Cyclist Bike-Share Analysis
Jasmine Dalrymple
2025-11-05
Inroduction
The Cyclistic Bike Share Case Study is my capstone project for the Google Data Analytics Certificate. For the analysis I will be using R programing language and RStudio. In this project, I will follow the data analysis process which I learned from the course: ask, prepare, process, analyze, share and act to analyze the data.
Scenario
You are a junior data analyst working on the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.
Ask
Lily Moreno,The director of marketing and your manager, the has assigned you the question to answer: How do annual members and casual riders use Cyclistic bikes differently?
Business Objective
The business objective of the case study is to identify opportunities for marketing campaigns to convert casual riders into annual members. This will be done through analysis of bike trip data and the understanding user behavior and preferences. The ultimate goal is to increase profit.
Business Task
Analyze Cyclistic’s historical bike trip data Identify trends and Patterns of casual and member riders Understand behavior and preferences of these two user groups Identify ways to convert casual riders into annual members Use insights from analysis to inform development of new marketing strategy for the company Goal increase profitability and drive future growth.
Stakeholders
Lily Moreno: Director of Marketing at Cyclistic, who is responsible for implementing the marketing campaigns at Cyclistic.
Cyclistic’s marketing team: They will be responsible for conducting the analysis and developing the marketing strategy based on the insights gained.
Cyclistic’s investors and shareholders: They have a financial interest in the company’s success and may be interested in the results of the analysis and any changes to the marketing strategy.
Prepare
Data Source
The data for this analysis can be accessed through the provided link. It includes 12 months of historical trip data in 2019 and 2020 from Cyclistic, a fictional bike share company based in Chicago.
Credibility of Data
Motivate, Inc. collected the data for this analysis directly through its management of the Cyclistic Bike Share program for the City of Chicago. The data is comprehensive and consistent, because it includes information on all rides taken by users; not just a sample. It is also current, as it is released on a monthly basis by the City of Chicago. The data is made available to the public by the City of Chicago.
Process
Here, we perform the data cleaning, ensure integrity, and that it is complete, correct, and relevant.
Install Required Packages
- tidyverse
- conflicted
- ggplot2
library(tidyverse)
library(conflicted)
library(ggplot2)Download and store data
read_csv("Divvy_Trips_2019_Q1.csv")## Rows: 365069 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): start_time, end_time, from_station_name, to_station_name, usertype,...
## dbl (5): trip_id, bikeid, from_station_id, to_station_id, birthyear
## num (1): tripduration
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 365,069 Ă— 12
## trip_id start_time end_time bikeid tripduration from_station_id
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 21742443 2019-01-01 0:04:37 2019-01-01 0… 2167 390 199
## 2 21742444 2019-01-01 0:08:13 2019-01-01 0… 4386 441 44
## 3 21742445 2019-01-01 0:13:23 2019-01-01 0… 1524 829 15
## 4 21742446 2019-01-01 0:13:45 2019-01-01 0… 252 1783 123
## 5 21742447 2019-01-01 0:14:52 2019-01-01 0… 1170 364 173
## 6 21742448 2019-01-01 0:15:33 2019-01-01 0… 2437 216 98
## 7 21742449 2019-01-01 0:16:06 2019-01-01 0… 2708 177 98
## 8 21742450 2019-01-01 0:18:41 2019-01-01 0… 2796 100 211
## 9 21742451 2019-01-01 0:18:43 2019-01-01 0… 6205 1727 150
## 10 21742452 2019-01-01 0:19:18 2019-01-01 0… 3939 336 268
## # â„ą 365,059 more rows
## # â„ą 6 more variables: from_station_name <chr>, to_station_id <dbl>,
## # to_station_name <chr>, usertype <chr>, gender <chr>, birthyear <dbl>read_csv("Divvy_Trips_2020_Q1.csv")## Rows: 426887 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, started_at, ended_at, start_station_name, e...
## dbl (6): start_station_id, end_station_id, start_lat, start_lng, end_lat, en...
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 426,887 Ă— 13
## ride_id rideable_type started_at ended_at start_station_name start_station_id
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 EACB19… docked_bike 2020-01-2… 2020-01… Western Ave & Lel… 239
## 2 8FED87… docked_bike 2020-01-3… 2020-01… Clark St & Montro… 234
## 3 789F3C… docked_bike 2020-01-0… 2020-01… Broadway & Belmon… 296
## 4 C9A388… docked_bike 2020-01-0… 2020-01… Clark St & Randol… 51
## 5 943BC3… docked_bike 2020-01-3… 2020-01… Clinton St & Lake… 66
## 6 6D9C8A… docked_bike 2020-01-1… 2020-01… Wells St & Hubbar… 212
## 7 31EB9B… docked_bike 2020-01-1… 2020-01… Desplaines St & R… 96
## 8 A2B24E… docked_bike 2020-01-1… 2020-01… Desplaines St & R… 96
## 9 5E3F01… docked_bike 2020-01-3… 2020-01… Wells St & Hubbar… 212
## 10 19DC57… docked_bike 2020-01-3… 2020-01… Clark St & Lake St 38
## # â„ą 426,877 more rows
## # â„ą 7 more variables: end_station_name <chr>, end_station_id <dbl>,
## # start_lat <dbl>, start_lng <dbl>, end_lat <dbl>, end_lng <dbl>,
## # member_casual <chr>Data Wrangling and combining
Rename the files to make them consistant with the 2020 data
(Divvy_Trips_2019_Q1 <- rename(Divvy_Trips_2019_Q1
,ride_id = trip_id
,rideable_type = bikeid
,started_at = start_time
,ended_at = end_time
,start_station_name = from_station_name
,start_station_id = from_station_id
,end_station_name = to_station_name
,end_station_id = to_station_id
,member_casual = usertype
))## # A tibble: 365,069 Ă— 12
## ride_id started_at ended_at rideable_type tripduration start_station_id
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 21742443 2019-01-01 0:0… 2019-01… 2167 390 199
## 2 21742444 2019-01-01 0:0… 2019-01… 4386 441 44
## 3 21742445 2019-01-01 0:1… 2019-01… 1524 829 15
## 4 21742446 2019-01-01 0:1… 2019-01… 252 1783 123
## 5 21742447 2019-01-01 0:1… 2019-01… 1170 364 173
## 6 21742448 2019-01-01 0:1… 2019-01… 2437 216 98
## 7 21742449 2019-01-01 0:1… 2019-01… 2708 177 98
## 8 21742450 2019-01-01 0:1… 2019-01… 2796 100 211
## 9 21742451 2019-01-01 0:1… 2019-01… 6205 1727 150
## 10 21742452 2019-01-01 0:1… 2019-01… 3939 336 268
## # â„ą 365,059 more rows
## # â„ą 6 more variables: start_station_name <chr>, end_station_id <dbl>,
## # end_station_name <chr>, member_casual <chr>, gender <chr>, birthyear <dbl>Coveret the ride_id and rideable_type to stack correctly
Divvy_Trips_2019_Q1 <- mutate(Divvy_Trips_2019_Q1, ride_id = as.character(ride_id)
,rideable_type = as.character(rideable_type))Make one data frame with all the data and remove data that isnt consistant through the years
all_trips <- bind_rows(Divvy_Trips_2019_Q1, Divvy_Trips_2020_Q1)#, q3_2019)#, q4_2019, q1_2020)all_trips <- all_trips %>%
select(-c(start_lat, start_lng, end_lat, end_lng, birthyear, gender, "tripduration"))Clean and add data to prepare for analysis
What needs to be cleaned?
- There are two names for member and two for casual, so to get rid of the names subscriber and customer I reassigned the value names.
all_trips <- all_trips %>%
mutate(member_casual = recode(member_casual
,"Subscriber" = "member"
,"Customer" = "casual"))- Remove the data with negative ride_length because that means it was being checked for quality by Divvy, and not being used by riders.
all_trips$ride_length <- as.numeric(difftime(all_trips$ended_at, all_trips$started_at, units = "mins"))
all_trips <- all_trips %>% dplyr::filter(start_station_name != "HQ QR", ride_length >= 0)Add columns that list the date, month, day, and year of each ride
all_trips$date <- as.Date(all_trips$started_at)
all_trips$month <- format(as.Date(all_trips$date), "%m")
all_trips$day <- format(as.Date(all_trips$date), "%d")
all_trips$year <- format(as.Date(all_trips$date), "%Y")
all_trips$day_of_week <- format(as.Date(all_trips$date), "%A")Add a ride_lengh and convert from factor to numeric
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))Analysis
I used summery to find the min, max, median, and mean if the ride_lenths
``` r
summary(all_trips$ride_length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 331 539 1189 912 10628422Arrange by the days of the week a compare the casual riders and members
all_trips$day_of_week <- ordered(all_trips$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))
aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = mean)## all_trips$member_casual all_trips$ride_length
## 1 casual 5370.7685
## 2 member 795.1324aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = median)## all_trips$member_casual all_trips$ride_length
## 1 casual 1393
## 2 member 508aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = max)## all_trips$member_casual all_trips$ride_length
## 1 casual 10628422
## 2 member 6096428aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = min)## all_trips$member_casual all_trips$ride_length
## 1 casual 2
## 2 member 1The average ride time by each day for members vs casual users
aggregate(all_trips$ride_length ~ all_trips$member_casual + all_trips$day_of_week, FUN = mean)## all_trips$member_casual all_trips$day_of_week all_trips$ride_length
## 1 casual Sunday 5059.1813
## 2 member Sunday 972.1609
## 3 casual Monday 4750.7627
## 4 member Monday 822.2134
## 5 casual Tuesday 4560.8190
## 6 member Tuesday 769.4416
## 7 casual Wednesday 4480.3724
## 8 member Wednesday 711.9838
## 9 casual Thursday 8450.1557
## 10 member Thursday 707.2093
## 11 casual Friday 6088.9402
## 12 member Friday 796.6713
## 13 casual Saturday 4946.4956
## 14 member Saturday 973.7094The relationship between rides and days of the week
all_trips %>%
mutate(weekday = wday(started_at, label = TRUE)) %>%
group_by(member_casual, weekday) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, weekday) ## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.## # A tibble: 14 Ă— 4
## # Groups: member_casual [2]
## member_casual weekday number_of_rides average_duration
## <chr> <ord> <int> <dbl>
## 1 casual Sun 18652 5059.
## 2 casual Mon 5591 4751.
## 3 casual Tue 7311 4561.
## 4 casual Wed 7690 4480.
## 5 casual Thu 7147 8450.
## 6 casual Fri 8013 6089.
## 7 casual Sat 13473 4946.
## 8 member Sun 60197 972.
## 9 member Mon 110430 822.
## 10 member Tue 127974 769.
## 11 member Wed 121902 712.
## 12 member Thu 125228 707.
## 13 member Fri 115168 797.
## 14 member Sat 59413 974.Visualizations
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Act
Key Takeaways
- Annual members primarily use the bike-sharing service for commuting purposes, while casual riders tend to use it for leisure, especially on weekends. -Annual members exhibit a more consistent use of service throughout the week compared to casual riders. -Casual riders tend to have longer ride duration’s, averaging around 400% longer than annual members. -Saturday has highest count of rented bikes.
Reccomendations
- Offer discounts or incentives for casual riders during weekdays to encourage more usage during the week.
- Introduce a rewards system for casual riders making it easier to convert to a membership
- Provide a echo friendly option for riders by offering discounts to businesses. This could motivate more casual riders to bike to work and save money on gas and parking.
Conclusion
This analysis provides valuable insights into the preferences and behaviors of members and casual riders. By tailoring strategies to the identified differences and preferences, Cyclistic can effectively convert casual riders into portential members.