有条件地求和r中的动态列 [英] Conditionally sum dynamic columns in r

问题描述

我试图有条件地对许多列求和，具体取决于它们是否大于或小于0.我很惊讶我找不到 dplyr 或 data.table 为此工作.我想为一个大的data.frame计算4个新列(要计算的列在文章的底部).

I am trying to conditionally sum across many columns depending on if they are greater than or less than 0. I am surprised I cannot find a dplyr or data.table work around for this. I want to calculate 4 new columns for a large data.frame (columns to calculate are at bottom of post).

dat2=matrix(nrow=10,rnorm(100));colnames(dat2)=paste0('V',rep(1:10))

dat2 %>% as.data.frame() %>%
  rowwise() %>%
  select_if(function(col){mean(col)>0}) %>%
  mutate(sum_pos=rowSums(.))  ##Obviously doesn't work

这些是我想计算的简单统计信息(是的；这些apply语句有效，但是我想在dplyr链中做其他事情，所以这就是为什么我要寻找 dplyr 或 data.table 方式.每个给定行的正数列或负数列均不同，因此我无法获取列列表(必须逐行动态地完成)).

These are the simple statistics I want to calculate (yes; these apply statements work, but there are other things in my dplyr chain I want to do, so thats why I am looking for a dplyr or data.table way. The columns that are positive or negative for each given row are different, so I cannot grab a list of columns (must be done dynamically, by row).

#Calculate these, but in a dplyr chain?
n_pos=apply(dat2,1,function(x) sum((x>0)))
n_neg=apply(dat2,1,function(x) sum((x<0)))
sum_pos=apply(dat2,1,function(x) sum(x[(x>0)]))
sum_neg=apply(dat2,1,function(x) sum(x[(x<0)]))

推荐答案

我们不需要 rowwise 和 rowSums ，因为 rowSums 可以做到没有任何分组的总和

We don't need rowwise with rowSums as rowSums can do the sum without any groupings

library(dplyr)
dat2 %>%
   as.data.frame() %>%  
   select_if(~ is.numeric(.) && mean(.) > 0) %>% 
   mutate(sum_pos = rowSums(.))

---

根据描述，似乎不是 mean 条件，而是与正负值分别按行， sum 和

---

Based on the description, it seems that it is not the mean condition, but related to rowwise, sum of the positive and negative values separately

dat2 %>%
   as.data.frame %>%
   mutate(sum_pos = rowSums(. * NA^(. < 0), na.rm = TRUE),
           sum_neg = rowSums(.[1:10] * NA^(.[1:10] > 0), na.rm = TRUE) )

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