En el siguiente capitulo veremos sobre que es, su manipulacion y lo que se necesita para realizar un estudio hacia los dataframes.
Son funciones ya predeterminadas del RStudio, algunos ejemplos son:
#Funciones incorporadas
vector <- c(14,20,18,16,16,15)
#sumatoria
sumatoria <- sum(vector)
print(sumatoria)[1] 99#media
media <- mean(vector)
print(media)[1] 16.5#maximo
maximo <- max(vector)
print(maximo)[1] 20#minimo
minimo <- min(vector)
print(minimo)[1] 14Con la ayuda de un paquete dslabs y los diversos paquetes que nos brinden esas funciones realizaremos ejemplos de manipulacion ded atos en el apartado Dataframe.
Los modulos en Rstudio usan la funcion function, la que nos permite almacenarlo y reutilizarlo cuando necesitemos el uso del codigo.
# function
calcular_promedio <- function(x) {
mean(x)
}
edades <- c(14,23,11,16,13,26)
Promedio_e <- calcular_promedio(edades)
print(Promedio_e)[1] 17.16667Para el uso de un dataframe en Rstudio, se requieren de librería que te permitan realizar funciones que el RStudio no posee para el trabajo en tablas estadisticas.
Las librerias principales que instalaremos y usaremos seran:
Es un paquete que nos permite realizar varias funciones que nos permitan la manipulacion, exploracion, importacion y visualizacion de datos. Esta compuesto de varios paquetes entre los mas importantes estan: ggplot2, dplyr, entre otros.
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionComo lo mencionamos anteriormente, esta dentro del paquete tidyverse. La libreria ggplot2 nos permite visualizar en formas de graficos y tablas los datos.
library(ggplot2)Esta libreria nos permite realizar una serie de funciones para la manipulación de data frames. Algunas de las funciones que proporciona este paquete son: filter(), count(), group_by(), entre otros.
library(dplyr)Es una libreria que nos proporciona dataframes para su manipulacion y manejo.
library(dslabs)Nos sirve para poder leer diversos archivos de diferentes tipos
library(readxl)Un dataframe en Rstudio es una base de datos de dos dimensiones que pueden contener diferentes tipos de datos, siendo usadas para la realizacion de su analisis, manipulacion e importacion.
Cargamos un dataframe de la libreria dslabs
data("murders")Mostramos los primeros registros
head(murders) state abb region population total
1 Alabama AL South 4779736 135
2 Alaska AK West 710231 19
3 Arizona AZ West 6392017 232
4 Arkansas AR South 2915918 93
5 California CA West 37253956 1257
6 Colorado CO West 5029196 65La funcion “class” identifica el tipo de objeto que tenemos.
class(murders)[1] "data.frame"La funcion dim nos da el numero de filas y columnas que posee nuestro dataframe.
dim(murders)[1] 51 5La funcion names da el nombre de las columnas del dataframe
names(murders)[1] "state" "abb" "region" "population" "total" La funcion str, ademas de dar el nombre de las columnas, brinda el tipo de variable que posee cada una
str(murders)'data.frame': 51 obs. of 5 variables:
$ state : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
$ abb : chr "AL" "AK" "AZ" "AR" ...
$ region : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
$ population: num 4779736 710231 6392017 2915918 37253956 ...
$ total : num 135 19 232 93 1257 ...Es una forma de encadenar funciones a las diversas funciones haciendo que sea mas estructurado el código. Se usara para la realizacion de las demas funciones
Reordena las filas de un dataframe
murders_ar<- murders%>%arrange(total)
murders_ar state abb region population total
1 Vermont VT Northeast 625741 2
2 North Dakota ND North Central 672591 4
3 New Hampshire NH Northeast 1316470 5
4 Wyoming WY West 563626 5
5 Hawaii HI West 1360301 7
6 South Dakota SD North Central 814180 8
7 Maine ME Northeast 1328361 11
8 Idaho ID West 1567582 12
9 Montana MT West 989415 12
10 Rhode Island RI Northeast 1052567 16
11 Alaska AK West 710231 19
12 Iowa IA North Central 3046355 21
13 Utah UT West 2763885 22
14 West Virginia WV South 1852994 27
15 Nebraska NE North Central 1826341 32
16 Oregon OR West 3831074 36
17 Delaware DE South 897934 38
18 Minnesota MN North Central 5303925 53
19 Kansas KS North Central 2853118 63
20 Colorado CO West 5029196 65
21 New Mexico NM West 2059179 67
22 Nevada NV West 2700551 84
23 Arkansas AR South 2915918 93
24 Washington WA West 6724540 93
25 Connecticut CT Northeast 3574097 97
26 Wisconsin WI North Central 5686986 97
27 District of Columbia DC South 601723 99
28 Oklahoma OK South 3751351 111
29 Kentucky KY South 4339367 116
30 Massachusetts MA Northeast 6547629 118
31 Mississippi MS South 2967297 120
32 Alabama AL South 4779736 135
33 Indiana IN North Central 6483802 142
34 South Carolina SC South 4625364 207
35 Tennessee TN South 6346105 219
36 Arizona AZ West 6392017 232
37 New Jersey NJ Northeast 8791894 246
38 Virginia VA South 8001024 250
39 North Carolina NC South 9535483 286
40 Maryland MD South 5773552 293
41 Ohio OH North Central 11536504 310
42 Missouri MO North Central 5988927 321
43 Louisiana LA South 4533372 351
44 Illinois IL North Central 12830632 364
45 Georgia GA South 9920000 376
46 Michigan MI North Central 9883640 413
47 Pennsylvania PA Northeast 12702379 457
48 New York NY Northeast 19378102 517
49 Florida FL South 19687653 669
50 Texas TX South 25145561 805
51 California CA West 37253956 1257Nos muestra solamente las columnas seleccinadas
murders_sel <- murders %>% select(region, population, state)
murders_sel region population state
1 South 4779736 Alabama
2 West 710231 Alaska
3 West 6392017 Arizona
4 South 2915918 Arkansas
5 West 37253956 California
6 West 5029196 Colorado
7 Northeast 3574097 Connecticut
8 South 897934 Delaware
9 South 601723 District of Columbia
10 South 19687653 Florida
11 South 9920000 Georgia
12 West 1360301 Hawaii
13 West 1567582 Idaho
14 North Central 12830632 Illinois
15 North Central 6483802 Indiana
16 North Central 3046355 Iowa
17 North Central 2853118 Kansas
18 South 4339367 Kentucky
19 South 4533372 Louisiana
20 Northeast 1328361 Maine
21 South 5773552 Maryland
22 Northeast 6547629 Massachusetts
23 North Central 9883640 Michigan
24 North Central 5303925 Minnesota
25 South 2967297 Mississippi
26 North Central 5988927 Missouri
27 West 989415 Montana
28 North Central 1826341 Nebraska
29 West 2700551 Nevada
30 Northeast 1316470 New Hampshire
31 Northeast 8791894 New Jersey
32 West 2059179 New Mexico
33 Northeast 19378102 New York
34 South 9535483 North Carolina
35 North Central 672591 North Dakota
36 North Central 11536504 Ohio
37 South 3751351 Oklahoma
38 West 3831074 Oregon
39 Northeast 12702379 Pennsylvania
40 Northeast 1052567 Rhode Island
41 South 4625364 South Carolina
42 North Central 814180 South Dakota
43 South 6346105 Tennessee
44 South 25145561 Texas
45 West 2763885 Utah
46 Northeast 625741 Vermont
47 South 8001024 Virginia
48 West 6724540 Washington
49 South 1852994 West Virginia
50 North Central 5686986 Wisconsin
51 West 563626 WyomingAgrega una nueva columna al dataframe
murders<- mutate(murders, average_for_person=total/population)
murders state abb region population total average_for_person
1 Alabama AL South 4779736 135 2.824424e-05
2 Alaska AK West 710231 19 2.675186e-05
3 Arizona AZ West 6392017 232 3.629527e-05
4 Arkansas AR South 2915918 93 3.189390e-05
5 California CA West 37253956 1257 3.374138e-05
6 Colorado CO West 5029196 65 1.292453e-05
7 Connecticut CT Northeast 3574097 97 2.713972e-05
8 Delaware DE South 897934 38 4.231937e-05
9 District of Columbia DC South 601723 99 1.645275e-04
10 Florida FL South 19687653 669 3.398069e-05
11 Georgia GA South 9920000 376 3.790323e-05
12 Hawaii HI West 1360301 7 5.145920e-06
13 Idaho ID West 1567582 12 7.655102e-06
14 Illinois IL North Central 12830632 364 2.836961e-05
15 Indiana IN North Central 6483802 142 2.190073e-05
16 Iowa IA North Central 3046355 21 6.893484e-06
17 Kansas KS North Central 2853118 63 2.208111e-05
18 Kentucky KY South 4339367 116 2.673201e-05
19 Louisiana LA South 4533372 351 7.742581e-05
20 Maine ME Northeast 1328361 11 8.280881e-06
21 Maryland MD South 5773552 293 5.074866e-05
22 Massachusetts MA Northeast 6547629 118 1.802179e-05
23 Michigan MI North Central 9883640 413 4.178622e-05
24 Minnesota MN North Central 5303925 53 9.992600e-06
25 Mississippi MS South 2967297 120 4.044085e-05
26 Missouri MO North Central 5988927 321 5.359892e-05
27 Montana MT West 989415 12 1.212838e-05
28 Nebraska NE North Central 1826341 32 1.752137e-05
29 Nevada NV West 2700551 84 3.110476e-05
30 New Hampshire NH Northeast 1316470 5 3.798036e-06
31 New Jersey NJ Northeast 8791894 246 2.798032e-05
32 New Mexico NM West 2059179 67 3.253724e-05
33 New York NY Northeast 19378102 517 2.667960e-05
34 North Carolina NC South 9535483 286 2.999324e-05
35 North Dakota ND North Central 672591 4 5.947151e-06
36 Ohio OH North Central 11536504 310 2.687123e-05
37 Oklahoma OK South 3751351 111 2.958934e-05
38 Oregon OR West 3831074 36 9.396843e-06
39 Pennsylvania PA Northeast 12702379 457 3.597751e-05
40 Rhode Island RI Northeast 1052567 16 1.520093e-05
41 South Carolina SC South 4625364 207 4.475323e-05
42 South Dakota SD North Central 814180 8 9.825837e-06
43 Tennessee TN South 6346105 219 3.450936e-05
44 Texas TX South 25145561 805 3.201360e-05
45 Utah UT West 2763885 22 7.959810e-06
46 Vermont VT Northeast 625741 2 3.196211e-06
47 Virginia VA South 8001024 250 3.124600e-05
48 Washington WA West 6724540 93 1.382994e-05
49 West Virginia WV South 1852994 27 1.457101e-05
50 Wisconsin WI North Central 5686986 97 1.705649e-05
51 Wyoming WY West 563626 5 8.871131e-06Permite colocar una condicion que de ser cumplida se mostrara.
murders_fil <- murders %>% filter(total<50 & average_for_person<1)
murders_fil state abb region population total average_for_person
1 Alaska AK West 710231 19 2.675186e-05
2 Delaware DE South 897934 38 4.231937e-05
3 Hawaii HI West 1360301 7 5.145920e-06
4 Idaho ID West 1567582 12 7.655102e-06
5 Iowa IA North Central 3046355 21 6.893484e-06
6 Maine ME Northeast 1328361 11 8.280881e-06
7 Montana MT West 989415 12 1.212838e-05
8 Nebraska NE North Central 1826341 32 1.752137e-05
9 New Hampshire NH Northeast 1316470 5 3.798036e-06
10 North Dakota ND North Central 672591 4 5.947151e-06
11 Oregon OR West 3831074 36 9.396843e-06
12 Rhode Island RI Northeast 1052567 16 1.520093e-05
13 South Dakota SD North Central 814180 8 9.825837e-06
14 Utah UT West 2763885 22 7.959810e-06
15 Vermont VT Northeast 625741 2 3.196211e-06
16 West Virginia WV South 1852994 27 1.457101e-05
17 Wyoming WY West 563626 5 8.871131e-06Summarize genera una tabla con un resumen estadistico de las variables seleccionadas. Esto se asocia con group_by que agrupa los casos a partir de la columna escogida.
murders%>% group_by(region)%>%summarise(media_1= mean(total),desviación_1=sd(total), sum_total=sum(total))# A tibble: 4 × 4
region media_1 desviación_1 sum_total
<fct> <dbl> <dbl> <dbl>
1 Northeast 163. 200. 1469
2 South 247. 213. 4195
3 North Central 152. 154. 1828
4 West 147 339. 1911Es una tecnica que busca explorar los datos que se ingresan a traves de un sistema, mayormente enun diagrama de flujo de datos
Para cargar un dataset en Rstudio haremos lo siguiente:
#Le asignaremos un nombre a nuestro link
url <- "https://raw.githubusercontent.com/korbinianheimerl/predictingMissingValues/main/train.csv"
#Leemos los datos y los cargamos en una variable llamada market_df
market_df <- read.csv(url)Usamos head para ver sus principales datos
head(market_df) PassengerId Survived Pclass
1 1 0 3
2 2 1 1
3 3 1 3
4 4 1 1
5 5 0 3
6 6 0 3
Name Sex Age SibSp Parch
1 Braund, Mr. Owen Harris male 22 1 0
2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
3 Heikkinen, Miss. Laina female 26 0 0
4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
5 Allen, Mr. William Henry male 35 0 0
6 Moran, Mr. James male NA 0 0
Ticket Fare Cabin Embarked
1 A/5 21171 7.2500 S
2 PC 17599 71.2833 C85 C
3 STON/O2. 3101282 7.9250 S
4 113803 53.1000 C123 S
5 373450 8.0500 S
6 330877 8.4583 Qdata_csv <- read.csv("C:/Users/User/Desktop/Rstudio/Datasets/train.csv")Usamos head para ver sus principales datos
head(data_csv) PassengerId Survived Pclass
1 1 0 3
2 2 1 1
3 3 1 3
4 4 1 1
5 5 0 3
6 6 0 3
Name Sex Age SibSp Parch
1 Braund, Mr. Owen Harris male 22 1 0
2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
3 Heikkinen, Miss. Laina female 26 0 0
4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
5 Allen, Mr. William Henry male 35 0 0
6 Moran, Mr. James male NA 0 0
Ticket Fare Cabin Embarked
1 A/5 21171 7.2500 S
2 PC 17599 71.2833 C85 C
3 STON/O2. 3101282 7.9250 S
4 113803 53.1000 C123 S
5 373450 8.0500 S
6 330877 8.4583 Qdata_xlsx <- read_excel("C:/Users/User/Desktop/Rstudio/Datasets/updated_df_withage.xlsx")New names:
• `` -> `...1`Usamos head para ver sus principales datos
head(data_xlsx)# A tibble: 6 × 9
...1 Survived Pclass Sex Age SibSp Parch Fare Ageismissing
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
1 0 0 3 1 22 1 0 7.25 FALSE
2 1 1 1 0 38 1 0 71.3 FALSE
3 2 1 3 0 26 0 0 7.92 FALSE
4 3 1 1 0 35 1 0 53.1 FALSE
5 4 0 3 1 35 0 0 8.05 FALSE
6 5 0 3 1 29.1 0 0 8.46 TRUE