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Assign multiple columns using := in data.table, by group

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How to add Multiple columns to data table?

Method 1: Using := A column can be added to an existing data table using := operator. Here ':' represents the fixed values and '=' represents the assignment of values. So, they together represent the assignment of fixed values. Therefore, with the help of “:=” we will add 2 columns in the above table.

How to add Multiple columns to DataFrame in R?

More specifically, you will learn 1) to add a column using base R (i.e., by using the $-operator and brackets, 2) add a column using the add_column() function (i.e., from tibble), 3) add multiple columns, and 4) to add columns from one dataframe to another.

How do I add multiple columns in R?

To concatenate two columns you can use the <code>paste()</code> function. For example, if you want to combine the two columns A and B in the dataframe df you can use the following code: <code>df['AB'] <- paste(df$A, df$B)</code>.


This now works in v1.8.3 on R-Forge. Thanks for highlighting it!

x <- data.table(a = 1:3, b = 1:6) 
f <- function(x) {list("hi", "hello")} 
x[ , c("col1", "col2") := f(), by = a][]
#    a b col1  col2
# 1: 1 1   hi hello
# 2: 2 2   hi hello
# 3: 3 3   hi hello
# 4: 1 4   hi hello
# 5: 2 5   hi hello
# 6: 3 6   hi hello

x[ , c("mean", "sum") := list(mean(b), sum(b)), by = a][]
#    a b col1  col2 mean sum
# 1: 1 1   hi hello  2.5   5
# 2: 2 2   hi hello  3.5   7
# 3: 3 3   hi hello  4.5   9
# 4: 1 4   hi hello  2.5   5
# 5: 2 5   hi hello  3.5   7
# 6: 3 6   hi hello  4.5   9 

mynames = c("Name1", "Longer%")
x[ , (mynames) := list(mean(b) * 4, sum(b) * 3), by = a]
#     a b col1  col2 mean sum Name1 Longer%
# 1: 1 1   hi hello  2.5   5    10      15
# 2: 2 2   hi hello  3.5   7    14      21
# 3: 3 3   hi hello  4.5   9    18      27
# 4: 1 4   hi hello  2.5   5    10      15
# 5: 2 5   hi hello  3.5   7    14      21
# 6: 3 6   hi hello  4.5   9    18      27


x[ , get("mynames") := list(mean(b) * 4, sum(b) * 3), by = a][]  # same
#    a b col1  col2 mean sum Name1 Longer%
# 1: 1 1   hi hello  2.5   5    10      15
# 2: 2 2   hi hello  3.5   7    14      21
# 3: 3 3   hi hello  4.5   9    18      27
# 4: 1 4   hi hello  2.5   5    10      15
# 5: 2 5   hi hello  3.5   7    14      21
# 6: 3 6   hi hello  4.5   9    18      27

x[ , eval(mynames) := list(mean(b) * 4, sum(b) * 3), by = a][]   # same
#    a b col1  col2 mean sum Name1 Longer%
# 1: 1 1   hi hello  2.5   5    10      15
# 2: 2 2   hi hello  3.5   7    14      21
# 3: 3 3   hi hello  4.5   9    18      27
# 4: 1 4   hi hello  2.5   5    10      15
# 5: 2 5   hi hello  3.5   7    14      21
# 6: 3 6   hi hello  4.5   9    18      27

Older version using the with argument (we discourage this argument when possible):

x[ , mynames := list(mean(b) * 4, sum(b) * 3), by = a, with = FALSE][] # same
#    a b col1  col2 mean sum Name1 Longer%
# 1: 1 1   hi hello  2.5   5    10      15
# 2: 2 2   hi hello  3.5   7    14      21
# 3: 3 3   hi hello  4.5   9    18      27
# 4: 1 4   hi hello  2.5   5    10      15
# 5: 2 5   hi hello  3.5   7    14      21
# 6: 3 6   hi hello  4.5   9    18      27

The following shorthand notation might be useful. All credit goes to Andrew Brooks, specifically this article.

dt[,`:=`(avg=mean(mpg), med=median(mpg), min=min(mpg)), by=cyl]