使用在dplyr中返回多个输出的函数将多个列添加到data.frame [英] Add multiple columns to data.frame using a function that returns multiple outputs in dplyr

问题描述

我正在计算连续变量的不同分位数的中位数和值.我想一步添加所有列.这有可能做到这一点吗?以下是可重现的示例.

  df<-data.frame(group = rep(c('group1'，'group2')，50)，x = rnorm(100)，y = rnorm(100))df％>％collect('variable'，'value'，-group)％&％;％group_by(组，变量)％&％;％summarise(中位数=舍入(分位数(value，0.5，na.rm = T)，2)，iqr25 =圆(quantile(value，0.25，na.rm = T)，2)，iqr75 =舍入(分位数(value，0.75，na.rm = T)，2)) 

输出

 #小贴士:4 x 5#个群组:群组[2]组变量中位数iqr25 iqr75< fct>< chr>< dbl>< dbl>< dbl>1组1 x 0.06 -0.74 1.042组1 y -0.36 -1.03 0.453组2 x -0.04 -0.85 0.624组2是0.06 -0.56 0.89 

可以在不编写3次分位数函数的情况下完成此汇总步骤吗?

我在使用它方面做了一些工作.但是有什么好方法吗?

  df％>％collect('variable'，'value'，-group)％&％;％group_by(组，变量)％&％;％summarise(s = toString(round(quantile(value，c(0.25,0.5,0.75)，na.rm = T)，2)))％>％分隔成= c('q25'，'median'，'q75')，sep ='，') 

解决方案

您可以在 group_by 之后嵌套数据，然后 map 到分位数

  df％>％collect('variable'，'value'，-group)％&％;％group_by(组，变量)％&％;％nest()％&％;％mutate(quant = map(data，〜quantile(.$ value，probs = c(0.25，0.5，0.75)))，量化= map(quant，t)，量化=地图(quant，as.data.frame)，quant = map(quant，setNames，c("iqr25"，"median"，"iqr75"))，)％&％;％嵌套(数量)％&％;％选择(-数据)#小动作:4 x 5组变量iqr25中值iqr75< fct>< chr>< dbl>< dbl>< dbl>1组1 x -0.876 -0.173 0.4712组2 x -0.372 0.0507 0.5193组1 y -0.785 -0.109 0.6184组2 y -0.944 -0.117 0.647 

I am calculating median and values for different quantiles for a continuous variable. I want to add all the columns in a single step. Is this possible to do this. Following is a reproducible example.

df <- data.frame(group = rep(c('group1','group2'),50),
             x = rnorm(100), 
             y = rnorm(100))
df %>% 
gather('variable','value', -group) %>% 
group_by(group, variable) %>% 
summarise(median = round(quantile(value,0.5, na.rm = T),2),
          iqr25 = round(quantile(value,0.25, na.rm = T),2),
          iqr75 = round(quantile(value,0.75, na.rm = T),2))

OUTPUT

# A tibble: 4 x 5
# Groups:   group [2]
  group  variable median iqr25 iqr75
  <fct>  <chr>     <dbl> <dbl> <dbl>
1 group1 x          0.06 -0.74  1.04
2 group1 y         -0.36 -1.03  0.45
3 group2 x         -0.04 -0.85  0.62
4 group2 y          0.06 -0.56  0.89

Can this summarise step be done without writing the quantile function 3 times.

I did a work around using this. But is there a nice way to do this.

df %>% 
gather('variable','value', -group) %>% 
group_by(group, variable) %>% 
summarise(s = toString(round(quantile(value, c(0.25,0.5,0.75),na.rm = T),2))) %>% 
separate(s, into = c('q25','median','q75'), sep = ',')

解决方案

You can nest the data after the group_by and then map to quantile

df %>% 
  gather('variable','value', -group) %>% 
  group_by(group, variable) %>% 
  nest() %>% 
  mutate(quant = map(data, ~quantile(.$value, probs = c(0.25, 0.5, 0.75))),
         quant = map(quant, t),
         quant = map(quant, as.data.frame),
         quant = map(quant, setNames, c("iqr25", "median", "iqr75")),

         ) %>% 
  unnest(quant) %>% 
  select(-data)

# A tibble: 4 x 5
  group  variable  iqr25  median iqr75
  <fct>  <chr>     <dbl>   <dbl> <dbl>
1 group1 x        -0.876 -0.173  0.471
2 group2 x        -0.372  0.0507 0.519
3 group1 y        -0.785 -0.109  0.618
4 group2 y        -0.944 -0.117  0.647

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