{flicker} is a collection of useful wrapper functions and extensions to the {dplyr} API which also work with Spark.
You can install:
- the development version from GitHub with
# install.packages("remotes")
remotes::install_github("nathaneastwood/flicker")- the latest release from CRAN with
install.packages("flicker")These functions offer the benefit over the scoped variants of being able to explicitly specify the parameters for each expression to evaluate.
library(flicker)
mtcars %>%
summarise_groups(
.groups = c("am", "cyl"),
avgMpg = mean(mpg, na.rm = TRUE),
avgDisp = mean(disp, na.rm = TRUE)
)
# # A tibble: 6 x 4
# am cyl avgMpg avgDisp
# <dbl> <dbl> <dbl> <dbl>
# 1 0 4 22.9 136.
# 2 0 6 19.1 205.
# 3 0 8 15.0 358.
# 4 1 4 28.1 93.6
# 5 1 6 20.6 155
# 6 1 8 15.4 326These functions are subtly different from the scoped _if() variants of
{dplyr} functions in that they can evaluate any predicate. They are
useful when used within a chain of commands.
previous_result <- 42
mtcars %>% filter_when(previous_result < 42, cyl == 4)
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
mtcars %>% filter_when(previous_result >= 42, cyl == 4)
# mpg cyl disp hp drat wt qsec vs am gear carb
# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2But we can also perform these checks as if using the scoped variants of {dplyr} functions.
mtcars %>% filter_when("mpg" %in% colnames(.), cyl == 4)
# mpg cyl disp hp drat wt qsec vs am gear carb
# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2This function will union the records from multiple data sets returning only the requested columns.
a <- data.frame(col1 = 1:5, col2 = 6, col3 = rnorm(5))
b <- data.frame(col1 = 1:3, col2 = 4, col3 = rnorm(3))
c <- data.frame(col1 = c(0, 1, 1, 2, 3, 5, 8), col3 = rnorm(7))
union_select(.data = list(a, b, c), c("col1", "col3"))
# col1 col3
# 1 1 -1.5936792
# 2 2 0.4583218
# 3 3 -0.7568519
# 4 4 1.3170420
# 5 5 -0.6419245
# 6 1 0.5815826
# 7 2 1.6735513
# 8 3 0.8638010
# 9 0 -1.2806895
# 10 1 0.1915506
# 11 1 -0.1021699
# 12 2 -0.9799384
# 13 3 -1.2197154
# 14 5 -0.9946515
# 15 8 -0.1872739As of {dplyr} 1.0.0, cross joins have been available through the use of
full_join(by = character()) but this is not a natural way to perform
the operation in my opinion. {flicker} provides a way to perform cross
joins for earlier versions of {dplyr}.
x <- data.frame(id = 1:2, val = rnorm(2))
y <- data.frame(run = 1:2, res = rnorm(2))
cross_join(x, y)
# id val run res
# 1 1 0.17952160 1 0.2839802
# 2 1 0.17952160 2 -0.2063309
# 3 2 0.08163433 1 0.2839802
# 4 2 0.08163433 2 -0.2063309