hystreet is a company that collects data on pedestrian traffic in shopping streets of different German cities. After registering you can access and download the data via their website.
Until now the package is not on CRAN but you can install it from GitHub with the following command:
if (!require("devtools"))
install.packages("devtools")
devtools::install_github("JohannesFriedrich/hystReet")
To use this package, you will first need to get a hystreet API key. To do so, you first need to set up an account on https://hystreet.com/. After that you can request an API key via e-mail. Once your request has been granted, you will find you key in your hystreet account profile.
Now you have three options:
Once you have your key, you can save it as an environment variable for the current session by running the following command:
Sys.setenv(HYSTREET_API_TOKEN = "PASTE YOUR API TOKEN HERE")
- Alternatively, you can set it permanently with the help of
usethis::edit_r_environ()
by adding the following line to your.Renviron
:
HYSTREET_API_TOKEN = PASTE YOUR API TOKEN HERE
- If you don’t want to save your API token here, you can enter it for
each function of this package using the
API_token
parameter.
Function name | Description | Example |
---|---|---|
get_hystreet_stats() | request common statistics about the hystreet project | get_hystreet_stats() |
get_hystreet_locations() | request all available locations | get_hystreet_locations() |
get_hystreet_station_data() | request data from a stations | get_hystreet_station_data(71) |
set_hystreet_token() | set your API token | set_hystreet_token(123456789) |
The function get_hystreet_stats()
summarises the number of available
stations and the sum of all counted pedestrians.
library(hystReet)
## Loading required package: httr
## Loading required package: jsonlite
##
## Attaching package: 'jsonlite'
## The following object is masked from 'package:purrr':
##
## flatten
stats <- get_hystreet_stats()
stats
stations |
today_count |
---|---|
119 |
1085039 |
The function get_hystreet_locations()
generates a data frame with all
available stations of the project.
locations <- get_hystreet_locations()
locations
id |
name |
city |
---|---|---|
251 |
Annastraße |
Augsburg |
209 |
Lange Straße |
Oldenburg |
151 |
Stiftstraße |
Stuttgart |
253 |
Neustraße |
Bocholt |
168 |
Hohe Straße (Nord) |
Köln |
93 |
Große Bleichen |
Hamburg |
135 |
Schönbornstraße |
Würzburg |
252 |
Holm |
Flensburg |
131 |
Sack |
Braunschweig |
142 |
Hauptstraße (Süd) |
Erlangen |
The (probably) most interesting function is
get_hystreet_station_data()
. Using the hystreetID it is possible to
request a specific station. By default, all the data from the current
day are received. With the query
argument it is possible to define the
time and sampling frame of the data more precisely: from
: datetime of
earliest measurement (default: today 00:00:00:): e.g. “2018-10-01
12:00:00” or “2018-10-01” to
: datetime of latest measurement
(default: today 23:59:59): e.g. “2018-12-01 12:00:00” or “2018-12-01”
resoution
: Resultion for the measurement (default: hour): “day”,
“hour”, “month”, “week”
data <- get_hystreet_station_data(
hystreetId = 71,
query = list(from = "2018-12-01", to = "2018-12-31", resolution = "day"))
Let´s see if we can find the busiest days in December 2018. Saturdays were probably quite busy, while there should have been substantially less pedestrian traffic on the 24th and 25th of December, both of which are holidays in Germany.
data <- get_hystreet_station_data(
hystreetId = 71,
query = list(from = "2018-12-01", to = "2019-01-01", resolution = "hour"))
ggplot(data$measurements, aes(x = timestamp, y = pedestrians_count, colour = weekdays(timestamp))) +
geom_path(group = 1) +
scale_x_datetime(date_breaks = "7 days") +
scale_x_datetime(labels = date_format("%d.%m.%Y")) +
labs(x = "Date",
y = "Pedestrians",
colour = "Day")
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
Now let´s compare data from different stations:
- Load the data
data_73 <- get_hystreet_station_data(
hystreetId = 73,
query = list(from = "2019-01-01", to = "2019-01-31", resolution = "day"))$measurements %>%
select(pedestrians_count, timestamp) %>%
mutate(station = 73)
data_74 <- get_hystreet_station_data(
hystreetId = 74,
query = list(from = "2019-01-01", to = "2019-01-31", resolution = "day"))$measurements %>%
select(pedestrians_count, timestamp) %>%
mutate(station = 74)
data <- bind_rows(data_73, data_74)
ggplot(data, aes(x = timestamp, y = pedestrians_count, fill = weekdays(timestamp))) +
geom_bar(stat = "identity") +
scale_x_datetime(labels = date_format("%d.%m.%Y")) +
facet_wrap(~station, scales = "free_y") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1))
Now a little bit of big data analysis. Let´s find the station with the highest pedestrians per day ratio:
hystreet_ids <- get_hystreet_locations()
all_data <- lapply(hystreet_ids[,"id"], function(x){
temp <- get_hystreet_station_data(
hystreetId = x)
lifetime_count <- temp$statistics$lifetime_count
days_counted <- as.numeric(temp$metadata$latest_measurement_at - temp$metadata$earliest_measurement_at)
return(data.frame(
id = x,
station = paste0(temp$city, " (",temp$name,")"),
ratio = lifetime_count/days_counted))
})
ratio <- bind_rows(all_data)
Which stations have the highest ratio?
ratio %>%
top_n(5, ratio) %>%
arrange(desc(ratio))
## id station ratio
## 1 73 München (Neuhauser Straße) 91904.43
## 2 47 Köln (Schildergasse (West)) 86939.41
## 3 165 München (Kaufingerstraße) 71187.87
## 4 48 Köln (Hohe Straße (Mitte)) 65037.64
## 5 63 Hannover (Georgstraße) 64433.47
Now let´s visualise the top 10 locations:
ggplot(ratio %>%
top_n(10,ratio), aes(station, ratio)) +
geom_bar(stat = "identity") +
labs(x = "Location",
y = "Pedestrians per day") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1))