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############################################################################
### ###
### FADNUTILS: ###
### AN R PACKAGE TO EASILY LOAD AND MANIPULATE FADN DATA ###
### ###
############################################################################
## In order to use fadnUtils, we must load fadnUtils and other packages.
## R version >=3.6.1 and < 4.0.0
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############################################################################
### ###
### SETUP: ###
### DIRECTORY NAMES AND CONDITIONAL FILE ###
### ###
############################################################################
# fadnUtils always work with a user defined data.dir
# Let's assume that the user has not created one yet.
# The following line creates a data.dir folder somewhere in our computer
# We must also have created the raw_str_map.file and pass it as an argument
# to the function. This file is copied to the data.dir folder. Thus, we can
# see the structure of the data contained in a data.dir folder by inspecting
# the raw_str_map.file residing in it.
##################################################################
## Install and load packages ##
##################################################################
requiredPackages = c('fadnUtils','data.table', 'devtools','jsonlite', 'ggplot2')
for(p in requiredPackages){
if(!require(p,character.only = TRUE)) install.packages(p)
library(p,character.only = TRUE)
}
#################################################################
## DIRECTORY NAMES ##
#################################################################
CurrentProjectDirectory = "D:/public/yang/MIND_STEP/New_test_fadnUtils"
##################################################################
## Required files ##
##################################################################
# the path of the fadn files for loading
fadn.data.dir = "D:/public/data/fadn/lieferung_20210414/csv/"
# A json file for extraction
# ceate a data.dir
create.data.dir(folder.path = CurrentProjectDirectory)
# Once the data.dir is created, we must declare that we are working with it
set.data.dir(CurrentProjectDirectory)
get.data.dir()
# After you create a data dir, below is a list of "real-world" example files:
# CurrentProjectDirectory/
# +-- csv
# +-- fadnUtils.metadata.json
# +-- rds
# \-- spool
# \-- readme.txt
############################################################################
### ###
### SECTION 1: ###
### IMPORT CSV FADN DATA ###
### ###
############################################################################
# .............. IMPORT DATA IN TWO STEPS ..........................................#
# However, you can import the file in two steps, one for converting
# the csv to fadn.raw.str (csv-data to raw r-data) and
# one for converting the fadn.raw.rds to fadn.str.rds (raw r-data
# to structured r-data).
#################################################################
## STEP 1: CONVERT CSV TO FADN.RAW.RDS ##
#################################################################
##-----------------------------
## load each file separately
##-----------------------------
# load for a specific country "DEU" and from a specific year "2009"
convert.to.fadn.raw.rds(
file.path = paste0(fadn.data.dir ,"DEU2009.csv"),
sepS = ",",
fadn.country = "DEU",
fadn.year = 2009
#keep.csv = T # copy csv file in csv.dir
)
##-----------------------------
## load all csv files in a folder
##-----------------------------
"csv2raw function takes csv files in a folder and converts them into raw data"
allcsv2raw <- function(LocationofCSVFiles){
# list all csv files
csv_file_names <- list.files(path = LocationofCSVFiles, pattern= "*.csv$")
#csv_file_names <- "DEU, BEL"
for (file in csv_file_names){
# extract first 3 char
country = substr(file, 1, 3)
# extract 4-7 char
year = substr(file, 4, 7)
#year = as.numeric(gsub("\\D+", "", file))
convert.to.fadn.raw.rds(
file.path = paste0(fadn.data.dir,file),
sepS = ",",
fadn.country = country,
fadn.year = year
#keep.csv = T # copy csv file in csv.dir
}
}
allcsv2raw(fadn.data.dir)
##-----------------------------
## load specific year and country
##-----------------------------
"C.Y2raw function takes selected countries and years, then converts them into raw data"
C.Y2raw <- function(countries, years){
for (country in countries){
for (year in years){
convert.to.fadn.raw.rds(
file.path = paste0(fadn.data.dir,file),
sepS = ",",
fadn.country = country,
fadn.year = year
#keep.csv = T # copy csv file in csv.dir
}
}
}
# load countries: BEL, DEU and NED
countriesList = c("BEL", "DEU", "NED")
yearsList = c(2009,2010,2011,2018)
C.Y2raw(countries = countriesList, years =yearsList )
show.data.dir.contents()
# If you converted the csv to raw r-data successfully, raw r-data files are saved in "rds" folder,
# the project's files and folders look like this:
# New_test_fadnUtils/
# +-- csv
# +-- fadnUtils.metadata.json
# +-- rds
# | +-- fadn.raw.2009.BEL.compressed.rds
# | +-- fadn.raw.2009.BEL.rds
# | +-- fadn.raw.2010.BEL.compressed.rds
# | +-- fadn.raw.2010.BEL.rds
# | +-- fadn.raw.2011.BEL.compressed.rds
# | +-- fadn.raw.2011.BEL.rds
# | +-- fadn.raw.2012.BEL.compressed.rds
# | \-- fadn.raw.2012.BEL.rds
# \-- spool
# \-- readme.txt
##################################################################
## STEP 2: CONVERT FADN.RAW.RDS TO FADN.STR.RDS ##
##################################################################
#######################################################################################################
# Notices:#
###########
## Before converting raw r-data into str r-data, it is recommended to use check.column() method
## so that all variables in this json file can be converted.
## The conversion of the raw r-data file to a structured r-data file is driven by a human-readable file,
## called raw_str_map.json.
## This json file is saved in extraction_dir by default.
## if you want to use raw_str_map.json by default, please put this file in extraction_dir.
## Or the user can define a external json file where it is
## and how to caculate the str r-data.
#######################################################################################################
rds.dir = paste0(get.data.dir(),"/rds/")
# set a str name for for saving the str r-data in rds.dir
new.str.name = "test"
# set a extraction_dir
dir.create(paste0(rds.dir, new.str.name))
new.extraction.dir = paste0(rds.dir, new.str.name)
##-----------------------------------------------------------------------------
## Step 2.0: Check the variables of loaded a raw rds data and a json file --
##-----------------------------------------------------------------------------
# Save the modifed json file
list_vars = check.column(importfilepath = paste0(rds.dir, "fadn.raw.2009.BEL.rds"), # a rds file or a csv file
jsonfile = "C:/Users/yang_x/Desktop/new-Version/inst/examples/2014_after - Kopie.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
)
# Let's see the unmathcted variables in this json file
print(list_vars)
##---------------------------------------------------------------
## Step 2.1: convert convert the raw r-data into str r-data --
##---------------------------------------------------------------
#check the default json file in extraction_dir
if ("raw_str_map.json" %in% list.files(new.extraction.dir, pattern = "\\.json$")){
cat(new.extraction.dir, "has a raw_str_map.json.", "\n")
}else{warning("please put a raw_str_map.json in ", new.extraction.dir,"\n", "Or using a external json file (option 2)", "\n")}
##::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
## option 1: convert the file separately using a raw_str_map.json in extraction_dir
## making sure that a raw_str_map.json is in extraction_dir
##::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
convert.to.fadn.str.rds(fadn.country = "BEL",
fadn.year = 2009,
str.name = new.str.name # extraction_dir
)
##::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
## option 2: convert the file separately using a external json file
##::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# If force_external_raw_str_map is TRUE
# this external json file will be copied to extraction_dir as raw_str_map.json,
convert.to.fadn.str.rds(fadn.country = "BEL",
fadn.year = 2009,
raw_str_map.file = "D:/public/yang/MIND_STEP/new_sample/test01/raw_str_map.json", # a external json file
str.name = new.str.name, # extraction_dir
force_external_raw_str_map = T,
DEBUG = F
)
##::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
## option 3: convert mutilple raw r-data files in rds.dir into str r-data in extraction_dir
##::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
"raw2str function will help us to convert the raw r-data into the str r-data in rds.dir completely.
This function takes a user-defined raw_str_map.file and a logical constant which is FALSE by default,
and converting raw data to str data."
raw2str <- function(Current_raw_str_map.file = NULL, overwrite_external_json = F){
# extract first 3 char
country = substr(file, 15, 17)
# extract number
year = as.numeric(gsub("\\D+", "", file))
cat("converting the str data for country: ", country, " and year: ", year, "\n")
tryCatch(
expr = {
convert.to.fadn.str.rds(fadn.country = country,
fadn.year = year,
raw_str_map.file = Current_raw_str_map.file,
force_external_raw_str_map = overwrite_external_json,
str.name = new.str.name
)
},
warning = function(w){
message('Caught an warning!')
print(w)
},
error = function(e) {
message("Caught an error! Please check the objects in json file using check.column() (see more in USE_CASE_4.R).")
#cat("Wrong, can't convert the str r-data!",sep = "\n")
print(e)
}
)
}
}
#---------------------------------------------------------------
# option 3.1: using a raw_str_map.json by defaut
#---------------------------------------------------------------
# convert str data with a default json file, make sure that a raw_str_map.json in extraction_dir
raw2str()
# or
raw2str(Current_raw_str_map.file = "D:/public/yang/MIND_STEP/new_sample/test01/raw_str_map.json", overwrite_external_json = F)
#---------------------------------------------------------------
# option 3.2: using a external json file
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#---------------------------------------------------------------
# convert str data using a external json file
raw2str(Current_raw_str_map.file = "D:/public/yang/MIND_STEP/new_sample/test01/raw_str_map.json", overwrite_external_json = T)
show.data.dir.contents()
# If str r-data was converted, the str r-data is saved in "test"(new.str.name) folder as below.
# New_test_fadnUtils/
# +-- csv
# +-- fadnUtils.metadata.json
# +-- rds
# | +-- fadn.raw.2009.BEL.compressed.rds
# | +-- fadn.raw.2009.BEL.rds
# | +-- fadn.raw.2010.BEL.compressed.rds
# | +-- fadn.raw.2010.BEL.rds
# | +-- fadn.raw.2011.BEL.compressed.rds
# | +-- fadn.raw.2011.BEL.rds
# | +-- fadn.raw.2012.BEL.compressed.rds
# | +-- fadn.raw.2012.BEL.rds
# | \-- test
# | +-- fadn.str.2009.BEL.rds
# | +-- raw_str_map.json
# | \-- rewrite_2014_after_copy.json
# \-- spool
# +-- my_logfile.txt
# \-- readme.txt
###########################################################################
### ###
### SECTION 2: ###
### LOAD R-DATA FROM A DATA.DIR ###
### ###
###########################################################################
#################################################################
## LOAD RAW R-DATA ##
#################################################################
### We can either load raw r-data files (the original FADN csv in r-friendly format),
### or structured r-data files (the original data transformed into meaningful
### information)
# To load raw r-data, only for BEL and 2009
my.raw.data.BEL = load.fadn.raw.rds(
countries = "BEL",
years = 2009
)
# my.data is a single large data.table, with the original csv columns and rows
nrow(my.raw.data.BEL) #Number of rows
names(my.raw.data.BEL) #Column names
length(names(my.raw.data.BEL)) #Number of columns
str(my.raw.data.BEL) #Overall structure
##################################################################
## LOAD STRUCTURED R-DATA ##
##################################################################
#To load structured data, for BEL and 2009
my.str.data.2009.BEL = load.fadn.str.rds(
countries = "BEL",
years = 2009,
extraction_dir = new.str.name # Location of the str r-data
)
# You can see that my.data is a list, with three elements: info, costs, crops
# You can access each individual element like this
str(my.str.data.2009.BEL$info)
str(my.str.data.2009.BEL$costs) # NULL
str(my.str.data.2009.BEL$crops)
# The first columns of each of the above elements (info, costs, crops)
# are created according to the ID section of the raw_str_map
names(my.str.data.2009.BEL$info)
names(my.str.data.2009.BEL$costs) # NULL
names(my.str.data.2009.BEL$crops)
# info and costs data.tables are in wide-format (each observation in a single row,
# all attributes of a single observation in different columns).
# crops element is in long format (one observation is in many rows,
#
#
# See https://seananderson.ca/2013/10/19/reshape/ for
# discussion of the two types of data formats
head(my.str.data.2009.BEL$info)
head(my.str.data.2009.BEL$costs) # NULL
head(my.str.data.2009.BEL$crops)
# Also on the attributes section of each of the above elements, we can access
# the column formulas and descriptions, as defined in the raw_str_map file.
View(
attr(my.str.data.2009.BEL$info,"column.descriptions")
# View(
# attr(my.str.data.2009.BEL$costs,"column.descriptions")
# ) # NULL
attr(my.str.data.2009.BEL$crops,"column.descriptions")
)
# Especially for the crops element, we can also see the description
# CROP column
View(
attr(my.str.data.2009.BEL$crops,"crops.descriptions")
)
#################################################################
## LOAD COUNTRIES-YEARS COMBINATIONS ##
#################################################################
### In the previous examples, we showed how to load data for one country and
### one year In the following examples we show more combinations.
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#To load for DEU and NED for year 2015
my.data = load.fadn.str.rds(countries = c("DEU","NED"), years = c(2009,2010,2011), extraction_dir = new.str.name )
#To load for DEU and NED for all years
my.data = load.fadn.str.rds(countries = c("DEU","NED"),extraction_dir = new.str.name )
#To load all available countries for year 2015
my.data = load.fadn.str.rds(years = 2015, extraction_dir = new.str.name)
#To load all availabel data
my.data = load.fadn.str.rds(extraction_dir = new.str.name)
#################################################################
## HOW TO STORE THE LOAD ##
#################################################################
#TODOS
# Since loading data sometimes takes time and create big datasets
# fadUtils offers a way to save the dataset created from the load call
# The first step is to store the loaded data
# Provide the object to save, a name and a description
# store.rds.data(my.data,"everything","all countries and years are here")
############################################################################
############################################################################
### ###
### PERFORM ANALYSIS ###
### ###
############################################################################
############################################################################
#We load structured data for all available countries and years
my.str.data = load.fadn.str.rds(extraction_dir = "a")
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##----------------------------------------------------------------
## HOW MANY FARMS FOR EACH COUNTY AND EACH YEAR --
##----------------------------------------------------------------
# we use the info DT, and group by YEAR-COUNTRY
my.str.data$info[,.N,by=list(YEAR,COUNTRY)]
#We can also use dcast, to show a more tabular format
dcast(
my.str.data$info,
YEAR~COUNTRY,
fun.aggregate = length,
value.var =
)
# We can also export to clipboard, using the write.excel utility function
# After running the following command, open excel and paste. The result will appear.
write.excel(
dcast(
my.str.data$info,
YEAR~COUNTRY,
fun.aggregate = length,
value.var =
)
)
##---------------------------------------------------------------
## ALL CROP AREAS PER COUNTRY-YEAR --
##---------------------------------------------------------------
# First, calculate the weighted area
my.str.data$crops[
VARIABLE=="LEVL",
VALUE.w:=WEIGHT*VALUE/1000
]
# Then dcast that variable
dcast(
my.str.data$crops[VARIABLE=="LEVL"],
COUNTRY+CROP~YEAR,
value.var = "VALUE.w",
fun.aggregate = sum,
na.rm = T
)
##---------------------------------------------------------------
## ALL CROP PRODUCTION PER COUNTRY-YEAR --
##---------------------------------------------------------------
dcast(
my.str.data$crops[VARIABLE=="GROF",VALUE.w:=WEIGHT*VALUE/1000],
COUNTRY+CROP~YEAR,
value.var = "VALUE.w",
fun.aggregate = sum,
na.rm = T
)
##---------------------------------------------------------------
## BARLEY PRODUCTION PER COUNTRY-YEAR --
##---------------------------------------------------------------
dcast(
my.str.data$crops[
VARIABLE=="GROF" & CROP=="BARL",
VALUE.w:=WEIGHT*VALUE/1000
],
COUNTRY~YEAR,
value.var = "VALUE.w",
fun.aggregate = sum,
na.rm = T
)
##----------------------------------------------------------------
## DISTRIBUTION OF NUMBER OF CROPS PER COUNTRY-YEAR --
##----------------------------------------------------------------
crops.data = my.str.data$crops #catering for easier access at next steps
#this contains the number of crops for each farm-country-year/
# Be carefule, we hav to filter to count only the LEVL variable
crops.data.Ncrops = crops.data[VARIABLE=="LEVL",.N,by=list(COUNTRY,YEAR,ID)]
# This displays the quantiles of the number of crops
crops.data.Ncrops[,as.list(quantile(N)),by=list(YEAR,COUNTRY)][order(COUNTRY)]
# R excels on graphic representation of results
library(ggplot2)
ggplot(crops.data.Ncrops,aes(y=N,x=1)) +
geom_boxplot() +
facet_grid(YEAR~COUNTRY) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)+
ylab("Number of Crops")
##---------------------------------------------------------------------------
## COLLECT COMMON ID FROM LOADED STRUCTURED R-DATA AND LOADED RAW R_DATA --
##---------------------------------------------------------------------------
# Collection the common id from loaded str r-data
collected.common.id_str = collect.common.id(my.str.data)
# Collection the common id from loaded raw r-data
collected.common.id_raw = collect.common.id(my.data)
##---------------------------------------------------------------
## CALCULATION BASED ON COLLECETED COMMON ID --
##---------------------------------------------------------------
# sommaries for infos
# sample and representend number of farms
my.str.data$info[,list(Nobs_sample=.N,Nobs_represented=sum(WEIGHT)),
by=.(COUNTRY,YEAR)]
# only for full sample (common id over years in selected data)
my.str.data$info[ID %in% collected.common.id_str[[1]],
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list(Nobs_sample=.N,
Nobs_represented=sum(WEIGHT)),
by=.(COUNTRY,YEAR)]
# some summaries for crops
# unweighted and weighted sum over countries, years, crops and variables (EAAP GROF INTF LEVL SHARE UVAG UVSA)
# EAA: Economic Accounts of Agriculture
# EAAP: EAA value at producer prices
# GROF
my.str.data$crops[ ,list(VALUE=sum(VALUE),
VALUE_weighted=sum(VALUE*WEIGHT)),
by=.(COUNTRY,YEAR,CROP,VARIABLE)]
# only for full sample (common id over years in selected data)
my.str.data$crops[ID %in% collected.common.id_str[[1]],
list(VALUE=sum(VALUE),
VALUE_weighted=sum(VALUE*WEIGHT)),
by=.(COUNTRY,YEAR,CROP,VARIABLE)]
my.str.data$crops[ID %in% collected.common.id_str[,common_id],
list(VALUE=sum(VALUE),
VALUE_weighted=sum(VALUE*WEIGHT)),
by=.(COUNTRY,YEAR,CROP,VARIABLE,ID)]
##---------------------------------------------------------------
## Load fadn raw data and search the the number of common id for adjacent combination years
##---------------------------------------------------------------
"find all adjacent combinations in a list"
myFun <- function(Data) {
A <- lapply(1:(length(Data)), 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)
}
"add a string to the facet label text and split it in two lines"
label_facet <- function(original_var, custom_name){
lev <- levels(as.factor(original_var))
lab <- paste0(lev, " \n ",custom_name)
names(lab) <- lev
return(lab)
}
"multi-panel plots using facet_wrap() for dynamic choice"
p = df %>%
# reorder by Num_id
#ggplot( aes(x = reorder(Years, -Num_id) ,y=Num_id))
ggplot( aes(x = Years ,y=Num_id))+
geom_bar( stat="identity",
width=0.5,
#fill = rainbow(n=length(df$Num_id))
fill = "#00abff"
) +
coord_flip()+
#labs(title = paste0("Plot of the Number of common ID for country: ", country ), fill = "Years") +
xlab("Years") +
ylab("Number of common ID") +
geom_text(aes(label=Num_id), vjust=0.5, colour="black", size=3.5)+
theme(axis.text.x=element_text(color = "black", size=6, angle=0, vjust=.8, hjust=0.8)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = n)) +
facet_wrap( ~ group, scales="free",
#labeller=names
labeller = labeller(group = label_facet(df$group, "adjacent combinations"))
)+
ggtitle(paste0("Number of common ID for country: ", country )) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
p
}
"load raw data and get the number of common id for selected countries over all exist adjacent combinations years.
then save the number of common id to an excel sheet and plot"
output_common_id <- function(countries_list, saveExcel = TRUE, excelname , savePlots = TRUE){
plots.dir = paste0(get.data.dir(), "/plots/")
if (!dir.exists(plots.dir)) dir.create(plots.dir)
xlsx_file_dir <- paste0(get.data.dir(), "/spool/")
if (saveExcel==TRUE) {wb <- createWorkbook(paste0(xlsx_file_dir, excelname))}
for (country in countries_list){
raw_file_names <- dir(rds.dir, pattern = paste0(country, "\\.rds$") )
years_list = as.numeric(gsub("\\D+", "", raw_file_names))
data = load.fadn.raw.rds(countries = country, years = year_items)
my.data[[name]] = data
common.id = collect.common.id(my.data[[data_list]])
Big.Num.Common.id[[data_list]] = nrow(common.id)
}
DF = do.call(rbind, Big.Num.Common.id)
DF = data.frame(DF)
colnames(DF) <- "Num_id"
DF$Years <- row.names(DF)
if (!is.null(wb)) {
if (!(country %in% names(wb))) {
addWorksheet(wb, country)}
writeData(wb,country, DF)
}
if (savePlots == TRUE){
library(ggplot2)
library(stringr)
DF$Length <- str_count(DF$Years)
DF$group <- cut(DF$Length, breaks=c(1,5,14,18,25,30,35,40,48,55,58,70,Inf))
levels(DF$group) <- c("1 year","2 years","3 years","4 years","5 years","6 years","7 years",
"8 years","9 years","10 years","11, 12 years",">12 years")
p <- figure(country,DF, 35)}
else{p <- figure(country,DF, 20)}
filename = paste0(plots.dir,country ,"_plot.png"),
width = 18, height = 8)
}
}
if (saveExcel == TRUE) {
saveWorkbook(wb, paste0(xlsx_file_dir, excelname), overwrite = T)
cat(excelname," is saved in ",xlsx_file_dir, "\n")}
if (savePlots == TRUE) cat("plots are saved in", plots.dir, "\n")
# get all countires in fadn str data
countries = unique(my.str.data$info$COUNTRY)
ID_list <- output_common_id(countries_list = countries)
# get Germany: DEU and Kroatien: HRV
DEU_list <- output_common_id(c("HRV", "DEU"), saveExcel = TRUE, excelname = "HRV_DEU.xlsx", savePlots = TRUE)