Analysis-and-Visualization-for-Divvy-Bike-Share.knit

BACKGROUND:

In 2016, a fictional company called Cyclistic launched a bike-share program which has grown over the years across Chicago. The company has a fleet of over 5,000 bicycles that are geotracked and locked into a network of over 600 stations. Cyclistic uses a marketing strategy of flexible pricing plans to attract broad customer segments. These pricing plans include: single-ride passes, full-day passes, and annual memberships. Single-ride or full-day pass customers are considered casual riders while annual membership customers are considered Cyclistic members.

Finance analysts find that annual memberships are more profitable than casual riderships and the new business task is to convert casual riders into members. In order to do so, the marketing analysis team need a better understanding by asking the following three questions that will guide future marketing:

How do annual members and casual riders use Cyclistic bikes differently?
Why would casual riders buy Cyclistic annual memberships?
How can Cyclistic use digital media to influence casual riders to become members?

STEP 1: ASK QUESTIONS AND DEFINE THE PROBLEM:

The aim of this analysis is to focus on the first question: How do annual members and casual riders use Cyclistic bikes differently? Insights from this analysis will inform marketing strategies aimed at converting casual riders into annual members. This action will maximize the number of annual members and secure future growth of Cyclistic bike share program. The primary stakeholders are: Cyclistic executive team and its Director of Marketing. Secondary stakeholders are Cyclistic marketing analytics team. The key deliverable is an analysis and visualization of the differences between annual members and casual riders use of the bike share program and inform marketing strategies for bringing in more annual members to Cyclistic bike share program.

The real world data for this analysis is collected and owned by the city of Chicago, and is made available to the public by a partnership agreement between the city of Chicago and Motivate International Inc., which also operates the Cyclistic bike share program in the city. The historical data sets can be found in a list available at the following html link. The licensing, privacy, security, and accessibility of the data are also managed by Motivate #International Inc. This is a link to the license agreement: link.

The next sections of this report will describe the five other steps in the data analysis process of: prepare, process, analyze, share, and act, used in the analysis.

STEP 2: PREPARE DATA BY COLLECTING AND STORING:

In preparation for this analysis, twelve data zipped CSV files were downloaded onto a PC where files could be extracted in .csv format. Individually each file was uploaded to the Rstudio global environment for data analysis after installing and loading r packages.

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.5     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.0.2     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(rmarkdown)
library(knitr)

Set Work Directory.

knitr::opts_knit$set(root.dir = "F:/Capstone Project Case Study 1 (Jan.11)/data")
getwd()

## [1] "F:/Capstone Project Case Study 1 (Jan.11)/data"

Use of “here” package creates paths to project root directory and data sets, refer to link.

X202101_tripdata <- read_csv("202101-tripdata.csv")

## Rows: 96834 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202102_tripdata <- read_csv("202102-tripdata.csv")

## Rows: 49622 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202103_tripdata <- read_csv("202103-tripdata.csv")

## Rows: 228496 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202104_tripdata <- read_csv("202104-tripdata.csv")

## Rows: 337230 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202105_tripdata <- read_csv("202105-tripdata.csv")

## Rows: 531633 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202106_tripdata <- read_csv("202106-tripdata.csv")

## Rows: 729595 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202107_tripdata <- read_csv("202107-tripdata.csv")

## Rows: 822410 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202108_tripdata <- read_csv("202108-tripdata.csv")

## Rows: 804352 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202109_tripdata <- read_csv("202109-tripdata.csv")

## Rows: 756147 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202110_tripdata <- read_csv("202110-tripdata.csv")

## Rows: 631226 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202111_tripdata <- read_csv("202111-tripdata.csv")

## Rows: 359978 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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.

X202112_tripdata <- read_csv("202112-tripdata.csv")

## Rows: 247540 Columns: 13

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): ride_id, rideable_type, started_at, ended_at, start_station_name, s...
## dbl (4): start_lat, start_lng, end_lat, end_lng

## 
## 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 3: PROCESS DATA BY CLEANING, CHECKING AND COMBINING INTO SINGLE DATA FRAME:

Suggested script for using R, click here link. Data frames inspected for incongruencies

str(X202101_tripdata)

## spec_tbl_df [96,834 x 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        : chr [1:96834] "2021-01-23 16:14" "2021-01-27 18:43" "2021-01-21 22:35" "2021-01-07 13:31" ...
##  $ ended_at          : chr [1:96834] "2021-01-23 16:24" "2021-01-27 18:47" "2021-01-21 22:37" "2021-01-07 13:42" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202102_tripdata)

## spec_tbl_df [49,622 x 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        : chr [1:49622] "2021-02-12 16:14" "2021-02-14 17:52" "2021-02-09 19:10" "2021-02-02 17:49" ...
##  $ ended_at          : chr [1:49622] "2021-02-12 16:21" "2021-02-14 18:12" "2021-02-09 19:19" "2021-02-02 17:54" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202103_tripdata)

## spec_tbl_df [228,496 x 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        : chr [1:228496] "2021-03-16 8:32" "2021-03-28 1:26" "2021-03-11 21:17" "2021-03-11 13:26" ...
##  $ ended_at          : chr [1:228496] "2021-03-16 8:36" "2021-03-28 1:36" "2021-03-11 21:33" "2021-03-11 13:55" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202104_tripdata)

## spec_tbl_df [337,230 x 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        : chr [1:337230] "2021-04-12 18:25" "2021-04-27 17:27" "2021-04-03 12:42" "2021-04-17 9:17" ...
##  $ ended_at          : chr [1:337230] "2021-04-12 18:56" "2021-04-27 18:31" "2021-04-07 11:40" "2021-04-17 9:42" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202105_tripdata)

## spec_tbl_df [531,633 x 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        : chr [1:531633] "2021-05-30 11:58" "2021-05-30 11:29" "2021-05-30 14:24" "2021-05-30 14:25" ...
##  $ ended_at          : chr [1:531633] "2021-05-30 12:10" "2021-05-30 12:14" "2021-05-30 14:25" "2021-05-30 14:41" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202106_tripdata)

## spec_tbl_df [729,595 x 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        : chr [1:729595] "2021-06-13 14:31" "2021-06-04 11:18" "2021-06-04 9:49" "2021-06-03 19:56" ...
##  $ ended_at          : chr [1:729595] "2021-06-13 14:34" "2021-06-04 11:24" "2021-06-04 9:55" "2021-06-03 20:21" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202107_tripdata)

## spec_tbl_df [822,410 x 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        : chr [1:822410] "2021-07-02 14:44" "2021-07-07 16:57" "2021-07-25 11:30" "2021-07-08 22:08" ...
##  $ ended_at          : chr [1:822410] "2021-07-02 15:19" "2021-07-07 17:16" "2021-07-25 11:48" "2021-07-08 22:23" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202108_tripdata)

## spec_tbl_df [804,352 x 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        : chr [1:804352] "2021-08-10 17:15" "2021-08-10 17:23" "2021-08-21 2:34" "2021-08-21 6:52" ...
##  $ ended_at          : chr [1:804352] "2021-08-10 17:22" "2021-08-10 17:39" "2021-08-21 2:50" "2021-08-21 7:08" ...
##  $ 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_character(),
##   ..   ended_at = col_character(),
##   ..   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(X202109_tripdata)

## spec_tbl_df [756,147 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:756147] "9DC7B962304CBFD8" "F930E2C6872D6B32" "6EF72137900BB910" "78D1DE133B3DBF55" ...
##  $ rideable_type     : chr [1:756147] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
##  $ started_at        : chr [1:756147] "2021-09-28 16:07" "2021-09-28 14:24" "2021-09-28 0:20" "2021-09-28 14:51" ...
##  $ ended_at          : chr [1:756147] "2021-09-28 16:09" "2021-09-28 14:40" "2021-09-28 0:23" "2021-09-28 15:00" ...
##  $ start_station_name: chr [1:756147] NA NA NA NA ...
##  $ start_station_id  : chr [1:756147] NA NA NA NA ...
##  $ end_station_name  : chr [1:756147] NA NA NA NA ...
##  $ end_station_id    : chr [1:756147] NA NA NA NA ...
##  $ start_lat         : num [1:756147] 41.9 41.9 41.8 41.8 41.9 ...
##  $ start_lng         : num [1:756147] -87.7 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num [1:756147] 41.9 42 41.8 41.8 41.9 ...
##  $ end_lng           : num [1:756147] -87.7 -87.7 -87.7 -87.7 -87.7 ...
##  $ member_casual     : chr [1:756147] "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_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(X202110_tripdata)

## spec_tbl_df [631,226 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:631226] "620BC6107255BF4C" "4471C70731AB2E45" "26CA69D43D15EE14" "362947F0437E1514" ...
##  $ rideable_type     : chr [1:631226] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
##  $ started_at        : chr [1:631226] "2021-10-22 12:46" "2021-10-21 9:12" "2021-10-16 16:28" "2021-10-16 16:17" ...
##  $ ended_at          : chr [1:631226] "2021-10-22 12:49" "2021-10-21 9:14" "2021-10-16 16:36" "2021-10-16 16:19" ...
##  $ start_station_name: chr [1:631226] "Kingsbury St & Kinzie St" NA NA NA ...
##  $ start_station_id  : chr [1:631226] "KA1503000043" NA NA NA ...
##  $ end_station_name  : chr [1:631226] NA NA NA NA ...
##  $ end_station_id    : chr [1:631226] NA NA NA NA ...
##  $ start_lat         : num [1:631226] 41.9 41.9 41.9 41.9 41.9 ...
##  $ start_lng         : num [1:631226] -87.6 -87.7 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num [1:631226] 41.9 41.9 41.9 41.9 41.9 ...
##  $ end_lng           : num [1:631226] -87.6 -87.7 -87.7 -87.7 -87.7 ...
##  $ member_casual     : chr [1:631226] "member" "member" "member" "member" ...
##  - 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_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(X202111_tripdata)

## spec_tbl_df [359,978 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:359978] "7C00A93E10556E47" "90854840DFD508BA" "0A7D10CDD144061C" "2F3BE33085BCFF02" ...
##  $ rideable_type     : chr [1:359978] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
##  $ started_at        : chr [1:359978] "2021-11-27 13:27" "2021-11-27 13:38" "2021-11-26 22:03" "2021-11-27 9:56" ...
##  $ ended_at          : chr [1:359978] "2021-11-27 13:46" "2021-11-27 13:56" "2021-11-26 22:05" "2021-11-27 10:01" ...
##  $ start_station_name: chr [1:359978] NA NA NA NA ...
##  $ start_station_id  : chr [1:359978] NA NA NA NA ...
##  $ end_station_name  : chr [1:359978] NA NA NA NA ...
##  $ end_station_id    : chr [1:359978] NA NA NA NA ...
##  $ start_lat         : num [1:359978] 41.9 42 42 41.9 41.9 ...
##  $ start_lng         : num [1:359978] -87.7 -87.7 -87.7 -87.8 -87.6 ...
##  $ end_lat           : num [1:359978] 42 41.9 42 41.9 41.9 ...
##  $ end_lng           : num [1:359978] -87.7 -87.7 -87.7 -87.8 -87.6 ...
##  $ member_casual     : chr [1:359978] "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_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(X202112_tripdata)

## spec_tbl_df [247,540 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:247540] "46F8167220E4431F" "73A77762838B32FD" "4CF42452054F59C5" "3278BA87BF698339" ...
##  $ rideable_type     : chr [1:247540] "electric_bike" "electric_bike" "electric_bike" "classic_bike" ...
##  $ started_at        : chr [1:247540] "2021-12-07 15:06" "2021-12-11 3:43" "2021-12-15 23:10" "2021-12-26 16:16" ...
##  $ ended_at          : chr [1:247540] "2021-12-07 15:13" "2021-12-11 4:10" "2021-12-15 23:23" "2021-12-26 16:30" ...
##  $ start_station_name: chr [1:247540] "Laflin St & Cullerton St" "LaSalle Dr & Huron St" "Halsted St & North Branch St" "Halsted St & North Branch St" ...
##  $ start_station_id  : chr [1:247540] "13307" "KP1705001026" "KA1504000117" "KA1504000117" ...
##  $ end_station_name  : chr [1:247540] "Morgan St & Polk St" "Clarendon Ave & Leland Ave" "Broadway & Barry Ave" "LaSalle Dr & Huron St" ...
##  $ end_station_id    : chr [1:247540] "TA1307000130" "TA1307000119" "13137" "KP1705001026" ...
##  $ start_lat         : num [1:247540] 41.9 41.9 41.9 41.9 41.9 ...
##  $ start_lng         : num [1:247540] -87.7 -87.6 -87.6 -87.6 -87.7 ...
##  $ end_lat           : num [1:247540] 41.9 42 41.9 41.9 41.9 ...
##  $ end_lng           : num [1:247540] -87.7 -87.7 -87.6 -87.6 -87.6 ...
##  $ member_casual     : chr [1:247540] "member" "casual" "member" "member" ...
##  - 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_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>

Convert to Posixct date-time class, for start time. Then create new
column with calculation of difference between end time and start time.

```r
X202101_tripdata$ended_at <- as.POSIXct(X202101_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202102_tripdata$ended_at <- as.POSIXct(X202102_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202103_tripdata$ended_at <- as.POSIXct(X202103_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202104_tripdata$ended_at <- as.POSIXct(X202104_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202105_tripdata$ended_at <- as.POSIXct(X202105_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202106_tripdata$ended_at <- as.POSIXct(X202106_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202107_tripdata$ended_at <- as.POSIXct(X202107_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202108_tripdata$ended_at <- as.POSIXct(X202108_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202109_tripdata$ended_at <- as.POSIXct(X202109_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202110_tripdata$ended_at <- as.POSIXct(X202110_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202111_tripdata$ended_at <- as.POSIXct(X202111_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
X202112_tripdata$ended_at <- as.POSIXct(X202112_tripdata$ended_at, format = "%Y-%m-%d %H:%M", scientific = FALSE)
```

Stack individual month data frames into one big data frame

all_trips<-bind_rows(X202101_tripdata,X202102_tripdata,X202103_tripdata,X202104_tripdata,X202105_tripdata,X202106_tripdata,X202107_tripdata,X202108_tripdata,X202109_tripdata,X202110_tripdata,X202111_tripdata,X202112_tripdata)

all_trips<-all_trips%>%
  select(-c(ride_id, start_station_name, start_station_id, end_station_name, end_station_id,start_lat, start_lng, end_lat, end_lng))

STEP 4: ANALYZE DATA TO FIND PATTERNS, RELATIONSHIPS AND TRENDS:

Suggested script, click here to link. “Cleaning Data” tutorial by Greg Martin, click here link . Reference manipulating data tutorial by Greg Martin, click here link

colnames(all_trips)

## [1] "rideable_type" "started_at"    "ended_at"      "member_casual"

nrow(all_trips)

## [1] 5595063

dim(all_trips)

## [1] 5595063       4

head(all_trips)

## # A tibble: 6 x 4
##   rideable_type started_at       ended_at            member_casual
##   <chr>         <chr>            <dttm>              <chr>        
## 1 electric_bike 2021-01-23 16:14 2021-01-23 16:24:00 member       
## 2 electric_bike 2021-01-27 18:43 2021-01-27 18:47:00 member       
## 3 electric_bike 2021-01-21 22:35 2021-01-21 22:37:00 member       
## 4 electric_bike 2021-01-07 13:31 2021-01-07 13:42:00 member       
## 5 electric_bike 2021-01-23 2:24  2021-01-23 02:24:00 casual       
## 6 electric_bike 2021-01-09 14:24 2021-01-09 15:17:00 casual

tail(all_trips)

## # A tibble: 6 x 4
##   rideable_type started_at       ended_at            member_casual
##   <chr>         <chr>            <dttm>              <chr>        
## 1 electric_bike 2021-12-24 15:42 2021-12-24 19:29:00 casual       
## 2 electric_bike 2021-12-12 13:36 2021-12-12 13:56:00 casual       
## 3 electric_bike 2021-12-06 19:37 2021-12-06 19:44:00 member       
## 4 electric_bike 2021-12-02 8:57  2021-12-02 09:05:00 member       
## 5 electric_bike 2021-12-13 9:00  2021-12-13 09:14:00 member       
## 6 classic_bike  2021-12-13 8:45  2021-12-13 08:49:00 member

str(all_trips)

## tibble [5,595,063 x 4] (S3: tbl_df/tbl/data.frame)
##  $ rideable_type: chr [1:5595063] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
##  $ started_at   : chr [1:5595063] "2021-01-23 16:14" "2021-01-27 18:43" "2021-01-21 22:35" "2021-01-07 13:31" ...
##  $ ended_at     : POSIXct[1:5595063], format: "2021-01-23 16:24:00" "2021-01-27 18:47:00" ...
##  $ member_casual: chr [1:5595063] "member" "member" "member" "member" ...

summary(all_trips)

##  rideable_type       started_at           ended_at                  
##  Length:5595063     Length:5595063     Min.   :2021-01-01 00:08:00  
##  Class :character   Class :character   1st Qu.:2021-06-07 00:44:00  
##  Mode  :character   Mode  :character   Median :2021-08-01 02:21:00  
##                                        Mean   :2021-07-29 08:10:31  
##                                        3rd Qu.:2021-09-24 16:54:00  
##                                        Max.   :2022-01-03 17:32:00  
##  member_casual     
##  Length:5595063    
##  Class :character  
##  Mode  :character  
##                    
##                    
##

Remove variable “docked bike” from column “rideable_type”, as these bikes were docked and presumed not in use by definition.

table(all_trips$rideable_type)

## 
##  classic_bike   docked_bike electric_bike 
##       3251028        312343       2031692

all_trips <- subset(all_trips,rideable_type != "docked_bike")

Check data frame no longer contains docked_bike

table(all_trips$rideable_type)

## 
##  classic_bike electric_bike 
##       3251028       2031692

Add columns that list date, month, day of week

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$day_of_week<-format(as.Date(all_trips$date), "%A")

Give order to day of week

all_trips$day_of_week <- ordered(all_trips$day_of_week, levels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))

Create new column for ride_length calculation in minutes: Refer to online resource, click herelink

all_trips <-all_trips %>%  mutate(all_trips, ride_length = difftime(all_trips$ended_at, all_trips$started_at, tz="America/Chicago", units = "mins"))

Ensure ride_length is numeric

all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))

Remove ride_length <=0, as these do not reflect bike rides in use

all_trips_v2<-all_trips[!(all_trips$ride_length<=0),]

summary(all_trips_v2$ride_length)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     2.0   127.0   131.0   138.4   140.0  1621.0

Compare member and casual riders

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                 145.0265
## 2                     member                 133.6329

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median)

##   all_trips_v2$member_casual all_trips_v2$ride_length
## 1                     casual                      135
## 2                     member                      130

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max)

##   all_trips_v2$member_casual all_trips_v2$ride_length
## 1                     casual                     1621
## 2                     member                     1620

aggregate(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                        6

Create tables: Refer to Describe and Summarize data by Greg Martin, click here link .

First table = number of riders by month:

all_trips_v2 %>% 
group_by(member_casual, month) %>% 
summarise(number = n(), average_ride = mean(ride_length)) %>%     #count of riders and average ride for each month
pivot_wider(names_from = member_casual, 
            values_from = number) #Create table with columns: month, average ride, member counts, casual counts

## `summarise()` has grouped output by 'member_casual'. You can override using the `.groups` argument.

## # A tibble: 24 x 4
##    month average_ride casual member
##    <chr>        <dbl>  <int>  <int>
##  1 01            140.  16012     NA
##  2 02            153.   8860     NA
##  3 03            148.  68376     NA
##  4 04            148. 111887     NA
##  5 05            148. 213563     NA
##  6 06            147. 318965     NA
##  7 07            146. 384358     NA
##  8 08            145. 367606     NA
##  9 09            144. 328553     NA
## 10 10            142. 234358     NA
## # ... with 14 more rows

Second table = number of riders by day of week:

all_trips_v2 %>% 
group_by(member_casual, day_of_week) %>% 
summarise(number = n(), average_ride = mean(ride_length)) %>%     #count of riders for each day of week
pivot_wider(names_from = member_casual, 
            values_from = number)  #Create table with columns: day of week, member counts, casual counts

## `summarise()` has grouped output by 'member_casual'. You can override using the `.groups` argument.

## # A tibble: 14 x 4
##    day_of_week average_ride casual member
##    <ord>              <dbl>  <int>  <int>
##  1 Sunday              149. 410707     NA
##  2 Monday              145. 249931     NA
##  3 Tuesday             143. 244764     NA
##  4 Wednesday           142. 251321     NA
##  5 Thursday            142. 258390     NA
##  6 Friday              143. 321985     NA
##  7 Saturday            147. 479565     NA
##  8 Sunday              136.     NA 376142
##  9 Monday              133.     NA 416212
## 10 Tuesday             133.     NA 465513
## 11 Wednesday           133.     NA 477191
## 12 Thursday            133.     NA 451524
## 13 Friday              133.     NA 446428
## 14 Saturday            135.     NA 433047

STEP 5: SHARE DATA VISUALIZATIONS:

Refer to An introduction to data visualization using R programming by Greg Martin, click here link

Monthly bike share use

all_trips_v2 %>% 
  group_by(member_casual, month) %>% 
  summarise(number = n(), average_ride = mean(ride_length)) %>%  #count of average rides for riders by month
  ggplot(aes(x = month, y = average_ride, fill = member_casual))+
  geom_col(aes(x=month, y = average_ride))+ 
  facet_wrap(~member_casual)+ labs(title = "Monthly Divvy Bike Rider Use 2021")

## `summarise()` has grouped output by 'member_casual'. You can override using the `.groups` argument.

Daily bike share Use

all_trips_v2 %>% 
  group_by(member_casual, day_of_week) %>% 
  summarise(number = n(), average_ride = mean(ride_length)) %>%     #count of average rides for riders by weekday
  ggplot(aes(x = day_of_week, y = average_ride, fill = member_casual))+
  geom_col(aes(x = day_of_week, y = average_ride))+
  theme(axis.text.x = element_text(angle = 60, hjust = 1))+
  facet_wrap(~member_casual)+ labs(title = "Daily Divvy Bike Rider Use 2021")

## `summarise()` has grouped output by 'member_casual'. You can override using the `.groups` argument.

STEP 6: ACT ON CONCLUSIONS FROM DATA ANALYSIS:

Data visualization for monthly use shows casual riders used Cyclistic bike share program more than members, overall, within the 2021 year. Greater use by casual riders occurred over seven months between February to October or late Winter, through Spring/Summer and into early Fall. Data visualization for daily use shows that casual riders overall used the bike share program more often during a three day period between Saturday, Sunday and Monday compared to member riders. Data suggests to focus marketing strategies on casual riders during the months of May to October and expecially on Saturdays, Sundays and Mondays.