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.

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.

# install.packages("devtools")
#devtools::install_github("SzarR/medusa")

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
• 2022: 9,190
• 2023: 9,583

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.

#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:

#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.

#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.

#data_final <- make_final_data(demo = demo, dues = dues, staff=staff)

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