Iteratively create columns based on grouped variables - r

Question

I've got some data (below) where I want to iteratively add columns based on sums of current columns by some grouping variable, and I want to name the columns a pasted value of the current name + "_tot". I'm thinking a combination of dplyr and lapply is the way to go about it but I can't get the structure correct.
set.seed(1234)
data <- data.frame(
biz = sample(c("telco","shipping","tech"), 50, replace = TRUE),
region = sample(c("mideast","americas"), 50, replace = TRUE),
june = sample(1:50, 50, replace=TRUE),
july = sample(100:150, 50, replace=TRUE)
)
So, what I want to do is 1) group this data by "region", then add a new column for each of the following months that is the sum of that month's value (in the real dataframe, there are many periods that follow).
Basically, I want to apply this function
library(dplyr)
data %>% group_by(region) %>% mutate(june_tot = sum(june))
across every month, without having to specify "june" or "july". My initial take:
testfun <- function(df, col) {
name <- paste(col, "_tot", sep="")
data2 <- df %>% group_by(region) %>% summarise(name=sum(col))
return(data2)
}
but lapplying this doesn't work, because I have to specify the columns to call into the initial function. Just removing the "col" argument from the initial function doesn't work either, of course.
Any ideas how to lapply this sort of argument?

Answer 1

Here are possible solutions to your problems using dplyr (first, since that is what you tried), and followed by data.table as well as base R solutions:
dplyr:
cols <- lapply(names(data)[-(1:2)], as.name)
names(cols) <- paste0(names(data)[-(1:2)], "_tot")
data %>% group_by(region) %>% mutate_each_q(funs(sum), cols)
Assumes every column but the first two are monthly data. An explanation by line:
we use as.name and lapply to generate a list of the columns names we want to mutate as symbols
we give the new names we want (i.e. month_tot) to the list of symbols from 1.
we use the mutate_each_q (known as mutate_each_ in dplyr 0.3.0.2) to apply sum to the list of expressions we created in 1. and 2.
This is the (sample) result:
Source: local data frame [50 x 6]
Groups: region
biz region june july june_tot july_tot
1 shipping mideast 17 124 780 3339
2 telco americas 11 101 465 2901
3 telco mideast 27 131 780 3339
4 tech americas 24 135 465 2901
... rows omitted
data.table:
new.names <- paste0(tail(names(data), 2L), "_tot") # Make new names
data.table(data)[,
(new.names):=lapply(.SD, sum), # `lapply` `sum` to the selected columns (those in .SD), and assign to `new.names` columns
by=region, .SDcols=-1 # group by `region`, and exclude first column from `.SD` (note `region` is excluded as well by reason of being in `by`
][] # extra `[]` just to force printing
Here, similar logic, except we use the special .SD object that represents every column in the data.table that we are not grouping by.
base:
do.call(
cbind,
list(
data,
setNames(
lapply(data[-(1:2)], function(x) ave(x, data$region, FUN=sum)),
paste0(names(data[-(1:2)]), "_tot")
) ) )
Here we use ave to compute the per region sums, use lapply to apply ave to each column, and use do.call(cbind, ...) to reconstruct the final data frame.

Answer 2

Try:
> for(i in 3:4) print(tapply(data[[i]], data$region, sum))
americas mideast
563 768
americas mideast
2538 3802
You can get all outputs in a list if you want.

Answer 3

Restructuring the data works well for this.
require(tidyr)
# wide to long
d2 <- gather(data = data,key = month,value = monthval,-c(biz,region))
# get totals and rename month
month_tots <- aggregate(x = list(total = d2$monthval),by = list(region = d2$region,month = d2$month),sum)
month_tots$month <- paste0(month_tots$month,'_tot')
# long to wide
month_tots <- spread(data = month_tots,key = month,value = total)
# recombine
merge(data,month_tots,by = 'region',all.x = T)