<jupyter_start><jupyter_text>Passantenzählung Schönbornstraße seit 2019https://opendata.wuerzburg.de/explore/dataset/hystreet_ab_1-1-2019/information/<jupyter_code>library(tidyverse)
data <- read_delim("hystreet_ab_1-1-2019.csv", delim = ";") %>% select(-min_temperature, -details)
library(lubridate)
data <- data %>% mutate(monat=month(timestamp, label=TRUE), jahr=year(timestamp), tag=day(timestamp))
data %>% group_by(jahr, monat) %>% summarize(count=sum(pedestrians_count))
data %>%
    filter(monat=="Apr", jahr==2019) %>%
    mutate(tag=day(timestamp)) %>%
    filter(tag==2)
options(repr.plot.width=12, repr.plot.height=10, repr.plot.res=150)
data %>% 
    group_by(jahr, monat) %>% 
    summarize(count=sum(pedestrians_count)) %>%
    ggplot(aes(x=jahr, y=count, fill=monat)) + geom_col() + facet_wrap(~monat)
data %>% mutate(timestamp_day = floor_date(timestamp, unit = "day")) %>%
    group_by(timestamp_day) %>%
    summarize(mean_temp=mean(temperature)) %>%
    ggplot(aes(x=timestamp_day, y=mean_temp)) + geom_line()
data %>% mutate(timestamp_day = floor_date(timestamp, unit = "day")) %>%
    group_by(timestamp_day) %>%
    summarize(mean_temp=mean(temperature)) %>%
    filter(year(timestamp_day)>2020, mean_temp < -5)
data %>% mutate(stunde=hour(timestamp), wochentag=weekdays(timestamp)) %>%
    mutate(
        wochentag = fct_relevel(wochentag, "Montag", "Dienstag", "Mittwoch"),
    ) %>%
    ggplot(aes(x=stunde, y=pedestrians_count, fill=monat)) + geom_col() + facet_wrap(~wochentag)
data %>% mutate(stunde=hour(timestamp), wochentag=weekdays(timestamp)) %>%
    mutate(
        wochentag = fct_relevel(wochentag, "Montag", "Dienstag", "Mittwoch"),
    ) %>%
    ggplot(aes(x=stunde, y=pedestrians_count, fill=wochentag)) + geom_col() + facet_grid(jahr~monat)<jupyter_output><empty_output>
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