The bike ride share data shows behavioral data between subscription members and casual riders. The data was collected from Lyft Bikes and Scooters, LLC (“Bikeshare”) spreadsheet

Project Objective:

The main goal of this project is to analyze the differences in bike usage patterns between casual riders and annual members. This analysis will provide valuable insights to design data-driven marketing strategies to convert casual riders into annual members. By utilizing Cyclistic’s historical bike trip data, the project seeks to uncover key behavioral trends that will guide the development of targeted marketing campaigns. These campaigns will focus on increasing annual memberships, which are more profitable for the company compared to casual riders.

Data Analysis:
- This project centers on analyzing Cyclistic’s historical bike trip data, which includes details such as trip duration, start and end locations, and the type of pass used (casual or annual membership).
- The analysis will explore key differences between casual riders and annual members, focusing on factors like trip frequency, duration, popular stations, and preferred times of day or week for riding.
Marketing Strategy Development:

Insights from the data analysis will help the marketing team craft strategies to effectively target casual riders, especially through digital and social media campaigns.
The project will emphasize identifying optimal opportunities for seasonal promotions, localized marketing efforts, and targeted offers aimed at boosting conversions to annual memberships.

Cleaning Steps Taken

removed duplicates
Verified data types
combined all data into a single spreadsheet

R Code

The R code below is used to organize and analyze the dataset

#installing and loading packages 
install.packages("tidyverse")                 
library(tidyverse)
library(conflicted)
install.packages("xlsx")
library("xlsx")

conflict_prefer("filter","dplyr")
conflict_prefer("lag","dplyr")

install.packages("readxl")
library("readxl")

#reading combined excel sheet into R 
all_trips<- excel_sheets("year_to_date.xlsx") %>% 
  map_df(~read_xlsx("year_to_date.xlsx",.))

all_trips <- rename(all_trips, day_of_week = day_of_the_week)

#checking colunmns and data types
View(all_trips)
head(all_trips$ride_length)
tail(all_trips)
nrow(all_trips)
dim(all_trips)
str(all_trips)

#removing empty columns
all_trips <- subset(all_trips , select = -c(16,17)) 

#creating day, month and year columns
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")

#formatting weekdays to weekday names instead of 1-7
all_trips$day_of_week <- format(as.Date(all_trips$date),"%A")               
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)

min(all_trips$ride_length)
#converting ride length to numeric for calculations
all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
is.numeric(all_trips$ride_length)

#removing negative values and headquarter trips by the company
all_trips_v2 <- all_trips[!(all_trips$start_station_name == "HQ QR" | all_trips$ride_length<0),] 

#removing columns that wont be used for analysis
all_trips_v2 <- subset(all_trips , select = -c(start_lat,end_lat,start_lng,end_lng,start_station_id,end_station_id, end_station_name)) 
#remove missing values 
all_trips_v2 <- na.omit(all_trips_v2)
View(all_trips_v2)

#min,max,median, and mean values for ride length
summary(all_trips_v2$ride_length) 

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = mean) 
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median)  
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max)
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = min)

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

#organizing days in order
all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))

rides_by_usetype <-all_trips_v2 %>% 
  mutate(weekday = wday(started_at,label = TRUE)) %>% #creates weekday feild
  group_by(member_casual,weekday) %>% #groups by usertype and weekday
  summarise(number_of_rides = n(),average_duration = mean(ride_length)) %>% #calculates the number of rides and average duration 
  arrange(member_casual,weekday) %>% #sorts data 
  filter(!is.na(weekday)) %>% #filter out missing data
  ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual))+
  geom_col(position = "dodge")
  
average_ride <- all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>% 
  group_by(member_casual, weekday) %>% 
  summarise(number_of_rides = n()
            ,average_duration = mean(ride_length)) %>% 
  filter(!is.na(weekday)) %>% #filter out missing data
  arrange(member_casual, weekday)  %>% 
  ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +
  geom_col(position = "dodge")

print(rides_by_usetype)
print(average_ride)

#creating a table to find the busiest stations for casual riders
stations <- all_trips_v2 %>% 
  select(member_casual, start_station_name) %>% 
  filter(member_casual == "casual") %>% 
  count(start_station_name)

print(stations)

#exporting a spreadsheet for year to date spreadsheet and stations names for casual members for further visualization and analysis

write.csv(all_trips_v2,file = 'year_to_date_finished.csv' ) #exporting data to csv format 
write_csv(stations, file = 'casual_stations.csv') #saving stations to target for casual members to identify opportunity

View(all_trips_v2)

                                    
#exporting a spreadsheet for average ride lengths for further visualization and analysis
counts <- aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)
write.csv(counts, file = 'avg_ride_length.csv') #saving number of trip per day to a csv file

Average Trip Duration

We can observe that subscription riders have pretty consistent ride durations during the week with a spike in the weekends, while casual riders have much longer trips especially on the weekends.

Number of Trips

We can observe that the number of trips increase on the weekends for casual riders and subscription members on the weekdays. This leads one to believe subscription members are using the bikes for daily commute.