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https://github.com/datalorax/sundry

A sundry of convenience functions
https://github.com/datalorax/sundry

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A sundry of convenience functions

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README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```



# sundry

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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](https://www.tidyverse.org) and thus are pipe friendly (`%>%`), and

work with other functions like `dplyr::group_by`. The package is somewhat perpetually under development. 



## Installation



You can install sundry from github with:



```{r gh-installation, eval = FALSE}

# install.packages("devtools")

devtools::install_github("datalorax/sundry")

```



## Examples



Below are a few examples of functions in the package.



### Batch import data and bind into a single data frame



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.



```{r read_files}

library(sundry)

library(tidyverse)

by_species <- iris %>%

  split(.$Species) %>%

  map(select, -Species)



str(by_species)



# 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

d %>% 

  count(file)



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



Quickly calculate descriptive stats for any set of variables.



```{r descriptive_stats, message = FALSE}

library(dplyr)

library(sundry)

storms %>% 

  descrips(wind, pressure)



storms %>% 

  group_by(year) %>% 

  descrips(wind, pressure)



storms %>% 

  group_by(year) %>% 

  descrips(wind, pressure,

           .funs = funs(qtile25 = quantile(., 0.25),

                        median, 

                        qtile75 = quantile(., 0.75)))

```



### Remove empty rows from specific columns

This 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.



```{r load_data}

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)

```



The data above have missing 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. 



```{r label, options}

d %>% 

  rm_empty_rows(number_level_4:percent_level_1) %>% 

  select(district_id, number_level_4:percent_level_1) 

```



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`. 



### Filter by Functions

Select rows according to functions. For example, select only the rows with the minimum and maximum values of a specific variable.



```{r filter_by_funs}

storms %>%

 filter_by_funs(wind, funs(min, max))



storms %>%

  group_by(year) %>%

  filter_by_funs(wind, funs(min, median, max)) %>%

  arrange(year)

```