数据操作基本功之slice操作（tidyverse就是好）

library(tidyverse)

rm(list = ls()) 
options(stringsAsFactors = T)

#基本用法不贴结果了哈
data("mtcars")
mtcars %>% slice(1L)

# Similar to tail(mtcars, 1):
mtcars %>% slice(n())
mtcars %>% slice(5:n())
# Rows can be dropped with negative indices:
slice(mtcars, -(1:4))

# First and last rows based on existing order
mtcars %>% slice_head(n = 5)
mtcars %>% slice_tail(n = 5)

# 取mpg最大及最小的5行数据
mtcars %>% slice_min(mpg, n = 2)
mtcars %>% slice_max(mpg, n = 2)

# slice_min() 和 slice_max() 在极值有相同数值时会出现多行
# 如果要排除这种情况，可应用with_ties = FALSE，会默认第一次出现的结果
mtcars %>% slice_min(cyl, n = 1)
mtcars %>% slice_min(cyl, n = 1, with_ties = FALSE)

slice_min的用法

# 随机抽取行
mtcars %>% slice_sample(n = 5)
mtcars %>% slice_sample(n = 5, replace = TRUE)

随机抽取行

# 依据数据中分类变量的比例进行抽取
mtcars$vs <- as.factor(mtcars$vs)
mtcars %>% slice_sample(weight_by = vs, n = 3)
mtcars %>% slice_sample(weight_by = vs, prop  = 0.1)

这个功能在随机分组中比较有用

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