I want to change the values of a column, which to be called by it's index, using pipe -
require(dplyr)
mtcars[, 1] = 4 * mtcars[,2]
I was wondering if above calculation can be done using pipe
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Not sure it is possible. Have a read at [this](https://stackoverflow.com/questions/32618744/dplyr-how-to-reference-columns-by-column-index-rather-than-column-name-using-mu) – Sotos Feb 25 '20 at 15:59
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You can do something like `mtcars[, 1] <- mtcars %>% pull(1) %>% multiply_by(2)`. – tmfmnk Feb 25 '20 at 16:07
2 Answers
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You can use magrittr and %<>%:
mtcars -> df1
library(dplyr)
library(magrittr)
df1 %<>%
mutate_at(vars(1), list(~ df1[[2]] * 4))
#> # A tibble: 32 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 24 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 24 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 16 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 24 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 32 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 24 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 32 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 16 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 16 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 24 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ... with 22 more rows
M--
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This is quite awesome. However when I change df1[[2]] to .[[2]] I get wired result. Any idea how to fix that? – Bogaso Feb 25 '20 at 16:42
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@Bogaso within `mutate_at` when you use **`.`** it refers to the `vars`, not the original dataset. When you use `.[[2]]` you are actually referring to the second row of `vars(1)` (i.e. `df1[1,2]`). – M-- Feb 25 '20 at 16:53
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Thanks. So if I need to refer the original dataset under this framework, calling by name is only way? I want to avoid calling by name to keep the process tidy (this is the fundamental idea working with pipe) – Bogaso Feb 25 '20 at 17:14
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1@M-- If you have a `group_by`, then do you use `df1[[2]]`. I tried with `.data[[2]]`, but it is not working because the `.data` seems to be the column in `mutate_all` – akrun Feb 25 '20 at 18:09
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@Bogaso keeping tidy and readability is important, but performance is also something you should think of. Glad that the other answer provides what you need. It's actually a smart one. – M-- Feb 25 '20 at 21:31
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@akrun if I wanted to stick to this along with `group_by`, I would've probably explored `purrr` solutions. makes sense? p.s. (this whole question is a bit unclear, not in terms of what it wants, but why it wants it that way) – M-- Feb 25 '20 at 21:35
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1@M-- But, it can be done with `.data` as well, only issue is that `.data` requires the column names with `.data[[` (.data[[2]] is returning error for me. Not sure whether it is the feature or a bug). In the devel version`mtcars %>% group_by(vs) %>% mutate(across(cols = c(1), ~ mean(.data[[names(mtcars)[2]]])))` – akrun Feb 25 '20 at 21:38
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1@akrun I thought you want to avoid using `mtcars` (i.e. LHS name) within your pipes. You're right, `.data` works the way you have it :) – M-- Feb 25 '20 at 21:40
2
Another option could be:
mtcars %<>%
mutate_at(vars(1), ~ !!select(., 2) %>% pull() * 4)
mpg cyl disp hp drat wt qsec vs am gear carb
1 24 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 24 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 16 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 24 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 32 8 360.0 175 3.15 3.440 17.02 0 0 3 2
6 24 6 225.0 105 2.76 3.460 20.22 1 0 3 1
7 32 8 360.0 245 3.21 3.570 15.84 0 0 3 4
8 16 4 146.7 62 3.69 3.190 20.00 1 0 4 2
9 16 4 140.8 95 3.92 3.150 22.90 1 0 4 2
10 24 6 167.6 123 3.92 3.440 18.30 1 0 4 4
tmfmnk
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Perfect! One quick Q - Do we really need '%<>%' operator? I found it is equally working with "%>%" operator. – Bogaso Feb 25 '20 at 17:45
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1If you want to update the LHS with the new results, then yes. Take a look at `help("%<>%")`. – tmfmnk Feb 25 '20 at 18:02