Ride_Share
Onyedika Obiakarije
12/7/2021
Cyclistics Bike-Share Analysis Case Study
This is the first case study of Google Capstone. In this case study, I will perform an analysis. I will be following the data analysis process: ask, prepare, process, analyze, share, and act.
Asking Phases.
Scenario
You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.
Characters and teams
Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assertive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.
Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.
Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them.
Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.
Guiding Questions
● What is the problem you are trying to solve?
Build a profile for annual member and convert casual riders into annual members.
● How can your insights drive business decisions?
My insights will help the marketing team increase annual member.
Prepare
Data from https://divvy-tripdata.s3.amazonaws.com/index.html. The data I will be using is from December 2020 to November 2021.
Guiding Questions
● Where is your data located?
Data is located here https://divvy-tripdata.s3.amazonaws.com/index.html.
● How is the data organized?
The data set is separated by months with identical label columns.
● Are there issues with bias or credibility in this data? Does your data ROCCC?
I do not think there are any bias because the dataset belongs to their client.The data is credibility and it is reliable, original, comprehensive, current and cited.
● How are you addressing licensing, privacy, security, and accessibility?
The data do not have any personal information and the company handle the licensing.
● How did you verify the data’s integrity?
All dataset has identical columns and has the correct type of data.
● How does it help you answer your question?
It has relevant data to answer my questions.
● Are there any problems with the data?
Process
library(tidyverse) #for data wrangling## Warning: package 'tidyverse' was built under R version 4.0.5## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.0.5## Warning: package 'tibble' was built under R version 4.0.5## Warning: package 'tidyr' was built under R version 4.0.5## Warning: package 'readr' was built under R version 4.0.5## Warning: package 'purrr' was built under R version 4.0.5## Warning: package 'dplyr' was built under R version 4.0.5## Warning: package 'forcats' was built under R version 4.0.5## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(lubridate) #for time and date## Warning: package 'lubridate' was built under R version 4.0.5##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(dplyr) #for bind_row
library(ggplot2) #for visualize# Set working directories.
setwd("C:/Users/harry/Desktop/Google Capstone Project")# load data sets.
Dec_2020 <- read_csv("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.Jan_2021 <- read_csv("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.Feb_2021 <- read_csv("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.Mar_2021 <- read_csv("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.Apr_2021 <- read_csv("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.May_2021 <- read_csv("202104-divvy-tripdata.csv")## Rows: 337230 Columns: 13## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at##
## 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.Jun_2021 <- read_csv("202105-divvy-tripdata.csv")## Rows: 531633 Columns: 13## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at##
## 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.Jul_2021 <- read_csv("202106-divvy-tripdata.csv")## Rows: 729595 Columns: 13## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at##
## 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.Aug_2021 <- read_csv("202107-divvy-tripdata.csv")## Rows: 822410 Columns: 13## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at##
## 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.Sep_2021 <- read_csv("202108-divvy-tripdata.csv")## Rows: 804352 Columns: 13## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (7): ride_id, rideable_type, start_station_name, start_station_id, end_...
## dbl (4): start_lat, start_lng, end_lat, end_lng
## dttm (2): started_at, ended_at##
## 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.Oct_2021 <- read_csv("202109-divvy-tripdata.csv")## Rows: 756147 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.Nov_2021 <- read_csv("202110-divvy-tripdata.csv")## Rows: 631226 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.# a quick check to see if all column names are named equally
colnames(Dec_2020)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(Mar_2021)## [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(Jun_2021)## [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(Sep_2021)## [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"# Convert some columns to characters.
Dec_2020 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Jan_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Feb_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Mar_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Apr_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
May_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Jun_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Jul_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Aug_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Sep_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Oct_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))
Nov_2021 <- mutate(Dec_2020, start_station_id = as.character(start_station_id),
end_station_id = as.character(end_station_id))#Combining the data sets into Seasons
Winter <- bind_rows(Dec_2020, Jan_2021, Feb_2021) #as q1
Spring <- bind_rows(Mar_2021,Apr_2021, May_2021) #as q2
Summer <- bind_rows(Jun_2021, Jul_2021, Aug_2021) #as q3
Autumn <- bind_rows(Sep_2021, Oct_2021, Nov_2021) #as q4#Inspect the data
str(Winter)## spec_tbl_df [779,148 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:779148] "BD0A6FF6FFF9B921" "96A7A7A4BDE4F82D" "C61526D06582BDC5" "E533E89C32080B9E" ...
## $ rideable_type : chr [1:779148] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:779148], format: "2020-11-01 13:36:00" "2020-11-01 10:03:26" ...
## $ ended_at : POSIXct[1:779148], format: "2020-11-01 13:45:40" "2020-11-01 10:14:45" ...
## $ start_station_name: chr [1:779148] "Dearborn St & Erie St" "Franklin St & Illinois St" "Lake Shore Dr & Monroe St" "Leavitt St & Chicago Ave" ...
## $ start_station_id : chr [1:779148] "110" "672" "76" "659" ...
## $ end_station_name : chr [1:779148] "St. Clair St & Erie St" "Noble St & Milwaukee Ave" "Federal St & Polk St" "Stave St & Armitage Ave" ...
## $ end_station_id : chr [1:779148] "211" "29" "41" "185" ...
## $ start_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:779148] -87.6 -87.6 -87.6 -87.7 -87.6 ...
## $ end_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:779148] -87.6 -87.7 -87.6 -87.7 -87.6 ...
## $ member_casual : chr [1:779148] "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(Spring)## spec_tbl_df [779,148 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:779148] "BD0A6FF6FFF9B921" "96A7A7A4BDE4F82D" "C61526D06582BDC5" "E533E89C32080B9E" ...
## $ rideable_type : chr [1:779148] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:779148], format: "2020-11-01 13:36:00" "2020-11-01 10:03:26" ...
## $ ended_at : POSIXct[1:779148], format: "2020-11-01 13:45:40" "2020-11-01 10:14:45" ...
## $ start_station_name: chr [1:779148] "Dearborn St & Erie St" "Franklin St & Illinois St" "Lake Shore Dr & Monroe St" "Leavitt St & Chicago Ave" ...
## $ start_station_id : chr [1:779148] "110" "672" "76" "659" ...
## $ end_station_name : chr [1:779148] "St. Clair St & Erie St" "Noble St & Milwaukee Ave" "Federal St & Polk St" "Stave St & Armitage Ave" ...
## $ end_station_id : chr [1:779148] "211" "29" "41" "185" ...
## $ start_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:779148] -87.6 -87.6 -87.6 -87.7 -87.6 ...
## $ end_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:779148] -87.6 -87.7 -87.6 -87.7 -87.6 ...
## $ member_casual : chr [1:779148] "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(Summer)## spec_tbl_df [779,148 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:779148] "BD0A6FF6FFF9B921" "96A7A7A4BDE4F82D" "C61526D06582BDC5" "E533E89C32080B9E" ...
## $ rideable_type : chr [1:779148] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:779148], format: "2020-11-01 13:36:00" "2020-11-01 10:03:26" ...
## $ ended_at : POSIXct[1:779148], format: "2020-11-01 13:45:40" "2020-11-01 10:14:45" ...
## $ start_station_name: chr [1:779148] "Dearborn St & Erie St" "Franklin St & Illinois St" "Lake Shore Dr & Monroe St" "Leavitt St & Chicago Ave" ...
## $ start_station_id : chr [1:779148] "110" "672" "76" "659" ...
## $ end_station_name : chr [1:779148] "St. Clair St & Erie St" "Noble St & Milwaukee Ave" "Federal St & Polk St" "Stave St & Armitage Ave" ...
## $ end_station_id : chr [1:779148] "211" "29" "41" "185" ...
## $ start_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:779148] -87.6 -87.6 -87.6 -87.7 -87.6 ...
## $ end_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:779148] -87.6 -87.7 -87.6 -87.7 -87.6 ...
## $ member_casual : chr [1:779148] "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(Autumn)## spec_tbl_df [779,148 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:779148] "BD0A6FF6FFF9B921" "96A7A7A4BDE4F82D" "C61526D06582BDC5" "E533E89C32080B9E" ...
## $ rideable_type : chr [1:779148] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : POSIXct[1:779148], format: "2020-11-01 13:36:00" "2020-11-01 10:03:26" ...
## $ ended_at : POSIXct[1:779148], format: "2020-11-01 13:45:40" "2020-11-01 10:14:45" ...
## $ start_station_name: chr [1:779148] "Dearborn St & Erie St" "Franklin St & Illinois St" "Lake Shore Dr & Monroe St" "Leavitt St & Chicago Ave" ...
## $ start_station_id : chr [1:779148] "110" "672" "76" "659" ...
## $ end_station_name : chr [1:779148] "St. Clair St & Erie St" "Noble St & Milwaukee Ave" "Federal St & Polk St" "Stave St & Armitage Ave" ...
## $ end_station_id : chr [1:779148] "211" "29" "41" "185" ...
## $ start_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:779148] -87.6 -87.6 -87.6 -87.7 -87.6 ...
## $ end_lat : num [1:779148] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:779148] -87.6 -87.7 -87.6 -87.7 -87.6 ...
## $ member_casual : chr [1:779148] "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>#Combine all data into one
all_trips <- bind_rows(Winter, Spring, Summer, Autumn)# Remove unnecessary columns
all_trips <- subset(all_trips, select = -c(start_lat, start_lng, end_lat, end_lng))colnames(all_trips) #Show colnames## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "member_casual"nrow(all_trips) #no_ of rows## [1] 3116592dim(all_trips) #Dimensions of the data## [1] 3116592 9head(all_trips) #load the first 6 row of the data## # A tibble: 6 x 9
## ride_id rideable_type started_at ended_at start_station_n~
## <chr> <chr> <dttm> <dttm> <chr>
## 1 BD0A6FF6FFF9B921 electric_bike 2020-11-01 13:36:00 2020-11-01 13:45:40 Dearborn St & E~
## 2 96A7A7A4BDE4F82D electric_bike 2020-11-01 10:03:26 2020-11-01 10:14:45 Franklin St & I~
## 3 C61526D06582BDC5 electric_bike 2020-11-01 00:34:05 2020-11-01 01:03:06 Lake Shore Dr &~
## 4 E533E89C32080B9E electric_bike 2020-11-01 00:45:16 2020-11-01 00:54:31 Leavitt St & Ch~
## 5 1C9F4EF18C168C60 electric_bike 2020-11-01 15:43:25 2020-11-01 16:16:52 Buckingham Foun~
## 6 7259585D8276D338 electric_bike 2020-11-14 15:55:17 2020-11-14 16:44:38 Wabash Ave & 16~
## # ... with 4 more variables: start_station_id <chr>, end_station_name <chr>,
## # end_station_id <chr>, member_casual <chr>summary(all_trips) #Shows the statistical summary## ride_id rideable_type started_at
## Length:3116592 Length:3116592 Min. :2020-11-01 00:00:08
## Class :character Class :character 1st Qu.:2020-11-06 20:13:53
## Mode :character Mode :character Median :2020-11-10 16:17:15
## Mean :2020-11-13 05:20:02
## 3rd Qu.:2020-11-19 15:59:47
## Max. :2020-11-30 23:56:22
## ended_at start_station_name start_station_id
## Min. :2020-11-01 00:02:20 Length:3116592 Length:3116592
## 1st Qu.:2020-11-06 20:36:08 Class :character Class :character
## Median :2020-11-10 16:32:15 Mode :character Mode :character
## Mean :2020-11-13 05:39:45
## 3rd Qu.:2020-11-19 16:18:54
## Max. :2020-12-01 17:28:22
## end_station_name end_station_id member_casual
## Length:3116592 Length:3116592 Length:3116592
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## #drop values with NA
all_trips <- na.omit(all_trips)
# Filter out started_at data that are less than ended_at data, avoiding negative value
all_trips <- all_trips %>% filter(all_trips$started_at < all_trips$ended_at)
#Create a new column and name it ride_length
all_trips$ride_length <- all_trips$ended_at - all_trips$started_at
#Format the colummn to hour, minutes, and seconds
all_trips$ride_length <- hms::hms(seconds_to_period(all_trips$ride_length))
#Convert ride_length to number for easy calculation.
all_trips <- mutate(all_trips, ride_length = as.numeric(ride_length))
#Create a new column called day_of_week
all_trips$day_of_week <- wday(all_trips$started_at, label = TRUE)
#Save file as a CSV
#all_trips %>% write.csv("clean_ride.csv")Guiding Questions
● What tools are you choosing and why?
I am using R. I am using R because of the amount of data and to expand my knowledge in R.
● Have you ensured your data’s integrity?
Yes, the dataset has identical columns.
● What steps have you taken to ensure that your data is clean?
I converted some columns, so that I would be able to merge it. Next, I remove the NA data and unnecessary columns. I filter out the started_at data that are less than the ended_at data, then I create two new columns and named them ride_length and day_of_week.Next, I formatted the ride_length column to hour, minutes, and seconds.
● How can you verify that your data is clean and ready to analyze?
● Have you documented your cleaning process so you can review and share those results?
Yes, all document are store in R markdown.
Analyze
# Descriptive analysis on ride_length
mean(all_trips$ride_length) #average ride## [1] 1203.182quantile(all_trips$ride_length, .25) #Q1## 25%
## 388quantile(all_trips$ride_length, .50) #Median## 50%
## 692#median(all_trips$ride_length) #midpoint
quantile(all_trips$ride_length, .75) #Q3## 75%
## 1285max(all_trips$ride_length) #longest ride## [1] 2156040min(all_trips$ride_length) #shortest ride## [1] 1sd(all_trips$ride_length) #Standard Deviation ## [1] 8762.412summary(all_trips$ride_length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 388 692 1203 1285 2156040#Compare ride_length of member_casual
aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = mean)## all_trips$member_casual all_trips$ride_length
## 1 casual 2008.8105
## 2 member 809.4879aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = median)## all_trips$member_casual all_trips$ride_length
## 1 casual 1028
## 2 member 584aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = max)## all_trips$member_casual all_trips$ride_length
## 1 casual 2156040
## 2 member 89996aggregate(all_trips$ride_length ~ all_trips$member_casual, FUN = min)## all_trips$member_casual all_trips$ride_length
## 1 casual 1
## 2 member 1#Average ride_length by each day for member_casual column.
aggregate(all_trips$ride_length ~ all_trips$member_casual + all_trips$day_of_week, FUN = mean)## all_trips$member_casual all_trips$day_of_week all_trips$ride_length
## 1 casual Sun 2459.6935
## 2 member Sun 882.9196
## 3 casual Mon 1725.7497
## 4 member Mon 745.4346
## 5 casual Tue 1728.4028
## 6 member Tue 761.2670
## 7 casual Wed 1535.9677
## 8 member Wed 754.6756
## 9 casual Thu 1827.8159
## 10 member Thu 779.3516
## 11 casual Fri 1919.1907
## 12 member Fri 816.4063
## 13 casual Sat 2236.8528
## 14 member Sat 934.1594#Number of rides by ride type
all_trips %>%
mutate(weekday = wday(started_at, label = TRUE)) %>% #weekday field
group_by(member_casual, weekday) %>% #group by usertype and weekday
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>% #average duration
arrange(member_casual, weekday) %>% #Sort
#visualization
ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) +
geom_col(position = "dodge")## `summarise()` has grouped output by 'member_casual'. You can override using the `.groups` argument.
#Average duration
all_trips %>%
mutate(weekday = wday(started_at, label = TRUE)) %>%
group_by(member_casual, weekday) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, weekday) %>%
ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +
geom_col(position = "dodge")## `summarise()` has grouped output by 'member_casual'. You can override using the `.groups` argument.
#Distributed within the weekday
all_trips %>%
group_by(day_of_week) %>%
summarise(count = length(ride_id),
'%' = (length(ride_id) / nrow(all_trips)) * 100,
'member' = (sum(member_casual == "member") / length(ride_id)) * 100,
'casual' = (sum(member_casual == "casual") / length(ride_id)) * 100,
'Member x Casual Perc Diferrent' = member - casual)## # A tibble: 7 x 6
## day_of_week count `%` member casual `Member x Casual Perc Diferrent`
## <ord> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Sun 391584 14.7 59.1 40.9 18.2
## 2 Mon 408768 15.3 74.2 25.8 48.4
## 3 Tue 312036 11.7 74.5 25.5 49.0
## 4 Wed 322392 12.1 74.7 25.3 49.4
## 5 Thu 350700 13.2 71.1 28.9 42.3
## 6 Fri 403296 15.1 67.2 32.8 34.3
## 7 Sat 474216 17.8 54.9 45.1 9.89ggplot(all_trips, aes(day_of_week, fill=member_casual)) +
geom_bar() +
labs(x="Weekday", title = "Distibution by weekday") 
#coord_flip()#Distributed by ride type
all_trips %>%
group_by(rideable_type) %>%
summarise(count = length(ride_id),
'%' = (length(ride_id) / nrow(all_trips)) * 100,
'member' = (sum(member_casual == "member") / length(ride_id)) * 100,
'casual' = (sum(member_casual == "casual") / length(ride_id)) * 100,
'Member x Casual Perc Diferrent' = member - casual)## # A tibble: 2 x 6
## rideable_type count `%` member casual `Member x Casual Perc Diferrent`
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 docked_bike 1805208 67.8 69.6 30.4 39.2
## 2 electric_bike 857784 32.2 62.1 37.9 24.2ggplot(all_trips, aes(rideable_type, fill=member_casual)) +
geom_bar() +
labs(x="Riderable type", title = "Distibution by type of bikes") 
Guiding Questions
● How should you organize your data to perform analysis on it?
All data sets should be combine into one data set.
● Has your data been properly formatted?
Yes, my data has been properly formatted. All the column has their collect data type.
● What surprises did you discover in the data?
Casual have more ride than members.
● What trends or relationships did you find in the data?