R/tidyverse: calculating standard deviation across rows

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