Cyclistic project 2023-24
Juan Aurelio García Morata
2024-09-15
Chicago
INTRODUCTION
This is part of the final project of the Google Coursera Data analyst course. The final assignment for the course requires to analyze data from a company and publish the results.
Cyclistic is a fictitious bike sharing company based in Chicago. I am working as a junior data analyst as part of the marketing team and I have to analyze the previous year’s data and present the results to the marketing department.
SCENARIO
There are two types of riders at the moment: members and casual riders. Cyclistic has realized that annual members are more profitable than casual riders. Therefore, the company would like to maximize the amount of members. The company would like to make membership more attractive for casual riders.
TASK
The purpose of my analysis is to find out how different is the use of bikes for members and casual riders.
PREPARATION
The data can be found at link and the license is at link. I downloaded the last 12 months of data from July 20203 to June 20204. The data is too large to work with it on spreadsheets so I have done my analysis using R.
=====================
STEP 1: COLLECT DATA
=====================
Getting necessary packages for analysis
library(tidyverse) #helps wrangle data## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── 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 errorslibrary(lubridate) #helps wrangle date attributes
library(ggplot2) #helps visualize data
library(dplyr) #helps with data manipulation
library(fixr) #helps fix data
library(readr) #helps reading csv filesSet working directory
I granted access to R studio to my desktop so I can work easily with the files I need.
setwd("C:/Users/Juan/Desktop/cases/Bikes/month")Import monthly csv files
july23 <- read_csv("202307-divvy-tripdata.csv")## Rows: 767650 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.aug23 <- read_csv("202308-divvy-tripdata.csv")## Rows: 771693 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.sep23 <- read_csv("202309-divvy-tripdata.csv")## Rows: 666371 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.oct23 <- read_csv("202310-divvy-tripdata.csv")## Rows: 537113 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.nov23 <- read_csv("202311-divvy-tripdata.csv")## Rows: 362518 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.dec23 <- read_csv("202312-divvy-tripdata.csv")## Rows: 224073 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.jan24 <- read_csv("202401-divvy-tripdata.csv")## Rows: 144873 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.feb24 <- read_csv("202402-divvy-tripdata.csv")## Rows: 223164 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.marc24 <- read_csv("202403-divvy-tripdata.csv")## Rows: 301687 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.april24 <- read_csv("202404-divvy-tripdata.csv")## Rows: 415025 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.may24 <- read_csv("202405-divvy-tripdata.csv")## Rows: 609493 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.june24 <- read_csv("202406-divvy-tripdata.csv")## Rows: 710721 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.==== STEP 2: WRANGLE DATA AND COMBINE INTO A SINGLE FILE ====
Checking all column names match
colnames(july23)## [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(aug23)## [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(sep23)## [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(oct23)## [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(nov23)## [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(dec23)## [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(jan24)## [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(feb24)## [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(marc24)## [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(april24)## [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(may24)## [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(june24)## [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"All tables have matching columns so I can proceed to bind all the data in one table.
Bind data in one data frame
all_trips <- bind_rows(july23,aug23,sep23,oct23,nov23,dec23,jan24,feb24,marc24,april24,
may24,june24)
str(all_trips)## spc_tbl_ [5,734,381 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:5734381] "9340B064F0AEE130" "D1460EE3CE0D8AF8" "DF41BE31B895A25E" "9624A293749EF703" ...
## $ rideable_type : chr [1:5734381] "electric_bike" "classic_bike" "classic_bike" "electric_bike" ...
## $ started_at : POSIXct[1:5734381], format: "2023-07-23 20:06:14" "2023-07-23 17:05:07" ...
## $ ended_at : POSIXct[1:5734381], format: "2023-07-23 20:22:44" "2023-07-23 17:18:37" ...
## $ start_station_name: chr [1:5734381] "Kedzie Ave & 110th St" "Western Ave & Walton St" "Western Ave & Walton St" "Racine Ave & Randolph St" ...
## $ start_station_id : chr [1:5734381] "20204" "KA1504000103" "KA1504000103" "13155" ...
## $ end_station_name : chr [1:5734381] "Public Rack - Racine Ave & 109th Pl" "Milwaukee Ave & Grand Ave" "Damen Ave & Pierce Ave" "Clinton St & Madison St" ...
## $ end_station_id : chr [1:5734381] "877" "13033" "TA1305000041" "TA1305000032" ...
## $ start_lat : num [1:5734381] 41.7 41.9 41.9 41.9 42 ...
## $ start_lng : num [1:5734381] -87.7 -87.7 -87.7 -87.7 -87.7 ...
## $ end_lat : num [1:5734381] 41.7 41.9 41.9 41.9 42 ...
## $ end_lng : num [1:5734381] -87.7 -87.6 -87.7 -87.6 -87.6 ...
## $ member_casual : chr [1:5734381] "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>Now I have all my data in one table which is necessary for full analysis.
Drop unnecessary columns for analysis
Latitude and longitude are not neeede for the analysis
all_trips <- all_trips %>%
select(-c(start_lat, start_lng, end_lat, end_lng))==== STEP 3: CLEAN UP AND ADD DATA TO PREPARE FOR ANALYSIS ======
Inspect the new table that has been created
colnames(all_trips) #List of column names## [1] "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) #How many rows are in data frame?## [1] 5734381dim(all_trips) #Dimensions of the data frame?## [1] 5734381 9head(all_trips) #See the first 6 rows of data frame. Also tail(all_trips)## # A tibble: 6 × 9
## ride_id rideable_type started_at ended_at
## <chr> <chr> <dttm> <dttm>
## 1 9340B064F0AEE130 electric_bike 2023-07-23 20:06:14 2023-07-23 20:22:44
## 2 D1460EE3CE0D8AF8 classic_bike 2023-07-23 17:05:07 2023-07-23 17:18:37
## 3 DF41BE31B895A25E classic_bike 2023-07-23 10:14:53 2023-07-23 10:24:29
## 4 9624A293749EF703 electric_bike 2023-07-21 08:27:44 2023-07-21 08:32:40
## 5 2F68A6A4CDB4C99A classic_bike 2023-07-08 15:46:42 2023-07-08 15:58:08
## 6 9AEE973E6B941A9C classic_bike 2023-07-10 08:44:47 2023-07-10 08:49:41
## # ℹ 5 more variables: start_station_name <chr>, start_station_id <chr>,
## # end_station_name <chr>, end_station_id <chr>, member_casual <chr>str(all_trips) #See list of columns and data types (numeric, character, etc)## tibble [5,734,381 × 9] (S3: tbl_df/tbl/data.frame)
## $ ride_id : chr [1:5734381] "9340B064F0AEE130" "D1460EE3CE0D8AF8" "DF41BE31B895A25E" "9624A293749EF703" ...
## $ rideable_type : chr [1:5734381] "electric_bike" "classic_bike" "classic_bike" "electric_bike" ...
## $ started_at : POSIXct[1:5734381], format: "2023-07-23 20:06:14" "2023-07-23 17:05:07" ...
## $ ended_at : POSIXct[1:5734381], format: "2023-07-23 20:22:44" "2023-07-23 17:18:37" ...
## $ start_station_name: chr [1:5734381] "Kedzie Ave & 110th St" "Western Ave & Walton St" "Western Ave & Walton St" "Racine Ave & Randolph St" ...
## $ start_station_id : chr [1:5734381] "20204" "KA1504000103" "KA1504000103" "13155" ...
## $ end_station_name : chr [1:5734381] "Public Rack - Racine Ave & 109th Pl" "Milwaukee Ave & Grand Ave" "Damen Ave & Pierce Ave" "Clinton St & Madison St" ...
## $ end_station_id : chr [1:5734381] "877" "13033" "TA1305000041" "TA1305000032" ...
## $ member_casual : chr [1:5734381] "member" "member" "member" "member" ...summary(all_trips) #Statistical summary of data. Mainly for numeric## ride_id rideable_type started_at
## Length:5734381 Length:5734381 Min. :2023-07-01 00:00:00.00
## Class :character Class :character 1st Qu.:2023-08-27 15:55:29.00
## Mode :character Mode :character Median :2023-11-09 14:41:58.00
## Mean :2023-12-16 20:05:31.20
## 3rd Qu.:2024-04-23 17:55:40.00
## Max. :2024-06-30 23:55:17.06
## ended_at start_station_name start_station_id
## Min. :2023-07-01 00:01:26.00 Length:5734381 Length:5734381
## 1st Qu.:2023-08-27 16:19:14.00 Class :character Class :character
## Median :2023-11-09 14:55:49.00 Mode :character Mode :character
## Mean :2023-12-16 20:23:50.00
## 3rd Qu.:2024-04-23 18:08:50.00
## Max. :2024-06-30 23:59:57.93
## end_station_name end_station_id member_casual
## Length:5734381 Length:5734381 Length:5734381
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## Add columns that list the date, month, day, and year of each ride
all_trips$date <- as.Date(all_trips$started_at) #The default format is yyyy-mm-dd
all_trips$month <- format(as.Date(all_trips$date), "%m")
all_trips$day <- format(as.Date(all_trips$date), "%d")
all_trips$year <- format(as.Date(all_trips$date), "%Y")
all_trips$day_of_week <- format(as.Date(all_trips$date), "%A")Let’s create a new column for ride length and then clean the results we don’t need (negative and 0 values).
Add a “ride_length” calculation to all_trips (in seconds)
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)Convert “ride_length” from Factor to numeric so we can run calculations on the data
is.factor(all_trips$ride_length)## [1] FALSEall_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
is.numeric(all_trips$ride_length)## [1] TRUECheck negative values
check_for_negative_values(all_trips)Replace negative and 0 values with NA values
all_trips[all_trips <= 0] <- NA Remove rows with NA values
all_trips <- na.omit(all_trips)Remove negative and 0 values in data frame
all_trips_2 <- all_trips[apply(all_trips!=0, 1, all),]Check negative values again
check_for_negative_values(all_trips_2)## Data frame does not contain negative values.## NULLThe data is clean.
Find number of casual and members riders
table(all_trips_2$member_casual)##
## casual member
## 1503559 2770268We can see clearly that there are many more casual riders than members.
========= STEP 4: CONDUCT DESCRIPTIVE ANALYSIS =========
Descriptive analysis on ride_length (all figures in seconds)
mean(all_trips_2$ride_length) #straight average (total ride length / rides)## [1] 990.8093median(all_trips_2$ride_length) #midpoint number in the ascending array of ride lengths## [1] 604max(all_trips_2$ride_length) #longest ride## [1] 413473min(all_trips_2$ride_length) #shortest ride## [1] 0.155Condense the four lines above to one line using summary() on the specific attribute
summary(all_trips_2$ride_length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2 347.0 604.0 990.8 1082.0 413473.0Compare members and casual users
aggregate(all_trips_2$ride_length ~ all_trips_2$member_casual, FUN = mean)## all_trips_2$member_casual all_trips_2$ride_length
## 1 casual 1434.4350
## 2 member 750.0321aggregate(all_trips_2$ride_length ~ all_trips_2$member_casual, FUN = median)## all_trips_2$member_casual all_trips_2$ride_length
## 1 casual 796
## 2 member 529aggregate(all_trips_2$ride_length ~ all_trips_2$member_casual, FUN = max)## all_trips_2$member_casual all_trips_2$ride_length
## 1 casual 413473
## 2 member 89859aggregate(all_trips_2$ride_length ~ all_trips_2$member_casual, FUN = min)## all_trips_2$member_casual all_trips_2$ride_length
## 1 casual 0.155
## 2 member 0.263Order days of the week
all_trips_2$day_of_week <- ordered(all_trips_2$day_of_week, levels=c("domingo", "lunes", "martes", "miércoles", "jueves", "viernes", "sábado"))
aggregate(all_trips_2$ride_length ~ all_trips_2$member_casual + all_trips_2$day_of_week, FUN = mean)## all_trips_2$member_casual all_trips_2$day_of_week all_trips_2$ride_length
## 1 casual domingo 1658.9865
## 2 member domingo 846.7964
## 3 casual lunes 1406.8795
## 4 member lunes 717.3063
## 5 casual martes 1272.5568
## 6 member martes 723.1518
## 7 casual miércoles 1254.6979
## 8 member miércoles 724.1508
## 9 casual jueves 1225.2893
## 10 member jueves 709.9829
## 11 casual viernes 1380.3573
## 12 member viernes 733.7183
## 13 casual sábado 1609.3650
## 14 member sábado 835.3832Casual riders are using the bikes for a longer period of time at any dady of the week. This could be explained as many tourists to the city could be using the bikes for visiting.
Now, let’s run the average ride time by each day for members vs casual users
aggregate(all_trips_2$ride_length ~ all_trips_2$member_casual + all_trips_2$day_of_week, FUN = mean)## all_trips_2$member_casual all_trips_2$day_of_week all_trips_2$ride_length
## 1 casual domingo 1658.9865
## 2 member domingo 846.7964
## 3 casual lunes 1406.8795
## 4 member lunes 717.3063
## 5 casual martes 1272.5568
## 6 member martes 723.1518
## 7 casual miércoles 1254.6979
## 8 member miércoles 724.1508
## 9 casual jueves 1225.2893
## 10 member jueves 709.9829
## 11 casual viernes 1380.3573
## 12 member viernes 733.7183
## 13 casual sábado 1609.3650
## 14 member sábado 835.3832analyze ridership data by type and weekday
all_trips_2 %>%
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) ## `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
## <chr> <ord> <int> <dbl>
## 1 casual "do\\." 268375 1659.
## 2 casual "lu\\." 175574 1407.
## 3 casual "ma\\." 173707 1273.
## 4 casual "mi\\." 178181 1255.
## 5 casual "ju\\." 182745 1225.
## 6 casual "vi\\." 213507 1380.
## 7 casual "sá\\." 311470 1609.
## 8 member "do\\." 313737 847.
## 9 member "lu\\." 395057 717.
## 10 member "ma\\." 437338 723.
## 11 member "mi\\." 447921 724.
## 12 member "ju\\." 440940 710.
## 13 member "vi\\." 385370 734.
## 14 member "sá\\." 349905 835.analyze ridership data by type and weekday
all_trips_2 %>%
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) ## `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
## <chr> <ord> <int> <dbl>
## 1 casual "do\\." 268375 1659.
## 2 casual "lu\\." 175574 1407.
## 3 casual "ma\\." 173707 1273.
## 4 casual "mi\\." 178181 1255.
## 5 casual "ju\\." 182745 1225.
## 6 casual "vi\\." 213507 1380.
## 7 casual "sá\\." 311470 1609.
## 8 member "do\\." 313737 847.
## 9 member "lu\\." 395057 717.
## 10 member "ma\\." 437338 723.
## 11 member "mi\\." 447921 724.
## 12 member "ju\\." 440940 710.
## 13 member "vi\\." 385370 734.
## 14 member "sá\\." 349905 835.Let’s visualize the number of rides by rider type
all_trips_2 %>%
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 = number_of_rides, fill = member_casual)) +
geom_col(position = "dodge") + scale_fill_manual(values = c ("casual" = "darkblue", "member" = "darkorange"))## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Although there are more casual users than members, it is clear that members use the bikes more frequently. This could be explain due to the fact that many members are most probably using the bikes to commute so the rides are shorter but more frequent.
Let’s create a visualization for average duration
all_trips_2 %>%
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") + scale_fill_manual(values = c ("casual" = "purple", "member" = "yellow"))## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
This graph shows clearly that casual riders are using the bikes for longer periods of time. Many temporary visitor to the city probably using the bikes to move around and their therefore their trips are longer.
Find row with maximum ride length
rides<- all_trips_2%>%
select(start_station_name,end_station_name,ride_length)
summary(rides)## start_station_name end_station_name ride_length
## Length:4273827 Length:4273827 Min. : 0.2
## Class :character Class :character 1st Qu.: 347.0
## Mode :character Mode :character Median : 604.0
## Mean : 990.8
## 3rd Qu.: 1082.0
## Max. :413473.0max_ride <- dplyr::filter(rides, ride_length == max(ride_length))
max_start_station <- max_ride$start_station_name
max_end_station <- max_ride$end_station_nameFind the row with the minimum ride length
rides<- all_trips_2%>%
select(start_station_name,end_station_name,ride_length)
summary(rides)## start_station_name end_station_name ride_length
## Length:4273827 Length:4273827 Min. : 0.2
## Class :character Class :character 1st Qu.: 347.0
## Mode :character Mode :character Median : 604.0
## Mean : 990.8
## 3rd Qu.: 1082.0
## Max. :413473.0min_ride <- dplyr::filter(rides, ride_length == min(ride_length))
min_start_station <- min_ride$start_station_name
min_end_station <- min_ride$end_station_nameDisplay the results
cat("Maximum ride length starts at:", max_start_station,",","and ends at:", max_end_station, "\n")## Maximum ride length starts at: Larrabee St & Webster Ave , and ends at: Michigan Ave & Oak Stcat("Minimum ride length starts at:", min_start_station, ",","and ends at:", min_end_station, "\n")## Minimum ride length starts at: Millennium Park , and ends at: Millennium ParkCombine max_ride and min_ride
combined_rides<-rbind(max_ride,min_ride)Visualize ride lengths by start and end stations
ggplot(combined_rides, aes(x = str_wrap(start_station_name,width = 10), y = ride_length, fill = end_station_name)) +
geom_bar(stat = "identity", fill = "#FF6666") +
geom_text(aes(label = ride_length), vjust = -0.5) +
labs(title = "Maximum and Minimum Ride Length by Start and End Stations", x = "Start_Station_Name", y = "Ride_Length (minutes)") +
theme_minimal()+
theme(
plot.background = element_rect(color = "purple", linewidth = 1),
panel.border = element_rect(color = "lightblue", fill = NA, size = 0.5)
)+
scale_y_continuous(labels = scales::number_format(accuracy = 1))## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Create a visualization for the number of rides by rider type
ggplot(data = all_trips_2) +
geom_bar(mapping=aes(x=member_casual, fill=member_casual)) +
labs(title = "Number of Rides: Member vs. Casual") +
scale_fill_manual(values = c ("casual" = "purple", "member" = "lightblue"))
### Top 3 Start Stations
all_trips_2 %>%
group_by(start_station_name) %>%
summarize(count = n()) %>%
arrange(desc(count)) %>%
head(4)## # A tibble: 4 × 2
## start_station_name count
## <chr> <int>
## 1 Streeter Dr & Grand Ave 60601
## 2 DuSable Lake Shore Dr & Monroe St 40556
## 3 Michigan Ave & Oak St 35295
## 4 DuSable Lake Shore Dr & North Blvd 34665** The top stations are found on the water front where most amenities are found link
Top 3 End Stations
all_trips_2 %>%
group_by(end_station_name) %>%
summarize(count = n()) %>%
arrange(desc(count)) %>%
head(4)## # A tibble: 4 × 2
## end_station_name count
## <chr> <int>
## 1 Streeter Dr & Grand Ave 62076
## 2 DuSable Lake Shore Dr & Monroe St 38878
## 3 DuSable Lake Shore Dr & North Blvd 37448
## 4 Michigan Ave & Oak St 35976Create visualization for members vs casual per month
all_trips_2 %>%
ggplot(aes(started_at)) +
geom_histogram(color = "#000000", aes(fill = member_casual)) +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~member_casual) +
scale_fill_manual(values = c ("casual" = "purple", "member" = "lightyellow")) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
** All riders use the service during the warmer months of the year and
it drops during the coldest months. Chicago is very cold during the
winter months so that explains it.
CONCLUSIONS
- The analysis shows that there are more casual users than members.
- Casual riders ride for longer periods of time but members ride more frequently
- The most popular stations are near the waterfront where more of the touristic sights are found.
All these finding suggest that if Cyclistic would like to get more members, these are my recommendations:
At the moments there are only a two options for casual members, one ride pass and one day pass. It would be interesting to create new passes such as Weekend pass, a three day pass, Weekly pass and Monthly pass.
Attracting prices could be a good way of converting recurrent casual rider into full membership. This could be done by introducing discounts or giving a free month rides per year.
Advertising at main stations, popular destinations and on social media the new rates and discounts should be a priority once the new plans are put in place.
##======== STEP 5: EXPORT SUMMARY FILE FOR FURTHER ANALYSIS ========
Create a csv file that we will visualize in Excel, Tableau, or my presentation software
counts <- aggregate(all_trips_2$ride_length ~ all_trips_2$member_casual +
all_trips_2$day_of_week, FUN = mean)
View(counts)
write.csv(counts, file ='/Users/Juan/Desktop/cases/Bikes/avg_ride_lenght.csv')