Jinny Park 2023-03-28
This notebook shows the capstone data analysis project done as a part of the Google Data Analytics certificate program.
This analysis is based on the Divvy case study “‘Sophisticated, Clear, and Polished’: Divvy and Data Visualization” written by Kevin Hartman (found here: https://artscience.blog/home/divvy-dataviz-case-study).
The goal of this capstone analysis is to answer the key question: “In what ways do members and casual riders use Divvy bikes differently in 2022?”
Since the Divvy bike trip data is collected monthly, this script first aggregates 12-month’s worth of Divvy bike ridership data in 2022 into a single dataframe. Data is cleaned and transformed to perform simple analysis on ride duration per weekday and ride membership type. Using ggplot, I provide easy-to-understand insights into ridership pattern for each membership type.
Load tidyverse for data import and wrangling, lubridate for date functions, and ggplot for visualization:
library(tidyverse) #helps wrangle data## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.1.8
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the �]8;;http://conflicted.r-lib.org/�conflicted package�]8;;� to force all conflicts to become errors
library(lubridate) #helps wrangle date attributes
library(ggplot2) #helps visualize data
library(readr)Check and set working directory:
getwd() ## [1] "/Users/jinnypark/Google_data_analytics_with_R/case-study-1-cyclistic/bike-trip-data-2022/bike-trip-data-csv-original"
setwd("~/Google_data_analytics_with_R/case-study-1-cyclistic/bike-trip-data-2022/bike-trip-data-csv-original")Before merging all twelve months’ worth of divvy ridership data, read in each month’s divvy dataset individually to check for any discrepancies across data.
jan <- read_csv("202201-divvy-tripdata.csv")## Rows: 103770 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 <- read_csv("202203-divvy-tripdata.csv")## Rows: 284042 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.
dec <- read_csv("202212-divvy-tripdata.csv")## Rows: 181806 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.
Then, check for column names of each dataset and make sure they all match, so they’re ready to be merged
colnames(jan)## [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)## [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(dec)## [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"
Collect all CSV file names in the folder as list_of_csv_files, then
read in all CSV files and aggregate them as one dataframe,
all_bike_trips
list_of_csv_files <- list.files(path = ".",
recursive = TRUE,
pattern = "\\.csv$",
full.names = TRUE)
all_bike_trips <- readr::read_csv(list_of_csv_files)## Rows: 6048463 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.
First, inspect the new aggregate table
glimpse(all_bike_trips) # quick overview## Rows: 6,048,463
## Columns: 13
## $ ride_id <chr> "C2F7DD78E82EC875", "A6CF8980A652D272", "BD0F91DFF7…
## $ rideable_type <chr> "electric_bike", "electric_bike", "classic_bike", "…
## $ started_at <dttm> 2022-01-13 11:59:47, 2022-01-10 08:41:56, 2022-01-…
## $ ended_at <dttm> 2022-01-13 12:02:44, 2022-01-10 08:46:17, 2022-01-…
## $ start_station_name <chr> "Glenwood Ave & Touhy Ave", "Glenwood Ave & Touhy A…
## $ start_station_id <chr> "525", "525", "TA1306000016", "KA1504000151", "TA13…
## $ end_station_name <chr> "Clark St & Touhy Ave", "Clark St & Touhy Ave", "Gr…
## $ end_station_id <chr> "RP-007", "RP-007", "TA1307000001", "TA1309000021",…
## $ start_lat <dbl> 42.01280, 42.01276, 41.92560, 41.98359, 41.87785, 4…
## $ start_lng <dbl> -87.66591, -87.66597, -87.65371, -87.66915, -87.624…
## $ end_lat <dbl> 42.01256, 42.01256, 41.92533, 41.96151, 41.88462, 4…
## $ end_lng <dbl> -87.67437, -87.67437, -87.66580, -87.67139, -87.627…
## $ member_casual <chr> "casual", "casual", "member", "casual", "member", "…
colnames(all_bike_trips) # all 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" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"
nrow(all_bike_trips) #how many rows?## [1] 6048463
dim(all_bike_trips) #dimensions of the dataframe?## [1] 6048463 13
head(all_bike_trips) #first 6 rows## # A tibble: 6 × 13
## ride_id ridea…¹ started_at ended_at start…² start…³
## <chr> <chr> <dttm> <dttm> <chr> <chr>
## 1 C2F7DD78E82EC… electr… 2022-01-13 11:59:47 2022-01-13 12:02:44 Glenwo… 525
## 2 A6CF8980A652D… electr… 2022-01-10 08:41:56 2022-01-10 08:46:17 Glenwo… 525
## 3 BD0F91DFF741C… classi… 2022-01-25 04:53:40 2022-01-25 04:58:01 Sheffi… TA1306…
## 4 CBB80ED419105… classi… 2022-01-04 00:18:04 2022-01-04 00:33:00 Clark … KA1504…
## 5 DDC963BFDDA51… classi… 2022-01-20 01:31:10 2022-01-20 01:37:12 Michig… TA1309…
## 6 A39C6F6CC0586… classi… 2022-01-11 18:48:09 2022-01-11 18:51:31 Wood S… 637
## # … with 7 more variables: end_station_name <chr>, end_station_id <chr>,
## # start_lat <dbl>, start_lng <dbl>, end_lat <dbl>, end_lng <dbl>,
## # member_casual <chr>, and abbreviated variable names ¹rideable_type,
## # ²start_station_name, ³start_station_id
tail(all_bike_trips) #last 6 rows## # A tibble: 6 × 13
## ride_id ridea…¹ started_at ended_at start…² start…³
## <chr> <chr> <dttm> <dttm> <chr> <chr>
## 1 B60EA061E2123… electr… 2023-02-04 17:52:34 2023-02-04 17:59:57 Clark … TA1305…
## 2 C04510F8EBB5E… classi… 2023-02-08 21:57:22 2023-02-08 22:08:06 Clark … TA1305…
## 3 187BA364FB265… electr… 2023-02-19 11:29:09 2023-02-19 11:39:11 Ogden … KA1504…
## 4 46B54F6B417D1… electr… 2023-02-07 09:01:33 2023-02-07 09:16:53 Clark … TA1305…
## 5 335B3CAD59F6C… electr… 2023-02-22 08:33:22 2023-02-22 08:50:11 Clark … TA1305…
## 6 03D59518BB151… classi… 2023-02-01 21:52:17 2023-02-01 22:04:17 Clark … TA1309…
## # … with 7 more variables: end_station_name <chr>, end_station_id <chr>,
## # start_lat <dbl>, start_lng <dbl>, end_lat <dbl>, end_lng <dbl>,
## # member_casual <chr>, and abbreviated variable names ¹rideable_type,
## # ²start_station_name, ³start_station_id
str(all_bike_trips) # Check the structure of the dataframe, make sure the data type is appropriate (numeric, character, etc).## spc_tbl_ [6,048,463 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:6048463] "C2F7DD78E82EC875" "A6CF8980A652D272" "BD0F91DFF741C66D" "CBB80ED419105406" ...
## $ rideable_type : chr [1:6048463] "electric_bike" "electric_bike" "classic_bike" "classic_bike" ...
## $ started_at : POSIXct[1:6048463], format: "2022-01-13 11:59:47" "2022-01-10 08:41:56" ...
## $ ended_at : POSIXct[1:6048463], format: "2022-01-13 12:02:44" "2022-01-10 08:46:17" ...
## $ start_station_name: chr [1:6048463] "Glenwood Ave & Touhy Ave" "Glenwood Ave & Touhy Ave" "Sheffield Ave & Fullerton Ave" "Clark St & Bryn Mawr Ave" ...
## $ start_station_id : chr [1:6048463] "525" "525" "TA1306000016" "KA1504000151" ...
## $ end_station_name : chr [1:6048463] "Clark St & Touhy Ave" "Clark St & Touhy Ave" "Greenview Ave & Fullerton Ave" "Paulina St & Montrose Ave" ...
## $ end_station_id : chr [1:6048463] "RP-007" "RP-007" "TA1307000001" "TA1309000021" ...
## $ start_lat : num [1:6048463] 42 42 41.9 42 41.9 ...
## $ start_lng : num [1:6048463] -87.7 -87.7 -87.7 -87.7 -87.6 ...
## $ end_lat : num [1:6048463] 42 42 41.9 42 41.9 ...
## $ end_lng : num [1:6048463] -87.7 -87.7 -87.7 -87.7 -87.6 ...
## $ member_casual : chr [1:6048463] "casual" "casual" "member" "casual" ...
## - attr(*, "spec")=
## .. cols(
## .. ride_id = col_character(),
## .. rideable_type = col_character(),
## .. started_at = col_datetime(format = ""),
## .. ended_at = col_datetime(format = ""),
## .. start_station_name = col_character(),
## .. start_station_id = col_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>
summary(all_bike_trips) #statistical summary of data mainly for numeric data## ride_id rideable_type started_at
## Length:6048463 Length:6048463 Min. :2022-01-01 00:00:05.0
## Class :character Class :character 1st Qu.:2022-06-01 08:26:47.0
## Mode :character Mode :character Median :2022-07-29 15:49:03.0
## Mean :2022-08-01 13:30:16.2
## 3rd Qu.:2022-09-28 16:34:54.0
## Max. :2023-02-28 23:59:31.0
##
## ended_at start_station_name start_station_id
## Min. :2022-01-01 00:01:48.00 Length:6048463 Length:6048463
## 1st Qu.:2022-06-01 08:41:42.00 Class :character Class :character
## Median :2022-07-29 16:07:41.00 Mode :character Mode :character
## Mean :2022-08-01 13:49:19.47
## 3rd Qu.:2022-09-28 16:48:54.00
## Max. :2023-03-06 15:09:53.00
##
## end_station_name end_station_id start_lat start_lng
## Length:6048463 Length:6048463 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. :45.64 Max. :-73.80
##
## end_lat end_lng member_casual
## Min. : 0.00 Min. :-88.14 Length:6048463
## 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 :6101 NA's :6101
Find all available values for Rideable Types and Member Types
unique(all_bike_trips$rideable_type) ## [1] "electric_bike" "classic_bike" "docked_bike"
unique(all_bike_trips$member_casual) ## [1] "casual" "member"
Currently, data can be only aggregated at the ride-level, which is too
granular. Calculate and add ride_length column by subtracting
started_at from ended_at with difftime()
(https://stat.ethz.ch/R-manual/R-devel/library/base/html/difftime.html)
all_bike_trips$ride_length <- difftime(all_bike_trips$ended_at, all_bike_trips$started_at)Add columns that list the date, month, day, and year of each ride. This allows us to aggregate ride data for each month, day, or year.
all_bike_trips$date <- as.Date(all_bike_trips$started_at) # Create `date` column with yyyy-mm-dd format as default
all_bike_trips$month <- format(as.Date(all_bike_trips$date), "%m") # Create `month` column based on `date` column's month value
all_bike_trips$day <- format(as.Date(all_bike_trips$date), "%d") # Create `day` column based on `date` column's day value
all_bike_trips$year <- format(as.Date(all_bike_trips$date), "%Y") # Create `year` column based on `date` column's year value
all_bike_trips$day_of_week <- format(as.Date(all_bike_trips$date), "%A") # Create `day_of_week` column, converting date into weekday (e.g., "Sunday")Inspect the structure of the columns including newly added ones.
str(all_bike_trips)## spc_tbl_ [6,048,463 × 19] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:6048463] "C2F7DD78E82EC875" "A6CF8980A652D272" "BD0F91DFF741C66D" "CBB80ED419105406" ...
## $ rideable_type : chr [1:6048463] "electric_bike" "electric_bike" "classic_bike" "classic_bike" ...
## $ started_at : POSIXct[1:6048463], format: "2022-01-13 11:59:47" "2022-01-10 08:41:56" ...
## $ ended_at : POSIXct[1:6048463], format: "2022-01-13 12:02:44" "2022-01-10 08:46:17" ...
## $ start_station_name: chr [1:6048463] "Glenwood Ave & Touhy Ave" "Glenwood Ave & Touhy Ave" "Sheffield Ave & Fullerton Ave" "Clark St & Bryn Mawr Ave" ...
## $ start_station_id : chr [1:6048463] "525" "525" "TA1306000016" "KA1504000151" ...
## $ end_station_name : chr [1:6048463] "Clark St & Touhy Ave" "Clark St & Touhy Ave" "Greenview Ave & Fullerton Ave" "Paulina St & Montrose Ave" ...
## $ end_station_id : chr [1:6048463] "RP-007" "RP-007" "TA1307000001" "TA1309000021" ...
## $ start_lat : num [1:6048463] 42 42 41.9 42 41.9 ...
## $ start_lng : num [1:6048463] -87.7 -87.7 -87.7 -87.7 -87.6 ...
## $ end_lat : num [1:6048463] 42 42 41.9 42 41.9 ...
## $ end_lng : num [1:6048463] -87.7 -87.7 -87.7 -87.7 -87.6 ...
## $ member_casual : chr [1:6048463] "casual" "casual" "member" "casual" ...
## $ ride_length : 'difftime' num [1:6048463] 177 261 261 896 ...
## ..- attr(*, "units")= chr "secs"
## $ date : Date[1:6048463], format: "2022-01-13" "2022-01-10" ...
## $ month : chr [1:6048463] "01" "01" "01" "01" ...
## $ day : chr [1:6048463] "13" "10" "25" "04" ...
## $ year : chr [1:6048463] "2022" "2022" "2022" "2022" ...
## $ day_of_week : chr [1:6048463] "Thursday" "Monday" "Tuesday" "Tuesday" ...
## - 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>
Convert “ride-length” from difftime to numeric so we can run calculations on the data (e.g., using summary())
all_bike_trips$ride_length <-
as.numeric(as.character(all_bike_trips$ride_length))
is.numeric(all_bike_trips$ride_length)## [1] TRUE
The dataframe includeds a few hundred entries with missing or “NA”
values. Just in case, check for “NA” values for $started_at or
$ended_at columns, which are crucial to analysis
(https://www.datasciencemadesimple.com/delete-or-drop-rows-in-r-with-conditions-2/)
all_bike_trips[(is.na(all_bike_trips$started_at)|is.na(all_bike_trips$ended_at)),]## # A tibble: 0 × 19
## # … with 19 variables: ride_id <chr>, rideable_type <chr>, started_at <dttm>,
## # ended_at <dttm>, start_station_name <chr>, start_station_id <chr>,
## # end_station_name <chr>, end_station_id <chr>, start_lat <dbl>,
## # start_lng <dbl>, end_lat <dbl>, end_lng <dbl>, member_casual <chr>,
## # ride_length <dbl>, date <date>, month <chr>, day <chr>, year <chr>,
## # day_of_week <chr>
Check for any negative ride lengths.
all_bike_trips[(all_bike_trips$ride_length<0),] #101 rows found## # A tibble: 101 × 19
## ride_id ridea…¹ started_at ended_at start…² start…³
## <chr> <chr> <dttm> <dttm> <chr> <chr>
## 1 2D97E3C98E16… classi… 2022-03-05 11:00:57 2022-03-05 10:55:01 DuSabl… TA1307…
## 2 7407049C5D89… electr… 2022-03-05 11:38:04 2022-03-05 11:37:57 Sheffi… TA1307…
## 3 0793C9208A64… electr… 2022-05-30 11:06:29 2022-05-30 11:06:17 Broadw… 13325
## 4 B897BE02B21F… electr… 2022-06-07 19:15:39 2022-06-07 17:05:37 <NA> <NA>
## 5 072E947E156D… electr… 2022-06-07 19:14:46 2022-06-07 17:07:45 W Armi… 20254.0
## 6 6A871510E302… electr… 2022-06-23 19:22:57 2022-06-23 19:21:46 <NA> <NA>
## 7 BF114472ABA0… electr… 2022-06-07 19:14:47 2022-06-07 17:05:42 Base -… Hubbar…
## 8 B3BE8FE661F7… electr… 2022-06-07 16:18:37 2022-06-07 16:07:28 <NA> <NA>
## 9 EF3CCF2B0563… electr… 2022-06-07 18:47:01 2022-06-07 17:05:41 <NA> <NA>
## 10 BBD84670E054… electr… 2022-06-07 19:11:33 2022-06-07 17:05:24 <NA> <NA>
## # … with 91 more rows, 13 more variables: end_station_name <chr>,
## # end_station_id <chr>, start_lat <dbl>, start_lng <dbl>, end_lat <dbl>,
## # end_lng <dbl>, member_casual <chr>, ride_length <dbl>, date <date>,
## # month <chr>, day <chr>, year <chr>, day_of_week <chr>, and abbreviated
## # variable names ¹rideable_type, ²start_station_name, ³start_station_id
Create a new version of the dataframe (v2) that drops negative ride lengths
all_bike_trips_v2 <- all_bike_trips[!(all_bike_trips$ride_length<0),]Descriptive analysis on ride_length (all figures in seconds)
mean(all_bike_trips_v2$ride_length) #straight average (total ride length divided by # of rides)## [1] 1143.419
median(all_bike_trips_v2$ride_length) #midpoint number in the ascending array of ride lengths## [1] 603
max(all_bike_trips_v2$ride_length) #longest ride## [1] 2483235
min(all_bike_trips_v2$ride_length) #shortest ride## [1] 0
Condense the above four lines of descriptive analysis into one by using
the summary() function
summary(all_bike_trips_v2$ride_length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 341 603 1143 1084 2483235
Compare ride length among members and casual riders
aggregate(all_bike_trips_v2$ride_length ~ all_bike_trips_v2$member_casual, FUN = mean)## all_bike_trips_v2$member_casual all_bike_trips_v2$ride_length
## 1 casual 1736.1354
## 2 member 752.1637
aggregate(all_bike_trips_v2$ride_length ~ all_bike_trips_v2$member_casual, FUN = median)## all_bike_trips_v2$member_casual all_bike_trips_v2$ride_length
## 1 casual 768
## 2 member 520
aggregate(all_bike_trips_v2$ride_length ~ all_bike_trips_v2$member_casual, FUN = max)## all_bike_trips_v2$member_casual all_bike_trips_v2$ride_length
## 1 casual 2483235
## 2 member 93594
aggregate(all_bike_trips_v2$ride_length ~ all_bike_trips_v2$member_casual, FUN = min)## all_bike_trips_v2$member_casual all_bike_trips_v2$ride_length
## 1 casual 0
## 2 member 0
aggregate(all_bike_trips_v2$ride_length ~ all_bike_trips_v2$member_casual + all_bike_trips_v2$day_of_week, FUN = mean)## all_bike_trips_v2$member_casual all_bike_trips_v2$day_of_week
## 1 casual Friday
## 2 member Friday
## 3 casual Monday
## 4 member Monday
## 5 casual Saturday
## 6 member Saturday
## 7 casual Sunday
## 8 member Sunday
## 9 casual Thursday
## 10 member Thursday
## 11 casual Tuesday
## 12 member Tuesday
## 13 casual Wednesday
## 14 member Wednesday
## all_bike_trips_v2$ride_length
## 1 1669.2406
## 2 742.2457
## 3 1730.4362
## 4 726.5003
## 5 1954.7222
## 6 837.4937
## 7 2031.5739
## 8 832.3083
## 9 1522.5367
## 10 727.2318
## 11 1529.7608
## 12 715.2197
## 13 1469.6641
## 14 716.0710
The days of the week are out of order in the above result; fix the order to start from Sunday.
all_bike_trips_v2$day_of_week <- ordered(all_bike_trips_v2$day_of_week, levels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))aggregate(all_bike_trips_v2$ride_length ~ all_bike_trips_v2$member_casual + all_bike_trips_v2$day_of_week, FUN = mean)## all_bike_trips_v2$member_casual all_bike_trips_v2$day_of_week
## 1 casual Sunday
## 2 member Sunday
## 3 casual Monday
## 4 member Monday
## 5 casual Tuesday
## 6 member Tuesday
## 7 casual Wednesday
## 8 member Wednesday
## 9 casual Thursday
## 10 member Thursday
## 11 casual Friday
## 12 member Friday
## 13 casual Saturday
## 14 member Saturday
## all_bike_trips_v2$ride_length
## 1 2031.5739
## 2 832.3083
## 3 1730.4362
## 4 726.5003
## 5 1529.7608
## 6 715.2197
## 7 1469.6641
## 8 716.0710
## 9 1522.5367
## 10 727.2318
## 11 1669.2406
## 12 742.2457
## 13 1954.7222
## 14 837.4937
The ridership increases over weekend for both casual and member cyclists. There are more casual ridership than member ridership overall.
aggregate(all_bike_trips_v2$ride_length ~ all_bike_trips_v2$member_casual + all_bike_trips_v2$rideable_type, FUN = mean)## all_bike_trips_v2$member_casual all_bike_trips_v2$rideable_type
## 1 casual classic_bike
## 2 member classic_bike
## 3 casual docked_bike
## 4 casual electric_bike
## 5 member electric_bike
## all_bike_trips_v2$ride_length
## 1 1715.7455
## 2 822.4180
## 3 7434.4912
## 4 957.0314
## 5 678.8516
The result shows longest ridership for classic bikes regardless of membership. It’s striking to see no members use docked bike; requires further analysis.
all_bike_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>% # creates weekday fields using wday()
group_by(member_casual, weekday) %>% #groups by user type and weekday
summarise(number_of_rides = n(), # calculates the number of rides and average duration
average_duration = mean(ride_length)) %>%
arrange(member_casual,weekday) # sort by member type, then 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 Sun 405343 2032.
## 2 casual Mon 290157 1730.
## 3 casual Tue 277488 1530.
## 4 casual Wed 284909 1470.
## 5 casual Thu 318061 1523.
## 6 casual Fri 343960 1669.
## 7 casual Sat 485083 1955.
## 8 member Sun 423534 832.
## 9 member Mon 520375 727.
## 10 member Tue 576626 715.
## 11 member Wed 569825 716.
## 12 member Thu 573040 727.
## 13 member Fri 504503 742.
## 14 member Sat 475458 837.
all_bike_trips_v2 %>%
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") +
labs(x = "Weekdays", y = "Average Duration", title = "Number of Rides per Membership type", subtitle = "Comparing ridership between casual and member rider types over weekdays", caption = "Data from 2022-01 to 2022-12")
This graph shows inverse relationship in ridership pattern among casual riders and members. Whereas members’ ridership peaked mid-week, casual ridership peaked over weekends.
all_bike_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>% # creates weekday fields using wday()
group_by(member_casual, rideable_type) %>% #groups by user type and bike type
summarise(number_of_rides = n(), # calculates the number of rides and average duration
average_duration = mean(ride_length)) %>%
arrange(rideable_type,member_casual) # sort by bike type, then membership type## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
## # A tibble: 5 × 4
## # Groups: member_casual [2]
## member_casual rideable_type number_of_rides average_duration
## <chr> <chr> <int> <dbl>
## 1 casual classic_bike 920886 1716.
## 2 member classic_bike 1860482 822.
## 3 casual docked_bike 181407 7434.
## 4 casual electric_bike 1302708 957.
## 5 member electric_bike 1782879 679.
all_bike_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>%
group_by(member_casual, rideable_type) %>%
summarise(number_of_rides = n(),
average_duration = mean(ride_length)) %>%
arrange(member_casual, rideable_type) %>%
ggplot(aes (x = member_casual, y = average_duration, fill = rideable_type)) + geom_col(position = "dodge") +
labs(x = "Membership Types", y = "Average Duration", title = "Average trip duration per bike types across membership type", subtitle = "Comparing ridership of Classic vs. Docked vs. Electric bikes across membership types", caption = "Data from 2022-01 to 2022-12")## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
all_bike_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>%
group_by(member_casual, rideable_type) %>%
summarise(number_of_rides = n(),
average_duration = mean(ride_length)) %>%
arrange(member_casual, rideable_type) %>%
ggplot(aes (x = member_casual, y = number_of_rides, fill = rideable_type)) + geom_col(position = "dodge") +
labs(x = "Membership Types", y = "Number of Rides", title = "Number of Rides per bike types across membership type", subtitle = "Comparing ridership of Classic vs. Docked vs. Electric bikes across membership types", caption = "Data from 2022-01 to 2022-12")## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Conclusion
Whereas the total number of rides are higher for members compared to casual riders, Casual riders average trip duration is higher than members’ rides. For docked bikes, the number of total rides are the lowest, yet the average trip duration is longest. This suggest that casual riders are spending more time on divvy bikes – especially on docked bikes – than those holding a membership. The shorter trip duration from members likely suggest biking as a transportation instead of biking as leisure. That is, work-related short commutes during the weekdays are likely shorter in duration than biking over weekend for leisure.
Create a csv file that can be used for further analysis via other software (E.g., Excel, Tableau)
counts <- aggregate(all_bike_trips_v2$ride_length ~
all_bike_trips_v2$member_casual + all_bike_trips_v2$day_of_week, FUN = mean)
write.csv(counts, file ='~/avg_ride_length.csv')