In fact, if you merge their global leaders dataset with the elections dataset, very often does the President of a country in a year not line up with who they say won the election in a country, year pair. Unless someone can point out to me what is going on, this is seriously poor quality data and needs to be removed less people use it.

Some of the parties from CLEA 2014 are not in CLEA 2016.

These parties were not yet removed from Party Facts when the CLEA 2016 data was imported on 3 August 2016.

Double check with CLEA team before finally removing these parties from Party Facts.

Here is a country summary and see list of parties (clea-2014-remove.xlsx). We need some informaton about the high number of parties from India.

clea14 <- read.csv("clea-2014/clea.csv", fileEncoding = "utf-8", as.is=TRUE)
clea14 %>% filter( ! ctr_pty %in% party$ctr_pty) %>% .[['country']] %>% table

AUS AUT CUW DEU IDN IND LUX MDA NLD SLE TUR USA 
  1   1   7   1   1  26   1   1   2   2   1   4 

Political Party Database Project

http://www.politicalpartydb.org/

https://dataverse.harvard.edu/dataverse/politicalparties

On Harvard Dataverse, where the stable versions of the Party Facts datasets are available, most data files have the .tab extension. There are three exceptions:

1. https://dataverse.harvard.edu/file.xhtml?fileId=4274155&version=1.1
2. https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/UUMC31/JEM0W5&version=1.0
3. https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/FTQAYT/L8EGUM&version=1.0

These have the .csv extension.

If this is not on purpose, perhaps all files could be of the same format for consistency reasons. I realised this difference when I was trying to write a function that would get these files systematically, where the extension makes a difference.

Perhaps this is not possible and/or desirable. In any case, let me say thank you for this great resource.

Integrated Party Organization Dataset (IPOD)

dataset includes ParlGov and CMP party IDs that can be used for linking

use tidyverse packages and style guide consistently

The file at https://github.com/hdigital/partyfactsdata/blob/master/import/clea/partyfacts-clea.csv appears to be missing some parties.

For example, Demokratikus Koalíció with partyfacts_id = 469 (see: https://partyfacts.herokuapp.com/data/partycodes/469/) and dataset_party_id = 348000004 (see: https://partyfacts.herokuapp.com/data/partyall/51923/) does not appear in the file.

Interestingly, Demokratikus Koalíció is listed in clea.csv at https://github.com/hdigital/partyfactsdata/blob/master/import/clea/clea.csv with party_id = 348000004.

Where should I look if I want a more complete/up-to-date version of partyfacts-clea.csv?

Alternatively, if such a file does not already exist or cannot be provided, do you know how I can generate a complete version of partyfacts-clea.csv on my own using the data available online at https://partyfacts.herokuapp.com/ ?

Create R script to run all import R scripts. This would assure that all files can be run to create the data for the import. Its a lightweight alternative to unit testing.

Datasets with cleaned-up party lists on Google drive ignore duplicates in Party Facts import.

Add these ignored party IDs in a new column and provide examples for dataset transformation.

Calculate years and the share of respondents supporting the party from the WVS Longitudinal Data to have more information for identifying WVS parties.

http://www.worldvaluessurvey.org/WVSDocumentationWVL.jsp

Marpor country names are adjusted to Party Facts country names for different names used.

Keep Marpor country names and rename Party Facts country names used for merging.