Bike Share Data Analysis
Abdullahi Faysal
2024-05-21
Case Study: Analyzing Bike-Share Data for Marketing Insights
The objective of this document is to analyze bike-share data to gain insights into customer behavior and preferences, specifically focusing on differentiating factors between casual riders and annual members.
Introduction
This document presents an analysis of bike-share data to understand customer behavior and preferences, focusing on casual riders and annual members.
The analysis will follow a structured approach, encompassing the key phases of data analysis: Ask, Prepare, Process, Analyze, Share, and Act.
Approach
Ask
Formulate research questions to guide the analysis
Define the business problem and objectives
Establish metrics for evaluating success
Prepare
Validate data quality and consistency
Merge and clean datasets for analysis
Ensure data integrity and reliability
Process
Cleanse and transform data for analysis
Document data processing steps for transparency
Analyze
Identify trends and patterns in customer behavior
Extract meaningful insights from the data
Develop data-driven recommendations
Act
Provide strategic recommendations based on analysis -Address business challenges and opportunities
Propose data-informed strategies for decision-making
1. Ask
Scenario
The marketing team aims to enhance customer retention by converting casual riders into annual members. Understanding the usage disparities between these two customer segments is crucial for devising targeted marketing campaigns.
Stakeholders:
- Marketing Director
- Executive Team
Objective
- The goal is to analyze how casual riders and annual members utilize the bike-share program differently based on available data.
Deliverables:
- Insights on usage patterns of casual riders and annual members
- Visualizations supporting key findings
- Recommendations for converting casual riders into annual members
2. Prepare
Data Source
The data for this analysis is sourced from the Divvy bike-share program, specifically the Divvy 2019 Q2, Divvy 2019 Q3, Divvy 2019 Q4, and Divvy 2020 Q1 datasets. These datasets contain trip details and customer information that will be used to analyze the behavior of casual riders and annual members.
Load Libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ 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(ggplot2)
library(lubridate)
library(dplyr)
library(readr)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(data.table)##
## Attaching package: 'data.table'
##
## The following objects are masked from 'package:lubridate':
##
## hour, isoweek, mday, minute, month, quarter, second, wday, week,
## yday, year
##
## The following objects are masked from 'package:dplyr':
##
## between, first, last
##
## The following object is masked from 'package:purrr':
##
## transposelibrary(tidyr)2. Process
Display columns names of each dataframe.
colnames(q2_2019)## [1] "01 - Rental Details Rental ID"
## [2] "01 - Rental Details Local Start Time"
## [3] "01 - Rental Details Local End Time"
## [4] "01 - Rental Details Bike ID"
## [5] "01 - Rental Details Duration In Seconds Uncapped"
## [6] "03 - Rental Start Station ID"
## [7] "03 - Rental Start Station Name"
## [8] "02 - Rental End Station ID"
## [9] "02 - Rental End Station Name"
## [10] "User Type"
## [11] "Member Gender"
## [12] "05 - Member Details Member Birthday Year"colnames(q3_2019)## [1] "trip_id" "start_time" "end_time"
## [4] "bikeid" "tripduration" "from_station_id"
## [7] "from_station_name" "to_station_id" "to_station_name"
## [10] "usertype" "gender" "birthyear"colnames(q4_2019)## [1] "trip_id" "start_time" "end_time"
## [4] "bikeid" "tripduration" "from_station_id"
## [7] "from_station_name" "to_station_id" "to_station_name"
## [10] "usertype" "gender" "birthyear"colnames(q1_2020)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"Renaming columns to ensure uniformity.
Certainly! Ensuring consistent column names across dataframes is crucial for effective data analysis. Let’s proceed with the cleaning process by renaming the columns using the rename() function.
(q4_2019 <- rename(q4_2019
,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: 704,054 × 12
## ride_id started_at ended_at rideable_type tripduration
## <dbl> <dttm> <dttm> <dbl> <dbl>
## 1 25223640 2019-10-01 00:01:39 2019-10-01 00:17:20 2215 940
## 2 25223641 2019-10-01 00:02:16 2019-10-01 00:06:34 6328 258
## 3 25223642 2019-10-01 00:04:32 2019-10-01 00:18:43 3003 850
## 4 25223643 2019-10-01 00:04:32 2019-10-01 00:43:43 3275 2350
## 5 25223644 2019-10-01 00:04:34 2019-10-01 00:35:42 5294 1867
## 6 25223645 2019-10-01 00:04:38 2019-10-01 00:10:51 1891 373
## 7 25223646 2019-10-01 00:04:52 2019-10-01 00:22:45 1061 1072
## 8 25223647 2019-10-01 00:04:57 2019-10-01 00:29:16 1274 1458
## 9 25223648 2019-10-01 00:05:20 2019-10-01 00:29:18 6011 1437
## 10 25223649 2019-10-01 00:05:20 2019-10-01 02:23:46 2957 8306
## # ℹ 704,044 more rows
## # ℹ 7 more variables: start_station_id <dbl>, start_station_name <chr>,
## # end_station_id <dbl>, end_station_name <chr>, member_casual <chr>,
## # gender <chr>, birthyear <dbl>(q3_2019 <- rename(q3_2019
,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: 1,640,718 × 12
## ride_id started_at ended_at rideable_type tripduration
## <dbl> <dttm> <dttm> <dbl> <dbl>
## 1 23479388 2019-07-01 00:00:27 2019-07-01 00:20:41 3591 1214
## 2 23479389 2019-07-01 00:01:16 2019-07-01 00:18:44 5353 1048
## 3 23479390 2019-07-01 00:01:48 2019-07-01 00:27:42 6180 1554
## 4 23479391 2019-07-01 00:02:07 2019-07-01 00:27:10 5540 1503
## 5 23479392 2019-07-01 00:02:13 2019-07-01 00:22:26 6014 1213
## 6 23479393 2019-07-01 00:02:21 2019-07-01 00:07:31 4941 310
## 7 23479394 2019-07-01 00:02:24 2019-07-01 00:23:12 3770 1248
## 8 23479395 2019-07-01 00:02:26 2019-07-01 00:28:16 5442 1550
## 9 23479396 2019-07-01 00:02:34 2019-07-01 00:28:57 2957 1583
## 10 23479397 2019-07-01 00:02:45 2019-07-01 00:29:14 6091 1589
## # ℹ 1,640,708 more rows
## # ℹ 7 more variables: start_station_id <dbl>, start_station_name <chr>,
## # end_station_id <dbl>, end_station_name <chr>, member_casual <chr>,
## # gender <chr>, birthyear <dbl>(q2_2019 <- rename(q2_2019
,ride_id = "01 - Rental Details Rental ID"
,rideable_type = "01 - Rental Details Bike ID"
,started_at = "01 - Rental Details Local Start Time"
,ended_at = "01 - Rental Details Local End Time"
,start_station_name = "03 - Rental Start Station Name"
,start_station_id = "03 - Rental Start Station ID"
,end_station_name = "02 - Rental End Station Name"
,end_station_id = "02 - Rental End Station ID"
,member_casual = "User Type"))## # A tibble: 1,108,163 × 12
## ride_id started_at ended_at rideable_type
## <dbl> <dttm> <dttm> <dbl>
## 1 22178529 2019-04-01 00:02:22 2019-04-01 00:09:48 6251
## 2 22178530 2019-04-01 00:03:02 2019-04-01 00:20:30 6226
## 3 22178531 2019-04-01 00:11:07 2019-04-01 00:15:19 5649
## 4 22178532 2019-04-01 00:13:01 2019-04-01 00:18:58 4151
## 5 22178533 2019-04-01 00:19:26 2019-04-01 00:36:13 3270
## 6 22178534 2019-04-01 00:19:39 2019-04-01 00:23:56 3123
## 7 22178535 2019-04-01 00:26:33 2019-04-01 00:35:41 6418
## 8 22178536 2019-04-01 00:29:48 2019-04-01 00:36:11 4513
## 9 22178537 2019-04-01 00:32:07 2019-04-01 01:07:44 3280
## 10 22178538 2019-04-01 00:32:19 2019-04-01 01:07:39 5534
## # ℹ 1,108,153 more rows
## # ℹ 8 more variables: `01 - Rental Details Duration In Seconds Uncapped` <dbl>,
## # start_station_id <dbl>, start_station_name <chr>, end_station_id <dbl>,
## # end_station_name <chr>, member_casual <chr>, `Member Gender` <chr>,
## # `05 - Member Details Member Birthday Year` <dbl>Using the str() function, we can notice the datatype for ride_id column and the rideable_type column in 2019 quarters is different than that of the 2020 quarter.
str(q2_2019)## spc_tbl_ [1,108,163 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : num [1:1108163] 22178529 22178530 22178531 22178532 22178533 ...
## $ started_at : POSIXct[1:1108163], format: "2019-04-01 00:02:22" "2019-04-01 00:03:02" ...
## $ ended_at : POSIXct[1:1108163], format: "2019-04-01 00:09:48" "2019-04-01 00:20:30" ...
## $ rideable_type : num [1:1108163] 6251 6226 5649 4151 3270 ...
## $ 01 - Rental Details Duration In Seconds Uncapped: num [1:1108163] 446 1048 252 357 1007 ...
## $ start_station_id : num [1:1108163] 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name : chr [1:1108163] "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : num [1:1108163] 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr [1:1108163] "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr [1:1108163] "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
## $ Member Gender : chr [1:1108163] "Male" "Female" "Male" "Male" ...
## $ 05 - Member Details Member Birthday Year : num [1:1108163] 1975 1984 1990 1993 1992 ...
## - attr(*, "spec")=
## .. cols(
## .. `01 - Rental Details Rental ID` = col_double(),
## .. `01 - Rental Details Local Start Time` = col_datetime(format = ""),
## .. `01 - Rental Details Local End Time` = col_datetime(format = ""),
## .. `01 - Rental Details Bike ID` = col_double(),
## .. `01 - Rental Details Duration In Seconds Uncapped` = col_number(),
## .. `03 - Rental Start Station ID` = col_double(),
## .. `03 - Rental Start Station Name` = col_character(),
## .. `02 - Rental End Station ID` = col_double(),
## .. `02 - Rental End Station Name` = col_character(),
## .. `User Type` = col_character(),
## .. `Member Gender` = col_character(),
## .. `05 - Member Details Member Birthday Year` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>str(q3_2019)## spc_tbl_ [1,640,718 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : num [1:1640718] 23479388 23479389 23479390 23479391 23479392 ...
## $ started_at : POSIXct[1:1640718], format: "2019-07-01 00:00:27" "2019-07-01 00:01:16" ...
## $ ended_at : POSIXct[1:1640718], format: "2019-07-01 00:20:41" "2019-07-01 00:18:44" ...
## $ rideable_type : num [1:1640718] 3591 5353 6180 5540 6014 ...
## $ tripduration : num [1:1640718] 1214 1048 1554 1503 1213 ...
## $ start_station_id : num [1:1640718] 117 381 313 313 168 300 168 313 43 43 ...
## $ start_station_name: chr [1:1640718] "Wilton Ave & Belmont Ave" "Western Ave & Monroe St" "Lakeview Ave & Fullerton Pkwy" "Lakeview Ave & Fullerton Pkwy" ...
## $ end_station_id : num [1:1640718] 497 203 144 144 62 232 62 144 195 195 ...
## $ end_station_name : chr [1:1640718] "Kimball Ave & Belmont Ave" "Western Ave & 21st St" "Larrabee St & Webster Ave" "Larrabee St & Webster Ave" ...
## $ member_casual : chr [1:1640718] "Subscriber" "Customer" "Customer" "Customer" ...
## $ gender : chr [1:1640718] "Male" NA NA NA ...
## $ birthyear : num [1:1640718] 1992 NA NA NA NA ...
## - attr(*, "spec")=
## .. cols(
## .. trip_id = col_double(),
## .. start_time = col_datetime(format = ""),
## .. end_time = col_datetime(format = ""),
## .. bikeid = col_double(),
## .. tripduration = col_number(),
## .. from_station_id = col_double(),
## .. from_station_name = col_character(),
## .. to_station_id = col_double(),
## .. to_station_name = col_character(),
## .. usertype = col_character(),
## .. gender = col_character(),
## .. birthyear = col_double()
## .. )
## - attr(*, "problems")=<externalptr>str(q4_2019)## spc_tbl_ [704,054 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : num [1:704054] 25223640 25223641 25223642 25223643 25223644 ...
## $ started_at : POSIXct[1:704054], format: "2019-10-01 00:01:39" "2019-10-01 00:02:16" ...
## $ ended_at : POSIXct[1:704054], format: "2019-10-01 00:17:20" "2019-10-01 00:06:34" ...
## $ rideable_type : num [1:704054] 2215 6328 3003 3275 5294 ...
## $ tripduration : num [1:704054] 940 258 850 2350 1867 ...
## $ start_station_id : num [1:704054] 20 19 84 313 210 156 84 156 156 336 ...
## $ start_station_name: chr [1:704054] "Sheffield Ave & Kingsbury St" "Throop (Loomis) St & Taylor St" "Milwaukee Ave & Grand Ave" "Lakeview Ave & Fullerton Pkwy" ...
## $ end_station_id : num [1:704054] 309 241 199 290 382 226 142 463 463 336 ...
## $ end_station_name : chr [1:704054] "Leavitt St & Armitage Ave" "Morgan St & Polk St" "Wabash Ave & Grand Ave" "Kedzie Ave & Palmer Ct" ...
## $ member_casual : chr [1:704054] "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
## $ gender : chr [1:704054] "Male" "Male" "Female" "Male" ...
## $ birthyear : num [1:704054] 1987 1998 1991 1990 1987 ...
## - attr(*, "spec")=
## .. cols(
## .. trip_id = col_double(),
## .. start_time = col_datetime(format = ""),
## .. end_time = col_datetime(format = ""),
## .. bikeid = col_double(),
## .. tripduration = col_number(),
## .. from_station_id = col_double(),
## .. from_station_name = col_character(),
## .. to_station_id = col_double(),
## .. to_station_name = col_character(),
## .. usertype = col_character(),
## .. gender = col_character(),
## .. birthyear = col_double()
## .. )
## - attr(*, "problems")=<externalptr>str(q1_2020)## spc_tbl_ [426,887 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:426887] "EACB19130B0CDA4A" "8FED874C809DC021" "789F3C21E472CA96" "C9A388DAC6ABF313" ...
## $ rideable_type : chr [1:426887] "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
## $ started_at : POSIXct[1:426887], format: "2020-01-21 20:06:59" "2020-01-30 14:22:39" ...
## $ ended_at : POSIXct[1:426887], format: "2020-01-21 20:14:30" "2020-01-30 14:26:22" ...
## $ start_station_name: chr [1:426887] "Western Ave & Leland Ave" "Clark St & Montrose Ave" "Broadway & Belmont Ave" "Clark St & Randolph St" ...
## $ start_station_id : num [1:426887] 239 234 296 51 66 212 96 96 212 38 ...
## $ end_station_name : chr [1:426887] "Clark St & Leland Ave" "Southport Ave & Irving Park Rd" "Wilton Ave & Belmont Ave" "Fairbanks Ct & Grand Ave" ...
## $ end_station_id : num [1:426887] 326 318 117 24 212 96 212 212 96 100 ...
## $ start_lat : num [1:426887] 42 42 41.9 41.9 41.9 ...
## $ start_lng : num [1:426887] -87.7 -87.7 -87.6 -87.6 -87.6 ...
## $ end_lat : num [1:426887] 42 42 41.9 41.9 41.9 ...
## $ end_lng : num [1:426887] -87.7 -87.7 -87.7 -87.6 -87.6 ...
## $ member_casual : chr [1:426887] "member" "member" "member" "member" ...
## - attr(*, "spec")=
## .. 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_double(),
## .. end_station_name = col_character(),
## .. end_station_id = col_double(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>Checking and converting column datatypes
column datatype of ride_id
str(q2_2019$ride_id)## num [1:1108163] 22178529 22178530 22178531 22178532 22178533 ...str(q3_2019$ride_id)## num [1:1640718] 23479388 23479389 23479390 23479391 23479392 ...str(q4_2019$ride_id)## num [1:704054] 25223640 25223641 25223642 25223643 25223644 ...str(q1_2020$ride_id)## chr [1:426887] "EACB19130B0CDA4A" "8FED874C809DC021" "789F3C21E472CA96" ...column datatype of rideable_type
str(q2_2019$rideable_type)## num [1:1108163] 6251 6226 5649 4151 3270 ...str(q3_2019$rideable_type)## num [1:1640718] 3591 5353 6180 5540 6014 ...str(q4_2019$rideable_type)## num [1:704054] 2215 6328 3003 3275 5294 ...str(q1_2020$rideable_type)## chr [1:426887] "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...To change datatypes of columns to character
q4_2019 <- mutate(q4_2019, ride_id = as.character(ride_id)
,rideable_type = as.character(rideable_type))
q3_2019 <- mutate(q3_2019, ride_id = as.character(ride_id)
,rideable_type = as.character(rideable_type))
q2_2019 <- mutate(q2_2019, ride_id = as.character(ride_id)
,rideable_type = as.character(rideable_type))Combine all the datasets into one single dataframe
all_divvy_trips <- bind_rows(q2_2019,q3_2019,q4_2019,q1_2020)Deleting unwanted columns
all_divvy_trips <- all_divvy_trips %>%
select(-c(start_lat,start_lng,end_lat,end_lng,birthyear,gender,
"01 - Rental Details Duration In Seconds Uncapped",
"05 - Member Details Member Birthday Year",
"Member Gender","tripduration"))Obtaining the unique values in the ‘member_casual’ column of the ‘all_trips’ dataset.
unique(all_divvy_trips$member_casual)## [1] "Subscriber" "Customer" "member" "casual"Recode values in the ‘member_casual’ column to their appropriate alternates
all_divvy_trips <- all_divvy_trips %>%
mutate(member_casual = recode(member_casual,"Subscriber" = "member", "Customer" = "casual"))
##### Display the resulting unique member types
cat("Unique member types after recoding:", "\n")## Unique member types after recoding:cat(unique(all_divvy_trips$member_casual), "\n")## member casualInsert new month, date, year and day columns
##### add a new column that is the date format of 'started_at' column
all_divvy_trips$date <- as.Date(all_divvy_trips$started_at)
##### extract month (01 for Jan, 02 for Feb, 03 for March,....)
all_divvy_trips$month <- format(as.Date(all_divvy_trips$date),"%m")
##### extract day (01,02,03,04,...)
all_divvy_trips$day <- format(as.Date(all_divvy_trips$date),"%d")
##### extract year (2019, 2020)
all_divvy_trips$year <- format(as.Date(all_divvy_trips$date),"%Y")
# extract day_of_week (Sunday, Monday, Tuesday,...)
all_divvy_trips$day_of_week <- format(as.Date(all_divvy_trips$date),"%A")Added ‘ride_length’ column with ride duration in seconds, calculated by subtracting start time from end time. Converted ‘ride_length’ data type to numeric.
all_divvy_trips <- all_divvy_trips %>%
mutate(ride_length = difftime(ended_at, started_at)) %>%
mutate(ride_length = as.numeric(as.character(.$ride_length)))Filter out rows with negative ride_length values or invalid start_station_name entries.
all_divvy_trips_v2 <- all_divvy_trips[!(all_divvy_trips$start_station_name == "HQ QR" | all_divvy_trips$ride_length < 0),]3. Analyze
Analyzing the ride_length variable.
# Mean
mean_result <- all_divvy_trips_v2 %>%
group_by(member_casual) %>%
summarise(mean_ride_length = mean(ride_length))
print(mean_result)## # A tibble: 2 × 2
## member_casual mean_ride_length
## <chr> <dbl>
## 1 casual 3553.
## 2 member 850.# Median
median_result <- all_divvy_trips_v2 %>%
group_by(member_casual) %>%
summarise(median_ride_length = median(ride_length))
print(median_result)## # A tibble: 2 × 2
## member_casual median_ride_length
## <chr> <dbl>
## 1 casual 1546
## 2 member 589# Maximum
max_result <- all_divvy_trips_v2 %>%
group_by(member_casual) %>%
summarise(max_ride_length = max(ride_length))
print(max_result)## # A tibble: 2 × 2
## member_casual max_ride_length
## <chr> <dbl>
## 1 casual 9387024
## 2 member 9056634# Minimum
min_result <- all_divvy_trips_v2 %>%
group_by(member_casual) %>%
summarise(min_ride_length = min(ride_length))
print(min_result)## # A tibble: 2 × 2
## member_casual min_ride_length
## <chr> <dbl>
## 1 casual 2
## 2 member 1Calculate the mean ride length by membership type and day of the week
mean_result <- all_divvy_trips_v2 %>%
group_by(member_casual, day_of_week) %>%
summarise(mean_ride_length = mean(ride_length)) %>%
ungroup()## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.print(mean_result)## # A tibble: 14 × 3
## member_casual day_of_week mean_ride_length
## <chr> <chr> <dbl>
## 1 casual Friday 3774.
## 2 casual Monday 3372.
## 3 casual Saturday 3332.
## 4 casual Sunday 3581.
## 5 casual Thursday 3683.
## 6 casual Tuesday 3596.
## 7 casual Wednesday 3719.
## 8 member Friday 825.
## 9 member Monday 843.
## 10 member Saturday 969.
## 11 member Sunday 920.
## 12 member Thursday 824.
## 13 member Tuesday 826.
## 14 member Wednesday 824.Create an ordered factor for day_of_week (Sunday to Saturday)
#ordered function
all_divvy_trips_v2$day_of_week <- ordered(all_divvy_trips_v2$day_of_week,levels=c("Sunday","Monday","Tuesday", "Wednesday","Thursday","Friday","Saturday"))Analyzing ridership data by member type and week by calculating number of rides and average ride duration
result <- all_divvy_trips_v2 %>%
mutate(weekday = lubridate::wday(started_at, label = TRUE)) %>%
group_by(member_casual, weekday) %>%
summarize(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.print(result)## # A tibble: 14 × 4
## # Groups: member_casual [2]
## member_casual weekday number_of_rides average_duration
## <chr> <ord> <int> <dbl>
## 1 casual Sun 181293 3581.
## 2 casual Mon 103296 3372.
## 3 casual Tue 90510 3596.
## 4 casual Wed 92457 3719.
## 5 casual Thu 102679 3683.
## 6 casual Fri 122404 3774.
## 7 casual Sat 209543 3332.
## 8 member Sun 267965 920.
## 9 member Mon 472196 843.
## 10 member Tue 508445 826.
## 11 member Wed 500329 824.
## 12 member Thu 484177 824.
## 13 member Fri 452790 825.
## 14 member Sat 287958 969.4. Share
Calculate the total number of rides per day of the week and create a bar plot to visualize the results
result <- all_divvy_trips_v2 %>%
mutate(weekday = weekdays(started_at, abbreviate = FALSE)) %>%
mutate(weekday = factor(weekday, levels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))) %>%
group_by(member_casual, weekday) %>%
summarize(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.ggplot(result, aes(x = weekday, y = number_of_rides, fill = member_casual)) +
geom_col(position = "dodge")+
labs(title = "Total Number of Rides per Day of the Week",
x = "Weekday",
y = "Number of Rides",
fill = "Member Type")
Calculate the average duration of rides per day of the week
# Define the desired order of weekdays
weekday_order <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
all_divvy_trips_v2 %>%
mutate(weekday_num = wday(started_at)) %>%
mutate(weekday = factor(weekday_num, levels = 1:7, labels = weekday_order)) %>%
group_by(member_casual, weekday) %>%
summarise(number_of_rides = n(), average_duration = mean(ride_length)) %>%
arrange(member_casual, weekday) %>%
ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +
geom_col(position = "dodge") +
labs(title = "Average Duration of Rides per Day of the Week",
x = "Weekday",
y = "Average Duration",
fill = "Member Type")## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Median ride length per day of week
weekday_order <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
all_divvy_trips_v2 %>%
mutate(weekday_num = wday(started_at)) %>%
mutate(weekday = factor(weekday_num, levels = 1:7, labels = weekday_order)) %>%
group_by(member_casual, weekday) %>%
summarize(number_of_rides = n(), median_duration = median(ride_length)) %>%
arrange(member_casual, weekday) %>%
ggplot(aes(x = weekday, y = median_duration, fill = member_casual)) +
geom_col(position = "dodge") +
labs(title = "Median Duration of Rides Per Day of the Week",
x = "Weekday",
y = "Median Duration",
fill = "member type")## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Calculate and visualize the average ride length per month
# Define the custom order of months starting with January
custom_month_order <- c("January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December")
all_divvy_trips_v2 %>%
mutate(month = factor(format(started_at, "%B"), levels = custom_month_order)) %>%
group_by(member_casual, month) %>%
summarize(number_of_rides = n(), average_duration = mean(ride_length)) %>%
arrange(member_casual, month) %>%
ggplot(aes(x = month, y = number_of_rides, fill = member_casual)) +
geom_col(position = "dodge") +
labs(title = "Average Ride Length per Month",
x = "Month",
y = "Number of Rides",
fill = "Member Type") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Summary
Analyzing historical bike data from 2019 — 2020, we observed how annual members and casual riders use Cyclistic bikes differently. Key findings include:
Both groups prefer Sunday and Saturday for longer rides, resulting in a U-shaped trend with a greater dip in the midweek for casual riders.
Annual members have consistent ride durations throughout the week, while casual riders have varying durations.
Annual members take more rides overall compared to casual riders.
Annual members ride more on weekdays, while casual riders ride more on weekends.
The busiest period for bike trips is July, August, and September, coinciding with the warmer summer months.
6: Act
In the Act phase, we will leverage the insights gained from our analysis of bike-share data to formulate actionable recommendations for stakeholders. By acting upon these recommendations, we aim to address the business problem effectively and facilitate data-driven decision-making.
Three Areas of Focus and Recommendations:
1. Seasonality
Insight
- There is an increase in demand for casual riders during Spring and Summer, presenting an opportunity for targeted advertising campaigns and promotional activities during these seasons.
Recommendation
Launch a captivating email campaign for casual riders in early Spring, extending it throughout the Summer to attract and retain customers.
Strategically schedule offers and promotions, such as early bird sign-ups and discounted memberships, encouraging potential riders to join ahead of the busy season. Highlight the advantages of becoming an annual member compared to remaining a casual rider.
2. Purpose of Bike Usage
Insight
- Annual members are more active mid-week with consistent ride lengths, while casual riders use bikes for weekend entertainment.
Recommendation
Introduce enticing weekend passes tailored to weekend activities and entertainment, providing casual riders with flexible options.
Leverage the power of digital marketing channels to effectively promote the new weekend passes, capturing the attention of potential riders.
Identify popular spots frequented by casual riders using a visually appealing map, establish collaborative partnerships, and offer exclusive membership discounts combined with entertainment pairings (e.g., restaurant discounts, entry passes to sports venues). Emphasize the unique advantages of becoming an annual member.
3. Usage Patterns
Insight
- Annual members predominantly use bikes on weekdays, whereas casual riders prefer weekends.
Recommendation
- Utilize captivating digital marketing channels to inspire bike usage during weekdays, positioning it as a sustainable and convenient commuting option. Highlight the benefits of biking for physical fitness, reducing traffic congestion, and contributing to a greener urban environment.