The sundry package is a personal R package filled with functions that
make my life a little easier when working on day-to-day analyses. Most
of the functions in the package are designed to be friendly with the
tidyverse and thus are pipe friendly
(%>%), and work with other functions like dplyr::group_by. The
package is somewhat perpetually under development.
You can install sundry from github with:
# install.packages("devtools")
devtools::install_github("datalorax/sundry")Below are a few examples of functions in the package.
Maybe my favorite function in the package is the read_files function,
which will read in n datasets and, if possible bind them together into
a single tibble (data frame). The function uses rio::import, which
makes it really nice because you don’t have to worry about file types
basically at all, and you can even read in data of different types all
at once.
library(sundry)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.0.0.9000 ✔ purrr 0.2.5
#> ✔ tibble 1.4.2 ✔ dplyr 0.7.7
#> ✔ tidyr 0.8.2 ✔ stringr 1.3.1
#> ✔ readr 1.1.1 ✔ forcats 0.3.0
#> ── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
by_species <- iris %>%
split(.$Species) %>%
map(select, -Species)
str(by_species)
#> List of 3
#> $ setosa :'data.frame': 50 obs. of 4 variables:
#> ..$ Sepal.Length: num [1:50] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#> ..$ Sepal.Width : num [1:50] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#> ..$ Petal.Length: num [1:50] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#> ..$ Petal.Width : num [1:50] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#> $ versicolor:'data.frame': 50 obs. of 4 variables:
#> ..$ Sepal.Length: num [1:50] 7 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 ...
#> ..$ Sepal.Width : num [1:50] 3.2 3.2 3.1 2.3 2.8 2.8 3.3 2.4 2.9 2.7 ...
#> ..$ Petal.Length: num [1:50] 4.7 4.5 4.9 4 4.6 4.5 4.7 3.3 4.6 3.9 ...
#> ..$ Petal.Width : num [1:50] 1.4 1.5 1.5 1.3 1.5 1.3 1.6 1 1.3 1.4 ...
#> $ virginica :'data.frame': 50 obs. of 4 variables:
#> ..$ Sepal.Length: num [1:50] 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 ...
#> ..$ Sepal.Width : num [1:50] 3.3 2.7 3 2.9 3 3 2.5 2.9 2.5 3.6 ...
#> ..$ Petal.Length: num [1:50] 6 5.1 5.9 5.6 5.8 6.6 4.5 6.3 5.8 6.1 ...
#> ..$ Petal.Width : num [1:50] 2.5 1.9 2.1 1.8 2.2 2.1 1.7 1.8 1.8 2.5 ...
# export as three different file types
rio::export(by_species$setosa, "setosa.csv")
rio::export(by_species$versicolor, "versicolor.xlsx")
rio::export(by_species$virginica, "virginica.sav")
# import them all back in as a single data frame
d <- read_files()
d
#> # A tibble: 150 x 5
#> file Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.100000 3.5 1.4 0.2
#> 2 setosa 4.9 3 1.4 0.2
#> 3 setosa 4.7 3.2 1.3 0.2
#> 4 setosa 4.600000 3.1 1.5 0.2
#> 5 setosa 5 3.6 1.4 0.2
#> 6 setosa 5.4 3.9 1.7 0.4
#> 7 setosa 4.600000 3.4 1.4 0.3
#> 8 setosa 5 3.4 1.5 0.2
#> 9 setosa 4.4 2.9 1.4 0.2
#> 10 setosa 4.9 3.1 1.5 0.1
#> # ... with 140 more rows
d %>%
count(file)
#> # A tibble: 3 x 2
#> file n
#> <chr> <int>
#> 1 setosa 50
#> 2 versicolor 50
#> 3 virginica 50
fs::file_delete(c("setosa.csv", "versicolor.xlsx", "virginica.sav"))The first argument is the directory, and defaults to the current working
directory. There’s also an optional pat argument you can supply to
read in only files with a specified pattern. You can also optionally
have the files read in as a list, rather than binding the data frames
together.
Quickly calculate descriptive stats for any set of variables.
library(dplyr)
library(sundry)
storms %>%
descrips(wind, pressure)
#> # A tibble: 2 x 6
#> variable n min max mean sd
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 pressure 10010 882 1022 992.1390 19.51678
#> 2 wind 10010 10 160 53.49500 26.21387
storms %>%
group_by(year) %>%
descrips(wind, pressure)
#> # A tibble: 82 x 7
#> year variable n min max mean sd
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1975 pressure 86 963 1014 994.6279 15.20530
#> 2 1975 wind 86 20 100 50.87209 23.60448
#> 3 1976 pressure 52 957 1012 988.7692 15.25678
#> 4 1976 wind 52 20 105 59.90385 24.78319
#> 5 1977 pressure 53 926 1015 995.3585 20.42443
#> 6 1977 wind 53 20 150 53.96226 29.55371
#> 7 1978 pressure 54 980 1012 1005.833 6.635383
#> 8 1978 wind 54 20 80 40.46296 13.88186
#> 9 1979 pressure 301 924 1014 994.8372 19.87369
#> 10 1979 wind 301 15 150 48.67110 30.31603
#> # ... with 72 more rows
storms %>%
group_by(year) %>%
descrips(wind, pressure,
.funs = funs(qtile25 = quantile(., 0.25),
median,
qtile75 = quantile(., 0.75)))
#> # A tibble: 82 x 5
#> year variable qtile25 median qtile75
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1975 pressure 984.5 997 1010.75
#> 2 1975 wind 25 52.5 65
#> 3 1976 pressure 978.5 992 1000.25
#> 4 1976 wind 38.75 60 80
#> 5 1977 pressure 994 1001 1010
#> 6 1977 wind 30 45 70
#> 7 1978 pressure 1006 1007 1009
#> 8 1978 wind 30 40 45
#> 9 1979 pressure 988 1002 1008
#> 10 1979 wind 25 35 65
#> # ... with 72 more rowsThis function is similar to janitor::remove_empty_rows, but allows you
to pass a set of columns (rather than looking across all columns). Rows
will be removed that are missing across all
columns.
d <- rio::import("http://www.oregon.gov/ode/educator-resources/assessment/TestResults2017/pagr_schools_ela_tot_ecd_ext_gnd_lep_1617.xlsx",
setclass = "tbl_df",
na = c("--", "*")) %>%
janitor::clean_names()
d %>%
select(district_id, number_level_4:percent_level_1)
#> # A tibble: 23,760 x 9
#> district_id number_level_4 percent_level_4 number_level_3
#> <dbl> <chr> <chr> <chr>
#> 1 2063 <NA> <NA> <NA>
#> 2 2063 <NA> <NA> <NA>
#> 3 2063 <NA> <NA> <NA>
#> 4 2063 <NA> <NA> <NA>
#> 5 2063 <NA> <NA> <NA>
#> 6 2063 <NA> <NA> <NA>
#> 7 2063 <NA> <NA> <NA>
#> 8 2063 <NA> <NA> <NA>
#> 9 2063 <NA> <NA> <NA>
#> 10 2063 <NA> <NA> <NA>
#> # ... with 23,750 more rows, and 5 more variables: percent_level_3 <chr>,
#> # number_level_2 <chr>, percent_level_2 <chr>, number_level_1 <chr>,
#> # percent_level_1 <chr>The data above have missing data data across many columns, but every row has at least some valid entries. Suppose I was only interested in data on schools with proficiency data.
d %>%
rm_empty_rows(number_level_4:percent_level_1) %>%
select(district_id, number_level_4:percent_level_1)
#> # A tibble: 15,081 x 9
#> district_id number_level_4 percent_level_4 number_level_3
#> <dbl> <chr> <chr> <chr>
#> 1 2113 5 45.5 5
#> 2 2113 5 29.4 8
#> 3 2113 2 20 5
#> 4 2113 5 33.299999999999997 7
#> 5 2113 2 20 5
#> 6 2113 0 0 6
#> 7 2113 7 53.8 5
#> 8 2113 5 41.7 4
#> 9 2113 5 41.7 6
#> 10 2113 - - -
#> # ... with 15,071 more rows, and 5 more variables: percent_level_3 <chr>,
#> # number_level_2 <chr>, percent_level_2 <chr>, number_level_1 <chr>,
#> # percent_level_1 <chr>The above returns all the rows that are not missing across the full set
of variables supplied (rows with partial missing are still returned).
The function can also be provided without any column arguments, and the
function will then mimic the behavior or janitor::remove_empty_rows.
Select rows according to functions. For example, select only the rows with the minimum and maximum values of a specific variable.
storms %>%
filter_by_funs(wind, funs(min, max))
#> # A tibble: 11 x 14
#> fun name year month day hour lat long status category wind
#> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <chr> <ord> <int>
#> 1 min Bonnie 1986 6 28 6 36.5 -91.3 tropi… -1 10
#> 2 min Bonnie 1986 6 28 12 37.2 -90 tropi… -1 10
#> 3 min AL031… 1987 8 16 18 30.9 -83.2 tropi… -1 10
#> 4 min AL031… 1987 8 17 0 31.4 -82.9 tropi… -1 10
#> 5 min AL031… 1987 8 17 6 31.8 -82.3 tropi… -1 10
#> 6 min Alber… 1994 7 7 0 32.7 -86.3 tropi… -1 10
#> 7 min Alber… 1994 7 7 6 32.7 -86.6 tropi… -1 10
#> 8 min Alber… 1994 7 7 12 32.800 -86.8 tropi… -1 10
#> 9 min Alber… 1994 7 7 18 33 -87 tropi… -1 10
#> 10 max Gilbe… 1988 9 14 0 19.7 -83.8 hurri… 5 160
#> 11 max Wilma 2005 10 19 12 17.3 -82.8 hurri… 5 160
#> # ... with 3 more variables: pressure <int>, ts_diameter <dbl>,
#> # hu_diameter <dbl>
storms %>%
group_by(year) %>%
filter_by_funs(wind, funs(min, median, max)) %>%
arrange(year)
#> # A tibble: 1,260 x 14
#> # Groups: year [41]
#> fun name year month day hour lat long status category wind
#> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <chr> <ord> <int>
#> 1 min Caro… 1975 9 1 6 25.2 -98.7 tropic… -1 20
#> 2 min Caro… 1975 9 1 12 25.3 -99 tropic… -1 20
#> 3 max Caro… 1975 8 31 0 24 -97 hurric… 3 100
#> 4 max Caro… 1975 8 31 6 24.1 -97.5 hurric… 3 100
#> 5 min Glor… 1976 9 26 12 23 -58 tropic… -1 20
#> 6 min Glor… 1976 9 26 18 23.7 -58.1 tropic… -1 20
#> 7 median Belle 1976 8 10 6 41 -73.2 tropic… 0 60
#> 8 median Glor… 1976 9 28 6 27.5 -58.2 tropic… 0 60
#> 9 median Glor… 1976 9 28 12 27.8 -58.6 tropic… 0 60
#> 10 median Glor… 1976 9 28 18 28.2 -59 tropic… 0 60
#> # ... with 1,250 more rows, and 3 more variables: pressure <int>,
#> # ts_diameter <dbl>, hu_diameter <dbl>