How to change an element of the original dataframe using dplyr

Question

I have a tibble:

library('tibble')

df <- tibble(
  ticker = c("first", "second", "third"),
  status = c(T,T,T)
)

> df
# A tibble: 3 x 2
  ticker status
 1 first  TRUE  
 2 second TRUE  
 3 third  TRUE

I want to change the status of 'first' to FALSE in the original df.

But when I run this code:

library('dplyr')

df %<>%
  filter(ticker=='first') %>%
  mutate(
   status = F
  )

I get my original df only with the first row

> df
# A tibble: 1 x 2
  ticker status
1 first  FALSE

Instead of:

> df
# A tibble: 3 x 2
  ticker status
1 first  FALSE 
2 second TRUE  
3 third  TRUE

Other solutions here: https://stackoverflow.com/questions/27909000/set-certain-values-to-na-with-dplyr ... — RLave, May 22 '19 at 13:13
`df %>% mutate(status = replace(status, ticker == "first", FALSE))` or just `df %>% mutate(status = ticker != "first")` — Ronak Shah, May 22 '19 at 13:14

score 2 · Answer 1 · answered May 22 '19 at 13:13

2

Using base R

 within(df, status[ticker == "first"] <- FALSE )

answered May 22 '19 at 13:13

Humpelstielzchen

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using base R it is much simpler df[df$ticker=='first', 'status'] <- F – Oktu May 23 '19 at 09:15
Sure, but if you have longer names for your dataframe it get's clearer and easier using within. That's the whole point of `within`. – Humpelstielzchen May 23 '19 at 09:21

Scipione Sarlo · Accepted Answer · 2019-05-22T13:18:16.967

Using the function case_when:

library('tibble')

df <- tibble(
    ticker = c("first", "second", "third"),
    status = c(T,T,T)
)

library(tidyverse)

df %>% 
    mutate(status = case_when(
        ticker == "first" ~ F,
        TRUE ~ T
    ))

This is the output:

# A tibble: 3 x 3
  ticker status `case_when(ticker == "first" ~ F, TRUE ~ T)`
  <chr>  <lgl>  <lgl>                                       
1 first  TRUE   FALSE                                       
2 second TRUE   TRUE                                        
3 third  TRUE   TRUE

How to change an element of the original dataframe using dplyr

2 Answers2