【R】《R for Data Science》学习笔记-程序篇

Functions

写呀写呀写代码，写成一个程序，你真棒！

Functions

写函数好处多多，使你的代码变得通俗易懂，容易维护，减少出错率。所以一定要学会写函数。

不使用return，最后代码不赋值即可作为返回值；
do not repeat yourself(DRY) principle

为什么要写函数的例子

df <- tibble::tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

## 最不好的做法
df$a <- (df$a - min(df$a, na.rm = TRUE)) / 
  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$b <- (df$b - min(df$b, na.rm = TRUE)) / 
  (max(df$b, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$c <- (df$c - min(df$c, na.rm = TRUE)) / 
  (max(df$c, na.rm = TRUE) - min(df$c, na.rm = TRUE))
df$d <- (df$d - min(df$d, na.rm = TRUE)) / 
  (max(df$d, na.rm = TRUE) - min(df$d, na.rm = TRUE))

## 改善了一点
x <- df$a
(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
rng <- range(x, na.rm = TRUE)
(x - rng[1]) / (rng[2] - rng[1])

## 写函数
rescale <- function(x){
 # 处理Inf和-Inf
 for (i in length(x)) {
  if(x[i] == -Inf){
   x[i] <- 0
  }else if(x[i] == Inf){
   x[i] <- 1
  }
 }
    
 rng <- range(x,na.rm = T)
 (x - rng[1])/(rng[2]-rng[1])

}

## 使用函数
rescale(c(1:10,Inf))
## 使用apply对df的每列进行循环处理
apply(df,2,rescale)

https://nicercode.github.io/intro/writing-functions.html

standard.error <- function(x) {
    v <- var(x)
    n <- length(x)
    sqrt(v/n)
}

variance <- function(x) {
    n <- length(x)
    m <- mean(x)
    (1/(n - 1)) * sum((x - m)^2)
}
## variance等同于var，真的么？
skewness <- function(x) {
    n <- length(x)
    v <- var(x)
    m <- mean(x)
    third.moment <- (1/(n - 2)) * sum((x - m)^3)
    third.moment/(var(x)^(3/2))
}

函数名最好前缀写一样，这样使用Tab可以在函数家族中找
注释#, to explain the “why” of your code,avoid comments that explain the “what” or the “how”

Ctrl + Shift + R可以用了生成下面格式的注释

# Load data --------------------------------------

# Plot data --------------------------------------

|| (or) and && (and) to combine multiple logical expressions;never use | or & in an if statement;any() or all() to collapse it to a single value （操作符的区别，不要在if语句中使用|或者&）
注意，x == NA doesn’t do anything useful!
如果写了太多的if语句，则需要考虑使用switch() 语句，还有个cut()（适用于离散型变量）
ifelse(test, yes, no)

问候函数编写（要点是字符串的切割）

greet <- function(){
  # lubridate::now()好像也可以返回当前时间
  sys_time <- as.character(Sys.time())
  split_time <- unlist(strsplit(sys_time,split = " |:"))
  hour <- as.numeric(split_time[2])
  if(hour<12 && hour >=6){
  return("good morning")
  }else if(hour >= 12 && hour <18){
  return("good afternoon")
  }else {
  return("good evening")
  }
}

greet()

函数的两个变量的长度，NA值进行判断，程序会中止接着，报错、提醒：stopifnot()
special argument: ... (pronounced dot-dot-dot)
http://adv-r.had.co.nz/Functions.html#lazy-evaluation
如果可以尽早设置返回值

Writing pipeable functions:

mtcars %>% 
show_missings() %>% 
mutate(mpg = ifelse(mpg < 20, NA, mpg)) %>% 
show_missings() 

环境

`+` <- function(x, y) {
  if (runif(1) < 0.1) {
    sum(x, y)
  } else {
    sum(x, y) * 1.1
  }
}
table(replicate(1000, 1 + 2))
#> 
#>   3 3.3 
#> 100 900
rm(`+`)

【R】《R for Data Science》学习笔记-程序篇

Functions

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