README.md
In nathaneastwood/sparkplugs: Common Wrappers and Missing Functionality from 'sparklyr' and 'dplyr'
{flicker} 
Overview
{flicker} is a collection of useful wrapper functions and extensions to
the {dplyr} API which also work with Spark.
Installation
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")
Usage
Grouped Operations
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 15.4 326
Scoped Variant “when”
These 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 2
But 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 2
Union Select
This 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 -0.1872739
Cross Joins
As 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
nathaneastwood/sparkplugs documentation built on Feb. 28, 2021, 4:57 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
{flicker}
Overview
{flicker} is a collection of useful wrapper functions and extensions to the {dplyr} API which also work with Spark.
Installation
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")
Usage
Grouped Operations
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 15.4 326
Scoped Variant “when”
These 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 2
But 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 2
Union Select
This 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 -0.1872739
Cross Joins
As 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
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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