Add a column to data frame resulting from the merging of some values from other columns with R

I have a data frame with many columns, among which I have the following:

COLUMN_1   COLUMN_2   COLUMN_3
red        blue       green
none       blue       none
red        none       green
red        none       none
none       none       none

I'd like to add a fourth column called COLORS based on a boolean which gives 'True' for every value 'red' of COLUMN_1, 'blue' of COLUMN_2, or 'green' of COLUMN_3, and gives 'False' if the value of COLUMN_1, COLUMN_2 and COLUMN_3 is always 'none'. Like this:

   COLUMN_1   COLUMN_2   COLUMN_3    COLORS
    red        blue       green      True
    none       blue       none       True
    red        none       green      True
    red        none       none       True
    none       none       none       False

A column without a boolean would also work:

COLUMN_1   COLUMN_2   COLUMN_3   COLORS
red        blue       green      Color
none       blue       none       Color
red        none       green      Color
red        none       none       Color
none       none       none       No color

library(dplyr) 

df %>%  
  mutate(
    COLORS = case_when(
      if_all(contains("COLUMN"), ~ .x == "none") ~ "No color", 
      TRUE ~ "Color"
    ))

# A tibble: 5 × 4
  COLUMN_1 COLUMN_2 COLUMN_3 COLORS  
  <chr>    <chr>    <chr>    <chr>   
1 red      blue     green    Color   
2 none     blue     none     Color   
3 red      none     green    Color   
4 red      none     none     Color   
5 none     none     none     No color

One option could be:

df %>%
 rowwise() %>%
 mutate(COLORS = any(across(everything()) == c("red", "blue", "green")))

  COLUMN_1 COLUMN_2 COLUMN_3 COLORS
  <chr>    <chr>    <chr>    <lgl> 
1 red      blue     green    TRUE  
2 none     blue     none     TRUE  
3 red      none     green    TRUE  
4 red      none     none     TRUE  
5 none     none     none     FALSE

If a given column would have a different color as expected, the it would return FALSE:

  COLUMN_1 COLUMN_2 COLUMN_3 COLORS
  <chr>    <chr>    <chr>    <lgl> 
1 red      blue     green    TRUE  
2 none     blue     none     TRUE  
3 red      none     green    TRUE  
4 red      none     none     TRUE  
5 blue     none     none     FALSE

It is not an efficient answer but an alternate

df2 <- df %>% 
  mutate(across(c(COLUMN_1,COLUMN_2,COLUMN_3), ~ ifelse(.x=='none', 1, 0), .names = 'n_{.col}'),
         new=rowSums(across(c(n_COLUMN_1,n_COLUMN_2,n_COLUMN_3))),
         COLORS=ifelse(new==3,'no color', 'color')) %>% 
  select(-contains('n_c'), -new)

^{Created on 2023-06-08 with reprex v2.0.2}

# A tibble: 5 × 4
  COLUMN_1 COLUMN_2 COLUMN_3 COLORS  
  <chr>    <chr>    <chr>    <chr>   
1 red      blue     green    color   
2 none     blue     none     color   
3 red      none     green    color   
4 red      none     none     color   
5 none     none     none     no color

We can try rowSums + col like below

transform(
    df,
    COLORS = rowSums(df == c("red", "blue", "green")[col(df)]) > 0
)

which gives

  COLUMN_1 COLUMN_2 COLUMN_3 COLORS
1      red     blue    green   TRUE
2     none     blue     none   TRUE
3      red     none    green   TRUE
4      red     none     none   TRUE
5     none     none     none  FALSE

library(dplyr)

df |>
  mutate(COLORS = rowSums(pick(starts_with("COLUMN")) != "none") > 0)

Note: this will return TRUE if there is a color "yellow".