A functional is a function that takes a function as an input and returns a vector as output.
Hadley Wickham, Advanced R
statistic <- function(f){ data <- c(10, 20, 30, 40, 62, 63) f(data)}statistic(mean)[1] 37.5statistic(sum)[1] 225lapply: loop over a list and evaluate a function on each element.
x <- list( a = 1:8, b = c(2.1, 3.2, 4.2, 5, 6))x$a[1] 1 2 3 4 5 6 7 8$b[1] 2.1 3.2 4.2 5.0 6.0#We are passing `mean` is an argument to lapplylapply(x, mean)$a[1] 4.5$b[1] 4.1lapply(x, sum)$a[1] 36$b[1] 20.5cv <- function(data){sd(data)/mean(data)}lapply(x, cv)$a[1] 0.5443311$b[1] 0.3706996x <- list( a = 1:8, b = c(2.1, 3.2, 4.2, 5, 6))x$a[1] 1 2 3 4 5 6 7 8$b[1] 2.1 3.2 4.2 5.0 6.0result_x <- list()result_xlist()for (i in 1:2){ result_x[[i]] <- mean(x[[i]])}result_x[[1]][1] 4.5[[2]][1] 4.1lapply: loop over a list and evaluate a function on each element.
x <- list( a = 1:8, b = c(2.1, 3.2, 4.2, 5, 6))x$a[1] 1 2 3 4 5 6 7 8$b[1] 2.1 3.2 4.2 5.0 6.0#We are passing `mean` is an argument to lapplysapply(x, mean) a b 4.5 4.1sapply(x, sum) a b 36.0 20.5Same as lapply but the output is a vector.
map() function in purrrlibrary(purrr)x <- list( a = 1:8, b = c(2.1, 3.2, 4.2, 5, 6))x$a[1] 1 2 3 4 5 6 7 8$b[1] 2.1 3.2 4.2 5.0 6.0map(x, mean)$a[1] 4.5$b[1] 4.1map() is lapply().map syntaxmap(YOUR_LIST, YOUR_FUNCTION)
map(c(4, 9, 16), sqrt)[[1]][1] 2[[2]][1] 3[[3]][1] 4map()iris_species <- split(iris, iris$Species)str(iris_species)List of 3 $ setosa :'data.frame': 50 obs. of 5 variables: ..$ Sepal.Length: num [1:50] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ..$ Sepal.Width : num [1:50] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... ..$ Petal.Length: num [1:50] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... ..$ Petal.Width : num [1:50] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... $ versicolor:'data.frame': 50 obs. of 5 variables: ..$ Sepal.Length: num [1:50] 7 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 ... ..$ Sepal.Width : num [1:50] 3.2 3.2 3.1 2.3 2.8 2.8 3.3 2.4 2.9 2.7 ... ..$ Petal.Length: num [1:50] 4.7 4.5 4.9 4 4.6 4.5 4.7 3.3 4.6 3.9 ... ..$ Petal.Width : num [1:50] 1.4 1.5 1.5 1.3 1.5 1.3 1.6 1 1.3 1.4 ... ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ... $ virginica :'data.frame': 50 obs. of 5 variables: ..$ Sepal.Length: num [1:50] 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 ... ..$ Sepal.Width : num [1:50] 3.3 2.7 3 2.9 3 3 2.5 2.9 2.5 3.6 ... ..$ Petal.Length: num [1:50] 6 5.1 5.9 5.6 5.8 6.6 4.5 6.3 5.8 6.1 ... ..$ Petal.Width : num [1:50] 2.5 1.9 2.1 1.8 2.2 2.1 1.7 1.8 1.8 2.5 ... ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 3 3 3 3 3 3 3 3 3 3 ...This creates a list of three data frames.
map() (cont.)head(iris_species)$setosa Sepal.Length Sepal.Width Petal.Length Petal.Width Species1 5.1 3.5 1.4 0.2 setosa2 4.9 3.0 1.4 0.2 setosa3 4.7 3.2 1.3 0.2 setosa4 4.6 3.1 1.5 0.2 setosa5 5.0 3.6 1.4 0.2 setosa6 5.4 3.9 1.7 0.4 setosa7 4.6 3.4 1.4 0.3 setosa8 5.0 3.4 1.5 0.2 setosa9 4.4 2.9 1.4 0.2 setosa10 4.9 3.1 1.5 0.1 setosa11 5.4 3.7 1.5 0.2 setosa12 4.8 3.4 1.6 0.2 setosa13 4.8 3.0 1.4 0.1 setosa14 4.3 3.0 1.1 0.1 setosa15 5.8 4.0 1.2 0.2 setosa16 5.7 4.4 1.5 0.4 setosa17 5.4 3.9 1.3 0.4 setosa18 5.1 3.5 1.4 0.3 setosa19 5.7 3.8 1.7 0.3 setosa20 5.1 3.8 1.5 0.3 setosa21 5.4 3.4 1.7 0.2 setosa22 5.1 3.7 1.5 0.4 setosa23 4.6 3.6 1.0 0.2 setosa24 5.1 3.3 1.7 0.5 setosa25 4.8 3.4 1.9 0.2 setosa26 5.0 3.0 1.6 0.2 setosa27 5.0 3.4 1.6 0.4 setosa28 5.2 3.5 1.5 0.2 setosa29 5.2 3.4 1.4 0.2 setosa30 4.7 3.2 1.6 0.2 setosa31 4.8 3.1 1.6 0.2 setosa32 5.4 3.4 1.5 0.4 setosa33 5.2 4.1 1.5 0.1 setosa34 5.5 4.2 1.4 0.2 setosa35 4.9 3.1 1.5 0.2 setosa36 5.0 3.2 1.2 0.2 setosa37 5.5 3.5 1.3 0.2 setosa38 4.9 3.6 1.4 0.1 setosa39 4.4 3.0 1.3 0.2 setosa40 5.1 3.4 1.5 0.2 setosa41 5.0 3.5 1.3 0.3 setosa42 4.5 2.3 1.3 0.3 setosa43 4.4 3.2 1.3 0.2 setosa44 5.0 3.5 1.6 0.6 setosa45 5.1 3.8 1.9 0.4 setosa46 4.8 3.0 1.4 0.3 setosa47 5.1 3.8 1.6 0.2 setosa48 4.6 3.2 1.4 0.2 setosa49 5.3 3.7 1.5 0.2 setosa50 5.0 3.3 1.4 0.2 setosa$versicolor Sepal.Length Sepal.Width Petal.Length Petal.Width Species51 7.0 3.2 4.7 1.4 versicolor52 6.4 3.2 4.5 1.5 versicolor53 6.9 3.1 4.9 1.5 versicolor54 5.5 2.3 4.0 1.3 versicolor55 6.5 2.8 4.6 1.5 versicolor56 5.7 2.8 4.5 1.3 versicolor57 6.3 3.3 4.7 1.6 versicolor58 4.9 2.4 3.3 1.0 versicolor59 6.6 2.9 4.6 1.3 versicolor60 5.2 2.7 3.9 1.4 versicolor61 5.0 2.0 3.5 1.0 versicolor62 5.9 3.0 4.2 1.5 versicolor63 6.0 2.2 4.0 1.0 versicolor64 6.1 2.9 4.7 1.4 versicolor65 5.6 2.9 3.6 1.3 versicolor66 6.7 3.1 4.4 1.4 versicolor67 5.6 3.0 4.5 1.5 versicolor68 5.8 2.7 4.1 1.0 versicolor69 6.2 2.2 4.5 1.5 versicolor70 5.6 2.5 3.9 1.1 versicolor71 5.9 3.2 4.8 1.8 versicolor72 6.1 2.8 4.0 1.3 versicolor73 6.3 2.5 4.9 1.5 versicolor74 6.1 2.8 4.7 1.2 versicolor75 6.4 2.9 4.3 1.3 versicolor76 6.6 3.0 4.4 1.4 versicolor77 6.8 2.8 4.8 1.4 versicolor78 6.7 3.0 5.0 1.7 versicolor79 6.0 2.9 4.5 1.5 versicolor80 5.7 2.6 3.5 1.0 versicolor81 5.5 2.4 3.8 1.1 versicolor82 5.5 2.4 3.7 1.0 versicolor83 5.8 2.7 3.9 1.2 versicolor84 6.0 2.7 5.1 1.6 versicolor85 5.4 3.0 4.5 1.5 versicolor86 6.0 3.4 4.5 1.6 versicolor87 6.7 3.1 4.7 1.5 versicolor88 6.3 2.3 4.4 1.3 versicolor89 5.6 3.0 4.1 1.3 versicolor90 5.5 2.5 4.0 1.3 versicolor91 5.5 2.6 4.4 1.2 versicolor92 6.1 3.0 4.6 1.4 versicolor93 5.8 2.6 4.0 1.2 versicolor94 5.0 2.3 3.3 1.0 versicolor95 5.6 2.7 4.2 1.3 versicolor96 5.7 3.0 4.2 1.2 versicolor97 5.7 2.9 4.2 1.3 versicolor98 6.2 2.9 4.3 1.3 versicolor99 5.1 2.5 3.0 1.1 versicolor100 5.7 2.8 4.1 1.3 versicolor$virginica Sepal.Length Sepal.Width Petal.Length Petal.Width Species101 6.3 3.3 6.0 2.5 virginica102 5.8 2.7 5.1 1.9 virginica103 7.1 3.0 5.9 2.1 virginica104 6.3 2.9 5.6 1.8 virginica105 6.5 3.0 5.8 2.2 virginica106 7.6 3.0 6.6 2.1 virginica107 4.9 2.5 4.5 1.7 virginica108 7.3 2.9 6.3 1.8 virginica109 6.7 2.5 5.8 1.8 virginica110 7.2 3.6 6.1 2.5 virginica111 6.5 3.2 5.1 2.0 virginica112 6.4 2.7 5.3 1.9 virginica113 6.8 3.0 5.5 2.1 virginica114 5.7 2.5 5.0 2.0 virginica115 5.8 2.8 5.1 2.4 virginica116 6.4 3.2 5.3 2.3 virginica117 6.5 3.0 5.5 1.8 virginica118 7.7 3.8 6.7 2.2 virginica119 7.7 2.6 6.9 2.3 virginica120 6.0 2.2 5.0 1.5 virginica121 6.9 3.2 5.7 2.3 virginica122 5.6 2.8 4.9 2.0 virginica123 7.7 2.8 6.7 2.0 virginica124 6.3 2.7 4.9 1.8 virginica125 6.7 3.3 5.7 2.1 virginica126 7.2 3.2 6.0 1.8 virginica127 6.2 2.8 4.8 1.8 virginica128 6.1 3.0 4.9 1.8 virginica129 6.4 2.8 5.6 2.1 virginica130 7.2 3.0 5.8 1.6 virginica131 7.4 2.8 6.1 1.9 virginica132 7.9 3.8 6.4 2.0 virginica133 6.4 2.8 5.6 2.2 virginica134 6.3 2.8 5.1 1.5 virginica135 6.1 2.6 5.6 1.4 virginica136 7.7 3.0 6.1 2.3 virginica137 6.3 3.4 5.6 2.4 virginica138 6.4 3.1 5.5 1.8 virginica139 6.0 3.0 4.8 1.8 virginica140 6.9 3.1 5.4 2.1 virginica141 6.7 3.1 5.6 2.4 virginica142 6.9 3.1 5.1 2.3 virginica143 5.8 2.7 5.1 1.9 virginica144 6.8 3.2 5.9 2.3 virginica145 6.7 3.3 5.7 2.5 virginica146 6.7 3.0 5.2 2.3 virginica147 6.3 2.5 5.0 1.9 virginica148 6.5 3.0 5.2 2.0 virginica149 6.2 3.4 5.4 2.3 virginica150 5.9 3.0 5.1 1.8 virginicamap() (cont.)Fit a linear model between Petal.Width ~ Petal.Length and extract the second coefficient.
library(magrittr)iris_species %>% map(~lm(Petal.Width ~ Petal.Length, data = .x)) %>% map(coef) %>% map_dbl(2) setosa versicolor virginica 0.2012451 0.3310536 0.1602970iris_species %>% map(~lm(Petal.Width ~ Petal.Length, data = .x)) %>% map(coef) %>% map_dbl(2) setosa versicolor virginica 0.2012451 0.3310536 0.1602970models <- lapply(iris_species, function(data) lm(Petal.Width ~ Petal.Length, data = data))vapply(models, function(x) coef(x)[[2]], double(1)) setosa versicolor virginica 0.2012451 0.3310536 0.1602970Or use for loop
iris_species %>% map(~lm(Petal.Width ~ Petal.Length, data = .x)) %>% map(coef) %>% map_dbl(2) setosa versicolor virginica 0.2012451 0.3310536 0.1602970intercepts <- double(length(iris_species))for (i in seq_along(iris_species)) { model <- lm(Petal.Width ~ Petal.Length, data = iris_species[[i]]) intercepts[[i]] <- coef(model)[[2]]}intercepts[1] 0.2012451 0.3310536 0.1602970map variants: modifydf <- data.frame( x = 1:3, y = 6:4)map(df, ~ .x * 10)$x[1] 10 20 30$y[1] 60 50 40# If you want to keep the output as a data frame, you can use modify()modify(df, ~ .x * 10) x y1 10 602 20 503 30 40modify returns the same type of output as the input.
map_dfmap_output <- map(mtcars, function(x) length(unique(x)))head(map_output, 3)$mpg[1] 25$cyl[1] 3$disp[1] 27map_df_output <- map_df(mtcars, function(x) length(unique(x)))head(map_df_output)# A tibble: 1 x 11 mpg cyl disp hp drat wt qsec vs am gear carb <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>1 25 3 27 22 22 29 30 2 2 3 6map/map_df/modifyset.seed(2020)x <- list(a=rnorm(5), b=rnorm(6))map(x, mean)$a[1] -0.8692886$b[1] 0.4089487map_df(x, mean)# A tibble: 1 x 2 a b <dbl> <dbl>1 -0.869 0.409modify(x, mean)$a[1] -0.8692886$b[1] 0.4089487Slides available at: hellor.netlify.app
All rights reserved by Thiyanga S. Talagala
Reference: Advanced R, Hadley Wickham
A functional is a function that takes a function as an input and returns a vector as output.
Hadley Wickham, Advanced R
statistic <- function(f){ data <- c(10, 20, 30, 40, 62, 63) f(data)}statistic(mean)[1] 37.5statistic(sum)[1] 225Keyboard shortcuts
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