Replace all NAs with -1 in r with dplyr - r

Question

I'm currently working with the tidyverse in R. After using mice to impute NAs some of the columns still have NAs due to the fact that they are poorly populated to begin with (I believe). As a final check I want to replace all of the remaining NAs with -1. It usually just happens in a single column depending on the dataset. Long story short I'm doing the same process on multiple locations and sometimes Col1 is populated wonderfully in region A, but badly in region B.
Currently I'm doing the following.
Clean.df <- df %>% mutate(
coalesce(Col1 ,-1),
coalesce(Col2, -1),
....)
And I'm doing that for 31 columns which makes me think there must be an easier way. I attempted read the coalesce documentation and tried to replace it with the name of the data frame, no luck.
Thanks for the insight.

Answer 1

Since you didn't provide any data, I am using a sample data frame to show how every NA in a data frame can be replaced with a given value (-1):
library(tidyverse)
# creating example dataset
example_df <- ggplot2::msleep
# looking at NAs
example_df
#> # A tibble: 83 x 11
#> name genus vore order conservation sleep_total sleep_rem sleep_cycle
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Chee~ Acin~ carni Carn~ lc 12.1 NA NA
#> 2 Owl ~ Aotus omni Prim~ <NA> 17 1.8 NA
#> 3 Moun~ Aplo~ herbi Rode~ nt 14.4 2.4 NA
#> 4 Grea~ Blar~ omni Sori~ lc 14.9 2.3 0.133
#> 5 Cow Bos herbi Arti~ domesticated 4 0.7 0.667
#> 6 Thre~ Brad~ herbi Pilo~ <NA> 14.4 2.2 0.767
#> 7 Nort~ Call~ carni Carn~ vu 8.7 1.4 0.383
#> 8 Vesp~ Calo~ <NA> Rode~ <NA> 7 NA NA
#> 9 Dog Canis carni Carn~ domesticated 10.1 2.9 0.333
#> 10 Roe ~ Capr~ herbi Arti~ lc 3 NA NA
#> # ... with 73 more rows, and 3 more variables: awake <dbl>, brainwt <dbl>,
#> # bodywt <dbl>
# replacing NAs with -1
purrr::map_dfr(.x = example_df,
.f = ~ tidyr::replace_na(data = ., -1))
#> # A tibble: 83 x 11
#> name genus vore order conservation sleep_total sleep_rem sleep_cycle
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Chee~ Acin~ carni Carn~ lc 12.1 -1 -1
#> 2 Owl ~ Aotus omni Prim~ -1 17 1.8 -1
#> 3 Moun~ Aplo~ herbi Rode~ nt 14.4 2.4 -1
#> 4 Grea~ Blar~ omni Sori~ lc 14.9 2.3 0.133
#> 5 Cow Bos herbi Arti~ domesticated 4 0.7 0.667
#> 6 Thre~ Brad~ herbi Pilo~ -1 14.4 2.2 0.767
#> 7 Nort~ Call~ carni Carn~ vu 8.7 1.4 0.383
#> 8 Vesp~ Calo~ -1 Rode~ -1 7 -1 -1
#> 9 Dog Canis carni Carn~ domesticated 10.1 2.9 0.333
#> 10 Roe ~ Capr~ herbi Arti~ lc 3 -1 -1
#> # ... with 73 more rows, and 3 more variables: awake <dbl>, brainwt <dbl>,
#> # bodywt <dbl>
Created on 2018-10-10 by the reprex package (v0.2.1)

Answer 2

An alternative to Indrajeet's answer that is pure dplyr. Using Indrajeet's recommendation of ggplot2::msleep:
library(dplyr)
ggplot2::msleep %>%
mutate_at(vars(sleep_rem, sleep_cycle), ~ if_else(is.na(.), -1, .))
# # A tibble: 83 x 11
# name genus vore order conservation sleep_total sleep_rem sleep_cycle awake
# <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 Chee~ Acin~ carni Carn~ lc 12.1 -1 -1 11.9
# 2 Owl ~ Aotus omni Prim~ <NA> 17 1.8 -1 7
# 3 Moun~ Aplo~ herbi Rode~ nt 14.4 2.4 -1 9.6
# 4 Grea~ Blar~ omni Sori~ lc 14.9 2.3 0.133 9.1
# 5 Cow Bos herbi Arti~ domesticated 4 0.7 0.667 20
# 6 Thre~ Brad~ herbi Pilo~ <NA> 14.4 2.2 0.767 9.6
# 7 Nort~ Call~ carni Carn~ vu 8.7 1.4 0.383 15.3
# 8 Vesp~ Calo~ <NA> Rode~ <NA> 7 -1 -1 17
# 9 Dog Canis carni Carn~ domesticated 10.1 2.9 0.333 13.9
# 10 Roe ~ Capr~ herbi Arti~ lc 3 -1 -1 21
# # ... with 73 more rows, and 2 more variables: brainwt <dbl>, bodywt <dbl>
If you want the nuclear option over all columns (numeric and character), then use:
ggplot2::msleep %>%
mutate_all(~ ifelse(is.na(.), -1, .))
# # A tibble: 83 x 11
# name genus vore order conservation sleep_total sleep_rem sleep_cycle awake
# <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 Chee~ Acin~ carni Carn~ lc 12.1 -1 -1 11.9
# 2 Owl ~ Aotus omni Prim~ -1 17 1.8 -1 7
# 3 Moun~ Aplo~ herbi Rode~ nt 14.4 2.4 -1 9.6
# 4 Grea~ Blar~ omni Sori~ lc 14.9 2.3 0.133 9.1
# 5 Cow Bos herbi Arti~ domesticated 4 0.7 0.667 20
# 6 Thre~ Brad~ herbi Pilo~ -1 14.4 2.2 0.767 9.6
# 7 Nort~ Call~ carni Carn~ vu 8.7 1.4 0.383 15.3
# 8 Vesp~ Calo~ -1 Rode~ -1 7 -1 -1 17
# 9 Dog Canis carni Carn~ domesticated 10.1 2.9 0.333 13.9
# 10 Roe ~ Capr~ herbi Arti~ lc 3 -1 -1 21
# # ... with 73 more rows, and 2 more variables: brainwt <dbl>, bodywt <dbl>
Note that I'm no longer using dplyr::if_else, since the function needs to be versatile with (or ignorant of) the different types. Since base::ifelse will happily/silently(/sloppily?) convert, we're good.