Replace all 0 values with 1 in dataframe with multiple rows and columns at once

Question

How can I replace all NA, NaN, 0, 0.00, 0.000 values with 1 in dataframe with multiple rows and columns at once? Thanks.

An example df:

a = c(233, 0, NA, 3455)
b = c(23, 0.000, NA, 345)
c = c(223, 0.00, NaN, 30055)

df = cbind.data.frame(a,b,c)

It's very helpful to provide data if you'd like help more quickly. That said, check out dplyr::mutate_all , as I think that should provide you a quick solution. — zack
– zack, Commented Jun 22, 2018 at 20:41
What have you tried so far? Can you provide an example input dataset and desired output dataset? Can you be more specific about "etc"? — wibeasley
– wibeasley, Commented Jun 22, 2018 at 20:43
I have updated my question with a simple example. I can replace column-wise but my actual dataframe has hundreds of columns and thousands of rows! — ip2018
– ip2018, Commented Jun 22, 2018 at 20:45

wibeasley · Accepted Answer · 2018-06-25 22:54:04Z

4

I like @zack's suggested dplyr::mutate_all(). Another option is purrr::map_df().

scrub <- function( x ) {
  x <- dplyr::if_else(dplyr::near(x, 0), 1, x)
  x <- dplyr::coalesce(x, 1)
  x
}

# Option 1 (These four lines are equivalent):
df %>%                                  # This needs `library(magrittr)` 
  purrr::map_df(scrub)
purrr::map_df(df, scrub)
purrr::map_df(df, ~scrub(.))
purrr::map_df(df, function(x) scrub(x))

# Option 2, suggested by @zack in the comments:
dplyr::mutate_all(df, scrub)

Here is the result from purrr::map_df(), which is a tibble. The dplyr::mutate_all() returns a data.frame.

# A tibble: 4 x 3
      a     b     c
  <dbl> <dbl> <dbl>
1   233    23   223
2     1     1     1
3     1     1     1
4  3455   345 30055

edited Jun 25, 2018 at 22:54

answered Jun 22, 2018 at 21:01

wibeasley

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1 Comment

wibeasley Over a year ago

@zack, for the sake of completeness, I included your purrr::map_df() suggestion from three days ago. I'm happy to remove it if you submit a separate answer.

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