In [27]:
library(DataComputing)
library(repr)
options(repr.plot.width=6.5, repr.plot.height=5)
In [4]:
Stations <- mosaic::read.file("http://tiny.cc/dcf/DC-Stations.csv")
data_site <- "http://tiny.cc/dcf/2014-Q4-Trips-History-Data-Small.rds"
Trips <- readRDS(gzcon(url(data_site)))
In [7]:
head(Trips)
Out[7]:
In [6]:
Trips %>%
ggplot(aes(x = sdate)) +
geom_density()
This is certainly just a kernel density estimation for the variable sdate
In [9]:
Trips %>%
mutate(time_of_day = lubridate::hour(sdate) + lubridate::minute(sdate) / 60) %>%
ggplot(aes(x=time_of_day)) +
geom_density()
In [19]:
Trips %>%
mutate(
time_of_day = lubridate::hour(sdate) + lubridate::minute(sdate) / 60,
day_of_week = lubridate::wday(sdate)
) %>%
ggplot(aes(x=time_of_day)) +
geom_density() +
facet_wrap(~day_of_week)
In [23]:
Trips %>%
mutate(
time_of_day = lubridate::hour(sdate) + lubridate::minute(sdate) / 60,
day_of_week = lubridate::wday(sdate)
) %>%
ggplot(aes(x=time_of_day)) +
geom_density(aes(fill=client), alpha=0.4, color=NA) +
facet_wrap(~day_of_week)
In [24]:
Trips %>%
mutate(
time_of_day = lubridate::hour(sdate) + lubridate::minute(sdate) / 60,
day_of_week = lubridate::wday(sdate)
) %>%
ggplot(aes(x=time_of_day)) +
geom_density(aes(fill=client), alpha=0.4, color=NA, position = position_stack()) +
facet_wrap(~day_of_week)
The last one is better.
In [29]:
Trips %>%
mutate(
time_of_day = lubridate::hour(sdate) + lubridate::minute(sdate) / 60,
wday = ifelse(lubridate::wday(sdate) %in% c(1,7), "weekend", "weekday")
) %>%
ggplot(aes(x=time_of_day)) +
geom_density(aes(fill=client), alpha=0.4, color=NA, position = position_stack()) +
facet_wrap(~wday)
In [ ]: