Multiplying similar named merged columns in R - r

Question

I have two dfs : df1 and df2 where the column names are dates. When I join the two df's I get columns like
date1.x, date1.y, date2.x, date2.y, date3.x, date3.y, date4.x, date4.y...........
I want to create new columns which have values which are multiplication of date1.x and date1.y and similarly for other date pairs as well.

Answer 1

df <- data.frame(id=11:13, date1.x=1:3, date2.x=4:6, date1.y=7:9, date2.y=10:12)
df
# id date1.x date2.x date1.y date2.y
# 1 11 1 4 7 10
# 2 12 2 5 8 11
# 3 13 3 6 9 12
grep("^date.*\\.x$", colnames(df), value = TRUE)
# [1] "date1.x" "date2.x"
datenms <- grep("^date.*\\.x$", colnames(df), value = TRUE)
### make sure all of our 'date#.x' columns have matching 'date#.y' columns
datenms <- datenms[ gsub("x$", "y", datenms) %in% colnames(df) ]
datenms
# [1] "date1.x" "date2.x"
subset(df, select = datenms)
# date1.x date2.x
# 1 1 4
# 2 2 5
# 3 3 6
subset(df, select = gsub("x$", "y", datenms))
# date1.y date2.y
# 1 7 10
# 2 8 11
# 3 9 12
subset(df, select = datenms) * subset(df, select = gsub("x$", "y", datenms))
# date1.x date2.x
# 1 7 40
# 2 16 55
# 3 27 72

Answer 2

There are a number of ways to do this, but I suggest that it is a good practice to get used to transforming your data into a format that is easy to work with. The first answer showed you one way to do what you want without transforming your data. My answer will show you how to transform the data so that calculation (this one and others) are easy, and then how to perform the calculation once the data is tidy.
Making your data tidy helps to perform easier aggregations, to graph results, to perform feature engineering for models, etc.
library(dplyr)
library(tidyr)
df <- data.frame(id=11:13, date1.x=1:3, date2.x=4:6, date1.y=7:9, date2.y=10:12)
df
# id date1.x date2.x date1.y date2.y
# 1 11 1 4 7 10
# 2 12 2 5 8 11
# 3 13 3 6 9 12
# Convert the data to a tidy format that is easier for computers to calculate
tidy_df <- df %>%
pivot_longer(
cols = starts_with("date"), # We are tidying any column starting with date
names_to = c("date_num","date_source"), # creating two columns for names
values_to = c("date_value"), # creating one column for values
names_prefix = "date", # removing the "date" prefix
names_sep = "\\." # splitting the names on the period `.`
)
tidy_df
# id date_num date_source date_value
# <int> <chr> <chr> <int>
# 1 11 1 x 1
# 2 11 2 x 4
# 3 11 1 y 7
# 4 11 2 y 10
# 5 12 1 x 2
# 6 12 2 x 5
# 7 12 1 y 8
# 8 12 2 y 11
# 9 13 1 x 3
# 10 13 2 x 6
# 11 13 1 y 9
# 12 13 2 y 12
# Now that the data is tidy we can do easier dataframe grouping and aggregation
tidy_df %>%
group_by(id,date_num) %>%
summarise(date_value_mult = prod(date_value)) %>%
ungroup()
# id date_num date_value_mult
# <int> <chr> <dbl>
# 1 11 1 7
# 2 11 2 40
# 3 12 1 16
# 4 12 2 55
# 5 13 1 27
# 6 13 2 72
# If/When you eventually want the data in a more human readable format you can
# pivot the data back into a human readable format. This is likely after all
# computer calculations are done and you want to present the data. For storing
# the data (such as in a database) you would not need/want this step.
tidy_df %>%
group_by(id,date_num) %>%
summarise(date_value_mult = prod(date_value)) %>%
ungroup() %>%
pivot_wider(
names_from = date_num,
values_from = date_value_mult,
names_prefix = "date"
)
# id date1 date2
# <int> <dbl> <dbl>
# 1 11 7 40
# 2 12 16 55
# 3 13 27 72