library(imgw) # https://github.com/bczernecki/imgw
## Warning: replacing previous import 'XML::xml' by 'rvest::xml' when loading
## 'imgw'
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
miesieczne <- pobierz_miesieczne() # pobieranie danych
## Warning in readLines(file, n = thisblock): incomplete final line
## found on 'https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/
## dane_meteorologiczne/miesieczne/synop/s_m_d_format.txt'
## Warning in clean_metadata_miesieczne("https://dane.imgw.pl/data/
## dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/
## s_m_d_format.txt"): NAs introduced by coercion
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15
## [1] 16
## [1] 17
## [1] 18
## [1] 19
## [1] 20
## [1] 21
## [1] 22
## [1] 23
## [1] 24
## [1] 25
## [1] 26
grudzien <- filter(miesieczne, Miesiąc == 12)
grudzien <- grudzien %>% select(`Kod stacji`:`Miesiąc`, matches("Miesięczna suma usłonecznienia"))
colnames(grudzien) <- c("kod","nazwa", "rok", "mm","uslonecznienie")
szeroka <- grudzien %>% select(-kod, -mm) %>% spread(data = ., key = nazwa, value = uslonecznienie)
szeroka <- szeroka[,colSums(is.na(szeroka))<10]
tail(szeroka)
## rok BIAŁYSTOK BIELSKO-BIAŁA CHOJNICE ELBLĄG-MILEJEWO
## 53 2012 41.7 0.0 28.9 38.0
## 54 2013 49.4 0.0 35.5 30.9
## 55 2014 21.5 0.0 26.3 26.1
## 56 2015 56.9 96.2 49.9 57.2
## 57 2016 32.0 76.0 41.6 44.6
## 58 2017 18.7 51.4 26.4 25.9
## GORZÓW WIELKOPOLSKI HEL JELENIA GÓRA KALISZ KASPROWY WIERCH KATOWICE
## 53 21.3 35.4 73.9 37.7 60.1 49.9
## 54 46.4 23.1 95.9 78.1 123.8 84.4
## 55 27.1 46.6 0.0 24.8 61.3 43.6
## 56 52.1 57.4 105.9 71.0 136.0 83.2
## 57 56.8 41.9 94.3 73.0 106.0 55.1
## 58 41.0 62.5 60.2 41.6 61.7 34.9
## KĘTRZYN KIELCE-SUKÓW KŁODZKO KOŁO KOŁOBRZEG KOSZALIN KRAKÓW-BALICE ŁEBA
## 53 43.9 37.6 36.3 39.8 29.1 31.6 0 23.7
## 54 24.7 79.3 57.6 58.5 23.3 25.6 0 23.2
## 55 0.0 37.5 20.5 0.0 18.9 20.3 0 32.7
## 56 66.0 79.3 74.6 53.7 45.3 35.1 0 54.1
## 57 35.7 51.3 76.3 58.3 38.4 0.0 0 0.0
## 58 30.3 33.2 31.7 0.0 35.5 39.2 0 43.8
## LĘBORK LEGNICA LESKO LESZNO ŁÓDŹ LUBLIN-RADAWIEC MIKOŁAJKI MŁAWA
## 53 0.0 56.5 44.1 35.7 46.2 23.6 36.4 36.1
## 54 0.0 80.2 97.8 66.2 0.0 53.4 22.6 28.9
## 55 0.0 0.0 65.1 0.0 24.8 22.5 20.1 18.0
## 56 0.0 100.5 101.5 70.6 68.7 65.5 63.3 45.0
## 57 21.4 93.3 63.6 0.0 66.5 33.1 39.4 42.3
## 58 0.0 50.1 35.7 45.3 35.6 21.2 26.2 23.7
## NOWY SĄCZ OLSZTYN OPOLE PŁOCK POZNAŃ RACIBÓRZ RESKO-SMÓLSKO
## 53 44.7 0 48.0 45.9 22.9 52.5 17.2
## 54 112.5 0 99.4 49.9 50.5 100.5 25.9
## 55 47.7 0 32.6 0.0 25.9 0.0 0.0
## 56 124.7 0 95.8 56.5 49.4 99.9 NA
## 57 80.8 0 80.7 52.1 48.1 0.0 NA
## 58 54.5 0 43.1 29.8 38.9 43.9 NA
## RZESZÓW-JASIONKA SANDOMIERZ SIEDLCE SŁUBICE ŚNIEŻKA SULEJÓW SUWAŁKI
## 53 39.6 44.8 37.5 0.0 60.1 41.2 41.8
## 54 83.1 82.3 62.5 0.0 83.2 73.8 29.6
## 55 23.4 0.0 15.7 0.0 29.0 0.0 28.6
## 56 82.7 95.6 69.3 0.0 85.4 82.6 35.4
## 57 0.0 0.0 39.9 68.9 110.6 67.0 28.1
## 58 0.0 41.1 23.8 41.2 48.9 42.4 12.2
## ŚWINOUJŚCIE SZCZECIN TARNÓW TERESPOL TORUŃ USTKA WARSZAWA WIELUŃ
## 53 0.0 18.4 37.7 44.1 33.1 0.0 51.1 40.4
## 54 0.0 28.1 86.7 68.7 33.4 0.0 58.1 85.0
## 55 0.0 29.6 0.0 21.1 28.4 25.8 19.5 0.0
## 56 16.4 50.6 91.9 68.4 61.1 40.4 67.6 0.0
## 57 16.0 48.7 57.0 30.9 63.2 20.1 41.5 0.0
## 58 8.3 33.7 39.5 19.7 32.9 37.5 21.6 40.4
## WŁODAWA WROCŁAW ZAKOPANE ZIELONA GÓRA
## 53 43.0 67.1 47.6 38.6
## 54 75.3 85.4 93.8 65.8
## 55 17.4 42.4 53.9 34.3
## 56 76.4 99.8 97.6 73.5
## 57 29.7 83.4 82.0 83.6
## 58 22.2 44.1 49.3 36.1
staty <- grudzien %>% group_by(`nazwa`) %>% summarise(min=min(uslonecznienie, na.rm=T),
mean=round(mean(uslonecznienie, na.rm=T),1),
max=max(uslonecznienie, na.rm=T))
tail(staty)
## # A tibble: 6 x 4
## nazwa min mean max
## <chr> <dbl> <dbl> <dbl>
## 1 WROCŁAW 0 37 99.8
## 2 ZAKOPANE 19.7 52.4 131.
## 3 ZAMOŚĆ 0 23.1 56.2
## 4 ŻARNOWIEC 0 0 0
## 5 ZGORZELEC 0 19 38
## 6 ZIELONA GÓRA 15.3 37.8 83.6