Case Study:How Dose A Bike-share Navigate Speedy Success?
ebenezer akpati
2022-10-12
Introduction
I have been working on Google Data Analytics Professional certificate, powered by Coursera. This course took me through SQL, R, Excel and Tableau workbench environment. however, below is my working of capstone project: case study 1
Seneario
You are 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, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations
Ask/Business task
To understand how do annual members and casual riders use bike-share program differently in order to design marketing strategies aiming to convert Cyclistic casual riders into annual members.
Preparing/Understanding Dataset
I used the public data set located [here]https://divvy-tripdata.s3.amazonaws.com/index.html this data was made available by Motivate international Inc. Under this [license]https://ride.divvybikes.com/data-license-agreement this is a public dataset, therefore, is is not my personal dataset and there is no way to know how often same rider uses the bike service.
Data Description
I used the data set of a year period (September 2020 - August 2021), this said data was downloaded into my localdisk and uploaded into rstudio for analysis. The dataset contains trip details and each trip includes:
Trip id Type of bikes (classic, docked or electric) Trip start day and time Trip end day and time Trip start station name, id, latitude and longitude Trip end station name, id, latitude and longitude Type of customer (casual or member) The data is considered structured data because it is organized in a certain format, like rows and columns.
How did you verify the data’s integrity?
This is public data that you can use to explore how different customer types are using Cyclistic bikes. We are going to assume the data is credible.
Sort and filter the data
I filtered and use the “find and select” > “Go to special” > “Blanks” allowed to delete the blank row. If I found a blank row in any column, I erased the whole row because the data is public, and could not find the reason why the data was empty.
PROCESS
Choose your tools R for cleaning, analysis, and data visualization. I used Janitor,here and Dplyr. I removed duplicate records, deleted row with incomplete or unavailable data, used the clean name function to make sure all column where unqune and has no title space,
All the R code chunks which has been implemented to prepare and process the dataset has been documented below.
#Step 1 install and upload required parckages
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("lubridate")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("GGally")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("janitor")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("skimr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("here")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)install.packages("tidyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(tidyverse) ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## âś” ggplot2 3.3.6 âś” purrr 0.3.5
## âś” tibble 3.1.8 âś” dplyr 1.0.10
## âś” tidyr 1.2.1 âś” stringr 1.4.1
## âś” readr 2.1.3 âś” forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## âś– dplyr::filter() masks stats::filter()
## âś– dplyr::lag() masks stats::lag()library(ggplot2)
library(here)## here() starts at /cloud/projectlibrary(skimr)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(dplyr)
library(tidyr)
library(lubridate)##
## Attaching package: 'lubridate'
##
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2Step 2 Upload Divvy datasets (csv files) here
trips_2020dec <- read_csv("trips2020_dec.csv")## Rows: 131573 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2020nov <- read_csv("trips2020_nov.csv")## Rows: 259716 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
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2020oct <- read_csv("trips2020_oct.csv")## Rows: 388653 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
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2020sep <- read_csv("trips2020_sep.csv")## Rows: 532958 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, started_at, ended_at, start_station_name, e...
## dbl (6): start_station_id, end_station_id, start_lat, start_lng, end_lat, en...
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021jan <- read_csv("trips2021_jan.csv")## Rows: 96834 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021feb <- read_csv("2021_feb.csv")## Rows: 49622 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021mar <- read_csv("2021_mar.csv")## Rows: 228496 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021april <- read_csv("2021_april.csv")## Rows: 337230 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021may <- read_csv("2021_may.csv")## Rows: 531633 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021jun <- read_csv("2021_jun.csv")## Rows: 729595 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021july <- read_csv("2021_july.csv")## Rows: 822410 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.trips_2021aug <- read_csv("2021aug.csv")## Rows: 804352 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.Step 3 Inspect the dataframes and look for incongruencies
str(trips_2020dec)## spec_tbl_df [131,573 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:131573] "70B6A9A437D4C30D" "158A465D4E74C54A" "5262016E0F1F2F9A" "BE119628E44F871E" ...
## $ rideable_type : chr [1:131573] "classic_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:131573], format: "2020-12-27 12:44:29" "2020-12-18 17:37:15" ...
## $ ended_at : POSIXct[1:131573], format: "2020-12-27 12:55:06" "2020-12-18 17:44:19" ...
## $ start_station_name: chr [1:131573] "Aberdeen St & Jackson Blvd" NA NA NA ...
## $ start_station_id : chr [1:131573] "13157" NA NA NA ...
## $ end_station_name : chr [1:131573] "Desplaines St & Kinzie St" NA NA NA ...
## $ end_station_id : chr [1:131573] "TA1306000003" NA NA NA ...
## $ start_lat : num [1:131573] 41.9 41.9 41.9 41.9 41.8 ...
## $ start_lng : num [1:131573] -87.7 -87.7 -87.7 -87.7 -87.6 ...
## $ end_lat : num [1:131573] 41.9 41.9 41.9 41.9 41.8 ...
## $ end_lng : num [1:131573] -87.6 -87.7 -87.7 -87.7 -87.6 ...
## $ member_casual : chr [1:131573] "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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2020nov)## spec_tbl_df [259,716 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:259716] "BD0A6FF6FFF9B921" "96A7A7A4BDE4F82D" "C61526D06582BDC5" "E533E89C32080B9E" ...
## $ rideable_type : chr [1:259716] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:259716], format: "2020-11-01 13:36:00" "2020-11-01 10:03:26" ...
## $ ended_at : POSIXct[1:259716], format: "2020-11-01 13:45:40" "2020-11-01 10:14:45" ...
## $ start_station_name: chr [1:259716] "Dearborn St & Erie St" "Franklin St & Illinois St" "Lake Shore Dr & Monroe St" "Leavitt St & Chicago Ave" ...
## $ start_station_id : num [1:259716] 110 672 76 659 2 72 76 NA 58 394 ...
## $ end_station_name : chr [1:259716] "St. Clair St & Erie St" "Noble St & Milwaukee Ave" "Federal St & Polk St" "Stave St & Armitage Ave" ...
## $ end_station_id : num [1:259716] 211 29 41 185 2 76 72 NA 288 273 ...
## $ start_lat : num [1:259716] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:259716] -87.6 -87.6 -87.6 -87.7 -87.6 ...
## $ end_lat : num [1:259716] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:259716] -87.6 -87.7 -87.6 -87.7 -87.6 ...
## $ member_casual : chr [1:259716] "casual" "casual" "casual" "casual" ...
## - 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(trips_2020oct)## spec_tbl_df [388,653 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:388653] "ACB6B40CF5B9044C" "DF450C72FD109C01" "B6396B54A15AC0DF" "44A4AEE261B9E854" ...
## $ rideable_type : chr [1:388653] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:388653], format: "2020-10-31 19:39:43" "2020-10-31 23:50:08" ...
## $ ended_at : POSIXct[1:388653], format: "2020-10-31 19:57:12" "2020-11-01 00:04:16" ...
## $ start_station_name: chr [1:388653] "Lakeview Ave & Fullerton Pkwy" "Southport Ave & Waveland Ave" "Stony Island Ave & 67th St" "Clark St & Grace St" ...
## $ start_station_id : num [1:388653] 313 227 102 165 190 359 313 125 NA 174 ...
## $ end_station_name : chr [1:388653] "Rush St & Hubbard St" "Kedzie Ave & Milwaukee Ave" "University Ave & 57th St" "Broadway & Sheridan Rd" ...
## $ end_station_id : num [1:388653] 125 260 423 256 185 53 125 313 199 635 ...
## $ start_lat : num [1:388653] 41.9 41.9 41.8 42 41.9 ...
## $ start_lng : num [1:388653] -87.6 -87.7 -87.6 -87.7 -87.7 ...
## $ end_lat : num [1:388653] 41.9 41.9 41.8 42 41.9 ...
## $ end_lng : num [1:388653] -87.6 -87.7 -87.6 -87.7 -87.7 ...
## $ member_casual : chr [1:388653] "casual" "casual" "casual" "casual" ...
## - 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(trips_2020sep)## spec_tbl_df [532,958 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:532958] "2B22BD5F95FB2629" "A7FB70B4AFC6CAF2" "86057FA01BAC778E" "57F6DC9A153DB98C" ...
## $ rideable_type : chr [1:532958] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : chr [1:532958] "17/09/2020 14:27" "17/09/2020 15:07" "17/09/2020 15:09" "17/09/2020 18:10" ...
## $ ended_at : chr [1:532958] "17/09/2020 14:44" "17/09/2020 15:07" "17/09/2020 15:09" "17/09/2020 18:35" ...
## $ start_station_name: chr [1:532958] "Michigan Ave & Lake St" "W Oakdale Ave & N Broadway" "W Oakdale Ave & N Broadway" "Ashland Ave & Belle Plaine Ave" ...
## $ start_station_id : num [1:532958] 52 NA NA 246 24 94 291 NA NA NA ...
## $ end_station_name : chr [1:532958] "Green St & Randolph St" "W Oakdale Ave & N Broadway" "W Oakdale Ave & N Broadway" "Montrose Harbor" ...
## $ end_station_id : num [1:532958] 112 NA NA 249 24 NA 256 NA NA NA ...
## $ start_lat : num [1:532958] 41.9 41.9 41.9 42 41.9 ...
## $ start_lng : num [1:532958] -87.6 -87.6 -87.6 -87.7 -87.6 ...
## $ end_lat : num [1:532958] 41.9 41.9 41.9 42 41.9 ...
## $ end_lng : num [1:532958] -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ member_casual : chr [1:532958] "casual" "casual" "casual" "casual" ...
## - attr(*, "spec")=
## .. cols(
## .. ride_id = col_character(),
## .. rideable_type = col_character(),
## .. started_at = col_character(),
## .. ended_at = col_character(),
## .. 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(trips_2021jan)## spec_tbl_df [96,834 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:96834] "E19E6F1B8D4C42ED" "DC88F20C2C55F27F" "EC45C94683FE3F27" "4FA453A75AE377DB" ...
## $ rideable_type : chr [1:96834] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:96834], format: "2021-01-23 16:14:19" "2021-01-27 18:43:08" ...
## $ ended_at : POSIXct[1:96834], format: "2021-01-23 16:24:44" "2021-01-27 18:47:12" ...
## $ start_station_name: chr [1:96834] "California Ave & Cortez St" "California Ave & Cortez St" "California Ave & Cortez St" "California Ave & Cortez St" ...
## $ start_station_id : chr [1:96834] "17660" "17660" "17660" "17660" ...
## $ end_station_name : chr [1:96834] NA NA NA NA ...
## $ end_station_id : chr [1:96834] NA NA NA NA ...
## $ start_lat : num [1:96834] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:96834] -87.7 -87.7 -87.7 -87.7 -87.7 ...
## $ end_lat : num [1:96834] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:96834] -87.7 -87.7 -87.7 -87.7 -87.7 ...
## $ member_casual : chr [1:96834] "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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2021feb)## spec_tbl_df [49,622 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:49622] "89E7AA6C29227EFF" "0FEFDE2603568365" "E6159D746B2DBB91" "B32D3199F1C2E75B" ...
## $ rideable_type : chr [1:49622] "classic_bike" "classic_bike" "electric_bike" "classic_bike" ...
## $ started_at : POSIXct[1:49622], format: "2021-02-12 16:14:56" "2021-02-14 17:52:38" ...
## $ ended_at : POSIXct[1:49622], format: "2021-02-12 16:21:43" "2021-02-14 18:12:09" ...
## $ start_station_name: chr [1:49622] "Glenwood Ave & Touhy Ave" "Glenwood Ave & Touhy Ave" "Clark St & Lake St" "Wood St & Chicago Ave" ...
## $ start_station_id : chr [1:49622] "525" "525" "KA1503000012" "637" ...
## $ end_station_name : chr [1:49622] "Sheridan Rd & Columbia Ave" "Bosworth Ave & Howard St" "State St & Randolph St" "Honore St & Division St" ...
## $ end_station_id : chr [1:49622] "660" "16806" "TA1305000029" "TA1305000034" ...
## $ start_lat : num [1:49622] 42 42 41.9 41.9 41.8 ...
## $ start_lng : num [1:49622] -87.7 -87.7 -87.6 -87.7 -87.6 ...
## $ end_lat : num [1:49622] 42 42 41.9 41.9 41.8 ...
## $ end_lng : num [1:49622] -87.7 -87.7 -87.6 -87.7 -87.6 ...
## $ member_casual : chr [1:49622] "member" "casual" "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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2021mar)## spec_tbl_df [228,496 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:228496] "CFA86D4455AA1030" "30D9DC61227D1AF3" "846D87A15682A284" "994D05AA75A168F2" ...
## $ rideable_type : chr [1:228496] "classic_bike" "classic_bike" "classic_bike" "classic_bike" ...
## $ started_at : POSIXct[1:228496], format: "2021-03-16 08:32:30" "2021-03-28 01:26:28" ...
## $ ended_at : POSIXct[1:228496], format: "2021-03-16 08:36:34" "2021-03-28 01:36:55" ...
## $ start_station_name: chr [1:228496] "Humboldt Blvd & Armitage Ave" "Humboldt Blvd & Armitage Ave" "Shields Ave & 28th Pl" "Winthrop Ave & Lawrence Ave" ...
## $ start_station_id : chr [1:228496] "15651" "15651" "15443" "TA1308000021" ...
## $ end_station_name : chr [1:228496] "Stave St & Armitage Ave" "Central Park Ave & Bloomingdale Ave" "Halsted St & 35th St" "Broadway & Sheridan Rd" ...
## $ end_station_id : chr [1:228496] "13266" "18017" "TA1308000043" "13323" ...
## $ start_lat : num [1:228496] 41.9 41.9 41.8 42 42 ...
## $ start_lng : num [1:228496] -87.7 -87.7 -87.6 -87.7 -87.7 ...
## $ end_lat : num [1:228496] 41.9 41.9 41.8 42 42.1 ...
## $ end_lng : num [1:228496] -87.7 -87.7 -87.6 -87.6 -87.7 ...
## $ member_casual : chr [1:228496] "casual" "casual" "casual" "casual" ...
## - 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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2021april)## spec_tbl_df [337,230 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:337230] "6C992BD37A98A63F" "1E0145613A209000" "E498E15508A80BAD" "1887262AD101C604" ...
## $ rideable_type : chr [1:337230] "classic_bike" "docked_bike" "docked_bike" "classic_bike" ...
## $ started_at : POSIXct[1:337230], format: "2021-04-12 18:25:36" "2021-04-27 17:27:11" ...
## $ ended_at : POSIXct[1:337230], format: "2021-04-12 18:56:55" "2021-04-27 18:31:29" ...
## $ start_station_name: chr [1:337230] "State St & Pearson St" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Honore St & Division St" ...
## $ start_station_id : chr [1:337230] "TA1307000061" "KA1503000069" "20121" "TA1305000034" ...
## $ end_station_name : chr [1:337230] "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Southport Ave & Waveland Ave" ...
## $ end_station_id : chr [1:337230] "13235" "KA1503000069" "20121" "13235" ...
## $ start_lat : num [1:337230] 41.9 41.8 41.7 41.9 41.7 ...
## $ start_lng : num [1:337230] -87.6 -87.6 -87.7 -87.7 -87.7 ...
## $ end_lat : num [1:337230] 41.9 41.8 41.7 41.9 41.7 ...
## $ end_lng : num [1:337230] -87.7 -87.6 -87.7 -87.7 -87.7 ...
## $ member_casual : chr [1:337230] "member" "casual" "casual" "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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2021may)## spec_tbl_df [531,633 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:531633] "C809ED75D6160B2A" "DD59FDCE0ACACAF3" "0AB83CB88C43EFC2" "7881AC6D39110C60" ...
## $ rideable_type : chr [1:531633] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:531633], format: "2021-05-30 11:58:15" "2021-05-30 11:29:14" ...
## $ ended_at : POSIXct[1:531633], format: "2021-05-30 12:10:39" "2021-05-30 12:14:09" ...
## $ start_station_name: chr [1:531633] NA NA NA NA ...
## $ start_station_id : chr [1:531633] NA NA NA NA ...
## $ end_station_name : chr [1:531633] NA NA NA NA ...
## $ end_station_id : chr [1:531633] NA NA NA NA ...
## $ start_lat : num [1:531633] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:531633] -87.6 -87.6 -87.7 -87.7 -87.7 ...
## $ end_lat : num [1:531633] 41.9 41.8 41.9 41.9 41.9 ...
## $ end_lng : num [1:531633] -87.6 -87.6 -87.7 -87.7 -87.7 ...
## $ member_casual : chr [1:531633] "casual" "casual" "casual" "casual" ...
## - 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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2021jun)## spec_tbl_df [729,595 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:729595] "99FEC93BA843FB20" "06048DCFC8520CAF" "9598066F68045DF2" "B03C0FE48C412214" ...
## $ rideable_type : chr [1:729595] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:729595], format: "2021-06-13 14:31:28" "2021-06-04 11:18:02" ...
## $ ended_at : POSIXct[1:729595], format: "2021-06-13 14:34:11" "2021-06-04 11:24:19" ...
## $ start_station_name: chr [1:729595] NA NA NA NA ...
## $ start_station_id : chr [1:729595] NA NA NA NA ...
## $ end_station_name : chr [1:729595] NA NA NA NA ...
## $ end_station_id : chr [1:729595] NA NA NA NA ...
## $ start_lat : num [1:729595] 41.8 41.8 41.8 41.8 41.8 ...
## $ start_lng : num [1:729595] -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ end_lat : num [1:729595] 41.8 41.8 41.8 41.8 41.8 ...
## $ end_lng : num [1:729595] -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ member_casual : chr [1:729595] "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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2021july)## spec_tbl_df [822,410 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:822410] "0A1B623926EF4E16" "B2D5583A5A5E76EE" "6F264597DDBF427A" "379B58EAB20E8AA5" ...
## $ rideable_type : chr [1:822410] "docked_bike" "classic_bike" "classic_bike" "classic_bike" ...
## $ started_at : POSIXct[1:822410], format: "2021-07-02 14:44:36" "2021-07-07 16:57:42" ...
## $ ended_at : POSIXct[1:822410], format: "2021-07-02 15:19:58" "2021-07-07 17:16:09" ...
## $ start_station_name: chr [1:822410] "Michigan Ave & Washington St" "California Ave & Cortez St" "Wabash Ave & 16th St" "California Ave & Cortez St" ...
## $ start_station_id : chr [1:822410] "13001" "17660" "SL-012" "17660" ...
## $ end_station_name : chr [1:822410] "Halsted St & North Branch St" "Wood St & Hubbard St" "Rush St & Hubbard St" "Carpenter St & Huron St" ...
## $ end_station_id : chr [1:822410] "KA1504000117" "13432" "KA1503000044" "13196" ...
## $ start_lat : num [1:822410] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:822410] -87.6 -87.7 -87.6 -87.7 -87.7 ...
## $ end_lat : num [1:822410] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:822410] -87.6 -87.7 -87.6 -87.7 -87.7 ...
## $ member_casual : chr [1:822410] "casual" "casual" "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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>str(trips_2021aug)## spec_tbl_df [804,352 Ă— 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:804352] "99103BB87CC6C1BB" "EAFCCCFB0A3FC5A1" "9EF4F46C57AD234D" "5834D3208BFAF1DA" ...
## $ rideable_type : chr [1:804352] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:804352], format: "2021-08-10 17:15:49" "2021-08-10 17:23:14" ...
## $ ended_at : POSIXct[1:804352], format: "2021-08-10 17:22:44" "2021-08-10 17:39:24" ...
## $ start_station_name: chr [1:804352] NA NA NA NA ...
## $ start_station_id : chr [1:804352] NA NA NA NA ...
## $ end_station_name : chr [1:804352] NA NA NA NA ...
## $ end_station_id : chr [1:804352] NA NA NA NA ...
## $ start_lat : num [1:804352] 41.8 41.8 42 42 41.8 ...
## $ start_lng : num [1:804352] -87.7 -87.7 -87.7 -87.7 -87.6 ...
## $ end_lat : num [1:804352] 41.8 41.8 42 42 41.8 ...
## $ end_lng : num [1:804352] -87.7 -87.6 -87.7 -87.7 -87.6 ...
## $ member_casual : chr [1:804352] "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_character(),
## .. end_station_name = col_character(),
## .. end_station_id = col_character(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>#I Changed the data structure start_station_id and end_station_id in Sept, Oct and Nov into character so that i can stack my data ito one fram
trips_2020sep <- mutate(trips_2020sep, start_station_id = as.character(start_station_id)
,end_station_id = as.character(end_station_id))
trips_2020oct <- mutate(trips_2020oct, start_station_id = as.character(start_station_id)
,end_station_id = as.character(end_station_id))
trips_2020nov <- mutate(trips_2020nov, start_station_id = as.character(start_station_id)
,end_station_id = as.character(end_station_id))#I changed date time in trips_2020sep to POSIXct
trips_2020sep$started_at<-parse_date_time(trips_2020sep$started_at, orders = c("dmy_HMS","dmy_HM"))
trips_2020sep$ended_at<-parse_date_time(trips_2020sep$ended_at, orders = c("dmy_HMS","dmy_HM"))Create a function to get time difference even when time stamps are inverted.
time_difference <- function(end_time, start_time){
t_diff_1 <- as.POSIXct(end_time) - as.POSIXct(start_time)
t_diff_2 <- as.POSIXct(start_time) - as.POSIXct(end_time)
ifelse (end_time > start_time, t_diff_1, t_diff_2)
}Create a single dataframe by joining months dataframes.
all_trips <- bind_rows(trips_2021jan, trips_2021feb, trips_2021mar,
trips_2021april, trips_2021may, trips_2021jun,
trips_2021july, trips_2021aug, trips_2020sep,
trips_2020oct, trips_2020nov, trips_2020dec) %>%
# Select, rename, create columns, change data types, and filter
rename(bike_type = rideable_type, rider_type = member_casual) %>%
mutate(ended_at = as.POSIXct(ended_at),
started_at = as.POSIXct(started_at),
day_of_week = weekdays(started_at),
rides_month = months(started_at),
ride_length = time_difference(ended_at, started_at)) %>%
select(-ride_id, -start_station_name, -start_station_id, -end_station_name,
-end_station_id, -start_lat, -start_lng, -end_lat, -end_lng,
-started_at, -ended_at) %>%
filter(ride_length != 0)STEP 4: CLEAN UP AND ADD DATA TO PREPARE FOR ANALYSIS
#Inspect the new table that has been created
colnames(all_trips) #List of column names## [1] "bike_type" "rider_type" "day_of_week" "rides_month" "ride_length"nrow(all_trips) #How many rows are in data frame?## [1] 4905647dim(all_trips) #Dimensions of the data frame?## [1] 4905647 5head(all_trips) #See the first 6 rows of data frame. Also tail(all_trips)str(all_trips) #See list of columns and data types (numeric, character, etc)## tibble [4,905,647 Ă— 5] (S3: tbl_df/tbl/data.frame)
## $ bike_type : chr [1:4905647] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ rider_type : chr [1:4905647] "member" "member" "member" "member" ...
## $ day_of_week: chr [1:4905647] "Saturday" "Wednesday" "Thursday" "Thursday" ...
## $ rides_month: chr [1:4905647] "January" "January" "January" "January" ...
## $ ride_length: num [1:4905647] 625 244 80 702 43 ...summary(all_trips) #Statistical summary of data. Mainly for numerics## bike_type rider_type day_of_week rides_month
## Length:4905647 Length:4905647 Length:4905647 Length:4905647
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## ride_length
## Min. : 1
## 1st Qu.: 430
## Median : 772
## Mean : 1535
## 3rd Qu.: 1397
## Max. :3356649Finish cleaning and check data integrity
clean_names(all_trips) ##there are 4905647 observation left and 5 variable
STEP 5: CONDUCT DESCRIPTIVE ANALYSIS
#Number of Rides Completed
tabyl(all_trips, rider_type) %>%
adorn_pct_formatting(digits = 1)## Total number of ride by ride type
ggplot(data = all_trips) +
geom_bar(mapping = aes(x = rider_type, fill = rider_type)) +
xlab("Rider Type") + ylab("Number of Rides") +
labs(title = "Number of Rides Completed by Rider Type",
caption = "Data collected by Cyclistic Bike-Share in previous 12 months",
fill = "Rider type") + scale_fill_viridis_d()
# Observations: There are more members customers by (54%) than casual customers(45%) when completed trips were compared Compare members and casual users
aggregate(all_trips$ride_length ~ all_trips$rider_type, FUN = mean)aggregate(all_trips$ride_length ~ all_trips$rider_type, FUN = median)aggregate(all_trips$ride_length ~ all_trips$rider_type, FUN = max)aggregate(all_trips$ride_length ~ all_trips$rider_type, FUN = min)aggregate(all_trips$ride_length ~ all_trips$rider_type + all_trips$day_of_week, FUN = mean)# Notice that the days of the week are out of order. Let's fix that
all_trips$day_of_week <- ordered(all_trips$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))all_trips %>%
group_by(rider_type, day_of_week) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(rider_type, day_of_week) %>%
ggplot(aes(x = day_of_week, y = number_of_rides, fill = rider_type)) +
geom_col(position = "dodge") ## `summarise()` has grouped output by 'rider_type'. You can override using the
## `.groups` argument.
labs(title="Total Time on Rides by Rider_Type",
subtitle="Sample of September 2020 and August 2021 rides",
caption ="Data collected by Cyclistic Bike-Share",
fill = "Rider type") + scale_fill_viridis_d()## NULL##plot 2# The highest number of casual customers was on Saturday and Sunday, this is week ends this could be attributed to people tend to go out for leisure more on week ends, the highest for members user is Tuesday and the lowest is on sunder, however, ther is no much change within the week for member users. Now, let’s run the average ride time by each day for members vs casual users
all_trips%>%
group_by(rider_type, day_of_week) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(rider_type, day_of_week) %>%
ggplot(aes(x = day_of_week, y = average_duration, fill = rider_type)) +
geom_col(position = "dodge") +
labs(title= "Average rides each day by rider type",
subtitle= "For the period between September 2020 and August 2021") ## `summarise()` has grouped output by 'rider_type'. You can override using the
## `.groups` argument.
##plot3# Observations: The casual customers spent more time than members customers weeklyBike preference by user type
#Total number of rides by prevrance
tabyl(all_trips, bike_type) %>%
adorn_pct_formatting(digits = 1)ggplot(data = all_trips) +
geom_bar(mapping = aes(x = bike_type, fill = bike_type)) +
xlab("Bike Type") + ylab("Number of Rides") +
labs(title = "Bike preference and rider_type",
subtitle= "June 2020 to May 2021",
fill = "Bike type") + scale_fill_viridis_d() + facet_grid(~rider_type)
## plot 4#Observations: The docked bike is the most chosen bike_type by casual and member customers. The electric bike is the least chosen bike by casual and member customers.Number of rides completed by month by user name
tabyl(all_trips, rides_month) %>%
adorn_pct_formatting(digits = 1) ggplot(data = all_trips) +
geom_bar(mapping = aes(x = ordered(rides_month,
levels = c("January", "February", "March",
"April", "May", "June", "July",
"August", "September","October",
"November", "December")),
fill = rides_month)) +
xlab("Month") + ylab("Number of Rides") +
labs(title = "Numbers of rides completed by month",
subtitle= "September2020 to August 2021",
fill = "Month") + facet_grid(~rider_type) + scale_fill_viridis_d()+
theme(axis.text.x=element_text(angle = 90))
## plot 5#Observations: The casual and member customers highest month is July and August respectively. The lowest number of rides completed by casual customers are between December to February where it picks up and the same applies to member customers too. The highest number of rides completed by month for members were July, August and September. The casual customers highest months were July and August. the lowest months can be atributed to winter season as a result of the cold, however, members performance was better this period which could be that the were using it to go to work.
RIDE LENGTH :comparing average ride_Length by ride_type and by rides_month
# Average ride_length by rider_type and by rides_month
avg_ride_month_tbl <- aggregate(all_trips$ride_length ~ all_trips$rider_type +
all_trips$rides_month, FUN = mean)
ggplot(data = avg_ride_month_tbl) +
geom_col(mapping = aes(x = ordered(`all_trips$rides_month`,
levels = c("January", "February", "March",
"April", "May", "June", "July",
"August", "September","October",
"November", "December")),
y = `all_trips$ride_length`,
fill = `all_trips$rides_month`)) +
xlab("Month") + ylab("Average ride length (in seconds)") +
labs(title="Average Ride Length by Month",
caption ="Data collected by Cyclistic Bike-Share in previous 12 months",
fill = "Month") + facet_grid(~`all_trips$rider_type`) +
scale_fill_viridis_d() +
theme(axis.text.x=element_text(angle = 90))
## plot 6 # Observations: Again, in each month the casual customers spent more time than members customers. The differences are casual customers time spent reduces from August to January until it hits February.#Total ride_length by rider_type and by rides_month
total_ride_month_tbl <- aggregate(all_trips$ride_length ~ all_trips$rider_type +
all_trips$rides_month, FUN = sum)
ggplot(data = total_ride_month_tbl) +
geom_col(mapping = aes(x = ordered(`all_trips$rides_month`,
levels = c("January", "February", "March",
"April", "May", "June", "July",
"August", "September","October",
"November", "December")),
y = `all_trips$ride_length`,
fill = `all_trips$rides_month`)) +
xlab("Month") + ylab("Rides sum (in seconds)") +
labs(title="Total Time on Rides by Month",
caption ="Data collected by Cyclistic Bike-Share in previous 12 months",
fill = "Month") + facet_grid(~`all_trips$rider_type`) +
scale_fill_viridis_d()+
theme(axis.text.x=element_text(angle = 90))
##plot 7ACT
#Conclusions and findings Casual customers spent more time on average each day than members customers. To encourage casual customers to become members, Cyclistic could offer a free month of membership or Offer discounted membership during summer to attract casual riders. They can use this means to explain the benefits of becoming member customers by showing how much time casual customers spent than member customers.
Casual and member customers use the docked bike the most. To keep with the demand Cyclistic should keep a reserve of docked bikes ready to deploy whenever docked bikes are damaged. This is very important because casual customers use this type of bike more than the others.
The number of casual customers peaks in July and August, but the spent time is still high than member customers. To encourage them to continue using the bike share service, special promotions of membership in other months such as October through February to raise the number of casual members, but also keep up with July and August.
From this years data analysis, there is a correlation between weather and bike usage: the warmer the time of year, the more rides are taken and more bike usage time. If stakeholders wish to expand the projects’ span, we could determine if there is a correlation between bike usage and weather by considering more years’ monthly data to see if it meets this years behavior.
thanks for your time, this is my first work and I will appriciate advice and constructive criticism
```r
write.csv(all_trips, file= "all_trips.csv")
write.csv(avg_ride_month_tbl, file= "avg_ride_month_tbl.csv")
write.csv(total_ride_month_tbl, file= "total_ride_month_tbl.csv")
save(all_trips, file = "all_trips.RData")
save(avg_ride_month_tbl, file = "avg_ride_month_tbl.RData")
save(total_ride_month_tbl, file = "total_ride_month_tbl.RData")
save(time_difference, file = "time_difference.RData")