<jupyter_start><jupyter_text>Temperatur in Würzburg<jupyter_code>library(tidyverse)
data <- read_csv("data/data_OBS_DEU_PT1H_T2M.csv")
head(data)
station <- read_csv("data/sdo_OBS_DEU_PT1H_T2M.csv")
station
data <- data %>% mutate(SDO_ID = if_else(SDO_ID==2600, "Kitzingen", "Würzburg"))<jupyter_output><empty_output><jupyter_text>Zusammenfassung für Würzburg und Kitzingen<jupyter_code>data %>% 
    group_by(SDO_ID) %>%
    summarise(min_Wert = min(Wert), max = max(Wert), mean=mean(Wert)) %>%
    mutate(range = max-min_Wert)<jupyter_output><empty_output><jupyter_text>Durchschnittstemperatur über die Zeit<jupyter_code>library(lubridate)
data$Zeitstempel[1:5] %>% year
data <- data %>% mutate(year = year(Zeitstempel))<jupyter_output><empty_output><jupyter_text>**In Kitzingen beginnen die Daten im März 2005, der Durchschnitt für 2005 ist also verfälscht**<jupyter_code>data %>% filter(SDO_ID == "Kitzingen", year==2005) %>% count(month(Zeitstempel))
data %>% 
        filter(SDO_ID=="Würzburg" | year > 2005) %>% # ignore Kitzingen 2005 it is incomplete (jan and feb missing)
        group_by(SDO_ID, year) %>%
        summarise(mean=mean(Wert)) %>%
        ggplot(aes(x = year, y = mean, col = SDO_ID)) + geom_line()<jupyter_output>`summarise()` has grouped output by 'SDO_ID'. You can override using the `.groups` argument.
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