Suppose we need to do the sum of each column in a dataset

set.seed(20)
df1 <- data.frame(ID = rep(c("A", "B", "C"), each = 3), V1 = rnorm(9), V2 = rnorm(9))
m1 <- as.matrix(df1[-1])

There are many ways to do this. Using base R, the best option would be colSums

colSums(df1[-1], na.rm = TRUE)

Here, we removed the first column as it is non-numeric and did the sum of each column, specifying the na.rm = TRUE (in case there are any NAs in the dataset)

This also works with matrix

colSums(m1, na.rm = TRUE)

This can be done in a loop with lapply/sapply/vapply

 lapply(df1[-1], sum, na.rm = TRUE)

It should be noted that the output is a list. If we need a vector output

 sapply(df1[-1], sum, na.rm = TRUE)

Or

 vapply(df1[-1], sum, na.rm = TRUE, numeric(1))

For matrices, if we want to loop through columns, then use apply with MARGIN = 1

 apply(m1, 2, FUN = sum, na.rm = TRUE)

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There are ways to do this with packages like dplyr or data.table

 library(dplyr)
 df1 %>%
     summarise_at(vars(matches("^V\\d+")), sum, na.rm = TRUE)

Here, we are passing a regular expression to match the column names that we need to get the sum in summarise_at. The regex will match all columns that start with V followed by one or more numbers (\\d+).

A data.table option is

library(data.table)   
setDT(df1)[, lapply(.SD, sum, na.rm = TRUE), .SDcols = 2:ncol(df1)]

We convert the 'data.frame' to 'data.table' (setDT(df1)), specified the columns to be applied the function in .SDcols and loop through the Subset of Data.table (.SD) and get the sum.

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If we need to use a group by operation, we can do this easily by specifying the group by column/columns

 df1 %>%
   group_by(ID) %>%   
   summarise_at(vars(matches("^V\\d+")), sum, na.rm = TRUE)

In cases where we need the sum of all the columns, summarise_each can be used instead of summarise_at

df1 %>%
    group_by(ID) %>%
    summarise_each(funs(sum(., na.rm = TRUE)))

The data.table option is

setDT(df1)[, lapply(.SD, sum, na.rm = TRUE), by = ID]