32 75.20 40
114 27 79.34 80
115 39 74.12 80
116 46 77.18 80
117 60 62.24 200
118 59 58.64 150
119 30 70.52 100
120 28 74.30 80
121 57 73.40 70
122 45 51.26 200
123 46 56.84 200
124 52 49.82 250
125 43 65.66 70
126 32 77.00 30
127 44 77.72 100
128 39 78.80 100
129 19 78.98 17
130 0 90.68 70
131 27 85.64 30
132 33 84.74 70
133 21 80.60 60
134 -2 83.30 40
135 0 78.08 100
136 0 80.24 120
137 10 70.88 70
138 7 71.42 40
139 56 51.62 70
140 35 55.04 140
141 31 66.92 70
142 35 66.92 40
143 17 63.50 100
144 25 68.72 140
145 18 59.90 120
146 30 52.70 70
147 25 51.26 150
148 38 47.66 200
149 0 72.14 70
150 -9 75.56 40
151 -10 78.98 50
152 -39 83.84 17
153 -40 67.64 80
154 -17 81.14 20
155 -2 82.22 7
156 13 75.02 30
157 27 64.04 50
158 23 54.50 80
159 -7 68.90 50
160 -13 68.72 60
161 12 58.64 60
162 13 48.92 80
163 -21 50.90 300
164 -19 54.32 200
165 10 60.44 150
166 -40 80.60 150
167 -29 61.70 300
168 -4 71.78 50
169 18 63.14 20
170 24 41.90 200
171 15 41.72 120
172 -28 53.60 70
173 -38 63.50 140
174 -36 60.08 150
175 -20 69.98 200
176 1 70.34 4
177 -17 67.82 30
178 -22 70.88 2
179 -15 68.90 0
180 59 41.90 60
181 -54 55.76 250
182 -43 63.50 150
183 -69 56.48 200
184 -50 61.34 200
185 -24 69.98 200
186 -40 59.90 300
187 -33 62.78 200
188 -16 64.76 300
189 2 42.98 50
190 -52 68.18 40
191 -26 51.08 150
192 -31 51.44 70
193 -48 70.70 200
194 -50 50.90 120
195 -22 48.56 150
196 -19 45.68 70
197 -28 45.32 150
198 -25 48.38 300
199 -10 58.64 100
200 -14 72.50 200
201 -36 51.26 70
202 18 46.94 40
203 8 39.92 1002.11.3 Importación de archivos Excel
R es capaz de leer un archivo Excel de la versión 2003 o anteriores. Para ello utilizaremos la función read_excel(). Como los archivos de Excel pueden tener varias hojas de datos, debemos indicar a la función cual es el número de hoja (sheetIndex) que queremos leer. Primero es necesario instalar la librería readxl
# install.packages("readxl")
library("readxl")Warning: package 'readxl' was built under R version 4.3.2Ahora como se mencionó al inicio se usara la función read_excel().
2.11.3.1 Ejemplo
Para la ejemplificación se usara el archivo “user_activity” cambiado a un archivo.xlsx para este ejemplo.
read_excel("./user_activity.xlsx", col_names = T)# A tibble: 4,622 × 1
userId,registerStartDate,registerFinalDate,steps,distance,runDistance,calor…¹
<chr>
1 10036,2017/10/09 00:00:00,2017/10/10 00:00:00,1625,1113,36,43,0,0,0,2017/10/…
2 10036,2017/10/10 00:00:00,2017/10/11 00:00:00,1722,1171,0,45,0,0,0,2017/10/0…
3 10036,2017/10/11 00:00:00,2017/10/12 00:00:00,203,123,0,8,0,0,0,2017/10/10 2…
4 10036,2017/10/12 00:00:00,2017/10/13 00:00:00,213,129,0,10,0,0,0,2017/10/11 …
5 10036,2017/10/13 00:00:00,2017/10/14 00:00:00,461,282,16,16,0,0,0,2017/10/12…
6 10036,2017/10/14 00:00:00,2017/10/15 00:00:00,305,184,0,16,0,0,0,2017/10/13 …
7 10036,2017/10/15 00:00:00,2017/10/16 00:00:00,173,105,9,10,0,0,0,2017/10/14 …
8 10036,2017/10/16 00:00:00,2017/10/17 00:00:00,375,227,29,21,0,0,0,2017/10/15…
9 10036,2017/10/17 00:00:00,2017/10/18 00:00:00,623,373,87,19,0,0,0,2017/10/16…
10 10036,2017/10/18 00:00:00,2017/10/19 00:00:00,312,179,31,15,0,0,0,2017/10/17…
# ℹ 4,612 more rows
# ℹ abbreviated name:
# ¹`userId,registerStartDate,registerFinalDate,steps,distance,runDistance,calories,deepSleepTime,shallowSleepTime,wakeTime,sleepStartDate,sleepStopDate`