CYCLISTIC BIKES APR TO JUN 2021

SETTING UP ENVIRONMENT.

INSTALL PACKAGES.

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(data.table)

## 
## Attaching package: 'data.table'
## 
## The following objects are masked from 'package:lubridate':
## 
##     hour, isoweek, mday, minute, month, quarter, second, wday, week,
##     yday, year
## 
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
## 
## The following object is masked from 'package:purrr':
## 
##     transpose

library(hms)

## 
## Attaching package: 'hms'
## 
## The following object is masked from 'package:lubridate':
## 
##     hms

library(here)

## here() starts at C:/Users/SWill/Documents/APR TO JUN CYCLISTIC BIKES

library(skimr)
library(janitor)

## 
## Attaching package: 'janitor'
## 
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

library(conflicted)
library(gtsummary)
library(scales)
library(RColorBrewer)
library(ggthemes)

SCIENTIFIC NOTATION RUINING YOUR GGPLOT CHARTS? TRY THE LINE OF CODE BELOW

options(scipen = 999)

USE ‘getwd()’ FUNCTION TO DISPLAY WORKING DIRECTORY.

getwd()

## [1] "C:/Users/SWill/Documents/APR TO JUN CYCLISTIC BIKES"

USE ‘setwd()’ FUNCTION TO SET WORKING DIRECTORY TO SIMPLIFY CALLS TO DATA.

setwd("C:/Users/SWill/Documents/APR TO JUN CYCLISTIC BIKES")

USE ‘spec_csv()’ FUNCTION TO CHECK THE DATA TYPES BEFORE READING THE DATA.

NOTICE ‘started_at’ AND ‘ended_at’ COLUMNS ARE ‘datetime’ DATA TYPE.

spec_csv("C:/Users/SWill/Desktop/CYCLISTIC BIKES/divvy-trip-data 01-12/202104-divvy-tripdata.csv")

## 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()
## )

spec_csv("C:/Users/SWill/Desktop/CYCLISTIC BIKES/divvy-trip-data 01-12/202105-divvy-tripdata.csv")

## 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()
## )

spec_csv("C:/Users/SWill/Desktop/CYCLISTIC BIKES/divvy-trip-data 01-12/202106-divvy-tripdata.csv")

## 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()
## )

UPLOAD DATASETS divvy-trip-data.csv FILES.

df_04 <- read.csv("C:/Users/SWill/Desktop/CYCLISTIC BIKES/divvy-trip-data 01-12/202104-divvy-tripdata.csv")
df_05 <- read.csv("C:/Users/SWill/Desktop/CYCLISTIC BIKES/divvy-trip-data 01-12/202105-divvy-tripdata.csv")
df_06 <- read.csv("C:/Users/SWill/Desktop/CYCLISTIC BIKES/divvy-trip-data 01-12/202106-divvy-tripdata.csv")

USE ‘bind_rows()’ FUNCTION TO STACK DATA FRAMES INTO ONE BIG DATA FRAME.

apr_to_jun <- bind_rows(df_04,df_05,df_06)

CHECK COLUMNS.

colnames(apr_to_jun)

##  [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"

USE ‘glimpse()’ FUNCTION TO GET A BETTER UNDERSTANDING OF THE DATA.

Rows: 1,598,458 Columns: 13

COLUMNS ‘started_at’ AND ‘ended_at’ ARE NOW ‘character’ DATA TYPE.

COLUMNS ‘end_station_name’ AND ‘end_station_id’ HAVE BLANK ROWS THAT NEED TO BE REMOVED.

glimpse(apr_to_jun)

## Rows: 1,598,458
## Columns: 13
## $ ride_id            <chr> "6C992BD37A98A63F", "1E0145613A209000", "E498E15508…
## $ rideable_type      <chr> "classic_bike", "docked_bike", "docked_bike", "clas…
## $ started_at         <chr> "2021-04-12 18:25:36", "2021-04-27 17:27:11", "2021…
## $ ended_at           <chr> "2021-04-12 18:56:55", "2021-04-27 18:31:29", "2021…
## $ start_station_name <chr> "State St & Pearson St", "Dorchester Ave & 49th St"…
## $ start_station_id   <chr> "TA1307000061", "KA1503000069", "20121", "TA1305000…
## $ end_station_name   <chr> "Southport Ave & Waveland Ave", "Dorchester Ave & 4…
## $ end_station_id     <chr> "13235", "KA1503000069", "20121", "13235", "20121",…
## $ start_lat          <dbl> 41.89745, 41.80577, 41.74149, 41.90312, 41.74149, 4…
## $ start_lng          <dbl> -87.62872, -87.59246, -87.65841, -87.67394, -87.658…
## $ end_lat            <dbl> 41.94815, 41.80577, 41.74149, 41.94815, 41.74149, 4…
## $ end_lng            <dbl> -87.66394, -87.59246, -87.65841, -87.66394, -87.658…
## $ member_casual      <chr> "member", "casual", "casual", "member", "casual", "…

USE ‘str()’ FUNCTION TO SEE LIST OF COLUMNS AND DATA TYPES NUMERIC, CHARACTER, DATETIME ETC.

‘data.frame’: 1598458 obs. of 13 variables:

str(apr_to_jun)

## 'data.frame':    1598458 obs. of  13 variables:
##  $ ride_id           : chr  "6C992BD37A98A63F" "1E0145613A209000" "E498E15508A80BAD" "1887262AD101C604" ...
##  $ rideable_type     : chr  "classic_bike" "docked_bike" "docked_bike" "classic_bike" ...
##  $ started_at        : chr  "2021-04-12 18:25:36" "2021-04-27 17:27:11" "2021-04-03 12:42:45" "2021-04-17 09:17:42" ...
##  $ ended_at          : chr  "2021-04-12 18:56:55" "2021-04-27 18:31:29" "2021-04-07 11:40:24" "2021-04-17 09:42:48" ...
##  $ start_station_name: chr  "State St & Pearson St" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Honore St & Division St" ...
##  $ start_station_id  : chr  "TA1307000061" "KA1503000069" "20121" "TA1305000034" ...
##  $ end_station_name  : chr  "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Southport Ave & Waveland Ave" ...
##  $ end_station_id    : chr  "13235" "KA1503000069" "20121" "13235" ...
##  $ start_lat         : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ start_lng         : num  -87.6 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ end_lng           : num  -87.7 -87.6 -87.7 -87.7 -87.7 ...
##  $ member_casual     : chr  "member" "casual" "casual" "member" ...

USE TIDYR TO SEPARATE “started_at” COLUMN TO A NEW COLUMN CALLED “start_date” and “start_time”.

USE TIDYR TO SEPARATE “ended_at” COLUMN TO A NEW COLUMN CALLED “end_date” and “end_time”.

apr_to_jun <- tidyr::separate(apr_to_jun, started_at, c("start_date", "start_time"), sep = " ", remove = FALSE)
apr_to_jun <- tidyr::separate(apr_to_jun, ended_at, c("end_date", "end_time"), sep = " ", remove = FALSE)

CHECK NEW COLUMNS.

colnames(apr_to_jun)

##  [1] "ride_id"            "rideable_type"      "started_at"        
##  [4] "start_date"         "start_time"         "ended_at"          
##  [7] "end_date"           "end_time"           "start_station_name"
## [10] "start_station_id"   "end_station_name"   "end_station_id"    
## [13] "start_lat"          "start_lng"          "end_lat"           
## [16] "end_lng"            "member_casual"

‘’data.frame’: 1598458 obs. of 17 variables:

str(apr_to_jun)

## 'data.frame':    1598458 obs. of  17 variables:
##  $ ride_id           : chr  "6C992BD37A98A63F" "1E0145613A209000" "E498E15508A80BAD" "1887262AD101C604" ...
##  $ rideable_type     : chr  "classic_bike" "docked_bike" "docked_bike" "classic_bike" ...
##  $ started_at        : chr  "2021-04-12 18:25:36" "2021-04-27 17:27:11" "2021-04-03 12:42:45" "2021-04-17 09:17:42" ...
##  $ start_date        : chr  "2021-04-12" "2021-04-27" "2021-04-03" "2021-04-17" ...
##  $ start_time        : chr  "18:25:36" "17:27:11" "12:42:45" "09:17:42" ...
##  $ ended_at          : chr  "2021-04-12 18:56:55" "2021-04-27 18:31:29" "2021-04-07 11:40:24" "2021-04-17 09:42:48" ...
##  $ end_date          : chr  "2021-04-12" "2021-04-27" "2021-04-07" "2021-04-17" ...
##  $ end_time          : chr  "18:56:55" "18:31:29" "11:40:24" "09:42:48" ...
##  $ start_station_name: chr  "State St & Pearson St" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Honore St & Division St" ...
##  $ start_station_id  : chr  "TA1307000061" "KA1503000069" "20121" "TA1305000034" ...
##  $ end_station_name  : chr  "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Southport Ave & Waveland Ave" ...
##  $ end_station_id    : chr  "13235" "KA1503000069" "20121" "13235" ...
##  $ start_lat         : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ start_lng         : num  -87.6 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ end_lng           : num  -87.7 -87.6 -87.7 -87.7 -87.7 ...
##  $ member_casual     : chr  "member" "casual" "casual" "member" ...

EXPLORE AND MANIPULATE DATA FRAME APR_TO_JUN.

COLUMN RIDEABLE TYPE.

EXPLORE…CHARACTER VARIABLE TYPE IN “rideable_type” COLUMN.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$rideable_type)

## [1] "character"

USE ‘unique ()’ FUNCTION TO FIND INDIVIDUAL VALUES IN COLUMN.

unique(apr_to_jun$rideable_type)

## [1] "classic_bike"  "docked_bike"   "electric_bike"

HOW MANY OBSERVATIONS FALL UNDER EACH USER TYPE?

table(apr_to_jun$rideable_type)

## 
##  classic_bike   docked_bike electric_bike 
##        958732        119783        519943

sort(table(apr_to_jun$rideable_type), decreasing = TRUE)

## 
##  classic_bike electric_bike   docked_bike 
##        958732        519943        119783

BAR PLOT OF DATA DISTRIBUTION OF ‘rideable_type’ COLUMN.

barplot(sort(table(apr_to_jun$rideable_type), decreasing = TRUE))

CHANGE VARIABLE FROM CHARACTER TO FACTOR.

apr_to_jun$rideable_type <- as.factor(apr_to_jun$rideable_type)

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$rideable_type)

## [1] "factor"

USE ‘levels’ FUNCTION TO CHECK FACTOR.

levels(apr_to_jun$rideable_type)

## [1] "classic_bike"  "docked_bike"   "electric_bike"

NOTE RIDEABLE TYPE IS NOW A FACTOR.

glimpse(apr_to_jun)

## Rows: 1,598,458
## Columns: 17
## $ ride_id            <chr> "6C992BD37A98A63F", "1E0145613A209000", "E498E15508…
## $ rideable_type      <fct> classic_bike, docked_bike, docked_bike, classic_bik…
## $ started_at         <chr> "2021-04-12 18:25:36", "2021-04-27 17:27:11", "2021…
## $ start_date         <chr> "2021-04-12", "2021-04-27", "2021-04-03", "2021-04-…
## $ start_time         <chr> "18:25:36", "17:27:11", "12:42:45", "09:17:42", "12…
## $ ended_at           <chr> "2021-04-12 18:56:55", "2021-04-27 18:31:29", "2021…
## $ end_date           <chr> "2021-04-12", "2021-04-27", "2021-04-07", "2021-04-…
## $ end_time           <chr> "18:56:55", "18:31:29", "11:40:24", "09:42:48", "14…
## $ start_station_name <chr> "State St & Pearson St", "Dorchester Ave & 49th St"…
## $ start_station_id   <chr> "TA1307000061", "KA1503000069", "20121", "TA1305000…
## $ end_station_name   <chr> "Southport Ave & Waveland Ave", "Dorchester Ave & 4…
## $ end_station_id     <chr> "13235", "KA1503000069", "20121", "13235", "20121",…
## $ start_lat          <dbl> 41.89745, 41.80577, 41.74149, 41.90312, 41.74149, 4…
## $ start_lng          <dbl> -87.62872, -87.59246, -87.65841, -87.67394, -87.658…
## $ end_lat            <dbl> 41.94815, 41.80577, 41.74149, 41.94815, 41.74149, 4…
## $ end_lng            <dbl> -87.66394, -87.59246, -87.65841, -87.66394, -87.658…
## $ member_casual      <chr> "member", "casual", "casual", "member", "casual", "…

COLUMN STARTED_AT AND ENDED_AT.

EXPLORE…CHARACTER VARIABLE TYPE IN “started_at” AND ended_at” COLUMN.

DATA TYPE IN COLUMN “started_at” AND “end_at” WAS DATETIME BEFORE UPLOADING.

CONVERT “started_at” AND “ended_at” COLUMN FROM CHARACTER TO DATETIME

apr_to_jun$started_at <- as.POSIXlt(apr_to_jun$started_at, format="%Y-%m-%d %H:%M:%S", tz="UTC")
apr_to_jun$ended_at <- as.POSIXlt(apr_to_jun$ended_at, format="%Y-%m-%d %H:%M:%S", tz="UTC")

CONVERT “start_date” COLUMN FROM CHARACTER TO DATE FORMAT.

apr_to_jun$start_date <- as.POSIXlt(apr_to_jun$start_date)

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$start_date)

## [1] "POSIXlt" "POSIXt"

CONVERT “end_date” COLUMN FROM CHARACTER TO DATE FORMAT.

apr_to_jun$end_date <- as.POSIXlt(apr_to_jun$end_date)

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$end_date)

## [1] "POSIXlt" "POSIXt"

USE ‘str()’ FUNCTION TO SEE LIST OF COLUMNS AND DATA TYPES NUMERIC, CHARACTER, DATETIME ETC.

‘started_at’AND ’ended_at’ CHARACTER DATA TYPE IS NOW POSIXlt.

‘data.frame’: 1598458 obs. of 17 variables:

str(apr_to_jun)

## 'data.frame':    1598458 obs. of  17 variables:
##  $ ride_id           : chr  "6C992BD37A98A63F" "1E0145613A209000" "E498E15508A80BAD" "1887262AD101C604" ...
##  $ rideable_type     : Factor w/ 3 levels "classic_bike",..: 1 2 2 1 2 1 1 3 1 1 ...
##  $ started_at        : POSIXlt, format: "2021-04-12 18:25:36" "2021-04-27 17:27:11" ...
##  $ start_date        : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ start_time        : chr  "18:25:36" "17:27:11" "12:42:45" "09:17:42" ...
##  $ ended_at          : POSIXlt, format: "2021-04-12 18:56:55" "2021-04-27 18:31:29" ...
##  $ end_date          : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ end_time          : chr  "18:56:55" "18:31:29" "11:40:24" "09:42:48" ...
##  $ start_station_name: chr  "State St & Pearson St" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Honore St & Division St" ...
##  $ start_station_id  : chr  "TA1307000061" "KA1503000069" "20121" "TA1305000034" ...
##  $ end_station_name  : chr  "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Southport Ave & Waveland Ave" ...
##  $ end_station_id    : chr  "13235" "KA1503000069" "20121" "13235" ...
##  $ start_lat         : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ start_lng         : num  -87.6 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ end_lng           : num  -87.7 -87.6 -87.7 -87.7 -87.7 ...
##  $ member_casual     : chr  "member" "casual" "casual" "member" ...

COLUMN START_STATION_NAME START_STATION_ID END_STATION_NAME AND END_STATION_ID.

EXPLORE…CHARACTER VARIABLE TYPE IN “start_staion_name” AND “end_staion_name”

REPLACE ALL BLANK VALUES IN “start_station_name” COLUMN WITH NA VALUES.

apr_to_jun$start_station_name[apr_to_jun$start_station_name==""] <- NA

REPLACE ALL BLANK VALUES IN “start_station_id” COLUMN WITH NA VALUES.

apr_to_jun$start_station_id[apr_to_jun$start_station_id==""] <- NA

REPLACE ALL BLANK VALUES IN “end_station_name” COLUMN WITH NA VALUES.

apr_to_jun$end_station_name[apr_to_jun$end_station_name==""] <- NA

REPLACE ALL BLANK VALUES IN “end_station_id” COLUMN WITH NA VALUES.

apr_to_jun$end_station_id[apr_to_jun$end_station_id==""] <- NA

glimpse(apr_to_jun)

## Rows: 1,598,458
## Columns: 17
## $ ride_id            <chr> "6C992BD37A98A63F", "1E0145613A209000", "E498E15508…
## $ rideable_type      <fct> classic_bike, docked_bike, docked_bike, classic_bik…
## $ started_at         <dttm> 2021-04-12 18:25:36, 2021-04-27 17:27:11, 2021-04-…
## $ start_date         <dttm> 2021-04-12, 2021-04-27, 2021-04-03, 2021-04-17, 20…
## $ start_time         <chr> "18:25:36", "17:27:11", "12:42:45", "09:17:42", "12…
## $ ended_at           <dttm> 2021-04-12 18:56:55, 2021-04-27 18:31:29, 2021-04-…
## $ end_date           <dttm> 2021-04-12, 2021-04-27, 2021-04-07, 2021-04-17, 20…
## $ end_time           <chr> "18:56:55", "18:31:29", "11:40:24", "09:42:48", "14…
## $ start_station_name <chr> "State St & Pearson St", "Dorchester Ave & 49th St"…
## $ start_station_id   <chr> "TA1307000061", "KA1503000069", "20121", "TA1305000…
## $ end_station_name   <chr> "Southport Ave & Waveland Ave", "Dorchester Ave & 4…
## $ end_station_id     <chr> "13235", "KA1503000069", "20121", "13235", "20121",…
## $ start_lat          <dbl> 41.89745, 41.80577, 41.74149, 41.90312, 41.74149, 4…
## $ start_lng          <dbl> -87.62872, -87.59246, -87.65841, -87.67394, -87.658…
## $ end_lat            <dbl> 41.94815, 41.80577, 41.74149, 41.94815, 41.74149, 4…
## $ end_lng            <dbl> -87.66394, -87.59246, -87.65841, -87.66394, -87.658…
## $ member_casual      <chr> "member", "casual", "casual", "member", "casual", "…

REMOVE ROWS WITH NA VALUES IN ALL COLUMNS.

apr_to_jun <- apr_to_jun %>% drop_na()

‘data.frame’: 1357979 obs. of 17 variables:

str(apr_to_jun)

## 'data.frame':    1357979 obs. of  17 variables:
##  $ ride_id           : chr  "6C992BD37A98A63F" "1E0145613A209000" "E498E15508A80BAD" "1887262AD101C604" ...
##  $ rideable_type     : Factor w/ 3 levels "classic_bike",..: 1 2 2 1 2 1 1 3 1 1 ...
##  $ started_at        : POSIXlt, format: "2021-04-12 18:25:36" "2021-04-27 17:27:11" ...
##  $ start_date        : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ start_time        : chr  "18:25:36" "17:27:11" "12:42:45" "09:17:42" ...
##  $ ended_at          : POSIXlt, format: "2021-04-12 18:56:55" "2021-04-27 18:31:29" ...
##  $ end_date          : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ end_time          : chr  "18:56:55" "18:31:29" "11:40:24" "09:42:48" ...
##  $ start_station_name: chr  "State St & Pearson St" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Honore St & Division St" ...
##  $ start_station_id  : chr  "TA1307000061" "KA1503000069" "20121" "TA1305000034" ...
##  $ end_station_name  : chr  "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Southport Ave & Waveland Ave" ...
##  $ end_station_id    : chr  "13235" "KA1503000069" "20121" "13235" ...
##  $ start_lat         : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ start_lng         : num  -87.6 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ end_lng           : num  -87.7 -87.6 -87.7 -87.7 -87.7 ...
##  $ member_casual     : chr  "member" "casual" "casual" "member" ...

COLUMN MEMBER_CASUAL.

EXPLORE…CHARACTER VARIABLE TYPE IN “member_casual” COLUMN.

USE ‘unique ()’ FUNCTION TO FIND INDIVIDUAL VALUES IN COLUMN.

unique(apr_to_jun$member_casual)

## [1] "member" "casual"

HOW MANY OBSERVATIONS FALL UNDER EACH USER TYPE?

table(apr_to_jun$member_casual)

## 
## casual member 
## 641441 716538

sort(table(apr_to_jun$member_casual), decreasing = TRUE)

## 
## member casual 
## 716538 641441

BAR PLOT OF DATA DISTRIBUTION OF ‘member_casual’ COLUMN.

barplot(sort(table(apr_to_jun$member_casual), decreasing = TRUE))

CHANGE VARIABLE FROM CHARACTER TO FACTOR.

apr_to_jun$member_casual <- as.factor(apr_to_jun$member_casual)

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$member_casual)

## [1] "factor"

USE ‘levels’ FUNCTION TO CHECK FACTOR.

levels(apr_to_jun$member_casual)

## [1] "casual" "member"

NOTE MEMBER CASUAL IS NOW A FACTOR.

glimpse(apr_to_jun)

## Rows: 1,357,979
## Columns: 17
## $ ride_id            <chr> "6C992BD37A98A63F", "1E0145613A209000", "E498E15508…
## $ rideable_type      <fct> classic_bike, docked_bike, docked_bike, classic_bik…
## $ started_at         <dttm> 2021-04-12 18:25:36, 2021-04-27 17:27:11, 2021-04-…
## $ start_date         <dttm> 2021-04-12, 2021-04-27, 2021-04-03, 2021-04-17, 20…
## $ start_time         <chr> "18:25:36", "17:27:11", "12:42:45", "09:17:42", "12…
## $ ended_at           <dttm> 2021-04-12 18:56:55, 2021-04-27 18:31:29, 2021-04-…
## $ end_date           <dttm> 2021-04-12, 2021-04-27, 2021-04-07, 2021-04-17, 20…
## $ end_time           <chr> "18:56:55", "18:31:29", "11:40:24", "09:42:48", "14…
## $ start_station_name <chr> "State St & Pearson St", "Dorchester Ave & 49th St"…
## $ start_station_id   <chr> "TA1307000061", "KA1503000069", "20121", "TA1305000…
## $ end_station_name   <chr> "Southport Ave & Waveland Ave", "Dorchester Ave & 4…
## $ end_station_id     <chr> "13235", "KA1503000069", "20121", "13235", "20121",…
## $ start_lat          <dbl> 41.89745, 41.80577, 41.74149, 41.90312, 41.74149, 4…
## $ start_lng          <dbl> -87.62872, -87.59246, -87.65841, -87.67394, -87.658…
## $ end_lat            <dbl> 41.94815, 41.80577, 41.74149, 41.94815, 41.74149, 4…
## $ end_lng            <dbl> -87.66394, -87.59246, -87.65841, -87.66394, -87.658…
## $ member_casual      <fct> member, casual, casual, member, casual, casual, cas…

ADD A CALCULATED FIELD FOR NEW COLUMN “ride_length_secs”.

apr_to_jun$ride_length_secs <- difftime(apr_to_jun$ended_at,apr_to_jun$started_at)

CHECK DATA TYPE.

is.numeric(apr_to_jun$ride_length_secs)

## [1] FALSE

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$ride_length_secs)

## [1] "difftime"

CONVERT “ride_length_secs” FROM DIFFTIME TO NUMERIC TO RUN CALCULATIONS ON THE DATA.

apr_to_jun$ride_length_secs <- as.numeric(as.character(apr_to_jun$ride_length_secs))

CHECK DATA TYPE.

is.numeric(apr_to_jun$ride_length_secs)

## [1] TRUE

CREATE NEW COLUMN “ride_length_total” USING MUTATE FUNCTION.

apr_to_jun <- mutate(apr_to_jun, ride_length_total = ride_length_secs/60)

CHECK DATA TYPE.

is.numeric(apr_to_jun$ride_length_total)

## [1] TRUE

ADD COLUMN FOR DAY OF WEEK.

NUMERIC VALUE DAY OF WEEK SUNDAY = 1 MONDAY = 2 TUESDAY = 3 ETC, ETC…

apr_to_jun$weekday <- lubridate::wday(apr_to_jun$start_date)

CHARACTER DAY OF WEEK USING ABBREVIATED LABELS MON,TUE,WED ETC ETC…

apr_to_jun$weekday. <- lubridate::wday(apr_to_jun$start_date, label = TRUE)

CHANGE ‘weekday’ DATA TYPE.

apr_to_jun$weekday. <- as.factor(apr_to_jun$weekday.)

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$weekday.)

## [1] "ordered" "factor"

USE ‘levels’ FUNCTION TO CHECK FACTOR.

levels(apr_to_jun$weekday.)

## [1] "Sun" "Mon" "Tue" "Wed" "Thu" "Fri" "Sat"

NOTE WEEKDAY. IS AN ORDERED FACTOR.

glimpse(apr_to_jun)

## Rows: 1,357,979
## Columns: 21
## $ ride_id            <chr> "6C992BD37A98A63F", "1E0145613A209000", "E498E15508…
## $ rideable_type      <fct> classic_bike, docked_bike, docked_bike, classic_bik…
## $ started_at         <dttm> 2021-04-12 18:25:36, 2021-04-27 17:27:11, 2021-04-…
## $ start_date         <dttm> 2021-04-12, 2021-04-27, 2021-04-03, 2021-04-17, 20…
## $ start_time         <chr> "18:25:36", "17:27:11", "12:42:45", "09:17:42", "12…
## $ ended_at           <dttm> 2021-04-12 18:56:55, 2021-04-27 18:31:29, 2021-04-…
## $ end_date           <dttm> 2021-04-12, 2021-04-27, 2021-04-07, 2021-04-17, 20…
## $ end_time           <chr> "18:56:55", "18:31:29", "11:40:24", "09:42:48", "14…
## $ start_station_name <chr> "State St & Pearson St", "Dorchester Ave & 49th St"…
## $ start_station_id   <chr> "TA1307000061", "KA1503000069", "20121", "TA1305000…
## $ end_station_name   <chr> "Southport Ave & Waveland Ave", "Dorchester Ave & 4…
## $ end_station_id     <chr> "13235", "KA1503000069", "20121", "13235", "20121",…
## $ start_lat          <dbl> 41.89745, 41.80577, 41.74149, 41.90312, 41.74149, 4…
## $ start_lng          <dbl> -87.62872, -87.59246, -87.65841, -87.67394, -87.658…
## $ end_lat            <dbl> 41.94815, 41.80577, 41.74149, 41.94815, 41.74149, 4…
## $ end_lng            <dbl> -87.66394, -87.59246, -87.65841, -87.66394, -87.658…
## $ member_casual      <fct> member, casual, casual, member, casual, casual, cas…
## $ ride_length_secs   <dbl> 1879, 3858, 341859, 1506, 5477, 41, 86, 1550, 3174,…
## $ ride_length_total  <dbl> 31.3166667, 64.3000000, 5697.6500000, 25.1000000, 9…
## $ weekday            <dbl> 2, 3, 7, 7, 7, 1, 7, 3, 2, 7, 7, 7, 3, 1, 3, 5, 3, …
## $ weekday.           <ord> Mon, Tue, Sat, Sat, Sat, Sun, Sat, Tue, Mon, Sat, S…

EXPLORE NUMERIC VARIABLE TYPE IN “weekday” COLUMN.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

class(apr_to_jun$weekday)

## [1] "numeric"

USE ‘summary()’ FUNCTION TO SUMMARIZE VALUES IN DATA FRAME.

summary(apr_to_jun$weekday)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.000   4.000   4.046   6.000   7.000

BOX PLOT AKA IS A GRAPHICAL REPRESENTATION TO SUMMARIZE DATA AND IDENTIFY OUTLIERS.

boxplot(apr_to_jun$weekday, col = 'yellow')

HISTOGRAM TO VIZUALIZE DISTRIBUTION OF VALUES IN WEEKDAY COLUMN.

hist(apr_to_jun$weekday, col='pink')

NOTE WEEKDAY IS NOW A ‘dbl’.

glimpse(apr_to_jun)

## Rows: 1,357,979
## Columns: 21
## $ ride_id            <chr> "6C992BD37A98A63F", "1E0145613A209000", "E498E15508…
## $ rideable_type      <fct> classic_bike, docked_bike, docked_bike, classic_bik…
## $ started_at         <dttm> 2021-04-12 18:25:36, 2021-04-27 17:27:11, 2021-04-…
## $ start_date         <dttm> 2021-04-12, 2021-04-27, 2021-04-03, 2021-04-17, 20…
## $ start_time         <chr> "18:25:36", "17:27:11", "12:42:45", "09:17:42", "12…
## $ ended_at           <dttm> 2021-04-12 18:56:55, 2021-04-27 18:31:29, 2021-04-…
## $ end_date           <dttm> 2021-04-12, 2021-04-27, 2021-04-07, 2021-04-17, 20…
## $ end_time           <chr> "18:56:55", "18:31:29", "11:40:24", "09:42:48", "14…
## $ start_station_name <chr> "State St & Pearson St", "Dorchester Ave & 49th St"…
## $ start_station_id   <chr> "TA1307000061", "KA1503000069", "20121", "TA1305000…
## $ end_station_name   <chr> "Southport Ave & Waveland Ave", "Dorchester Ave & 4…
## $ end_station_id     <chr> "13235", "KA1503000069", "20121", "13235", "20121",…
## $ start_lat          <dbl> 41.89745, 41.80577, 41.74149, 41.90312, 41.74149, 4…
## $ start_lng          <dbl> -87.62872, -87.59246, -87.65841, -87.67394, -87.658…
## $ end_lat            <dbl> 41.94815, 41.80577, 41.74149, 41.94815, 41.74149, 4…
## $ end_lng            <dbl> -87.66394, -87.59246, -87.65841, -87.66394, -87.658…
## $ member_casual      <fct> member, casual, casual, member, casual, casual, cas…
## $ ride_length_secs   <dbl> 1879, 3858, 341859, 1506, 5477, 41, 86, 1550, 3174,…
## $ ride_length_total  <dbl> 31.3166667, 64.3000000, 5697.6500000, 25.1000000, 9…
## $ weekday            <dbl> 2, 3, 7, 7, 7, 1, 7, 3, 2, 7, 7, 7, 3, 1, 3, 5, 3, …
## $ weekday.           <ord> Mon, Tue, Sat, Sat, Sat, Sun, Sat, Tue, Mon, Sat, S…

NOTE WEEKDAY IS NOW NUMERIC.

str(apr_to_jun)

## 'data.frame':    1357979 obs. of  21 variables:
##  $ ride_id           : chr  "6C992BD37A98A63F" "1E0145613A209000" "E498E15508A80BAD" "1887262AD101C604" ...
##  $ rideable_type     : Factor w/ 3 levels "classic_bike",..: 1 2 2 1 2 1 1 3 1 1 ...
##  $ started_at        : POSIXlt, format: "2021-04-12 18:25:36" "2021-04-27 17:27:11" ...
##  $ start_date        : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ start_time        : chr  "18:25:36" "17:27:11" "12:42:45" "09:17:42" ...
##  $ ended_at          : POSIXlt, format: "2021-04-12 18:56:55" "2021-04-27 18:31:29" ...
##  $ end_date          : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ end_time          : chr  "18:56:55" "18:31:29" "11:40:24" "09:42:48" ...
##  $ start_station_name: chr  "State St & Pearson St" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Honore St & Division St" ...
##  $ start_station_id  : chr  "TA1307000061" "KA1503000069" "20121" "TA1305000034" ...
##  $ end_station_name  : chr  "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Southport Ave & Waveland Ave" ...
##  $ end_station_id    : chr  "13235" "KA1503000069" "20121" "13235" ...
##  $ start_lat         : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ start_lng         : num  -87.6 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ end_lng           : num  -87.7 -87.6 -87.7 -87.7 -87.7 ...
##  $ member_casual     : Factor w/ 2 levels "casual","member": 2 1 1 2 1 1 1 1 1 1 ...
##  $ ride_length_secs  : num  1879 3858 341859 1506 5477 ...
##  $ ride_length_total : num  31.3 64.3 5697.6 25.1 91.3 ...
##  $ weekday           : num  2 3 7 7 7 1 7 3 2 7 ...
##  $ weekday.          : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tue"<..: 2 3 7 7 7 1 7 3 2 7 ...

NEW COLUMN RIDE_LENGTH_SECS

DELETE RIDES UNDER 2 MINUTES (> 120) 1325970 ROWS REMAIN.

apr_to_jun <- subset(apr_to_jun, ride_length_secs > 120)

‘data.frame’: 1325970 obs. of 21 variables:

str(apr_to_jun)

## 'data.frame':    1325970 obs. of  21 variables:
##  $ ride_id           : chr  "6C992BD37A98A63F" "1E0145613A209000" "E498E15508A80BAD" "1887262AD101C604" ...
##  $ rideable_type     : Factor w/ 3 levels "classic_bike",..: 1 2 2 1 2 3 1 3 3 1 ...
##  $ started_at        : POSIXlt, format: "2021-04-12 18:25:36" "2021-04-27 17:27:11" ...
##  $ start_date        : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ start_time        : chr  "18:25:36" "17:27:11" "12:42:45" "09:17:42" ...
##  $ ended_at          : POSIXlt, format: "2021-04-12 18:56:55" "2021-04-27 18:31:29" ...
##  $ end_date          : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ end_time          : chr  "18:56:55" "18:31:29" "11:40:24" "09:42:48" ...
##  $ start_station_name: chr  "State St & Pearson St" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Honore St & Division St" ...
##  $ start_station_id  : chr  "TA1307000061" "KA1503000069" "20121" "TA1305000034" ...
##  $ end_station_name  : chr  "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Loomis Blvd & 84th St" "Southport Ave & Waveland Ave" ...
##  $ end_station_id    : chr  "13235" "KA1503000069" "20121" "13235" ...
##  $ start_lat         : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ start_lng         : num  -87.6 -87.6 -87.7 -87.7 -87.7 ...
##  $ end_lat           : num  41.9 41.8 41.7 41.9 41.7 ...
##  $ end_lng           : num  -87.7 -87.6 -87.7 -87.7 -87.7 ...
##  $ member_casual     : Factor w/ 2 levels "casual","member": 2 1 1 2 1 1 1 1 1 1 ...
##  $ ride_length_secs  : num  1879 3858 341859 1506 5477 ...
##  $ ride_length_total : num  31.3 64.3 5697.6 25.1 91.3 ...
##  $ weekday           : num  2 3 7 7 7 3 2 7 7 3 ...
##  $ weekday.          : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tue"<..: 2 3 7 7 7 3 2 7 7 3 ...

DELETE RIDES OVER 24 HOURS (> 86400) 1325386 ROWS REMAIN.

apr_to_jun <- subset(apr_to_jun, ride_length_secs < 86400)

‘data.frame’: 1325386 obs. of 21 variables:

str(apr_to_jun)

## 'data.frame':    1325386 obs. of  21 variables:
##  $ ride_id           : chr  "6C992BD37A98A63F" "1E0145613A209000" "1887262AD101C604" "C123548CAB2A32A5" ...
##  $ rideable_type     : Factor w/ 3 levels "classic_bike",..: 1 2 1 2 3 1 3 3 1 1 ...
##  $ started_at        : POSIXlt, format: "2021-04-12 18:25:36" "2021-04-27 17:27:11" ...
##  $ start_date        : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ start_time        : chr  "18:25:36" "17:27:11" "09:17:42" "12:42:25" ...
##  $ ended_at          : POSIXlt, format: "2021-04-12 18:56:55" "2021-04-27 18:31:29" ...
##  $ end_date          : POSIXlt, format: "2021-04-12" "2021-04-27" ...
##  $ end_time          : chr  "18:56:55" "18:31:29" "09:42:48" "14:13:42" ...
##  $ start_station_name: chr  "State St & Pearson St" "Dorchester Ave & 49th St" "Honore St & Division St" "Loomis Blvd & 84th St" ...
##  $ start_station_id  : chr  "TA1307000061" "KA1503000069" "TA1305000034" "20121" ...
##  $ end_station_name  : chr  "Southport Ave & Waveland Ave" "Dorchester Ave & 49th St" "Southport Ave & Waveland Ave" "Loomis Blvd & 84th St" ...
##  $ end_station_id    : chr  "13235" "KA1503000069" "13235" "20121" ...
##  $ start_lat         : num  41.9 41.8 41.9 41.7 41.8 ...
##  $ start_lng         : num  -87.6 -87.6 -87.7 -87.7 -87.6 ...
##  $ end_lat           : num  41.9 41.8 41.9 41.7 41.8 ...
##  $ end_lng           : num  -87.7 -87.6 -87.7 -87.7 -87.6 ...
##  $ member_casual     : Factor w/ 2 levels "casual","member": 2 1 2 1 1 1 1 1 1 1 ...
##  $ ride_length_secs  : num  1879 3858 1506 5477 1550 ...
##  $ ride_length_total : num  31.3 64.3 25.1 91.3 25.8 ...
##  $ weekday           : num  2 3 7 7 3 2 7 7 3 1 ...
##  $ weekday.          : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tue"<..: 2 3 7 7 3 2 7 7 3 1 ...

SORT DATA FRAME BY DATE AND TIMES.

apr_to_jun <- apr_to_jun %>% arrange(ymd_hms(apr_to_jun$started_at))

CREATE NEW DATA FRAME (apr_to_jun_v2) FROM DATA FRAME (apr_to_jun).

apr_to_jun_v2 <- apr_to_jun[c('rideable_type', 'started_at', 'start_date', 'member_casual', 'ride_length_secs', 'ride_length_total', 'weekday', 'weekday.')]

DESCRIPTIVE ANALYSIS ON RIDE LENGTH.

MINIMUM TRIP TIME.

min(apr_to_jun_v2$ride_length_secs)

## [1] 121

MIDDLE VALUE IN APR TO JUN DATASET.

median(apr_to_jun_v2$ride_length_secs)

## [1] 839

MAXIMUM TRIP TIME.

max(apr_to_jun_v2$ride_length_secs)

## [1] 86313

AVERAGE TRIP.

mean(apr_to_jun_v2$ride_length_secs)

## [1] 1382.554

THE DIFFERENCE BETWEEN MAXIMUM AND MINIMUM TRIP.

range(apr_to_jun_v2$ride_length_secs)

## [1]   121 86313

DIFFERENCE BETWEEN THE FIRST QUARTILE AND THIRD QUARTILE OF APR TO JUN.

IQR(apr_to_jun_v2$ride_length_secs)

## [1] 1036

COMPARE MEMBERS AND CASUAL RIDERS.

MEMBERS Vs CASUAL MINIMUM TRIP TIME.

aggregate(apr_to_jun_v2$ride_length_secs ~ apr_to_jun_v2$member_casual, FUN = min)

##   apr_to_jun_v2$member_casual apr_to_jun_v2$ride_length_secs
## 1                      casual                            121
## 2                      member                            121

MEMBERS Vs CASUAL MIDDLE VALUE IN APR TO JUN DATASET.

aggregate(apr_to_jun_v2$ride_length_secs ~ apr_to_jun_v2$member_casual, FUN = median)

##   apr_to_jun_v2$member_casual apr_to_jun_v2$ride_length_secs
## 1                      casual                           1144
## 2                      member                            654

MEMBERS Vs CASUAL MAXIMUM TRIP TIME.

aggregate(apr_to_jun_v2$ride_length_secs ~ apr_to_jun_v2$member_casual, FUN = max)

##   apr_to_jun_v2$member_casual apr_to_jun_v2$ride_length_secs
## 1                      casual                          86313
## 2                      member                          84355

MEMBERS Vs CASUAL AVERAGE TRIP.

aggregate(apr_to_jun_v2$ride_length_secs ~ apr_to_jun_v2$member_casual, FUN = mean)

##   apr_to_jun_v2$member_casual apr_to_jun_v2$ride_length_secs
## 1                      casual                      1940.8063
## 2                      member                       877.2253

AVERAGE RIDE TIME FOR EACH DAY FOR MEMBERS Vs CASUAL RIDERS.

aggregate(apr_to_jun_v2$ride_length_total ~ apr_to_jun_v2$member_casual + apr_to_jun_v2$weekday., FUN = mean)

##    apr_to_jun_v2$member_casual apr_to_jun_v2$weekday.
## 1                       casual                    Sun
## 2                       member                    Sun
## 3                       casual                    Mon
## 4                       member                    Mon
## 5                       casual                    Tue
## 6                       member                    Tue
## 7                       casual                    Wed
## 8                       member                    Wed
## 9                       casual                    Thu
## 10                      member                    Thu
## 11                      casual                    Fri
## 12                      member                    Fri
## 13                      casual                    Sat
## 14                      member                    Sat
##    apr_to_jun_v2$ride_length_total
## 1                         36.76020
## 2                         16.85183
## 3                         32.29270
## 4                         13.99979
## 5                         30.60623
## 6                         13.93400
## 7                         28.33423
## 8                         13.77299
## 9                         27.86540
## 10                        13.65438
## 11                        29.93355
## 12                        14.09953
## 13                        34.38729
## 14                        16.21189

apr_to_jun_v2 %>% 
  group_by(member_casual, weekday.) %>%  
  summarise(number_of_rides = n(),average_duration = mean(ride_length_total)) %>%       
  arrange(member_casual, weekday.)

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

## # A tibble: 14 × 4
## # Groups:   member_casual [2]
##    member_casual weekday. number_of_rides average_duration
##    <fct>         <ord>              <int>            <dbl>
##  1 casual        Sun               133179             36.8
##  2 casual        Mon                69867             32.3
##  3 casual        Tue                70259             30.6
##  4 casual        Wed                67159             28.3
##  5 casual        Thu                61054             27.9
##  6 casual        Fri                86280             29.9
##  7 casual        Sat               141919             34.4
##  8 member        Sun                93641             16.9
##  9 member        Mon                94734             14.0
## 10 member        Tue               106353             13.9
## 11 member        Wed               108626             13.8
## 12 member        Thu                94294             13.7
## 13 member        Fri                99013             14.1
## 14 member        Sat                99008             16.2

DATA VISUALIZATIONS AND SUMMARY

COUNT ‘member_casual’ FOR PIE CHART

CREATE DATA FRAME FOR PIE CHART

MEMBER Vs CASUAL APR TO JUN PIE CHART

apr_to_jun_v2_tot <- apr_to_jun_v2 %>% 
  group_by(member_casual) %>% 
  summarise(number_of_rides = n()) 

pie_cvm <- data.frame(group = c("casual", "member"), value = c(629717, 695669))

ggplot(pie_cvm, aes(x = "", y = value, fill = group)) +
  geom_col(width = 1) +
  coord_polar("y") +
  geom_text(aes(label = round(value, 3)), position = position_stack(vjust = 0.5))+
  scale_fill_brewer(palette = "Dark2")+
  labs(title = "April to June 2021 Totals.")+
  theme_economist()

MEMBER Vs CASUAL APR TO JUN DAILY TOTALS.

apr_to_jun_v2 %>% 
  group_by(member_casual, weekday.) %>% 
  summarise(number_of_rides = n()) %>% 
  arrange(member_casual, weekday.)  %>% 
  ggplot(aes(x = weekday., y = number_of_rides, fill = member_casual)) +
  scale_fill_brewer(palette = "Dark2")+
  labs(title = "Member Vs Casual, April to June 2021 Daily Totals.",
       x = "Weekday",
       y = "Number of Rides")+
  geom_col(position = "dodge")+
  theme_economist()

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

SUMMARY MEMBER Vs CASUAL APR TO JUN DAILY TOTALS.

apr_to_jun_v2 %>% select(!c(ride_length_secs, ride_length_total ,started_at, start_date, weekday, rideable_type)) %>% tbl_summary(by = member_casual)

Characteristic	casual, N = 629,717¹	member, N = 695,669¹
weekday.
Sun	133,179 (21%)	93,641 (13%)
Mon	69,867 (11%)	94,734 (14%)
Tue	70,259 (11%)	106,353 (15%)
Wed	67,159 (11%)	108,626 (16%)
Thu	61,054 (9.7%)	94,294 (14%)
Fri	86,280 (14%)	99,013 (14%)
Sat	141,919 (23%)	99,008 (14%)
¹ n (%)

MEMBER Vs CASUAL APR TO JUN RIDEABLE TYPE.

apr_to_jun_v2 %>% 
  group_by(member_casual, rideable_type) %>% 
  summarise(number_of_rides = n()) %>% 
  arrange(member_casual, rideable_type)  %>% 
  ggplot(aes(x = rideable_type, y = number_of_rides, fill = member_casual)) +
  scale_fill_brewer(palette = "Dark2")+
  labs(title = "Member Vs Casual, April to June 2021 Rideable Type.",
       x = "Rideable Type",
       y = "Number of Bikes")+
  geom_col(position = "dodge")+
  theme_economist()

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

SUMMARY MEMBER Vs CASUAL APR TO JUN RIDEABLE TYPE.

apr_to_jun_v2 %>% select(!c(ride_length_secs,ride_length_total ,started_at, start_date, weekday, weekday.)) %>% tbl_summary(by = member_casual)

Characteristic	casual, N = 629,717¹	member, N = 695,669¹
rideable_type
classic_bike	374,133 (59%)	558,445 (80%)
docked_bike	117,966 (19%)	0 (0%)
electric_bike	137,618 (22%)	137,224 (20%)
¹ n (%)

RIDEABLE TYPE APR TO JUN DAILY TOTALS.

apr_to_jun_v2 %>% 
  group_by(weekday., rideable_type) %>% 
  summarise(number_of_rides = n()) %>% 
  arrange(weekday.)  %>% 
  ggplot(aes(x = weekday., y = number_of_rides, fill = rideable_type)) +
  scale_fill_brewer(palette = "Dark2")+
  facet_wrap(~rideable_type)+
  labs(title = "Rideable Type April to June 2021 Daily Totals.",
       x = "Day Of Week",
       y = "Number of Bikes")+
  geom_col(position = "dodge")+
  theme_economist()

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

RIDEABLE TYPE APR TO JUN DAILY TOTALS.

apr_to_jun_v2 %>% 
  group_by(weekday., rideable_type) %>% 
  summarise(number_of_rides = n()) %>% 
  arrange(weekday.)  %>% 
  ggplot(aes(x = weekday., y = number_of_rides, fill = rideable_type)) +
  scale_fill_brewer(palette = "Dark2")+
  labs(title = "Rideable Type April to June 2021 Daily Totals.",
       x = "Day Of Week",
       y = "Number of Bikes")+
  geom_col(position = "dodge")+
  theme_economist()

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

SUMMARY RIDEABLE TYPE APR TO JUN DAILY TOTALS.

apr_to_jun_v2 %>% select(!c(ride_length_secs,ride_length_total ,started_at, start_date, weekday, member_casual )) %>% tbl_summary(by = rideable_type)

Characteristic	classic_bike, N = 932,578¹	docked_bike, N = 117,966¹	electric_bike, N = 274,842¹
weekday.
Sun	158,511 (17%)	28,175 (24%)	40,134 (15%)
Mon	116,292 (12%)	13,366 (11%)	34,943 (13%)
Tue	125,954 (14%)	12,262 (10%)	38,396 (14%)
Wed	126,066 (14%)	10,531 (8.9%)	39,188 (14%)
Thu	109,086 (12%)	9,562 (8.1%)	36,700 (13%)
Fri	127,996 (14%)	15,605 (13%)	41,692 (15%)
Sat	168,673 (18%)	28,465 (24%)	43,789 (16%)
¹ n (%)

RIDEABLE TYPE APR TO JUN TOTALS.

apr_to_jun_v2 %>% 
  group_by(rideable_type) %>% 
  summarise(number_of_rides = n()) %>% 
  arrange(rideable_type)  %>% 
  ggplot(aes(x = rideable_type, y = number_of_rides, fill = rideable_type)) +
  scale_fill_brewer(palette = "Dark2")+
  labs(title = "Rideable Type April to June 2021 Totals.",
       x = "Rideable Type",
       y = "Number of Bikes")+
  geom_col(position = "dodge")+
  theme_economist()

SUMMARY RIDEABLE TYPE APR TO JUN TOTALS.

apr_to_jun_v2 %>% select(!c(ride_length_secs,ride_length_total ,started_at, start_date, weekday, weekday., member_casual)) %>% tbl_summary()

Characteristic	N = 1,325,386¹
rideable_type
classic_bike	932,578 (70%)
docked_bike	117,966 (8.9%)
electric_bike	274,842 (21%)
¹ n (%)

MORE TO LEARN

SESSION INFORMATION

sessionInfo()

## R version 4.2.3 (2023-03-15 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8 
## [2] LC_CTYPE=English_United Kingdom.utf8   
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggthemes_4.2.4     RColorBrewer_1.1-3 scales_1.2.1       gtsummary_1.7.1   
##  [5] conflicted_1.2.0   janitor_2.2.0      skimr_2.1.5        here_1.0.1        
##  [9] hms_1.1.3          data.table_1.14.8  lubridate_1.9.2    forcats_1.0.0     
## [13] stringr_1.5.0      dplyr_1.1.2        purrr_1.0.1        readr_2.1.4       
## [17] tidyr_1.3.0        tibble_3.2.1       ggplot2_3.4.2      tidyverse_2.0.0   
## 
## loaded via a namespace (and not attached):
##  [1] rprojroot_2.0.3      digest_0.6.29        utf8_1.2.2          
##  [4] R6_2.5.1             repr_1.1.6           evaluate_0.16       
##  [7] highr_0.9            pillar_1.9.0         rlang_1.1.0         
## [10] rstudioapi_0.14      jquerylib_0.1.4      rmarkdown_2.22      
## [13] labeling_0.4.2       munsell_0.5.0        compiler_4.2.3      
## [16] xfun_0.38            pkgconfig_2.0.3      base64enc_0.1-3     
## [19] htmltools_0.5.5      tidyselect_1.2.0     fansi_1.0.3         
## [22] crayon_1.5.1         tzdb_0.3.0           withr_2.5.0         
## [25] commonmark_1.9.0     grid_4.2.3           jsonlite_1.8.4      
## [28] gtable_0.3.0         lifecycle_1.0.3      magrittr_2.0.3      
## [31] cli_3.6.1            stringi_1.7.8        cachem_1.0.6        
## [34] farver_2.1.1         broom.helpers_1.13.0 snakecase_0.11.0    
## [37] xml2_1.3.3           bslib_0.4.0          generics_0.1.3      
## [40] vctrs_0.6.1          tools_4.2.3          glue_1.6.2          
## [43] markdown_1.5         fastmap_1.1.0        yaml_2.3.5          
## [46] timechange_0.1.1     colorspace_2.0-3     gt_0.9.0            
## [49] memoise_2.0.1        knitr_1.39           sass_0.4.6

CYCLISTIC BIKES APR TO JUN 2021

Stephen Williams

2023-07-01

SETTING UP ENVIRONMENT.

INSTALL PACKAGES.

SCIENTIFIC NOTATION RUINING YOUR GGPLOT CHARTS? TRY THE LINE OF CODE BELOW

USE ‘getwd()’ FUNCTION TO DISPLAY WORKING DIRECTORY.

USE ‘setwd()’ FUNCTION TO SET WORKING DIRECTORY TO SIMPLIFY CALLS TO DATA.

USE ‘spec_csv()’ FUNCTION TO CHECK THE DATA TYPES BEFORE READING THE DATA.

NOTICE ‘started_at’ AND ‘ended_at’ COLUMNS ARE ‘datetime’ DATA TYPE.

UPLOAD DATASETS divvy-trip-data.csv FILES.

USE ‘bind_rows()’ FUNCTION TO STACK DATA FRAMES INTO ONE BIG DATA FRAME.

CHECK COLUMNS.

USE ‘glimpse()’ FUNCTION TO GET A BETTER UNDERSTANDING OF THE DATA.

Rows: 1,598,458 Columns: 13

COLUMNS ‘started_at’ AND ‘ended_at’ ARE NOW ‘character’ DATA TYPE.

COLUMNS ‘end_station_name’ AND ‘end_station_id’ HAVE BLANK ROWS THAT NEED TO BE REMOVED.

USE ‘str()’ FUNCTION TO SEE LIST OF COLUMNS AND DATA TYPES NUMERIC, CHARACTER, DATETIME ETC.

‘data.frame’: 1598458 obs. of 13 variables:

USE TIDYR TO SEPARATE “started_at” COLUMN TO A NEW COLUMN CALLED “start_date” and “start_time”.

USE TIDYR TO SEPARATE “ended_at” COLUMN TO A NEW COLUMN CALLED “end_date” and “end_time”.

CHECK NEW COLUMNS.

‘’data.frame’: 1598458 obs. of 17 variables:

EXPLORE AND MANIPULATE DATA FRAME APR_TO_JUN.

COLUMN RIDEABLE TYPE.

EXPLORE…CHARACTER VARIABLE TYPE IN “rideable_type” COLUMN.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

USE ‘unique ()’ FUNCTION TO FIND INDIVIDUAL VALUES IN COLUMN.

HOW MANY OBSERVATIONS FALL UNDER EACH USER TYPE?

BAR PLOT OF DATA DISTRIBUTION OF ‘rideable_type’ COLUMN.

CHANGE VARIABLE FROM CHARACTER TO FACTOR.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

USE ‘levels’ FUNCTION TO CHECK FACTOR.

NOTE RIDEABLE TYPE IS NOW A FACTOR.

COLUMN STARTED_AT AND ENDED_AT.

EXPLORE…CHARACTER VARIABLE TYPE IN “started_at” AND ended_at” COLUMN.

DATA TYPE IN COLUMN “started_at” AND “end_at” WAS DATETIME BEFORE UPLOADING.

CONVERT “started_at” AND “ended_at” COLUMN FROM CHARACTER TO DATETIME

CONVERT “start_date” COLUMN FROM CHARACTER TO DATE FORMAT.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

CONVERT “end_date” COLUMN FROM CHARACTER TO DATE FORMAT.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

USE ‘str()’ FUNCTION TO SEE LIST OF COLUMNS AND DATA TYPES NUMERIC, CHARACTER, DATETIME ETC.

‘started_at’AND ’ended_at’ CHARACTER DATA TYPE IS NOW POSIXlt.

‘data.frame’: 1598458 obs. of 17 variables:

COLUMN START_STATION_NAME START_STATION_ID END_STATION_NAME AND END_STATION_ID.

EXPLORE…CHARACTER VARIABLE TYPE IN “start_staion_name” AND “end_staion_name”

REPLACE ALL BLANK VALUES IN “start_station_name” COLUMN WITH NA VALUES.

REPLACE ALL BLANK VALUES IN “start_station_id” COLUMN WITH NA VALUES.

REPLACE ALL BLANK VALUES IN “end_station_name” COLUMN WITH NA VALUES.

REPLACE ALL BLANK VALUES IN “end_station_id” COLUMN WITH NA VALUES.

REMOVE ROWS WITH NA VALUES IN ALL COLUMNS.

‘data.frame’: 1357979 obs. of 17 variables:

COLUMN MEMBER_CASUAL.

EXPLORE…CHARACTER VARIABLE TYPE IN “member_casual” COLUMN.

USE ‘unique ()’ FUNCTION TO FIND INDIVIDUAL VALUES IN COLUMN.

HOW MANY OBSERVATIONS FALL UNDER EACH USER TYPE?

BAR PLOT OF DATA DISTRIBUTION OF ‘member_casual’ COLUMN.

CHANGE VARIABLE FROM CHARACTER TO FACTOR.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

USE ‘levels’ FUNCTION TO CHECK FACTOR.

NOTE MEMBER CASUAL IS NOW A FACTOR.

ADD A CALCULATED FIELD FOR NEW COLUMN “ride_length_secs”.

CHECK DATA TYPE.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

CONVERT “ride_length_secs” FROM DIFFTIME TO NUMERIC TO RUN CALCULATIONS ON THE DATA.

CHECK DATA TYPE.

CREATE NEW COLUMN “ride_length_total” USING MUTATE FUNCTION.

CHECK DATA TYPE.

ADD COLUMN FOR DAY OF WEEK.

NUMERIC VALUE DAY OF WEEK SUNDAY = 1 MONDAY = 2 TUESDAY = 3 ETC, ETC…

CHARACTER DAY OF WEEK USING ABBREVIATED LABELS MON,TUE,WED ETC ETC…

CHANGE ‘weekday’ DATA TYPE.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

USE ‘levels’ FUNCTION TO CHECK FACTOR.

NOTE WEEKDAY. IS AN ORDERED FACTOR.

EXPLORE NUMERIC VARIABLE TYPE IN “weekday” COLUMN.

USE ‘class’ FUNCTION TO CHECK DATA TYPE IN COLUMN.

USE ‘summary()’ FUNCTION TO SUMMARIZE VALUES IN DATA FRAME.

BOX PLOT AKA IS A GRAPHICAL REPRESENTATION TO SUMMARIZE DATA AND IDENTIFY OUTLIERS.