Cyclistic Case Study
Ali Gruett
2023-06-02
Analysis of how annual members and casual riders use Cyclistic bikes differently.
Load relevant packages.
Load all packages necessary to complete the analysis. Either set the working directory or make sure the current working directory is correct.
library(tidyverse) #data manipulation, exploration, and visualization
library(lubridate) #works with dates and times
library(ggplot2) #visualize data
library(dplyr) #good for data manipulation
library(ggmap) #visualizing data on a map
library(tidyr) #data cleaning
library(sf) #a standardized way to encode spatial vector data
library(mapview) #provides functions to create interactive visualizations of spatial data
library(reshape2)
getwd() #displays your working directory}Import data into R
Read data into R from the working directory. Data is in the form of 12 csv files. Name all the data frames created. Each data frame represents all trips taken during a month. The time frame is May 2022 - April 2023.
May_2022 <- read_csv("202205-divvy-tripdata.csv")
Jun_2022 <- read_csv("202206-divvy-tripdata.csv")
Jul_2022 <- read_csv("202207-divvy-tripdata.csv")
Aug_2022 <- read_csv("202208-divvy-tripdata.csv")
Sep_2022 <- read_csv("202209-divvy-tripdata.csv")
Oct_2022 <- read_csv("202210-divvy-tripdata.csv")
Nov_2022 <- read_csv("202211-divvy-tripdata.csv")
Dec_2022 <- read_csv("202212-divvy-tripdata.csv")
Jan_2023 <- read_csv("202301-divvy-tripdata.csv")
Feb_2023 <- read_csv("202302-divvy-tripdata.csv")
Mar_2023 <- read_csv("202303-divvy-tripdata.csv")
Apr_2023 <- read_csv("202304-divvy-tripdata.csv")Inspect dataframes to ensure data types and columns are correct and identical between all the data frames.This allows the data to append correctly.
str(May_2022)## spc_tbl_ [634,858 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:634858] "EC2DE40644C6B0F4" "1C31AD03897EE385" "1542FBEC830415CF" "6FF59852924528F8" ...
## $ rideable_type : chr [1:634858] "classic_bike" "classic_bike" "classic_bike" "classic_bike" ...
## $ started_at : POSIXct[1:634858], format: "2022-05-23 23:06:58" "2022-05-11 08:53:28" ...
## $ ended_at : POSIXct[1:634858], format: "2022-05-23 23:40:19" "2022-05-11 09:31:22" ...
## $ start_station_name: chr [1:634858] "Wabash Ave & Grand Ave" "DuSable Lake Shore Dr & Monroe St" "Clinton St & Madison St" "Clinton St & Madison St" ...
## $ start_station_id : chr [1:634858] "TA1307000117" "13300" "TA1305000032" "TA1305000032" ...
## $ end_station_name : chr [1:634858] "Halsted St & Roscoe St" "Field Blvd & South Water St" "Wood St & Milwaukee Ave" "Clark St & Randolph St" ...
## $ end_station_id : chr [1:634858] "TA1309000025" "15534" "13221" "TA1305000030" ...
## $ start_lat : num [1:634858] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:634858] -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ end_lat : num [1:634858] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:634858] -87.6 -87.6 -87.7 -87.6 -87.7 ...
## $ member_casual : chr [1:634858] "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>Complete this step for all months.
Append into one data frame.
all_trips <- bind_rows(May_2022,Jun_2022,Jul_2022,Aug_2022,Sep_2022,Oct_2022,Nov_2022,Dec_2022,Jan_2023,Feb_2023,Mar_2023,Apr_2023)Inspect the new data frame.
Observe the number of columns and rows, the structure of the data frame, get a preview of the data, names of the columns, and data type.
str(all_trips)## spc_tbl_ [5,859,061 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:5859061] "EC2DE40644C6B0F4" "1C31AD03897EE385" "1542FBEC830415CF" "6FF59852924528F8" ...
## $ rideable_type : chr [1:5859061] "classic_bike" "classic_bike" "classic_bike" "classic_bike" ...
## $ started_at : POSIXct[1:5859061], format: "2022-05-23 23:06:58" "2022-05-11 08:53:28" ...
## $ ended_at : POSIXct[1:5859061], format: "2022-05-23 23:40:19" "2022-05-11 09:31:22" ...
## $ start_station_name: chr [1:5859061] "Wabash Ave & Grand Ave" "DuSable Lake Shore Dr & Monroe St" "Clinton St & Madison St" "Clinton St & Madison St" ...
## $ start_station_id : chr [1:5859061] "TA1307000117" "13300" "TA1305000032" "TA1305000032" ...
## $ end_station_name : chr [1:5859061] "Halsted St & Roscoe St" "Field Blvd & South Water St" "Wood St & Milwaukee Ave" "Clark St & Randolph St" ...
## $ end_station_id : chr [1:5859061] "TA1309000025" "15534" "13221" "TA1305000030" ...
## $ start_lat : num [1:5859061] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:5859061] -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ end_lat : num [1:5859061] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:5859061] -87.6 -87.6 -87.7 -87.6 -87.7 ...
## $ member_casual : chr [1:5859061] "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>For columns that are character data types, view the length. For datetime and double, view the five number summary.
summary(all_trips)## ride_id rideable_type started_at
## Length:5859061 Length:5859061 Min. :2022-05-01 00:00:06.00
## Class :character Class :character 1st Qu.:2022-07-03 11:12:30.00
## Mode :character Mode :character Median :2022-08-28 12:44:57.00
## Mean :2022-09-19 13:39:54.23
## 3rd Qu.:2022-11-08 06:30:21.00
## Max. :2023-04-30 23:59:05.00
##
## ended_at start_station_name start_station_id
## Min. :2022-05-01 00:05:17.00 Length:5859061 Length:5859061
## 1st Qu.:2022-07-03 11:38:52.00 Class :character Class :character
## Median :2022-08-28 13:07:09.00 Mode :character Mode :character
## Mean :2022-09-19 13:58:50.35
## 3rd Qu.:2022-11-08 06:43:39.00
## Max. :2023-05-03 10:37:12.00
##
## end_station_name end_station_id start_lat start_lng
## Length:5859061 Length:5859061 Min. :41.64 Min. :-87.84
## Class :character Class :character 1st Qu.:41.88 1st Qu.:-87.66
## Mode :character Mode :character Median :41.90 Median :-87.64
## Mean :41.90 Mean :-87.65
## 3rd Qu.:41.93 3rd Qu.:-87.63
## Max. :42.07 Max. :-87.52
##
## end_lat end_lng member_casual
## Min. : 0.00 Min. :-88.14 Length:5859061
## 1st Qu.:41.88 1st Qu.:-87.66 Class :character
## Median :41.90 Median :-87.64 Mode :character
## Mean :41.90 Mean :-87.65
## 3rd Qu.:41.93 3rd Qu.:-87.63
## Max. :42.37 Max. : 0.00
## NA's :5973 NA's :5973View the first several rows of data.
head(all_trips)## # A tibble: 6 × 13
## ride_id rideable_type started_at ended_at
## <chr> <chr> <dttm> <dttm>
## 1 EC2DE40644C6B0F4 classic_bike 2022-05-23 23:06:58 2022-05-23 23:40:19
## 2 1C31AD03897EE385 classic_bike 2022-05-11 08:53:28 2022-05-11 09:31:22
## 3 1542FBEC830415CF classic_bike 2022-05-26 18:36:28 2022-05-26 18:58:18
## 4 6FF59852924528F8 classic_bike 2022-05-10 07:30:07 2022-05-10 07:38:49
## 5 483C52CAAE12E3AC classic_bike 2022-05-10 17:31:56 2022-05-10 17:36:57
## 6 C0A3AA5A614DCE01 classic_bike 2022-05-04 14:48:55 2022-05-04 14:56:04
## # ℹ 9 more variables: start_station_name <chr>, start_station_id <chr>,
## # end_station_name <chr>, end_station_id <chr>, start_lat <dbl>,
## # start_lng <dbl>, end_lat <dbl>, end_lng <dbl>, member_casual <chr>View the types of riders and bikes. Make sure there are no errors with data entry for these columns (ex: for members there should only be two types).
unique(all_trips$member_casual)## [1] "member" "casual"unique(all_trips$rideable_type)## [1] "classic_bike" "docked_bike" "electric_bike"Create a frequency table for the member_casual and rideable_type columns.
table(all_trips$member_casual)##
## casual member
## 2358307 3500754table(all_trips$rideable_type)##
## classic_bike docked_bike electric_bike
## 2642585 170518 3045958Members have taken more rides than casual riders during the past year (May 2022-April 2023). The most common type of bike ridden is the electric bike.
Preparing for analysis
Create columns that list the date, month, day, year, day of week, and hour of each ride. Create a new column that combines the start and end station names.
all_trips$date <- as_datetime(all_trips$started_at)
all_trips$month <- format(as.Date(all_trips$date),"%m")
all_trips$day <- format(as.Date(all_trips$date),"%d")
all_trips$year <- format(as.Date(all_trips$date),"%Y")
all_trips$day_of_week <- format(as.Date(all_trips$date),"%A")
all_trips$hour <- format(as_datetime(all_trips$date), "%H")
all_trips$start_end <- paste(all_trips$start_station_name, ",", all_trips$end_station_name)Add a calculated column for duration of the ride in minutes.
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at, units=c("mins"))Inspect the data frame to make sure the columns were added and appear correct.
str(all_trips)## spc_tbl_ [5,859,061 × 21] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:5859061] "EC2DE40644C6B0F4" "1C31AD03897EE385" "1542FBEC830415CF" "6FF59852924528F8" ...
## $ rideable_type : chr [1:5859061] "classic_bike" "classic_bike" "classic_bike" "classic_bike" ...
## $ started_at : POSIXct[1:5859061], format: "2022-05-23 23:06:58" "2022-05-11 08:53:28" ...
## $ ended_at : POSIXct[1:5859061], format: "2022-05-23 23:40:19" "2022-05-11 09:31:22" ...
## $ start_station_name: chr [1:5859061] "Wabash Ave & Grand Ave" "DuSable Lake Shore Dr & Monroe St" "Clinton St & Madison St" "Clinton St & Madison St" ...
## $ start_station_id : chr [1:5859061] "TA1307000117" "13300" "TA1305000032" "TA1305000032" ...
## $ end_station_name : chr [1:5859061] "Halsted St & Roscoe St" "Field Blvd & South Water St" "Wood St & Milwaukee Ave" "Clark St & Randolph St" ...
## $ end_station_id : chr [1:5859061] "TA1309000025" "15534" "13221" "TA1305000030" ...
## $ start_lat : num [1:5859061] 41.9 41.9 41.9 41.9 41.9 ...
## $ start_lng : num [1:5859061] -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ end_lat : num [1:5859061] 41.9 41.9 41.9 41.9 41.9 ...
## $ end_lng : num [1:5859061] -87.6 -87.6 -87.7 -87.6 -87.7 ...
## $ member_casual : chr [1:5859061] "member" "member" "member" "member" ...
## $ date : POSIXct[1:5859061], format: "2022-05-23 23:06:58" "2022-05-11 08:53:28" ...
## $ month : chr [1:5859061] "05" "05" "05" "05" ...
## $ day : chr [1:5859061] "23" "11" "26" "10" ...
## $ year : chr [1:5859061] "2022" "2022" "2022" "2022" ...
## $ day_of_week : chr [1:5859061] "Monday" "Wednesday" "Thursday" "Tuesday" ...
## $ hour : chr [1:5859061] "23" "08" "18" "07" ...
## $ start_end : chr [1:5859061] "Wabash Ave & Grand Ave , Halsted St & Roscoe St" "DuSable Lake Shore Dr & Monroe St , Field Blvd & South Water St" "Clinton St & Madison St , Wood St & Milwaukee Ave" "Clinton St & Madison St , Clark St & Randolph St" ...
## $ ride_length : 'difftime' num [1:5859061] 33.35 37.9 21.8333333333333 8.7 ...
## ..- attr(*, "units")= chr "mins"
## - 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>Ride length and hour are currently a character data type. Convert them to numeric.
all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
is.numeric(all_trips$ride_length)## [1] TRUEall_trips$hour <- as.numeric(as.character(all_trips$hour))
is.numeric(all_trips$hour)## [1] TRUEsummary(all_trips$ride_length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -10353.35 5.65 9.98 18.94 17.92 41387.25The summary shows the minimum for ride_length is a negative value. Remove any negative values and create a new data set (version 2).
all_trips_v2 <- all_trips[!(all_trips$ride_length<0),]summary(all_trips_v2)## ride_id rideable_type started_at
## Length:5858958 Length:5858958 Min. :2022-05-01 00:00:06.00
## Class :character Class :character 1st Qu.:2022-07-03 11:11:58.25
## Mode :character Mode :character Median :2022-08-28 12:44:51.50
## Mean :2022-09-19 13:39:50.03
## 3rd Qu.:2022-11-08 06:32:21.75
## Max. :2023-04-30 23:59:05.00
##
## ended_at start_station_name start_station_id
## Min. :2022-05-01 00:05:17.00 Length:5858958 Length:5858958
## 1st Qu.:2022-07-03 11:38:33.75 Class :character Class :character
## Median :2022-08-28 13:07:00.50 Mode :character Mode :character
## Mean :2022-09-19 13:58:46.32
## 3rd Qu.:2022-11-08 06:46:17.00
## Max. :2023-05-03 10:37:12.00
##
## end_station_name end_station_id start_lat start_lng
## Length:5858958 Length:5858958 Min. :41.64 Min. :-87.84
## Class :character Class :character 1st Qu.:41.88 1st Qu.:-87.66
## Mode :character Mode :character Median :41.90 Median :-87.64
## Mean :41.90 Mean :-87.65
## 3rd Qu.:41.93 3rd Qu.:-87.63
## Max. :42.07 Max. :-87.52
##
## end_lat end_lng member_casual
## Min. : 0.00 Min. :-88.14 Length:5858958
## 1st Qu.:41.88 1st Qu.:-87.66 Class :character
## Median :41.90 Median :-87.64 Mode :character
## Mean :41.90 Mean :-87.65
## 3rd Qu.:41.93 3rd Qu.:-87.63
## Max. :42.37 Max. : 0.00
## NA's :5973 NA's :5973
## date month day
## Min. :2022-05-01 00:00:06.00 Length:5858958 Length:5858958
## 1st Qu.:2022-07-03 11:11:58.25 Class :character Class :character
## Median :2022-08-28 12:44:51.50 Mode :character Mode :character
## Mean :2022-09-19 13:39:50.03
## 3rd Qu.:2022-11-08 06:32:21.75
## Max. :2023-04-30 23:59:05.00
##
## year day_of_week hour start_end
## Length:5858958 Length:5858958 Min. : 0.00 Length:5858958
## Class :character Class :character 1st Qu.:11.00 Class :character
## Mode :character Mode :character Median :15.00 Mode :character
## Mean :14.21
## 3rd Qu.:18.00
## Max. :23.00
##
## ride_length
## Min. : 0.00
## 1st Qu.: 5.65
## Median : 9.98
## Mean : 18.94
## 3rd Qu.: 17.92
## Max. :41387.25
## Analysis
1. Comparison of ride length and total number of rides between members and casual riders.
Ride length analysis.
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN=mean)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 28.49466
## 2 member 12.50031aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN=max)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 41387.250
## 2 member 1559.667aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN=min)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 0
## 2 member 0aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN=median)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 12.516667
## 2 member 8.666667Count the total number of rides taken by members and casual riders.
nrow(subset(all_trips_v2,member_casual=="member"))## [1] 3500705nrow(subset(all_trips_v2,member_casual=="casual"))## [1] 2358253Member’s trip lengths are shorter by an average of 15.99 minutes and members are using the bikes more than casual riders.
2. Comparison of rides taken each day of the week between members and casual riders.
Order the days of the week Sun-Sat.
all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))
all_trips_v2 %>%
group_by(member_casual, day_of_week) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, day_of_week) ## # A tibble: 14 × 4
## # Groups: member_casual [2]
## member_casual day_of_week number_of_rides average_duration
## <chr> <ord> <int> <dbl>
## 1 casual Sunday 388811 33.4
## 2 casual Monday 275748 28.4
## 3 casual Tuesday 272648 25.3
## 4 casual Wednesday 284575 24.2
## 5 casual Thursday 318467 24.7
## 6 casual Friday 350081 27.5
## 7 casual Saturday 467923 32.2
## 8 member Sunday 402066 13.8
## 9 member Monday 484560 12.0
## 10 member Tuesday 544393 12.0
## 11 member Wednesday 556913 11.9
## 12 member Thursday 560877 12.1
## 13 member Friday 497473 12.4
## 14 member Saturday 454423 13.9The n() function counts the number of observations by the groups identified in the summarize function
Graph as a column chart.
options(scipen=999)
all_trips_v2 %>%
group_by(member_casual, day_of_week) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, day_of_week) %>%
ggplot(aes(x = day_of_week, y = number_of_rides, fill = member_casual)) +
geom_col(position = "dodge") +
labs(x="Day of Week", y="Number of Rides",
title="Total Number of Rides Taken Each Day of the Week",
subtitle="Members vs. Casual Riders") +
guides(fill=guide_legend(title="Rider Type"))
all_trips_v2 %>%
group_by(member_casual, day_of_week) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, day_of_week) %>%
ggplot(aes(x = day_of_week, y = average_duration, fill = member_casual)) +
geom_col(position = "dodge") +
labs(x="Day of Week", y="Average Duration of Ride",
title="Average Ride Duration on Each Day of Week",
subtitle="Members vs. Casual Riders")+
guides(fill=guide_legend(title="Rider Type"))
Casual riders use Cyclistic bikes more often on the weekends while members take more rides on weekdays. The duration of member’s rides stays fairly consistent throughout the week with a slight increase in duration on the weekends. Casual riders take significantly longer rides on the weekends with their shortest rides being taken on Wednesdays.
3. Comparison of start times between members and casual riders
all_trips_v2 %>%
group_by(member_casual,hour) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, hour)## # A tibble: 48 × 4
## # Groups: member_casual [2]
## member_casual hour number_of_rides average_duration
## <chr> <dbl> <int> <dbl>
## 1 casual 0 47112 30.4
## 2 casual 1 30385 35.7
## 3 casual 2 18802 37.1
## 4 casual 3 11007 41.8
## 5 casual 4 7680 36.8
## 6 casual 5 12848 27.0
## 7 casual 6 31240 22.3
## 8 casual 7 54058 19.3
## 9 casual 8 72823 19.4
## 10 casual 9 74458 25.2
## # ℹ 38 more rowsoptions(scipen=999)
all_trips_v2 %>%
group_by(member_casual,hour) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, hour) %>%
ggplot(aes(x=hour, y=number_of_rides, fill = member_casual)) +
geom_col(position = "dodge")+
labs(x="Hour of Day", y="Total Number of Rides",
title="Total Number of Rides Taken Each Hour of the Day",
subtitle="Members vs. Casual Riders")+
guides(fill=guide_legend(title="Rider Type"))
all_trips_v2 %>%
group_by(member_casual,hour) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, hour) %>%
ggplot(aes(x=hour, y=average_duration, fill = member_casual)) +
geom_col(position = "dodge") +
labs(x="Hour of Day", y="Average Duration of Ride",
title="Average Ride Duration of Each Hour of the Day",
subtitle="Members vs. Casual Riders")+
guides(fill=guide_legend(title="Rider Type"))
Casual riders take their longest rides early in the morning (3am), though this is also the time when the least number of rides are taken. Their shortest trips start at 7 or 8am. The duration is fairly consistent from 10am-midnight, with a slight dip from 4-6 pm. The number of trips taken by casual riders is lowest at 4am, then gradually increases until it peaks at 5pm, followed by a gradual decrease.
For members, the duration of trips stays fairly consistent across different times of the day, with a slight decrease in the morning hours (5-9am). The number of trips taken for members peaks in the morning (8am) and early in the evening (5pm).
This suggests many members are using the bikes to commute while casual riders may take the bikes for leisure rides mostly in the evening.
4. Comparison of start station and end station usage between members and casual riders
Remove all trips that don’t have either a start or end station name.
all_trips_v3 <- all_trips_v2[!(is.na(all_trips_v2$start_station_name) | is.na(all_trips_v2$end_station_name)), ]nrow(subset(all_trips_v3,member_casual=="member"))## [1] 2743031nrow(subset(all_trips_v3,member_casual=="casual"))## [1] 1791144Determine the most frequently used start and end stations grouped by rider type:
all_trips_v3 %>%
group_by(member_casual, start_station_name) %>%
filter(member_casual=="member") %>%
summarize(number_of_trips=n()) %>%
arrange(desc(number_of_trips))## # A tibble: 1,431 × 3
## # Groups: member_casual [1]
## member_casual start_station_name number_of_trips
## <chr> <chr> <int>
## 1 member Kingsbury St & Kinzie St 23814
## 2 member Clark St & Elm St 21656
## 3 member Clinton St & Washington Blvd 20489
## 4 member Wells St & Concord Ln 20226
## 5 member Loomis St & Lexington St 19832
## 6 member University Ave & 57th St 19663
## 7 member Ellis Ave & 60th St 19517
## 8 member Clinton St & Madison St 18338
## 9 member Wells St & Elm St 18221
## 10 member Broadway & Barry Ave 16817
## # ℹ 1,421 more rowsall_trips_v3 %>%
group_by(member_casual, start_station_name) %>%
filter(member_casual=="casual") %>%
summarize(number_of_trips=n()) %>%
arrange(desc(number_of_trips))## # A tibble: 1,511 × 3
## # Groups: member_casual [1]
## member_casual start_station_name number_of_trips
## <chr> <chr> <int>
## 1 casual Streeter Dr & Grand Ave 54340
## 2 casual DuSable Lake Shore Dr & Monroe St 30409
## 3 casual Michigan Ave & Oak St 23851
## 4 casual Millennium Park 23723
## 5 casual DuSable Lake Shore Dr & North Blvd 22156
## 6 casual Shedd Aquarium 19567
## 7 casual Theater on the Lake 17321
## 8 casual Wells St & Concord Ln 15077
## 9 casual Dusable Harbor 13385
## 10 casual Indiana Ave & Roosevelt Rd 12913
## # ℹ 1,501 more rowsCasual riders use less stations than members. The most popular station makes up 3.03% (54,340/1,791,144) of the total number of trips (using v3 data) of casual riders. The top 10 most popular stations make up 12.99% (232,742/1,791,144) of the total number of trips.
In comparison, the most popular station for members makes up 0.87% (23,814/2,743,031) of the total number of trips taken by members. The top 10 most popular stations make up 7.24% (23,814/2,743,031) of the total number of trips.
Please note these numbers exclude trips where the start station or end station name was missing.
Determine the most common trips members and casual riders are taking:
all_trips_v3 %>%
group_by(start_end) %>%
filter(member_casual=="member") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))## # A tibble: 144,280 × 3
## start_end number_of_rides average_duration
## <chr> <int> <dbl>
## 1 Ellis Ave & 60th St , University Ave & 57th… 6548 4.41
## 2 University Ave & 57th St , Ellis Ave & 60th… 6179 4.58
## 3 Ellis Ave & 60th St , Ellis Ave & 55th St 5953 4.88
## 4 Ellis Ave & 55th St , Ellis Ave & 60th St 5531 5.14
## 5 State St & 33rd St , Calumet Ave & 33rd St 3980 4.32
## 6 Calumet Ave & 33rd St , State St & 33rd St 3838 4.03
## 7 Loomis St & Lexington St , Morgan St & Polk… 3515 4.92
## 8 Morgan St & Polk St , Loomis St & Lexington… 3342 5.20
## 9 University Ave & 57th St , Kimbark Ave & 53… 2696 7.08
## 10 Kimbark Ave & 53rd St , University Ave & 57… 2409 6.78
## # ℹ 144,270 more rowsall_trips_v3 %>%
group_by(start_end) %>%
filter(member_casual=="casual") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))## # A tibble: 133,576 × 3
## start_end number_of_rides average_duration
## <chr> <int> <dbl>
## 1 Streeter Dr & Grand Ave , Streeter Dr & Gra… 10478 40.0
## 2 DuSable Lake Shore Dr & Monroe St , DuSable… 6618 33.5
## 3 DuSable Lake Shore Dr & Monroe St , Streete… 5105 27.3
## 4 Michigan Ave & Oak St , Michigan Ave & Oak … 4643 44.9
## 5 Millennium Park , Millennium Park 4031 37.7
## 6 Montrose Harbor , Montrose Harbor 2982 50.1
## 7 Streeter Dr & Grand Ave , DuSable Lake Shor… 2772 27.7
## 8 Streeter Dr & Grand Ave , Millennium Park 2684 33.6
## 9 Shedd Aquarium , Shedd Aquarium 2570 22.4
## 10 DuSable Lake Shore Dr & North Blvd , DuSabl… 2451 36.6
## # ℹ 133,566 more rowsThe top 10 trip routes make up 1.60% (43,991/2,743,031) of total member rides and 2.48% (44,334/1,791,144) of total casual rides. The top route for casual riders makes up .58% of total casual rides. The top route for members is .24% (6,548/2,743,031). Casual riders are taking the same routes more often than members.
5. Comparison of the number of rides and ride duration by day of the week for each rider type.
A. CASUAL RIDERS
Create new data frame with the total number of trips broken out by day of the week. It will contain casual rider trips only.
casual_start_end <- all_trips_v3 %>%
group_by(start_end) %>%
filter(member_casual=="casual") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length),
sat=length(which(day_of_week=="Saturday")),
mon=length(which(day_of_week=="Monday")),
tue=length(which(day_of_week=="Tuesday")),
wed=length(which(day_of_week=="Wednesday")),
thu=length(which(day_of_week=="Thursday")),
fri=length(which(day_of_week=="Friday")),
sun=length(which(day_of_week=="Sunday"))) %>%
arrange(desc(number_of_rides))Create data frame for the top 10 rows of the casual_start_end data frame.
top10_casual_start_end <- casual_start_end %>%
slice(1:10)Transform the top 10 rows from wide to long format.
casual_start_end_long <- melt(top10_casual_start_end,
id.vars = c("start_end", "number_of_rides","average_duration"))
casual_start_end_long$average_duration=NULL
casual_start_end_long$number_of_rides=NULLGraph
casual_start_end_long %>%
group_by(start_end) %>%
ggplot(aes(x=variable,y=value,group=start_end, color=start_end))+
geom_line() +
labs(x="Day of Week", y="Number of Rides",
title="Top 10 Most Popular Routes Taken by Casual Riders",
subtitle="Number of Rides per Day of Week") +
guides(fill=guide_legend(title="Start, End Station Name"))
B. MEMBERS
Repeat the same steps for members.
member_start_end<- all_trips_v3 %>%
group_by(start_end) %>%
filter(member_casual=="member") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length),
sat=length(which(day_of_week=="Saturday")),
mon=length(which(day_of_week=="Monday")),
tue=length(which(day_of_week=="Tuesday")),
wed=length(which(day_of_week=="Wednesday")),
thu=length(which(day_of_week=="Thursday")),
fri=length(which(day_of_week=="Friday")),
sun=length(which(day_of_week=="Sunday"))) %>%
arrange(desc(number_of_rides))top10_member_start_end <- member_start_end %>%
slice(1:10)member_start_end_long <- melt(top10_member_start_end,
id.vars = c("start_end", "number_of_rides","average_duration"))
member_start_end_long$average_duration=NULL
member_start_end_long$number_of_rides=NULLmember_start_end_long %>%
group_by(start_end) %>%
ggplot(aes(x=variable,y=value,group=start_end, color=start_end))+
geom_line()+
labs(x="Day of Week", y="Number of Rides",
title="Top 10 Most Popular Routes Taken by Members",
subtitle="Number of Rides per Day of Week") +
guides(fill=guide_legend(title="Start, End Station Name"))
At the top 10 most popular stations, casual riders mostly ride on the weekends while members mostly ride on weekdays.
summary(member_start_end$average_duration)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 12.64 19.12 21.95 27.64 1455.88summary(casual_start_end$average_duration)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.017 14.533 21.942 30.171 33.726 10722.967As seen previously, member’s rides are, on average, shorter in duration than casual riders.
6. Comparison of rides taken each month between members and casual riders
all_trips_v2 %>%
group_by(member_casual,month) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, month) %>%
ggplot(aes(x=month, y=number_of_rides, fill = member_casual)) +
geom_col(position = "dodge") +
labs(x="Month", y="Number of Rides",
title="Number of Rides Taken Each Month",
subtitle="Members vs. Casual Riders") +
guides(fill=guide_legend(title="Rider Type"))
all_trips_v2 %>%
group_by(member_casual,month) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, month) %>%
ggplot(aes(x=month, y=average_duration, fill = member_casual)) +
geom_col(position = "dodge") +
labs(x="Month", y="Average Duration of Ride",
title="Average Duration of Rides Taken Each Month",
subtitle="Members vs. Casual Riders") +
guides(fill=guide_legend(title="Rider Type"))
all_trips_v2 %>%
group_by(member_casual,month) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, month) %>%
ggplot(aes(x=month, y=average_duration, color = member_casual, size=number_of_rides)) +
geom_point()+
labs(x="Month", y="Average Duration of Ride",
title="Avg Duration and Number of Rides Taken by Month",
subtitle="Members vs. Casual Riders")
all_trips_v2 %>%
group_by(member_casual,month) %>%
summarise(number_of_rides = n()
,average_duration = mean(ride_length)) %>%
arrange(member_casual, month) %>%
ggplot(aes(x=month, y=number_of_rides, color = member_casual, size=average_duration)) +
geom_point()+
labs(x="Month", y="Number of Rides",
title="Number and Avg Duration of Rides Taken by Month",
subtitle="Members vs. Casual Riders")
The duration of rides each month for casual riders is significantly greater than members. For casual riders and members, the duration of rides is higher in the summer months, though this is more significant for casual riders.
For casual riders and members, there are a greater number of rides taken during the summer months than in the winter months, though the difference is more pronounced for casual riders. The number of rides taken by members and casual riders is closest during the month of July. Members are taking more rides than casual riders during the winter months.
This suggests that members are using the bikes year-round as a means of transportation while casual riders may use the bikes more for leisure during the summer months.
7. Average Duration vs. Number of Rides for Members and Casual Riders.
Used data within two standard deviations of the mean to be able to see trends more clearly.
mean(member_start_end$average_duration)## [1] 21.94725sd(member_start_end$average_duration)## [1] 20.32174member_start_end %>%
filter(average_duration<63.99886) %>%
ggplot(aes(x=number_of_rides, y=average_duration))+
geom_point() +
labs(x="Average Duration of Ride", y="Number of Rides",
title="Average Duration vs. Number of Rides Taken by Members")
mean(casual_start_end$average_duration)## [1] 30.17134sd(casual_start_end$average_duration)## [1] 54.47108casual_start_end %>%
filter(average_duration<160.05513) %>%
ggplot(aes(x=number_of_rides, y=average_duration))+
geom_point() +
labs(x="Average Duration of Ride", y="Number of Rides",
title="Average Duration vs. Number of Rides Taken by Casual Riders")
Each point represents a route (start station/end station combination). This is another way to graphically show insights seen previously. Members’ rides are shorter on average. There are fewer popular routes for casual riders and they are more frequently used compared to the most popular routes taken by members. One route in particular was taken over 10,000 times by casual riders versus the most popular member route being taken only approximately 6,500 times.
Next, to visualize where the most popular start and end stations are located, the latitude and longitudes provided for the stations will be used to map out where they are in Chicago. The map will be compared to Google Maps to determine whether there are any landmarks which may provide greater context for why these locations are popular for members and casual riders.
8. Visualizing the Data on a Map of Chicago
A. MEMBERS
Most popular start locations.
map_member_start<-all_trips_v3 %>%
group_by(start_station_name, start_lat,start_lng) %>%
filter(member_casual=="member") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))FALSE `summarise()` has grouped output by 'start_station_name', 'start_lat'. You can
FALSE override using the `.groups` argument.top100_map_member_start <- map_member_start[1:100, ]mapview(top100_map_member_start,xcol="start_lng",ycol="start_lat", crs=4296, grid=FALSE, cex="number_of_rides")Most popular end locations.
map_member_end <-all_trips_v3 %>%
group_by(end_station_name,end_lat,end_lng) %>%
filter(member_casual=="member") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))FALSE `summarise()` has grouped output by 'end_station_name', 'end_lat'. You can
FALSE override using the `.groups` argument.top100_map_member_end <- map_member_end[1:100, ]mapview(top100_map_member_end,xcol="end_lng",ycol="end_lat",crs=4296, grid=FALSE,cex="number_of_rides")Most popular routes.
map_member_start_end<- all_trips_v3 %>%
group_by(start_end,start_lat,start_lng,end_lat, end_lng) %>%
filter(member_casual=="member") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))FALSE `summarise()` has grouped output by 'start_end', 'start_lat', 'start_lng',
FALSE 'end_lat'. You can override using the `.groups` argument.top100_map_member_start_end <- map_member_start_end[1:100, ]sum(top100_map_member_start_end$number_of_rides)/sum(map_member_start_end$number_of_rides)## [1] 0.03585741mapview(top100_map_member_start_end,xcol="start_lng", ycol="start_lat", crs=4296, grid=FALSE, cex="number_of_rides")mapview(top100_map_member_start_end,xcol="end_lng", ycol="end_lat", crs=4296, grid=FALSE, cex="number_of_rides")B. CASUAL RIDERS
Most popular start locations.
map_casual_start<-all_trips_v3 %>%
group_by(start_station_name, start_lat,start_lng) %>%
filter(member_casual=="casual") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))FALSE `summarise()` has grouped output by 'start_station_name', 'start_lat'. You can
FALSE override using the `.groups` argument.top100_map_casual_start <- map_casual_start[1:100, ]mapview(top100_map_casual_start,xcol="start_lng",ycol="start_lat", crs=4296, grid=FALSE, cex="number_of_rides")Most popular end locations.
map_casual_end <-all_trips_v3 %>%
group_by(end_station_name,end_lat,end_lng) %>%
filter(member_casual=="casual") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))FALSE `summarise()` has grouped output by 'end_station_name', 'end_lat'. You can
FALSE override using the `.groups` argument.top100_map_casual_end <- map_casual_end[1:100, ]mapview(top100_map_casual_end,xcol="end_lng",ycol="end_lat",crs=4296, grid=FALSE,cex="number_of_rides")Most popular routes.
map_casual_start_end<- all_trips_v3 %>%
group_by(start_end,start_lat,start_lng,end_lat, end_lng) %>%
filter(member_casual=="casual") %>%
summarize(number_of_rides=n(),
average_duration=mean(ride_length)) %>%
arrange(desc(number_of_rides))FALSE `summarise()` has grouped output by 'start_end', 'start_lat', 'start_lng',
FALSE 'end_lat'. You can override using the `.groups` argument.top100_map_casual_start_end <- map_casual_start_end[1:100, ]sum(top100_map_casual_start_end$number_of_rides)/sum(map_casual_start_end$number_of_rides)## [1] 0.05339325mapview(top100_map_casual_start_end,xcol="start_lng", ycol="start_lat", crs=4296, grid=FALSE, cex="number_of_rides")mapview(top100_map_casual_start_end,xcol="end_lng", ycol="end_lat", crs=4296, grid=FALSE, cex="number_of_rides")When compared to Google Maps landmarks, member trips are concentrated around colleges while casual rider trips are concentrated around tourist landmarks.
This supports the hypothesis that members are primarily using the bikes as a means of reliable transportation while casual users are using them to sight-see and visit popular locations. Since many members start and end locations are near colleges, the demographic of members may be college students.