R table function: how to sum instead of counting? [duplicate]

Answer 1

Another possibility consists in using the aggregate() function:

profit_dat <- aggregate(Profit ~ Category + Mode, data=df, sum)
#> profit_dat
#  Category Mode Profit
#1        X    K     36
#2        Y    K     17
#3        X    L     11
#4        Y    L     26
#5        Z    L      8
#6        X    M     11
#7        Y    M     28
#8        Z    M     15

Answer 2

We can use xtabs from base R. By default, the xtabs gets the sum

xtabs(Profit~Category+Mode, df)
#           Mode
#Category  K  L  M
#       X 36 11 11
#       Y 17 26 28
#       Z  0  8 15

Or another base R option that is more flexible to apply different FUN is tapply.

with(df, tapply(Profit, list(Category, Mode), FUN=sum))
#  K  L  M
#X 36 11 11
#Y 17 26 28
#Z NA  8 15

---

Or we can use dcast to convert from 'long' to 'wide' format. It is more flexible as we can specify the fun.aggregate to sum, mean, median etc.

library(reshape2)
dcast(df, Category~Mode, value.var='Profit', sum)
# Category  K  L  M
#1        X 36 11 11
#2        Y 17 26 28
#3        Z  0  8 15

---

If you need it in the 'long' format, here is one option with data.table. We convert the 'data.frame' to 'data.table' (setDT(df)), grouped by 'Category' and 'Mode', we get the sum of 'Profit'.

library(data.table)
setDT(df)[, list(Profit= sum(Profit)) , by = .(Category, Mode)]

Answer 3

I prefer using dplyr (and ggplot2) for most data analysis:

library(dplyr)

group_by(df, Category, Mode) %>%
  summarise(sum = sum, count=n())

https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html