As a junior data analyst working in 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, their team wants to understand how
casual riders and annual members use Cyclistic bikes differently.
For the purpose of this case study, the datasets shared by the
company are appropriate and will answer the business questions. The data
has been made available by Motivate International Inc. under license.
Decided to download data for four quarters.
After downloading the bike share data, unzip the files and create a
folder on your desktop or Drive to house the files. Use appropriate
file-naming conventions.
We have used data for four quarters and combined them to a single
dataframe for twelve months. The time period for analysis is from April
2019 to March 2020.
step 1. install the required packages in R for completing the
analysis
options(repos = list(CRAN="http://cran.rstudio.com/"))
install.packages("tidyverse")
## Installing package into 'C:/Users/DELL/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)
## package 'tidyverse' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\DELL\AppData\Local\Temp\RtmpMjSoXn\downloaded_packages
install.packages("lubridate")
## Installing package into 'C:/Users/DELL/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)
## package 'lubridate' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\DELL\AppData\Local\Temp\RtmpMjSoXn\downloaded_packages
install.packages("ggplot2")
## Installing package into 'C:/Users/DELL/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)
## package 'ggplot2' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\DELL\AppData\Local\Temp\RtmpMjSoXn\downloaded_packages
step 2. load the libraries
library(tidyverse)
library(lubridate)
library(ggplot2)
step 3. set your working directory or folder and change the file
path according to folder in your computer.
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_knit$set(root.dir = 'C:/Users/DELL/Documents/S/Google_dataanalytics/case_study_1/data')
step 6. We can see that column names are inconsistent. “q2_2019” has
a lot different column names than other quarters. So, to make the column
names same we will follow the standard used in “q1_2020”. We will use
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))
(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))
(q2_2019 <- rename(q2_2019
,ride_id = "X01...Rental.Details.Rental.ID"
,rideable_type = "X01...Rental.Details.Bike.ID"
,tripduration = "X01...Rental.Details.Duration.In.Seconds.Uncapped"
,started_at = "X01...Rental.Details.Local.Start.Time"
,ended_at = "X01...Rental.Details.Local.End.Time"
,start_station_name = "X03...Rental.Start.Station.Name"
,start_station_id = "X03...Rental.Start.Station.ID"
,end_station_name = "X02...Rental.End.Station.Name"
,end_station_id = "X02...Rental.End.Station.ID"
,member_casual = "User.Type"
,gender = "Member.Gender"
,birthyear = "X05...Member.Details.Member.Birthday.Year"))
Step 7. Re-check column names for consistency. Now, they are
consistently named.
colnames(q3_2019)
## [1] "ride_id" "started_at" "ended_at"
## [4] "rideable_type" "tripduration" "start_station_id"
## [7] "start_station_name" "end_station_id" "end_station_name"
## [10] "member_casual" "gender" "birthyear"
colnames(q4_2019)
## [1] "ride_id" "started_at" "ended_at"
## [4] "rideable_type" "tripduration" "start_station_id"
## [7] "start_station_name" "end_station_id" "end_station_name"
## [10] "member_casual" "gender" "birthyear"
colnames(q2_2019)
## [1] "ride_id" "started_at" "ended_at"
## [4] "rideable_type" "tripduration" "start_station_id"
## [7] "start_station_name" "end_station_id" "end_station_name"
## [10] "member_casual" "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"
Step 8. Inspect the data frames and look for incongruities an find
data type of each column
str(q1_2020)
## 'data.frame': 426887 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 : int 239 234 296 51 66 212 96 96 212 38 ...
## $ end_station_name : chr "Clark St & Leland Ave" "Southport Ave & Irving Park Rd" "Wilton Ave & Belmont Ave" "Fairbanks Ct & Grand Ave" ...
## $ end_station_id : int 326 318 117 24 212 96 212 212 96 100 ...
## $ 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" ...
str(q4_2019)
## 'data.frame': 704054 obs. of 12 variables:
## $ ride_id : int 25223640 25223641 25223642 25223643 25223644 25223645 25223646 25223647 25223648 25223649 ...
## $ started_at : chr "2019-10-01 00:01:39" "2019-10-01 00:02:16" "2019-10-01 00:04:32" "2019-10-01 00:04:32" ...
## $ ended_at : chr "2019-10-01 00:17:20" "2019-10-01 00:06:34" "2019-10-01 00:18:43" "2019-10-01 00:43:43" ...
## $ rideable_type : int 2215 6328 3003 3275 5294 1891 1061 1274 6011 2957 ...
## $ tripduration : chr "940.0" "258.0" "850.0" "2,350.0" ...
## $ start_station_id : int 20 19 84 313 210 156 84 156 156 336 ...
## $ start_station_name: chr "Sheffield Ave & Kingsbury St" "Throop (Loomis) St & Taylor St" "Milwaukee Ave & Grand Ave" "Lakeview Ave & Fullerton Pkwy" ...
## $ end_station_id : int 309 241 199 290 382 226 142 463 463 336 ...
## $ end_station_name : chr "Leavitt St & Armitage Ave" "Morgan St & Polk St" "Wabash Ave & Grand Ave" "Kedzie Ave & Palmer Ct" ...
## $ member_casual : chr "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
## $ gender : chr "Male" "Male" "Female" "Male" ...
## $ birthyear : int 1987 1998 1991 1990 1987 1994 1991 1995 1993 NA ...
str(q3_2019)
## 'data.frame': 1640718 obs. of 12 variables:
## $ ride_id : int 23479388 23479389 23479390 23479391 23479392 23479393 23479394 23479395 23479396 23479397 ...
## $ started_at : chr "2019-07-01 00:00:27" "2019-07-01 00:01:16" "2019-07-01 00:01:48" "2019-07-01 00:02:07" ...
## $ ended_at : chr "2019-07-01 00:20:41" "2019-07-01 00:18:44" "2019-07-01 00:27:42" "2019-07-01 00:27:10" ...
## $ rideable_type : int 3591 5353 6180 5540 6014 4941 3770 5442 2957 6091 ...
## $ tripduration : chr "1,214.0" "1,048.0" "1,554.0" "1,503.0" ...
## $ start_station_id : int 117 381 313 313 168 300 168 313 43 43 ...
## $ start_station_name: chr "Wilton Ave & Belmont Ave" "Western Ave & Monroe St" "Lakeview Ave & Fullerton Pkwy" "Lakeview Ave & Fullerton Pkwy" ...
## $ end_station_id : int 497 203 144 144 62 232 62 144 195 195 ...
## $ end_station_name : chr "Kimball Ave & Belmont Ave" "Western Ave & 21st St" "Larrabee St & Webster Ave" "Larrabee St & Webster Ave" ...
## $ member_casual : chr "Subscriber" "Customer" "Customer" "Customer" ...
## $ gender : chr "Male" "" "" "" ...
## $ birthyear : int 1992 NA NA NA NA 1990 NA NA NA NA ...
str(q2_2019)
## 'data.frame': 1108163 obs. of 12 variables:
## $ ride_id : int 22178529 22178530 22178531 22178532 22178533 22178534 22178535 22178536 22178537 22178538 ...
## $ started_at : chr "2019-04-01 00:02:22" "2019-04-01 00:03:02" "2019-04-01 00:11:07" "2019-04-01 00:13:01" ...
## $ ended_at : chr "2019-04-01 00:09:48" "2019-04-01 00:20:30" "2019-04-01 00:15:19" "2019-04-01 00:18:58" ...
## $ rideable_type : int 6251 6226 5649 4151 3270 3123 6418 4513 3280 5534 ...
## $ tripduration : chr "446.0" "1,048.0" "252.0" "357.0" ...
## $ start_station_id : int 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : int 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
## $ gender : chr "Male" "Female" "Male" "Male" ...
## $ birthyear : int 1975 1984 1990 1993 1992 1999 1969 1991 NA NA ...
Step 9. Our next aim is to stack all four dataframes over one
another into a single dataframe. So, as a first step, we will convert
data type of “ride_id” and “rideable_type” columns to character so that
they can stack correctly.
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))
Step 10. Next, we can stack all four data frames into one big data
frame using bind_rows() function
all_trips <- bind_rows(q2_2019, q3_2019, q4_2019, q1_2020)
Step 11. To make data more consistent, remove lat, long, birthyear,
and gender fields as this data was dropped from data collection since
2020
all_trips <- all_trips %>%
select(-c(start_lat, start_lng, end_lat, end_lng, birthyear, gender, tripduration))
Step 12. Inspect the new table all_trips that has been created and
also re-check column names for consistency
colnames(all_trips)
## [1] "ride_id" "started_at" "ended_at"
## [4] "rideable_type" "start_station_id" "start_station_name"
## [7] "end_station_id" "end_station_name" "member_casual"
Step 13. Now, we might feel our data is ready for analysis. But,
lets re-check again by computing number of rows, column names, using
summary() function and cross checking dimensions of data frame.
Basically, carefully inspect the new table all_trips that has been
created.
colnames(all_trips)
## [1] "ride_id" "started_at" "ended_at"
## [4] "rideable_type" "start_station_id" "start_station_name"
## [7] "end_station_id" "end_station_name" "member_casual"
nrow(all_trips)
## [1] 3879822
dim(all_trips)
## [1] 3879822 9
head(all_trips)
## ride_id started_at ended_at rideable_type
## 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
## start_station_id start_station_name end_station_id
## 1 81 Daley Center Plaza 56
## 2 317 Wood St & Taylor St 59
## 3 283 LaSalle St & Jackson Blvd 174
## 4 26 McClurg Ct & Illinois St 133
## 5 202 Halsted St & 18th St 129
## 6 420 Ellis Ave & 55th St 426
## end_station_name member_casual
## 1 Desplaines St & Kinzie St Subscriber
## 2 Wabash Ave & Roosevelt Rd Subscriber
## 3 Canal St & Madison St Subscriber
## 4 Kingsbury St & Kinzie St Subscriber
## 5 Blue Island Ave & 18th St Subscriber
## 6 Ellis Ave & 60th St Subscriber
str(all_trips)
## 'data.frame': 3879822 obs. of 9 variables:
## $ ride_id : chr "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : chr "2019-04-01 00:02:22" "2019-04-01 00:03:02" "2019-04-01 00:11:07" "2019-04-01 00:13:01" ...
## $ ended_at : chr "2019-04-01 00:09:48" "2019-04-01 00:20:30" "2019-04-01 00:15:19" "2019-04-01 00:18:58" ...
## $ rideable_type : chr "6251" "6226" "5649" "4151" ...
## $ start_station_id : int 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : int 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
summary(all_trips)
## ride_id started_at ended_at rideable_type
## Length:3879822 Length:3879822 Length:3879822 Length:3879822
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## start_station_id start_station_name end_station_id end_station_name
## Min. : 1.0 Length:3879822 Min. : 1.0 Length:3879822
## 1st Qu.: 77.0 Class :character 1st Qu.: 77.0 Class :character
## Median :174.0 Mode :character Median :174.0 Mode :character
## Mean :202.9 Mean :203.8
## 3rd Qu.:291.0 3rd Qu.:291.0
## Max. :675.0 Max. :675.0
## NA's :1
## member_casual
## Length:3879822
## Class :character
## Mode :character
##
##
##
##
Step 14. After careful scrutiny, there are a few problems we will
need to fix. In the “member_casual” column, there are two names for
members (“member” and “Subscriber”) and two names for casual riders
(“Customer” and “casual”). We will need to consolidate that from four to
two labels.First check how many observations fall under each user
type.
table(all_trips$member_casual)
##
## casual Customer member Subscriber
## 48480 857474 378407 2595461
Step 15. We can see that there is data under four labels in member
type category. So, to change four types of membership to two, we use the
following code chunk:
all_trips <- all_trips %>%
mutate(member_casual = recode(member_casual
,"Subscriber" = "member"
,"Customer" = "casual"))
Step 16. Again re-run check for number of labels in member_casual
using table() function.
table(all_trips$member_casual)
##
## casual member
## 905954 2973868
Step 17. Now, there are only two labels. Next, Add separate columns
for the date, month, day, and year of each ride. This will allow us to
aggregate ride data for each month, day, or year. Otherwise, we could
only aggregate at the ride level with “ride_id”.
Step 18. Create a column called “ride_length.” Calculate the length
of each ride by subtracting the column “ended_at” from the column
“started_at”. We can do this by using difftime() function to get time in
seconds.
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)
Step 19. Next, let’s inspect the structure of the columns in the
dataframe “all_trips”
str(all_trips)
## 'data.frame': 3879822 obs. of 14 variables:
## $ ride_id : chr "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : chr "2019-04-01 00:02:22" "2019-04-01 00:03:02" "2019-04-01 00:11:07" "2019-04-01 00:13:01" ...
## $ ended_at : chr "2019-04-01 00:09:48" "2019-04-01 00:20:30" "2019-04-01 00:15:19" "2019-04-01 00:18:58" ...
## $ rideable_type : chr "6251" "6226" "5649" "4151" ...
## $ start_station_id : int 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : int 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr "member" "member" "member" "member" ...
## $ date : Date, format: "2019-04-01" "2019-04-01" ...
## $ month : chr "Apr" "Apr" "Apr" "Apr" ...
## $ day_of_week : chr "Mon" "Mon" "Mon" "Mon" ...
## $ year : chr "2019" "2019" "2019" "2019" ...
## $ ride_length : 'difftime' num 446 1048 252 357 ...
## ..- attr(*, "units")= chr "secs"
Step 20. Convert data type of “ride_length” from factor to numeric
so that we can run calculations on the data like maximum, mean, average,
minimum etc.
is.factor(all_trips$ride_length)
## [1] FALSE
all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
Step 21. Re-check whether datatype has been converted from factor to
numeric
is.numeric(all_trips$ride_length)
## [1] TRUE
Step 22. Remove “bad” data. The data frame includes a few hundred
entries, when bikes were taken out of docks and checked for quality by
company. These values can be identified by negative value in
“ride_length”. We will create a new version of the data frame (v2) where
rows with negative “ride_length” data is being removed.
all_trips_v2 <- all_trips[!(all_trips$start_station_name == "HQ QR" | all_trips$ride_length<0),]
Step 23. Again carefully inspect the structure of the columns. We
can see that number of rows have reduced from 3879822 to 3876042. So,
there were 3780 rows with negative “ride_length”
str(all_trips_v2)
## 'data.frame': 3876042 obs. of 14 variables:
## $ ride_id : chr "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : chr "2019-04-01 00:02:22" "2019-04-01 00:03:02" "2019-04-01 00:11:07" "2019-04-01 00:13:01" ...
## $ ended_at : chr "2019-04-01 00:09:48" "2019-04-01 00:20:30" "2019-04-01 00:15:19" "2019-04-01 00:18:58" ...
## $ rideable_type : chr "6251" "6226" "5649" "4151" ...
## $ start_station_id : int 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : int 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr "member" "member" "member" "member" ...
## $ date : Date, format: "2019-04-01" "2019-04-01" ...
## $ month : chr "Apr" "Apr" "Apr" "Apr" ...
## $ day_of_week : chr "Mon" "Mon" "Mon" "Mon" ...
## $ year : chr "2019" "2019" "2019" "2019" ...
## $ ride_length : num 446 1048 252 357 1007 ...
Step 24. Now, we can use the data for twelve months to do further
analysis as we have completed a number of cleaning tasks and made it
consistent.
Next, we can do a descriptive analysis on ride_length (all values in
seconds) using following code chunk:
summary(all_trips_v2$ride_length)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 412 712 1479 1289 9387024
Step 25. Our Aim is to find data about how casual riders use bike
share compared to Annual members. So, we compare various parameters like
average “ride_length” and number of trips for both categories of
users.
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week,
FUN = mean)
## all_trips_v2$member_casual all_trips_v2$day_of_week all_trips_v2$ride_length
## 1 casual Fri 3773.8351
## 2 member Fri 824.5305
## 3 casual Mon 3372.2869
## 4 member Mon 842.5726
## 5 casual Sat 3331.9138
## 6 member Sat 968.9337
## 7 casual Sun 3581.4054
## 8 member Sun 919.9746
## 9 casual Thu 3682.9847
## 10 member Thu 823.9278
## 11 casual Tue 3596.3599
## 12 member Tue 826.1427
## 13 casual Wed 3718.6619
## 14 member Wed 823.9996
Step 26. Notice how the days of the week are out of order. We can
fix that by using the following code chunk:
all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week,
levels=c("Sun", "Mon", "Tue",
"Wed", "Thu", "Fri", "Sat"))
Step 27. Now, let’s compare the average “ride_length” again by each
day for both category of users i.e. annual members and casual
users.
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week,
FUN = mean)
## all_trips_v2$member_casual all_trips_v2$day_of_week all_trips_v2$ride_length
## 1 casual Sun 3581.4054
## 2 member Sun 919.9746
## 3 casual Mon 3372.2869
## 4 member Mon 842.5726
## 5 casual Tue 3596.3599
## 6 member Tue 826.1427
## 7 casual Wed 3718.6619
## 8 member Wed 823.9996
## 9 casual Thu 3682.9847
## 10 member Thu 823.9278
## 11 casual Fri 3773.8351
## 12 member Fri 824.5305
## 13 casual Sat 3331.9138
## 14 member Sat 968.9337
Step 28. Next, we can analyze and find insights about user type by
day of the week. We can first use Mutate() function to creates weekday
field using wday(). The, followed by grouping data by user type and day
of the week.
Then, we calculate number of rides and average duration of each ride
to sort them by user type and day of the week. We use data pipes to
complete all these analysis in the following code chunk:
all_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>% ## creates weekday field using wday()
group_by(member_casual, weekday) %>% ## groups by user type and weekday
summarise(number_of_rides = n(), average_duration = mean(ride_length)) %>% ## calculates the number of rides and average duration
arrange(member_casual, weekday)
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
## # 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.
Step 29. Now, Let’s visualize the number of rides v/s average
duration plot grouped by rider type. We use ggplot() function to get a
column barchart where “dodge” is used to group by “member_casual”
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)) %>%
arrange(member_casual, weekday) %>%
ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +
geom_col(position = "dodge")+
labs(title = "Average duration v/s day of the week")
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
1) From the above graph, we can see that average duration of
“ridelength” for annual members is much lower than that for casual
riders.So, if we target these casual riders, by introducing offers
catering to this segment, we can increase annual membership enrollments
into the company.
2) Amongst, casual riders, the average duration of “trip_length”
is high on Fridays and Sundays. So, ads for memberships can be targeted
on these days. Weekend offers might also be considered to increase
revenue from casual riders.
Step 30. Let’s visualize the number of rides v/s weekday plot
grouped by user type. Again,we use “dodge” for grouped column plots side
by side
all_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>% ## creates weekday field using wday()
group_by(member_casual, weekday) %>% ## groups by user type and weekday
summarise(number_of_rides = n(), average_duration = mean(ride_length)) %>% ## calculates the number of rides and average duration
arrange(member_casual, weekday) %>%
ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) +
geom_col(position = "dodge")+
labs(title = "Number of rides v/s day of the week")
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
1) From the above graph, it is clear that number of trips by
annual members are higher compared to casual riders especially during
weekdays. Further analysis needs to be done to determine wether higher
number of trips by annual members correspond to home-work commute during
weekdays.
2) Again, we can observe casual riders have taken more number of
trips during weekends compared to weekdays. Further analysis needs to be
done to determine the reason for an increase in number of trips by
casual users during weekend.
3) This graph also suggests the possibility of exploring weekend
offers and ads targeting casual riders to convert to annual
members.
Step 31. Next, we can export a SUMMARY FILE for further analysis. We
Create a new csv file that we will use to visualize in Excel, Tableau,
or my presentation software. By using write.csv() function, a new file
with “all_trips_v2” data is created.
write.csv(all_trips_v2, "C:/Users/DELL/Documents/S/Google_dataanalytics/case_study_1/data/all_trips_final.csv",
row.names=FALSE)
Step 32. We also export a summary table from the data analysed in
code chunk titled “Insights”
counts <- aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)
write.csv(counts, file = 'C:/Users/DELL/Documents/S/Google_dataanalytics/case_study_1/data/avg_ride_length.csv')
We come to the end of bike-share case study! We have compared the
data about casual users and annual members to obtain insights which
would help in increasing memberships. Please do leave feedback regarding
this analysis like what can be improved or included.