计算跨行的标准偏差 [英] Calculating standard deviation across rows

问题描述

假设我有以下数据:

colA <- c("SampA", "SampB", "SampC")
colB <- c(21, 20, 30)
colC <- c(15, 14, 12)
colD <- c(10, 22, 18)
df <- data.frame(colA, colB, colC, colD)
df
#    colA colB colC colD
# 1 SampA   21   15   10
# 2 SampB   20   14   22
# 3 SampC   30   12   18

我想获取 B-D 列中值的行均值和标准差.

I want to get the row means and standard deviations for the values in columns B-D.

我可以按如下方式计算 rowMeans:

I can calculate the rowMeans as follows:

library(dplyr)
df %>% select(., matches("colB|colC|colD")) %>% mutate(rmeans = rowMeans(.))
#   colB colC colD   rmeans
# 1   21   15   10 15.33333
# 2   20   14   22 18.66667
# 3   30   12   18 20.00000

但是当我尝试使用 sd() 计算标准偏差时，它抛出了一个错误.

But when I try to calculate the standard deviation using sd(), it throws up an error.

df %>% select(., matches("colB|colC|colD")) %>% mutate(rsds = sapply(., sd(.)))
Error in is.data.frame(x) : 
  (list) object cannot be coerced to type 'double'

所以我的问题是:我如何计算这里的标准偏差?

So my question is: how do I calculate the standard deviations here?

我尝试了 sapply() 和 sd() 阅读了第一个答案 此处.

I tried sapply() with sd() having read the first answer here.

附加不一定要寻找整洁"的解决方案(基础 R 也可以正常工作).

Additional edit: not necessarily looking for a 'tidy' solution (base R also works just fine).

推荐答案

试试这个 (使用)，使用 matrixStats 包中的 rowSds，

Try this (using), withrowSds from the matrixStats package,

library(dplyr)
library(matrixStats)

columns <- c('colB', 'colC', 'colD')

df %>% 
  mutate(Mean= rowMeans(.[columns]), stdev=rowSds(as.matrix(.[columns])))

退货

   colA colB colC colD     Mean    stdev
1 SampA   21   15   10 15.33333 5.507571
2 SampB   20   14   22 18.66667 4.163332
3 SampC   30   12   18 20.00000 9.165151

您的数据

colA <- c("SampA", "SampB", "SampC")
colB <- c(21, 20, 30)
colC <- c(15, 14, 12)
colD <- c(10, 22, 18)
df <- data.frame(colA, colB, colC, colD)
df

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