dplyr数据透视表 [英] dplyr pivot table

问题描述

我想获取一个具有递减值的数据透视表.

I want to obtain a pivot table with descending value.

library(dplyr)
library(tidyr)

h<-mtcars %>%
  group_by(cyl, gear) %>%
  tally() %>%
  spread(gear, n, fill = 0)
h<-h%>% add_rownames("index")

i<-mtcars %>%
  group_by(cyl, gear) %>%
  tally() %>%
  spread(cyl, n, fill = 0)

获取值的总和

j<-i%>%
  select(-1)%>%
  summarise_each(funs(sum))

k<-t(j)
k<- as.data.frame(k)
k<-tbl_df(k)
k<-k%>%add_rownames("index")

l<-left_join(h,k,by="index")
l<-l%>%
  select(-1)%>%
  arrange(desc(V1))

在dplyr中还有另一种方法吗?

Is there another way to do the same in dplyr?

推荐答案

我们按'cyl'，'gear'分组，获得频率计数(tally())，从'long'改成'wide'(使用<tidyr中的c1>)，ungroup删除属性(以前，它不使用该属性)，使用mutate创建"V1"作为每一行的sum(使用rowSums)并最后根据'V1'中的值arrange(order)行.

We group by 'cyl', 'gear', get the frequency count (tally()), reshape from 'long' to 'wide' (using spread from tidyr), ungroup to remove the attributes (previously, it used to work without this), use mutate to create 'V1' as the sum of each row (using rowSums) and finally arrange (order) the rows based on values in 'V1'.

library(dplyr)
library(tidyr)

mtcars %>%
   group_by(cyl, gear) %>% 
   tally() %>%
   spread(gear, n, fill=0) %>% 
   ungroup() %>%
   mutate(V1= rowSums(.[-1])) %>% 
   arrange(desc(V1))
#    cyl     3     4     5    V1
#  <dbl> <dbl> <dbl> <dbl> <dbl>
#1     8    12     0     2    14
#2     4     1     8     2    11
#3     6     2     4     1     7

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