在 R 中的分组数据帧中使用来自大型数据帧的多分位数组 [英] Use multiquantile groups from a large dataframe in a grouped dataframe in R

问题描述

我有下一个问题，我有一个大数据框，我必须从一个变量中提取分位数，但要按实例分组:

list_q <- list()对于(我在 3:5){tmp <- mtcars%>%过滤器(齿轮== i)%>%拉(mpg) %>%分位数(probs = seq(0, 1, 0.25)，na.rm = TRUE)list_q[[i]] <- tmp}列表_q

使用此输出:

<代码>[[3]]0% 25% 50% 75% 100%10.4 14.5 15.5 18.4 21.5[[4]]0% 25% 50% 75% 100%17.800 21.000 22.800 28.075 33.900[[5]]0% 25% 50% 75% 100%15.0 15.8 19.7 26.0 30.4

现在，我需要对变量均值进行分组并确定它属于哪个分位数，但使用原始度量:

a <- mtcars %>%group_by(齿轮，碳水化合物)%>%总结(mpg_mean = mean(mpg))%>%取消分组()齿轮碳水化合物mpg_mean<dbl><dbl><dbl>1 3 1 20.32 3 2 17.23 3 3 16.34 3 4 12.65 4 1 29.16 4 2 24.87 4 4 19.88 5 2 28.29 5 4 15.810 5 6 19.711 5 8 15

所以我可以这样做:

<预><代码>g3＜-a％＞％过滤器(齿轮== 3)%>%变异(分位数 = 切割(mpg_mean，list_q[[3]]，标签 = FALSE，include.lowest = TRUE))g4＜-a％＞％过滤器(齿轮== 4)%>%变异(分位数 = cut(mpg_mean，list_q[[4]]，标签 = FALSE，include.lowest = TRUE))g5＜-a％＞％过滤器(齿轮== 5)%>%变异(分位数 = 切割(mpg_mean，list_q[[5]]，标签 = FALSE，include.lowest = TRUE))绑定行(g3，g4，g5)

获得:

# tibble: 11 x 4齿轮碳水化合物 mpg_mean 分位数<dbl><dbl><dbl><int>1 3 1 20.3 42 3 2 17.2 33 3 3 16.3 34 3 4 12.6 15 4 1 29.1 46 4 2 24.8 37 4 4 19.8 18 5 2 28.2 49 5 4 15.8 110 5 6 19.7 211 5 8 15 1

我想知道是否有更有效的方法

解决方案

我们可以先group_by gear 并将mpg的分位数存储在一个列表.然后我们也可以 group_by carb 获得 mean 的 mpg 值，并使用之前存储在列表中的分位数cut 这是mpg 列的意思.

库(dplyr)mtcars %>%group_by(齿轮)%>%变异(gear_q = 列表(分位数(mpg)))%>%group_by(carb, add = TRUE) %>%总结(mpg_mean = mean(mpg)，gear_q = list(first(gear_q))) %>%变异(分位数=切割(mpg_mean，第一(gear_q)，标签 = FALSE，include.lowest = TRUE)) %>%选择(-gear_q)# 齿轮碳水化合物 mpg_mean 分位数# <dbl><dbl><dbl><int># 1 3 1 20.3 4# 2 3 2 17.2 3# 3 3 3 16.3 3# 4 3 4 12.6 1# 5 4 1 29.1 4# 6 4 2 24.8 3# 7 4 4 19.8 1# 8 5 2 28.2 4# 9 5 4 15.8 1#10 5 6 19.7 2#11 5 8 15 1

I have the next problem, I have a large dataframe, in which I have to extract the quantiles from a variable but by group, by instance:

list_q <- list()

for (i in 3:5){

  tmp <- mtcars %>% 
    filter(gear == i) %>% 
    pull(mpg) %>% 
    quantile(probs = seq(0, 1, 0.25), na.rm = TRUE)

  list_q[[i]] <- tmp  

}

list_q

With this output:

[[3]]
  0%  25%  50%  75% 100% 
10.4 14.5 15.5 18.4 21.5 

[[4]]
    0%    25%    50%    75%   100% 
17.800 21.000 22.800 28.075 33.900 

[[5]]
  0%  25%  50%  75% 100% 
15.0 15.8 19.7 26.0 30.4 

Now, I need to group the variable means and determine which quantile it belongs but using the original measures:

a <- mtcars %>% 
  group_by(gear, carb) %>% 
  summarize(mpg_mean = mean(mpg)) %>% 
  ungroup()

    gear  carb mpg_mean
   <dbl> <dbl>    <dbl>
 1     3     1     20.3
 2     3     2     17.2
 3     3     3     16.3
 4     3     4     12.6
 5     4     1     29.1
 6     4     2     24.8
 7     4     4     19.8
 8     5     2     28.2
 9     5     4     15.8
10     5     6     19.7
11     5     8     15 

So I could do this:

g3 <- a %>% 
  filter(gear == 3) %>% 
  mutate(quantile = cut(mpg_mean, list_q[[3]], labels = FALSE, include.lowest = TRUE))

g4 <- a %>% 
  filter(gear == 4) %>% 
  mutate(quantile = cut(mpg_mean, list_q[[4]], labels = FALSE, include.lowest = TRUE))

g5 <- a %>% 
  filter(gear == 5) %>% 
  mutate(quantile = cut(mpg_mean, list_q[[5]], labels = FALSE, include.lowest = TRUE))

bind_rows(g3, g4, g5)

Obtaining:

# A tibble: 11 x 4
    gear  carb mpg_mean quantile
   <dbl> <dbl>    <dbl>    <int>
 1     3     1     20.3        4
 2     3     2     17.2        3
 3     3     3     16.3        3
 4     3     4     12.6        1
 5     4     1     29.1        4
 6     4     2     24.8        3
 7     4     4     19.8        1
 8     5     2     28.2        4
 9     5     4     15.8        1
10     5     6     19.7        2
11     5     8     15          1

I wish to know if there is a way to do this more efficiently

解决方案

We can first group_by gear and store the quantiles for mpg in a list. We can then also group_by carb to get mean of mpg value and use the quantiles stored in the list previously to cut this mean of mpg column.

library(dplyr)

mtcars %>% 
  group_by(gear) %>% 
  mutate(gear_q = list(quantile(mpg))) %>%
  group_by(carb, add = TRUE) %>%
  summarize(mpg_mean = mean(mpg), 
            gear_q = list(first(gear_q))) %>%
  mutate(quantile = cut(mpg_mean, first(gear_q), 
                        labels = FALSE, include.lowest = TRUE)) %>%
  select(-gear_q)

#    gear  carb mpg_mean quantile
#   <dbl> <dbl>    <dbl>    <int>
# 1     3     1     20.3        4
# 2     3     2     17.2        3
# 3     3     3     16.3        3
# 4     3     4     12.6        1
# 5     4     1     29.1        4
# 6     4     2     24.8        3
# 7     4     4     19.8        1
# 8     5     2     28.2        4
# 9     5     4     15.8        1
#10     5     6     19.7        2
#11     5     8     15          1

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