fruit.R

library(tidyverse)

fruit_prices <- tibble(
  Month = c("Jan", "Feb", "Mar"),
  Apples = c(1.79, 1.82, 1.8),
  Pears = c(1.29, 1.34, 1.5),
  Oranges = c(.89, .92, .85)
)

# A typical mutate, create a new column that is the sum of three columns
fruit_prices %>% 
  mutate(Sum = Apples + Pears + Oranges)

# # A tibble: 3 × 5
#   Month Apples Pears Oranges   Sum
#   <chr>  <dbl> <dbl>   <dbl> <dbl>
# 1 Jan     1.79  1.29    0.89  3.97
# 2 Feb     1.82  1.34    0.92  4.08
# 3 Mar     1.8   1.5     0.85  4.15

# Mutate with rowwise + c_across to select individual columns by name
fruit_prices %>% 
  rowwise() %>% 
  mutate(Sum = sum(c_across(c("Apples", "Pears", "Oranges")))) %>% 
  ungroup()

# # A tibble: 3 × 5
#   Month Apples Pears Oranges   Sum
#   <chr>  <dbl> <dbl>   <dbl> <dbl>
# 1 Jan     1.79  1.29    0.89  3.97
# 2 Feb     1.82  1.34    0.92  4.08
# 3 Mar     1.8   1.5     0.85  4.15

# Same as above but uses the `:` shorthand, selects all columns between Apples and Oranges
fruit_prices %>% 
  rowwise() %>% 
  mutate(Sum = sum(c_across(Apples:Oranges))) %>% 
  ungroup()

# # A tibble: 3 × 5
#   Month Apples Pears Oranges   Sum
#   <chr>  <dbl> <dbl>   <dbl> <dbl>
# 1 Jan     1.79  1.29    0.89  3.97
# 2 Feb     1.82  1.34    0.92  4.08
# 3 Mar     1.8   1.5     0.85  4.15

# Sum only the Apples and Oranges columns
fruit_prices %>% 
  rowwise() %>% 
  mutate(`A+O Sum` = sum(c_across(c("Apples", "Oranges")))) %>% 
  ungroup()

# # A tibble: 3 × 5
# Month Apples Pears Oranges `A+O Sum`
#   <chr>  <dbl> <dbl>   <dbl>     <dbl>
# 1 Jan     1.79  1.29    0.89      2.68
# 2 Feb     1.82  1.34    0.92      2.74
# 3 Mar     1.8   1.5     0.85      2.65

# Using contains from tidyselect, only ums columns that contain "p"
fruit_prices %>% 
  rowwise() %>% 
  mutate(P_Sum = sum(c_across(contains("p")))) %>% 
  ungroup()

# # A tibble: 3 × 5
#   Month Apples Pears Oranges P_Sum
#   <chr>  <dbl> <dbl>   <dbl> <dbl>
# 1 Jan     1.79  1.29    0.89  3.08
# 2 Feb     1.82  1.34    0.92  3.16
# 3 Mar     1.8   1.5     0.85  3.3 

# Drop Month and sum all the remaining columns
fruit_prices %>% 
  select(-Month) %>% 
  rowwise() %>% 
  mutate(Sum = sum(c_across(everything()))) %>% 
  ungroup()

# # A tibble: 3 × 4
#   Apples Pears Oranges   Sum
#    <dbl> <dbl>   <dbl> <dbl>
# 1   1.79  1.29    0.89  3.97
# 2   1.82  1.34    0.92  4.08
# 3   1.8   1.5     0.85  4.15

# Only sum the numeric columns
fruit_prices %>% 
  rowwise() %>% 
  mutate(Sum = sum(c_across(where(is.numeric)))) %>% 
  ungroup()

# # A tibble: 3 × 5
#   Month Apples Pears Oranges   Sum
#   <chr>  <dbl> <dbl>   <dbl> <dbl>
# 1 Jan     1.79  1.29    0.89  3.97
# 2 Feb     1.82  1.34    0.92  4.08
# 3 Mar     1.8   1.5     0.85  4.15