hacksaw
hacksaw is as an adhesive between various dplyr and purrr operations, with some extra tidyverse-like functionality (e.g. keeping NAs, shifting row values) and shortcuts (e.g. filtering patterns, casting, plucking, etc.).
Installation
You can install the released version of hacksaw from CRAN with:
install.packages("hacksaw")Or install the development version from GitHub with:
remotes::install_github("daranzolin/hacksaw")Split operations
hacksaw’s assortment of split operations recycle the original data
frame. This is useful when you want to run slightly different code on
the same object multiple times (e.g. assignment) or you want to take
advantage of some list functionality (e.g. purrr, lengths(), %->%,
etc.).
The useful%<-% and %->% operators are re-exported from the zeallot
package.
filter
library(hacksaw)
library(tidyverse)
iris %>%
filter_split(
large_petals = Petal.Length > 5.1,
large_sepals = Sepal.Length > 6.4
) %>%
map(summary)
#> $large_petals
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Min. :6.100 Min. :2.500 Min. :5.200 Min. :1.400
#> 1st Qu.:6.400 1st Qu.:2.900 1st Qu.:5.525 1st Qu.:1.900
#> Median :6.700 Median :3.000 Median :5.700 Median :2.100
#> Mean :6.862 Mean :3.071 Mean :5.826 Mean :2.094
#> 3rd Qu.:7.200 3rd Qu.:3.200 3rd Qu.:6.075 3rd Qu.:2.300
#> Max. :7.900 Max. :3.800 Max. :6.900 Max. :2.500
#> Species
#> setosa : 0
#> versicolor: 0
#> virginica :34
#>
#>
#>
#>
#> $large_sepals
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> Min. :6.500 Min. :2.500 Min. :4.400 Min. :1.30 setosa : 0
#> 1st Qu.:6.700 1st Qu.:3.000 1st Qu.:5.050 1st Qu.:1.65 versicolor: 9
#> Median :6.800 Median :3.000 Median :5.700 Median :2.00 virginica :26
#> Mean :6.971 Mean :3.071 Mean :5.569 Mean :1.94
#> 3rd Qu.:7.200 3rd Qu.:3.200 3rd Qu.:6.050 3rd Qu.:2.25
#> Max. :7.900 Max. :3.800 Max. :6.900 Max. :2.50select
Include multiple columns and select helpers within c():
iris %>%
select_split(
sepal_data = c(Species, starts_with("Sepal")),
petal_data = c(Species, starts_with("Petal"))
) %>%
str()
#> List of 2
#> $ sepal_data:'data.frame': 150 obs. of 3 variables:
#> ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#> ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#> $ petal_data:'data.frame': 150 obs. of 3 variables:
#> ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#> ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...count
Count across multiple variables:
mtcars %>%
count_split(
cyl,
carb,
gear
)
#> [[1]]
#> cyl n
#> 1 8 14
#> 2 4 11
#> 3 6 7
#>
#> [[2]]
#> carb n
#> 1 2 10
#> 2 4 10
#> 3 1 7
#> 4 3 3
#> 5 6 1
#> 6 8 1
#>
#> [[3]]
#> gear n
#> 1 3 15
#> 2 4 12
#> 3 5 5rolling_count_split
Rolling counts, left-to-right
mtcars %>%
rolling_count_split(
cyl,
carb,
gear
)
#> [[1]]
#> cyl n
#> 1 4 11
#> 2 6 7
#> 3 8 14
#>
#> [[2]]
#> cyl carb n
#> 1 4 1 5
#> 2 4 2 6
#> 3 6 1 2
#> 4 6 4 4
#> 5 6 6 1
#> 6 8 2 4
#> 7 8 3 3
#> 8 8 4 6
#> 9 8 8 1
#>
#> [[3]]
#> cyl carb gear n
#> 1 4 1 3 1
#> 2 4 1 4 4
#> 3 4 2 4 4
#> 4 4 2 5 2
#> 5 6 1 3 2
#> 6 6 4 4 4
#> 7 6 6 5 1
#> 8 8 2 3 4
#> 9 8 3 3 3
#> 10 8 4 3 5
#> 11 8 4 5 1
#> 12 8 8 5 1distinct
Easily get the unique values of multiple columns:
starwars %>%
distinct_split(skin_color, eye_color, homeworld) %>%
str() # lengths() is also useful
#> List of 3
#> $ : chr [1:31] "fair" "gold" "white, blue" "white" ...
#> $ : chr [1:15] "blue" "yellow" "red" "brown" ...
#> $ : chr [1:49] "Tatooine" "Naboo" "Alderaan" "Stewjon" ...mutate
iris %>%
mutate_split(
Sepal.Length2 = Sepal.Length * 2,
Sepal.Length3 = Sepal.Length * 3
) %>%
str()
#> List of 2
#> $ :'data.frame': 150 obs. of 6 variables:
#> ..$ Sepal.Length : num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#> ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#> ..$ Petal.Length : num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#> ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#> ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ Sepal.Length2: num [1:150] 10.2 9.8 9.4 9.2 10 10.8 9.2 10 8.8 9.8 ...
#> $ :'data.frame': 150 obs. of 6 variables:
#> ..$ Sepal.Length : num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#> ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#> ..$ Petal.Length : num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#> ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#> ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ Sepal.Length3: num [1:150] 15.3 14.7 14.1 13.8 15 16.2 13.8 15 13.2 14.7 ...group_by
Separate groups:
mtcars %>%
group_by_split(cyl, gear, am, across(c(cyl, gear))) %>%
map(tally, wt = vs)
#> [[1]]
#> # A tibble: 3 x 2
#> cyl n
#> <dbl> <dbl>
#> 1 4 10
#> 2 6 4
#> 3 8 0
#>
#> [[2]]
#> # A tibble: 3 x 2
#> gear n
#> <dbl> <dbl>
#> 1 3 3
#> 2 4 10
#> 3 5 1
#>
#> [[3]]
#> # A tibble: 2 x 2
#> am n
#> <dbl> <dbl>
#> 1 0 7
#> 2 1 7
#>
#> [[4]]
#> # A tibble: 8 x 3
#> # Groups: cyl [3]
#> cyl gear n
#> <dbl> <dbl> <dbl>
#> 1 4 3 1
#> 2 4 4 8
#> 3 4 5 1
#> 4 6 3 2
#> 5 6 4 2
#> 6 6 5 0
#> 7 8 3 0
#> 8 8 5 0rolling_group_by_split
Rolling groups, left-to-right:
mtcars %>%
rolling_group_by_split(
cyl,
carb,
gear
) %>%
map(summarize, mean_mpg = mean(mpg))
#> [[1]]
#> # A tibble: 3 x 2
#> cyl mean_mpg
#> <dbl> <dbl>
#> 1 4 26.7
#> 2 6 19.7
#> 3 8 15.1
#>
#> [[2]]
#> # A tibble: 9 x 3
#> # Groups: cyl [3]
#> cyl carb mean_mpg
#> <dbl> <dbl> <dbl>
#> 1 4 1 27.6
#> 2 4 2 25.9
#> 3 6 1 19.8
#> 4 6 4 19.8
#> 5 6 6 19.7
#> 6 8 2 17.2
#> 7 8 3 16.3
#> 8 8 4 13.2
#> 9 8 8 15
#>
#> [[3]]
#> # A tibble: 12 x 4
#> # Groups: cyl, carb [9]
#> cyl carb gear mean_mpg
#> <dbl> <dbl> <dbl> <dbl>
#> 1 4 1 3 21.5
#> 2 4 1 4 29.1
#> 3 4 2 4 24.8
#> 4 4 2 5 28.2
#> 5 6 1 3 19.8
#> 6 6 4 4 19.8
#> 7 6 6 5 19.7
#> 8 8 2 3 17.2
#> 9 8 3 3 16.3
#> 10 8 4 3 12.6
#> 11 8 4 5 15.8
#> 12 8 8 5 15nest_by
mtcars %>%
nest_by_split(cyl, gear) %>%
map(mutate, model = list(lm(mpg ~ wt, data = data)))
#> [[1]]
#> # A tibble: 3 x 3
#> # Rowwise: cyl
#> cyl data model
#> <dbl> <list<tbl_df[,10]>> <list>
#> 1 4 [11 × 10] <lm>
#> 2 6 [7 × 10] <lm>
#> 3 8 [14 × 10] <lm>
#>
#> [[2]]
#> # A tibble: 3 x 3
#> # Rowwise: gear
#> gear data model
#> <dbl> <list<tbl_df[,10]>> <list>
#> 1 3 [15 × 10] <lm>
#> 2 4 [12 × 10] <lm>
#> 3 5 [5 × 10] <lm>rolling_nest_by
mtcars %>%
rolling_nest_by_split(cyl, gear) %>%
map(mutate, model = list(lm(mpg ~ wt, data = data)))
#> [[1]]
#> # A tibble: 3 x 3
#> # Rowwise: cyl
#> cyl data model
#> <dbl> <list<tbl_df[,10]>> <list>
#> 1 4 [11 × 10] <lm>
#> 2 6 [7 × 10] <lm>
#> 3 8 [14 × 10] <lm>
#>
#> [[2]]
#> # A tibble: 8 x 4
#> # Rowwise: cyl, gear
#> cyl gear data model
#> <dbl> <dbl> <list<tbl_df[,9]>> <list>
#> 1 4 3 [1 × 9] <lm>
#> 2 4 4 [8 × 9] <lm>
#> 3 4 5 [2 × 9] <lm>
#> 4 6 3 [2 × 9] <lm>
#> 5 6 4 [4 × 9] <lm>
#> 6 6 5 [1 × 9] <lm>
#> 7 8 3 [12 × 9] <lm>
#> 8 8 5 [2 × 9] <lm>transmute
iris %>%
transmute_split(Sepal.Length * 2, Petal.Width + 5) %>%
str()
#> List of 2
#> $ : num [1:150] 10.2 9.8 9.4 9.2 10 10.8 9.2 10 8.8 9.8 ...
#> $ : num [1:150] 5.2 5.2 5.2 5.2 5.2 5.4 5.3 5.2 5.2 5.1 ...slice
iris %>%
slice_split(1:10, 11:15, 30:50) %>%
str()
#> List of 3
#> $ :'data.frame': 10 obs. of 5 variables:
#> ..$ Sepal.Length: num [1:10] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9
#> ..$ Sepal.Width : num [1:10] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1
#> ..$ Petal.Length: num [1:10] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5
#> ..$ Petal.Width : num [1:10] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1
#> ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1
#> $ :'data.frame': 5 obs. of 5 variables:
#> ..$ Sepal.Length: num [1:5] 5.4 4.8 4.8 4.3 5.8
#> ..$ Sepal.Width : num [1:5] 3.7 3.4 3 3 4
#> ..$ Petal.Length: num [1:5] 1.5 1.6 1.4 1.1 1.2
#> ..$ Petal.Width : num [1:5] 0.2 0.2 0.1 0.1 0.2
#> ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1
#> $ :'data.frame': 21 obs. of 5 variables:
#> ..$ Sepal.Length: num [1:21] 4.7 4.8 5.4 5.2 5.5 4.9 5 5.5 4.9 4.4 ...
#> ..$ Sepal.Width : num [1:21] 3.2 3.1 3.4 4.1 4.2 3.1 3.2 3.5 3.6 3 ...
#> ..$ Petal.Length: num [1:21] 1.6 1.6 1.5 1.5 1.4 1.5 1.2 1.3 1.4 1.3 ...
#> ..$ Petal.Width : num [1:21] 0.2 0.2 0.4 0.1 0.2 0.2 0.2 0.2 0.1 0.2 ...
#> ..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...Use the var_max and var_min helpers to easily get minimum and
maximum values of a variable:
iris %>%
slice_split(
largest_sepals = var_max(Sepal.Length, 4),
smallest_sepals = var_min(Sepal.Length, 4)
)#
#> $largest_sepals
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 7.7 3.8 6.7 2.2 virginica
#> 2 7.7 2.6 6.9 2.3 virginica
#> 3 7.7 2.8 6.7 2.0 virginica
#> 4 7.9 3.8 6.4 2.0 virginica
#>
#> $smallest_sepals
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 4.4 2.9 1.4 0.2 setosa
#> 2 4.3 3.0 1.1 0.1 setosa
#> 3 4.4 3.0 1.3 0.2 setosa
#> 4 4.4 3.2 1.3 0.2 setosaprecision_split
precision_split splits the mtcars data frame into two: one with mpg
greater than 20, one with mpg less than 20:
mtcars %>%
precision_split(mpg > 20) %->% c(lt20mpg, gt20mpg)
str(gt20mpg)
#> 'data.frame': 14 obs. of 11 variables:
#> $ mpg : num 21 21 22.8 21.4 24.4 22.8 32.4 30.4 33.9 21.5 ...
#> $ cyl : num 6 6 4 6 4 4 4 4 4 4 ...
#> $ disp: num 160 160 108 258 147 ...
#> $ hp : num 110 110 93 110 62 95 66 52 65 97 ...
#> $ drat: num 3.9 3.9 3.85 3.08 3.69 3.92 4.08 4.93 4.22 3.7 ...
#> $ wt : num 2.62 2.88 2.32 3.21 3.19 ...
#> $ qsec: num 16.5 17 18.6 19.4 20 ...
#> $ vs : num 0 0 1 1 1 1 1 1 1 1 ...
#> $ am : num 1 1 1 0 0 0 1 1 1 0 ...
#> $ gear: num 4 4 4 3 4 4 4 4 4 3 ...
#> $ carb: num 4 4 1 1 2 2 1 2 1 1 ...
str(lt20mpg)
#> 'data.frame': 18 obs. of 11 variables:
#> $ mpg : num 18.7 18.1 14.3 19.2 17.8 16.4 17.3 15.2 10.4 10.4 ...
#> $ cyl : num 8 6 8 6 6 8 8 8 8 8 ...
#> $ disp: num 360 225 360 168 168 ...
#> $ hp : num 175 105 245 123 123 180 180 180 205 215 ...
#> $ drat: num 3.15 2.76 3.21 3.92 3.92 3.07 3.07 3.07 2.93 3 ...
#> $ wt : num 3.44 3.46 3.57 3.44 3.44 ...
#> $ qsec: num 17 20.2 15.8 18.3 18.9 ...
#> $ vs : num 0 1 0 1 1 0 0 0 0 0 ...
#> $ am : num 0 0 0 0 0 0 0 0 0 0 ...
#> $ gear: num 3 3 3 4 4 3 3 3 3 3 ...
#> $ carb: num 2 1 4 4 4 3 3 3 4 4 ...eval_split
Evaluate any expression:
mtcars %>%
eval_split(
select(hp, mpg),
filter(mpg > 25),
mutate(pounds = wt*1000)
) %>%
str()
#> List of 3
#> $ :'data.frame': 32 obs. of 2 variables:
#> ..$ hp : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#> $ :'data.frame': 6 obs. of 11 variables:
#> ..$ mpg : num [1:6] 32.4 30.4 33.9 27.3 26 30.4
#> ..$ cyl : num [1:6] 4 4 4 4 4 4
#> ..$ disp: num [1:6] 78.7 75.7 71.1 79 120.3 ...
#> ..$ hp : num [1:6] 66 52 65 66 91 113
#> ..$ drat: num [1:6] 4.08 4.93 4.22 4.08 4.43 3.77
#> ..$ wt : num [1:6] 2.2 1.61 1.83 1.94 2.14 ...
#> ..$ qsec: num [1:6] 19.5 18.5 19.9 18.9 16.7 ...
#> ..$ vs : num [1:6] 1 1 1 1 0 1
#> ..$ am : num [1:6] 1 1 1 1 1 1
#> ..$ gear: num [1:6] 4 4 4 4 5 5
#> ..$ carb: num [1:6] 1 2 1 1 2 2
#> $ :'data.frame': 32 obs. of 12 variables:
#> ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#> ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
#> ..$ disp : num [1:32] 160 160 108 258 360 ...
#> ..$ hp : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
#> ..$ drat : num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
#> ..$ wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
#> ..$ qsec : num [1:32] 16.5 17 18.6 19.4 17 ...
#> ..$ vs : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...
#> ..$ am : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
#> ..$ gear : num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
#> ..$ carb : num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
#> ..$ pounds: num [1:32] 2620 2875 2320 3215 3440 ...Casting
Tired of mutate(var = as.[character|numeric|logical](var))?
starwars %>% cast_character(height, mass) %>% str(max.level = 2)
#> tibble [87 × 14] (S3: tbl_df/tbl/data.frame)
#> $ name : chr [1:87] "Luke Skywalker" "C-3PO" "R2-D2" "Darth Vader" ...
#> $ height : chr [1:87] "172" "167" "96" "202" ...
#> $ mass : chr [1:87] "77" "75" "32" "136" ...
#> $ hair_color: chr [1:87] "blond" NA NA "none" ...
#> $ skin_color: chr [1:87] "fair" "gold" "white, blue" "white" ...
#> $ eye_color : chr [1:87] "blue" "yellow" "red" "yellow" ...
#> $ birth_year: num [1:87] 19 112 33 41.9 19 52 47 NA 24 57 ...
#> $ sex : chr [1:87] "male" "none" "none" "male" ...
#> $ gender : chr [1:87] "masculine" "masculine" "masculine" "masculine" ...
#> $ homeworld : chr [1:87] "Tatooine" "Tatooine" "Naboo" "Tatooine" ...
#> $ species : chr [1:87] "Human" "Droid" "Droid" "Human" ...
#> $ films :List of 87
#> $ vehicles :List of 87
#> $ starships :List of 87
iris %>% cast_character(contains(".")) %>% str(max.level = 1)
#> 'data.frame': 150 obs. of 5 variables:
#> $ Sepal.Length: chr "5.1" "4.9" "4.7" "4.6" ...
#> $ Sepal.Width : chr "3.5" "3" "3.2" "3.1" ...
#> $ Petal.Length: chr "1.4" "1.4" "1.3" "1.5" ...
#> $ Petal.Width : chr "0.2" "0.2" "0.2" "0.2" ...
#> $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...hacksaw also includes cast_numeric and cast_logical.
Keeping NAs
The reverse of tidyr::drop_na, strangely omitted in the original
tidyverse.
df <- tibble(x = c(1, 2, NA, NA, NA), y = c("a", NA, "b", NA, NA))
df %>% keep_na()
#> # A tibble: 2 x 2
#> x y
#> <dbl> <chr>
#> 1 NA <NA>
#> 2 NA <NA>
df %>% keep_na(x)
#> # A tibble: 3 x 2
#> x y
#> <dbl> <chr>
#> 1 NA b
#> 2 NA <NA>
#> 3 NA <NA>
df %>% keep_na(x, y)
#> # A tibble: 2 x 2
#> x y
#> <dbl> <chr>
#> 1 NA <NA>
#> 2 NA <NA>Coercive joins
I seldom care if my join keys are incompatible. The *_join2 suite of
functions coerce either the left or right table accordingly.
df1 <- tibble(x = 1:10, b = 1:10, y = letters[1:10])
df2 <- tibble(x = as.character(1:10), z = letters[11:20])
left_join2(df1, df2)
#> Joining, by = "x"
#> # A tibble: 10 x 4
#> x b y z
#> <chr> <int> <chr> <chr>
#> 1 1 1 a k
#> 2 2 2 b l
#> 3 3 3 c m
#> 4 4 4 d n
#> 5 5 5 e o
#> 6 6 6 f p
#> 7 7 7 g q
#> 8 8 8 h r
#> 9 9 9 i s
#> 10 10 10 j tShifting row values
Shift values across rows in either direction. Sometimes useful when importing irregularly-shaped tabular data.
df <- tibble(
s = c(NA, 1, NA, NA),
t = c(NA, NA, 1, NA),
u = c(NA, NA, 2, 5),
v = c(5, 1, 9, 2),
x = c(1, 5, 6, 7),
y = c(NA, NA, 8, NA),
z = 1:4
)
df
#> # A tibble: 4 x 7
#> s t u v x y z
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 NA NA NA 5 1 NA 1
#> 2 1 NA NA 1 5 NA 2
#> 3 NA 1 2 9 6 8 3
#> 4 NA NA 5 2 7 NA 4
shift_row_values(df)
#> # A tibble: 4 x 7
#> s t u v x y z
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 5 1 1 NA NA NA NA
#> 2 1 1 5 2 NA NA NA
#> 3 1 2 9 6 8 3 NA
#> 4 5 2 7 4 NA NA NA
shift_row_values(df, at = 1:3)
#> # A tibble: 4 x 7
#> s t u v x y z
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 5 1 1 NA NA NA NA
#> 2 1 1 5 2 NA NA NA
#> 3 1 2 9 6 8 3 NA
#> 4 NA NA 5 2 7 NA 4
shift_row_values(df, at = 1:2, .dir = "right")
#> # A tibble: 4 x 7
#> s t u v x y z
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 NA NA NA NA 5 1 1
#> 2 NA NA NA 1 1 5 2
#> 3 NA 1 2 9 6 8 3
#> 4 NA NA 5 2 7 NA 4Filtering, keeping, and discarding patterns
A wrapper around filter(grepl(..., var)):
starwars %>%
filter_pattern(homeworld, "oo") %>%
distinct(homeworld)
#> # A tibble: 2 x 1
#> homeworld
#> <chr>
#> 1 Tatooine
#> 2 NabooUse keep_pattern and discard_pattern for lists and vectors.
Plucking values
A wrapper around x[p][i]:
df <- tibble(
id = c(1, 1, 1, 2, 2, 2, 3, 3),
tested = c("no", "no", "yes", "no", "no", "no", "yes", "yes"),
year = c(2015:2017, 2010:2012, 2019:2020)
)
df %>%
group_by(id) %>%
mutate(year_first_tested = pluck_when(year, tested == "yes"))
#> # A tibble: 8 x 4
#> # Groups: id [3]
#> id tested year year_first_tested
#> <dbl> <chr> <int> <int>
#> 1 1 no 2015 2017
#> 2 1 no 2016 2017
#> 3 1 yes 2017 2017
#> 4 2 no 2010 NA
#> 5 2 no 2011 NA
#> 6 2 no 2012 NA
#> 7 3 yes 2019 2019
#> 8 3 yes 2020 2019