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# plot the common id over the years
path <- "D:/public/yang/MIND_STEP/new_sample/fadn_data_common_id.xlsx"
sheetsnames <- excel_sheets(path)
length(sheetsnames)
lst <- lapply(1:28, function(i) read_excel(path, sheet = i))
lst_test <- Map(function(x, y) {names(x)[1] <- y; x},
lst, sheetsnames)
names(lst_test) <- sheetsnames
new_colnames <- c("Years","Num_id")
new_lst_ = lapply(lst_test, setNames, new_colnames)
###
new_lst = new_lst_
for (i in 1:28){
new_lst[[i]]$Length <- str_count(new_lst[[i]]$Years)
new_lst[[i]]$group <- cut(new_lst[[i]]$Length, breaks=c(1,10,14,18,25,30,35,40,48,55,58,70,Inf))
#new_lst[[i]]$group <- cut(new_lst[[i]]$Length, breaks=c(1,10,14,18,25,30,35,40,48,55,58,70,Inf))
}
label_facet <- function(original_var, custom_name){
lev <- levels(as.factor(original_var))
#lev <- c()
lab <- paste0(custom_name, ": ", lev)
names(lab) <- lev
return(lab)
}
names <- as_labeller(
c(`(1,10]` = "2 years", `(14,18]` = "3 years",`(18,25]` = "4 years",
`(25,30]` = "5 years",`(30,35]` = "6 years", `(35,40]` = "7 years",
`(40,48]` = "8 years", `(48,55]`= "9 years", `(55,58]` = "10 years",
`(58,70]`="11 and 12 years", `(70,Inf]` = "more than 12"))
figure <- function(country, df, n){
p = df %>%
#ggplot( aes(x = reorder(Years, -Num_id) ,y=Num_id))
ggplot( aes(x = Years ,y=Num_id))+
geom_bar( stat="identity", position = position_dodge(width = 0.8),width=0.5, fill = rainbow(n=length(df$Num_id)) ) +
coord_flip()+
#labs(title = paste0("Plot of the Number of common ID for country: ", country ), fill = "Years") +
xlab("Years") +
ylab("Number of common ID") +
geom_text(aes(label=Num_id), vjust=0.5, colour="black", size=3.5)+
theme(axis.text.x=element_text(color = "black", size=6, angle=0, vjust=.8, hjust=0.8)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = n)) +
facet_wrap( ~ group, scales="free",
labeller=names
#labeller(group = label_facet(df$group, "grouping"))
)+
ggtitle(paste0("Number of common ID for country: ", country )) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
p
}
for (i in 1:28){
if (sheetsnames[i] =="NED"){
p <- figure(sheetsnames[i],new_lst[[i]], 35)
}
else{
p <- figure(sheetsnames[i],new_lst[[i]], 20)
}
ggsave(plot = p,
filename = paste0("D:/public/data/fadn/lieferung_20210414/common_id_plots/",sheetsnames[i] ,"_plot.png"),
width = 18, height = 8)
}
figure("BEL",new_lst$BEL, 35)
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library(fadnUtils)
library(writexl)
library(jsonlite)
library(xlsx)
# FADN Data DIR
dir = "D:/public/data/fadn/lieferung_20210414/csv/"
setwd(dir)
# Get all csv file in FADN data dir
csv_files = list.files(path = dir, pattern= "*.csv$")
csv_list = data.frame(csv_files)
colnames(csv_list) = "names"
# split string into two columns at data frame based on "."
df = data.frame(do.call('rbind', strsplit(as.character(csv_list$names),'.',fixed=TRUE)))
df
# split data frame string into 2 columns
df = separate(df, X1, into = c("country", "Year"), sep = 3, remove = FALSE)
table(df$country)
table(df$Year)
countires = unique(df$country)
years = unique(df$Year)
# 28 countries
#
# Get all csv files for country "DEU"
DEU_csv <- grep("^DEU", csv_list$names, value = TRUE)
length(DEU_csv)
#++++++++++++++++++++++++++++
# set a project dir
set.data.dir("D:/public/yang/MIND_STEP/new_sample")
get.data.dir()
# convert CSV into Raw data for DEU
for(file in csv_files) {
print(file)
# convert into data table
country = substr(file, 1, 3)
# extract 4-7 char
year = substr(file, 4, 7)
convert.to.fadn.raw.rds(
file.path = file,
sepS = ",",
fadn.country = country,
fadn.year = year,
col.id = "ID")
}
show.data.dir.contents()
# CONVERT FADN.RAW.RDS TO FADN.STR.RDS
rds.dir = paste0(get.data.dir(),"/rds/")
# set a str name for for saving the str r-data in rds.dir
new.str.name = "DEU"
# set a extraction_dir
dir.create(paste0(rds.dir, new.str.name))
new.extraction.dir = paste0(rds.dir, new.str.name)
# Save the modifed json file
list_vars = check.column(importfilepath = paste0(rds.dir, "fadn.raw.2018.BEL.rds"), # a rds file or a csv file
jsonfile = "D:/public/yang/MIND_STEP/2014_after_copy.json", # a json file
rewrite_json = TRUE, # write a new json file without unmatched variables
extraction_dir = new.extraction.dir # save the new json in extraction_dir
)
#********************************
year_list = c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018)
# find all adjacent combinations in a list
myFun <- function(Data) {
A <- lapply(2:(length(Data)-1L), sequence)
B <- lapply(rev(lengths(A))-1L, function(x) c(0, sequence(x)))
unlist(lapply(seq_along(A), function(x) {
lapply(B[[x]], function(y) Data[A[[x]]+y])
}), recursive = FALSE, use.names = FALSE)
}
adjacent_list = myFun(year_list)
adjacent_list[[45]] = year_list
my.data = list()
for (year_items in adjacent_list) {
name = toString(year_items)
print(class(name))
data = load.fadn.raw.rds(countries = "DEU", years = year_items)
my.data[[name]] = data
}
Big.Num.Common.id = list()
for (data_list in 1:length(my.data)){
data = my.data[data_list]
# Retrieving column names
name = names(data)
print("******************************")
colnames(data[[name]])[which( names(data[[name]]) == "ID")] <- "id"
common.id = collect.common.id(data[[name]])
Big.Num.Common.id[[name]] = nrow(common.id)
}
DF = do.call(rbind, Big.Num.Common.id)
DF = data.frame(DF)
DF$Years <- rownames(DF)
colnames(DF) <- "the number of common id"
rownames(DF) <- NULL
write_xlsx(DF, "D:/public/yang/MIND_STEP/new_sample/DEUData_common_id.xlsx")
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