Cyclistic Bike-Share Analysis
John Rosman IV
12/19/2021
Collecting, cleaning, preparing, analyzing and visualizing
STEP 1: Collecting
## Rows: 1108163 Columns: 12## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): 03 - Rental Start Station Name, 02 - Rental End Station Name, User...
## dbl (5): 01 - Rental Details Rental ID, 01 - Rental Details Bike ID, 03 - R...
## dttm (2): 01 - Rental Details Local Start Time, 01 - Rental Details Local En...##
## 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: 1640718 Columns: 12## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): from_station_name, to_station_name, usertype, gender
## dbl (5): trip_id, bikeid, from_station_id, to_station_id, birthyear
## dttm (2): start_time, end_time##
## 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: 704054 Columns: 12## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): from_station_name, to_station_name, usertype, gender
## dbl (5): trip_id, bikeid, from_station_id, to_station_id, birthyear
## dttm (2): start_time, end_time##
## 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: 426887 Columns: 13## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): ride_id, rideable_type, start_station_name, end_station_name, memb...
## dbl (6): start_station_id, end_station_id, start_lat, start_lng, end_lat, e...
## dttm (2): started_at, ended_at##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.compared colnames across the 4 datasets
looking for inconsistencies
## [1] "trip_id" "start_time" "end_time"
## [4] "bikeid" "tripduration" "from_station_id"
## [7] "from_station_name" "to_station_id" "to_station_name"
## [10] "usertype" "gender" "birthyear"## [1] "trip_id" "start_time" "end_time"
## [4] "bikeid" "tripduration" "from_station_id"
## [7] "from_station_name" "to_station_id" "to_station_name"
## [10] "usertype" "gender" "birthyear"## [1] "01 - Rental Details Rental ID"
## [2] "01 - Rental Details Local Start Time"
## [3] "01 - Rental Details Local End Time"
## [4] "01 - Rental Details Bike ID"
## [5] "01 - Rental Details Duration In Seconds Uncapped"
## [6] "03 - Rental Start Station ID"
## [7] "03 - Rental Start Station Name"
## [8] "02 - Rental End Station ID"
## [9] "02 - Rental End Station Name"
## [10] "User Type"
## [11] "Member Gender"
## [12] "05 - Member Details Member Birthday Year"## [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"Renamed columns to make them consistent with q1_2020
(q4_2019 <- rename(q4_2019
,ride_id = trip_id
,rideable_type = bikeid
,started_at = start_time
,ended_at = end_time
,start_station_name = from_station_name
,start_station_id = from_station_id
,end_station_name = to_station_name
,end_station_id = to_station_id
,member_casual = usertype))## # A tibble: 704,054 × 12
## ride_id started_at ended_at rideable_type tripduration
## <dbl> <dttm> <dttm> <dbl> <dbl>
## 1 25223640 2019-10-01 00:01:39 2019-10-01 00:17:20 2215 940
## 2 25223641 2019-10-01 00:02:16 2019-10-01 00:06:34 6328 258
## 3 25223642 2019-10-01 00:04:32 2019-10-01 00:18:43 3003 850
## 4 25223643 2019-10-01 00:04:32 2019-10-01 00:43:43 3275 2350
## 5 25223644 2019-10-01 00:04:34 2019-10-01 00:35:42 5294 1867
## 6 25223645 2019-10-01 00:04:38 2019-10-01 00:10:51 1891 373
## 7 25223646 2019-10-01 00:04:52 2019-10-01 00:22:45 1061 1072
## 8 25223647 2019-10-01 00:04:57 2019-10-01 00:29:16 1274 1458
## 9 25223648 2019-10-01 00:05:20 2019-10-01 00:29:18 6011 1437
## 10 25223649 2019-10-01 00:05:20 2019-10-01 02:23:46 2957 8306
## # … with 704,044 more rows, and 7 more variables: start_station_id <dbl>,
## # start_station_name <chr>, end_station_id <dbl>, end_station_name <chr>,
## # member_casual <chr>, gender <chr>, birthyear <dbl>(q3_2019 <- rename(q3_2019
,ride_id = trip_id
,rideable_type = bikeid
,started_at = start_time
,ended_at = end_time
,start_station_name = from_station_name
,start_station_id = from_station_id
,end_station_name = to_station_name
,end_station_id = to_station_id
,member_casual = usertype))## # A tibble: 1,640,718 × 12
## ride_id started_at ended_at rideable_type tripduration
## <dbl> <dttm> <dttm> <dbl> <dbl>
## 1 23479388 2019-07-01 00:00:27 2019-07-01 00:20:41 3591 1214
## 2 23479389 2019-07-01 00:01:16 2019-07-01 00:18:44 5353 1048
## 3 23479390 2019-07-01 00:01:48 2019-07-01 00:27:42 6180 1554
## 4 23479391 2019-07-01 00:02:07 2019-07-01 00:27:10 5540 1503
## 5 23479392 2019-07-01 00:02:13 2019-07-01 00:22:26 6014 1213
## 6 23479393 2019-07-01 00:02:21 2019-07-01 00:07:31 4941 310
## 7 23479394 2019-07-01 00:02:24 2019-07-01 00:23:12 3770 1248
## 8 23479395 2019-07-01 00:02:26 2019-07-01 00:28:16 5442 1550
## 9 23479396 2019-07-01 00:02:34 2019-07-01 00:28:57 2957 1583
## 10 23479397 2019-07-01 00:02:45 2019-07-01 00:29:14 6091 1589
## # … with 1,640,708 more rows, and 7 more variables: start_station_id <dbl>,
## # start_station_name <chr>, end_station_id <dbl>, end_station_name <chr>,
## # member_casual <chr>, gender <chr>, birthyear <dbl>(q2_2019 <- rename(q2_2019
,ride_id = "01 - Rental Details Rental ID"
,rideable_type = "01 - Rental Details Bike ID"
,started_at = "01 - Rental Details Local Start Time"
,ended_at = "01 - Rental Details Local End Time"
,start_station_name = "03 - Rental Start Station Name"
,start_station_id = "03 - Rental Start Station ID"
,end_station_name = "02 - Rental End Station Name"
,end_station_id = "02 - Rental End Station ID"
,member_casual = "User Type"))## # A tibble: 1,108,163 × 12
## ride_id started_at ended_at rideable_type
## <dbl> <dttm> <dttm> <dbl>
## 1 22178529 2019-04-01 00:02:22 2019-04-01 00:09:48 6251
## 2 22178530 2019-04-01 00:03:02 2019-04-01 00:20:30 6226
## 3 22178531 2019-04-01 00:11:07 2019-04-01 00:15:19 5649
## 4 22178532 2019-04-01 00:13:01 2019-04-01 00:18:58 4151
## 5 22178533 2019-04-01 00:19:26 2019-04-01 00:36:13 3270
## 6 22178534 2019-04-01 00:19:39 2019-04-01 00:23:56 3123
## 7 22178535 2019-04-01 00:26:33 2019-04-01 00:35:41 6418
## 8 22178536 2019-04-01 00:29:48 2019-04-01 00:36:11 4513
## 9 22178537 2019-04-01 00:32:07 2019-04-01 01:07:44 3280
## 10 22178538 2019-04-01 00:32:19 2019-04-01 01:07:39 5534
## # … with 1,108,153 more rows, and 8 more variables:
## # 01 - Rental Details Duration In Seconds Uncapped <dbl>,
## # start_station_id <dbl>, start_station_name <chr>, end_station_id <dbl>,
## # end_station_name <chr>, member_casual <chr>, Member Gender <chr>,
## # 05 - Member Details Member Birthday Year <dbl>Inspected the dataframes and looked for incongruities
## spec_tbl_df [426,887 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : chr [1:426887] "EACB19130B0CDA4A" "8FED874C809DC021" "789F3C21E472CA96" "C9A388DAC6ABF313" ...
## $ rideable_type : chr [1:426887] "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
## $ started_at : POSIXct[1:426887], format: "2020-01-21 20:06:59" "2020-01-30 14:22:39" ...
## $ ended_at : POSIXct[1:426887], format: "2020-01-21 20:14:30" "2020-01-30 14:26:22" ...
## $ start_station_name: chr [1:426887] "Western Ave & Leland Ave" "Clark St & Montrose Ave" "Broadway & Belmont Ave" "Clark St & Randolph St" ...
## $ start_station_id : num [1:426887] 239 234 296 51 66 212 96 96 212 38 ...
## $ end_station_name : chr [1:426887] "Clark St & Leland Ave" "Southport Ave & Irving Park Rd" "Wilton Ave & Belmont Ave" "Fairbanks Ct & Grand Ave" ...
## $ end_station_id : num [1:426887] 326 318 117 24 212 96 212 212 96 100 ...
## $ start_lat : num [1:426887] 42 42 41.9 41.9 41.9 ...
## $ start_lng : num [1:426887] -87.7 -87.7 -87.6 -87.6 -87.6 ...
## $ end_lat : num [1:426887] 42 42 41.9 41.9 41.9 ...
## $ end_lng : num [1:426887] -87.7 -87.7 -87.7 -87.6 -87.6 ...
## $ member_casual : chr [1:426887] "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_double(),
## .. end_station_name = col_character(),
## .. end_station_id = col_double(),
## .. start_lat = col_double(),
## .. start_lng = col_double(),
## .. end_lat = col_double(),
## .. end_lng = col_double(),
## .. member_casual = col_character()
## .. )
## - attr(*, "problems")=<externalptr>## spec_tbl_df [704,054 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : num [1:704054] 25223640 25223641 25223642 25223643 25223644 ...
## $ started_at : POSIXct[1:704054], format: "2019-10-01 00:01:39" "2019-10-01 00:02:16" ...
## $ ended_at : POSIXct[1:704054], format: "2019-10-01 00:17:20" "2019-10-01 00:06:34" ...
## $ rideable_type : num [1:704054] 2215 6328 3003 3275 5294 ...
## $ tripduration : num [1:704054] 940 258 850 2350 1867 ...
## $ start_station_id : num [1:704054] 20 19 84 313 210 156 84 156 156 336 ...
## $ start_station_name: chr [1:704054] "Sheffield Ave & Kingsbury St" "Throop (Loomis) St & Taylor St" "Milwaukee Ave & Grand Ave" "Lakeview Ave & Fullerton Pkwy" ...
## $ end_station_id : num [1:704054] 309 241 199 290 382 226 142 463 463 336 ...
## $ end_station_name : chr [1:704054] "Leavitt St & Armitage Ave" "Morgan St & Polk St" "Wabash Ave & Grand Ave" "Kedzie Ave & Palmer Ct" ...
## $ member_casual : chr [1:704054] "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
## $ gender : chr [1:704054] "Male" "Male" "Female" "Male" ...
## $ birthyear : num [1:704054] 1987 1998 1991 1990 1987 ...
## - attr(*, "spec")=
## .. cols(
## .. trip_id = col_double(),
## .. start_time = col_datetime(format = ""),
## .. end_time = col_datetime(format = ""),
## .. bikeid = col_double(),
## .. tripduration = col_number(),
## .. from_station_id = col_double(),
## .. from_station_name = col_character(),
## .. to_station_id = col_double(),
## .. to_station_name = col_character(),
## .. usertype = col_character(),
## .. gender = col_character(),
## .. birthyear = col_double()
## .. )
## - attr(*, "problems")=<externalptr>## spec_tbl_df [1,640,718 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : num [1:1640718] 23479388 23479389 23479390 23479391 23479392 ...
## $ started_at : POSIXct[1:1640718], format: "2019-07-01 00:00:27" "2019-07-01 00:01:16" ...
## $ ended_at : POSIXct[1:1640718], format: "2019-07-01 00:20:41" "2019-07-01 00:18:44" ...
## $ rideable_type : num [1:1640718] 3591 5353 6180 5540 6014 ...
## $ tripduration : num [1:1640718] 1214 1048 1554 1503 1213 ...
## $ start_station_id : num [1:1640718] 117 381 313 313 168 300 168 313 43 43 ...
## $ start_station_name: chr [1:1640718] "Wilton Ave & Belmont Ave" "Western Ave & Monroe St" "Lakeview Ave & Fullerton Pkwy" "Lakeview Ave & Fullerton Pkwy" ...
## $ end_station_id : num [1:1640718] 497 203 144 144 62 232 62 144 195 195 ...
## $ end_station_name : chr [1:1640718] "Kimball Ave & Belmont Ave" "Western Ave & 21st St" "Larrabee St & Webster Ave" "Larrabee St & Webster Ave" ...
## $ member_casual : chr [1:1640718] "Subscriber" "Customer" "Customer" "Customer" ...
## $ gender : chr [1:1640718] "Male" NA NA NA ...
## $ birthyear : num [1:1640718] 1992 NA NA NA NA ...
## - attr(*, "spec")=
## .. cols(
## .. trip_id = col_double(),
## .. start_time = col_datetime(format = ""),
## .. end_time = col_datetime(format = ""),
## .. bikeid = col_double(),
## .. tripduration = col_number(),
## .. from_station_id = col_double(),
## .. from_station_name = col_character(),
## .. to_station_id = col_double(),
## .. to_station_name = col_character(),
## .. usertype = col_character(),
## .. gender = col_character(),
## .. birthyear = col_double()
## .. )
## - attr(*, "problems")=<externalptr>## spec_tbl_df [1,108,163 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ride_id : num [1:1108163] 22178529 22178530 22178531 22178532 22178533 ...
## $ started_at : POSIXct[1:1108163], format: "2019-04-01 00:02:22" "2019-04-01 00:03:02" ...
## $ ended_at : POSIXct[1:1108163], format: "2019-04-01 00:09:48" "2019-04-01 00:20:30" ...
## $ rideable_type : num [1:1108163] 6251 6226 5649 4151 3270 ...
## $ 01 - Rental Details Duration In Seconds Uncapped: num [1:1108163] 446 1048 252 357 1007 ...
## $ start_station_id : num [1:1108163] 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name : chr [1:1108163] "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : num [1:1108163] 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr [1:1108163] "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr [1:1108163] "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
## $ Member Gender : chr [1:1108163] "Male" "Female" "Male" "Male" ...
## $ 05 - Member Details Member Birthday Year : num [1:1108163] 1975 1984 1990 1993 1992 ...
## - attr(*, "spec")=
## .. cols(
## .. `01 - Rental Details Rental ID` = col_double(),
## .. `01 - Rental Details Local Start Time` = col_datetime(format = ""),
## .. `01 - Rental Details Local End Time` = col_datetime(format = ""),
## .. `01 - Rental Details Bike ID` = col_double(),
## .. `01 - Rental Details Duration In Seconds Uncapped` = col_number(),
## .. `03 - Rental Start Station ID` = col_double(),
## .. `03 - Rental Start Station Name` = col_character(),
## .. `02 - Rental End Station ID` = col_double(),
## .. `02 - Rental End Station Name` = col_character(),
## .. `User Type` = col_character(),
## .. `Member Gender` = col_character(),
## .. `05 - Member Details Member Birthday Year` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>STEP 2: Clean and Prepare
## [1] "ride_id" "started_at" "ended_at"
## [4] "rideable_type" "start_station_id" "start_station_name"
## [7] "end_station_id" "end_station_name" "member_casual"## [1] 3879822## [1] 3879822 9## # A tibble: 6 × 9
## ride_id started_at ended_at rideable_type start_station_id
## <chr> <dttm> <dttm> <chr> <dbl>
## 1 221785… 2019-04-01 00:02:22 2019-04-01 00:09:48 6251 81
## 2 221785… 2019-04-01 00:03:02 2019-04-01 00:20:30 6226 317
## 3 221785… 2019-04-01 00:11:07 2019-04-01 00:15:19 5649 283
## 4 221785… 2019-04-01 00:13:01 2019-04-01 00:18:58 4151 26
## 5 221785… 2019-04-01 00:19:26 2019-04-01 00:36:13 3270 202
## 6 221785… 2019-04-01 00:19:39 2019-04-01 00:23:56 3123 420
## # … with 4 more variables: start_station_name <chr>, end_station_id <dbl>,
## # end_station_name <chr>, member_casual <chr>## tibble [3,879,822 × 9] (S3: tbl_df/tbl/data.frame)
## $ ride_id : chr [1:3879822] "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : POSIXct[1:3879822], format: "2019-04-01 00:02:22" "2019-04-01 00:03:02" ...
## $ ended_at : POSIXct[1:3879822], format: "2019-04-01 00:09:48" "2019-04-01 00:20:30" ...
## $ rideable_type : chr [1:3879822] "6251" "6226" "5649" "4151" ...
## $ start_station_id : num [1:3879822] 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr [1:3879822] "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : num [1:3879822] 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr [1:3879822] "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr [1:3879822] "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...## ride_id started_at ended_at
## Length:3879822 Min. :2019-04-01 00:02:22 Min. :2019-04-01 00:09:48
## Class :character 1st Qu.:2019-06-23 07:49:09 1st Qu.:2019-06-23 08:20:27
## Mode :character Median :2019-08-14 17:43:38 Median :2019-08-14 18:02:04
## Mean :2019-08-26 00:49:59 Mean :2019-08-26 01:14:37
## 3rd Qu.:2019-10-12 12:10:21 3rd Qu.:2019-10-12 12:36:16
## Max. :2020-03-31 23:51:34 Max. :2020-05-19 20:10:34
##
## rideable_type start_station_id start_station_name end_station_id
## Length:3879822 Min. : 1.0 Length:3879822 Min. : 1.0
## Class :character 1st Qu.: 77.0 Class :character 1st Qu.: 77.0
## Mode :character Median :174.0 Mode :character Median :174.0
## Mean :202.9 Mean :203.8
## 3rd Qu.:291.0 3rd Qu.:291.0
## Max. :675.0 Max. :675.0
## NA's :1
## end_station_name member_casual
## Length:3879822 Length:3879822
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## Reassign to the desired values wanted to change to be uniform
Added columns that list date, month and year of each ride
Added a “ride_length” calculation to all_trips (in seconds)
https://stat.ethz.ch/R-manual/R-devel/library/base/html/difftime.html
## tibble [3,879,822 × 15] (S3: tbl_df/tbl/data.frame)
## $ ride_id : chr [1:3879822] "22178529" "22178530" "22178531" "22178532" ...
## $ started_at : POSIXct[1:3879822], format: "2019-04-01 00:02:22" "2019-04-01 00:03:02" ...
## $ ended_at : POSIXct[1:3879822], format: "2019-04-01 00:09:48" "2019-04-01 00:20:30" ...
## $ rideable_type : chr [1:3879822] "6251" "6226" "5649" "4151" ...
## $ start_station_id : num [1:3879822] 81 317 283 26 202 420 503 260 211 211 ...
## $ start_station_name: chr [1:3879822] "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
## $ end_station_id : num [1:3879822] 56 59 174 133 129 426 500 499 211 211 ...
## $ end_station_name : chr [1:3879822] "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
## $ member_casual : chr [1:3879822] "member" "member" "member" "member" ...
## $ date : Date[1:3879822], format: "2019-04-01" "2019-04-01" ...
## $ month : chr [1:3879822] "04" "04" "04" "04" ...
## $ day : chr [1:3879822] "01" "01" "01" "01" ...
## $ year : chr [1:3879822] "2019" "2019" "2019" "2019" ...
## $ day_of_week : chr [1:3879822] "Monday" "Monday" "Monday" "Monday" ...
## $ ride_length : 'difftime' num [1:3879822] 446 1048 252 357 ...
## ..- attr(*, "units")= chr "secs"## [1] TRUESTEP 3: Descriptive Analysis
Descriptive analysis on ride_length (all figures in seconds)
straight average 1479.139 (total ride length / rides)
## [1] 1479.139712s midpoint number in the ascending array of ride lengths
## [1] 712longest ride, Time duration of 9387024s
## [1] 9387024shortest ride, 1s
## [1] 1## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 412 712 1479 1289 9387024Compare members and casual users
## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 3552.7502
## 2 member 850.0662## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 1546
## 2 member 589## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 9387024
## 2 member 9056634## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 2
## 2 member 1Average weekly ride length, members casual users
## all_trips_v2$member_casual all_trips_v2$day_of_week all_trips_v2$ride_length
## 1 casual Friday 3773.8351
## 2 member Friday 824.5305
## 3 casual Monday 3372.2869
## 4 member Monday 842.5726
## 5 casual Saturday 3331.9138
## 6 member Saturday 968.9337
## 7 casual Sunday 3581.4054
## 8 member Sunday 919.9746
## 9 casual Thursday 3682.9847
## 10 member Thursday 823.9278
## 11 casual Tuesday 3596.3599
## 12 member Tuesday 826.1427
## 13 casual Wednesday 3718.6619
## 14 member Wednesday 823.9996Ordered average weekly ride length, members casual users
## all_trips_v2$member_casual all_trips_v2$day_of_week all_trips_v2$ride_length
## 1 casual Sunday 3581.4054
## 2 member Sunday 919.9746
## 3 casual Monday 3372.2869
## 4 member Monday 842.5726
## 5 casual Tuesday 3596.3599
## 6 member Tuesday 826.1427
## 7 casual Wednesday 3718.6619
## 8 member Wednesday 823.9996
## 9 casual Thursday 3682.9847
## 10 member Thursday 823.9278
## 11 casual Friday 3773.8351
## 12 member Friday 824.5305
## 13 casual Saturday 3331.9138
## 14 member Saturday 968.9337Analyzed ridership data by type and 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 181293 3581.
## 2 casual Mon 103296 3372.
## 3 casual Tue 90510 3596.
## 4 casual Wed 92457 3719.
## 5 casual Thu 102679 3683.
## 6 casual Fri 122404 3774.
## 7 casual Sat 209543 3332.
## 8 member Sun 267965 920.
## 9 member Mon 472196 843.
## 10 member Tue 508445 826.
## 11 member Wed 500329 824.
## 12 member Thu 484177 824.
## 13 member Fri 452790 825.
## 14 member Sat 287958 969.Created a data frame
## Length Class Mode
## 1 character characterSTEP 4: Key Visualizations
Avg rides per week, Members and Casual riders
## `summarise()` has grouped output by 'member_casual'. You can override using the `.groups` argument.
Avg ride length per week, Members and Casual riders
