Notes for R

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Some notes for R.

Filter rows according to values of multiple columns

Use filter_all, filter_at or filter_if from dplyr.1

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# You can take the intersection of the replicated expressions:
filter_all(mtcars, all_vars(. > 150))
#>  [1] mpg  cyl  disp hp   drat wt   qsec vs   am   gear carb
#> <0 rows> (or 0-length row.names)

# Or the union:
filter_all(mtcars, any_vars(. > 150))
#>     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> 2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> 3  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> 4  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> 5  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> 6  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> 7  19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> 8  17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> 9  16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> 10 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> 11 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> 12 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> 13 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> 14 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> 15 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> 16 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> 17 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> 18 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> 19 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> 20 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> 21 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8


# You can vary the selection of columns on which to apply the
# predicate. filter_at() takes a vars() specification:
filter_at(mtcars, vars(starts_with("d")), any_vars((. %% 2) == 0))
#>     mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> 1  21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> 2  21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> 3  22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> 4  21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> 5  18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
#> 6  14.3   8  360 245 3.21 3.570 15.84  0  0    3    4
#> 7  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
#> 8  10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
#> 9  14.7   8  440 230 3.23 5.345 17.42  0  0    3    4
#> 10 15.5   8  318 150 2.76 3.520 16.87  0  0    3    2
#> 11 15.2   8  304 150 3.15 3.435 17.30  0  0    3    2
#> 12 13.3   8  350 245 3.73 3.840 15.41  0  0    3    4
#> 13 19.2   8  400 175 3.08 3.845 17.05  0  0    3    2

# And filter_if() selects variables with a predicate function:
filter_if(mtcars, ~ all(floor(.) == .), all_vars(. != 0))
#>    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 1 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> 2 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> 3 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> 4 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> 5 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> 6 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> 7 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Join path and/or filename

Use file.path().2

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> file.path("usr", "local", "lib")
[1] "usr/local/lib"
>

List files in a dir

Use list.files().3

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list.files(path = ".", pattern = NULL, all.files = FALSE,
           full.names = FALSE, recursive = FALSE,
           ignore.case = FALSE, include.dirs = FALSE, no.. = FALSE)

Read multiple files into one dataframe

Use do.call + lapply.4

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dataset <- do.call("rbind",lapply(file_list, FUN=function(files){read.table(files, header=TRUE, sep="\t")}))

Select helpers in dplyr

Select helpers5:

  • starts_with(): starts with a prefix
  • ends_with(): ends with a prefix
  • contains(): contains a literal string
  • matches(): matches a regular expression
  • num_range(): a numerical range like x01, x02, x03.
  • one_of(): variables in character vector.
  • everything(): all variables.

Keep strings matching a pattern

Use stringr::str_subset.6

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fruit <- c("apple", "banana", "pear", "pinapple")

str_subset(fruit, "a")
#> [1] "apple"    "banana"   "pear"     "pinapple"
str_subset(fruit, "^a")
#> [1] "apple"
str_subset(fruit, "a$")
#> [1] "banana"
str_subset(fruit, "b")
#> [1] "banana"
str_subset(fruit, "[aeiou]")
#> [1] "apple"    "banana"   "pear"     "pinapple"

# Missings never match
str_subset(c("a", NA, "b"), ".")
#> [1] "a" "b"

References

1. https://dplyr.tidyverse.org/reference/filter_all.html ↩
2. https://stackoverflow.com/questions/13110076/function-to-concatenate-paths ↩
3. https://stat.ethz.ch/R-manual/R-devel/library/base/html/list.files.html ↩
4. https://psychwire.wordpress.com/2011/06/03/merge-all-files-in-a-directory-using-r-into-a-single-dataframe/#comment-24 ↩
5. https://dplyr.tidyverse.org/reference/select_helpers.html ↩
6. https://stringr.tidyverse.org/reference/str_subset.html ↩