Unemployment in Europe

A couple of years I have made plots of unemployment and its change over the years. At first this was a bigger and complex piece of code. As things have progressed, the code can now become pretty concise. There are just plenty of packages to do the heavy lifting. So, this year I tried to make the code easy to read and reasonably documented.

Data

Data is from Eurostat. Since we have the joy of the Eurostat package, suffice to say this is dataset une_rt_m. Since the get_eurostat function gave me codes for things such as country and gender, the first step is to use a dictionary to decode. Subsequently, the country names are a bit sanitized and data is selected.
library(eurostat)
library(ggplot2)
library(KernSmooth)
library(plyr)
library(dplyr)

library(scales) # to access breaks/formatting functions
r1 <- get_eurostat(‘une_rt_m’)%>%
    mutate(.,geo=as.character(geo)) # character preferred for merge
r2 <- get_eurostat_dic(‘geo’) %>%
    rename(.,geo=V1) %>%
    mutate(.,
# part of country name within braces removed        
        country=gsub(‘\\(.*$’,”,V2),
        country=gsub(‘ $’,”,country),
        country=ifelse(geo==’EA19′,paste(country,'(19)’),country)) %>%
    select(.,geo,country) %>%
    right_join(.,r1) %>%
# keep only total, drop sexes
    filter(.,sex==’T’) %>%
# filter out old Euro area and keep only EU28 , EA19    
    filter(.,!grepl(‘EA..’,geo)|  geo==’EA19′) %>% 
    filter(.,!(geo %in% c(‘EU15′,’EU25′,’EU27’)) ) %>%         
# SA is seasonably adjusted    
    filter(.,s_adj==’SA’) %>% 
    mutate(.,country=factor(country)) %>%
    select(.,-sex,-s_adj)

Plots

Results

Average unemployment

First derivative