Group by using base R

by

Here’s another base R solution using by

do.call(rbind, by(df, df[, 1:3], 
                  function(x) cbind(x[1, 1:3], sum(x$sales), mean(x$units))))

Or using “split\apply\combine” theory

t(sapply(split(df, df[, 1:3], drop = TRUE), 
                   function(x) c(sumSales = sum(x$sales), meanUnits = mean(x$units))))

Or similarly

do.call(rbind, lapply(split(df, df[, 1:3], drop = TRUE), 
                     function(x) c(sumSales = sum(x$sales), meanUnits = mean(x$units))))

Edit: it seems like df is of class data.table (but you for some reason asked for base R solution only), here’s how you would do it with your data.table object

df[, .(sumSales = sum(sales), meanUnits = mean(units)), keyby = .(year, quarter, Channel)]
#     year quarter Channel sumSales meanUnits
#  1: 2013      Q1     AAA     4855      15.0
#  2: 2013      Q1     BBB     2231      12.0
#  3: 2013      Q2     AAA     4004      17.5
#  4: 2013      Q2     BBB     2057      23.0
#  5: 2013      Q3     AAA     2558      21.0
#  6: 2013      Q3     BBB     4807      21.0
#  7: 2013      Q4     AAA     4291      12.0
#  8: 2013      Q4     BBB     1128      25.0
#  9: 2014      Q1     AAA     2169      23.0
# 10: 2014      Q1     CCC     3912      16.5
# 11: 2014      Q2     AAA     2613      21.0
# 12: 2014      Q2     BBB     1533      11.0
# 13: 2014      Q2     CCC     2114      23.0
# 14: 2014      Q3     BBB     5219      13.0
# 15: 2014      Q3     CCC     1614      15.0
# 16: 2014      Q4     AAA     2695      14.0
# 17: 2014      Q4     BBB     4177      15.0

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