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Yomi.blaze93
Yomi.blaze93

Reputation: 425

format all numeric columns with comma to seprate the v

I have a data table below.

how do I format all columns with variables with comma values?

I know of the scales package but if I use the scales package I won't be able to use the table for some calculating operations any longer

i want something that will still retain the table format type as numeric.

Customers telex manay players
babs 3434323424 937387573 96222221
bobs 53545322 758464938 122134
pint 43 7453537384 223444
red 35435 624353 345654
yello 4567 44 334

I want the output to look like this table below

Customers telex manay players
babs 3,434,323,424 937,387,573 96,222,221
bobs 53,545,322 758,464,938 122,134
pint 43 7,453,537,384 223,444
red 35,435 624,353 345,654
yello 4,567 44 334

Upvotes: 3

Views: 2258

Answers (3)

Or Gadish
Or Gadish

Reputation: 157

You can use the scales::label_comma() function. Note that the format for using this is scales::label_comma()(x) since a label function is generated and then you call it on your numerical vector.

As noted in similar answers above, this converts the column type to character. To convert back to number, you can use readr::parse_number.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

df <- tibble(
  Customers = c("babs", "bobs", "pint", "red", "yellow"),
  telex = c(3434323424, 53545322, 43, 35435, 4567),
  manay = c(937387573, 758464938, 7453537384, 624353, 44),
  players = c(96222221, 122134, 223444, 345654, 334)
)
df
#> # A tibble: 5 x 4
#>   Customers      telex      manay  players
#>   <chr>          <dbl>      <dbl>    <dbl>
#> 1 babs      3434323424  937387573 96222221
#> 2 bobs        53545322  758464938   122134
#> 3 pint              43 7453537384   223444
#> 4 red            35435     624353   345654
#> 5 yellow          4567         44      334

df_with_comma <- df %>% 
  mutate(across(telex:players, scales::label_comma()))
df_with_comma
#> # A tibble: 5 x 4
#>   Customers telex         manay         players   
#>   <chr>     <chr>         <chr>         <chr>     
#> 1 babs      3,434,323,424 937,387,573   96,222,221
#> 2 bobs      53,545,322    758,464,938   122,134   
#> 3 pint      43            7,453,537,384 223,444   
#> 4 red       35,435        624,353       345,654   
#> 5 yellow    4,567         44            334

df_reverted <- df_with_comma %>% 
  mutate(across(telex:players, readr::parse_number))
df_reverted
#> # A tibble: 5 x 4
#>   Customers      telex      manay  players
#>   <chr>          <dbl>      <dbl>    <dbl>
#> 1 babs      3434323424  937387573 96222221
#> 2 bobs        53545322  758464938   122134
#> 3 pint              43 7453537384   223444
#> 4 red            35435     624353   345654
#> 5 yellow          4567         44      334

Created on 2022-06-07 by the reprex package (v2.0.1)

Upvotes: 2

maRvin
maRvin

Reputation: 335

format() does this.

library(tidyverse)

df <- tibble(
  Customers = c("babs", "bobs", "pint", "red", "yellow"),
  telex = c(3434323424, 53545322, 43, 35435, 4567),
  manay = c(937387573, 758464938, 7453537384, 624353, 44),
  players = c(96222221, 122134, 223444, 345654, 334)
)

df <- df %>%
  mutate(across(telex:players, ~ format(., big.mark = ",", scientific = F)))

But note that your data changes to type "character".

Upvotes: 3

Matt
Matt

Reputation: 7385

You can use formatC:

library(dplyr)

df <- data.table::fread('Customers  telex   manay   players
babs    3434323424  937387573   96222221
bobs    53545322    758464938   122134
pint    43  7453537384  223444
red     35435   624353  345654
yello   4567    44  334')

df %>% 
  mutate(across(telex:players, ~as.numeric(.))) %>% 
  mutate(across(telex:players, ~formatC(., format="d", big.mark=",")))

Which gives:

  Customers      telex       manay    players
1:      babs         NA 937,387,573 96,222,221
2:      bobs 53,545,322 758,464,938    122,134
3:      pint         43          NA    223,444
4:       red     35,435     624,353    345,654
5:     yello      4,567          44        334

Upvotes: 0

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