Cyclistic
Mohd J.
2023-11-17
Cyclistic_Exercise_Full_Year_Analysis
This analysis is for case study 1 from the Google Data Analytics Certificate (Cyclistic). It’s originally based on the case study “‘Sophisticated, Clear, and Polished’: Divvy and Data Visualization” written by Kevin Hartman (found here: https://artscience.blog/home/divvy-dataviz-case-study).
We will be using the Divvy data set for the case
study. The purpose of this script is to consolidate downloaded Divvy
data into a single data frame and then conduct simple analysis to help
answer the key question:
“In what ways do members and
casual riders use Divvy bikes differently?”
using the following script https://docs.google.com/document/d/1gUs7-pu4iCHH3PTtkC1pMvHfmyQGu0hQBG5wvZOzZkA/copy
Cyclistic case study
Install required packages
tidyverse for
data import and wrangling
libridate for date functions
ggplot
for visualization
Markdown for creating HTML report
install packages
install.packages("dplyr")
install.packages("lubridate")
install.packages("ggplot2")
install.packages("markdown")
install.packages("data.table")
install.packages("kableExtra")
install.packages("knitr")
install.packages("tidyverse")
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(lubridate)
library(ggplot2)
library(markdown)
library(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(kableExtra)##
## Attaching package: 'kableExtra'
##
## The following object is masked from 'package:dplyr':
##
## group_rowslibrary(knitr)
library(dplyr)Displays working directory and set new directory
getwd()## [1] "C:/Users/tsheef/Desktop/case study Cyclistic"setwd("/Users/tsheef/Desktop/case study Cyclistic")STEP 1: COLLECT DATA
Upload Divvy data sets (csv files)
https://www.divvybikes.com/data-license-agreement
q2_2019 <- read_csv("Divvy_Trips_2019_Q2.csv")## Rows: 1108163 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): 03 - Rental Start Station Name, 02 - Rental End Station Name, User...
## dbl (5): 01 - Rental Details Rental ID, 01 - Rental Details Bike ID, 03 - R...
## num (1): 01 - Rental Details Duration In Seconds Uncapped
## dttm (2): 01 - Rental Details Local Start Time, 01 - Rental Details Local En...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.q3_2019 <- read_csv("Divvy_Trips_2019_Q3.csv")## Rows: 1640718 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): from_station_name, to_station_name, usertype, gender
## dbl (5): trip_id, bikeid, from_station_id, to_station_id, birthyear
## num (1): tripduration
## dttm (2): start_time, end_time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.q4_2019 <- read_csv("Divvy_Trips_2019_Q4.csv")## Rows: 704054 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): from_station_name, to_station_name, usertype, gender
## dbl (5): trip_id, bikeid, from_station_id, to_station_id, birthyear
## num (1): tripduration
## dttm (2): start_time, end_time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.q1_2020 <- read_csv("Divvy_Trips_2020_Q1.csv")## Rows: 426887 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): ride_id, rideable_type, start_station_name, end_station_name, memb...
## dbl (6): start_station_id, end_station_id, start_lat, start_lng, end_lat, e...
## dttm (2): started_at, ended_at
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.STEP 2: WRANGLE DATA AND COMBINE INTO A SINGLE FILE
Compare
column names each of the files
While the names don’t have to be in the same order
They DO need to match perfectly before we can use a
Command to join them into one file
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(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(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"Rename columns to make them consistent with q1_2020 (as this will be the supposed going-forward table design for Divvy)
(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>Inspect the data frames and look for inconsistencies
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>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(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(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>Convert ride_id and rideable_type 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)) Stack individual quarter’s data frames into one big data frame
all_trips <- bind_rows(q2_2019,q3_2019,q4_2019,q1_2020)Remove lat, long, birth year, and gender fields as this data was dropped beginning in 2020
all_trips <- all_trips %>%
select(-c(birthyear, gender, "01 - Rental Details Duration In Seconds Uncapped", "05 - Member Details Member Birthday Year", "Member Gender", "tripduration"))STEP 3: CLEAN UP AND ADD DATA TO PREPARE FOR ANALYSIS
Inspect
the new table 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"
## [10] "start_lat" "start_lng" "end_lat"
## [13] "end_lng"nrow(all_trips)## [1] 3879822dim(all_trips) ## [1] 3879822 13head(all_trips) ## # A tibble: 6 × 13
## ride_id started_at ended_at rideable_type start_station_id
## <chr> <dttm> <dttm> <chr> <dbl>
## 1 221785… 2019-04-01 00:02:22 2019-04-01 00:09:48 6251 81
## 2 221785… 2019-04-01 00:03:02 2019-04-01 00:20:30 6226 317
## 3 221785… 2019-04-01 00:11:07 2019-04-01 00:15:19 5649 283
## 4 221785… 2019-04-01 00:13:01 2019-04-01 00:18:58 4151 26
## 5 221785… 2019-04-01 00:19:26 2019-04-01 00:36:13 3270 202
## 6 221785… 2019-04-01 00:19:39 2019-04-01 00:23:56 3123 420
## # ℹ 8 more variables: start_station_name <chr>, end_station_id <dbl>,
## # end_station_name <chr>, member_casual <chr>, start_lat <dbl>,
## # start_lng <dbl>, end_lat <dbl>, end_lng <dbl>str(all_trips) ## tibble [3,879,822 × 13] (S3: tbl_df/tbl/data.frame)
## $ ride_id : chr [1:3879822] "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : POSIXct[1:3879822], format: "2019-04-01 00:02:22" "2019-04-01 00:03:02" ...
## $ ended_at : POSIXct[1:3879822], format: "2019-04-01 00:09:48" "2019-04-01 00:20:30" ...
## $ rideable_type : chr [1:3879822] "6251" "6226" "5649" "4151" ...
## $ start_station_id : num [1:3879822] 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr [1:3879822] "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : num [1:3879822] 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr [1:3879822] "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr [1:3879822] "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
## $ start_lat : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...
## $ start_lng : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...
## $ end_lat : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...
## $ end_lng : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...summary(all_trips) ## ride_id started_at
## Length:3879822 Min. :2019-04-01 00:02:22.00
## Class :character 1st Qu.:2019-06-23 07:49:09.25
## Mode :character Median :2019-08-14 17:43:38.00
## Mean :2019-08-26 00:49:59.38
## 3rd Qu.:2019-10-12 12:10:21.00
## Max. :2020-03-31 23:51:34.00
##
## ended_at rideable_type start_station_id
## Min. :2019-04-01 00:09:48.00 Length:3879822 Min. : 1.0
## 1st Qu.:2019-06-23 08:20:27.75 Class :character 1st Qu.: 77.0
## Median :2019-08-14 18:02:04.00 Mode :character Median :174.0
## Mean :2019-08-26 01:14:37.06 Mean :202.9
## 3rd Qu.:2019-10-12 12:36:16.75 3rd Qu.:291.0
## Max. :2020-05-19 20:10:34.00 Max. :675.0
##
## start_station_name end_station_id end_station_name member_casual
## Length:3879822 Min. : 1.0 Length:3879822 Length:3879822
## Class :character 1st Qu.: 77.0 Class :character Class :character
## Mode :character Median :174.0 Mode :character Mode :character
## Mean :203.8
## 3rd Qu.:291.0
## Max. :675.0
## NA's :1
## start_lat start_lng end_lat end_lng
## Min. :42 Min. :-88 Min. :42 Min. :-88
## 1st Qu.:42 1st Qu.:-88 1st Qu.:42 1st Qu.:-88
## Median :42 Median :-88 Median :42 Median :-88
## Mean :42 Mean :-88 Mean :42 Mean :-88
## 3rd Qu.:42 3rd Qu.:-88 3rd Qu.:42 3rd Qu.:-88
## Max. :42 Max. :-88 Max. :42 Max. :-88
## NA's :3452935 NA's :3452935 NA's :3452936 NA's :3452936There are a few problems we will need to fix:
(1) 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.
(2) The data can only be aggregated at the ride-level, which is too
granular. We will want to add some additional columns of data – such as
day, month, year – that provide additional opportunities to aggregate
the data.
(3) We will want to add a calculated field for length of ride since
the 2020Q1 data did not have the “trip duration” column. We will add
“ride_length” to the entire data frame for consistency.
(4) There are some rides where trip duration shows up as negative,
including several hundred rides where Divvy took bikes out of
circulation for Quality Control reasons. We will want to delete these
rides.
In the “member_casual” column, replace “Subscriber” with “member”
and “Customer” with “casual”
Before 2020, Divvy used different labels for these two types of
riders … we will want to make our dataframe consistent with their
current nomenclature
N.B.: “Level” is a special property of a column that is retained
even if a subset does not contain any values from a specific level
Begin by seeing how many observations fall under each user type
table(all_trips$member_casual)##
## casual Customer member Subscriber
## 48480 857474 378407 2595461Reassign to the desired values (we will go with the current 2020 labels)
all_trips <- all_trips %>%
mutate(member_casual = recode(member_casual
,"Subscriber" = "member"
,"Customer" = "casual"))Check to make sure the proper number of observations were reassigned
table(all_trips$member_casual)##
## casual member
## 905954 2973868Add columns that list the date, month, day, and year of each ride
This will allow us to aggregate ride data for each month, day, or
year … before completing these operations we could only aggregate at the
ride level
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")
all_trips$time_start <- format(as.POSIXct(all_trips$started_at,format="%H:%M:%S"),"%H:%M")
all_trips$time_end <- format(as.POSIXct(all_trips$ended_at,format="%H:%M:%S"),"%H:%M")Add a “ride_length” calculation to all_trips (in seconds)
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)Inspect the structure of the columns
str(all_trips)## tibble [3,879,822 × 21] (S3: tbl_df/tbl/data.frame)
## $ ride_id : chr [1:3879822] "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : POSIXct[1:3879822], format: "2019-04-01 00:02:22" "2019-04-01 00:03:02" ...
## $ ended_at : POSIXct[1:3879822], format: "2019-04-01 00:09:48" "2019-04-01 00:20:30" ...
## $ rideable_type : chr [1:3879822] "6251" "6226" "5649" "4151" ...
## $ start_station_id : num [1:3879822] 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr [1:3879822] "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : num [1:3879822] 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr [1:3879822] "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr [1:3879822] "member" "member" "member" "member" ...
## $ start_lat : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...
## $ start_lng : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...
## $ end_lat : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...
## $ end_lng : num [1:3879822] NA NA NA NA NA NA NA NA NA NA ...
## $ date : Date[1:3879822], format: "2019-04-01" "2019-04-01" ...
## $ month : chr [1:3879822] "04" "04" "04" "04" ...
## $ day : chr [1:3879822] "01" "01" "01" "01" ...
## $ year : chr [1:3879822] "2019" "2019" "2019" "2019" ...
## $ day_of_week : chr [1:3879822] "Monday" "Monday" "Monday" "Monday" ...
## $ time_start : chr [1:3879822] "00:02" "00:03" "00:11" "00:13" ...
## $ time_end : chr [1:3879822] "00:09" "00:20" "00:15" "00:18" ...
## $ ride_length : 'difftime' num [1:3879822] 446 1048 252 357 ...
## ..- attr(*, "units")= chr "secs"Convert “ride_length” from Factor to numeric so we can run calculations on the data
is.factor(all_trips$ride_length)## [1] FALSEall_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
is.numeric(all_trips$ride_length)## [1] TRUERemove “bad” data
The data frame includes a few hundred entries when bikes were taken
out of docks and checked for quality by Divvy or ride_length was
negative
We will create a new version of the data frame (v2) since data is
being removed
all_trips_v2 <- all_trips[!(all_trips$start_station_name == "HQ QR" | all_trips$ride_length<0),]str(all_trips_v2)## tibble [3,876,042 × 21] (S3: tbl_df/tbl/data.frame)
## $ ride_id : chr [1:3876042] "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : POSIXct[1:3876042], format: "2019-04-01 00:02:22" "2019-04-01 00:03:02" ...
## $ ended_at : POSIXct[1:3876042], format: "2019-04-01 00:09:48" "2019-04-01 00:20:30" ...
## $ rideable_type : chr [1:3876042] "6251" "6226" "5649" "4151" ...
## $ start_station_id : num [1:3876042] 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr [1:3876042] "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : num [1:3876042] 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr [1:3876042] "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr [1:3876042] "member" "member" "member" "member" ...
## $ start_lat : num [1:3876042] NA NA NA NA NA NA NA NA NA NA ...
## $ start_lng : num [1:3876042] NA NA NA NA NA NA NA NA NA NA ...
## $ end_lat : num [1:3876042] NA NA NA NA NA NA NA NA NA NA ...
## $ end_lng : num [1:3876042] NA NA NA NA NA NA NA NA NA NA ...
## $ date : Date[1:3876042], format: "2019-04-01" "2019-04-01" ...
## $ month : chr [1:3876042] "04" "04" "04" "04" ...
## $ day : chr [1:3876042] "01" "01" "01" "01" ...
## $ year : chr [1:3876042] "2019" "2019" "2019" "2019" ...
## $ day_of_week : chr [1:3876042] "Monday" "Monday" "Monday" "Monday" ...
## $ time_start : chr [1:3876042] "00:02" "00:03" "00:11" "00:13" ...
## $ time_end : chr [1:3876042] "00:09" "00:20" "00:15" "00:18" ...
## $ ride_length : num [1:3876042] 446 1048 252 357 1007 ...STEP 4: CONDUCT DESCRIPTIVE ANALYSIS
Descriptive analysis on ride_length (all figures in seconds)
Mean(all_trips_v2 ride_length) straight average (total ride length /
rides)
Median(all_trips_v2 ride_length) midpoint number in the
ascending array of ride lengths
Max(all_trips_v2 ride_length)
longest ride
Min(all_trips_v2 ride_length) shortest ride
You
can condense the four lines above to one line using summary() on the
specific attribute
summary(all_trips_v2$ride_length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 412 712 1479 1289 9387024Compare members and casual users
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = mean)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 3552.7502
## 2 member 850.0662aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 1546
## 2 member 589aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 9387024
## 2 member 9056634aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = min)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 2
## 2 member 1See the average ride time by each day for members vs casual users
aggregate(all_trips_v2$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$ride_length
## 1 casual Friday 3773.8351
## 2 member Friday 824.5305
## 3 casual Monday 3372.2869
## 4 member Monday 842.5726
## 5 casual Saturday 3331.9138
## 6 member Saturday 968.9337
## 7 casual Sunday 3581.4054
## 8 member Sunday 919.9746
## 9 casual Thursday 3682.9847
## 10 member Thursday 823.9278
## 11 casual Tuesday 3596.3599
## 12 member Tuesday 826.1427
## 13 casual Wednesday 3718.6619
## 14 member Wednesday 823.9996Notice that the days of the week are out of order. Let’s fix that.
all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))Now, let’s run the average ride time by each day for members vs casual users
aggregate(all_trips_v2$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 Sunday 3581.4054
## 2 member Sunday 919.9746
## 3 casual Monday 3372.2869
## 4 member Monday 842.5726
## 5 casual Tuesday 3596.3599
## 6 member Tuesday 826.1427
## 7 casual Wednesday 3718.6619
## 8 member Wednesday 823.9996
## 9 casual Thursday 3682.9847
## 10 member Thursday 823.9278
## 11 casual Friday 3773.8351
## 12 member Friday 824.5305
## 13 casual Saturday 3331.9138
## 14 member Saturday 968.9337analyze ridership data by type and weekday
creates weekday field using wday()
groups by usertype and weekday
calculates the number of rides and average duration
calculates the average duration
sorts
all_trips_v2 %>%
mutate(weekday = wday(started_at)) %>%
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 × 4
## # Groups: member_casual [2]
## member_casual weekday number_of_rides average_duration
## <chr> <int> <int> <dbl>
## 1 casual 1 181293 3581.
## 2 casual 2 103296 3372.
## 3 casual 3 90510 3596.
## 4 casual 4 92457 3719.
## 5 casual 5 102679 3683.
## 6 casual 6 122404 3774.
## 7 casual 7 209543 3332.
## 8 member 1 267965 920.
## 9 member 2 472196 843.
## 10 member 3 508445 826.
## 11 member 4 500329 824.
## 12 member 5 484177 824.
## 13 member 6 452790 825.
## 14 member 7 287958 969.Let’s visualize the ratio of Members To Casuals
membership <- count(all_trips_v2,member_casual , name = "count")
ggplot(membership, aes(x = member_casual, y = count)) +
geom_col(fill="lightblue",width = 0.5)+
geom_text(aes(label=count), vjust = 2, fontface = "bold") +
scale_y_continuous(labels =function(y) format(y,big.mark =",",scientific = FALSE))
Let’s visualize the number of rides by rider type
all_trips_v2 %>%
mutate(weekday = wday(started_at)) %>%
group_by(member_casual, weekday) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, weekday) %>%
ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) +
geom_col(position = position_dodge(width = 0.5))+
scale_y_continuous(labels =function(y) format(y,big.mark =",",scientific = FALSE))## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Let’s create a visualization for average duration
all_trips_v2 %>%
mutate(weekday = wday(started_at)) %>%
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 = position_dodge(width = 0.5))## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Let’s create a visualization for the top 5 departure Stations for members
departure_station_members <- all_trips_v2 %>%
filter(member_casual == "member" ) %>%
group_by(start_station_name,.drop = FALSE, ) %>%
count(sort = TRUE , name = "count") %>%
head(5)
ggplot(departure_station_members, aes(x = count, y = start_station_name)) +
geom_col(fill="blue",width = 0.5)
Let’s create a visualization for the top 5 departure Stations for casuals
departure_station_casual <- all_trips_v2 %>%
filter(member_casual == "casual" ) %>%
group_by(start_station_name,.drop = FALSE, ) %>%
count(sort = TRUE, name = "count") %>%
head(5)
ggplot(departure_station_casual, aes(x = count, y = start_station_name)) +
geom_col(fill="red",width = 0.5)
Let’s create a visualization for the top 5 Arrival Stations for members
arrival_station_members <- all_trips_v2 %>%
filter(member_casual == "member" ) %>%
group_by(end_station_name,.drop = FALSE, ) %>%
count(sort = TRUE, name = "count") %>%
head(5)
ggplot(arrival_station_members, aes(x = count, y = end_station_name)) +
geom_col(fill="blue",width = 0.5)
Let’s create a visualization for the top 5 Arrival Stations for casuals
arrival_station_casual <- all_trips_v2 %>%
filter(member_casual == "casual" ) %>%
group_by(end_station_name,.drop = FALSE, ) %>%
count(sort = TRUE, name = "count") %>%
head(5)
ggplot(arrival_station_casual, aes(x = count, y = end_station_name)) +
geom_col(fill="red",width = 0.5)
Let’s create a visualization for the Starting time for Casuals And members by each day of the week
ggplot(data = all_trips) +
geom_bar(mapping = aes(x = time_start, fill = member_casual)) +
facet_grid(~factor(day_of_week, levels=c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday')))
Let’s create a visualization for the ending time for Casual And members by day of the week
ggplot(data = all_trips) +
geom_bar(mapping = aes(x = time_end, fill = member_casual)) +
facet_grid(~factor(day_of_week, levels=c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday')))
lets create a table of all starting stations with coordination to check later on Google Maps
start_stations_location <- q1_2020 %>%
add_count(start_station_name, sort = TRUE) %>%
distinct(start_station_name, .keep_all = TRUE) %>%
select(c(start_station_name, n, start_lat,start_lng))
kable(head(start_stations_location,10),format = "html", align = "lcll") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| start_station_name | n | start_lat | start_lng |
|---|---|---|---|
| Canal St & Adams St | 7813 | 41.8793 | -87.6399 |
| Clinton St & Madison St | 6797 | 41.8822 | -87.6411 |
| Clinton St & Washington Blvd | 5941 | 41.8834 | -87.6412 |
| Kingsbury St & Kinzie St | 4626 | 41.8892 | -87.6385 |
| Columbus Dr & Randolph St | 4425 | 41.8847 | -87.6195 |
| HQ QR | 3767 | 41.8899 | -87.6803 |
| Franklin St & Monroe St | 3711 | 41.8803 | -87.6352 |
| Canal St & Madison St | 3637 | 41.8821 | -87.6398 |
| Clinton St & Lake St | 3580 | 41.8856 | -87.6418 |
| Larrabee St & Kingsbury St | 3550 | 41.8978 | -87.6429 |
lets create a table of all ending stations with coordination to check later on Google Maps
end_stations_location <- q1_2020 %>%
add_count(end_station_name, sort = TRUE) %>%
distinct(end_station_name, .keep_all = TRUE) %>%
select(c(end_station_name, n, end_lat,end_lng))
kable(head(end_stations_location,10),format = "html", align = "lcll") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| end_station_name | n | end_lat | end_lng |
|---|---|---|---|
| Canal St & Adams St | 8323 | 41.8793 | -87.6399 |
| Clinton St & Washington Blvd | 7166 | 41.8834 | -87.6412 |
| Clinton St & Madison St | 6854 | 41.8822 | -87.6411 |
| Kingsbury St & Kinzie St | 4615 | 41.8892 | -87.6385 |
| Michigan Ave & Washington St | 4227 | 41.8840 | -87.6247 |
| HQ QR | 3766 | 41.8899 | -87.6803 |
| Franklin St & Monroe St | 3748 | 41.8803 | -87.6352 |
| Canal St & Madison St | 3706 | 41.8821 | -87.6398 |
| Clinton St & Lake St | 3646 | 41.8856 | -87.6418 |
| St. Clair St & Erie St | 3618 | 41.8944 | -87.6227 |
lets visualize the top 5 departure stations for members on Google Maps (red markers)
Top5_departure_station_members <-left_join(departure_station_members,start_stations_location,by = "start_station_name") %>%
select(c(start_station_name, count, start_lat,start_lng))
kable(Top5_departure_station_members, format = "html", align = "lcll") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) | start_station_name | count | start_lat | start_lng |
|---|---|---|---|
| Canal St & Adams St | 51948 | 41.8793 | -87.6399 |
| Clinton St & Madison St | 46191 | 41.8822 | -87.6411 |
| Clinton St & Washington Blvd | 43590 | 41.8834 | -87.6412 |
| Columbus Dr & Randolph St | 31053 | 41.8847 | -87.6195 |
| Franklin St & Monroe St | 30982 | 41.8803 | -87.6352 |
Click on the image to view in Google Maps
lets visualize the top 5 departure stations for casuals on Google Maps (red markers)
Top5_departure_station_casual <-left_join(departure_station_casual,start_stations_location,by = "start_station_name") %>%
select(c(start_station_name, count, start_lat,start_lng))
kable(Top5_departure_station_casual, format = "html", align = "lcll") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| start_station_name | count | start_lat | start_lng |
|---|---|---|---|
| Streeter Dr & Grand Ave | 53415 | 41.8923 | -87.6120 |
| Lake Shore Dr & Monroe St | 39686 | 41.8810 | -87.6167 |
| Millennium Park | 21901 | 41.8810 | -87.6241 |
| Michigan Ave & Oak St | 21633 | 41.9010 | -87.6238 |
| Shedd Aquarium | 20781 | 41.8672 | -87.6154 |
Click on the image to view in Google Maps
lets visualize the top 5 arrival stations for members on Google Maps (red markers)
Top5_arrival_station_members <-left_join(arrival_station_members, end_stations_location,by = "end_station_name") %>%
select(c(end_station_name, count, end_lat,end_lng))
kable(Top5_arrival_station_members, format = "html", align = "lcll") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| end_station_name | count | end_lat | end_lng |
|---|---|---|---|
| Canal St & Adams St | 48839 | 41.8793 | -87.6399 |
| Clinton St & Washington Blvd | 47633 | 41.8834 | -87.6412 |
| Clinton St & Madison St | 44285 | 41.8822 | -87.6411 |
| Daley Center Plaza | 30845 | 41.8842 | -87.6296 |
| Kingsbury St & Kinzie St | 30404 | 41.8892 | -87.6385 |
Click on the image to view in Google Maps
lets visualize the top 5 arrival stations for casuals on Google Maps (red markers)
Top5_arrival_station_casual <-left_join(arrival_station_casual, end_stations_location,by = "end_station_name") %>%
select(c(end_station_name, count, end_lat,end_lng))
kable(Top5_arrival_station_casual, format = "html", align = "lcll") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| end_station_name | count | end_lat | end_lng |
|---|---|---|---|
| Streeter Dr & Grand Ave | 67507 | 41.8923 | -87.6120 |
| Lake Shore Dr & Monroe St | 31051 | 41.8810 | -87.6167 |
| Millennium Park | 25509 | 41.8810 | -87.6241 |
| Michigan Ave & Oak St | 23982 | 41.9010 | -87.6238 |
| Lake Shore Dr & North Blvd | 23477 | 41.9117 | -87.6268 |
Click on the image to view in Google Maps
Our observations and analysts
After Cleaning, organizing the data and analyzing each graph we had the following observations:
First number of casual customers is 905954 while number of members is 2973868
Let’s visualize the ratio of Members To Casuals
Second number of rides are far more on the members side than casuals.
Let’s visualize the number of rides by rider type
Third casuals duration time is far more than members duration time
Let’s create a visualization for average duration
Forth visualizing the number of rides, it is clear that members take more rides than casuals, every day of the week.
Let’s visualize the number of rides by rider type
Fifth visualizing the average duration we see that Casuals Spend much more time per trip than members
Let’s create a visualization for average duration
Sixth visualizing the Starting time for Casuals compared to members by each day of the week, we can see that members tend to peak at normal working hours, mostly. while casuals start off peak times mostly in the afternoons and late at night. while on week ends casuals out number members most of the day.
Let’s create a visualization for the Starting time for Casuals And mambers by each day of the week
Seventh visualizing the end time for members compared to Casuals by each day of the week, we can see that members tend to peak at normal working hours, mostly. while casuals start off-peak times mostly again in the afternoons and late at night. while on week ends casuals out number members most of the day.
Let’s create a visualization for the ending time for Casual And members by day of the week
Eighth visualizing the top 5 departure Stations for members we check their locations on Google maps where it shows random type of departure points
lets visualize the top 5 departure stations for members on Google Maps (red markers)
Ninth visualizing the top 5 departure Stations for casuals. we check their locations on Google maps
where it shows random type of departure points.
lets visualize the top 5 departure stations for casuals on Google Maps (red markers)
Tenth visualizing the top 5 arrival Stations for members. we check their locations on Google maps
where it shows business type of arrival points
lets visualize the top 5 arrival stations for members on Google Maps (red markers)
Eleventh visualizing the top 5 arrival Stations for casuals. we check their locations on Google maps
where it shows entertainment type of arrival points.
Based on all the previous analyses and the Eleven observations, to answer our key Question: <br>
“In what ways do members and casual riders use Divvy bikes differently?”
we can summarize that:
most members, while they use their bikes much more frequently, they tend to use it for less duration time they use their bikes departing at peak working hours and arriving back at peak working hours departing from random locations probably their homes to business location then back. indicating that they mostly commute between work and home.
while casuals though they use their bikes much less number of times but for much longer duration, they tend to use their bikes mostly off peak times and late after-work from random location to entertainment locations as we can induce from Google maps therefore casuals are mostly using bicycles for entertainment.
Members |
Casuals | |
Count |
2973868 | 905954 |
Count % |
77% | 23% |
Frequency of use |
much higher | |
Duration of use |
much higher | |
weekdays Departure Time |
mostly peak | mostly off peak |
weekdays Departure location |
random | random |
weekdays arrival Time |
mostly peak | mostly of peak |
weekdays arrival location |
mostly Business | mostly entertainment |
weekend departure time |
mostly early | mostly early |
weekend departure location |
random | random |
weekend arrival time |
mostly late | mostly late |
weekend arrival location |
entertainment | entertainment |
indicating that they mostly commute bet ween work and home |
indicating that they are mostly using bicycles for entertainment. |
Finally based on the above how can we convert casuals to members ?
firstly, we need to convince casuals that they can use these bikes not only for leisure but also for going to work by passing a massage like
““why don’t you extend your leisure time?””
OR