EGU GA participant stats

Intro

As a German at the European Geosciences Union General Assembly (EGU GA) 2018, you sometimes feel like there are just a lot of people from Germany at the conference. I sometimes feel bad for everybody else as we happily chat away in German, not always taking notice of excluding others from a conversation. But is that really true? There are a lot of Germans in Europe, and many research institutions with scientsts from all over the world. So let’s load some packages and take a look at the data!

library("eurostat")
library("lubridate")
library("dplyr")
library("rvest")
library("stringr")
library("knitr")

Get population data

Download popoulation data from Eurostat using the eurostat package.

(We could probably do better here and only take into account working population…)

code_pop <- eurostat::search_eurostat("Population on 1 January", type = "table") %>%
  head(n = 1) %>% .$code
data_pop <- get_eurostat(code_pop, lang = "en", cache = FALSE)

data_pop_2017 <- data_pop %>%
  filter(year(time) == 2017) %>%
  label_eurostat(lang = "en") %>%
  select(geo, values)

# adjust country names to match EGU website
data_pop_2017 <- data_pop_2017 %>%
  mutate(country = case_when(
      grepl(x = .$geo, pattern = ".*Macedonia*") ~ "Macedonia, The Former Yugoslav Republic Of",
      grepl(x = .$geo, pattern = "Bosnia.*") ~ "Bosnia And Herzegovina",
      grepl(x = .$geo, pattern = "Germany.*") ~ "Germany",
      TRUE ~ as.character(geo)
    )
  ) %>%
  select(country, inhabitants = values)

Download participant data

webpage <- read_html("https://egu2018.eu/")

participant_stats <- html_table(webpage)[[1]]
names(participant_stats) <- c("country", "participants")
participant_stats <- participant_stats %>%
  mutate(participants = str_replace_all(participant_stats$participants, "[\\.,\\,]", "")) %>%
  mutate(participants = as.integer(participants))

kable(head(participant_stats))

EGU’s participant data supports the personal impression: Germany is the country with most participants at the GA, beating United Kingdom with a comfortable margin of 1066 people.

Analyse

• join the two datasets (and loose all non European countries as a side-effect)
• calculate ratio participant count / 100 000 inhabitants

data <- left_join(participant_stats, data_pop_2017, by = "country") %>%
  filter(!is.na(inhabitants)) %>%
  mutate(`participants/100 000 inhabitants` = participants/inhabitants * 100000)

options(scipen = 999)
options(knitr.kable.NA = '')

kable(summary(data), caption = "Summary statistics of EGU participant data")

The average number of participants per 100 000 inhabitants is 1.9354365.

Result table

data_arranged <- data %>% arrange(desc(`participants/100 000 inhabitants`))
kable(data_arranged, row.names = TRUE)

Turns out Germany is above average, but not “overrepresented” as much as other countries such as Iceland, Austria, Switzerland, Norway, Andorra, Finland.

What did we learn? ¯\_(ツ)_/¯ Nothing exciting, really, just that I will be more relaxed about meeting “German” colleagues at future EGUs, because while it seems we’re crushing the place in numbers, we are not.

The number that really matters for EGU as an international conference remains 106 - the number of countries represented by EGU attendees.

Metadata

Participant data courtesy of EGU/Copernicus (https://ego2018.eu).

Population data courtesy of European Union via Eurostat.

sessionInfo()

## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] bindrcpp_0.2.2  knitr_1.20      stringr_1.3.0   rvest_0.3.2    
## [5] xml2_1.2.0      dplyr_0.7.4     lubridate_1.7.3 eurostat_3.1.5 
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.16       highr_0.6          compiler_3.4.4    
##  [4] pillar_1.2.1       RColorBrewer_1.1-2 bindr_0.1.1       
##  [7] class_7.3-14       tools_3.4.4        digest_0.6.15     
## [10] jsonlite_1.5       evaluate_0.10.1    tibble_1.4.2      
## [13] lattice_0.20-35    pkgconfig_2.0.1    rlang_0.2.0       
## [16] curl_3.2           yaml_2.1.18        e1071_1.6-8       
## [19] httr_1.3.1         hms_0.4.2          tidyselect_0.2.4  
## [22] classInt_0.1-24    rprojroot_1.3-2    grid_3.4.4        
## [25] glue_1.2.0         R6_2.2.2           rmarkdown_1.9     
## [28] sp_1.2-7           selectr_0.4-1      readr_1.1.1       
## [31] tidyr_0.8.0        purrr_0.2.4        magrittr_1.5      
## [34] backports_1.1.2    htmltools_0.3.6    assertthat_0.2.0  
## [37] stringi_1.1.7