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