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HugoCzerniawski Feb 14th, 2020 73 Never
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  1. library(ggplot2)
  2. library(tidyverse)
  3. library(zoo)
  4. #pd <- import('pandas')
  5.  
  6.  
  7. dataFramesConversion <- function(index){
  8.   df <- data.frame()
  9.   start=3+14*(index -1)
  10.   end=16+14*(index -1)
  11.  
  12.   for(i in 1:12)
  13.     df <- rbind(df,data.frame(matrix(unlist(AAAdata[(start+14*10*(i-1)):(end+14*10*(i-1))]),nrow = 17)))
  14.  
  15.   return(df)
  16. }
  17.  
  18.  
  19. voivodeshipYearAverage <- function(Frame){
  20.   means <- data.frame()
  21.   for(j in 1:17){
  22.     numbers <- list(0,0,0,0,0,0,0,0,0,0,0,0,0,0)
  23.     for(i in 1:12){
  24.       numbers <- mapply("+", numbers, Frame[j+17*(i-1),] , SIMPLIFY = FALSE)
  25.     }
  26.     #  print(data.frame(matrix(unlist(numbers),nrow =1)))
  27.     means <- rbind(means,data.frame(matrix(unlist(numbers),nrow =1)))
  28.   }
  29.  
  30.   return (list(unlist(numbers)/12))
  31. }
  32.  
  33.  
  34. #View(data)
  35. #name(data)
  36.  
  37. #df <- data.frame(matrix(unlist(data[3:16]),nrow = 17))
  38. #view(df)
  39. main <- function(){
  40.   AAAdata<-(read.csv(file = ".\\CENY_2917_CTAB_20200213132521.csv",header = TRUE,sep = ';', dec = ',', stringsAsFactors = FALSE, encoding = 'UTF-8'))
  41.   voivodeships <- c("Polska","Dolnoslaskie","Kujawsko-Pomorskie","Lubelskie","Lubuskie","Lodzkie","Malopolskie",
  42.                     "Mazowieckie","Opolskie","Podkarpackie","Podlaskie","Pomorskie","Slaskie","Swietokrzyskie",
  43.                     "Warminsko-Mazurskie","Wielkopolskie","Zachodniopomorskie")
  44.   years <- c("2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019")
  45.  
  46.   Ryz <- dataFramesConversion(1)
  47.   RyzAvg <- voivodeshipYearAverage(Ryz)
  48.  
  49.   Rostbef <- dataFramesConversion(2)
  50.   RostbefAvg <- voivodeshipYearAverage(Rostbef)
  51.  
  52.   Szynka <- dataFramesConversion(3)
  53.   SzynkaAvg <- voivodeshipYearAverage(Szynka)
  54.  
  55.   Mleko <- dataFramesConversion(4)
  56.   MlekoAvg <- voivodeshipYearAverage(Mleko)
  57.  
  58.   Jaja <- dataFramesConversion(5)
  59.   JajaAvg <- voivodeshipYearAverage(Jaja)
  60.  
  61.   Garnitur <- dataFramesConversion(6)
  62.   GarniturAvg <- voivodeshipYearAverage(Garnitur)
  63.  
  64.   Rajstopy <- dataFramesConversion(7)
  65.   RajstopyAvg <- voivodeshipYearAverage(Rajstopy)
  66.  
  67.   Spodnie <- dataFramesConversion(8)
  68.   SpodnieAvg <- voivodeshipYearAverage(Spodnie)
  69.  
  70.   podzelowanieObuwia <- dataFramesConversion(9)
  71.   podzelowanieObuwiaAvg <- voivodeshipYearAverage(podzelowanieObuwia)
  72.  
  73.   Pasta <- dataFramesConversion(10)
  74.   PastaAvg <- voivodeshipYearAverage(Pasta)
  75.  
  76.   return()
  77. }
  78.  
  79. main()
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