Ford Go Bike
In this notebook I download and unzip the Ford Go Bike data.
library(tidyverse)
library(tictoc)
library(ggmap)
library(skimr)
library(lubridate)
library(forcats)
library(kableExtra)Set working directory.
setwd("~/GitHub/Stat6620/fordgobike")Create a directory /data in your directory. Download the files. First one is not zipped, the remaining are zipped.
URL <- "https://s3.amazonaws.com/fordgobike-data/2017-fordgobike-tripdata.csv"
download.file(URL, destfile = "./data/2017-fordgobike-tripdata.csv", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 112M 0 169k 0 0 169k 0 0:11:19 0:00:01 0:11:18 162k
0 112M 0 1002k 0 0 501k 0 0:03:49 0:00:02 0:03:47 489k
2 112M 2 2362k 0 0 787k 0 0:02:26 0:00:03 0:02:23 775k
4 112M 4 4759k 0 0 1189k 0 0:01:36 0:00:04 0:01:32 1176k
7 112M 7 8736k 0 0 1747k 0 0:01:05 0:00:05 0:01:00 1769k
12 112M 12 13.9M 0 0 2385k 0 0:00:48 0:00:06 0:00:42 2828k
18 112M 18 20.9M 0 0 3066k 0 0:00:37 0:00:07 0:00:30 4092k
26 112M 26 29.8M 0 0 3827k 0 0:00:30 0:00:08 0:00:22 5650k
35 112M 35 40.4M 0 0 4607k 0 0:00:25 0:00:09 0:00:16 7341k
46 112M 46 52.7M 0 0 5404k 0 0:00:21 0:00:10 0:00:11 9060k
59 112M 59 66.5M 0 0 6197k 0 0:00:18 0:00:11 0:00:07 10.5M
72 112M 72 81.7M 0 0 6979k 0 0:00:16 0:00:12 0:00:04 12.1M
87 112M 87 98.9M 0 0 7796k 0 0:00:14 0:00:13 0:00:01 13.8M
100 112M 100 112M 0 0 8861k 0 0:00:13 0:00:13 --:--:-- 15.1MURL <- "https://s3.amazonaws.com/fordgobike-data/201801-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201801-fordgobike-tripdata.csv.zip", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
8 3251k 8 288k 0 0 288k 0 0:00:11 0:00:01 0:00:10 260k
50 3251k 50 1631k 0 0 815k 0 0:00:03 0:00:02 0:00:01 773k
100 3251k 100 3251k 0 0 1625k 0 0:00:02 0:00:02 --:--:-- 1136kURL <- "https://s3.amazonaws.com/fordgobike-data/201802-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201802-fordgobike-tripdata.csv.zip", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
6 3698k 6 237k 0 0 237k 0 0:00:15 0:00:01 0:00:14 226k
56 3698k 56 2073k 0 0 1036k 0 0:00:03 0:00:02 0:00:01 1013k
100 3698k 100 3698k 0 0 1849k 0 0:00:02 0:00:02 --:--:-- 1498kURL <- "https://s3.amazonaws.com/fordgobike-data/201803-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201803-fordgobike-tripdata.csv.zip", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 3901k 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
16 3901k 16 662k 0 0 662k 0 0:00:05 0:00:01 0:00:04 476k
91 3901k 91 3551k 0 0 1775k 0 0:00:02 0:00:02 --:--:-- 1485k
100 3901k 100 3901k 0 0 1950k 0 0:00:02 0:00:02 --:--:-- 1600kURL <- "https://s3.amazonaws.com/fordgobike-data/201804-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201804-fordgobike-tripdata.csv.zip", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
2 4613k 2 101k 0 0 101k 0 0:00:45 --:--:-- 0:00:45 125k
26 4613k 26 1240k 0 0 1240k 0 0:00:03 0:00:01 0:00:02 696k
100 4613k 100 4613k 0 0 2306k 0 0:00:02 0:00:02 --:--:-- 1677kURL <- "https://s3.amazonaws.com/fordgobike-data/201805-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201805-fordgobike-tripdata.csv.zip", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
1 6269k 1 86674 0 0 86674 0 0:01:14 --:--:-- 0:01:14 98k
18 6269k 18 1138k 0 0 1138k 0 0:00:05 0:00:01 0:00:04 612k
73 6269k 73 4588k 0 0 2294k 0 0:00:02 0:00:02 --:--:-- 1604k
100 6269k 100 6269k 0 0 2089k 0 0:00:03 0:00:03 --:--:-- 1995kURL <- "https://s3.amazonaws.com/fordgobike-data/201806-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201806-fordgobike-tripdata.csv.zip", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 6901k 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
9 6901k 9 628k 0 0 628k 0 0:00:10 0:00:01 0:00:09 478k
39 6901k 39 2753k 0 0 1376k 0 0:00:05 0:00:02 0:00:03 1190k
94 6901k 94 6527k 0 0 2175k 0 0:00:03 0:00:03 --:--:-- 1970k
100 6901k 100 6901k 0 0 2300k 0 0:00:03 0:00:03 --:--:-- 2035kURL <- "https://s3.amazonaws.com/fordgobike-data/201807-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201807-fordgobike-tripdata.csv.zip", method="curl") % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 7057k 0 17042 0 0 17042 0 0:07:04 --:--:-- 0:07:04 30269
12 7057k 12 866k 0 0 866k 0 0:00:08 0:00:01 0:00:07 565k
54 7057k 54 3824k 0 0 1912k 0 0:00:03 0:00:02 0:00:01 1510k
100 7057k 100 7057k 0 0 2352k 0 0:00:03 0:00:03 --:--:-- 2280kLoop over the one value in the url and filename that changes.
URL <- "https://s3.amazonaws.com/fordgobike-data/2017-fordgobike-tripdata.csv"
download.file(URL, destfile = "./data/2017-fordgobike-tripdata.csv", method="curl")
for (i in 1:7) {
URL <- paste0("https://s3.amazonaws.com/fordgobike-data/20180",i,"-fordgobike-tripdata.csv.zip")
download.file(URL, destfile = paste0("./data/20180",i,"-fordgobike-tripdata.csv.zip"), method="curl")
}Unzip downloaded files.
unzip("./data/201801-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201802-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201803-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201804-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201805-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201806-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201807-fordgobike-tripdata.csv.zip",exdir="./data")Clean up data directory.
fn <- "./data/201801-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)[1] TRUEfn <- "./data/201802-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)[1] TRUEfn <- "./data/201803-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)[1] TRUEfn <- "./data/201804-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)[1] TRUEfn <- "./data/201805-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)[1] TRUEfn <- "./data/201806-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)[1] TRUEfn <- "./data/201807-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)[1] TRUERead the.csv files
fordgobike2017 <- read_csv(file="./data/2017-fordgobike-tripdata.csv")
fordgobike201801 <- read_csv(file="./data/201801-fordgobike-tripdata.csv")
fordgobike201802 <- read_csv(file="./data/201802-fordgobike-tripdata.csv")
fordgobike201803 <- read_csv(file="./data/201803-fordgobike-tripdata.csv")
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|=====================================================================================| 100% 22 MBfordgobike201804 <- read_csv(file="./data/201804-fordgobike-tripdata.csv")
fordgobike201805 <- read_csv(file="./data/201805-fordgobike-tripdata.csv")
fordgobike201806 <- read_csv(file="./data/201806-fordgobike-tripdata.csv")
fordgobike201807 <- read_csv(file="./data/201807-fordgobike-tripdata.csv")
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|=====================================================================================| 100% 40 MBCheck the head() and tail() of the data.frames that are loaded.
head(fordgobike2017) [38;5;246m# A tibble: 6 x 15[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m8[24m[4m0[24m110 2017-12-31 [38;5;246m16:57:39[39m 2018-01-01 [38;5;246m15:12:50[39m 74 Laguna St at Hayes St
[38;5;250m2[39m [4m7[24m[4m8[24m800 2017-12-31 [38;5;246m15:56:34[39m 2018-01-01 [38;5;246m13:49:55[39m 284 Yerba Buena Center for the~
[38;5;250m3[39m [4m4[24m[4m5[24m768 2017-12-31 [38;5;246m22:45:48[39m 2018-01-01 [38;5;246m11:28:36[39m 245 Downtown Berkeley BART
[38;5;250m4[39m [4m6[24m[4m2[24m172 2017-12-31 [38;5;246m17:31:10[39m 2018-01-01 [38;5;246m10:47:23[39m 60 8th St at Ringold St
[38;5;250m5[39m [4m4[24m[4m3[24m603 2017-12-31 [38;5;246m14:23:14[39m 2018-01-01 [38;5;246m02:29:57[39m 239 Bancroft Way at Telegraph ~
[38;5;250m6[39m [4m9[24m226 2017-12-31 [38;5;246m22:51:00[39m 2018-01-01 [38;5;246m01:24:47[39m 30 San Francisco Caltrain (To~
[38;5;246m# ... with 10 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m[39mhead(fordgobike201801)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m7[24m[4m5[24m284 2018-01-31 [38;5;246m22:52:35[39m 2018-02-01 [38;5;246m19:47:19[39m 120 Mission Dolores Park
[38;5;250m2[39m [4m8[24m[4m5[24m422 2018-01-31 [38;5;246m16:13:34[39m 2018-02-01 [38;5;246m15:57:17[39m 15 San Francisco Ferry Buildi~
[38;5;250m3[39m [4m7[24m[4m1[24m576 2018-01-31 [38;5;246m14:23:55[39m 2018-02-01 [38;5;246m10:16:52[39m 304 Jackson St at 5th St
[38;5;250m4[39m [4m6[24m[4m1[24m076 2018-01-31 [38;5;246m14:53:23[39m 2018-02-01 [38;5;246m07:51:20[39m 75 Market St at Franklin St
[38;5;250m5[39m [4m3[24m[4m9[24m966 2018-01-31 [38;5;246m19:52:24[39m 2018-02-01 [38;5;246m06:58:31[39m 74 Laguna St at Hayes St
[38;5;250m6[39m [4m6[24m477 2018-01-31 [38;5;246m22:58:44[39m 2018-02-01 [38;5;246m00:46:41[39m 236 Market St at 8th St
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mhead(fordgobike201802)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 598 2018-02-28 [38;5;246m23:59:47[39m 2018-03-01 [38;5;246m00:09:45[39m 284 Yerba Buena Center for the~
[38;5;250m2[39m 943 2018-02-28 [38;5;246m23:21:16[39m 2018-02-28 [38;5;246m23:36:59[39m 6 The Embarcadero at Sansome~
[38;5;250m3[39m [4m1[24m[4m8[24m587 2018-02-28 [38;5;246m18:20:55[39m 2018-02-28 [38;5;246m23:30:42[39m 93 4th St at Mission Bay Blvd~
[38;5;250m4[39m [4m1[24m[4m8[24m558 2018-02-28 [38;5;246m18:20:53[39m 2018-02-28 [38;5;246m23:30:12[39m 93 4th St at Mission Bay Blvd~
[38;5;250m5[39m 885 2018-02-28 [38;5;246m23:15:12[39m 2018-02-28 [38;5;246m23:29:58[39m 308 San Pedro Square
[38;5;250m6[39m 921 2018-02-28 [38;5;246m23:14:19[39m 2018-02-28 [38;5;246m23:29:40[39m 312 San Jose Diridon Station
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mhead(fordgobike201803)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m7[24m[4m1[24m766 2018-03-31 [38;5;246m16:58:33[39m 2018-04-01 [38;5;246m12:54:39[39m 4 Cyril Magnin St at Ellis St
[38;5;250m2[39m [4m6[24m[4m2[24m569 2018-03-31 [38;5;246m19:03:35[39m 2018-04-01 [38;5;246m12:26:25[39m 78 Folsom St at 9th St
[38;5;250m3[39m [4m5[24m[4m6[24m221 2018-03-31 [38;5;246m20:13:13[39m 2018-04-01 [38;5;246m11:50:14[39m 258 University Ave at Oxford St
[38;5;250m4[39m [4m8[24m[4m5[24m844 2018-03-31 [38;5;246m11:28:07[39m 2018-04-01 [38;5;246m11:18:52[39m 186 Lakeside Dr at 14th St
[38;5;250m5[39m [4m1[24m566 2018-03-31 [38;5;246m23:37:56[39m 2018-04-01 [38;5;246m00:04:02[39m 193 Grand Ave at Santa Clara A~
[38;5;250m6[39m 281 2018-03-31 [38;5;246m23:58:07[39m 2018-04-01 [38;5;246m00:02:49[39m 197 El Embarcadero at Grand Ave
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mhead(fordgobike201804)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m7[24m[4m2[24m393 2018-04-30 [38;5;246m22:49:32[39m 2018-05-01 [38;5;246m18:56:06[39m 4 Cyril Magnin St at Ellis St
[38;5;250m2[39m [4m8[24m[4m1[24m034 2018-04-30 [38;5;246m17:46:04[39m 2018-05-01 [38;5;246m16:16:39[39m 122 19th St at Mission St
[38;5;250m3[39m [4m8[24m[4m6[24m142 2018-04-30 [38;5;246m16:07:13[39m 2018-05-01 [38;5;246m16:02:56[39m 41 Golden Gate Ave at Polk St
[38;5;250m4[39m [4m6[24m[4m8[24m839 2018-04-30 [38;5;246m17:11:57[39m 2018-05-01 [38;5;246m12:19:16[39m 284 Yerba Buena Center for the~
[38;5;250m5[39m [4m5[24m[4m9[24m091 2018-04-30 [38;5;246m18:45:21[39m 2018-05-01 [38;5;246m11:10:13[39m 196 Grand Ave at Perkins St
[38;5;250m6[39m [4m6[24m[4m8[24m093 2018-04-30 [38;5;246m15:39:18[39m 2018-05-01 [38;5;246m10:34:12[39m 21 Montgomery St BART Station~
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mhead(fordgobike201805)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m5[24m[4m6[24m791 2018-05-31 [38;5;246m21:41:51[39m 2018-06-01 [38;5;246m13:28:22[39m 44 Civic Center/UN Plaza BART~
[38;5;250m2[39m [4m5[24m[4m2[24m797 2018-05-31 [38;5;246m18:39:53[39m 2018-06-01 [38;5;246m09:19:51[39m 186 Lakeside Dr at 14th St
[38;5;250m3[39m [4m4[24m[4m3[24m204 2018-05-31 [38;5;246m21:09:48[39m 2018-06-01 [38;5;246m09:09:52[39m 17 Embarcadero BART Station (~
[38;5;250m4[39m [4m6[24m[4m7[24m102 2018-05-31 [38;5;246m14:09:54[39m 2018-06-01 [38;5;246m08:48:17[39m 106 Sanchez St at 17th St
[38;5;250m5[39m [4m5[24m[4m8[24m883 2018-05-31 [38;5;246m16:07:23[39m 2018-06-01 [38;5;246m08:28:47[39m 16 Steuart St at Market St
[38;5;250m6[39m [4m2[24m[4m2[24m858 2018-05-31 [38;5;246m23:06:40[39m 2018-06-01 [38;5;246m05:27:38[39m 163 Lake Merritt BART Station
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mhead(fordgobike201806)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m5[24m[4m9[24m088 2018-06-30 [38;5;246m23:32:44[39m 2018-07-01 [38;5;246m15:57:33[39m 76 McCoppin St at Valencia St
[38;5;250m2[39m [4m6[24m[4m0[24m358 2018-06-30 [38;5;246m21:48:19[39m 2018-07-01 [38;5;246m14:34:18[39m 248 Telegraph Ave at Ashby Ave
[38;5;250m3[39m [4m6[24m[4m3[24m654 2018-06-30 [38;5;246m20:26:53[39m 2018-07-01 [38;5;246m14:07:47[39m 23 The Embarcadero at Steuart~
[38;5;250m4[39m [4m5[24m[4m0[24m508 2018-06-30 [38;5;246m20:29:59[39m 2018-07-01 [38;5;246m10:31:48[39m 58 Market St at 10th St
[38;5;250m5[39m [4m5[24m[4m1[24m697 2018-06-30 [38;5;246m18:24:56[39m 2018-07-01 [38;5;246m08:46:33[39m 196 Grand Ave at Perkins St
[38;5;250m6[39m [4m3[24m[4m6[24m708 2018-06-30 [38;5;246m20:25:34[39m 2018-07-01 [38;5;246m06:37:22[39m 8 The Embarcadero at Vallejo~
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<chr>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mhead(fordgobike201807)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m5[24m[4m9[24m989 2018-07-31 [38;5;246m18:20:32[39m 2018-08-01 [38;5;246m11:00:22[39m 197 El Embarcadero at Grand Ave
[38;5;250m2[39m [4m6[24m[4m0[24m232 2018-07-31 [38;5;246m17:24:26[39m 2018-08-01 [38;5;246m10:08:18[39m 77 11th St at Natoma St
[38;5;250m3[39m [4m4[24m[4m3[24m864 2018-07-31 [38;5;246m21:03:26[39m 2018-08-01 [38;5;246m09:14:30[39m NULL NULL
[38;5;250m4[39m [4m5[24m[4m1[24m522 2018-07-31 [38;5;246m18:54:23[39m 2018-08-01 [38;5;246m09:13:06[39m 114 Rhode Island St at 17th St
[38;5;250m5[39m [4m8[24m[4m3[24m380 2018-07-31 [38;5;246m09:22:29[39m 2018-08-01 [38;5;246m08:32:09[39m 213 32nd St at Adeline St
[38;5;250m6[39m [4m4[24m[4m9[24m546 2018-07-31 [38;5;246m18:44:11[39m 2018-08-01 [38;5;246m08:29:57[39m 139 Garfield Square (25th St a~
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<chr>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike2017)[38;5;246m# A tibble: 6 x 15[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 730 2017-06-28 [38;5;246m09:58:33[39m 2017-06-28 [38;5;246m10:10:44[39m 23 The Embarcadero at Steuart~
[38;5;250m2[39m 435 2017-06-28 [38;5;246m10:00:54[39m 2017-06-28 [38;5;246m10:08:10[39m 81 Berry St at 4th St
[38;5;250m3[39m 431 2017-06-28 [38;5;246m09:56:39[39m 2017-06-28 [38;5;246m10:03:51[39m 66 3rd St at Townsend St
[38;5;250m4[39m 424 2017-06-28 [38;5;246m09:47:36[39m 2017-06-28 [38;5;246m09:54:41[39m 21 Montgomery St BART Station~
[38;5;250m5[39m 366 2017-06-28 [38;5;246m09:47:41[39m 2017-06-28 [38;5;246m09:53:47[39m 58 Market St at 10th St
[38;5;250m6[39m 188 2017-06-28 [38;5;246m09:49:46[39m 2017-06-28 [38;5;246m09:52:55[39m 25 Howard St at 2nd St
[38;5;246m# ... with 10 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike201801)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 752 2018-01-01 [38;5;246m00:19:13[39m 2018-01-01 [38;5;246m00:31:46[39m 23 The Embarcadero at Steuart~
[38;5;250m2[39m 695 2018-01-01 [38;5;246m00:19:58[39m 2018-01-01 [38;5;246m00:31:33[39m 23 The Embarcadero at Steuart~
[38;5;250m3[39m 600 2018-01-01 [38;5;246m00:19:48[39m 2018-01-01 [38;5;246m00:29:49[39m 17 Embarcadero BART Station (~
[38;5;250m4[39m [4m1[24m151 2018-01-01 [38;5;246m00:09:31[39m 2018-01-01 [38;5;246m00:28:43[39m 97 14th St at Mission St
[38;5;250m5[39m 714 2018-01-01 [38;5;246m00:07:52[39m 2018-01-01 [38;5;246m00:19:47[39m 74 Laguna St at Hayes St
[38;5;250m6[39m 145 2018-01-01 [38;5;246m00:07:41[39m 2018-01-01 [38;5;246m00:10:06[39m 316 San Salvador St at 1st St
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike201802)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 404 2018-02-01 [38;5;246m00:16:47[39m 2018-02-01 [38;5;246m00:23:31[39m 89 Division St at Potrero Ave
[38;5;250m2[39m 136 2018-02-01 [38;5;246m00:20:14[39m 2018-02-01 [38;5;246m00:22:30[39m 182 19th Street BART Station
[38;5;250m3[39m 345 2018-02-01 [38;5;246m00:08:39[39m 2018-02-01 [38;5;246m00:14:25[39m 122 19th St at Mission St
[38;5;250m4[39m 439 2018-02-01 [38;5;246m00:02:25[39m 2018-02-01 [38;5;246m00:09:45[39m 284 Yerba Buena Center for the~
[38;5;250m5[39m 524 2018-02-01 [38;5;246m00:00:05[39m 2018-02-01 [38;5;246m00:08:49[39m 113 Franklin Square
[38;5;250m6[39m 319 2018-02-01 [38;5;246m00:00:39[39m 2018-02-01 [38;5;246m00:05:59[39m 72 Page St at Scott St
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike201803)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 594 2018-03-01 [38;5;246m00:39:50[39m 2018-03-01 [38;5;246m00:49:45[39m 66 3rd St at Townsend St
[38;5;250m2[39m 471 2018-03-01 [38;5;246m00:28:29[39m 2018-03-01 [38;5;246m00:36:20[39m 180 Telegraph Ave at 23rd St
[38;5;250m3[39m 285 2018-03-01 [38;5;246m00:17:32[39m 2018-03-01 [38;5;246m00:22:18[39m 183 Telegraph Ave at 19th St
[38;5;250m4[39m 408 2018-03-01 [38;5;246m00:13:37[39m 2018-03-01 [38;5;246m00:20:25[39m 27 Beale St at Harrison St
[38;5;250m5[39m 368 2018-03-01 [38;5;246m00:14:14[39m 2018-03-01 [38;5;246m00:20:22[39m 27 Beale St at Harrison St
[38;5;250m6[39m 164 2018-03-01 [38;5;246m00:13:31[39m 2018-03-01 [38;5;246m00:16:15[39m 240 Haste St at Telegraph Ave
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike201804)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 656 2018-04-01 [38;5;246m00:06:53[39m 2018-04-01 [38;5;246m00:17:50[39m 45 5th St at Howard St
[38;5;250m2[39m 887 2018-04-01 [38;5;246m00:00:08[39m 2018-04-01 [38;5;246m00:14:55[39m 194 Lakeshore Ave at Trestle G~
[38;5;250m3[39m 387 2018-04-01 [38;5;246m00:08:06[39m 2018-04-01 [38;5;246m00:14:33[39m 30 San Francisco Caltrain (To~
[38;5;250m4[39m 480 2018-04-01 [38;5;246m00:06:21[39m 2018-04-01 [38;5;246m00:14:21[39m 44 Civic Center/UN Plaza BART~
[38;5;250m5[39m 503 2018-04-01 [38;5;246m00:04:36[39m 2018-04-01 [38;5;246m00:13:00[39m 100 Bryant St at 15th St
[38;5;250m6[39m 192 2018-04-01 [38;5;246m00:02:03[39m 2018-04-01 [38;5;246m00:05:16[39m 176 MacArthur BART Station
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike201805)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 392 2018-05-01 [38;5;246m00:12:49[39m 2018-05-01 [38;5;246m00:19:22[39m 336 Potrero Ave and Mariposa St
[38;5;250m2[39m 645 2018-05-01 [38;5;246m00:07:24[39m 2018-05-01 [38;5;246m00:18:09[39m 186 Lakeside Dr at 14th St
[38;5;250m3[39m 135 2018-05-01 [38;5;246m00:15:53[39m 2018-05-01 [38;5;246m00:18:09[39m 244 Shattuck Ave at Hearst Ave
[38;5;250m4[39m 316 2018-05-01 [38;5;246m00:10:04[39m 2018-05-01 [38;5;246m00:15:20[39m 30 San Francisco Caltrain (To~
[38;5;250m5[39m 183 2018-05-01 [38;5;246m00:12:01[39m 2018-05-01 [38;5;246m00:15:05[39m 243 Bancroft Way at College Ave
[38;5;250m6[39m 78 2018-05-01 [38;5;246m00:02:01[39m 2018-05-01 [38;5;246m00:03:20[39m 106 Sanchez St at 17th St
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike201806)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 119 2018-06-01 [38;5;246m00:09:28[39m 2018-06-01 [38;5;246m00:11:28[39m 105 16th St at Prosper St
[38;5;250m2[39m 491 2018-06-01 [38;5;246m00:03:12[39m 2018-06-01 [38;5;246m00:11:24[39m 41 Golden Gate Ave at Polk St
[38;5;250m3[39m 507 2018-06-01 [38;5;246m00:02:28[39m 2018-06-01 [38;5;246m00:10:55[39m 118 Eureka Valley Recreation C~
[38;5;250m4[39m 377 2018-06-01 [38;5;246m00:04:10[39m 2018-06-01 [38;5;246m00:10:27[39m 186 Lakeside Dr at 14th St
[38;5;250m5[39m 391 2018-06-01 [38;5;246m00:03:37[39m 2018-06-01 [38;5;246m00:10:08[39m 30 San Francisco Caltrain (To~
[38;5;250m6[39m 283 2018-06-01 [38;5;246m00:02:37[39m 2018-06-01 [38;5;246m00:07:21[39m 36 Folsom St at 3rd St
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<chr>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mtail(fordgobike201807)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m 165 2018-07-01 [38;5;246m00:08:12[39m 2018-07-01 [38;5;246m00:10:58[39m 278 The Alameda at Bush St
[38;5;250m2[39m 130 2018-07-01 [38;5;246m00:07:39[39m 2018-07-01 [38;5;246m00:09:49[39m 253 Haste St at College Ave
[38;5;250m3[39m 445 2018-07-01 [38;5;246m00:02:16[39m 2018-07-01 [38;5;246m00:09:42[39m 179 Telegraph Ave at 27th St
[38;5;250m4[39m 490 2018-07-01 [38;5;246m00:00:41[39m 2018-07-01 [38;5;246m00:08:51[39m 307 SAP Center
[38;5;250m5[39m 219 2018-07-01 [38;5;246m00:04:32[39m 2018-07-01 [38;5;246m00:08:12[39m 70 Central Ave at Fell St
[38;5;250m6[39m 368 2018-07-01 [38;5;246m00:01:22[39m 2018-07-01 [38;5;246m00:07:31[39m 4 Cyril Magnin St at Ellis St
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<chr>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mdim(fordgobike2017)[1] 519700 15fordgobike2017 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m5[24m[4m1[24m[4m9[24m700fordgobike201801 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m9[24m[4m4[24m802fordgobike201802 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m1[24m[4m0[24m[4m6[24m718fordgobike201803 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m1[24m[4m1[24m[4m1[24m382fordgobike201804 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m1[24m[4m3[24m[4m1[24m169fordgobike201805 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m1[24m[4m7[24m[4m9[24m125fordgobike201806 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m1[24m[4m9[24m[4m5[24m968fordgobike201807 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m1[24m[4m9[24m[4m9[24m222glimpse(fordgobike201805)Observations: 179,125
Variables: 16
$ duration_sec <int> 56791, 52797, 43204, 67102, 58883, 22858, 2863, 3189, 3149, 313...
$ start_time <dttm> 2018-05-31 21:41:51, 2018-05-31 18:39:53, 2018-05-31 21:09:48,...
$ end_time <dttm> 2018-06-01 13:28:22, 2018-06-01 09:19:51, 2018-06-01 09:09:52,...
$ start_station_id <int> 44, 186, 17, 106, 16, 163, 197, 61, 61, 61, 61, 61, 211, 66, 19...
$ start_station_name <chr> "Civic Center/UN Plaza BART Station (Market St at McAllister St...
$ start_station_latitude <dbl> 37.78107, 37.80132, 37.79225, 37.76324, 37.79413, 37.79732, 37....
$ start_station_longitude <dbl> -122.4117, -122.2626, -122.3971, -122.4307, -122.3944, -122.265...
$ end_station_id <int> 78, 338, 93, 47, 30, 212, 197, 8, 8, 8, 8, 8, 7, 323, 34, 6, 19...
$ end_station_name <chr> "Folsom St at 9th St", "13th St at Franklin St", "4th St at Mis...
$ end_station_latitude <dbl> 37.77372, 37.80319, 37.77041, 37.78095, 37.77660, 37.82493, 37....
$ end_station_longitude <dbl> -122.4116, -122.2706, -122.3912, -122.3997, -122.3953, -122.260...
$ bike_id <int> 1230, 3414, 2677, 4224, 3392, 1235, 152, 1109, 2143, 3374, 3493...
$ user_type <chr> "Customer", "Subscriber", "Customer", "Subscriber", "Subscriber...
$ member_birth_year <int> NA, 1983, NA, 1979, 1986, 1992, 1985, NA, NA, NA, NA, NA, NA, 1...
$ member_gender <chr> NA, "Male", NA, "Male", "Male", "Male", "Male", NA, NA, NA, NA,...
$ bike_share_for_all_trip <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "N...glimpse(fordgobike201806)Observations: 195,968
Variables: 16
$ duration_sec <int> 59088, 60358, 63654, 50508, 51697, 36708, 46380, 7224, 4294, 22...
$ start_time <dttm> 2018-06-30 23:32:44, 2018-06-30 21:48:19, 2018-06-30 20:26:53,...
$ end_time <dttm> 2018-07-01 15:57:33, 2018-07-01 14:34:18, 2018-07-01 14:07:47,...
$ start_station_id <chr> "76", "248", "23", "58", "196", "8", "237", "284", "240", "133"...
$ start_station_name <chr> "McCoppin St at Valencia St", "Telegraph Ave at Ashby Ave", "Th...
$ start_station_latitude <dbl> 37.77166, 37.85596, 37.79146, 37.77662, 37.80889, 37.79995, 37....
$ start_station_longitude <dbl> -122.4224, -122.2598, -122.3910, -122.4174, -122.2565, -122.398...
$ end_station_id <chr> "95", "239", "50", "88", "272", "4", "237", "284", "240", "55",...
$ end_station_name <chr> "Sanchez St at 15th St", "Bancroft Way at Telegraph Ave", "2nd ...
$ end_station_latitude <dbl> 37.76622, 37.86881, 37.78053, 37.77003, 37.85058, 37.78588, 37....
$ end_station_longitude <dbl> -122.4311, -122.2588, -122.3903, -122.4117, -122.2782, -122.408...
$ bike_id <int> 2100, 653, 3235, 3675, 3232, 577, 1764, 779, 2491, 4225, 3972, ...
$ user_type <chr> "Subscriber", "Customer", "Subscriber", "Subscriber", "Customer...
$ member_birth_year <int> 1975, NA, 1962, 1992, 1989, NA, NA, 1989, 1996, 1963, 1981, 199...
$ member_gender <chr> "Male", NA, "Female", "Male", "Female", NA, NA, "Male", "Female...
$ bike_share_for_all_trip <chr> "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes", "Yes", ...glimpse(fordgobike201807)Observations: 199,222
Variables: 16
$ duration_sec <int> 59989, 60232, 43864, 51522, 83380, 49546, 42799, 50603, 54830, ...
$ start_time <dttm> 2018-07-31 18:20:32, 2018-07-31 17:24:26, 2018-07-31 21:03:26,...
$ end_time <dttm> 2018-08-01 11:00:22, 2018-08-01 10:08:18, 2018-08-01 09:14:30,...
$ start_station_id <chr> "197", "77", "NULL", "114", "213", "139", "337", "19", "247", "...
$ start_station_name <chr> "El Embarcadero at Grand Ave", "11th St at Natoma St", "NULL", ...
$ start_station_latitude <dbl> 37.80885, 37.77351, 37.41000, 37.76448, 37.82385, 37.75102, 37....
$ start_station_longitude <dbl> -122.2497, -122.4160, -121.9400, -122.4026, -122.2812, -122.411...
$ end_station_id <chr> "181", "356", "NULL", "345", "198", "356", "196", "16", "266", ...
$ end_station_name <chr> "Grand Ave at Webster St", "Valencia St at Clinton Park", "NULL...
$ end_station_latitude <dbl> 37.81138, 37.76919, 37.41000, 37.76647, 37.80781, 37.76919, 37....
$ end_station_longitude <dbl> -122.2652, -122.4223, -121.9400, -122.3983, -122.2645, -122.422...
$ bike_id <int> 1953, 3010, 4273, 1043, 1336, 697, 605, 800, 2432, 3839, 3492, ...
$ user_type <chr> "Customer", "Subscriber", "Subscriber", "Subscriber", "Subscrib...
$ member_birth_year <int> 1995, 1994, 1998, 1990, 1982, 1991, 1976, 1972, 1997, 1978, 199...
$ member_gender <chr> "Male", "Female", "Male", "Female", "Male", "Female", "Female",...
$ bike_share_for_all_trip <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No...fordgobike201806 <- fordgobike201806 %>%
mutate(start_station_id = as.integer(start_station_id),
end_station_id= as.integer(start_station_id) )NAs introduced by coercionfordgobike201807 <- fordgobike201807 %>%
mutate(start_station_id = as.integer(start_station_id),
end_station_id= as.integer(start_station_id) )NAs introduced by coercionfordgobike2018 <- bind_rows(fordgobike201801, fordgobike201802, fordgobike201803, fordgobike201804,
fordgobike201805, fordgobike201806, fordgobike201807)
glimpse(fordgobike2018)Observations: 1,018,386
Variables: 16
$ duration_sec <int> 75284, 85422, 71576, 61076, 39966, 6477, 453, 180, 996, 825, 13...
$ start_time <dttm> 2018-01-31 22:52:35, 2018-01-31 16:13:34, 2018-01-31 14:23:55,...
$ end_time <dttm> 2018-02-01 19:47:19, 2018-02-01 15:57:17, 2018-02-01 10:16:52,...
$ start_station_id <int> 120, 15, 304, 75, 74, 236, 110, 81, 134, 305, 98, 89, 223, 308,...
$ start_station_name <chr> "Mission Dolores Park", "San Francisco Ferry Building (Harry Br...
$ start_station_latitude <dbl> 37.76142, 37.79539, 37.34876, 37.77379, 37.77643, 37.80369, 37....
$ start_station_longitude <dbl> -122.4264, -122.3942, -121.8948, -122.4212, -122.4262, -122.282...
$ end_station_id <int> 285, 15, 296, 47, 19, 160, 134, 93, 4, 317, 4, 43, 86, 297, 186...
$ end_station_name <chr> "Webster St at O'Farrell St", "San Francisco Ferry Building (Ha...
$ end_station_latitude <dbl> 37.78352, 37.79539, 37.32600, 37.78095, 37.78898, 37.80532, 37....
$ end_station_longitude <dbl> -122.4312, -122.3942, -121.8771, -122.3997, -122.4035, -122.294...
$ bike_id <int> 2765, 2815, 3039, 321, 617, 1306, 3571, 1403, 3675, 1453, 1278,...
$ user_type <chr> "Subscriber", "Customer", "Customer", "Customer", "Subscriber",...
$ member_birth_year <int> 1986, NA, 1996, NA, 1991, NA, 1988, 1980, 1987, 1994, NA, 1993,...
$ member_gender <chr> "Male", NA, "Male", NA, "Male", NA, "Male", "Male", "Male", "Fe...
$ bike_share_for_all_trip <chr> "No", "No", "No", "No", "No", "No", "No", "No", "Yes", "Yes", "...fordgobike2018 %>% select(start_station_id,start_station_name, start_station_latitude,start_station_longitude) %>%
arrange(start_station_id) %>%
distinct() %>%
head() %>%
kable(, format = "rst")
================ ============================================ ====================== =======================
start_station_id start_station_name start_station_latitude start_station_longitude
================ ============================================ ====================== =======================
3 Powell St BART Station (Market St at 4th St) 37.78638 -122.4049
4 Cyril Magnin St at Ellis St 37.78588 -122.4089
5 Powell St BART Station (Market St at 5th St) 37.78390 -122.4084
6 The Embarcadero at Sansome St 37.80477 -122.4032
7 Frank H Ogawa Plaza 37.80456 -122.2717
8 The Embarcadero at Vallejo St 37.79995 -122.3985
================ ============================================ ====================== =======================dim(fordgobike2017)[1] 519700 15fordgobike2017 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m [4m5[24m[4m1[24m[4m9[24m700nrow(fordgobike201801) + nrow(fordgobike201802) + nrow(fordgobike201803) + nrow(fordgobike201804)[1] 444071dim(fordgobike2018)[1] 1018386 16fordgobike2018 %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m 1[4m0[24m[4m1[24m[4m8[24m386fordgobike <- bind_rows(fordgobike2017, fordgobike2018)
dim(fordgobike)[1] 1538086 16fordgobike %>% count()[38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m 1[4m5[24m[4m3[24m[4m8[24m086dim(fordgobike)[1] 1538086 16fordgobike <- fordgobike %>% mutate(age = 2018 - member_birth_year)
fordgobike %>% count() [38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m 1[4m5[24m[4m3[24m[4m8[24m086dim(fordgobike)[1] 1538086 17fordgobike <- fordgobike %>% mutate(year=year(start_time), month=month(start_time), day=day(start_time) )
fordgobike %>% count() [38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m 1[4m5[24m[4m3[24m[4m8[24m086dim(fordgobike)[1] 1538086 20fordgobike <- fordgobike %>% mutate(week_day = wday(start_time) )
levels <- c("M","T","W","TH","F","SAT","SUN")
fordgobike$week_day <- factor(fordgobike$week_day, levels = levels)
fordgobike %>% count() [38;5;246m# A tibble: 1 x 1[39m
n
[3m[38;5;246m<int>[39m[23m
[38;5;250m1[39m 1[4m5[24m[4m3[24m[4m8[24m086dim(fordgobike)[1] 1538086 21today()[1] "2018-09-03"now()[1] "2018-09-03 13:19:25 PDT"Age
fordgobike %>% group_by( age ) %>% count()fordgobike %>% group_by( age ) %>% summary() duration_sec start_time end_time start_station_id
Min. : 61.0 Min. :2017-06-28 09:47:36 Min. :2017-06-28 09:52:55 Min. : 3.0
1st Qu.: 361.0 1st Qu.:2017-11-14 10:08:31 1st Qu.:2017-11-14 10:21:12 1st Qu.: 28.0
Median : 569.0 Median :2018-03-15 07:10:23 Median :2018-03-15 07:24:04 Median : 79.0
Mean : 957.4 Mean :2018-02-22 12:28:46 Mean :2018-02-22 12:44:43 Mean :107.7
3rd Qu.: 897.0 3rd Qu.:2018-06-02 17:56:46 3rd Qu.:2018-06-02 18:19:06 3rd Qu.:173.0
Max. :86369.0 Max. :2018-07-31 23:57:19 Max. :2018-08-01 11:00:22 Max. :357.0
NA's :5245
start_station_name start_station_latitude start_station_longitude end_station_id
Length:1538086 Min. :37.31 Min. :-122.44 Min. : 3.0
Class :character 1st Qu.:37.77 1st Qu.:-122.41 1st Qu.: 27.0
Mode :character Median :37.78 Median :-122.40 Median : 77.0
Mean :37.77 Mean :-122.36 Mean :105.9
3rd Qu.:37.80 3rd Qu.:-122.39 3rd Qu.:171.0
Max. :45.51 Max. : -73.57 Max. :357.0
NA's :5245
end_station_name end_station_latitude end_station_longitude bike_id user_type
Length:1538086 Min. :37.28 Min. :-122.44 Min. : 10 Length:1538086
Class :character 1st Qu.:37.77 1st Qu.:-122.41 1st Qu.:1045 Class :character
Mode :character Median :37.78 Median :-122.40 Median :2072 Mode :character
Mean :37.77 Mean :-122.35 Mean :2021
3rd Qu.:37.80 3rd Qu.:-122.39 3rd Qu.:2952
Max. :45.51 Max. : -73.57 Max. :4307
member_birth_year member_gender bike_share_for_all_trip age year
Min. :1881 Length:1538086 Length:1538086 Min. : 18.0 Min. :2017
1st Qu.:1976 Class :character Class :character 1st Qu.: 29.0 1st Qu.:2017
Median :1984 Mode :character Mode :character Median : 34.0 Median :2018
Mean :1982 Mean : 36.2 Mean :2018
3rd Qu.:1989 3rd Qu.: 42.0 3rd Qu.:2018
Max. :2000 Max. :137.0 Max. :2018
NA's :137667 NA's :137667
month day week_day
Min. : 1.0 Min. : 1.00 M : 0
1st Qu.: 4.0 1st Qu.: 8.00 T : 0
Median : 6.0 Median :16.00 W : 0
Mean : 6.3 Mean :15.98 TH : 0
3rd Qu.: 9.0 3rd Qu.:24.00 F : 0
Max. :12.0 Max. :31.00 (Other): 0
NA's :1538086 skim(fordgobike)Skim summary statistics
n obs: 1538086
n variables: 21
Variable type: character
variable missing complete n min max empty n_unique
bike_share_for_all_trip 519700 1018386 1538086 2 3 0 2
end_station_name 0 1538086 1538086 4 63 0 316
member_gender 137326 1400760 1538086 4 6 0 3
start_station_name 0 1538086 1538086 4 63 0 316
user_type 0 1538086 1538086 8 10 0 2
Variable type: factor
variable missing complete n n_unique top_counts ordered
week_day 1538086 0 1538086 0 NA: 1538086, M: 0, T: 0, W: 0 FALSE
Variable type: integer
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
bike_id 0 1538086 1538086 2020.6 1152.29 10 1045 2072 2952 4307 ▇▆▆▇▇▇▅▃
day 0 1538086 1538086 15.98 8.78 1 8 16 24 31 ▇▇▇▇▆▇▇▇
duration_sec 0 1538086 1538086 957.38 2891.83 61 361 569 897 86369 ▇▁▁▁▁▁▁▁
end_station_id 5245 1532841 1538086 105.9 92.55 3 27 77 171 357 ▇▅▃▂▂▁▁▁
member_birth_year 137667 1400419 1538086 1981.8 10.56 1881 1976 1984 1989 2000 ▁▁▁▁▁▂▇▇
start_station_id 5245 1532841 1538086 107.7 92.97 3 28 79 173 357 ▇▅▃▂▂▁▁▁
Variable type: numeric
variable missing complete n mean sd p0 p25 p50 p75
age 137667 1400419 1538086 36.2 10.56 18 29 34 42
end_station_latitude 0 1538086 1538086 37.77 0.098 37.28 37.77 37.78 37.8
end_station_longitude 0 1538086 1538086 -122.35 0.15 -122.44 -122.41 -122.4 -122.39
month 0 1538086 1538086 6.3 3.06 1 4 6 9
start_station_latitude 0 1538086 1538086 37.77 0.098 37.31 37.77 37.78 37.8
start_station_longitude 0 1538086 1538086 -122.36 0.15 -122.44 -122.41 -122.4 -122.39
year 0 1538086 1538086 2017.66 0.47 2017 2017 2018 2018
p100 hist
137 ▇▇▂▁▁▁▁▁
45.51 ▇▁▁▁▁▁▁▁
-73.57 ▇▁▁▁▁▁▁▁
12 ▅▃▇▅▆▅▃▅
45.51 ▇▁▁▁▁▁▁▁
-73.57 ▇▁▁▁▁▁▁▁
2018 ▅▁▁▁▁▁▁▇
Variable type: POSIXct
variable missing complete n min max median n_unique
end_time 0 1538086 1538086 2017-06-28 2018-08-01 2018-03-15 1538010
start_time 0 1538086 1538086 2017-06-28 2018-07-31 2018-03-15 1538011fordgobike %>% ggplot(aes(x=age)) + geom_histogram()
fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age)) + geom_histogram()
fordgobike %>% filter(age <= 100) %>% ggplot(aes(x=age)) + geom_histogram()
fordgobike %>% filter(age > 100) %>% ggplot(aes(x=age)) + geom_histogram()
fordgobike %>% group_by( member_gender, age ) %>% count()fordgobike %>% ggplot(aes(x=age, class=member_gender)) + geom_histogram()
fordgobike %>% ggplot(aes(x=age, class=member_gender)) + geom_histogram(aes(y=..density..))
fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age)) + geom_histogram()
fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age, color=member_gender)) +
geom_histogram(position="identity") +
facet_grid(member_gender ~ .)
fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age, color=member_gender)) +
geom_histogram(aes(y=..density..),position="identity") +
facet_grid(member_gender ~ .)
Year and day of week.
fordgobike %>% ggplot(aes(x=year)) + geom_bar()
fordgobike %>% ggplot(aes(x=month)) + geom_bar() + facet_grid(year ~ .)
fordgobike %>% ggplot(aes(x=day)) + geom_bar() + facet_grid(year ~ .)
fordgobike2018 <- fordgobike2018 %>% filter(start_station_latitude < 38 & start_station_longitude < 120 )
fordgobike_subset <- fordgobike2018 %>% select(start_station_longitude,start_station_latitude)
fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude)) +
geom_point()
library(biganalytics)
# run in parallel, the doMC package runs on Windows
library(doParallel)
registerDoParallel(cores = 8)
head(fordgobike2018)[38;5;246m# A tibble: 6 x 16[39m
duration_sec start_time end_time start_station_id start_station_name
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<dttm>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m1[39m [4m7[24m[4m5[24m284 2018-01-31 [38;5;246m22:52:35[39m 2018-02-01 [38;5;246m19:47:19[39m 120 Mission Dolores Park
[38;5;250m2[39m [4m8[24m[4m5[24m422 2018-01-31 [38;5;246m16:13:34[39m 2018-02-01 [38;5;246m15:57:17[39m 15 San Francisco Ferry Buildi~
[38;5;250m3[39m [4m7[24m[4m1[24m576 2018-01-31 [38;5;246m14:23:55[39m 2018-02-01 [38;5;246m10:16:52[39m 304 Jackson St at 5th St
[38;5;250m4[39m [4m6[24m[4m1[24m076 2018-01-31 [38;5;246m14:53:23[39m 2018-02-01 [38;5;246m07:51:20[39m 75 Market St at Franklin St
[38;5;250m5[39m [4m3[24m[4m9[24m966 2018-01-31 [38;5;246m19:52:24[39m 2018-02-01 [38;5;246m06:58:31[39m 74 Laguna St at Hayes St
[38;5;250m6[39m [4m6[24m477 2018-01-31 [38;5;246m22:58:44[39m 2018-02-01 [38;5;246m00:46:41[39m 236 Market St at 8th St
[38;5;246m# ... with 11 more variables: start_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m, start_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_id [3m[38;5;246m<int>[38;5;246m[23m, end_station_name [3m[38;5;246m<chr>[38;5;246m[23m, end_station_latitude [3m[38;5;246m<dbl>[38;5;246m[23m,
# end_station_longitude [3m[38;5;246m<dbl>[38;5;246m[23m, bike_id [3m[38;5;246m<int>[38;5;246m[23m, user_type [3m[38;5;246m<chr>[38;5;246m[23m, member_birth_year [3m[38;5;246m<int>[38;5;246m[23m,
# member_gender [3m[38;5;246m<chr>[38;5;246m[23m, bike_share_for_all_trip [3m[38;5;246m<chr>[38;5;246m[23m[39mfordgobike_subset2 <- as.matrix(fordgobike_subset)
set.seed <- 123454321
tic()
cl <- bigkmeans(fordgobike_subset2, 3, nstart=8)
toc()3.84 sec elapsedhead(cl$cluster)[1] 2 2 3 2 2 1cl$centers [,1] [,2]
[1,] -122.2660 37.83117
[2,] -122.4072 37.77809
[3,] -121.8953 37.34168fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl$cluster)) +
geom_point()
fordgobike2018 <- fordgobike2018 %>% mutate(clust = cl$cluster)# City of Oakland c(-122.2711, 37.8044) )
# https://stackoverflow.com/questions/20621250/simple-approach-to-assigning-clusters-for-new-data-after-k-means-clustering
cl$centers [,1] [,2]
[1,] -122.2660 37.83117
[2,] -122.4072 37.77809
[3,] -121.8953 37.34168closest.cluster <- function(x) {
cluster.dist <- apply(cl$centers, 1, function(y) sqrt(sum((x-y)^2)))
return(which.min(cluster.dist)[1])
}
oak <- closest.cluster(c(-122.2711, 37.8044))
oak[1] 1oakland <- fordgobike2018 %>% filter(clust == oak)
oakland %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude)) +
geom_point()
tic()
cl.km <- kmeans(fordgobike_subset, 3)
toc()0.39 sec elapsedcl.km$centers start_station_longitude start_station_latitude
1 -122.2660 37.83117
2 -122.4072 37.77809
3 -121.8953 37.34168# City of Oakland c(-122.2711, 37.8044) )
fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl.km$cluster)) +
geom_point()
NAdim(cl$centers)[1] 3 2bayarea <- get_map(location = c(lon=cl$centers[oak,1], lat=cl$centers[oak,2]), zoom = 12, maptype = "roadmap")Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=37.831171,-122.26603&zoom=12&size=640x640&scale=2&maptype=roadmap&language=en-EN&sensor=falseggmap(bayarea)
ggmap(bayarea) +
geom_point(data = oakland, aes(x = start_station_longitude, y = start_station_latitude), size = 0.2, shape = 19) +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),
axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()
)
ggsave(filename="oakland.jpg", width = 4, height = 4, units = "cm", plot=last_plot())
ggmap(bayarea) +
geom_point(data = oakland, aes(x = start_station_longitude, y = start_station_latitude), size = 1, shape = 19) +
ggtitle("Oakland Ford Go Bike stations")
bayarea <- get_map(location = "hayward")Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=hayward&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=hayward&sensor=falseggmap(bayarea)
ggmap(bayarea) +
geom_point(data = fordgobike2018, aes(x = start_station_longitude, y = start_station_latitude, color = clust, alpha = 0.1), size = 0.2, shape = 19) +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),
axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(),
legend.position="none")
ggsave(filename="bayarea.jpg", width = 4, height = 4, units = "cm", plot=last_plot())
ggmap(bayarea) +
geom_point(data = fordgobike2018, aes(x = start_station_longitude, y = start_station_latitude, color = clust, alpha = 0.1), size = 1, shape = 19) +
ggtitle("Bay Area Ford Go Bike stations")
Gender of users
fordgobike2018 %>% ggplot(aes(x=member_gender, y=duration_sec)) + geom_bar(stat="Identity") +
ggtitle("Bay Area")
oakland %>% ggplot(aes(x=member_gender, y=duration_sec)) + geom_bar(stat="Identity") +
ggtitle("Oakland")
Duration of rides in the Bay Area
fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) 
fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) 
fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(member_gender ~ .)
fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(member_gender ~ .)
Durations of rides in Oakland
oakland %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) 
oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) 
oakland %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(member_gender ~ .)
oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(member_gender ~ .)
Duration by City
fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .) 
fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .) 
fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .)
fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .)
Duration in Oakland
oakland %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .) 
oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .) 
oakland %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .)
oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(clust ~ .)
fordgobike2018 %>% filter(clust == 1) %>%
group_by( member_gender ) %>%
summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
kable(format = "rst")
============= ========= ========
member_gender dur_mean dur_sd
============= ========= ========
Female 861.2938 2521.980
Male 690.1984 2130.362
Other 914.9140 2826.456
NA 2230.1873 6355.476
============= ========= ========fordgobike2018 %>% filter(clust == 2) %>%
group_by( member_gender ) %>%
summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
kable(format = "rst")
============= ========= ========
member_gender dur_mean dur_sd
============= ========= ========
Female 928.4383 2306.678
Male 748.3416 1876.665
Other 920.9302 2815.948
NA 2224.1842 5628.010
============= ========= ========fordgobike2018 %>% filter(clust == 3) %>%
group_by( member_gender ) %>%
summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
kable(format = "rst")
============= ========= ========
member_gender dur_mean dur_sd
============= ========= ========
Female 977.3565 3121.170
Male 752.0847 2361.596
Other 1158.0862 4597.671
NA 2506.8540 6302.026
============= ========= ========oakland %>%
group_by( member_gender ) %>%
summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
kable(format = "rst")
============= ========= ========
member_gender dur_mean dur_sd
============= ========= ========
Female 861.2938 2521.980
Male 690.1984 2130.362
Other 914.9140 2826.456
NA 2230.1873 6355.476
============= ========= ========---
title: "Ford Go Bike"
output:
  html_notebook: default
  pdf_document: default
---

In this notebook I download and unzip the [Ford Go Bike](https://www.fordgobike.com/) [data](https://www.fordgobike.com/system-data). 

```{r}
library(tidyverse)
library(tictoc)
library(ggmap)
library(skimr)
library(lubridate)
library(forcats)
library(kableExtra)
```

Set working directory.

```{r}
setwd("~/GitHub/Stat6620/fordgobike")
```

Create a directory /data in your directory.  Download the files.  First one is not zipped, the remaining are zipped.

```{r}
URL <- "https://s3.amazonaws.com/fordgobike-data/2017-fordgobike-tripdata.csv"
download.file(URL, destfile = "./data/2017-fordgobike-tripdata.csv", method="curl")
URL <- "https://s3.amazonaws.com/fordgobike-data/201801-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201801-fordgobike-tripdata.csv.zip", method="curl")
URL <- "https://s3.amazonaws.com/fordgobike-data/201802-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201802-fordgobike-tripdata.csv.zip", method="curl")
URL <- "https://s3.amazonaws.com/fordgobike-data/201803-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201803-fordgobike-tripdata.csv.zip", method="curl")
URL <- "https://s3.amazonaws.com/fordgobike-data/201804-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201804-fordgobike-tripdata.csv.zip", method="curl")
URL <- "https://s3.amazonaws.com/fordgobike-data/201805-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201805-fordgobike-tripdata.csv.zip", method="curl")
URL <- "https://s3.amazonaws.com/fordgobike-data/201806-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201806-fordgobike-tripdata.csv.zip", method="curl")
URL <- "https://s3.amazonaws.com/fordgobike-data/201807-fordgobike-tripdata.csv.zip"
download.file(URL, destfile = "./data/201807-fordgobike-tripdata.csv.zip", method="curl")
```

Loop over the one value in the url and filename that changes.

```{r, eval=FALSE}

URL <- "https://s3.amazonaws.com/fordgobike-data/2017-fordgobike-tripdata.csv"
download.file(URL, destfile = "./data/2017-fordgobike-tripdata.csv", method="curl")

for (i in 1:7) {
URL <- paste0("https://s3.amazonaws.com/fordgobike-data/20180",i,"-fordgobike-tripdata.csv.zip")
download.file(URL, destfile = paste0("./data/20180",i,"-fordgobike-tripdata.csv.zip"), method="curl")
}
```



Unzip downloaded files.

```{r}
unzip("./data/201801-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201802-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201803-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201804-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201805-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201806-fordgobike-tripdata.csv.zip",exdir="./data")
unzip("./data/201807-fordgobike-tripdata.csv.zip",exdir="./data")
```

Clean up data directory.

```{r}
fn <- "./data/201801-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)
fn <- "./data/201802-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)
fn <- "./data/201803-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)
fn <- "./data/201804-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)
fn <- "./data/201805-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)
fn <- "./data/201806-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)
fn <- "./data/201807-fordgobike-tripdata.csv.zip"
if (file.exists(fn)) file.remove(fn)
```

Read the.csv files

```{r message=FALSE}
fordgobike2017 <- read_csv(file="./data/2017-fordgobike-tripdata.csv")
fordgobike201801 <- read_csv(file="./data/201801-fordgobike-tripdata.csv")
fordgobike201802 <- read_csv(file="./data/201802-fordgobike-tripdata.csv")
fordgobike201803 <- read_csv(file="./data/201803-fordgobike-tripdata.csv")
fordgobike201804 <- read_csv(file="./data/201804-fordgobike-tripdata.csv")
fordgobike201805 <- read_csv(file="./data/201805-fordgobike-tripdata.csv")
fordgobike201806 <- read_csv(file="./data/201806-fordgobike-tripdata.csv")
fordgobike201807 <- read_csv(file="./data/201807-fordgobike-tripdata.csv")
```

Check the head() and tail() of the data.frames that are loaded.


```{r}
head(fordgobike2017) 
head(fordgobike201801)
head(fordgobike201802)
head(fordgobike201803)
head(fordgobike201804)
head(fordgobike201805)
head(fordgobike201806)
head(fordgobike201807)
```



```{r}
tail(fordgobike2017)
tail(fordgobike201801)
tail(fordgobike201802)
tail(fordgobike201803)
tail(fordgobike201804)
tail(fordgobike201805)
tail(fordgobike201806)
tail(fordgobike201807)
```


```{r}
dim(fordgobike2017)

fordgobike2017 %>% count()

```


```{r}

fordgobike201801 %>% count()
fordgobike201802 %>% count()
fordgobike201803 %>% count()
fordgobike201804 %>% count()
fordgobike201805 %>% count()
fordgobike201806 %>% count()
fordgobike201807 %>% count()

```


```{r}
glimpse(fordgobike201805)
glimpse(fordgobike201806)
glimpse(fordgobike201807)


fordgobike201806 <- fordgobike201806 %>%
        mutate(start_station_id = as.integer(start_station_id),
               end_station_id= as.integer(start_station_id) )

fordgobike201807 <- fordgobike201807 %>%
        mutate(start_station_id = as.integer(start_station_id),
               end_station_id= as.integer(start_station_id) )

fordgobike2018 <- bind_rows(fordgobike201801, fordgobike201802, fordgobike201803, fordgobike201804,
                            fordgobike201805, fordgobike201806, fordgobike201807)

glimpse(fordgobike2018)

```



```{r}
fordgobike2018 %>% select(start_station_id,start_station_name, start_station_latitude,start_station_longitude) %>%
  arrange(start_station_id) %>%
  distinct() %>%
  head() %>%
  kable(, format = "rst")
```




```{r}
dim(fordgobike2017)

fordgobike2017 %>% count()

nrow(fordgobike201801) + nrow(fordgobike201802) + nrow(fordgobike201803) + nrow(fordgobike201804)

dim(fordgobike2018)

fordgobike2018 %>% count()

fordgobike <- bind_rows(fordgobike2017, fordgobike2018)

dim(fordgobike)


fordgobike %>% count()

dim(fordgobike)

fordgobike <- fordgobike %>% mutate(age = 2018 - member_birth_year)

fordgobike %>% count() 

dim(fordgobike)

fordgobike <- fordgobike %>% mutate(year=year(start_time), month=month(start_time), day=day(start_time) )

fordgobike %>% count() 

dim(fordgobike)

fordgobike <- fordgobike %>% mutate(week_day = wday(start_time) )

levels <- c("M","T","W","TH","F","SAT","SUN")

fordgobike$week_day <- factor(fordgobike$week_day, levels = levels)

fordgobike %>% count() 

dim(fordgobike)

```



```{r}

today()

now()
```

Age

```{r warning=FALSE}
fordgobike %>% group_by( age ) %>% count()

fordgobike %>% group_by( age ) %>% summary()

skim(fordgobike)

fordgobike %>% ggplot(aes(x=age)) + geom_histogram()

fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age)) + geom_histogram()

fordgobike %>% filter(age <= 100) %>% ggplot(aes(x=age)) + geom_histogram()

fordgobike %>% filter(age > 100) %>% ggplot(aes(x=age)) + geom_histogram()
```


```{r}
fordgobike %>% group_by( member_gender, age ) %>% count()

fordgobike %>% ggplot(aes(x=age, class=member_gender)) + geom_histogram()

fordgobike %>% ggplot(aes(x=age, class=member_gender)) + geom_histogram(aes(y=..density..))

fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age)) + geom_histogram()
```

```{r}
fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age, color=member_gender)) + 
  geom_histogram(position="identity") + 
  facet_grid(member_gender ~ .)

fordgobike %>% filter(age <= 80) %>% ggplot(aes(x=age, color=member_gender)) + 
  geom_histogram(aes(y=..density..),position="identity") + 
  facet_grid(member_gender ~ .)

```

Year and day of week.

```{r}

fordgobike %>% ggplot(aes(x=year)) + geom_bar()
fordgobike %>% ggplot(aes(x=month)) + geom_bar() + facet_grid(year ~ .)
fordgobike %>% ggplot(aes(x=day)) + geom_bar() + facet_grid(year ~ .)


```

```{r}
fordgobike2018 <- fordgobike2018 %>% filter(start_station_latitude < 38 & start_station_longitude < 120 )

fordgobike_subset <- fordgobike2018 %>% select(start_station_longitude,start_station_latitude)

fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude)) +
  geom_point()
```





```{r}
library(biganalytics)

# run in parallel, the doMC package runs on Windows
library(doParallel)
registerDoParallel(cores = 8)

head(fordgobike2018)

fordgobike_subset2 <- as.matrix(fordgobike_subset)

set.seed <- 123454321

tic()

cl <- bigkmeans(fordgobike_subset2, 3, nstart=8)

toc()

head(cl$cluster)

cl$centers

fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl$cluster)) +
  geom_point()

fordgobike2018 <- fordgobike2018 %>% mutate(clust = cl$cluster)

```


```{r}
# City of Oakland  c(-122.2711, 37.8044) )

# https://stackoverflow.com/questions/20621250/simple-approach-to-assigning-clusters-for-new-data-after-k-means-clustering

cl$centers

closest.cluster <- function(x) {
  cluster.dist <- apply(cl$centers, 1, function(y) sqrt(sum((x-y)^2)))
  return(which.min(cluster.dist)[1])
}

oak <- closest.cluster(c(-122.2711, 37.8044))
oak

oakland <- fordgobike2018 %>% filter(clust == oak)

oakland %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude)) +
  geom_point()
```



```{r}
tic()

cl.km <- kmeans(fordgobike_subset, 3)

toc()

cl.km$centers

# City of Oakland  c(-122.2711, 37.8044) )

fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl.km$cluster)) +
  geom_point()
  

```

```{r}
dim(cl$centers)

bayarea <- get_map(location = c(lon=cl$centers[oak,1], lat=cl$centers[oak,2]), zoom = 12, maptype = "roadmap")

ggmap(bayarea)

ggmap(bayarea) +
  geom_point(data = oakland, aes(x = start_station_longitude, y = start_station_latitude), size = 0.2, shape = 19) +
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),
        axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()
        )

ggsave(filename="oakland.jpg", width = 4, height = 4, units = "cm", plot=last_plot())

ggmap(bayarea) +
  geom_point(data = oakland, aes(x = start_station_longitude, y = start_station_latitude), size = 1, shape = 19) +
  ggtitle("Oakland Ford Go Bike stations")
```



```{r}
bayarea <- get_map(location = "hayward")

ggmap(bayarea)

ggmap(bayarea) +
  geom_point(data = fordgobike2018, aes(x = start_station_longitude, y = start_station_latitude, color  = clust, alpha = 0.1), size = 0.2, shape = 19) +
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),
        axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(),
        legend.position="none")

ggsave(filename="bayarea.jpg", width = 4, height = 4, units = "cm", plot=last_plot())

ggmap(bayarea) +
  geom_point(data = fordgobike2018, aes(x = start_station_longitude, y = start_station_latitude, color  = clust, alpha = 0.1), size = 1, shape = 19) +
  ggtitle("Bay Area Ford Go Bike stations")
```


Gender of users

```{r}

fordgobike2018 %>% ggplot(aes(x=member_gender, y=duration_sec)) + geom_bar(stat="Identity") +
  ggtitle("Bay Area")

oakland %>% ggplot(aes(x=member_gender, y=duration_sec)) + geom_bar(stat="Identity") +
  ggtitle("Oakland")
```

Duration of rides in the Bay Area

```{r}
fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() +
  geom_density(aes(y=..density..)) 

fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() +
  geom_density(aes(y=..density..)) 

fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(member_gender ~ .)

fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(member_gender ~ .)
```

Durations of rides in Oakland


```{r}
oakland %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() +
  geom_density(aes(y=..density..)) 

oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() +
  geom_density(aes(y=..density..)) 

oakland %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(member_gender ~ .)

oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(member_gender ~ .)
```

Duration by City

```{r}
fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() +
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 

fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() +
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 

fordgobike2018 %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 

fordgobike2018 %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 
```

Duration in Oakland

```{r}
oakland %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() +
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 

oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() +
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 

oakland %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 

oakland %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(clust ~ .) 
```



```{r}

fordgobike2018 %>% filter(clust == 1) %>% 
  group_by( member_gender ) %>%
  summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
  kable(format = "rst")


fordgobike2018 %>% filter(clust == 2) %>% 
  group_by( member_gender ) %>%
  summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
  kable(format = "rst")


fordgobike2018 %>% filter(clust == 3) %>% 
  group_by( member_gender ) %>%
  summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
  kable(format = "rst")

```


```{r}
oakland %>%
  group_by( member_gender ) %>%
  summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec)) %>%
  kable(format = "rst")

```


