Multiply Columns by Substrings in R

Question

Suppose I have a dataframe that has multiple components and their properties listed out in multiple columns and I want to run multiple functions against the columns. My approach was to try and base it off the substring in each column header, but I haven't been able to figure out how to do that. Below is an example of the data frame.

Basket   F_Type_1   F_Qty_1   F_P_1   F_Type_2   F_Qty_2   F_P_2 
AAA       Apple     10        2.5     Banana     9         2
BBB       Peach     5         6       Melon      20        5

I essentially want to cbind two new columns to the end of this dataframe that multiplies Qty and P so you get two new columns at the end like below.

F_Total_1   F_Total_2
25          18
30          100

The input is dynamic so on occasion it could be 2 fruits or 10 fruits in certain baskets. But I can figure that portion out, it's moreso trying to figure out how to multiply columns based on the Substrings '1' or '2'.

I appreciate all your help and any other approaches you may have!

Answer 1

We create a function that finds the specific names, and then calculates the rowwise products. The heavy lifter of this function is the mapply function. We add a final step to rename the resultant data.frame .

fun1 <- function(data){
  qty_names <- names(data)[grepl(pattern = "Qty", x = names(data))]

  p_names <- names(data)[grepl(pattern = "P", x = names(data))]

  setNames(
    data.frame(
      mapply(qty_names, p_names, 
             FUN = function(n1, n2) apply(data[c(n1,n2)], 1, prod))),
      paste0('F_Total_', 1:length(p_names)))


}

cbind(dat, fun1(dat))

  Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 F_Total_1 F_Total_2
1    AAA    Apple      10   2.5   Banana       9     2        25        18
2    BBB    Peach       5   6.0    Melon      20     5        30       100

Answer 2

Create data

library(data.table)
df <- fread("
Basket   F_Type_1   F_Qty_1   F_P_1   F_Type_2   F_Qty_2   F_P_2 
AAA       Apple     10        2.5     Banana     9         2
BBB       Peach     5         6       Melon      20        5
")

df
#    Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2
# 1:    AAA    Apple      10   2.5   Banana       9     2
# 2:    BBB    Peach       5   6.0    Melon      20     5

For numbers from 1 to sum(grepl('F_P_', names(df))) , set F_Total_{number} as F_Qty_{number}*F_P_{number}

for(i in seq(sum(grepl('F_P_', names(df)))))
  df[, paste0('F_Total_', i) := Reduce(`*`, .SD)
     , .SDcols = paste0(c('F_Qty_', 'F_P_'), i)]

df
#    Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 F_Total_1 F_Total_2
# 1:    AAA    Apple      10   2.5   Banana       9     2        25        18
# 2:    BBB    Peach       5   6.0    Melon      20     5        30       100

Or in base R with df as a data.frame

for(i in seq(sum(grepl('F_P_', names(df)))))
  df[paste0('F_Total_', i)] <- Reduce(`*`, df[paste0(c('F_Qty_', 'F_P_'), i)])

Answer 3

In base R, you can use the [[]] form to access column with a string, so you can loop easily

df <- read.table(text = "Basket   F_Type_1   F_Qty_1   F_P_1   F_Type_2   F_Qty_2   F_P_2 
AAA       Apple     10        2.5     Banana     9         2
BBB       Peach     5         6       Melon      20        5",header = T)

for(i in 1:2)
{
  df[[paste0("F_Total_",i)]] <- as.numeric(df[[paste0("F_P_",i)]])*as.numeric(df[[paste0("F_Qty_",i)]])
}

  Basket F_Type_1 F_Qty_1 F_P_1 F_Type_2 F_Qty_2 F_P_2 F_Total_1 F_Total_2
1    AAA    Apple      10   2.5   Banana       9     2        25        18
2    BBB    Peach       5   6.0    Melon      20     5        30       100

Answer 4

Here's a method using tidyverse functions for reshaping your data. Basically using tidyr verbs, we reshape your data into a more "tidy" format.

# library(dplyr); library(tidyr)
dd %>% select(Basket, contains("_Qty_"), contains("_P_")) %>% 
  gather("key", "value", -Basket) %>% 
  separate(key, c("F", "Val", "Grp")) %>% 
  group_by(Basket, Grp) %>% 
  spread(Val, value) %>% 
  mutate(Total=P*Qty, GrpN=paste0("Total_", Grp)) %>% 
  ungroup() %>% 
  select(Basket, GrpN, Total) %>% 
  spread(GrpN ,Total)

#   Basket Total_1 Total_2
#   <fct>    <dbl>   <dbl>
# 1 AAA         25      18
# 2 BBB         30     100

Answer 5

setNames(df[grepl("F_Qty",names(df))]*df[grepl("F_P",names(df))],paste0("F_Total_",1:2))

  F_Total_1 F_Total_2
1        25        18
2        30       100

Answer 6

Suggestion: If you could store your data in the long format, it will make future maintenance much cleaner.

Basket     Item     Type    Qty Price    Total
AAA           1    Apple     10   2.5       25
AAA           2    Banana     9     2       18
BBB           1    Peach      5   6.0       30
BBB           2    Melon     20     5      100
BBB           3    Orange    11   2.7     29.7

Then, when you really need the data in the above format, use the data.table package to transpose the data.

library(data.table)
fruits <- data.frame("Basket" = c("AAA", "AAA", "BBB", "BBB", "BBB"),
                 "Item" = c(1,2,1,2,3),
                 "Type" = c("Apple", "Banana", "Peach", "Melon", "Orange"),
                 "Qty" = c(10, 9, 5, 20, 11),
                 "Price" = c(2.5, 2, 6.0, 5, 2.7)
)

fruits$Total <- fruits$Qty * fruits$Price
fruits.New <- data.table::dcast(setDT(fruits),
                            formula = Basket ~ Item,
                            value.var = c("Type", "Price", "Qty", "Total"))

This way, you only have to maintain one formula and not to worry about apply , while giving you the flexibility to have variable column numbers too.

The column name should be relatively easy to fix if it's important.