R/data_prep.R

In fmichelsendis/NuclearEnergyData2018: Data from the NEA Nuclear Energy Data "Brown Book" 2018 edition

# library(tidyverse)
# library(readxl)
# library(devtools)
#
#
# df22<-read_xlsx("data-raw/T2-2-FMS.xlsx")%>%
#   gather(key="YEAR",value="TU_PROD_PERYEAR", 2:7)%>%
#   mutate(
#     YEAR=as.double(YEAR),
#     TU_PROD_PERYEAR=as.double(TU_PROD_PERYEAR)
#          )%>%
#   select(COUNTRY, YEAR, TU_PROD_PERYEAR)
#
#
# df23_a<-read_xlsx("data-raw/T2-3-FMS.xlsx")%>%
#   gather(key="YEAR",value="UREQS_LOW", 2,4,6,8,10,12,14)%>%
#   mutate(YEAR = substr(YEAR, 1, 4))%>%
#   select(COUNTRY, YEAR, UREQS_LOW)
#
# df23_b<-read_xlsx("data-raw/T2-3-FMS.xlsx")%>%
#   gather(key="YEAR",value="UREQS_HIGH", 3,5,7,9,11,13, 15)%>%
#   mutate(YEAR = substr(YEAR, 1, 4))%>%
#   select(COUNTRY, YEAR, UREQS_HIGH)
#
#   gather(key="YEAR",value="UREQS_HIGH", 2:8)%>%
#   select(COUNTRY, YEAR, UREQS_LOW, UREQS_HIGH)%>%
#   mutate(YEAR = substr(YEAR, 1, 4)#keeps only the year of the column-now-row name
#     )%>%
#   arrange(YEAR)
#
#
# df23<-merge(df23_a, df23_b)
#
#
# df24<-read_xlsx("data-raw/T2-4-FMS.xlsx")%>%
#   gather(key="YEAR", value="CC", 3:9)%>%
#   spread(key="CCTO", value = "CC") %>%
#   arrange(COUNTRY)
#
#
#
# df25_a<-read_xlsx("data-raw/T2-5-FMS.xlsx")%>%
#   gather(key="YEAR", value="CR_LOW", 3,5,7,9,11,13, 15)%>%
#   mutate(YEAR = substr(YEAR, 1, 4)#keeps only the year of the column-now-row name
#     ) %>%
#   select(COUNTRY, YEAR, CR, CR_LOW)
#
# df25_b<-read_xlsx("data-raw/T2-5-FMS.xlsx")%>%
#   gather(key="YEAR", value="CR_HIGH", 4,6,8,10,12,14, 16)%>%
#   mutate(YEAR = substr(YEAR, 1, 4)#keeps only the year of the column-now-row name
#     ) %>%
#   select(COUNTRY, YEAR, CR, CR_HIGH)
#
# df25<-merge(df25_a, df25_b, by = c("COUNTRY", "YEAR","CR"))
#
#
#
# df26<-read_xlsx("data-raw/T2-6-FMS.xlsx")%>%
#   gather(key="YEAR", value="ECAP", 3:9)%>%
#   mutate(YEAR=as.double(YEAR),
#          ECAP=as.double(ECAP))%>%
#   select(COUNTRY, YEAR, METHOD, ECAP)
#
#
#
# df27<-read_xlsx("data-raw/T2-7-FMS.xlsx")
#
# df27_a<-df27%>%
#   gather(key="YEAR", value="ER_LOW", 2,4,6,8,10,12,14)%>%
#   mutate(YEAR = substr(YEAR, 1, 4)#keeps only the year of the column-now-row name
#     ) %>%
#   select(COUNTRY, YEAR, ER_LOW)
#
# df27_b<-df27%>%
#   gather(key="YEAR", value="ER_HIGH", 3,5,7,9,11,13,15)%>%
#   mutate(YEAR = substr(YEAR, 1, 4)#keeps only the year of the column-now-row name
#     ) %>%
#   select(COUNTRY, YEAR, ER_HIGH)
#
# df27<-merge(df27_a, df27_b)
#
#
# df28<-read_xlsx("data-raw/T2-8-FMS.xlsx")%>%
#   gather(key="YEAR", value="FABCAP", 3:9)%>%
#   mutate(YEAR=as.double(YEAR),
#          FABCAP=as.double(FABCAP))%>%
#   select(COUNTRY, YEAR, FUELTYPE, FABCAP)%>%
#   arrange(COUNTRY)
#
#
#
# df29_a<-read_xlsx("data-raw/T2-9-FMS.xlsx")%>%
#   gather(key="YEAR", value="FABREQ_LOW", 3,5,7,9,11,13, 15)%>%
#   mutate(YEAR = substr(YEAR, 1, 4)#keeps only the year of the column-now-row name
#     ) %>%
#   select(COUNTRY, YEAR, FUELTYPE, FABREQ_LOW)
#
# df29_b<-read_xlsx("data-raw/T2-9-FMS.xlsx")%>%
#   gather(key="YEAR", value="FABREQ_HIGH", 4,6,8,10,12,14, 16)%>%
#   mutate(YEAR = substr(YEAR, 1, 4)#keeps only the year of the column-now-row name
#     ) %>%
#   select(COUNTRY, YEAR, FUELTYPE, FABREQ_HIGH)
#
# df29<-merge(df29_a, df29_b, by = c("COUNTRY", "YEAR","FUELTYPE"))
#
# df210<-read_xlsx("data-raw/T2-10-FMS.xlsx")%>%
#   gather(key="YEAR", value="SNFSTOCAP", 3:9)%>%
#   mutate(YEAR=as.double(YEAR),
#          SNFSTOCAP=as.double(SNFSTOCAP))%>%
#   select(COUNTRY, YEAR, FUELTYPE, SNFSTOCAP)%>%
#   arrange(COUNTRY)
#
#
#
# df211_a<-read_xlsx("data-raw/T2-11-FMS.xlsx")%>%
#   gather(key="YEAR", value="ARISINGS", 2,4,6,8,10,12,14)%>%
#   mutate(
#     YEAR = as.double(substr(YEAR, 1, 4)),
#     ARISINGS=as.double(ARISINGS)
#     )%>%
#   select(COUNTRY, YEAR, ARISINGS)
#
# df211_b<-read_xlsx("data-raw/T2-11-FMS.xlsx")%>%
#   gather(key="YEAR", value="INSTORAGE", 3,5,7,9,11,13,15)%>%
#   mutate(
#     YEAR = as.double(substr(YEAR, 1, 4)),
#     INSTORAGE=as.double(INSTORAGE)
#     )%>%
#   select(COUNTRY, YEAR, INSTORAGE)
#
# df211<-merge(df211_a, df211_b)
#
#
# df212<-read_xlsx("data-raw/T2-12-FMS.xlsx")%>%
#   gather(key="YEAR", value="REPROCAP", 3:9)%>%
#   mutate(YEAR=as.double(YEAR),
#          REPRO=as.double(REPROCAP))%>%
#   select(COUNTRY, YEAR, FUELTYPE, REPROCAP)%>%
#   arrange(COUNTRY)
#
# #write.csv(df210, "../data/T2-12-FMS-tidy.csv")
#
# df213<-read_xlsx("data-raw/T2-13-FMS.xlsx")%>%
#   gather(key="YEAR", value="PU_USE", 3:9)%>%
#   mutate(YEAR=as.double(YEAR),
#          RPU_USE=as.double(PU_USE))%>%
#   select(COUNTRY, YEAR, FUELTYPE, PU_USE)%>%
#   arrange(COUNTRY)
#
#
# ## Problem with original data !!
#
#
# # df214<-read_xlsx("data-raw/T2-14-FMS.xlsx")%>%
# #   gather(key="YEAR", value="PU_USE", 3:9)%>%
# #   mutate(YEAR=as.double(YEAR),
# #          RPU_USE=as.double(PU_USE))%>%
# #   select(COUNTRY, YEAR, FUELTYPE, PU_USE)%>%
# #   arrange(COUNTRY)
#
# #write.csv(df214, "../data/T2-14-FMS-tidy.csv")
# #datatable(df214)
#
#
#
# df215<-read_xlsx("data-raw/T2-15-FMS.xlsx")%>%
#   gather(key="YEAR", value="TOT_RET_USE", 2:5)%>%
#   mutate(YEAR=as.double(YEAR),
#          TOT_RET_USE=as.double(TOT_RET_USE))%>%
#   select(COUNTRY, YEAR, TOT_RET_USE)%>%
#   arrange(COUNTRY)
#
# #write.csv(df215, "../data/T2-15-FMS-tidy.csv")
#
#
# df216<-read_xlsx("data-raw/T2-16-FMS.xlsx")%>%
#   gather(key="YEAR", value="TOT_RU_PROD", 2:5)%>%
#   mutate(YEAR=as.double(YEAR),
#          TOT_RU_PROD=as.double(TOT_RU_PROD))%>%
#   select(COUNTRY, YEAR, TOT_RU_PROD)%>%
#   arrange(COUNTRY)
#
#
#
# T217 : FILE CORRUPTED FROM ORIGIN !
# df217<-read_xlsx("data-raw/T2-17-FMS.xlsx")%>%
#   gather(key="YEAR", value="TOT_RU_USE", 2:5)%>%
#   mutate(YEAR=as.double(YEAR),
#          TOT_RU_USE=as.double(TOT_RU_USE))%>%
#   select(COUNTRY, YEAR, TOT_RU_USE)%>%
#   arrange(COUNTRY)
#
#
# ##
#
# use_data(df22, overwrite = TRUE)
# use_data(df23, overwrite = TRUE)
# use_data(df24, overwrite = TRUE)
# use_data(df25, overwrite = TRUE)
# use_data(df26, overwrite = TRUE)
# use_data(df27, overwrite = TRUE)
# use_data(df28, overwrite = TRUE)
# use_data(df28, overwrite = TRUE)
# use_data(df29, overwrite = TRUE)
# use_data(df210, overwrite = TRUE)
# use_data(df211, overwrite = TRUE)
# use_data(df212, overwrite = TRUE)
# use_data(df213, overwrite = TRUE)
# use_data(df215, overwrite = TRUE)
# use_data(df216, overwrite = TRUE)
#
#
#
# # df22<-read_csv("../data/T2-2-FMS-tidy.csv")
# # df23<-read_csv("../data/T2-3-FMS-tidy.csv")
# # df24<-read_csv("../data/T2-4-FMS-tidy.csv")
# # df25<-read_csv("../data/T2-5-FMS-tidy.csv")
# # df26<-read_csv("../data/T2-6-FMS-tidy.csv")
# # df27<-read_csv("../data/T2-7-FMS-tidy.csv")
# # df28<-read_csv("../data/T2-8-FMS-tidy.csv")
# # df29<-read_csv("../data/T2-9-FMS-tidy.csv")
# # df210<-read_csv("../data/T2-10-FMS-tidy.csv")
# # df211<-read_csv("../data/T2-11-FMS-tidy.csv")
#
#
#
#