README.md
In halpo/short: A collection of shortcuts and time savers
short
Overview
The short package gives several shortcuts to common functionality.
using - easy loading packages
The using function is a simpler way of specifying multiple packages to
load. It additionally will attempt to download and install the packages
if not already installed.
table_1 - Table 1 Demographics and Summaries
The table_1 function can be used to generate a demographics and summary
of variables; i.e. the veritable "Table 1" for publications.
For each variable given, table_1 calls summarise_table_1 which
will summarize the variable for each value of a key, differentiating
variable, as well as overall. For character and factor variables,
the counts and percentages of each level is given.
For logical variables, the count of positives and the percentage is
given as the value. Numeric Variables are summarized by the minimum,
median, mean, maximum, and standard deviation.
Additional methods for table_1 can be added by adding methods to
summarise_table_1
Example
library(tidyverse)
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
## -- Attaching packages --------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.1.1 v purrr 0.3.2
## v tibble 2.1.1 v dplyr 0.8.1
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x purrr::as_function() masks short::as_function()
## x dplyr::filter() masks short::filter(), stats::filter()
## x tibble::has_name() masks short::has_name()
## x purrr::is_null() masks testthat::is_null()
## x dplyr::lag() masks short::lag(), stats::lag()
## x dplyr::matches() masks short::matches(), testthat::matches()
mtcars %>%
mutate( gear = ordered(paste(gear, 'Gears'))
, cyl = ordered(paste(cyl, 'Cylinders'))
) %>%
table_1( cyl
, 'Automatic Transmission' = am == 0
, 'Number of gears' = gear
, Horsepower = hp
)
## # A tibble: 9 x 6
## Variable Level `(All)` `4 Cylinders` `6 Cylinders` `8 Cylinders`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Automatic Tran~ Yes 19 (59.~ 3 (27.27%) 4 (57.14%) 12 (85.71%)
## 2 Number of gears 3 Gea~ 15 (46.~ 1 (9.09% ) 2 (28.57%) 12 (85.71%)
## 3 Number of gears 4 Gea~ 12 (37.~ 8 (72.73%) 4 (57.14%) <NA>
## 4 Number of gears 5 Gea~ 5 (15.6~ 2 (18.18%) 1 (14.29%) 2 (14.29%)
## 5 Horsepower Min " 52.0" " 52.0" 105.0 150.0
## 6 Horsepower Median 123.0 " 66.0" 110.0 180.0
## 7 Horsepower Mean 146.7 " 82.6" 122.3 209.2
## 8 Horsepower Max 335.0 113.0 175.0 335.0
## 9 Horsepower SD " 68.6" " 20.9" " 24.3" " 51.0"
with_margins - add marginals to and computation.
The with_margins function converts another function to operate over
all possible subsets of groups, including no groups.
Example
mtcars %>%
mutate( gear = ordered(paste(gear, 'Gears'))
, cyl = ordered(paste(cyl, 'Cylinders'))
) %>%
group_by(gear, cyl) %>%
with_margins(summarise_at)( vars(mpg, disp, hp, wt)
, funs(min, mean, max)
)
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## please use list() instead
##
## # Before:
## funs(name = f(.))
##
## # After:
## list(name = ~ f(.))
## This warning is displayed once per session.
## # A tibble: 15 x 14
## gear cyl mpg_min disp_min hp_min wt_min mpg_mean disp_mean hp_mean
## <ord> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 Ge~ 4 Cy~ 21.5 120. 97 2.46 21.5 120. 97
## 2 3 Ge~ 6 Cy~ 18.1 225 105 3.22 19.8 242. 108.
## 3 3 Ge~ 8 Cy~ 10.4 276. 150 3.44 15.0 358. 194.
## 4 4 Ge~ 4 Cy~ 21.4 71.1 52 1.62 26.9 103. 76
## 5 4 Ge~ 6 Cy~ 17.8 160 110 2.62 19.8 164. 116.
## 6 5 Ge~ 4 Cy~ 26 95.1 91 1.51 28.2 108. 102
## 7 5 Ge~ 6 Cy~ 19.7 145 175 2.77 19.7 145 175
## 8 5 Ge~ 8 Cy~ 15 301 264 3.17 15.4 326 300.
## 9 3 Ge~ (All) 10.4 120. 97 2.46 16.1 326. 176.
## 10 4 Ge~ (All) 17.8 71.1 52 1.62 24.5 123. 89.5
## 11 5 Ge~ (All) 15 95.1 91 1.51 21.4 202. 196.
## 12 (All) 4 Cy~ 21.4 71.1 52 1.51 26.7 105. 82.6
## 13 (All) 6 Cy~ 17.8 145 105 2.62 19.7 183. 122.
## 14 (All) 8 Cy~ 10.4 276. 150 3.17 15.1 353. 209.
## 15 (All) (All) 10.4 71.1 52 1.51 20.1 231. 147.
## # ... with 5 more variables: wt_mean <dbl>, mpg_max <dbl>, disp_max <dbl>,
## # hp_max <dbl>, wt_max <dbl>
spread_each - Spread Multiple Values
The spread_each function works similar to the tidyr::spread function
but works for spreading out multiple values.
Example
mtcars %>%
count(gear, cyl) %>%
group_by(gear) %>%
mutate(pct = n/sum(n)) %>%
spread_each(cyl, n, pct)
## # A tibble: 3 x 8
## # Groups: "gear" [1]
## gear `4.n` `4.pct` `6.n` `6.pct` `8.n` `8.pct` `"gear"`
## <dbl> <int> <dbl> <int> <dbl> <int> <dbl> <chr>
## 1 3 1 0.0667 2 0.133 12 0.8 gear
## 2 4 8 0.667 4 0.333 NA NA gear
## 3 5 2 0.4 1 0.2 2 0.4 gear
.T - Simple text specification
Similar to lazy loading of the tidyverse,
the .T function provides
a simple way to designate arguments should be interpreted as text.
The limiting factor is that the text must be syntactically correct,
however it come in very helpful.
Example
.T(first, second, 'a third argument with spaces')
## [1] "first" "second"
## [3] "a third argument with spaces"
Inline functions
The short package also provides many inline shortcuts.
Text concatenations
%\\n% - concatenate with a line break%<<% - concatenate with a space%<<<% - concatenate without space.
Pattern Matching
%~% - Matches the pattern on the left hand side.%!~% - Does not match the pattern on the left hand side.
Other
%or% - Similar to the rlang or purrr function %||% but checks
on length rather than is.null so will give the right hand
side for length 0 vectors as well.%inherits% - inline version of inherits%is a% - alternate inline version of inherits
String Filtering
There are two convenience function for filtering character vectors,
sift and the filter out version seive.
# limit to Mercs
mtcars %>% rownames() %>% sift("^Merc")
## [1] "Merc 240D" "Merc 230" "Merc 280" "Merc 280C" "Merc 450SE"
## [6] "Merc 450SL" "Merc 450SLC"
# Filter out everything starting with M
mtcars %>% rownames() %>% sieve("^M")
## [1] "Datsun 710" "Hornet 4 Drive" "Hornet Sportabout"
## [4] "Valiant" "Duster 360" "Cadillac Fleetwood"
## [7] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [10] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [13] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [16] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [19] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [22] "Volvo 142E"
halpo/short documentation built on July 18, 2019, 12:06 a.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.
short
Overview
The short package gives several shortcuts to common functionality.
using - easy loading packages
The using function is a simpler way of specifying multiple packages to
load. It additionally will attempt to download and install the packages
if not already installed.
table_1 - Table 1 Demographics and Summaries
The table_1 function can be used to generate a demographics and summary
of variables; i.e. the veritable "Table 1" for publications.
For each variable given, table_1 calls summarise_table_1 which
will summarize the variable for each value of a key, differentiating
variable, as well as overall. For character and factor variables,
the counts and percentages of each level is given.
For logical variables, the count of positives and the percentage is
given as the value. Numeric Variables are summarized by the minimum,
median, mean, maximum, and standard deviation.
Additional methods for table_1 can be added by adding methods to
summarise_table_1
Example
library(tidyverse)
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
## -- Attaching packages --------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.1.1 v purrr 0.3.2
## v tibble 2.1.1 v dplyr 0.8.1
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x purrr::as_function() masks short::as_function()
## x dplyr::filter() masks short::filter(), stats::filter()
## x tibble::has_name() masks short::has_name()
## x purrr::is_null() masks testthat::is_null()
## x dplyr::lag() masks short::lag(), stats::lag()
## x dplyr::matches() masks short::matches(), testthat::matches()
mtcars %>%
mutate( gear = ordered(paste(gear, 'Gears'))
, cyl = ordered(paste(cyl, 'Cylinders'))
) %>%
table_1( cyl
, 'Automatic Transmission' = am == 0
, 'Number of gears' = gear
, Horsepower = hp
)
## # A tibble: 9 x 6
## Variable Level `(All)` `4 Cylinders` `6 Cylinders` `8 Cylinders`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Automatic Tran~ Yes 19 (59.~ 3 (27.27%) 4 (57.14%) 12 (85.71%)
## 2 Number of gears 3 Gea~ 15 (46.~ 1 (9.09% ) 2 (28.57%) 12 (85.71%)
## 3 Number of gears 4 Gea~ 12 (37.~ 8 (72.73%) 4 (57.14%) <NA>
## 4 Number of gears 5 Gea~ 5 (15.6~ 2 (18.18%) 1 (14.29%) 2 (14.29%)
## 5 Horsepower Min " 52.0" " 52.0" 105.0 150.0
## 6 Horsepower Median 123.0 " 66.0" 110.0 180.0
## 7 Horsepower Mean 146.7 " 82.6" 122.3 209.2
## 8 Horsepower Max 335.0 113.0 175.0 335.0
## 9 Horsepower SD " 68.6" " 20.9" " 24.3" " 51.0"
with_margins - add marginals to and computation.
The with_margins function converts another function to operate over
all possible subsets of groups, including no groups.
Example
mtcars %>%
mutate( gear = ordered(paste(gear, 'Gears'))
, cyl = ordered(paste(cyl, 'Cylinders'))
) %>%
group_by(gear, cyl) %>%
with_margins(summarise_at)( vars(mpg, disp, hp, wt)
, funs(min, mean, max)
)
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## please use list() instead
##
## # Before:
## funs(name = f(.))
##
## # After:
## list(name = ~ f(.))
## This warning is displayed once per session.
## # A tibble: 15 x 14
## gear cyl mpg_min disp_min hp_min wt_min mpg_mean disp_mean hp_mean
## <ord> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 Ge~ 4 Cy~ 21.5 120. 97 2.46 21.5 120. 97
## 2 3 Ge~ 6 Cy~ 18.1 225 105 3.22 19.8 242. 108.
## 3 3 Ge~ 8 Cy~ 10.4 276. 150 3.44 15.0 358. 194.
## 4 4 Ge~ 4 Cy~ 21.4 71.1 52 1.62 26.9 103. 76
## 5 4 Ge~ 6 Cy~ 17.8 160 110 2.62 19.8 164. 116.
## 6 5 Ge~ 4 Cy~ 26 95.1 91 1.51 28.2 108. 102
## 7 5 Ge~ 6 Cy~ 19.7 145 175 2.77 19.7 145 175
## 8 5 Ge~ 8 Cy~ 15 301 264 3.17 15.4 326 300.
## 9 3 Ge~ (All) 10.4 120. 97 2.46 16.1 326. 176.
## 10 4 Ge~ (All) 17.8 71.1 52 1.62 24.5 123. 89.5
## 11 5 Ge~ (All) 15 95.1 91 1.51 21.4 202. 196.
## 12 (All) 4 Cy~ 21.4 71.1 52 1.51 26.7 105. 82.6
## 13 (All) 6 Cy~ 17.8 145 105 2.62 19.7 183. 122.
## 14 (All) 8 Cy~ 10.4 276. 150 3.17 15.1 353. 209.
## 15 (All) (All) 10.4 71.1 52 1.51 20.1 231. 147.
## # ... with 5 more variables: wt_mean <dbl>, mpg_max <dbl>, disp_max <dbl>,
## # hp_max <dbl>, wt_max <dbl>
spread_each - Spread Multiple Values
The spread_each function works similar to the tidyr::spread function
but works for spreading out multiple values.
Example
mtcars %>%
count(gear, cyl) %>%
group_by(gear) %>%
mutate(pct = n/sum(n)) %>%
spread_each(cyl, n, pct)
## # A tibble: 3 x 8
## # Groups: "gear" [1]
## gear `4.n` `4.pct` `6.n` `6.pct` `8.n` `8.pct` `"gear"`
## <dbl> <int> <dbl> <int> <dbl> <int> <dbl> <chr>
## 1 3 1 0.0667 2 0.133 12 0.8 gear
## 2 4 8 0.667 4 0.333 NA NA gear
## 3 5 2 0.4 1 0.2 2 0.4 gear
.T - Simple text specification
Similar to lazy loading of the tidyverse,
the .T function provides
a simple way to designate arguments should be interpreted as text.
The limiting factor is that the text must be syntactically correct,
however it come in very helpful.
Example
.T(first, second, 'a third argument with spaces')
## [1] "first" "second"
## [3] "a third argument with spaces"
Inline functions
The short package also provides many inline shortcuts.
Text concatenations
%\\n%- concatenate with a line break%<<%- concatenate with a space%<<<%- concatenate without space.
Pattern Matching
%~%- Matches the pattern on the left hand side.%!~%- Does not match the pattern on the left hand side.
Other
%or%- Similar to therlangorpurrrfunction%||%but checks onlengthrather thanis.nullso will give the right hand side for length 0 vectors as well.%inherits%- inline version ofinherits%is a%- alternate inline version ofinherits
String Filtering
There are two convenience function for filtering character vectors,
sift and the filter out version seive.
# limit to Mercs
mtcars %>% rownames() %>% sift("^Merc")
## [1] "Merc 240D" "Merc 230" "Merc 280" "Merc 280C" "Merc 450SE"
## [6] "Merc 450SL" "Merc 450SLC"
# Filter out everything starting with M
mtcars %>% rownames() %>% sieve("^M")
## [1] "Datsun 710" "Hornet 4 Drive" "Hornet Sportabout"
## [4] "Valiant" "Duster 360" "Cadillac Fleetwood"
## [7] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [10] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [13] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [16] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [19] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [22] "Volvo 142E"
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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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