Chapter 6 Base R
10월 1일 목요일, 202AIE17 송채은
6.1 Objects, variables, and assignment operator
x <- c(1,2,3)x## [1] 1 2 36.2 Functions
a function is a block of codes that is used to perform a single task when the function is called
- A function requires
- arguments : whose values will be used if the function is called
- body : which is a group of R expressions contained in a curly brace ({ and })
- Functions are a fundamental building block of R
- Packages are a collection of functions made by others
- our job is to build a pipeline of data flow by connecting many available functions
1. An Example of Functions
mean(c(1,2,3,4))## [1] 2.5a <- mean(c(1,2,3,4))mean() will not work with NA
mean(c(1,2,NA,4))## [1] NAWhen na.rm = TRUE, NA will be removed before computation
mean(c(1,2,NA,4), na.rm = TRUE)## [1] 2.3333332. User-Defined Functions
se <- function(x) {
v <- var(x)
n <- length(x)
return(sqrt(v/n))
}mySample <- rnorm(n=100, mean=20, sd=4)se(mySample)## [1] 0.41582473. Exercise on functions
x1 <- rnorm(100, mean=0, sd=1)x2 <- rnorm(100, mean=3, sd=2)chaeeun <- mean(x2)-mean(x1)/sd(x1)chaeeun## [1] 2.8209146.3 Operators
1 == 2## [1] FALSE"a" != "b"## [1] TRUE1 == 2 | "a" != "b"## [1] TRUE1 == 2 & "a" != "b"## [1] FALSE6.4 Data Structure
R has base structures and base data structures can be organized by their dimensionality element를 찾기 위한 Index의 갯수가 1개(1D), 2개(2D), n개(nD)
6.5 Vectors
Atomic Vectors(homogeneous)
- All elements of an atomic vector must be the same type and usually created with c()
- Logical (TRUE or FALSE) 논리
- integer 정수
- double 실수
- character 문자
- complex 복소수
- raw
Lists (heterogeneous) their elements can be of any type and lists are created by list()
6.5.1 Vector Has Three Properties
1. Type
typeof() returns the type of an object
typeof(c(1,2,3))## [1] "double"2. Length
length() returns the number of elements in a vector
length(c(1,2,3))## [1] 33. Attributes
attr() or attributes() returns additional arbitrary metadata
attributes(c(1,2,3))## NULLa <- c(x=1,y=2,z=3)names(a)## [1] "x" "y" "z"modifying existing names
names(a) <- c("l", "m", "n")a## l m n
## 1 2 3names are attributes
attributes(a)## $names
## [1] "l" "m" "n"head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1attributes(mtcars)## $names
## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb"
##
## $row.names
## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" "Hornet Sportabout"
## [6] "Valiant" "Duster 360" "Merc 240D" "Merc 230" "Merc 280"
## [11] "Merc 280C" "Merc 450SE" "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128" "Honda Civic" "Toyota Corolla"
## [21] "Toyota Corona" "Dodge Challenger" "AMC Javelin" "Camaro Z28" "Pontiac Firebird"
## [26] "Fiat X1-9" "Porsche 914-2" "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"
##
## $class
## [1] "data.frame"6.5.2 Type Conversion
All elements of a vector must belong to the same base data type. If that is not true, R will automatically force it by type conversion
v <- c(4, 7, 23.5, 76.2, 80, "rrt")v## [1] "4" "7" "23.5" "76.2" "80" "rrt"typeof(v)## [1] "character"Functions can automatically convert data type TRUE = 1, FALSE = 2
sum(c(TRUE, FALSE, TRUE))## [1] 2can convert data type with as.character(), as.double(), as.integer(), and as.logical()
a <- c(1,2,3)a## [1] 1 2 3b <- as.character(a)b## [1] "1" "2" "3"6.5.3 Generate a vector
a <- c(1,2,3,4,5)a## [1] 1 2 3 4 5c() also combine vectors
a <- c(1,2,3)b <- c(4,5,6)c <- c(a, b)c## [1] 1 2 3 4 5 6k:n generates a vector whose elements are the sequence of numbers from k to n
1:10## [1] 1 2 3 4 5 6 7 8 9 10seq() generates regular sequence seq(from, to)
seq(1, 10)## [1] 1 2 3 4 5 6 7 8 9 10seq(from, to, by)
seq(1, 10, 2)## [1] 1 3 5 7 9rep(x, times) replicates the values in x multiple times (x can be a number or vector)
rep(1, 5)## [1] 1 1 1 1 1rep(c(1,2), 5)## [1] 1 2 1 2 1 2 1 2 1 2rep(c(1,2), each = 5)## [1] 1 1 1 1 1 2 2 2 2 2rnorm(100)## [1] 1.05430020 -0.70747447 -0.32920300 -0.46654981 0.25314117 1.43733792 0.10544477 -1.05807539 0.43049233 -1.98590939
## [11] 1.49579578 -0.04953275 -0.95942917 -0.65197615 0.69324827 -0.72920304 -0.86872850 0.87616007 0.70878155 -0.29516434
## [21] -0.14410707 0.92548801 -0.44549329 0.19453602 -1.60115211 1.39350959 3.32606250 -0.79648802 -1.30996456 -0.46003636
## [31] -0.53669533 0.82104467 1.78821753 -1.97608002 1.52531876 -0.25585319 1.21006074 1.95607085 0.04230925 0.46803578
## [41] 2.01670378 1.19368046 1.20120153 -0.49669237 -0.18918695 -0.42362620 0.41901524 -0.39265277 -0.90423529 -1.68525596
## [51] -0.99853779 -0.05946837 -0.12272501 1.19990084 -0.25277591 0.06012662 -1.08792087 0.76878385 -0.06246595 -2.07270115
## [61] -1.31755700 -0.89706424 0.34031477 0.90086963 -0.11628450 -0.24003826 -1.18040086 -0.24690402 -0.83260657 -1.00306421
## [71] -0.30850349 -0.34659285 0.43716883 -2.36077103 -0.23116019 1.87449880 1.38009796 0.95581840 -0.08384473 0.26942021
## [81] 1.08039932 0.40561762 0.15912815 1.13572438 0.10386879 0.86585554 0.79493803 -0.43080560 0.77649804 -0.32221706
## [91] 1.50550855 0.32032912 0.24727775 -0.82197481 -1.42758024 1.10942098 1.39620746 0.08895578 0.46403878 -0.54669577library(ggplot2)qplot(rnorm(10000))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
runif(n, min, max) generates a vector of n random samples from a uniform distribution whose limits are min and max
runif(100, 0, 1)## [1] 0.472724686 0.307018266 0.720369834 0.565719134 0.260214073 0.291630986 0.882164101 0.051605914 0.455890893 0.165143798
## [11] 0.851892580 0.040269077 0.366332942 0.673759001 0.057252024 0.706318196 0.217254666 0.262953555 0.404861972 0.751126933
## [21] 0.126246933 0.472105791 0.134025459 0.689038690 0.783065907 0.164032331 0.101259435 0.565697048 0.060336350 0.765656369
## [31] 0.511318344 0.724996882 0.869950069 0.912974228 0.905732265 0.743389372 0.374935023 0.532313017 0.094557391 0.724723988
## [41] 0.379362820 0.882813673 0.996418042 0.331454388 0.511362910 0.083599654 0.792371807 0.218166791 0.266773995 0.006076652
## [51] 0.064438633 0.565171633 0.112191624 0.796797916 0.464214930 0.866476413 0.754352485 0.269016077 0.259146014 0.664641577
## [61] 0.988021544 0.914366963 0.661401265 0.768460279 0.666844471 0.408162299 0.735091421 0.749441098 0.768186237 0.223100694
## [71] 0.946561704 0.962713837 0.938126939 0.832991195 0.182621158 0.988980525 0.530086909 0.550687216 0.148937527 0.271781892
## [81] 0.011120157 0.151779840 0.667246365 0.367090994 0.024391824 0.755280936 0.867969546 0.033019193 0.211942921 0.705608915
## [91] 0.724538469 0.230190331 0.617799667 0.790173059 0.995027459 0.348505143 0.956797696 0.023629957 0.851972289 0.025694436qplot(runif(10000, 0, 1))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
6.5.4 Indexing or subsetting a Vector
access a particular element of a vector through an index between square brackets or indexing (subsetting) operator
1. Positive integers
return elements at the specified positions
x <- c(2,3,4,5,6,7)x[c(3,1)]## [1] 4 22. Negative integers
omit elements at the specified positions
x[-c(3,1)]## [1] 3 5 6 73. Logical vectors
select elements where the corresponding logical value is TRUE
x[c(TRUE, TRUE, FALSE, FALSE, TRUE, TRUE)]## [1] 2 3 6 7x > 3## [1] FALSE FALSE TRUE TRUE TRUE TRUEThis is called a logical indexing
x[x > 3]## [1] 4 5 6 7x[x > 3 & x < 5]## [1] 4v1 %in% v2 returns a logical vector indicating whether the elements of v1 are included in v2
c(1,2,3) %in% c(2,3,4,5,6)## [1] FALSE TRUE TRUEa <- c(1,2,3,4,5)a## [1] 1 2 3 4 5a[3] <- 100a## [1] 1 2 100 4 5a[c(1,5)] <- 100a## [1] 100 2 100 4 100a <- c(1,2,3,NA,5,6,NA)a## [1] 1 2 3 NA 5 6 NAis.na indicates which elements are missing
is.na(a)## [1] FALSE FALSE FALSE TRUE FALSE FALSE TRUETRUE and FALSE will be converted into 1 and 0
sum(is.na(a))## [1] 2!is.na(a)## [1] TRUE TRUE TRUE FALSE TRUE TRUE FALSEsum(!is.na(a))## [1] 5a[is.na(a)] <- 999a## [1] 1 2 3 999 5 6 999create a vector with names
a <- c(x = 1, y = 2, z = 3)a## x y z
## 1 2 3named vector can be indexed using their names
a[c("x", "z")]## x z
## 1 3R uses a “recycling rule” by repeating the shorter vector
i <- c(TRUE, FALSE)a <- c(1,2,3,4)a[i]## [1] 1 3v1 <- c(4,5,6,7)v2 <- c(10,10)v1 + v2## [1] 14 15 16 176.5.5 Arrange a vector
sort() sorts ascending order
sort(c(5,6,4))## [1] 4 5 6sorts into descending order
sort(c(5,6,4), decreasing = TRUE)## [1] 6 5 4rev() provides a reversed version of its argument
rev(c(5,6,4))## [1] 4 6 5rank() returns the sample ranks of the elements in a vector
rank(c(5,6,4))## [1] 2 3 1order() returns a permutation which rearranges 1) first sorts a vector in ascending order to produce c(4,5,6) 2) and returns the indices of the sorted element in the original vector
order(c(5,6,4))## [1] 3 1 2a <- c(5,6,4)a[order(a)]## [1] 4 5 6head(mtcars[order(mtcars$mpg), ])## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
## Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
## Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 86.5.6 Vectorization of Functions
Many R functions can be applied to a vector of values producing an equal-sized vector of result
v <- c(1,4,25)sqrt(v)## [1] 1 2 5v <- c(1,2,3)v^2## [1] 1 4 9v1 <- c(4,5,6,7)v2 <- c(10,2,1,2)v1+v2## [1] 14 7 7 9R uses a “recycling rule” by repeating the shorter vector
v1 <- c(4,5,6,7)v2 <- c(10,2)v1+v2## [1] 14 7 16 9mean will be subtracted from every element of v1
v1 <- c(1,2,3,4)v1 - mean(v1)## [1] -1.5 -0.5 0.5 1.56.5.7 Some more functions
table() creates a frequency table
a <- c(1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4)table(a)## a
## 1 2 3 4
## 2 3 3 5unique() returns a vector of unique elements
unique(a)## [1] 1 2 3 4a <- c(1,2,3,NA,5)By default, mean() produces NA when there’s NAs in a vector
mean(a)## [1] NAna.rm = TRUE removes NAs before computation
mean(a, na.rm = TRUE)## [1] 2.758. Generating Sequences
creating a vector containing integers between 1 and 10
1:10## [1] 1 2 3 4 5 6 7 8 9 105:0## [1] 5 4 3 2 1 0seq(from=1, to=3, by=0.5)## [1] 1.0 1.5 2.0 2.5 3.0seq(1,3,0.5)## [1] 1.0 1.5 2.0 2.5 3.0rep() replicates each term in formula
rep(5,3)## [1] 5 5 5rep(1:2, 3)## [1] 1 2 1 2 1 2rep(1:2, each=3)## [1] 1 1 1 2 2 2gl(k,n) generates sequences involving factors + k : the number of levels + n : the number of repetitions
gl(5,3)## [1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5
## Levels: 1 2 3 4 59. Exercise on vectors
a <- mtcars$mpglength(a)## [1] 32Calculate the mean of a using sum() and length() functions
sum(a)/length(a)## [1] 20.09062mean(a)## [1] 20.09062Calculate the variance of a using sd() function
sd(a)^2## [1] 36.3241Calculate the variance of a using var() function
sum((a-mean(a))^2)/(length(a)-1)## [1] 36.3241(a-mean(a))/sd(a)## [1] 0.15088482 0.15088482 0.44954345 0.21725341 -0.23073453 -0.33028740 -0.96078893 0.71501778 0.44954345 -0.14777380
## [11] -0.38006384 -0.61235388 -0.46302456 -0.81145962 -1.60788262 -1.60788262 -0.89442035 2.04238943 1.71054652 2.29127162
## [21] 0.23384555 -0.76168319 -0.81145962 -1.12671039 -0.14777380 1.19619000 0.98049211 1.71054652 -0.71190675 -0.06481307
## [31] -0.84464392 0.21725341Use scale() function to standardize a and compare the results with your manual calculation
scale(a)## [,1]
## [1,] 0.15088482
## [2,] 0.15088482
## [3,] 0.44954345
## [4,] 0.21725341
## [5,] -0.23073453
## [6,] -0.33028740
## [7,] -0.96078893
## [8,] 0.71501778
## [9,] 0.44954345
## [10,] -0.14777380
## [11,] -0.38006384
## [12,] -0.61235388
## [13,] -0.46302456
## [14,] -0.81145962
## [15,] -1.60788262
## [16,] -1.60788262
## [17,] -0.89442035
## [18,] 2.04238943
## [19,] 1.71054652
## [20,] 2.29127162
## [21,] 0.23384555
## [22,] -0.76168319
## [23,] -0.81145962
## [24,] -1.12671039
## [25,] -0.14777380
## [26,] 1.19619000
## [27,] 0.98049211
## [28,] 1.71054652
## [29,] -0.71190675
## [30,] -0.06481307
## [31,] -0.84464392
## [32,] 0.21725341
## attr(,"scaled:center")
## [1] 20.09062
## attr(,"scaled:scale")
## [1] 6.026948Calculate the difference between the largest and smallest numbers in a
max(a)-min(a)## [1] 23.5diff(range(a))## [1] 23.5Normalize the vector
(a-min(a))/(max(a)-min(a))## [1] 0.4510638 0.4510638 0.5276596 0.4680851 0.3531915 0.3276596 0.1659574 0.5957447 0.5276596 0.3744681 0.3148936 0.2553191
## [13] 0.2936170 0.2042553 0.0000000 0.0000000 0.1829787 0.9361702 0.8510638 1.0000000 0.4723404 0.2170213 0.2042553 0.1234043
## [25] 0.3744681 0.7191489 0.6638298 0.8510638 0.2297872 0.3957447 0.1957447 0.4680851qplot(a)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
To set aesthetics, wrap in I()
qplot(a, color = I("red"), fill = I("blue"))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
How many elements in a are larger than 20?
a>20## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
## [22] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUEwithin the subsetting operator (i.e., []) will create a vector with elements larger than 20
a[a>20]## [1] 21.0 21.0 22.8 21.4 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26.0 30.4 21.4length(a[a>20])## [1] 14How many elements in a are larger than 20?
sum(a>20)## [1] 14head(txhousing)## # A tibble: 6 x 9
## city year month sales volume median listings inventory date
## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abilene 2000 1 72 5380000 71400 701 6.3 2000
## 2 Abilene 2000 2 98 6505000 58700 746 6.6 2000.
## 3 Abilene 2000 3 130 9285000 58100 784 6.8 2000.
## 4 Abilene 2000 4 98 9730000 68600 785 6.9 2000.
## 5 Abilene 2000 5 141 10590000 67300 794 6.8 2000.
## 6 Abilene 2000 6 156 13910000 66900 780 6.6 2000.b <- txhousing$medianlength(b)## [1] 8602sum(is.na(b))## [1] 616sum(b, na.rm = TRUE) / length(b)## [1] 118955.8mean(b, na.rm = TRUE)## [1] 128131.4