Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,
eval = FALSE,
fig.align = "center")
## ----message=FALSE, warning=FALSE, include=FALSE, eval = TRUE-----------------
library(lubridate)
library(scales)
library(ggplot2)
library(dplyr)
library(hystReet)
## -----------------------------------------------------------------------------
# Sys.setenv(HYSTREET_API_TOKEN = "PASTE YOUR API TOKEN HERE")
## -----------------------------------------------------------------------------
# library(hystReet)
#
# stats <- get_hystreet_stats()
## -----------------------------------------------------------------------------
# locations <- get_hystreet_locations()
## ---- eval = TRUE, echo=FALSE-------------------------------------------------
knitr::kable(
locations[1:10,],
format = "html"
)
## -----------------------------------------------------------------------------
# location_71 <- get_hystreet_station_data(
# hystreetId = 71,
# query = list(from = "2021-12-01", to = "2022-01-01", resolution = "day"))
## -----------------------------------------------------------------------------
# location_71 <- get_hystreet_station_data(
# hystreetId = 71,
# query = list(from = "2021-12-01", to = "2022-01-01", resolution = "hour"))
## ---- eval = TRUE-------------------------------------------------------------
ggplot(location_71$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")
## -----------------------------------------------------------------------------
# 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)
## ----eval =TRUE---------------------------------------------------------------
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))
## ----message=FALSE, warning=FALSE---------------------------------------------
# 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)
## ---- eval = TRUE-------------------------------------------------------------
ratio %>%
top_n(5, ratio) %>%
arrange(desc(ratio))
## ---- eval = TRUE-------------------------------------------------------------
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))
## ----corona_effects_data------------------------------------------------------
# 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_effects_plot, eval = TRUE-----------------------------------------
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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