基于因子水平的子集数据框并创建以子集内变量为条件的新分位数变量

Question

我有一个这样的数据框：

set.seed(567) 
year= as.factor(c(rep("1998", 20), rep("1999", 16)))
lepsp= c(letters[seq(from = 1, to = 20 )], c('a','b','c'),letters[seq(from =8, to = 20 )]) 
freq= rpois(36, lambda=12)
df<-data.frame(year, lepsp, freq)

df<- 
  df %>%
  group_by(year) %>%
  mutate(rank = dense_rank(-freq))

我想通过 year 对 df 进行子集化，并创建一个名为 quant 的新列，该列将相应的四分位数分配给子集中的每个 freq 值。新列可以将分位数分配为 probs = seq(0, 1, 0.05)。最重要的是，我以后可以根据分位数分配类别，例如，低于 25% 的任何东西都被归类为罕见。因此，这些可以是广泛的四分位名称，但百分位数增量越小，我就越需要将某些东西归类为罕见 r 或常见 c。

输出应如下所示：

df<-data.frame(df, quant= c(75,50,25,50,50,25,75,50,25,75,75,100,50,100,100,50,25,25,75,25,75,50,50,75,75,25,25,50,50,50,25,75,75,25,75,50), 
               abucat= c("c", "r", "r","r","r", "r","c","r","r", "c", "c", "c", "r","c", "c","r" , "r", "r", "c", "r", "c","r","r","c","c","r",
 "r","r","r","r","r","c","c","r","c","r"))

我试过：

library(dplyr)

df<- 
  df %>%
  group_by(year) %>%
  mutate(quant = quantile(freq, probs= seq(0, 1, 0.25)))

Answer 1

我更新了代码以使用 case_when 以使其更直观。您应该能够看到每个 quant 被分类的情况以及相应的值。然后我使用 tidyr 分开将其分成 2 列。

library(dplyr)
library(tidyr)
set.seed(567) 
year= as.factor(c(rep("1998", 20), rep("1999", 16)))
lepsp= c(letters[seq(from = 1, to = 20 )], c('a','b','c'),letters[seq(from =8, to = 20 )]) 
freq= rpois(36, lambda=12)
df<-data.frame(year, lepsp, freq)

df<- 
  df %>%
  group_by(year) %>%
  mutate(rank = dense_rank(-freq))

df<-data.frame(df, quant= c(75,50,25,50,50,25,75,50,25,75,75,100,50,100,100,50,25,25,75,25,75,50,50,75,75,25,25,50,50,50,25,75,75,25,75,50), 
               abucat= c("c", "r", "r","r","r", "r","c","r","r", "c", "c", "c", "r","c", "c","r" , "r", "r", "c", "r", "c","r","r","c","c","r",
                         "r","r","r","r","r","c","c","r","c","r"))

df %>%
  group_by(year) %>%
  mutate(qtile = list(quantile(freq))) %>% 
  rowwise() %>% 
  mutate(q = case_when(freq <= qtile[2] ~ "25,r",
                           freq > qtile[2] & freq <=qtile[3] ~"50,r",
                           freq > qtile[3] & freq <=qtile[4] ~"75,c",
                           freq > qtile[4] ~ "100,c")) %>% 
  separate(q, c("quant","abucat")) %>% 
  select(-qtile)
#  Source: local data frame [36 x 6]
#  Groups: <by row>
#  
#  # A tibble: 36 x 6
#     year  lepsp  freq  rank quant abucat
#     <fct> <fct> <int> <int> <chr> <chr> 
#   1 1998  a        14     3 75    c     
#   2 1998  b        13     4 50    r     
#   3 1998  c         9     7 25    r     
#   4 1998  d        12     5 50    r     
#   5 1998  e        12     5 50    r     
#   6 1998  f         9     7 25    r     
#   7 1998  g        15     2 75    c     
#   8 1998  h        12     5 50    r     
#   9 1998  i        10     6 25    r     
#  10 1998  j        15     2 75    c     
#  # ... with 26 more rows

基于因子水平的子集数据框并创建以子集内变量为条件的新分位数变量

Subset dataframe based on levels of a factor and create new variable of quantiles conditional on variable within subset

r

quantile

dataframe