Case study · R
Bike-Share Rider Analysis
My Google Data Analytics capstone: cleaning and analyzing a year of Chicago bike-share trips in R to answer one question for a marketing team. How do annual members and casual riders use the bikes differently?
5.7MRides analyzed
62%Rides by members
2.3xLonger casual rides
5pmPeak hour for both
The ask
In the capstone scenario, Cyclistic, a Chicago bike-share company, found that annual members are more profitable than casual riders. Marketing wanted to convert casual riders into members, and needed to know how the two groups ride differently before designing a campaign.
The data is Divvy’s real trip history, made public by Motivate International Inc. under the Divvy data license.
How I built it
- Step 1Combined and checked the dataRead in 12 monthly CSV files, confirmed their columns matched, and bound them into one 5.7 million row table.
- Step 2Cleaned itStandardized column names, confirmed unique 16-character ride IDs, removed rides that ended before they started, labeled missing stations, and added date, hour, duration and distance fields.
- Step 3Analyzed and charted itCompared members and casual riders by ride length, day, hour, month, distance, bike type and start station in ggplot2.
What the data showed
- Casual riders take much longer rides: 0.70 hours on average, against 0.31 hours for members.
- Members ride like commuters, with weekday peaks around 8am and 5pm. Casual riders ride more on weekends.
- Both groups peak from June to August and drop sharply in winter, casual riders most of all.
- Casual riders favor electric bikes (58%) and are the only group using docked bikes.
- Casual riders cluster at Streeter Dr & Grand Ave, and their top five start stations do not overlap with members at all.
Recommendations
- Membership promotionsWeekend discounts and milestone rewards for trips or hours ridden, promoted in the app, aimed squarely at casual riders.
- Targeted messages at Streeter Dr & Grand AveCasual riders concentrate at this station, so it is the first place to promote membership benefits like priority bike access.
- Station feedback surveysShort surveys at the busiest casual rider stations to learn what would make those riders sign up.
The R code
Every cleaning and analysis step, as run in Posit Cloud.
Show the R code
# Install relevant packages if not available if(!require("tidyverse")) install.packages("tidyverse") if(!require("janitor")) install.packages("janitor") if(!require("geosphere")) install.packages("geosphere")
# Load the required packages
library(tidyverse)
library(janitor)
library(geosphere)
Loading required package: tidyverse
── Attaching core tidyverse packages ─────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4 ✔ forcats 1.0.0 ✔ stringr 1.5.0 ✔ ggplot2 3.4.2 ✔ tibble 3.2.1 ✔ lubridate 1.9.2 ✔ tidyr 1.3.0 ✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors Loading required package: janitor
Attaching package: ‘janitor’
The following objects are masked from ‘package:stats’: chisq.test, fisher.test
Loading required package: geosphere
The legacy packages maptools, rgdal, and rgeos, underpinning the sp package, which was just loaded, will retire in October 2023. Please refer to R-spatial evolution reports for details, especially https://r-spatial.org/r/2023/05/15/evolution4.html. It may be desirable to make the sf package available; package maintainers should consider adding sf to Suggests:. The sp package is now running under evolution status 2 (status 2 uses the sf package in place of rgdal)
# Read CSV and assign names to each data frame
trips_2022_08 <- read_csv("/kaggle/input/cyclistic-tripdata/202208-divvy-tripdata.csv")
trips_2022_09 <- read_csv("/kaggle/input/cyclistic-tripdata/202209-divvy-publictripdata.csv")
trips_2022_10 <- read_csv("/kaggle/input/cyclistic-tripdata/202210-divvy-tripdata.csv")
trips_2022_11 <- read_csv("/kaggle/input/cyclistic-tripdata/202211-divvy-tripdata.csv")
trips_2022_12 <- read_csv("/kaggle/input/cyclistic-tripdata/202212-divvy-tripdata.csv")
trips_2023_01 <- read_csv("/kaggle/input/cyclistic-tripdata/202301-divvy-tripdata.csv")
trips_2023_02 <- read_csv("/kaggle/input/cyclistic-tripdata/202302-divvy-tripdata.csv")
trips_2023_03 <- read_csv("/kaggle/input/cyclistic-tripdata/202303-divvy-tripdata.csv")
trips_2023_04 <- read_csv("/kaggle/input/cyclistic-tripdata/202304-divvy-tripdata.csv")
trips_2023_05 <- read_csv("/kaggle/input/cyclistic-tripdata/202305-divvy-tripdata.csv")
trips_2023_06 <- read_csv("/kaggle/input/cyclistic-tripdata/202306-divvy-tripdata.csv")
trips_2023_07 <- read_csv("/kaggle/input/cyclistic-tripdata/202307-divvy-tripdata.csv")
Rows: 785932 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.
Rows: 701339 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.
Rows: 558685 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.
Rows: 337735 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.
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.
Rows: 190301 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.
Rows: 190445 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.
Rows: 258678 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.
Rows: 426590 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.
Rows: 604827 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.
Rows: 719618 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.
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.
# Check whether the set of data frames are row-bindable compare_df_cols_same(
trips_2022_08,trips_2022_09,trips_2022_10, trips_2022_11,trips_2022_12,trips_2023_01, trips_2023_02,trips_2023_03,trips_2023_04, trips_2023_05,trips_2023_06,trips_2023_07 )
TRUE
# Combine all data frames into a single data frame total_trips_original <- bind_rows( trips_2022_08,trips_2022_09,trips_2022_10, trips_2022_11,trips_2022_12,trips_2023_01, trips_2023_02,trips_2023_03,trips_2023_04, trips_2023_05,trips_2023_06,trips_2023_07 )
# Standardize & clean column names
total_trips_cleaned <- clean_names(total_trips_original)
colnames(total_trips_cleaned)
'ride_id''rideable_type''started_at''ended_at''start_station_name''start_station_id''end_station_name''end_station_id''start_lat''start_lng''end_lat''end_lng''member_casual'
head(total_trips_cleaned)
str(total_trips_cleaned)
spc_tbl_ [5,723,606 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ ride_id : chr [1:5723606] "550CF7EFEAE0C618" "DAD198F405F9C5F5" "E6F2BC47B65CB7FD" "F597830181C2E13C" ...
$ rideable_type : chr [1:5723606] "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
$ started_at : POSIXct[1:5723606], format: "2022-08-07 21:34:15" "2022-08-08 14:39:21" ...
$ ended_at : POSIXct[1:5723606], format: "2022-08-07 21:41:46" "2022-08-08 14:53:23" ...
$ start_station_name: chr [1:5723606] NA NA NA NA ...
$ start_station_id : chr [1:5723606] NA NA NA NA ...
$ end_station_name : chr [1:5723606] NA NA NA NA ...
$ end_station_id : chr [1:5723606] NA NA NA NA ...
$ start_lat : num [1:5723606] 41.9 41.9 42 41.9 41.9 ...
$ start_lng : num [1:5723606] -87.7 -87.6 -87.7 -87.7 -87.7 ...
$ end_lat : num [1:5723606] 41.9 41.9 42 42 41.8 ...
$ end_lng : num [1:5723606] -87.7 -87.6 -87.7 -87.7 -87.7 ...
$ member_casual : chr [1:5723606] "casual" "casual" "casual" "casual" ...
- attr(*, "spec")=
.. cols(
.. ride_id = col_character(),
.. rideable_type = col_character(),
.. started_at = col_datetime(format = ""),
.. ended_at = col_datetime(format = ""),
.. start_station_name = col_character(),
.. start_station_id = col_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>
glimpse(total_trips_cleaned)
Rows: 5,723,606
Columns: 13
$ ride_id <chr> "550CF7EFEAE0C618", "DAD198F405F9C5F5", "E6F2BC47B6…
$ rideable_type <chr> "electric_bike", "electric_bike", "electric_bike", …
$ started_at <dttm> 2022-08-07 21:34:15, 2022-08-08 14:39:21, 2022-08-…
$ ended_at <dttm> 2022-08-07 21:41:46, 2022-08-08 14:53:23, 2022-08-…
$ start_station_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ start_station_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ end_station_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ end_station_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ start_lat <dbl> 41.93, 41.89, 41.97, 41.94, 41.85, 41.79, 41.89, 41…
$ start_lng <dbl> -87.69, -87.64, -87.69, -87.65, -87.65, -87.72, -87…
$ end_lat <dbl> 41.94, 41.92, 41.97, 41.97, 41.84, 41.82, 41.89, 41…
$ end_lng <dbl> -87.72, -87.64, -87.66, -87.69, -87.66, -87.69, -87…
$ member_casual <chr> "casual", "casual", "casual", "casual", "casual", "…
summary(total_trips_cleaned)
ride_id rideable_type started_at
Length:5723606 Length:5723606 Min. :2022-08-01 00:00:00
Class :character Class :character 1st Qu.:2022-09-28 13:56:43
Mode :character Mode :character Median :2023-02-16 13:53:51
Mean :2023-02-01 23:55:22
3rd Qu.:2023-06-03 07:41:37
Max. :2023-07-31 23:59:56
ended_at start_station_name start_station_id
Min. :2022-08-01 00:05:00 Length:5723606 Length:5723606
1st Qu.:2022-09-28 14:12:20 Class :character Class :character
Median :2023-02-16 14:04:56 Mode :character Mode :character
Mean :2023-02-02 00:13:43
3rd Qu.:2023-06-03 08:00:15
Max. :2023-08-12 04:53:41
end_station_name end_station_id start_lat start_lng
Length:5723606 Length:5723606 Min. :41.64 Min. :-87.92
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. :-88.16 Length:5723606
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. :42.18 Max. : 0.00
NA's :6102 NA's :6102
num_duplicate_rows <- sum(
duplicated(total_trips_cleaned$ride_id))
cat("Number of rows with duplicates:", num_duplicate_rows, "\n")
Number of rows with duplicates: 0
n_distinct(total_trips_cleaned)
5723606
sum(nchar(total_trips_cleaned$ride_id)!=16)
0
max_date <- total_trips_cleaned %>% summarise(max_date = max(started_at))
min_date <- total_trips_cleaned %>% summarise(min_date = min(started_at))
print(max_date)
print(min_date)
# A tibble: 1 × 1
max_date
<dttm>
1 2023-07-31 23:59:56
# A tibble: 1 × 1
min_date
<dttm>
1 2022-08-01 00:00:00
rows_before_mutation <- nrow(total_trips_cleaned)
na_count <- sapply(total_trips_cleaned, function(col) sum(is.na(col)))
print(na_count)
ride_id rideable_type started_at ended_at
0 0 0 0
start_station_name start_station_id end_station_name end_station_id
868772 868904 925008 925149
start_lat start_lng end_lat end_lng
0 0 6102 6102
member_casual
0
columns_to_mutate <- c("start_station_name", "start_station_id",
"end_station_name", "end_station_id")
total_trips_prepared <- total_trips_cleaned %>%
mutate(across(all_of(columns_to_mutate), ~ replace_na(., "missing")))
missing_recount <- sapply(total_trips_prepared[columns_to_mutate],
function(col) sum(col == "missing"))
print(missing_recount)
start_station_name start_station_id end_station_name end_station_id
868772 868904 925008 925149
total_trips_transformed <- total_trips_prepared %>%
filter(started_at <= ended_at)
rows_filtered_out <- nrow(total_trips_prepared) - nrow(total_trips_transformed)
cat("Number of rows filtered out:", rows_filtered_out, "\n")
Number of rows filtered out: 121
missing_summary <- sapply(total_trips_transformed, function(col) sum(is.na(col)))
total_inputs <- nrow(total_trips_transformed)
percent_difference <- (missing_summary / total_inputs) * 100
distinct_responses <- sapply(total_trips_transformed, function(col) length(unique(col)))
summary_table <- data.frame(
column = names(total_trips_transformed),
missing_Count = missing_summary,
total_Inputs = rep(total_inputs, length(total_trips_transformed)),
percent_difference = percent_difference,
distinct_responses = distinct_responses
)
print(summary_table)
column missing_Count total_Inputs
ride_id ride_id 0 5723485
rideable_type rideable_type 0 5723485
started_at started_at 0 5723485
ended_at ended_at 0 5723485
start_station_name start_station_name 0 5723485
start_station_id start_station_id 0 5723485
end_station_name end_station_name 0 5723485
end_station_id end_station_id 0 5723485
start_lat start_lat 0 5723485
start_lng start_lng 0 5723485
end_lat end_lat 6102 5723485
end_lng end_lng 6102 5723485
member_casual member_casual 0 5723485
percent_difference distinct_responses
ride_id 0.0000000 5723485
rideable_type 0.0000000 3
started_at 0.0000000 4820913
ended_at 0.0000000 4833032
start_station_name 0.0000000 1802
start_station_id 0.0000000 1503
end_station_name 0.0000000 1800
end_station_id 0.0000000 1506
start_lat 0.0000000 782347
start_lng 0.0000000 740445
end_lat 0.1066134 13868
end_lng 0.1066134 13983
member_casual 0.0000000 2
total_trips_transformed <- total_trips_transformed %>%
mutate(
date = as.Date(started_at),
year = year(started_at),
month = month(started_at, label = TRUE),
day = wday(started_at, label = TRUE),
hour = hour(started_at),
duration_s = as.numeric(difftime(ended_at, started_at, units = "secs")),
duration_h = round(duration_s / 2400,3),
ride_distance_km = distGeo(
matrix(c(total_trips_transformed$start_lng, total_trips_transformed$start_lat), ncol = 2),
matrix(c(total_trips_transformed$end_lng, total_trips_transformed$end_lat), ncol = 2)
) / 1000
)
glimpse(total_trips_transformed)
Rows: 5,723,485
Columns: 21
$ ride_id <chr> "550CF7EFEAE0C618", "DAD198F405F9C5F5", "E6F2BC47B6…
$ rideable_type <chr> "electric_bike", "electric_bike", "electric_bike", …
$ started_at <dttm> 2022-08-07 21:34:15, 2022-08-08 14:39:21, 2022-08-…
$ ended_at <dttm> 2022-08-07 21:41:46, 2022-08-08 14:53:23, 2022-08-…
$ start_station_name <chr> "missing", "missing", "missing", "missing", "missin…
$ start_station_id <chr> "missing", "missing", "missing", "missing", "missin…
$ end_station_name <chr> "missing", "missing", "missing", "missing", "missin…
$ end_station_id <chr> "missing", "missing", "missing", "missing", "missin…
$ start_lat <dbl> 41.93, 41.89, 41.97, 41.94, 41.85, 41.79, 41.89, 41…
$ start_lng <dbl> -87.69, -87.64, -87.69, -87.65, -87.65, -87.72, -87…
$ end_lat <dbl> 41.94, 41.92, 41.97, 41.97, 41.84, 41.82, 41.89, 41…
$ end_lng <dbl> -87.72, -87.64, -87.66, -87.69, -87.66, -87.69, -87…
$ member_casual <chr> "casual", "casual", "casual", "casual", "casual", "…
$ date <date> 2022-08-07, 2022-08-08, 2022-08-08, 2022-08-08, 20…
$ year <dbl> 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 202…
$ month <ord> Aug, Aug, Aug, Aug, Aug, Aug, Aug, Aug, Aug, Aug, A…
$ day <ord> Sun, Mon, Mon, Mon, Sun, Mon, Mon, Sun, Sun, Sun, S…
$ hour <int> 21, 14, 15, 2, 20, 13, 14, 20, 21, 23, 20, 11, 22, …
$ duration_s <dbl> 451, 842, 644, 903, 352, 781, 536, 1077, 683, 669, …
$ duration_h <dbl> 0.188, 0.351, 0.268, 0.376, 0.147, 0.325, 0.223, 0.…
$ ride_distance_km <dbl> 2.7247194, 3.3321432, 2.4866899, 4.7012385, 1.38687…
ride_summary <- total_trips_transformed %>%
group_by(member_casual) %>%
summarise(total_rides = n()) %>%
mutate(percentage = (total_rides / sum(total_rides)) * 100)
print(ride_summary)
# A tibble: 2 × 3
member_casual total_rides percentage
<chr> <int> <dbl>
1 casual 2169497 37.9
2 member 3553988 62.1
summary(total_trips_transformed$duration_h)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.136 0.240 0.459 0.428 1286.535
aggregate(total_trips_transformed$duration_h ~ total_trips_transformed$member_casual, FUN = mean)
aggregate(total_trips_transformed$duration_h ~ total_trips_transformed$member_casual, FUN = median)
aggregate(total_trips_transformed$duration_h ~ total_trips_transformed$member_casual, FUN = max)
aggregate(total_trips_transformed$duration_h ~ total_trips_transformed$member_casual, FUN = min)
A data.frame: 2 × 2
total_trips_transformed$member_casual total_trips_transformed$duration_h
<chr> <dbl>
casual 0.7035874
member 0.3096731
A data.frame: 2 × 2
total_trips_transformed$member_casual total_trips_transformed$duration_h
<chr> <dbl>
casual 0.296
member 0.213
A data.frame: 2 × 2
total_trips_transformed$member_casual total_trips_transformed$duration_h
<chr> <dbl>
casual 1286.535
member 38.992
A data.frame: 2 × 2
total_trips_transformed$member_casual total_trips_transformed$duration_h
<chr> <dbl>
casual 0
member 0
aggregate(total_trips_transformed$duration_h ~ total_trips_transformed$member_casual +
total_trips_transformed$day, FUN = mean)
A data.frame: 14 × 3
total_trips_transformed$member_casual total_trips_transformed$day total_trips_transformed$duration_h
<chr> <ord> <dbl>
casual Sun 0.8258949
member Sun 0.3421641
casual Mon 0.6817051
member Mon 0.2958516
casual Tue 0.6286331
member Tue 0.2977303
casual Wed 0.6018506
member Wed 0.2943977
casual Thu 0.5961304
member Thu 0.2972516
casual Fri 0.6846345
member Fri 0.3088395
casual Sat 0.8146626
member Sat 0.3467641
average_dur <- total_trips_transformed %>%
group_by(member_casual, day) %>%
summarise(number_of_rides = n(),
average_duration = mean(duration_h)) %>%
arrange(member_casual, day) %>%
ggplot(aes(x = day, y = average_duration, fill = member_casual)) +
geom_col(position = "dodge") +
labs(title = "Average Ride Duration per Week",
x = "Day of the Week",
y = "Average Duration (hours)",
fill = "Member/Casual")
print(average_dur)
total_riders <- total_trips_transformed %>%
group_by(member_casual, day) %>%
summarise(number_of_rides = n(),
average_duration= mean(duration_h)) %>%
arrange(member_casual, day) %>%
ggplot(aes(x=day,y=number_of_rides, fill=member_casual))+
geom_col(position = "dodge") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Total Number of Rides per Day",
x = "Day of the Week",
y = "Number of Rides",
fill = "Member/Casual")
print(total_riders)
`summarise()` has grouped output by 'member_casual'. You can override using the
`.groups` argument.
casual_hourly <- total_trips_transformed %>%
group_by(hour, member_casual) %>%
summarise(number_of_rides = n()) %>%
ggplot(aes(x = hour, y = number_of_rides, fill = member_casual)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Total Number of Rides per Hour",
x = "Hour",
y = "Number of Rides",
fill = "Member/Casual") +
theme_minimal() +
scale_y_continuous(labels = scales::comma)
print(casual_hourly)
`summarise()` has grouped output by 'hour'. You can override using the
`.groups` argument.
casual_monthly <- total_trips_transformed %>%
group_by(month, member_casual) %>%
summarise(number_of_rides = n()) %>%
ggplot(aes(x = month, y = number_of_rides, fill = member_casual)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Total Number of Rides per Month",
x = "Month",
y = "Number of Rides",
fill = "Member/Casual") +
theme_minimal()+
scale_y_continuous(labels = scales::comma)
print(casual_monthly)
`summarise()` has grouped output by 'month'. You can override using the
`.groups` argument.
distance_month <- total_trips_transformed %>%
group_by(month, member_casual) %>%
filter(!is.na(ride_distance_km) | ride_distance_km != 0) %>%
summarize(average_distance = mean(ride_distance_km)) %>%
ggplot(aes(x = month, y = average_distance,
color = member_casual, group = member_casual)) +
geom_line() +
labs(title = "Average Distance per Month",
x = "Month",
y = "Average Distance",
color = "Member/Casual") +
theme_minimal()
print(distance_month)
`summarise()` has grouped output by 'month'. You can override using the
`.groups` argument.
distance_day_line <- total_trips_transformed %>%
group_by(day, member_casual) %>%
filter(!is.na(ride_distance_km) | ride_distance_km != 0) %>%
summarize(average_distance = mean(ride_distance_km)) %>%
ggplot(aes(x = day, y = average_distance, color = member_casual,
group = member_casual)) +
geom_line() +
labs(title = "Average Distance per Day",
x = "Day",
y = "Average Distance",
color = "Member/Casual") +
theme_minimal()
print(distance_day_line)
`summarise()` has grouped output by 'day'. You can override using the `.groups`
argument.
distance_hour_line <- total_trips_transformed %>%
group_by(hour, member_casual) %>%
filter(!is.na(ride_distance_km) | ride_distance_km != 0) %>%
summarize(average_distance = mean(ride_distance_km)) %>%
ggplot(aes(x = hour, y = average_distance, color = member_casual)) +
geom_line() +
labs(title = "Average Distance per Hour",
x = "Hour",
y = "Average Distance",
color = "Member/Casual") +
theme_minimal()
print(distance_hour_line)
`summarise()` has grouped output by 'hour'. You can override using the
`.groups` argument.
pie_chart_member <- total_trips_transformed %>%
filter(member_casual == "member") %>%
group_by(rideable_type) %>%
summarise(count = n()) %>%
mutate(percentage = count / sum(count) * 100) %>%
arrange(desc(percentage)) %>%
mutate(position = cumsum(percentage) - percentage / 2) %>%
ggplot(aes(x = "", y = percentage, fill = rideable_type)) +
geom_bar(stat = "identity", width = 1, position = position_stack(vjust = 0.5),
color = "white") +
geom_text(aes(label = ifelse(percentage %in% c(0, 25, 50, 100), "",
paste0(round(percentage), "%"))),
position = position_stack(vjust = 0.5),
check_overlap = TRUE,
color = "white") +
coord_polar(theta = "y") +
scale_x_discrete(breaks = NULL) +
labs(title = "Bike Type Distribution for Member Riders",
fill = "Bike Type") +
theme_void() +
theme(legend.position = "right")
print(pie_chart_member)
pie_chart_casual <- total_trips_transformed %>%
filter(member_casual == "casual") %>%
group_by(rideable_type) %>%
summarise(count = n()) %>%
mutate(percentage = count / sum(count) * 100) %>%
arrange(desc(percentage)) %>%
mutate(position = cumsum(percentage) - percentage / 2) %>%
ggplot(aes(x = "", y = percentage, fill = rideable_type)) +
geom_bar(stat = "identity", width = 1, position = position_stack(vjust = 0.5),
color = "white") +
geom_text(aes(label = ifelse(percentage %in% c(0, 25, 50, 100), "",
paste0(round(percentage), "%"))),
position = position_stack(vjust = 0.5),
check_overlap = TRUE,
color = "white") +
coord_polar(theta = "y") +
scale_x_discrete(breaks = NULL) +
labs(title = "Bike Type Distribution for Casual Riders",
fill = "Bike Type") +
theme_void() +
theme(legend.position = "right")
print(pie_chart_casual)
top_start_stations <- total_trips_transformed %>%
group_by(member_casual, start_station_name) %>%
filter(start_station_name != "missing") %>%
summarise(total_rides = n()) %>%
arrange(member_casual, desc(total_rides)) %>%
group_by(member_casual) %>%
top_n(10)
print(top_start_stations)
# List the top 10 end stations
top_end_stations <- total_trips_transformed %>%
group_by(member_casual, end_station_name) %>%
filter(end_station_name != "missing") %>%
summarise(total_rides = n()) %>%
arrange(member_casual, desc(total_rides)) %>%
group_by(member_casual) %>%
top_n(10)
print(top_end_stations)
`summarise()` has grouped output by 'member_casual'. You can override using the
`.groups` argument.
Selecting by total_rides
# A tibble: 20 × 3
# Groups: member_casual [2]
member_casual start_station_name total_rides
<chr> <chr> <int>
1 casual Streeter Dr & Grand Ave 50025
2 casual DuSable Lake Shore Dr & Monroe St 30193
3 casual Michigan Ave & Oak St 23119
4 casual Millennium Park 22437
5 casual DuSable Lake Shore Dr & North Blvd 20973
6 casual Shedd Aquarium 18706
7 casual Theater on the Lake 16757
8 casual Wells St & Concord Ln 14061
9 casual Dusable Harbor 13933
10 casual Indiana Ave & Roosevelt Rd 12346
11 member Kingsbury St & Kinzie St 25282
12 member Clark St & Elm St 23971
13 member Clinton St & Washington Blvd 23101
14 member Wells St & Concord Ln 21505
15 member University Ave & 57th St 20876
16 member Loomis St & Lexington St 20525
17 member Ellis Ave & 60th St 19772
18 member Wells St & Elm St 19495
19 member Clinton St & Madison St 19178
20 member Broadway & Barry Ave 18679
`summarise()` has grouped output by 'member_casual'. You can override using the
`.groups` argument.
Selecting by total_rides
# A tibble: 20 × 3
# Groups: member_casual [2]
member_casual end_station_name total_rides
<chr> <chr> <int>
1 casual Streeter Dr & Grand Ave 52721
2 casual DuSable Lake Shore Dr & Monroe St 27567
3 casual Michigan Ave & Oak St 24321
4 casual Millennium Park 24130
5 casual DuSable Lake Shore Dr & North Blvd 23064
6 casual Theater on the Lake 17863
7 casual Shedd Aquarium 16629
8 casual Wells St & Concord Ln 13586
9 casual Dusable Harbor 12950
10 casual Clark St & Armitage Ave 12285
11 member Kingsbury St & Kinzie St 25487
12 member Clinton St & Washington Blvd 24148
13 member Clark St & Elm St 23829
14 member Wells St & Concord Ln 22340
15 member University Ave & 57th St 21035
16 member Loomis St & Lexington St 20812
17 member Clinton St & Madison St 20242
18 member Wells St & Elm St 19611
19 member Ellis Ave & 60th St 19491
20 member Broadway & Barry Ave 18968
# Create a gradient color bar chart for casual riders
casual_bar_chart <- top_start_stations %>%
filter(member_casual == "casual") %>%
ggplot(aes(x = reorder(start_station_name, total_rides), y = total_rides,
fill = total_rides)) +
geom_bar(stat = "identity") + # No need to specify fill color here
scale_fill_gradient(low = "lightblue", high = "darkblue") +
labs(title = "Top Start Stations for Casual Riders",
x = "Start Station Name",
y = "Total Rides") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none") # Remove legend
print(casual_bar_chart)
# Create a gradient color bar chart for member riders
member_bar_chart <- top_start_stations %>%
filter(member_casual == "member") %>%
ggplot(aes(x = reorder(start_station_name, total_rides), y = total_rides,
fill = total_rides)) +
geom_bar(stat = "identity") + # No need to specify fill color here
scale_fill_gradient(low = "peachpuff", high = "darkorange") +
labs(title = "Top Start Stations for Member Riders",
x = "Start Station Name",
y = "Total Rides") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none")
print(member_bar_chart)Next projectOrders Dashboard →
Get in touch
