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README.Rmd
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README.Rmd
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---
output:
github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
options(width = 110)
```
> Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.
>
> -- *"[For Big-Data Scientists, 'Janitor Work' Is Key Hurdle to Insight](http://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html)" - The New York Times, 2014*
# janitor <img src="man/figures/logo_small.png" align="right" />
***********************
[](https://travis-ci.org/sfirke/janitor)
[](https://codecov.io/github/sfirke/janitor?branch=master)
[](https://www.tidyverse.org/lifecycle/#stable)
[](https://cran.r-project.org/package=janitor)
**janitor** has simple functions for examining and cleaning dirty data. It was built with beginning and intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.
The main janitor functions:
* perfectly format data.frame column names;
* create and format frequency tables of one, two, or three variables - think an improved `table()`; and
* isolate partially-duplicate records.
The tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel.
janitor is a [#tidyverse](https://github.com/hadley/tidyverse/blob/master/vignettes/manifesto.Rmd)-oriented package. Specifically, it plays nicely with the `%>%` pipe and is optimized for cleaning data brought in with the [readr](https://github.com/tidyverse/readr) and [readxl](https://github.com/tidyverse/readxl) packages.
### Installation
You can install:
* the most recent officially-released version from CRAN with
```R
install.packages("janitor")
````
* the latest development version from GitHub with
```R
install.packages("devtools")
devtools::install_github("sfirke/janitor")
```
## Using janitor
A full description of each function, organized by topic, can be found in janitor's [catalog of functions vignette](http://sfirke.github.io/janitor/articles/janitor.html). There you will find functions not mentioned in this README, like `compare_df_cols()` which provides a summary of differences in column names and types when given a set of data.frames.
Below are quick examples of how janitor tools are commonly used.
### Cleaning dirty data
Take this roster of teachers at a fictional American high school, stored in the Microsoft Excel file [dirty_data.xlsx](https://github.com/sfirke/janitor/blob/master/dirty_data.xlsx):
Dirtiness includes:
* Dreadful column names
* Rows and columns containing Excel formatting but no data
* Dates stored as numbers
* Values spread inconsistently over the "Certification" columns
Here's that data after being read in to R:
```{r, warning = FALSE, message = FALSE}
library(pacman) # for loading packages
p_load(readxl, janitor, dplyr, here)
roster_raw <- read_excel(here("dirty_data.xlsx")) # available at http://github.com/sfirke/janitor
glimpse(roster_raw)
```
Excel formatting led to an untitled empty column and 5 empty rows at the bottom of the table (only 12 records have any actual data). Bad column names are preserved.
Name cleaning comes in two flavors. `make_clean_names()` operates on character vectors and can be used during data import:
```{r, warning = FALSE, message = FALSE}
roster_raw_cleaner <- read_excel(here("dirty_data.xlsx"),
.name_repair = make_clean_names)
# Tells read_excel() how to repair repetitive column names, overriding the
# default repair setting
glimpse(roster_raw_cleaner)
```
This can be further cleaned:
```{r}
roster <- roster_raw_cleaner %>%
remove_empty(c("rows", "cols")) %>%
mutate(hire_date = excel_numeric_to_date(hire_date),
cert = coalesce(certification, certification_2)) %>% # from dplyr
select(-certification, -certification_2) # drop unwanted columns
roster
```
`clean_names()` is a convenience version that can be used for piped data.frame workflows:
```{r}
data("iris")
head(iris)
```
```{r}
iris %>%
clean_names() %>%
head()
```
### Examining dirty data
#### Finding duplicates
Use `get_dupes()` to identify and examine duplicate records during data cleaning. Let's see if any teachers are listed more than once:
```{r}
roster %>% get_dupes(first_name, last_name)
```
Yes, some teachers appear twice. We ought to address this before counting employees.
#### Tabulating tools
A variable (or combinations of two or three variables) can be tabulated with `tabyl()`. The resulting data.frame can be tweaked and formatted
with the suite of `adorn_` functions for quick analysis and printing of pretty results in a report. `adorn_` functions can be helpful with non-tabyls, too.
`tabyl` can be called two ways:
* On a vector, when tabulating a single variable - e.g., `tabyl(roster$subject)`
* On a data.frame, specifying 1, 2, or 3 variable names to tabulate : `roster %>% tabyl(subject, employee_status)`.
* Here the data.frame is passed in with the `%>%` pipe; this allows `tabyl` to be used in an analysis pipeline
#### tabyl()
Like `table()`, but pipe-able, data.frame-based, and fully featured.
One variable:
```{r}
roster %>%
tabyl(subject)
```
Two variables:
```{r}
roster %>%
filter(hire_date > as.Date("1950-01-01")) %>%
tabyl(employee_status, full_time)
```
Three variables:
```{r}
roster %>%
tabyl(full_time, subject, employee_status, show_missing_levels = FALSE)
```
##### Adorning tabyls
The `adorn_` functions dress up the results of these tabulation calls for fast, basic reporting. Here are some of the functions that augment a summary table for reporting:
```{r}
roster %>%
tabyl(employee_status, full_time) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting() %>%
adorn_ns() %>%
adorn_title("combined")
```
Pipe that right into `knitr::kable()` in your RMarkdown report.
These modular adornments can be layered to reduce R's deficit against Excel and SPSS when it comes to quick, informative counts.
## Contact me
You are welcome to:
* submit suggestions and report bugs: https://github.com/sfirke/janitor/issues
* let me know what you think on twitter <a href="https://twitter.com/samfirke">@samfirke</a>
* compose a friendly e-mail to: <img src = "http://samfirke.com/wp-content/uploads/2016/07/email_address_whitespace_top.png" alt = "samuel.firke AT gmail" width = "210"/>