Styjun

Say I have the following data:

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

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

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

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'

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

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

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

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])))

Returns

 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

Your data

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

Here is another way using pmap to get the rowwise mean and sd

library(purrr)
library(dplyr)
library(tidur_
f1 <- function(x) tibble(Mean = mean(x), SD = sd(x))
df %>% 
 # select the numeric columns
 select_if(is.numeric) %>%
 # apply the f1 rowwise to get the mean and sd in transmute
 transmute(out = pmap(., ~ f1(c(...)))) %>% 
 # unnest the list column
 unnest %>%
 # bind with the original dataset
 bind_cols(df, .)
# colA colB colC colD Mean SD
#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

Package magrittr pipes %>% are not a good way to process by rows.

Maybe the following is what you want.

df %>% 
 select(-colA) %>%
 t() %>% as.data.frame() %>%
 summarise_all(sd)
# V1 V2 V3
#1 5.507571 4.163332 9.165151

A different tidyverse approach could be:

df %>%
 rowid_to_column() %>%
 gather(var, val, -c(colA, rowid)) %>%
 group_by(rowid) %>%
 summarise(rsds = sd(val)) %>%
 left_join(df %>%
 rowid_to_column(), by = c("rowid" = "rowid")) %>%
 select(-rowid)

 rsds colA colB colC colD
 <dbl> <fct> <dbl> <dbl> <dbl>
1 5.51 SampA 21 15 10
2 4.16 SampB 20 14 22
3 9.17 SampC 30 12 18

Here it, first, creates a row ID. Second, it performs a wide-to-long data transformation, excluding the "colA" and row ID. Third, it groups by row ID and calculates the standard deviation. Finally, it joins it with the original df on row ID.

Or alternatively, using rowwise() and do():

 df %>% 
 rowwise() %>%
 do(data.frame(., rsds = sd(unlist(.[2:length(.)]))))

 colA colB colC colD rsds
* <fct> <dbl> <dbl> <dbl> <dbl>
1 SampA 21 15 10 5.51
2 SampB 20 14 22 4.16
3 SampC 30 12 18 9.17

You can use pmap, or rowwise (or group by colA) along with mutate :

library(tidyverse)
df %>% mutate(sd = pmap(.[-1], ~sd(c(...)))) # same as transform(df, sd = apply(df[-1],1,sd))
#> colA colB colC colD sd
#> 1 SampA 21 15 10 5.507571
#> 2 SampB 20 14 22 4.163332
#> 3 SampC 30 12 18 9.165151

df %>% rowwise() %>% mutate(sd = sd(c(colB,colC,colD)))
#> Source: local data frame [3 x 5]
#> Groups: <by row>
#> 
#> # A tibble: 3 x 5
#> colA colB colC colD sd
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 SampA 21 15 10 5.51
#> 2 SampB 20 14 22 4.16
#> 3 SampC 30 12 18 9.17

df %>% group_by(colA) %>% mutate(sd = sd(c(colB,colC,colD)))
#> # A tibble: 3 x 5
#> # Groups: colA [3]
#> colA colB colC colD sd
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 SampA 21 15 10 5.51
#> 2 SampB 20 14 22 4.16
#> 3 SampC 30 12 18 9.17