Cyclistic - Casual Riders vs Members

July 27, 2022

Business Task

Identify how casual riders use Cyclistic bikes differently than Cyclistic members.

Identifying these differences will determine if and how Cyclistic can convert casual riders into annual members.

Data Source

Analysis has been conducted using June 2021 to May 2022 Cyclistic trip data, provided by Motivate International Inc.

Source: https://divvy-tripdata.s3.amazonaws.com/index.html

Preparing Data

Create data frames for individual trip data files

df1 <- read_csv("~/cyclisticCaseStudy/Data/202106-divvy-tripdata.csv")

## Rows: 729595 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df2 <- read_csv("~/cyclisticCaseStudy/Data/202107-divvy-tripdata.csv")

## Rows: 822410 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df3 <- read_csv("~/cyclisticCaseStudy/Data/202108-divvy-tripdata.csv")

## Rows: 804352 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df4 <- read_csv("~/cyclisticCaseStudy/Data/202109-divvy-tripdata.csv")

## Rows: 756147 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df5 <- read_csv("~/cyclisticCaseStudy/Data/202110-divvy-tripdata.csv")

## Rows: 631226 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df6 <- read_csv("~/cyclisticCaseStudy/Data/202111-divvy-tripdata.csv")

## Rows: 359978 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df7 <- read_csv("~/cyclisticCaseStudy/Data/202112-divvy-tripdata.csv")

## Rows: 247540 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df8 <- read_csv("~/cyclisticCaseStudy/Data/202201-divvy-tripdata.csv")

## Rows: 103770 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df9 <- read_csv("~/cyclisticCaseStudy/Data/202202-divvy-tripdata.csv")

## Rows: 115609 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df10 <- read_csv("~/cyclisticCaseStudy/Data/202203-divvy-tripdata.csv")

## Rows: 284042 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df11 <- read_csv("~/cyclisticCaseStudy/Data/202204-divvy-tripdata.csv")

## Rows: 371249 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df12 <- read_csv("~/cyclisticCaseStudy/Data/202205-divvy-tripdata.csv")

## Rows: 634858 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl  (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Check column details for each data frame

spec(df1)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df2)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df3)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df4)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df5)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df6)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df7)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df8)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df9)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df10)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df11)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

spec(df12)

## cols(
##   ride_id = col_character(),
##   rideable_type = col_character(),
##   started_at = col_datetime(format = ""),
##   ended_at = col_datetime(format = ""),
##   start_station_name = col_character(),
##   start_station_id = col_character(),
##   end_station_name = col_character(),
##   end_station_id = col_character(),
##   start_lat = col_double(),
##   start_lng = col_double(),
##   end_lat = col_double(),
##   end_lng = col_double(),
##   member_casual = col_character()
## )

Merge Trip Data into one data frame

tripdata_df <- rbind(df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12)

Creating Additional Data For Analysis

Add ride length

tripdata_df$ride_length <- with(tripdata_df, ended_at - started_at) 

#relocate ride_length after dates
tripdata_df <- tripdata_df %>% relocate(ride_length, .before = start_station_name) 

#convert ride length to numeric format
tripdata_df$ride_length <- as.numeric(as.character(tripdata_df$ride_length))

Add day of the week

tripdata_df$day_of_week <- with(tripdata_df, weekdays(started_at))
tripdata_df <- tripdata_df %>% relocate(day_of_week, .before = start_station_name) #relocate day of the week after ride_length

Verifying/Cleaning the Data

Verify Column Names

colnames(tripdata_df)

##  [1] "ride_id"            "rideable_type"      "started_at"        
##  [4] "ended_at"           "ride_length"        "day_of_week"       
##  [7] "start_station_name" "start_station_id"   "end_station_name"  
## [10] "end_station_id"     "start_lat"          "start_lng"         
## [13] "end_lat"            "end_lng"            "member_casual"

Check all trip IDs are unique

length(unique(tripdata_df$ride_id)) == length(tripdata_df$ride_id)

## [1] TRUE

Check date fields

class(tripdata_df$started_at)

## [1] "POSIXct" "POSIXt"

Check member classifications

unique(tripdata_df$member_casual)

## [1] "member" "casual"

Check rideable types

unique(tripdata_df$rideable_type)

## [1] "electric_bike" "classic_bike"  "docked_bike"

Remove zero and negative ride duration data

tripdata_df <- tripdata_df[(tripdata_df$ride_length > 0),]
#check min ride lenght
min(tripdata_df$ride_length)

## [1] 1

Analysis

Data Frame Summary

##    ride_id          rideable_type        started_at                 
##  Length:5860130     Length:5860130     Min.   :2021-06-01 00:00:38  
##  Class :character   Class :character   1st Qu.:2021-07-29 10:42:03  
##  Mode  :character   Mode  :character   Median :2021-09-23 17:33:16  
##                                        Mean   :2021-10-26 03:46:57  
##                                        3rd Qu.:2022-01-14 11:14:06  
##                                        Max.   :2022-05-31 23:59:56  
##                                                                     
##     ended_at                    ride_length      day_of_week       
##  Min.   :2021-06-01 00:06:22   Min.   :      1   Length:5860130    
##  1st Qu.:2021-07-29 11:02:00   1st Qu.:    382   Class :character  
##  Median :2021-09-23 17:49:24   Median :    681   Mode  :character  
##  Mean   :2021-10-26 04:07:39   Mean   :   1242                     
##  3rd Qu.:2022-01-14 11:29:08   3rd Qu.:   1236                     
##  Max.   :2022-06-02 11:35:01   Max.   :3356649                     
##                                                                    
##  start_station_name start_station_id   end_station_name   end_station_id    
##  Length:5860130     Length:5860130     Length:5860130     Length:5860130    
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##    start_lat       start_lng         end_lat         end_lng      
##  Min.   :41.64   Min.   :-87.84   Min.   :41.39   Min.   :-88.97  
##  1st Qu.:41.88   1st Qu.:-87.66   1st Qu.:41.88   1st Qu.:-87.66  
##  Median :41.90   Median :-87.64   Median :41.90   Median :-87.64  
##  Mean   :41.90   Mean   :-87.65   Mean   :41.90   Mean   :-87.65  
##  3rd Qu.:41.93   3rd Qu.:-87.63   3rd Qu.:41.93   3rd Qu.:-87.63  
##  Max.   :45.64   Max.   :-73.80   Max.   :42.17   Max.   :-87.49  
##                                   NA's   :5036    NA's   :5036    
##  member_casual     
##  Length:5860130    
##  Class :character  
##  Mode  :character  
##                    
##                    
##                    
##

Average Ride Lengths

## [1] 13.04521

## [1] 30.55286

Weekday Usage

How do casual riders use Cyclistic bikes?

Casual riders prefer to use bikes on the weekends
Rides are typically longer, with the average time being 30 minutes
Usage for casual riders peaks on the weekend, but drops dramatically during the week

How do annual members use Cyclistic bikes?

Members primarily use bikes during the week
Rides are typically shorter than casual riders, but are taken more frequently
Bikes are used as a means to commute to and from work

Recommendations

Cyclistic should encourage more casual users to ride on the weekdays, thus creating more value in annual memberships for these users.

Make commuting via bicycles more attractive or practical
- Explore partnerships with local businesses
- Actively promote the addition of bike lanes and safety corridors
Market the service as a daily fitness solution
- Explore partnerships with gyms, hospitals, etc.
- Offer discounted membership pricing during the New Year’s holiday

Follow-Up

Additional data gathering could provide further insights:

Survey of users on commuting habits
Survey of users on fitness habits
Billing information (kept confidential) to determine percentage of memberships purchased in bulk
- Insights into who is purchasing plans for their employees, members, etc.