Downloading my_data for the assignment

1. Intro

The objective of this .Rmd file is to download data from Eurostat to be used for the students in their assignments

We are going to need some packages, then we are going to load them:

library(tidyverse)     #- install.packages("tidyverse")
library(eurostat)      #- install.packages("eurostat")
library(gt)            #- install.packages("gt")
library(DT)            #- install.packages("DT")

2. Countries available from Eurostat

First, we will see the list of available countries in the Eurostat database. They are in the data.frame called eu_countries

country_codes <- eu_countries       #- avalailable countries in Eurostat data

Let’s see the countries list as a table

#gt::gt(country_codes)
DT::datatable(country_codes)

3. You have to chose your country

Every one of you should choose one different country.

During the lessons/e-meetings we will use data for Spain, then here I choose Spain

my_country <- "ES"   #-  CHOOSE YOUR COUNTRY -  CHOOSE YOUR COUNTRY

We can now start to download the data. I repeat, in the lessons I will use data for Spain , BUT for your assignment you should use other country. Each of you a different country

---

4. Downloading the data for the assignment

OK. The code below will download some data for the country you have chosen

1. First, we have to set some parameters to say to the eurostat package API which series we want to download

geo_f <- my_country
s_adj_f <- c("SCA")     #- Seasonally and calendar adjusted data
unit_f  <- c( "CP_MEUR" , "PD05_EUR", "CLV_I10" )     #- units: CP_MEUR[Current prices, million euro], PD05_EUR[Price index (implicit deflator), 2005=100, euro], CLV_I10[Chain linked volumes, index 2010=100]
na_item_f <-c("B1GQ")    #- economic series. B1GQ: Gross domestic product at market prices
filtros <- list(geo = geo_f, na_item = na_item_f , unit = unit_f, s_adj = s_adj_f)

2. Already downloading the data for the country you have chosen (my_country)

df_l <- get_eurostat("namq_10_gdp", filters = filtros, type = "label", stringsAsFactors = F,   select_time = 'Q')   #- data with labels
df <- get_eurostat("namq_10_gdp", filters = filtros, type = "code", stringsAsFactors = F,   select_time = 'Q')   #-  data with codes

3. “Cleaning” the data for the country chosen

#- the data are in long format: converting to the wide (more traditional) format
df_la <- df_l %>% tidyr::spread(unit, values)
dfa   <- df   %>% tidyr::spread(unit, values)

#- Getting the range of available data (non-NA observations)
first <- min(which(!is.na(dfa[,5])))  #- first non-NA observation
last <- max(which(!is.na(dfa[,5])))   #- last non-NA observation
data_c <- dfa[first:last,4:7]  #- getting only the valid observations of the last 3 columns (4:6)

#- Renaming & transforming the series
data_c <- data_c %>% rename(GDP = CP_MEUR, Def = PD05_EUR, Vol = CLV_I10)   #- renaming our data
data_c <- data_c %>% mutate(GDPr = GDP/Def*100)              #- real GDP
data_c <- data_c

let’s see what we have in the data.frame data_c

data_c %>%  slice(1, nrow(data_c)) %>% gt::gt()

time	Vol	GDP	Def	GDPr
1995-01-01	65.8	112945.8	70.583	160018.4
2019-10-01	111.2	315710.0	116.780	270345.9

4. Dow loading data for the EU15

geo_f   <- c("EU15")      #- countries: EU15
filtros <- list(geo = geo_f, na_item = na_item_f , unit = unit_f, s_adj = s_adj_f)
df_l <- get_eurostat("namq_10_gdp", filters = filtros, type = "label", stringsAsFactors = F,   select_time = 'Q')   #- data with labels
df <- get_eurostat("namq_10_gdp", filters = filtros, type = "code", stringsAsFactors = F,   select_time = 'Q')   #-  data with codes
df_l <- df_l %>% tidyr::spread(unit, values)
df   <- df   %>% tidyr::spread(unit, values)
data_15 <- df[first:last,4:7]  #- getting only the valid observations of the last 3 columns (4:6)
data_15 <- data_15 %>% rename(GDP_15 = CP_MEUR, Def_15 = PD05_EUR, Vol_15 = CLV_I10)   #- renaming our data
data_15 <- data_15 %>% mutate(GDPr_15 = GDP_15/Def_15*100)              #- real GDP
#data_15 <-  data_15[,-1]  #- removing first column

let’s see what we have in the data.frame data_15

data_15 %>% slice(1, nrow(data_15)) %>% gt::gt()

time	Vol_15	GDP_15	Def_15	GDPr_15
1995-01-01	76.1	1738293	80.959	2147127
2019-10-01	113.7	3779499	117.775	3209084

4. Downloading data for the US

geo_f   <- c("US")      #- countries: US
na_item_f <- c("B1GQ", "PD05_NAC")    #- economic series. B1GQ: Gross domestic product at market prices
filtros <- list(geo = geo_f, na_item = na_item_f ,  s_adj = s_adj_f)
df_l <- get_eurostat("naidq_10_gdp", filters = filtros, type = "label", stringsAsFactors = F,   select_time = 'Q')   #- data with labels
df <- get_eurostat("naidq_10_gdp", filters = filtros, type = "code", stringsAsFactors = F,   select_time = 'Q')   #-  data with codes
df_l <- df_l %>% tidyr::spread(unit, values)
df   <- df   %>% tidyr::spread(unit, values)
data_us <- df[first:last,c(4,5,9,10)]  #- getting the data we need
data_us <- data_us %>% rename(GDP_us = CP_MNAC, GDP_us_e = CP_MEUR, Vol_us = CLV_I10)   #- renaming our data
data_us <- data_us %>% mutate(tc_e_d = GDP_us/GDP_us_e)              #- exchange rate
#data_us <-  data_us[,-1]  #- removing first column

let’s see what we have in the data.frame data_us

data_us %>% slice(1, nrow(data_us)) %>% gt::gt()

time	Vol_us	GDP_us_e	GDP_us	tc_e_d
1995-01-01	67.6	1476928	1880572	1.2733
2019-10-01	123.2	4906766	5432281	1.1071

---

3. Merging the data

We have download data for the country you have chosen, for E15, and for the US. We have to join the data in a unique data.frame. But they have to share the same sample.

df <- full_join(data_c, data_15, by = c("time" = "time")) #- merging df_c with df_15
df <- full_join(df, data_us, by = c("time" = "time"))

Good, we already have all the data we need in one data.frame. We have to save it because you will do your assignment with this data

---

4. Saving the data in a file

For example we can save the data in .csv format:

#- exporting df as a .csv file. The follwing 2 lines of code make the same
# write_csv(df, "my_data_ES.csv")                         #- saving df as csv 
# write_csv(df, paste0("my_data_", my_country, ".csv"))   #- saving df as csv

Or in .rds format

#- saving df as a .rds file
# write_csv(df, paste0("my_data_", my_country, ".rds"))  #- saving df as rds

Please uncomment the two previous chunks to effectively save your data because you are going to need it for your assignment

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Web creada por Pedro J. Pérez. © 2020. If you find any bugs please report them to pedro.j.perez@uv.es.