README.Rmd

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output: github_document
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# medusa <img src="man/figures/logo.png" align="right" width="120" />
  
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Medusa was developed to automate the data cleaning workflow of membership
analytic reports. Traditionally, the process of data cleaning was performed
manually in Excel, leading to an inefficient and cumbersome process. The end
result of this effort was a semi-annual membership report with visualizations
for key demographic variables that describes the SIOP membership population. The
purpose of the medusa package was to streamline the ingestion, data cleaning and
visualization processes associated with this project. In the current iteration
of medusa, the data cleaning and table generating steps have been automated.

## Installation

Medusa is currently available as a developmental package from this repository. 
The instructions below will walk you through installing it on your local machine.
As a side note, it relies heavily on the **tidyverse** package ecosystem to clean
and tidy data. You will be prompted to install all required packages if they are
not detected in your current R environment.

``` {r}
# install.packages("devtools")
#devtools::install_github("SzarR/medusa")
```

## Current Counts

Current counts after data cleaning for each of the years equate to:

* 2017: 9,905
* 2018: 10,079
* 2019: 9,842
* 2020: 9,660
* 2021: 9,100

## Example

There are two types of functions within *medusa*. The first, are the individual
step functions. Each such step function cleans a specific part of the demographic
dataset. An array of step functions are associated within the second type of
function, the wrapper function. There are three wrapper functions:

* make_demo_data( ) = Runs a series of 14 functions to clean raw demographics data
* make_dues( ) = Runs a series of 2 functions to clean raw dues data
* make_final_data( ) = Takes the output of the above to create the final data table

These wrapper functions are the primary means of interacting with the R package. 
A typical workflow would be:

First, we read in the xlsx datasets that house the data we'd like to clean.

```{r workflow_example}
#library(medusa)
#library(readxl)
#library(tidyverse)

#demo_raw <- read_xls(
#  path = "~/R-lang/MAS_EB_Reporting/data/Demographics 7-6-20.xls")

#dues_0620 <- read_xlsx(
#  path = "~/R-lang/MAS_EB_Reporting/data/Dues June 2020.xlsx",
#  sheet = "Sheet1")

#dues_0520 <- read_xlsx(
#  path = "~/R-lang/MAS_EB_Reporting/data/May 2020 Dues.xlsx",
#  sheet = "Sheet1")
```

Once the raw data has been loaded into your local R environment, we begin to
clean it. The line below takes the original demographic tibble and cleans all
relevant columns, producing a cleaned tibble as its output:

```{r}
#demo <- make_demo_data(df = demo_raw)
```

The next wrapper function takes any number of tibbles as an argument, along
with a *year* argument to specify which SIOP year analytics are being performed 
on. Afterward, standard data cleaning is then performed, resulting in a cleaned
dues tibble.

```{r}
#dues <- make_dues(dues_0520, dues_0620, ..., year = 2020)
```

Finally, once the demo and dues files have been cleaned accordingly, we can run
the merge to obtain the resultant tibble of active SIOP members for the year
specified.

```{r}
#data_final <- make_final_data(demo = demo, dues = dues, staff=staff)
```

This final dataset then serves as the baseline for all data analytics.