Case study: How does a bike-share navigate speedy success?
Nisha P
2024-07-16
Overview
Cyclistic, a prominent bike-share company headquartered in Chicago, has rapidly gained traction in the city’s transportation landscape. In an effort to delve deeper into their customer base and refine their marketing strategies, Cyclistic seeks to understand the distinct behaviors and preferences of casual riders versus annual members.
The company recognizes the need to tailor its marketing approach to effectively convert casual riders into committed annual members. By leveraging data-driven insights, Cyclistic aims to develop a comprehensive understanding of how these two customer segments interact with their services differently.
Business Task
The objective of this business task is to develop a comprehensive marketing strategy for Cyclistic that addresses the distinct needs and behaviors of both annual members and casual riders. By answering the following three questions, we aim to optimize marketing efforts, increase customer engagement, and drive conversions from casual riders to annual members.
- Understanding Usage Patterns: Analyze Cyclistic’s dataset to identify differences in how annual members and casual riders utilize Cyclistic bikes.
Data Background
The dataset was acquired from the link https://divvy-tripdata.s3.amazonaws.com/index.html Motivate International Inc made the data available under this https://divvybikes.com/data-license-agreement
For this project, I downloaded data for twelve months (January to December 2020). The zipped CSVs were downloaded and unzipped into a folder.
Below shown the dataset of a cyclistic biketrip data for the year 2020.The dataset has 3541683 rows and 13 column.
Due to the large size of data we use R to analyse effectively.
R Programming
- Loading Packages The R package is a collection of R functions, data sets, and compiled code that extends the functionality of R.
In R, the library() function is used to load R packages into your current R session
library (tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary (janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary (lubridate)
library (scales)##
## Attaching package: 'scales'
##
## The following object is masked from 'package:purrr':
##
## discard
##
## The following object is masked from 'package:readr':
##
## col_factorlibrary(geosphere)
rm(list=ls())- Read CSV file
Below given are the year 2020 dataset of cyclistic bike share program which are downloaded and saved as CSV files. Here read.csv() is used for reading the csv files.
df1 <- read.csv("Divvy_Trips_2020_Q1.csv")
df2 <- read.csv("202004-divvy-tripdata.csv")
df3 <- read.csv("202005-divvy-tripdata.csv")
df4 <- read.csv("202006-divvy-tripdata.csv")
df5 <- read.csv("202007-divvy-tripdata.csv")
df6 <- read.csv("202008-divvy-tripdata.csv")
df7 <- read.csv("202009-divvy-tripdata.csv")
df8 <- read.csv("202010-divvy-tripdata.csv")
df9 <- read.csv("202011-divvy-tripdata.csv")
df10 <- read.csv("202012-divvy-tripdata.csv")
df20 <- rbind(df1,df2,df3,df4,df5,df6,df7,df8,df9,df10)In R, the head() function is used to view the first few rows of a data frame or a matrix. It allows you to quickly inspect the structure and content of your data without displaying the entire dataset.
head(df20)## ride_id rideable_type started_at ended_at
## 1 EACB19130B0CDA4A docked_bike 2020-01-21 20:06:59 2020-01-21 20:14:30
## 2 8FED874C809DC021 docked_bike 2020-01-30 14:22:39 2020-01-30 14:26:22
## 3 789F3C21E472CA96 docked_bike 2020-01-09 19:29:26 2020-01-09 19:32:17
## 4 C9A388DAC6ABF313 docked_bike 2020-01-06 16:17:07 2020-01-06 16:25:56
## 5 943BC3CBECCFD662 docked_bike 2020-01-30 08:37:16 2020-01-30 08:42:48
## 6 6D9C8A6938165C11 docked_bike 2020-01-10 12:33:05 2020-01-10 12:37:54
## start_station_name start_station_id end_station_name
## 1 Western Ave & Leland Ave 239 Clark St & Leland Ave
## 2 Clark St & Montrose Ave 234 Southport Ave & Irving Park Rd
## 3 Broadway & Belmont Ave 296 Wilton Ave & Belmont Ave
## 4 Clark St & Randolph St 51 Fairbanks Ct & Grand Ave
## 5 Clinton St & Lake St 66 Wells St & Hubbard St
## 6 Wells St & Hubbard St 212 Desplaines St & Randolph St
## end_station_id start_lat start_lng end_lat end_lng member_casual
## 1 326 41.9665 -87.6884 41.9671 -87.6674 member
## 2 318 41.9616 -87.6660 41.9542 -87.6644 member
## 3 117 41.9401 -87.6455 41.9402 -87.6530 member
## 4 24 41.8846 -87.6319 41.8918 -87.6206 member
## 5 212 41.8856 -87.6418 41.8899 -87.6343 member
## 6 96 41.8899 -87.6343 41.8846 -87.6446 memberThe str() function in R is used to compactly display the internal structure of an R object. It is particularly useful for understanding the structure of complex objects such as data frames, lists, and matrices.
str(df20)## 'data.frame': 3541683 obs. of 13 variables:
## $ ride_id : chr "EACB19130B0CDA4A" "8FED874C809DC021" "789F3C21E472CA96" "C9A388DAC6ABF313" ...
## $ rideable_type : chr "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
## $ started_at : chr "2020-01-21 20:06:59" "2020-01-30 14:22:39" "2020-01-09 19:29:26" "2020-01-06 16:17:07" ...
## $ ended_at : chr "2020-01-21 20:14:30" "2020-01-30 14:26:22" "2020-01-09 19:32:17" "2020-01-06 16:25:56" ...
## $ start_station_name: chr "Western Ave & Leland Ave" "Clark St & Montrose Ave" "Broadway & Belmont Ave" "Clark St & Randolph St" ...
## $ start_station_id : chr "239" "234" "296" "51" ...
## $ end_station_name : chr "Clark St & Leland Ave" "Southport Ave & Irving Park Rd" "Wilton Ave & Belmont Ave" "Fairbanks Ct & Grand Ave" ...
## $ end_station_id : chr "326" "318" "117" "24" ...
## $ start_lat : num 42 42 41.9 41.9 41.9 ...
## $ start_lng : num -87.7 -87.7 -87.6 -87.6 -87.6 ...
## $ end_lat : num 42 42 41.9 41.9 41.9 ...
## $ end_lng : num -87.7 -87.7 -87.7 -87.6 -87.6 ...
## $ member_casual : chr "member" "member" "member" "member" ...- Cleaning Data
Janitor is an R package that provides a set of functions to clean and preprocess data in R data frames
df20_cleanedcols <- janitor::remove_empty(df20,which =c("cols"))
df20_cleanedrows <- janitor::remove_empty(df20,which =c("rows"))
dim(df20_cleanedcols)## [1] 3541683 13dim(df20_cleanedrows)## [1] 3541683 13- Removing duplicates and NA values
In R, the* is.na() function and the na.omit() function are used for handling missing values (NAs) in data.
sum(is.na(df20))## [1] 190198#omit NA
df20_clean <- na.omit(df20)
glimpse(df20_clean)## Rows: 3,406,932
## Columns: 13
## $ ride_id <chr> "EACB19130B0CDA4A", "8FED874C809DC021", "789F3C21E4…
## $ rideable_type <chr> "docked_bike", "docked_bike", "docked_bike", "docke…
## $ started_at <chr> "2020-01-21 20:06:59", "2020-01-30 14:22:39", "2020…
## $ ended_at <chr> "2020-01-21 20:14:30", "2020-01-30 14:26:22", "2020…
## $ start_station_name <chr> "Western Ave & Leland Ave", "Clark St & Montrose Av…
## $ start_station_id <chr> "239", "234", "296", "51", "66", "212", "96", "96",…
## $ end_station_name <chr> "Clark St & Leland Ave", "Southport Ave & Irving Pa…
## $ end_station_id <chr> "326", "318", "117", "24", "212", "96", "212", "212…
## $ start_lat <dbl> 41.9665, 41.9616, 41.9401, 41.8846, 41.8856, 41.889…
## $ start_lng <dbl> -87.6884, -87.6660, -87.6455, -87.6319, -87.6418, -…
## $ end_lat <dbl> 41.9671, 41.9542, 41.9402, 41.8918, 41.8899, 41.884…
## $ end_lng <dbl> -87.6674, -87.6644, -87.6530, -87.6206, -87.6343, -…
## $ member_casual <chr> "member", "member", "member", "member", "member", "…The unique() function in R is used to extract the unique elements from an object, such as a vector, data frame, or list.
df20_clean <- df20_clean %>%
filter(df20_clean$start_station_name!="")
glimpse(df20_clean)## Rows: 3,395,233
## Columns: 13
## $ ride_id <chr> "EACB19130B0CDA4A", "8FED874C809DC021", "789F3C21E4…
## $ rideable_type <chr> "docked_bike", "docked_bike", "docked_bike", "docke…
## $ started_at <chr> "2020-01-21 20:06:59", "2020-01-30 14:22:39", "2020…
## $ ended_at <chr> "2020-01-21 20:14:30", "2020-01-30 14:26:22", "2020…
## $ start_station_name <chr> "Western Ave & Leland Ave", "Clark St & Montrose Av…
## $ start_station_id <chr> "239", "234", "296", "51", "66", "212", "96", "96",…
## $ end_station_name <chr> "Clark St & Leland Ave", "Southport Ave & Irving Pa…
## $ end_station_id <chr> "326", "318", "117", "24", "212", "96", "212", "212…
## $ start_lat <dbl> 41.9665, 41.9616, 41.9401, 41.8846, 41.8856, 41.889…
## $ start_lng <dbl> -87.6884, -87.6660, -87.6455, -87.6319, -87.6418, -…
## $ end_lat <dbl> 41.9671, 41.9542, 41.9402, 41.8918, 41.8899, 41.884…
## $ end_lng <dbl> -87.6674, -87.6644, -87.6530, -87.6206, -87.6343, -…
## $ member_casual <chr> "member", "member", "member", "member", "member", "…- Organising Data
Lubridate is an R package designed to make it easier to work with dates and times in R. It provides a set of functions that simplify common tasks such as parsing, manipulating, and formatting dates and times.we use parse date ymd_hms() and as.Date() for changing the Started_at and ended_at column format.
Difftime() is used for calculating the difference in time. This helps us to find and analyse the duration of each ride.
df <- df20_clean
df$started_date <- as.Date(df$started_at)
df$ended_date <- as.Date(df$ended_at)
#time as hours and minutes
df$started_at <- lubridate::ymd_hms(df$started_at)
df$ended_at <- lubridate::ymd_hms(df$ended_at)
df$Start_time <- format(df$started_at,"%H:%M:%S")
df$End_time <- format(df$ended_at,"%H:%M:%S")
df$day_of_the_week <- weekdays(df$started_at)
df$month <- month(df$started_at, label = TRUE, abbr = TRUE)
df$trip_duration <- (as.double(difftime(df$ended_at,df$started_at)))/60
df<-df %>%
filter(trip_duration > 0)
glimpse(df) ## Rows: 3,384,262
## Columns: 20
## $ ride_id <chr> "EACB19130B0CDA4A", "8FED874C809DC021", "789F3C21E4…
## $ rideable_type <chr> "docked_bike", "docked_bike", "docked_bike", "docke…
## $ started_at <dttm> 2020-01-21 20:06:59, 2020-01-30 14:22:39, 2020-01-…
## $ ended_at <dttm> 2020-01-21 20:14:30, 2020-01-30 14:26:22, 2020-01-…
## $ start_station_name <chr> "Western Ave & Leland Ave", "Clark St & Montrose Av…
## $ start_station_id <chr> "239", "234", "296", "51", "66", "212", "96", "96",…
## $ end_station_name <chr> "Clark St & Leland Ave", "Southport Ave & Irving Pa…
## $ end_station_id <chr> "326", "318", "117", "24", "212", "96", "212", "212…
## $ start_lat <dbl> 41.9665, 41.9616, 41.9401, 41.8846, 41.8856, 41.889…
## $ start_lng <dbl> -87.6884, -87.6660, -87.6455, -87.6319, -87.6418, -…
## $ end_lat <dbl> 41.9671, 41.9542, 41.9402, 41.8918, 41.8899, 41.884…
## $ end_lng <dbl> -87.6674, -87.6644, -87.6530, -87.6206, -87.6343, -…
## $ member_casual <chr> "member", "member", "member", "member", "member", "…
## $ started_date <date> 2020-01-21, 2020-01-30, 2020-01-09, 2020-01-06, 20…
## $ ended_date <date> 2020-01-21, 2020-01-30, 2020-01-09, 2020-01-06, 20…
## $ Start_time <chr> "20:06:59", "14:22:39", "19:29:26", "16:17:07", "08…
## $ End_time <chr> "20:14:30", "14:26:22", "19:32:17", "16:25:56", "08…
## $ day_of_the_week <chr> "Tuesday", "Thursday", "Thursday", "Monday", "Thurs…
## $ month <ord> Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, J…
## $ trip_duration <dbl> 7.516667, 3.716667, 2.850000, 8.816667, 5.533333, 4…The distHaversine functionin R, from the geosphere package, is used to calculate the great-circle distance between two points on the Earth’s surface given their latitude and longitude coordinates. This distance is calculated using the Haversine formula, which accounts for the spherical shape of the Earth.
# Distance Calculation using distHaversine function
df$distance <- mapply(function(lat1, lon1, lat2, lon2) {
distHaversine(c(lon1, lat1), c(lon2, lat2))
}, df$start_lat, df$start_lng, df$end_lat, df$end_lng)
#change to km
df$distance <- df$distance/1000
glimpse(df)## Rows: 3,384,262
## Columns: 21
## $ ride_id <chr> "EACB19130B0CDA4A", "8FED874C809DC021", "789F3C21E4…
## $ rideable_type <chr> "docked_bike", "docked_bike", "docked_bike", "docke…
## $ started_at <dttm> 2020-01-21 20:06:59, 2020-01-30 14:22:39, 2020-01-…
## $ ended_at <dttm> 2020-01-21 20:14:30, 2020-01-30 14:26:22, 2020-01-…
## $ start_station_name <chr> "Western Ave & Leland Ave", "Clark St & Montrose Av…
## $ start_station_id <chr> "239", "234", "296", "51", "66", "212", "96", "96",…
## $ end_station_name <chr> "Clark St & Leland Ave", "Southport Ave & Irving Pa…
## $ end_station_id <chr> "326", "318", "117", "24", "212", "96", "212", "212…
## $ start_lat <dbl> 41.9665, 41.9616, 41.9401, 41.8846, 41.8856, 41.889…
## $ start_lng <dbl> -87.6884, -87.6660, -87.6455, -87.6319, -87.6418, -…
## $ end_lat <dbl> 41.9671, 41.9542, 41.9402, 41.8918, 41.8899, 41.884…
## $ end_lng <dbl> -87.6674, -87.6644, -87.6530, -87.6206, -87.6343, -…
## $ member_casual <chr> "member", "member", "member", "member", "member", "…
## $ started_date <date> 2020-01-21, 2020-01-30, 2020-01-09, 2020-01-06, 20…
## $ ended_date <date> 2020-01-21, 2020-01-30, 2020-01-09, 2020-01-06, 20…
## $ Start_time <chr> "20:06:59", "14:22:39", "19:29:26", "16:17:07", "08…
## $ End_time <chr> "20:14:30", "14:26:22", "19:32:17", "16:25:56", "08…
## $ day_of_the_week <chr> "Tuesday", "Thursday", "Thursday", "Monday", "Thurs…
## $ month <ord> Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, J…
## $ trip_duration <dbl> 7.516667, 3.716667, 2.850000, 8.816667, 5.533333, 4…
## $ distance <dbl> 1.7394455, 0.8343444, 0.6211318, 1.2326158, 0.78450…- Summarize the data
## summary of the dataframe by removing unnecessary column
df <- df %>%
select(-start_station_id,-end_station_id,-start_lat,-end_lat,-start_lng,-end_lng)
glimpse(df)## Rows: 3,384,262
## Columns: 15
## $ ride_id <chr> "EACB19130B0CDA4A", "8FED874C809DC021", "789F3C21E4…
## $ rideable_type <chr> "docked_bike", "docked_bike", "docked_bike", "docke…
## $ started_at <dttm> 2020-01-21 20:06:59, 2020-01-30 14:22:39, 2020-01-…
## $ ended_at <dttm> 2020-01-21 20:14:30, 2020-01-30 14:26:22, 2020-01-…
## $ start_station_name <chr> "Western Ave & Leland Ave", "Clark St & Montrose Av…
## $ end_station_name <chr> "Clark St & Leland Ave", "Southport Ave & Irving Pa…
## $ member_casual <chr> "member", "member", "member", "member", "member", "…
## $ started_date <date> 2020-01-21, 2020-01-30, 2020-01-09, 2020-01-06, 20…
## $ ended_date <date> 2020-01-21, 2020-01-30, 2020-01-09, 2020-01-06, 20…
## $ Start_time <chr> "20:06:59", "14:22:39", "19:29:26", "16:17:07", "08…
## $ End_time <chr> "20:14:30", "14:26:22", "19:32:17", "16:25:56", "08…
## $ day_of_the_week <chr> "Tuesday", "Thursday", "Thursday", "Monday", "Thurs…
## $ month <ord> Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, J…
## $ trip_duration <dbl> 7.516667, 3.716667, 2.850000, 8.816667, 5.533333, 4…
## $ distance <dbl> 1.7394455, 0.8343444, 0.6211318, 1.2326158, 0.78450…summary(df)## ride_id rideable_type started_at
## Length:3384262 Length:3384262 Min. :2020-01-01 00:04:44.00
## Class :character Class :character 1st Qu.:2020-06-15 18:10:09.00
## Mode :character Mode :character Median :2020-08-06 11:47:32.00
## Mean :2020-07-25 10:44:55.88
## 3rd Qu.:2020-09-20 19:27:40.25
## Max. :2020-12-31 23:59:59.00
##
## ended_at start_station_name end_station_name
## Min. :2020-01-01 00:10:54.00 Length:3384262 Length:3384262
## 1st Qu.:2020-06-15 18:36:25.25 Class :character Class :character
## Median :2020-08-06 12:14:02.00 Mode :character Mode :character
## Mean :2020-07-25 11:12:57.50
## 3rd Qu.:2020-09-20 19:57:06.25
## Max. :2021-01-03 08:54:11.00
##
## member_casual started_date ended_date
## Length:3384262 Min. :2020-01-01 Min. :2020-01-01
## Class :character 1st Qu.:2020-06-15 1st Qu.:2020-06-15
## Mode :character Median :2020-08-06 Median :2020-08-06
## Mean :2020-07-24 Mean :2020-07-24
## 3rd Qu.:2020-09-20 3rd Qu.:2020-09-20
## Max. :2020-12-31 Max. :2021-01-03
##
## Start_time End_time day_of_the_week month
## Length:3384262 Length:3384262 Length:3384262 Aug :605652
## Class :character Class :character Class :character Jul :548617
## Mode :character Mode :character Mode :character Sep :498228
## Jun :342040
## Oct :337375
## Nov :221916
## (Other):830434
## trip_duration distance
## Min. : 0.02 Min. : 0.0000
## 1st Qu.: 7.77 1st Qu.: 0.8578
## Median : 14.20 Median : 1.6617
## Mean : 28.03 Mean : 2.1940
## 3rd Qu.: 26.10 3rd Qu.: 2.9989
## Max. :156450.40 Max. :48.4250
## ##calculate riable_type usage
sum_df <- df %>%
select(rideable_type,member_casual,started_at,start_station_name,day_of_the_week,month,trip_duration,distance) %>%
group_by(rideable_type,member_casual) %>%
summarise(Total_Duration = sum(trip_duration),Count = n(),Total_distance = sum(distance)) %>%
ungroup()## `summarise()` has grouped output by 'rideable_type'. You can override using the
## `.groups` argument.glimpse(sum_df)## Rows: 6
## Columns: 5
## $ rideable_type <chr> "classic_bike", "classic_bike", "docked_bike", "docked_…
## $ member_casual <chr> "casual", "member", "casual", "member", "casual", "memb…
## $ Total_Duration <dbl> 265619.2, 760908.7, 58895684.5, 29011077.5, 3089328.6, …
## $ Count <int> 11288, 59248, 1140595, 1810763, 147271, 215097
## $ Total_distance <dbl> 22488.75, 112575.68, 2422406.88, 3948045.61, 367277.44,…## member Vs Casual distribution
Member_type<- df %>%
group_by(member_casual) %>%
summarise(Count = n(),Total_duration = sum(trip_duration),Total_distance = sum(distance)) %>%
ungroup()
glimpse(Member_type)## Rows: 2
## Columns: 4
## $ member_casual <chr> "casual", "member"
## $ Count <int> 1299154, 2085108
## $ Total_duration <dbl> 62250632, 32600893
## $ Total_distance <dbl> 2812173, 4612903# Daily ride
ride_per_day <- df %>%
group_by(started_date,member_casual) %>%
summarise(Avg_Trip = mean(trip_duration),Avg_distance = mean(distance),Count = n()) %>%
arrange(started_date) %>%
ungroup()## `summarise()` has grouped output by 'started_date'. You can override using the
## `.groups` argument.glimpse(ride_per_day)## Rows: 728
## Columns: 5
## $ started_date <date> 2020-01-01, 2020-01-01, 2020-01-02, 2020-01-02, 2020-01…
## $ member_casual <chr> "casual", "member", "casual", "member", "casual", "membe…
## $ Avg_Trip <dbl> 82.751572, 12.622806, 102.745953, 11.360752, 31.038227, …
## $ Avg_distance <dbl> 1.856419, 1.807829, 2.067135, 1.945271, 2.050374, 1.8494…
## $ Count <int> 477, 1664, 663, 5816, 453, 5437, 390, 2797, 431, 2604, 2…## weekly ride
Weekly_ride <- df %>%
group_by(day_of_the_week,member_casual) %>%
summarise(Avg_Trip = mean(trip_duration),
Avg_distance = mean(distance),Count = n()) %>%
arrange(day_of_the_week) %>%
ungroup()## `summarise()` has grouped output by 'day_of_the_week'. You can override using
## the `.groups` argument.glimpse(Weekly_ride)## Rows: 14
## Columns: 5
## $ day_of_the_week <chr> "Friday", "Friday", "Monday", "Monday", "Saturday", "S…
## $ member_casual <chr> "casual", "member", "casual", "member", "casual", "mem…
## $ Avg_Trip <dbl> 45.87021, 15.37449, 46.87540, 14.86429, 48.97663, 17.8…
## $ Avg_distance <dbl> 2.176026, 2.186232, 2.024397, 2.152693, 2.304111, 2.34…
## $ Count <int> 191493, 312182, 134204, 280497, 299915, 297969, 245412…# monthly Ride
monthly_ride <- df %>%
group_by(month, member_casual) %>%
summarise(Avg_Trip = mean(trip_duration),
Avg_distance = mean(distance),Count = n()) %>%
arrange(month) %>%
ungroup()## `summarise()` has grouped output by 'month'. You can override using the
## `.groups` argument.glimpse(monthly_ride)## Rows: 24
## Columns: 5
## $ month <ord> Jan, Jan, Feb, Feb, Mar, Mar, Apr, Apr, May, May, Jun, J…
## $ member_casual <chr> "casual", "member", "casual", "member", "casual", "membe…
## $ Avg_Trip <dbl> 161.64949, 11.14904, 127.63017, 12.80652, 63.12443, 14.3…
## $ Avg_distance <dbl> 1.944444, 1.783580, 1.955242, 1.768710, 1.962172, 1.9842…
## $ Count <int> 7785, 136099, 12860, 126715, 27625, 115593, 23566, 61054…# Popular start station
Popular_top_start_stations <- df %>%
count(start_station_name) %>%
arrange(desc(n)) %>%
head(10)# Top 20 start station
top_start_stations <- df %>%
group_by(start_station_name,member_casual) %>%
count(start_station_name) %>%
arrange(desc(n)) %>%
head(20) %>%
ungroup()#Top 20 end Station
top_end_stations <- df %>%
group_by(end_station_name,member_casual) %>%
count(end_station_name) %>%
arrange(desc(n)) %>%
head(20) %>%
ungroup()head(top_start_stations)## # A tibble: 6 × 3
## start_station_name member_casual n
## <chr> <chr> <int>
## 1 Streeter Dr & Grand Ave casual 25862
## 2 Clark St & Elm St member 20219
## 3 Lake Shore Dr & Monroe St casual 19893
## 4 Millennium Park casual 18376
## 5 Kingsbury St & Kinzie St member 16448
## 6 St. Clair St & Erie St member 15825head(top_end_stations)## # A tibble: 6 × 3
## end_station_name member_casual n
## <chr> <chr> <int>
## 1 Streeter Dr & Grand Ave casual 28463
## 2 Clark St & Elm St member 20882
## 3 Millennium Park casual 19419
## 4 Lake Shore Dr & Monroe St casual 19253
## 5 St. Clair St & Erie St member 17655
## 6 Kingsbury St & Kinzie St member 16630# Top 20 Average distance
dis_df <- df %>%
group_by(start_station_name,member_casual) %>%
summarise(Avg_distance = mean(distance)) %>%
arrange(desc(Avg_distance)) %>%
head(20) %>%
ungroup()## `summarise()` has grouped output by 'start_station_name'. You can override
## using the `.groups` argument.head(dis_df)## # A tibble: 6 × 3
## start_station_name member_casual Avg_distance
## <chr> <chr> <dbl>
## 1 Vincennes Ave & 104th St member 7.19
## 2 Dodge Ave & Main St casual 6.87
## 3 Michigan Ave & 71st St member 6.65
## 4 Ashland Ave & 74th St member 6.08
## 5 Stony Island Ave & 90th St member 6.06
## 6 Dauphin Ave & 103rd St member 5.78#hourly Bike Demand
df <- df %>%
mutate(start_hour = lubridate::hour(started_at))hourly_need <- df %>%
group_by(member_casual,start_hour) %>%
summarise(number_of_trips = n()) %>%
ungroup()## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.- Data Visualization
The ggplot() function is the primary function used in the ggplot2 package, a popular data visualization package in R. It is used to create and customize plots based on a grammar of graphics approach, allowing users to create complex and highly customization visualizations with relatively simple syntax.
Here’s how the ggplot() works to visualize.
1. Member Vs Casual Distribution
Member_type$Percentage <- round(Member_type$Count/sum(Member_type$Count)*100)
ggplot(Member_type,mapping = aes(x = " ", y = Percentage, fill = member_casual)) +
geom_col(color = "black") +
geom_text(aes(label=paste(member_casual, paste(Percentage,"%"),sep="\n")), position = position_stack(vjust=0.5), color="black") +
labs(title = "Members vs Casual Distribution") +
coord_polar(theta = "y") +
scale_fill_brewer() +
theme_bw()
2.Riders Bike type Usage
##Most used bike type
ggplot(sum_df,mapping = aes(x = rideable_type ,y = Count,fill = rideable_type)) +
geom_bar(stat = "identity") +
facet_wrap(~member_casual, nrow = 1) +
theme(legend.position = "none") +
labs(title = "Rider Bike type Usage",x = "Bike_type",y = "Count")
3.Rides Done on Per Day
# Total Ride per day
ggplot(ride_per_day, aes(x = started_date, y = Count,fill = factor(member_casual))) +
geom_col()+ labs( title ="Ride taken Per Day",
x = "Date",
y = "Count") +
theme_minimal()
4.Weekly Ride Count
##Total Ride per Week
ggplot(Weekly_ride, aes(x = day_of_the_week, y = Count,fill = factor(member_casual))) +
geom_col()+ labs( title ="Weekly Ride Count",
x = "Day of Week",
y = "Count") +
theme_minimal()
5.Monthly Ride Count
##Monthy Ride Count
ggplot(monthly_ride, aes(x = month, y = Count ,fill = factor(member_casual))) +
geom_col()+ labs( title ="Monthly Ride Count",
x = "Month(year 2020)",
y = "Count") +theme_minimal()+
theme(axis.text.x = element_text(angle = 45))
6. Average Distance Covered from Various Start Station
## Average Distance ride by member type in a year
ggplot(dis_df, aes(x = Avg_distance, y = reorder(start_station_name,Avg_distance), fill = factor(member_casual))) +
geom_col() +
labs(title = "Large Distance Covered from Various Start Station(Top 20)",
x = "Distance Covered (km)",
y = "Station Name",
fill = "Rider Type") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 90))
7.Popular Top Start Station
Popular_top_start_stations %>%
ggplot() + geom_col(aes(x=n,y= reorder(start_station_name,n)))+ scale_x_continuous(labels = comma)+
labs(title = "Top 10 popular Start Station", y = "No of Rides")
8. Rider most preferred Stations
#Top Start Station Name
ggplot(top_start_stations, aes(x = n, y = reorder(start_station_name,n), fill = factor(member_casual))) +
geom_col(stat = "identity", position = "dodge") +
labs(title = "Top 20 Start Station Name",
x = "Ride Count",
y = "Station Name",
fill = "Rider Type") +
theme_minimal()## Warning in geom_col(stat = "identity", position = "dodge"): Ignoring unknown
## parameters: `stat`
#Top End Station Name
ggplot(top_end_stations, aes(x = n, y = reorder(end_station_name,n), fill = factor(member_casual))) +
geom_col(stat = "identity", position = "dodge") +
labs(title = "Top 20 End Station Name",
x = "Ride Count",
y = "Station Name",
fill = "Rider Type") +
theme_minimal()## Warning in geom_col(stat = "identity", position = "dodge"): Ignoring unknown
## parameters: `stat`
9.Hourly Bike Demand
hourly_need %>%
ggplot()+geom_line(aes(x=start_hour,y= number_of_trips,color = member_casual))+
labs(title = "Hourly Bike Demand",
x = "Hour",
y = "No of Trips",
fill = "Member_casual") +
scale_x_continuous(limits = c(0,24), name ="Hours")+
theme_minimal()
Recommendations for Stakeholders:
1.Enhance Membership Programs:
Since 62%* of the users are members, there is an opportunity to further strengthen membership benefits to retain and attract more long-term users. Consider offering loyalty programs, discounts for long-term memberships, or exclusive benefits during peak seasons (April to September).
2.Bike Type Optimization:
Given that both members and casual riders prefer docked bikes, ensure that there are sufficient docked bikes available at high-demand stations, especially during peak hours. Consider investing in more docked bikes and maintaining a balance with other bike types.
3.Seasonal Promotions:
Since ride frequency increases from April and decreases after September, plan for seasonal promotions and marketing campaigns to maximize ridership during these months. This could include discounted rides, special events, or partnerships with local businesses to encourage more usage.
4.Improve Weekend Services:
With higher ride volumes on weekends, ensure there are adequate resources and bike availability. Consider running special weekend events or promotions to further boost ridership.
5.Focus on High-Demand Stations:
Vincennes Ave & 104th Station and Streeter Dr & Grand Ave are key stations with high ride counts and distances covered. Enhance services at these stations, such as better bike maintenance, increased docking stations, and potentially setting up customer service points.
6.Adjust for Peak Hours:
With peak demand between 3 pm to 6 pm, allocate more bikes and ensure efficient redistribution of bikes to meet demand. Consider offering incentives for riders who choose to ride outside of these peak hours to balance the load.
Recommendations for Marketing Team:
1.Targeted Marketing Campaigns:
Develop targeted campaigns to convert casual riders to members. Highlight the benefits of membership, such as cost savings, exclusive access to promotions, and convenience.
2.Leverage Popular Stations:
Use the popularity of stations like Streeter Dr & Grand Ave to create event-based marketing. For instance, set up pop-up events, offer free refreshments, or partner with nearby attractions to draw in more riders.
3.Promote During Peak Seasons:
Utilize data showing increased rides from April to September to launch time-limited offers and campaigns. Engage with riders through social media, email newsletters, and local advertisements to promote these offers.
4.Weekend Specials:
Since weekends see higher ridership, promote special weekend passes or family packages to attract group rides. Collaborate with local tourist attractions or restaurants to offer combined deals.
5.Highlight Environmental Impact:
Emphasize the environmental benefits of using bike share programs in your marketing materials. Share statistics on carbon footprint reduction and promote the sustainable aspect of biking to attract eco-conscious riders.
6.Dynamic Pricing:
Implement dynamic pricing strategies during peak hours and seasons to manage demand and encourage off-peak usage. Offer discounted rates for rides starting early in the morning or late at night.
By addressing these, Cyclistic can enhance user experience, optimize operations, and effectively increase ridership and membership.