https://github.com/expersso/eurostatR

Reproducible and programmatic access to Eurostat
https://github.com/expersso/eurostatR

Last synced: about 2 months ago
JSON representation

Reproducible and programmatic access to Eurostat

• Host: GitHub
• URL: https://github.com/expersso/eurostatR
• Owner: expersso
• Created: 2016-01-03T12:25:30.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-01-04T15:21:28.000Z (over 8 years ago)
• Last Synced: 2024-05-21T02:54:53.175Z (4 months ago)
• Language: R
• Size: 21.5 KB
• Stars: 4
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output:

  md_document:

    variant: markdown_github

---

```{r options, echo=FALSE}

knitr::opts_chunk$set(fig.path = "", message = FALSE, warning = FALSE)

```

### Introduction

The `eurostatR` package package provides an `R` interface to the [Eurostat API](https://ec.europa.eu/eurostat).

To install the development version:

```{r install, eval=FALSE}

library(devtools)

install_github("expersso/eurostatR")

```

### Example usage

Start by retrieving a dataframe with all available data sets:

```{r}

library(eurostatR)

flows <- get_dataflows()

head(flows)

```

Now, let's say we're interested in the share of employees with a doctorate,

broken down by occupation:

```{r}

flows[grep("doctorate", flows$description),]

id <- "cdh_e_occ1"

```

Before getting the data, we need to know the available dimensions for this data

set:

```{r}

dims <- get_dimensions(id)

str(dims)

```

The last value of the returned list (`dims$key`) gives the general form of the

key that we need to request. By inspecting the dimensions in the list, we find

that `FREQ` should be `A` (annual), `Y_GRAD` should be `TOTAL`, and `UNIT`

should be `PC` (percent). The dimension `ISCO88` gives the different

occupations, and since we want all of these, we leave that dimension empty.

Finally we want to look at date for Germany and the US (`DE` and `US`), which we

specify with a `+`.

```{r}

key <- "cdh_e_occ1.A.TOTAL.PC..DE+US"

doctorates <- get_data(key)

head(doctorates)

```

We can now plot these data as a parallel coordinates plot:

```{r plot, fig.width = 6, fig.height = 4}

library(dplyr)

library(ggplot2)

doctorates <- left_join(doctorates, dims$dimensions$ISCO88, 

                        by = c("isco88" = "id"))

# Factor order for occupation variable, in descending order

lvls <- doctorates %>% 

  filter(geo == "US") %>% 

  arrange(obsvalue) %>% 

  .$description

doctorates %>% 

  filter(obstime == 2006) %>% 

  filter(!is.na(obsvalue)) %>% 

  filter(description != "Professionals") %>% 

  ggplot(aes(x = factor(description, lvls), 

             y = obsvalue, fill = geo, color = geo, group = geo)) +

  geom_line() +

  geom_point() +

  coord_flip() +

  theme_light(8) +

  labs(y = "% share of employees", x = NULL,

       title = "Employed doctorate holders by occupation")

```

The chart tells us e.g. that doctorates are much more prevalent among teaching

professionals in the US than they are in Germany.

### Disclaimer

This package is in no way officially related to, or endorsed by, the Eurostat.