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)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 101k 0 0 101k 0 0:18:53 0:00:01 0:18:52 97916
0 112M 0 390k 0 0 195k 0 0:09:49 0:00:02 0:09:47 189k
0 112M 0 764k 0 0 254k 0 0:07:31 0:00:03 0:07:28 249k
1 112M 1 1155k 0 0 288k 0 0:06:38 0:00:04 0:06:34 284k
1 112M 1 1580k 0 0 316k 0 0:06:04 0:00:05 0:05:59 319k
1 112M 1 2039k 0 0 339k 0 0:05:38 0:00:06 0:05:32 387k
2 112M 2 2583k 0 0 369k 0 0:05:12 0:00:07 0:05:05 438k
2 112M 2 3348k 0 0 418k 0 0:04:35 0:00:08 0:04:27 516k
3 112M 3 4487k 0 0 498k 0 0:03:51 0:00:09 0:03:42 666k
5 112M 5 6017k 0 0 601k 0 0:03:11 0:00:10 0:03:01 887k
6 112M 6 7649k 0 0 695k 0 0:02:45 0:00:11 0:02:34 1122k
8 112M 8 9349k 0 0 779k 0 0:02:27 0:00:12 0:02:15 1353k
9 112M 9 10.5M 0 0 827k 0 0:02:19 0:00:13 0:02:06 1482k
10 112M 10 11.9M 0 0 876k 0 0:02:11 0:00:14 0:01:57 1557k
12 112M 12 13.5M 0 0 924k 0 0:02:04 0:00:15 0:01:49 1570k
13 112M 13 15.0M 0 0 962k 0 0:01:59 0:00:16 0:01:43 1550k
14 112M 14 16.6M 0 0 1000k 0 0:01:55 0:00:17 0:01:38 1533k
16 112M 16 18.2M 0 0 1036k 0 0:01:51 0:00:18 0:01:33 1581k
17 112M 17 20.1M 0 0 1086k 0 0:01:46 0:00:19 0:01:27 1672k
19 112M 19 22.4M 0 0 1149k 0 0:01:40 0:00:20 0:01:20 1822k
22 112M 22 25.1M 0 0 1226k 0 0:01:33 0:00:21 0:01:12 2070k
25 112M 25 28.7M 0 0 1336k 0 0:01:26 0:00:22 0:01:04 2475k
29 112M 29 33.4M 0 0 1490k 0 0:01:17 0:00:23 0:00:54 3124k
35 112M 35 39.3M 0 0 1680k 0 0:01:08 0:00:24 0:00:44 3940k
42 112M 42 47.3M 0 0 1939k 0 0:00:59 0:00:25 0:00:34 5101k
50 112M 50 56.9M 0 0 2242k 0 0:00:51 0:00:26 0:00:25 6508k
60 112M 60 68.2M 0 0 2589k 0 0:00:44 0:00:27 0:00:17 8104k
71 112M 71 80.2M 0 0 2935k 0 0:00:39 0:00:28 0:00:11 9582k
80 112M 80 90.0M 0 0 3180k 0 0:00:36 0:00:29 0:00:07 10.1M
89 112M 89 100M 0 0 3439k 0 0:00:33 0:00:30 0:00:03 10.6M
99 112M 99 112M 0 0 3709k 0 0:00:31 0:00:31 --:--:-- 11.0M
100 112M 100 112M 0 0 3715k 0 0:00:31 0:00:31 --:--:-- 11.0MURL <- "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
2 3251k 2 86674 0 0 86674 0 0:00:38 --:--:-- 0:00:38 98k
25 3251k 25 815k 0 0 815k 0 0:00:03 0:00:01 0:00:02 438k
100 3251k 100 3251k 0 0 1625k 0 0:00:02 0:00:02 --:--:-- 1162kURL <- "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 3698k 0 34450 0 0 34450 0 0:01:49 --:--:-- 0:01:49 44053
23 3698k 23 883k 0 0 883k 0 0:00:04 0:00:01 0:00:03 495k
100 3698k 100 3698k 0 0 1849k 0 0:00:02 0:00:02 --:--:-- 1384kURL <- "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
4 3901k 4 168k 0 0 168k 0 0:00:23 0:00:01 0:00:22 165k
47 3901k 47 1835k 0 0 1835k 0 0:00:02 0:00:01 0:00:01 924k
100 3901k 100 3901k 0 0 1950k 0 0:00:02 0:00:02 --:--:-- 1486kURL <- "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
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
8 4613k 8 373k 0 0 373k 0 0:00:12 0:00:01 0:00:11 298k
63 4613k 63 2906k 0 0 1453k 0 0:00:03 0:00:02 0:00:01 1291k
100 4613k 100 4613k 0 0 2306k 0 0:00:02 0:00:02 --:--:-- 1789kUnzip 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")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] TRUERead the.csv files
fordgobike2017 <- read_csv(file="./data/2017-fordgobike-tripdata.csv")Parsed with column specification:
cols(
duration_sec = col_integer(),
start_time = col_datetime(format = ""),
end_time = col_datetime(format = ""),
start_station_id = col_integer(),
start_station_name = col_character(),
start_station_latitude = col_double(),
start_station_longitude = col_double(),
end_station_id = col_integer(),
end_station_name = col_character(),
end_station_latitude = col_double(),
end_station_longitude = col_double(),
bike_id = col_integer(),
user_type = col_character(),
member_birth_year = col_integer(),
member_gender = col_character()
)
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|=================================================================================================| 100% 112 MBfordgobike201801 <- read_csv(file="./data/201801-fordgobike-tripdata.csv")Parsed with column specification:
cols(
duration_sec = col_integer(),
start_time = col_datetime(format = ""),
end_time = col_datetime(format = ""),
start_station_id = col_integer(),
start_station_name = col_character(),
start_station_latitude = col_double(),
start_station_longitude = col_double(),
end_station_id = col_integer(),
end_station_name = col_character(),
end_station_latitude = col_double(),
end_station_longitude = col_double(),
bike_id = col_integer(),
user_type = col_character(),
member_birth_year = col_integer(),
member_gender = col_character(),
bike_share_for_all_trip = col_character()
)fordgobike201802 <- read_csv(file="./data/201802-fordgobike-tripdata.csv")Parsed with column specification:
cols(
duration_sec = col_integer(),
start_time = col_datetime(format = ""),
end_time = col_datetime(format = ""),
start_station_id = col_integer(),
start_station_name = col_character(),
start_station_latitude = col_double(),
start_station_longitude = col_double(),
end_station_id = col_integer(),
end_station_name = col_character(),
end_station_latitude = col_double(),
end_station_longitude = col_double(),
bike_id = col_integer(),
user_type = col_character(),
member_birth_year = col_integer(),
member_gender = col_character(),
bike_share_for_all_trip = col_character()
)fordgobike201803 <- read_csv(file="./data/201803-fordgobike-tripdata.csv")Parsed with column specification:
cols(
duration_sec = col_integer(),
start_time = col_datetime(format = ""),
end_time = col_datetime(format = ""),
start_station_id = col_integer(),
start_station_name = col_character(),
start_station_latitude = col_double(),
start_station_longitude = col_double(),
end_station_id = col_integer(),
end_station_name = col_character(),
end_station_latitude = col_double(),
end_station_longitude = col_double(),
bike_id = col_integer(),
user_type = col_character(),
member_birth_year = col_integer(),
member_gender = col_character(),
bike_share_for_all_trip = col_character()
)fordgobike201804 <- read_csv(file="./data/201804-fordgobike-tripdata.csv")Parsed with column specification:
cols(
duration_sec = col_integer(),
start_time = col_datetime(format = ""),
end_time = col_datetime(format = ""),
start_station_id = col_integer(),
start_station_name = col_character(),
start_station_latitude = col_double(),
start_station_longitude = col_double(),
end_station_id = col_integer(),
end_station_name = col_character(),
end_station_latitude = col_double(),
end_station_longitude = col_double(),
bike_id = col_integer(),
user_type = col_character(),
member_birth_year = col_integer(),
member_gender = col_character(),
bike_share_for_all_trip = col_character()
)Check the head() and tail() of the data.frames that are loaded.
head(fordgobike2017)head(fordgobike201801)head(fordgobike201802)head(fordgobike201803)head(fordgobike201804)tail(fordgobike2017)tail(fordgobike201801)tail(fordgobike201802)tail(fordgobike201803)tail(fordgobike201804)dim(fordgobike2017)[1] 519700 15fordgobike2017 %>% count()nrow(fordgobike201801) + nrow(fordgobike201802) + nrow(fordgobike201803) + nrow(fordgobike201804)[1] 444071fordgobike2018 <- bind_rows(fordgobike201801, fordgobike201802, fordgobike201803, fordgobike201804)
dim(fordgobike2018)[1] 444071 16fordgobike2018 %>% count()fordgobike <- bind_rows(fordgobike2017, fordgobike2018)
dim(fordgobike)[1] 963771 16fordgobike %>% count()dim(fordgobike)[1] 963771 16fordgobike <- fordgobike %>% mutate(age = 2018 - member_birth_year)
fordgobike %>% count() dim(fordgobike)[1] 963771 17fordgobike <- fordgobike %>% mutate(year=year(start_time), month=month(start_time), day=day(start_time) )
fordgobike %>% count() dim(fordgobike)[1] 963771 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() dim(fordgobike)[1] 963771 21today()[1] "2018-05-31"now()[1] "2018-05-31 06:54:05 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.: 368.0 1st Qu.:2017-10-04 11:40:04 1st Qu.:2017-10-04 11:56:47 1st Qu.: 27.0
Median : 575.0 Median :2017-12-14 16:35:16 Median :2017-12-14 16:49:41 Median : 75.0
Mean : 996.8 Mean :2017-12-16 02:25:36 Mean :2017-12-16 02:42:13 Mean :102.9
3rd Qu.: 900.0 3rd Qu.:2018-03-01 17:17:44 3rd Qu.:2018-03-01 17:27:42 3rd Qu.:162.0
Max. :86369.0 Max. :2018-04-30 23:58:45 Max. :2018-05-01 18:56:06 Max. :345.0
start_station_name start_station_latitude start_station_longitude end_station_id end_station_name
Length:963771 Min. :37.31 Min. :-122.4 Min. : 3.0 Length:963771
Class :character 1st Qu.:37.77 1st Qu.:-122.4 1st Qu.: 25.0 Class :character
Mode :character Median :37.78 Median :-122.4 Median : 71.0 Mode :character
Mean :37.77 Mean :-122.4 Mean :100.3
3rd Qu.:37.80 3rd Qu.:-122.4 3rd Qu.:158.0
Max. :37.88 Max. :-121.9 Max. :345.0
end_station_latitude end_station_longitude bike_id user_type member_birth_year
Min. :37.31 Min. :-122.4 Min. : 10 Length:963771 Min. :1886
1st Qu.:37.77 1st Qu.:-122.4 1st Qu.: 914 Class :character 1st Qu.:1975
Median :37.78 Median :-122.4 Median :1929 Mode :character Median :1983
Mean :37.77 Mean :-122.4 Mean :1846 Mean :1981
3rd Qu.:37.80 3rd Qu.:-122.4 3rd Qu.:2737 3rd Qu.:1989
Max. :37.88 Max. :-121.9 Max. :4073 Max. :2000
NA's :100885
member_gender bike_share_for_all_trip age year month day
Length:963771 Length:963771 Min. : 18 Min. :2017 Min. : 1.000 Min. : 1.00
Class :character Class :character 1st Qu.: 29 1st Qu.:2017 1st Qu.: 3.000 1st Qu.: 8.00
Mode :character Mode :character Median : 35 Median :2017 Median : 7.000 Median :16.00
Mean : 37 Mean :2017 Mean : 6.459 Mean :15.96
3rd Qu.: 43 3rd Qu.:2018 3rd Qu.:10.000 3rd Qu.:24.00
Max. :132 Max. :2018 Max. :12.000 Max. :31.00
NA's :100885
week_day
M : 0
T : 0
W : 0
TH : 0
F : 0
(Other): 0
NA's :963771 skim(fordgobike)Skim summary statistics
n obs: 963771
n variables: 21
Variable type: character
variable missing complete n min max empty n_unique
1 bike_share_for_all_trip 519700 444071 963771 2 3 0 2
2 end_station_name 0 963771 963771 9 63 0 299
3 member_gender 100658 863113 963771 4 6 0 3
4 start_station_name 0 963771 963771 9 63 0 299
5 user_type 0 963771 963771 8 10 0 2
Variable type: factor
variable missing complete n n_unique top_counts ordered
1 week_day 963771 0 963771 0 NA: 963771, M: 0, T: 0, W: 0 FALSE
Variable type: integer
variable missing complete n mean sd min p25 median p75 max
1 bike_id 0 963771 963771 1846.03 1056.45 10 914 1929 2737 4073
2 day 0 963771 963771 15.96 8.79 1 8 16 24 31
3 duration_sec 0 963771 963771 996.78 3092.37 61 368 575 900 86369
4 end_station_id 0 963771 963771 100.33 89.47 3 25 71 158 345
5 member_birth_year 100885 862886 963771 1981 10.61 1886 1975 1983 1989 2000
6 start_station_id 0 963771 963771 102.91 90.23 3 27 75 162 345
hist
1 <U+2587><U+2586><U+2586><U+2586><U+2587><U+2587><U+2585><U+2582>
2 <U+2587><U+2587><U+2587><U+2587><U+2586><U+2587><U+2587><U+2587>
3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
4 <U+2587><U+2585><U+2583><U+2581><U+2582><U+2581><U+2581><U+2581>
5 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2583><U+2587><U+2587>
6 <U+2587><U+2585><U+2583><U+2582><U+2582><U+2581><U+2581><U+2581>
Variable type: numeric
variable missing complete n mean sd min p25 median p75 max
1 age 100885 862886 963771 37 10.61 18 29 35 43 132
2 end_station_latitude 0 963771 963771 37.77 0.095 37.31 37.77 37.78 37.8 37.88
3 end_station_longitude 0 963771 963771 -122.36 0.11 -122.44 -122.41 -122.4 -122.39 -121.87
4 month 0 963771 963771 6.46 3.8 1 3 7 10 12
5 start_station_latitude 0 963771 963771 37.77 0.095 37.31 37.77 37.78 37.8 37.88
6 start_station_longitude 0 963771 963771 -122.36 0.11 -122.44 -122.41 -122.4 -122.39 -121.87
7 year 0 963771 963771 2017.46 0.5 2017 2017 2017 2018 2018
hist
1 <U+2587><U+2587><U+2583><U+2581><U+2581><U+2581><U+2581><U+2581>
2 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587><U+2581>
3 <U+2587><U+2581><U+2582><U+2581><U+2581><U+2581><U+2581><U+2581>
4 <U+2587><U+2585><U+2585><U+2581><U+2582><U+2587><U+2585><U+2587>
5 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587><U+2581>
6 <U+2587><U+2581><U+2582><U+2581><U+2581><U+2581><U+2581><U+2581>
7 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
|==================================================================== | 80% ~1 s remaining
Variable type: POSIXct
variable missing complete n min max median n_unique
1 end_time 0 963771 963771 2017-06-28 2018-05-01 2017-12-14 963733
2 start_time 0 963771 963771 2017-06-28 2018-04-30 2017-12-14 963739
|======================================================================================|100% ~0 s remaining 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()
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 ~ .)
fordgobike %>% ggplot(aes(x=as.factor(week_day))) + geom_bar()
library(biganalytics)Loading required package: bigmemory
Loading required package: bigmemory.sri
Loading required package: foreach
Attaching package: 㤼㸱foreach㤼㸲
The following objects are masked from 㤼㸱package:purrr㤼㸲:
accumulate, when
Loading required package: biglm
Loading required package: DBI# run in parallel, the doMC package runs on Windows
library(doParallel)Loading required package: iterators
Loading required package: parallelregisterDoParallel(cores = 8)
fordgobike_subset <- fordgobike[,c(6,7)]
fordgobike_subset2 <- as.matrix(fordgobike_subset)
cl <- bigkmeans(fordgobike_subset2, 3, nstart=8)
fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl$cluster)) +
geom_point()
fordgobike_subset <- fordgobike[,c(6,7)]
cl <- kmeans(fordgobike_subset, 3)
fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl$cluster)) +
geom_point()
NAcl$centers start_station_latitude start_station_longitude
1 37.33454 -121.8909
2 37.82669 -122.2651
3 37.77905 -122.4070bayarea <- get_map(location = "hayward")Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=hayward&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=hayward&sensor=falseggmap(bayarea) +
geom_point(data = fordgobike_subset, aes(x = start_station_longitude, y = start_station_latitude, color = cl$cluster, alpha = 0.1), size = 2, shape = 19) # 21
fordgobike <- fordgobike %>% mutate(city = cl$cluster)
fordgobike %>% head()fordgobike_subset <- fordgobike[,c(10,11)]
cl <- kmeans(fordgobike_subset, 3)
fordgobike_subset %>% ggplot(aes(x=end_station_longitude, y=end_station_latitude, color=cl$cluster)) +
geom_point()
cl$centers end_station_latitude end_station_longitude
1 37.77950 -122.4059
2 37.33461 -121.8909
3 37.82582 -122.2658
fordgobike %>% ggplot(aes(x=member_gender, y=duration_sec)) + geom_bar(stat="Identity")
fordgobike %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) 
fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) 
fordgobike %>% 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 ~ .)
fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(member_gender ~ .)
fordgobike %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(city ~ .) 
fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(city ~ .) 
fordgobike %>% ggplot(aes(x=duration_sec, y=..density..)) +
scale_x_continuous(limits = c(0, 10000)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(city ~ .)
fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) +
geom_histogram() +
geom_density(aes(y=..density..)) +
facet_grid(city ~ .)
---
title: "Ford Go Bike"
output: html_notebook
---

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)
```

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")
```

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")
```

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)
```

Read the.csv files

```{r}
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")
```

Check the head() and tail() of the data.frames that are loaded.


```{r}
head(fordgobike2017)
head(fordgobike201801)
head(fordgobike201802)
head(fordgobike201803)
head(fordgobike201804)
```



```{r}
tail(fordgobike2017)
tail(fordgobike201801)
tail(fordgobike201802)
tail(fordgobike201803)
tail(fordgobike201804)
```

```{r}
dim(fordgobike2017)

fordgobike2017 %>% count()

nrow(fordgobike201801) + nrow(fordgobike201802) + nrow(fordgobike201803) + nrow(fordgobike201804)

fordgobike2018 <- bind_rows(fordgobike201801, fordgobike201802, fordgobike201803, 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}
fordgobike %>% ggplot(aes(x=as.factor(week_day))) + geom_bar()
```



```{r}
library(biganalytics)

# run in parallel, the doMC package runs on Windows
library(doParallel)
registerDoParallel(cores = 8)

fordgobike_subset <- fordgobike[,c(6,7)]

fordgobike_subset2 <- as.matrix(fordgobike_subset)

cl <- bigkmeans(fordgobike_subset2, 3, nstart=8)

fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl$cluster)) +
  geom_point()
```



```{r}

fordgobike_subset <- fordgobike[,c(6,7)]

cl <- kmeans(fordgobike_subset, 3)

fordgobike_subset %>% ggplot(aes(x=start_station_longitude, y=start_station_latitude, color=cl$cluster)) +
  geom_point()
  

```

```{r}
cl$centers
```


```{r}
bayarea <- get_map(location = "hayward")

ggmap(bayarea) +
  geom_point(data = fordgobike_subset, aes(x = start_station_longitude, y = start_station_latitude, color  = cl$cluster, alpha = 0.1), size = 2, shape = 19)  # 21
```


```{r}
fordgobike <- fordgobike %>% mutate(city = cl$cluster)

fordgobike %>% head()
```




```{r}
fordgobike_subset <- fordgobike[,c(10,11)]

cl <- kmeans(fordgobike_subset, 3)

fordgobike_subset %>% ggplot(aes(x=end_station_longitude, y=end_station_latitude, color=cl$cluster)) +
  geom_point()
```


```{r}
cl$centers
```


```{r}
bayarea <- get_map(location = "hayward")

ggmap(bayarea) +
  geom_point(data = fordgobike_subset, aes(x = end_station_longitude, y = end_station_latitude, color  = cl$cluster, alpha = 0.1), size = 2, shape = 19)  # 21
```

```{r}

fordgobike %>% ggplot(aes(x=member_gender, y=duration_sec)) + geom_bar(stat="Identity")
```


```{r}
fordgobike %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() +
  geom_density(aes(y=..density..)) 

fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() +
  geom_density(aes(y=..density..)) 

fordgobike %>% 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 ~ .)

fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(member_gender ~ .)
```

```{r}
fordgobike %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() +
  geom_density(aes(y=..density..)) +
  facet_grid(city ~ .) 

fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() +
  geom_density(aes(y=..density..)) +
  facet_grid(city ~ .) 

fordgobike %>% ggplot(aes(x=duration_sec, y=..density..)) + 
  scale_x_continuous(limits = c(0, 10000)) +
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(city ~ .) 

fordgobike %>% ggplot(aes(log(x=duration_sec), y=..density..)) + 
  geom_histogram() + 
  geom_density(aes(y=..density..)) +
  facet_grid(city ~ .) 
```



```{r}

fordgobike %>% filter(city == 1) %>% 
  group_by( member_gender ) %>%
  summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec))

fordgobike %>% filter(city == 2) %>% 
  group_by( member_gender ) %>%
  summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec))

fordgobike %>% filter(city == 3) %>% 
  group_by( member_gender ) %>%
  summarize(dur_mean = mean(duration_sec), dur_sd = sd(duration_sec))
```

