Case Study: Turning Casual Riders To Members

Analyze real data sets from a bike sharing company to find how casual and member riders use company bikes differently. The analysis should be an important tool for the marketing analyst team to figure out a strategy that converts casual riders into annual members.

How do Casual and Annual Members use Cyclistic bikes differently?

• Imported data from Excel to Google sheets

• Checked for data errors (Nulls, Misspellings, Wrong Data Types, Etc.) by sorting and filtering

• Made copies of the data before manipulating the data

• Froze the header row

• Formatted column to have matching data types

• Created two new columns (ride_length, day_of_the_week) where I calculated the day of the week and the duration of each bike ride using functions

• Found the Mean and Max for "ride_length", and the Mode for "day_of_the_week" using functions

• Created pivot tables comparing the average ride duration and the total number of bike rides between casual and member riders for each day of the week

• Installed and loaded necessary packages

• Set the working directory

• Uploaded four CSV files (data sets)

• Compared the column names of data sets column and then renamed them to match each other

• Compared the data types of each column and mutated them to match each other

• Stacked all four data sets and created one big data frame

• Got a summary of the data

• Reassigned observations

• Added columns to the data frame

• Created a copy of the data frame and deleted unnecessary columns

• Collected the mean, median, max, and the min of the ride length

• Created visuals from RStudio and exported them to a local file

• Wrote over 200 lines of code