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Getting started with the R-package hystReet

Johannes Friedrich

2022-11-24

1 Introduction

hystreet is a company collecting pedestrains in german cities. After registering you can download the data for free from 19 cities.

2 Installation

Until now the package is not on CRAN but you can download it via GitHub with the following command:

3 API Keys

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:

  1. Once you have your key, save it as an environment variable for the current session by running the following:
Sys.setenv(HYSTREET_API_TOKEN = "PASTE YOUR API TOKEN HERE")
  1. Alternatively, you can set it permanently with the help of usethis::edit_r_environ() by adding the line to your .Renviron:
HYSTREET_API_TOKEN = PASTE YOUR API TOKEN HERE
  1. If you don’t want to save it here, you can input it in each function using the API_token parameter.

4 Usage

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)

4.1 Load some statistics

The function ‘get_hystreet_stats()’ summaries the number of available stations and the sum of all counted pedestrians.

library(hystReet)

stats <- get_hystreet_stats()

4.2 Request all stations

The function ‘get_hystreet_locations()’ requests all available stations of the project.

locations <- get_hystreet_locations()
id name city
360 Brüderstraße (Mitte) Soest
308 Leipziger Straße (West) Halle (Saale)
76 Königstraße (Mitte) Stuttgart
351 Johann-Philipp-Straße Trier
309 Hochstraße (Nord) Krefeld
140 Fleischstraße (Nord) Trier
53 Schadowstraße (West) Düsseldorf
55 Flinger Straße (Ost) Düsseldorf
108 Große Straße (Mitte) Osnabrück
348 Holstenstraße (Nord) Kiel

4.3 Request data from a specific station

The (probably) most interesting function is ‘get_hystreet_station_data()’. With 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 set the received data more precise:

location_71 <- get_hystreet_station_data(
  hystreetId = 71,
  query = list(from = "2021-12-01", to = "2022-01-01", resolution = "day"))

5 Some ideas to visualise the data

Let´s see if we can see the most frequent days before Christmas … I think it could be Saturday ;-). Also nice to see the 25th and 26th of December … holidays in Germany :-).

location_71 <- get_hystreet_station_data(
    hystreetId = 71, 
    query = list(from = "2021-12-01", to = "2022-01-01", resolution = "hour"))
ggplot(location_71$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.

5.1 Compare different stations

Now let´s compare different stations:

  1. Load the data
location_73 <- get_hystreet_station_data(
    hystreetId = 73, 
    query = list(from = "2022-01-01", to = "2022-01-31", resolution = "day"))$measurements %>% 
  select(pedestrians_count, timestamp) %>% 
  mutate(station = 73)

location_74 <- get_hystreet_station_data(
    hystreetId = 74, 
    query = list(from = "2022-01-01", to = "2019-01-22", resolution = "day"))$measurements %>% 
    select(pedestrians_count, timestamp) %>% 
  mutate(station = 74)

data_73_74 <- bind_rows(location_73, location_74)
ggplot(data_73_74, 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))

5.2 Highest ratio (pedestrians/day)

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(ID){
  temp <- get_hystreet_station_data(
    hystreetId = ID,
    query = list(from = "2021-01-01", to = "2021-12-31", resolution = "day"))
  
    lifetime_count <- temp$statistics$timerange_count
    days_counted <- as.integer(ymd(temp$metadata$measured_to)  - ymd(temp$metadata$measured_from))
    
    return(data.frame(
      id = ID,
      station = paste0(temp$city, " (",temp$name,")"),
      ratio = lifetime_count/days_counted))
  
})

ratio <- bind_rows(all_data)

What stations have the highest ratio?

ratio %>% 
  top_n(5, ratio) %>% 
  arrange(desc(ratio))
##    id                           station    ratio
## 1  73  München (Neuhauser Straße (Ost)) 45510.15
## 2 165         München (Kaufingerstraße) 42458.42
## 3 305    Wien (Kärntner Straße (Mitte)) 40067.34
## 4 306 Wien (Mariahilfer Straße (Mitte)) 39642.14
## 5  63            Hannover (Georgstraße) 38442.91

Now let´s visualise the top 10 cities:

ggplot(ratio %>% 
         top_n(10,ratio), aes(station, ratio)) +
  geom_bar(stat = "identity") +
  labs(x = "City",
       y = "Pedestrians per day") + 
    theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 45, hjust = 1))

5.3 Corona effects

The Hystreet-API is a great source of analysing the social effects of the Corona pandemic in 2020. Let´s collect all german stations since March 2020 and analyse the pedestrian count until 10th June 2020.

data <- lapply(hystreet_ids[,"id"], function(ID){

    temp <- get_hystreet_station_data(
        hystreetId = ID,
        query = list(from = "2020-03-01", to = "2020-06-10", resolution = "day")
    )
    
    return(data.frame(
    name = temp$name,
    city = temp$city,
    timestamp = format(as.POSIXct(temp$measurements$timestamp), "%Y-%m-%d"),
    pedestrians_count = temp$measurements$pedestrians_count,
    legend = paste(temp$city, temp$name, sep = " - ")
  ))
   
}) 

corona_data_all <- bind_rows(data)
corona_data_all %>% 
ggplot(aes(ymd(timestamp), pedestrians_count, colour = legend)) +
geom_line(alpha = 0.2) +
    scale_x_date(labels = date_format("%d.%m.%Y"),
               breaks = date_breaks("7 days")
     ) +
  theme(legend.position = "none",
        legend.title = element_text("Legende"),
         axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(x = "Date",
       y = "Persons/Day")

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They may not be fully stable and should be used with caution. We make no claims about them.
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