Google Data Analytics Capstone #1

Cyclistic Bike Share

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclisitc, a bike-share company in Chicago. The marketing team has been tasked with analyzing bike trip data collected and identify trends. From these insights, the marketing team will design a new marketing strategy to convert casual riders to annual members. Recommendation will need to be approved by executives; thus, claims must be backed up with compelling data insights and professional data visualizations.

The director has assigned you to answer the first question: How do annual members and casual riders use Cyclistic bikes differently?

###Company Background

Cyclistic launched a successful bike-share offering in 2016. Since then, it has grown into a fleet of 5,824 bicycles that are tracked geographically. The bikes can be unlocked from any of the 692 stations available across the city of Chicago and returned to any station in the network at any time.

Cyclistic’s marketing strategy has been centered around building awareness around their product and appealing to broad consumer segments. One appraoch that has proven successful was the flexibility of their pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase memberships are referred as Cyclistic members and the rest are referred as casual riders.

The company’s financial analysts have come to the conclusion that the company can make more money on Cyclistic members than on casual riders. Although, the pricing flexibility has attracted customers, Moreno believes that maximizing company members will be the key to growing profits. Rather than creating a marketing campaign targeting a broad market segment, Moreno believes there is a good chance to convert casual riders into annual members.

Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

###Business Task

Analyze usage patterns of Cyclistic bike users to help develop marketing strategy.

Things I might want to know: Length of time spent on bike. What time people used the bikes (What day, What period of the year, What period of the day). What locations are popular for users.

###Tools

R for data cleaning and data visualization.

###Dataset

Cyclistic’s dataset, provided by Motivate International Inc under this license.

###R Packages

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.6     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(ggplot2)
library(skimr)
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

###Cleaning the Data

Apr2020 <- read_csv('/Users/FAHsieh/Downloads/202004-divvy-tripdata.csv')
## Rows: 84776 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.
May2020 <- read_csv('/Users/FAHsieh/Downloads/202005-divvy-tripdata.csv')
## Rows: 200274 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.
Jun2020 <- read_csv('/Users/FAHsieh/Downloads/202006-divvy-tripdata.csv')
## Rows: 343005 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.
Jul2020 <- read_csv('/Users/FAHsieh/Downloads/202007-divvy-tripdata.csv')
## Rows: 551480 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.
Aug2020 <- read_csv('/Users/FAHsieh/Downloads/202008-divvy-tripdata.csv')
## Rows: 622361 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.
Sep2020 <- read_csv('/Users/FAHsieh/Downloads/202009-divvy-tripdata.csv')
## Rows: 532958 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.
Oct2020 <- read_csv('/Users/FAHsieh/Downloads/202010-divvy-tripdata.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
## 
## 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.
Nov2020 <- read_csv('/Users/FAHsieh/Downloads/202011-divvy-tripdata.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
## 
## 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.
Dec2020 <- read_csv('/Users/FAHsieh/Downloads/202012-divvy-tripdata.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
## 
## 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.
Jan2021 <- read_csv('/Users/FAHsieh/Downloads/202101-divvy-tripdata.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
## 
## 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.
Feb2021 <- read_csv('/Users/FAHsieh/Downloads/202102-divvy-tripdata.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
## 
## 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.
Mar2021 <- read_csv('/Users/FAHsieh/Downloads/202103-divvy-tripdata.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
## 
## 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.

After loading the data into r, we need to combine the datasets into one single dataframe.

colnames(Apr2020) #Check column names are the same
##  [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"
colnames(May2020)
##  [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"
colnames(Jun2020)
##  [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"
colnames(Jul2020)
##  [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"
colnames(Aug2020)
##  [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"
colnames(Sep2020)
##  [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"
colnames(Oct2020)
##  [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"
colnames(Nov2020)
##  [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"
colnames(Dec2020)
##  [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"
colnames(Jan2021)
##  [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"
colnames(Feb2021)
##  [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"
colnames(Mar2021)
##  [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"
str(Apr2020) #Check column datatypes
## spec_tbl_df [84,776 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:84776] "A847FADBBC638E45" "5405B80E996FF60D" "5DD24A79A4E006F4" "2A59BBDF5CDBA725" ...
##  $ rideable_type     : chr [1:84776] "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
##  $ started_at        : POSIXct[1:84776], format: "2020-04-26 17:45:14" "2020-04-17 17:08:54" ...
##  $ ended_at          : POSIXct[1:84776], format: "2020-04-26 18:12:03" "2020-04-17 17:17:03" ...
##  $ start_station_name: chr [1:84776] "Eckhart Park" "Drake Ave & Fullerton Ave" "McClurg Ct & Erie St" "California Ave & Division St" ...
##  $ start_station_id  : num [1:84776] 86 503 142 216 125 173 35 434 627 377 ...
##  $ end_station_name  : chr [1:84776] "Lincoln Ave & Diversey Pkwy" "Kosciuszko Park" "Indiana Ave & Roosevelt Rd" "Wood St & Augusta Blvd" ...
##  $ end_station_id    : num [1:84776] 152 499 255 657 323 35 635 382 359 508 ...
##  $ start_lat         : num [1:84776] 41.9 41.9 41.9 41.9 41.9 ...
##  $ start_lng         : num [1:84776] -87.7 -87.7 -87.6 -87.7 -87.6 ...
##  $ end_lat           : num [1:84776] 41.9 41.9 41.9 41.9 42 ...
##  $ end_lng           : num [1:84776] -87.7 -87.7 -87.6 -87.7 -87.7 ...
##  $ member_casual     : chr [1:84776] "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(May2020)
## spec_tbl_df [200,274 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:200274] "02668AD35674B983" "7A50CCAF1EDDB28F" "2FFCDFDB91FE9A52" "58991CF1DB75BA84" ...
##  $ rideable_type     : chr [1:200274] "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
##  $ started_at        : POSIXct[1:200274], format: "2020-05-27 10:03:52" "2020-05-25 10:47:11" ...
##  $ ended_at          : POSIXct[1:200274], format: "2020-05-27 10:16:49" "2020-05-25 11:05:40" ...
##  $ start_station_name: chr [1:200274] "Franklin St & Jackson Blvd" "Clark St & Wrightwood Ave" "Kedzie Ave & Milwaukee Ave" "Clarendon Ave & Leland Ave" ...
##  $ start_station_id  : num [1:200274] 36 340 260 251 261 206 261 180 331 219 ...
##  $ end_station_name  : chr [1:200274] "Wabash Ave & Grand Ave" "Clark St & Leland Ave" "Kedzie Ave & Milwaukee Ave" "Lake Shore Dr & Wellington Ave" ...
##  $ end_station_id    : num [1:200274] 199 326 260 157 206 22 261 180 300 305 ...
##  $ start_lat         : num [1:200274] 41.9 41.9 41.9 42 41.9 ...
##  $ start_lng         : num [1:200274] -87.6 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num [1:200274] 41.9 42 41.9 41.9 41.8 ...
##  $ end_lng           : num [1:200274] -87.6 -87.7 -87.7 -87.6 -87.6 ...
##  $ member_casual     : chr [1:200274] "member" "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(Jun2020)
## spec_tbl_df [343,005 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:343005] "8CD5DE2C2B6C4CFC" "9A191EB2C751D85D" "F37D14B0B5659BCF" "C41237B506E85FA1" ...
##  $ rideable_type     : chr [1:343005] "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
##  $ started_at        : POSIXct[1:343005], format: "2020-06-13 23:24:48" "2020-06-26 07:26:10" ...
##  $ ended_at          : POSIXct[1:343005], format: "2020-06-13 23:36:55" "2020-06-26 07:31:58" ...
##  $ start_station_name: chr [1:343005] "Wilton Ave & Belmont Ave" "Federal St & Polk St" "Daley Center Plaza" "Broadway & Cornelia Ave" ...
##  $ start_station_id  : num [1:343005] 117 41 81 303 327 327 41 115 338 84 ...
##  $ end_station_name  : chr [1:343005] "Damen Ave & Clybourn Ave" "Daley Center Plaza" "State St & Harrison St" "Broadway & Berwyn Ave" ...
##  $ end_station_id    : num [1:343005] 163 81 5 294 117 117 81 303 164 53 ...
##  $ start_lat         : num [1:343005] 41.9 41.9 41.9 41.9 41.9 ...
##  $ start_lng         : num [1:343005] -87.7 -87.6 -87.6 -87.6 -87.7 ...
##  $ end_lat           : num [1:343005] 41.9 41.9 41.9 42 41.9 ...
##  $ end_lng           : num [1:343005] -87.7 -87.6 -87.6 -87.7 -87.7 ...
##  $ member_casual     : chr [1:343005] "casual" "member" "member" "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(Jul2020)
## spec_tbl_df [551,480 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:551480] "762198876D69004D" "BEC9C9FBA0D4CF1B" "D2FD8EA432C77EC1" "54AE594E20B35881" ...
##  $ rideable_type     : chr [1:551480] "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
##  $ started_at        : POSIXct[1:551480], format: "2020-07-09 15:22:02" "2020-07-24 23:56:30" ...
##  $ ended_at          : POSIXct[1:551480], format: "2020-07-09 15:25:52" "2020-07-25 00:20:17" ...
##  $ start_station_name: chr [1:551480] "Ritchie Ct & Banks St" "Halsted St & Roscoe St" "Lake Shore Dr & Diversey Pkwy" "LaSalle St & Illinois St" ...
##  $ start_station_id  : num [1:551480] 180 299 329 181 268 635 113 211 176 31 ...
##  $ end_station_name  : chr [1:551480] "Wells St & Evergreen Ave" "Broadway & Ridge Ave" "Clark St & Wellington Ave" "Clark St & Armitage Ave" ...
##  $ end_station_id    : num [1:551480] 291 461 156 94 301 289 140 31 191 142 ...
##  $ start_lat         : num [1:551480] 41.9 41.9 41.9 41.9 41.9 ...
##  $ start_lng         : num [1:551480] -87.6 -87.6 -87.6 -87.6 -87.6 ...
##  $ end_lat           : num [1:551480] 41.9 42 41.9 41.9 41.9 ...
##  $ end_lng           : num [1:551480] -87.6 -87.7 -87.6 -87.6 -87.6 ...
##  $ member_casual     : chr [1:551480] "member" "member" "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(Aug2020)
## spec_tbl_df [622,361 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ride_id           : chr [1:622361] "322BD23D287743ED" "2A3AEF1AB9054D8B" "67DC1D133E8B5816" "C79FBBD412E578A7" ...
##  $ rideable_type     : chr [1:622361] "docked_bike" "electric_bike" "electric_bike" "electric_bike" ...
##  $ started_at        : POSIXct[1:622361], format: "2020-08-20 18:08:14" "2020-08-27 18:46:04" ...
##  $ ended_at          : POSIXct[1:622361], format: "2020-08-20 18:17:51" "2020-08-27 19:54:51" ...
##  $ start_station_name: chr [1:622361] "Lake Shore Dr & Diversey Pkwy" "Michigan Ave & 14th St" "Columbus Dr & Randolph St" "Daley Center Plaza" ...
##  $ start_station_id  : num [1:622361] 329 168 195 81 658 658 196 67 153 177 ...
##  $ end_station_name  : chr [1:622361] "Clark St & Lincoln Ave" "Michigan Ave & 14th St" "State St & Randolph St" "State St & Kinzie St" ...
##  $ end_station_id    : num [1:622361] 141 168 44 47 658 658 49 229 225 305 ...
##  $ start_lat         : num [1:622361] 41.9 41.9 41.9 41.9 41.9 ...
##  $ start_lng         : num [1:622361] -87.6 -87.6 -87.6 -87.6 -87.7 ...
##  $ end_lat           : num [1:622361] 41.9 41.9 41.9 41.9 41.9 ...
##  $ end_lng           : num [1:622361] -87.6 -87.6 -87.6 -87.6 -87.7 ...
##  $ member_casual     : chr [1:622361] "member" "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(Sep2020)
## spec_tbl_df [532,958 × 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        : POSIXct[1:532958], format: "2020-09-17 14:27:11" "2020-09-17 15:07:31" ...
##  $ ended_at          : POSIXct[1:532958], format: "2020-09-17 14:44:24" "2020-09-17 15:07:45" ...
##  $ 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_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(Oct2020)
## spec_tbl_df [388,653 × 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(Nov2020)
## spec_tbl_df [259,716 × 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(Dec2020)
## spec_tbl_df [131,573 × 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(Jan2021)
## spec_tbl_df [96,834 × 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(Feb2021)
## spec_tbl_df [49,622 × 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(Mar2021)
## spec_tbl_df [228,496 × 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>
Apr2020 <- mutate(Apr2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id)) #Columns end_station_id and start_station_id cannot be combined as num data type, convert to character data type
May2020 <- mutate(May2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Jun2020 <- mutate(Jun2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Jul2020 <- mutate(Jul2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Aug2020 <- mutate(Aug2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Sep2020 <- mutate(Sep2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Oct2020 <- mutate(Oct2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Nov2020 <- mutate(Nov2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Dec2020 <- mutate(Dec2020, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Jan2021 <- mutate(Jan2021, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Feb2021 <- mutate(Feb2021, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Mar2021 <- mutate(Mar2021, start_station_id = as.character(start_station_id), end_station_id = as.character(end_station_id))
Combined_trips <- bind_rows(Apr2020, May2020, Jun2020, Jul2020, Aug2020, Sep2020, Oct2020, Nov2020, Dec2020, Jan2021, Feb2021, Mar2021) #Combine dataframes
Combined_trips <- select(Combined_trips, -c(start_lat, end_lat, start_lng, end_lng)) #Remove Longitude and Latitude data. This will not be very useful for our analysis

After binding the datasets into one dataframe, we now need to clean the data.

summary(Combined_trips) #Check overviews of the dataframe
##    ride_id          rideable_type        started_at                    
##  Length:3489748     Length:3489748     Min.   :2020-04-01 00:00:30.00  
##  Class :character   Class :character   1st Qu.:2020-07-14 19:38:28.00  
##  Mode  :character   Mode  :character   Median :2020-08-29 14:50:36.50  
##                                        Mean   :2020-09-10 01:21:45.98  
##                                        3rd Qu.:2020-10-20 18:14:13.00  
##                                        Max.   :2021-03-31 23:59:08.00  
##     ended_at                      start_station_name start_station_id  
##  Min.   :2020-04-01 00:10:45.00   Length:3489748     Length:3489748    
##  1st Qu.:2020-07-14 20:13:07.75   Class :character   Class :character  
##  Median :2020-08-29 15:21:13.00   Mode  :character   Mode  :character  
##  Mean   :2020-09-10 01:46:31.98                                        
##  3rd Qu.:2020-10-20 18:28:46.25                                        
##  Max.   :2021-04-06 11:00:11.00                                        
##  end_station_name   end_station_id     member_casual     
##  Length:3489748     Length:3489748     Length:3489748    
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
## 
skim(Combined_trips)
Data summary
Name Combined_trips
Number of rows 3489748
Number of columns 9
_______________________
Column type frequency:
character 7
POSIXct 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ride_id 0 1.00 16 16 0 3489539 0
rideable_type 0 1.00 11 13 0 3 0
start_station_name 122175 0.96 10 53 0 708 0
start_station_id 122801 0.96 1 35 0 1259 0
end_station_name 143242 0.96 10 53 0 706 0
end_station_id 143703 0.96 1 35 0 1259 0
member_casual 0 1.00 6 6 0 2 0

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
started_at 0 1 2020-04-01 00:00:30 2021-03-31 23:59:08 2020-08-29 14:50:36 3040228
ended_at 0 1 2020-04-01 00:10:45 2021-04-06 11:00:11 2020-08-29 15:21:13 3027775 
Combined_trips$start_date <- as.Date(Combined_trips$started_at) #Create new Column with just date. Separate function could be used as well; but crashed my computer
Combined_trips$start_month <- format(Combined_trips$start_date, "%b") #New column with just the month
Combined_trips$start_day_of_month <- format(Combined_trips$start_date, "%d") #New column with the day of the month
Combined_trips$day_of_week <- weekdays(Combined_trips$start_date, abbreviate = TRUE) #New column with the day of the week
Combined_trips$ride_time <- difftime(Combined_trips$ended_at, Combined_trips$started_at, units = "secs") #New column with ride time in seconds
skim(Combined_trips$ride_time) #Check for weird data
Data summary
Name Combined_trips$ride_time
Number of rows 3489748
Number of columns 1
_______________________
Column type frequency:
difftime 1
________________________
Group variables None

Variable type: difftime

skim_variable n_missing complete_rate min max median n_unique
data 0 1 -1742998 secs 3523202 secs 871 secs 26460 
Combined_trips2 <- filter(Combined_trips, Combined_trips$ride_time > 0) #Remove all rows where ride time is less than or equal to 0 
skim(Combined_trips2$ride_time) #Check again
Data summary
Name Combined_trips2$ride_time
Number of rows 3478810
Number of columns 1
_______________________
Column type frequency:
difftime 1
________________________
Group variables None

Variable type: difftime

skim_variable n_missing complete_rate min max median n_unique
data 0 1 1 secs 3523202 secs 874 secs 25630 
Combined_trips2$start_time <- format(Combined_trips2$started_at, "%H:%M")
Combined_trips2$time_of_day = "Morning(5-11)"
Combined_trips2$time_of_day[Combined_trips2$start_time >= "11:00" & Combined_trips2$start_time <= "13:00"] = "Noon(11-13)"
Combined_trips2$time_of_day[Combined_trips2$start_time >= "13:00" & Combined_trips2$start_time <= "20:00"] = "Afternoon(13-20)"
Combined_trips2$time_of_day[Combined_trips2$start_time >= "20:00" & Combined_trips2$start_time <= "5:00"] = "Night(20-5)"
aggregate(Combined_trips2$ride_time, list(Combined_trips2$day_of_week), FUN=mean)
Average_trips_day <- data.frame(day_of_week=c("2022-08-01", "2022-08-02", "2022-08-03", "2022-08-04", "2022-08-05", "2022-08-06", "2022-08-07"), average_ride=c("2698.8843", "2429.4913", "2419.1462", "2578.5452", "2566.3872", "2817.5686", "3044.9107", "920.3126", "907.9542", "918.9792", "912.6536", "948.2213", "1067.7615", "1093.1743"), member_casual=c("Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Member", "Member", "Member", "Member", "Member", "Member", "Member"))
Average_trips_day$average_ride <- as.numeric(Average_trips_day$average_ride)
Average_trips_day$day_of_week <- as.Date(Average_trips_day$day_of_week)
aggregate(Combined_trips2$ride_time, list(Combined_trips2$start_month, Combined_trips2$member_casual), FUN=mean)
 Average_trips_month <- data.frame(month=c("2021-01-01", "2021-02-01", "2021-03-01", "2020-04-01", "2020-05-01", "2020-06-01", "2020-07-01", "2020-08-01", "2020-09-01", "2020-10-01", "2020-11-01", "2020-12-01"), average_ride=as.numeric(c("1541.0754", "2692.6862", "2289.6601", "4388.5533", "3073.2654", "3100.2874", "3597.2850", "2696.3853", "2293.3985", " 1815.5378", "1909.3637", "1611.1376", "772.3780", "1081.4072", "838.2379", "1288.8205", "1186.4038", "1123.9922", "1066.1054", "1010.1743", "932.5190", "843.0398", "815.3985", "764.9993")), member_casual=c("Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Casual", "Member", "Member", "Member", "Member", "Member", "Member", "Member", "Member", "Member", "Member", "Member", "Member"))
Average_trips_month$month <- as.Date(Average_trips_month$month, format = "%Y-%m-%d")
Popular_start_stations <-  aggregate(Combined_trips2$start_station_name, list(Combined_trips2$member_casual, Combined_trips2$start_station_name), FUN = length)
Popular_Casual_Start_Station <- subset(Popular_start_stations, Popular_start_stations$Group.1 == "casual")
Popular_Casual_Start_Station <- Popular_Casual_Start_Station[order(Popular_Casual_Start_Station$x, decreasing = TRUE),][1:10,]
Popular_Member_Start_Station <- subset(Popular_start_stations, Popular_start_stations$Group.1 == "member")
Popular_Member_Start_Station <- Popular_Member_Start_Station[order(Popular_Member_Start_Station$x, decreasing = TRUE),][1:10,]
Popular_end_stations <- aggregate(Combined_trips2$end_station_name, list(Combined_trips2$member_casual, Combined_trips2$end_station_name), FUN = length)
Popular_Member_End_Station <- subset(Popular_end_stations, Popular_end_stations$Group.1 == "member")
Popular_Member_End_Station <- Popular_Member_End_Station[order(Popular_Member_End_Station$x, decreasing = TRUE),][1:10,]
Popular_Casual_End_Station <- subset(Popular_end_stations, Popular_end_stations$Group.1 == "casual")
Popular_Casual_End_Station <- Popular_Casual_End_Station[order(Popular_Casual_End_Station$x, decreasing = TRUE),][1:10,]
Popular_end_stations <- rbind(Popular_Casual_End_Station, Popular_Member_End_Station)
Popular_start_stations <- rbind(Popular_Casual_Start_Station, Popular_Member_Start_Station)

###Analyzing the Data

Lets look at some of the trends over the year.

## Warning: Ignoring unknown aesthetics: fill

This graph shows that there are not many differences in riding behavior between members of Cyclistic and casual riders over the year. Both groups of consumers tend to ride more during the summer towards the beginning of fall as seen by the rise in both lines around June and the slow decline aftwerwards. After the winter, we see both groups start riding bikes more as we would expect with warmer weather. However, the steep decline towards the beginning of April 2021 seems to dispute this theory. The steep decline could be due to the company running quality checks on their bike fleet, rendering them unable to serve riders.

This graph compares the behavior of member and casual riders over the course of a week and time of day. The main difference is that members are much more consistent and use the bikes at around the same level throughout the week. On the other hand, casual riders are much more likely to use the bikes on the weekends (Friday-Sunday). Both members and casual riders are more likely to use the bikes during the afternoon (between 1-7pm) and very rarely use the bikes during the night. Additionally, members are more likely to use their bikes in then casual riders are.

This graph compares member and casual behavior over the course of the year similarly to the graph above; however, instead of comparing behavior between days of the week, we compare the behavior of these 2 groups over the months of the year. We find that they are very similar trends in the number of riders over the course of the year. Both casual and member riders tend to ride bikes more often in the months June through August and avoid riding bikes in December through Febuary. This makes sense as this follows the pattern of the seasons. You would expect more people to bike when it is warmer. The data also repeats the theme that people are more likely to bike in the afternoon for both groups, but members are more likely to use their bikes in the morning.

One interesting aspect of the data is that Casual riders tend to use their bikes longer than member riders do. Above, we can see the that the consistent trend in the data is that Casual riders ride their bikes for a duration over double that of Members on average.

This trend is not just specific to a certain time of the year. As you can see in the above data visualization, this trend occurs across the whole entire year.

Here are the top 10 stations for both members and casual riders. Casual riders tend to seek the more touristy parts of the city, which suggests that they are likely students, families, couples, and tourists visiting the city. Only the Theater on the Lake station and the Clark St. & Elm St. station are on both the top 10 lists for members and casual riders.

###Conclusion

Riders are heavily affected by the time of year. Both existing members and casual riders tend to avoid using their bikes during the colder parts of the year. Thus, any marketing campaign should take this into account and should be launched between the months of June and September, when rider activity is at the highest. In addition, it would be smart to consider the areas popular with both casual riders and members. Casual riders who frequent these areas are possible new members. On the other hand, avoid areas with only casual riders or members. Areas with mostly just casual riders suggests that these places are for tourists and those riders are likely not to stay in the city and have any incentive to sign up for a membership. You should also avoid places with just members because then you would be trying to convert members into members, a wasted exercise.

As a final thought, our data allows us to tell how members and casual riders behave differently, but does not reveal much about what caused people to sign up. Thus, the best course of action would be to continue market research on existing members to find out what factors lead to their subscription.