## ----include = FALSE---------------------------------------------------------- library(magrittr) options(scipen = 3) knitr::opts_chunk$set(comment = "#>", collapse = TRUE) ## ----------------------------------------------------------------------------- library(magrittr) car_data <- mtcars %>% subset(hp > 100) %>% aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>% transform(kpl = mpg %>% multiply_by(0.4251)) %>% print ## ----------------------------------------------------------------------------- car_data <- transform(aggregate(. ~ cyl, data = subset(mtcars, hp > 100), FUN = function(x) round(mean(x), 2)), kpl = mpg*0.4251) ## ---- eval = FALSE------------------------------------------------------------ # car_data %>% # (function(x) { # if (nrow(x) > 2) # rbind(head(x, 1), tail(x, 1)) # else x # }) ## ----------------------------------------------------------------------------- car_data %>% { if (nrow(.) > 0) rbind(head(., 1), tail(., 1)) else . } ## ----------------------------------------------------------------------------- 1:10 %>% (substitute(f(), list(f = sum))) ## ---- fig.keep='none'--------------------------------------------------------- rnorm(200) %>% matrix(ncol = 2) %T>% plot %>% # plot usually does not return anything. colSums ## ---- eval = FALSE------------------------------------------------------------ # iris %>% # subset(Sepal.Length > mean(Sepal.Length)) %$% # cor(Sepal.Length, Sepal.Width) # # data.frame(z = rnorm(100)) %$% # ts.plot(z) ## ---- eval = FALSE------------------------------------------------------------ # iris$Sepal.Length %<>% sqrt ## ----------------------------------------------------------------------------- rnorm(1000) %>% multiply_by(5) %>% add(5) %>% { cat("Mean:", mean(.), "Variance:", var(.), "\n") head(.) } ## ---- results = 'hide'-------------------------------------------------------- rnorm(100) %>% `*`(5) %>% `+`(5) %>% { cat("Mean:", mean(.), "Variance:", var(.), "\n") head(.) }