替代apply家族的map家族

高效数据处理组合

tidyverse包中就是这种组合：tidyr + dplyr + purrr，如下所示：

> library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 2.2.1     ✔ purrr   0.2.4
✔ tibble  1.4.1     ✔ dplyr   0.7.4
✔ tidyr   0.7.2     ✔ stringr 1.2.0
✔ readr   1.1.1     ✔ forcats 0.2.0
── Conflicts ─────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

学习笔记

• 学习资源
  • purrr tutorial
  • R for Data Science的第21章节（其实在前面的章节也谈到上一个学习资源）
• 学习摘记

library(tidyverse)

# 构造一个tibble
df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

# 算每列数据的中位数
median(df$a)
median(df$b)

# 重复做写很麻烦，写个循环
output <- vector("double", ncol(df))
for (i in seq_along(df)){
  # seq_along()更加安全比起length()
  output[[i]] <- median(df[[i]])
}
output


## 练习题
mean_loop <- function(df){
output <- vector("double", ncol(df))
for (i in seq_along(df)){
  output[[i]] <- mean(df[[i]])
}
print(output)
}

mean_loop(mtcars)


# vapply() is a safe alternative to sapply()
# because you supply an additional argument that defines the type.
# The only problem with vapply() is that it’s a lot of typing:
# vapply(df, is.numeric, logical(1)) is equivalent to map_lgl(df, is.numeric).
# One advantage of vapply() over purrr’s map functions is that it can also produce matrices
# — the map functions only ever produce vectors.

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