Multiply pairs of columns using dplyr in R - r

Question

I have a dataframe with crime data and associated "prices", organized by country and year (although I don't think this is important here). Here is a subset of my data:
> crime
# A tibble: 8 x 8
iso year theft robbery burglary theft_price robbery_price burglary_price
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ALB 2003 3694 199 874 32.9 115 49.3
2 ALB 2004 3694 199 874 38.2 134 57.3
3 ALB 2005 3694 199 874 42.8 150 64.2
4 ALB 2006 3450 164 779 47.0 165 70.5
5 AUS 2003 722334 14634 586266 408.4 1427 612.4
6 AUS 2004 636717 14634 512551 481.3 1683 721.2
7 AUS 2005 598700 14634 468558 536.7 1877 804.5
8 AUS 2006 594111 14634 433974 564.8 1973 846.5
I want to create new columns that contain the product of each crime type with its price, so theft x theft_price = theft_prod, etc. In my actual dataset I have more crime types so I need something that is scalable to more variables than this subset contains.
I like the syntax of the dplyr package, so I to use something like this, but I cannot find the solution. I think it is not possible to reference other variables than the ones in vars(). Correct?
crime %>%
mutate_at(vars(theft, robbery, burglary),
funs(prod = . * ????))
Thanks.

Answer 1

Use dplyr and tidyr:
library(dplyr); library(tidyr);
df %>%
gather(crime, value, -iso, -year) %>%
separate(crime, c('crime', 'type'), sep='_', fill = 'right') %>%
replace_na(list(type = 'amount')) %>%
spread(type, value) %>%
transmute(
iso = iso, year = year,
crime = paste(crime, 'prod', sep = '_'),
prod = amount * price
) %>%
spread(crime, prod)
# iso year burglary_prod robbery_prod theft_prod
#1 ALB 2003 43088.2 22885 121532.6
#2 ALB 2004 50080.2 26666 141110.8
#3 ALB 2005 56110.8 29850 158103.2
#4 ALB 2006 54919.5 27060 162150.0
#5 AUS 2003 359029298.4 20882718 295001205.6
#6 AUS 2004 369651781.2 24629022 306451892.1
#7 AUS 2005 376954911.0 27468018 321322290.0
#8 AUS 2006 367358991.0 28872882 335553892.8
Another option without data reshaping, assuming the columns' names follow the crime_price convention:
library(tidyverse)
# find out the crimes columns
crimes = grep('^(?!.*_price$)', names(df)[-c(1,2)], perl = T, value = T)
# construct the crimes prices columns
crimes_prices = paste(crimes, 'price', sep = '_')
crimes_prod = paste(crimes, 'prod', sep = '_')
# loop through crime and crime price columns and multiply them
map2(crimes, crimes_prices, ~ df[[.x]] * df[[.y]]) %>%
set_names(crimes_prod) %>%
as_tibble() %>%
bind_cols(select(df, iso, year))
# A tibble: 8 x 5
# theft_prod robbery_prod burglary_prod iso year
# <dbl> <int> <dbl> <fct> <int>
#1 121533. 22885 43088. ALB 2003
#2 141111. 26666 50080. ALB 2004
#3 158103. 29850 56111. ALB 2005
#4 162150 27060 54920. ALB 2006
#5 295001206. 20882718 359029298. AUS 2003
#6 306451892. 24629022 369651781. AUS 2004
#7 321322290 27468018 376954911 AUS 2005
#8 335553893. 28872882 367358991 AUS 2006

Answer 2

Doing this kind of manipulation in the tidyverse is best done by making sure your data is tidy by reshaping it. A purrr approach is also possible but is likely reliant on the order of your columns, which might not always be reliable. Instead, you can do the following:
gather up all your measure columns
mutate a new column measure_type that indicates whether it is a count or price, and remove the _price from crime_type. Now we have separate columns for the type of crime and the metric we are using for that crime. Each row is a single iso-year-crime-metric combination.
spread the crime types back out so now we have separate count and price columns for all crimes, and then multiply with mutate.
(optional) if you want to put it back in your wide format, we just gather up count and price and our new product column, unite to combine with the crime type and spread back out.
library(tidyverse)
tbl <- read_table2(
"iso year theft robbery burglary theft_price robbery_price burglary_price
ALB 2003 3694 199 874 32.9 115 49.3
ALB 2004 3694 199 874 38.2 134 57.3
ALB 2005 3694 199 874 42.8 150 64.2
ALB 2006 3450 164 779 47.0 165 70.5
AUS 2003 722334 14634 586266 408.4 1427 612.4
AUS 2004 636717 14634 512551 481.3 1683 721.2
AUS 2005 598700 14634 468558 536.7 1877 804.5
AUS 2006 594111 14634 433974 564.8 1973 846.5"
)
tidy_tbl <- tbl %>%
gather(crime_type, measure, -iso, - year) %>%
mutate(
measure_type = if_else(str_detect(crime_type, "_price$"), "price", "count"),
crime_type = str_remove(crime_type, "_price")
) %>%
spread(measure_type, measure) %>%
mutate(product = count * price)
tidy_tbl
#> # A tibble: 24 x 6
#> iso year crime_type count price product
#> <chr> <int> <chr> <dbl> <dbl> <dbl>
#> 1 ALB 2003 burglary 874 49.3 43088.
#> 2 ALB 2003 robbery 199 115 22885
#> 3 ALB 2003 theft 3694 32.9 121533.
#> 4 ALB 2004 burglary 874 57.3 50080.
#> 5 ALB 2004 robbery 199 134 26666
#> 6 ALB 2004 theft 3694 38.2 141111.
#> 7 ALB 2005 burglary 874 64.2 56111.
#> 8 ALB 2005 robbery 199 150 29850
#> 9 ALB 2005 theft 3694 42.8 158103.
#> 10 ALB 2006 burglary 779 70.5 54920.
#> # ... with 14 more rows
tidy_tbl %>%
gather(measure_type, measure, count:product) %>%
unite("colname", crime_type, measure_type) %>%
spread(colname, measure)
#> # A tibble: 8 x 11
#> iso year burglary_count burglary_price burglary_product robbery_count
#> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 ALB 2003 874 49.3 43088. 199
#> 2 ALB 2004 874 57.3 50080. 199
#> 3 ALB 2005 874 64.2 56111. 199
#> 4 ALB 2006 779 70.5 54920. 164
#> 5 AUS 2003 586266 612. 359029298. 14634
#> 6 AUS 2004 512551 721. 369651781. 14634
#> 7 AUS 2005 468558 804. 376954911 14634
#> 8 AUS 2006 433974 846. 367358991 14634
#> # ... with 5 more variables: robbery_price <dbl>, robbery_product <dbl>,
#> # theft_count <dbl>, theft_price <dbl>, theft_product <dbl>
Created on 2018-08-15 by the reprex package (v0.2.0).