R loop over a list

You can do this with tidyverse:

1. First we use str_detect from stringr package (it is in tidyverse) to identify those rows with red ind variables.
2. Then we add 1 and 2 (=row_number) to names and multiple value by -1 to get the negative values.
3. we use bind_rows from dplyr package (it is in tidyverse) to bind to original dataframe.
4. Then we use an ifelse statement to set variables red to 0 in value (assuming the original values are > 0.
5. we use as_tibble() to remove the rownames and finally arrange

library(tidyverse)

df %>% 
  filter(str_detect(variables, "red")) %>% 
  mutate(names = paste0(names, row_number()),
         value = value*-1) %>% 
  bind_rows(df) %>% 
  mutate(value = ifelse(str_detect(variables, "red") &
                          value > 0, 0, value)) %>% 
  as_tibble() %>% 
  arrange(names)

  names   variables           value
  <chr>   <chr>               <dbl>
1 colour  c(red, blue)            0
2 colour  c(yellow, blue)        32
3 colour  c(green, red, pink)     0
4 colour  c(pink, purple)        14
5 colour1 c(red, blue)          -10
6 colour2 c(green, red, pink)   -81
7 shape   c(circle, triangle)     5
8 shape   c(rectangle)           31

structure(list(names = c("colour", "colour", "colour", "colour", 
"shape", "shape"), variables = c("c(red, blue)", "c(yellow, blue)", 
"c(green, red, pink)", "c(pink, purple)", "c(circle, triangle)", 
"c(rectangle)"), value = c(10L, 32L, 81L, 14L, 5L, 31L)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))

Here a solution with Base R only, using TarJae's construction of the data, for a dataframe x:

# Create a vector with the row indeces that contain the target string:
target <- grep( 'red', x[ , 2 ] )
# Put these rows away in a separate data.frame:
stash <- x[ target, ]
# Set the value of the original rows to `0`
x[ target, 3 ] <- 0
# Set the values in the separated rows to their negative value 
stash[ , 3 ] <- stash[ , 3 ] * -1
# Modify `names` as desired:
for( i in 1 : length( stash[ , 1 ] ) ) 
    stash[ i, 1 ] <-  paste( stash[ i, 1 ], i, sep = "" )

# Insert the modified data (supposes that the dataframe is expected 
# to be sorted on the first column):
x <- rbind( x, stash )
x <- x[ order(x[ 1 ] ),  ]

That gives you

> x
     names           variables value
1   colour        c(red, blue)     0
2   colour     c(yellow, blue)    32
3   colour c(green, red, pink)     0
4   colour     c(pink, purple)    14
11 colour1        c(red, blue)   -10
31 colour2 c(green, red, pink)   -81
5    shape c(circle, triangle)     5
6    shape        c(rectangle)    31

This works as well if you replace 'red' with 'blue' or with 'rectangle'. For the extended question in your comment: One could create a vector with the targets (such as c( "red", "triangle" ), then loop over the code above, writing each output into a list. More sample data and information about your desired output would be needed, though. As a first step, you could try:

crit <- c( "rectangle", "red", "blue" )     # vector with target values
y <- list()                                 # initialize receiving list
for( j in 1 : length( crit ) )
{ 
    target <- grep( crit[ j ], x[ , 2 ] )
    stash <- x[ target, ] 
    stash[ , 3 ] <- stash[ , 3 ] * -1
    # Modify `names` as desired:
    for( i in 1 : length( stash[ , 1 ] ) ) 
        stash[ i, 1 ] <-  paste( stash[ i, 1 ], ( 10 * i + j ), sep = "" )

    # write to list element instead of overwriting original dataframe
    y[[ j ]] <- rbind( x, stash )
    y[[ j ]][ target, 3 ] <- 0      # Set the value of the original rows to `0`
    y[[ j ]] <- y[[ j ]][ order( y[[ j ]][ 1 ] ),  ]
    # here, you have a list with individual dataframes for each target value
    
    # you could merge into one joint dataframe with
    z <- Reduce( function( x, y, ... ) merge( x, y, all = TRUE, ... ), y )
}
    

That produces

> z
      names           variables value
1    colour c(green, red, pink)     0
2    colour c(green, red, pink)    81
3    colour     c(pink, purple)    14
4    colour        c(red, blue)     0
5    colour        c(red, blue)    10
6    colour     c(yellow, blue)     0
7    colour     c(yellow, blue)    32
8  colour12        c(red, blue)   -10
9  colour13        c(red, blue)   -10
10 colour22 c(green, red, pink)   -81
11 colour23     c(yellow, blue)   -32
12    shape c(circle, triangle)     5
13    shape        c(rectangle)     0
14    shape        c(rectangle)    31
15  shape11        c(rectangle)   -31

and may be a good start for further work