Introduction

Using the essurvey package is fairly easy. There are are two main families of functions: import_* and show_*. They each complement each other and allow the user to almost never have to go to the European Social Survey (ESS) website. The only scenario where you need to enter the ESS website is to validate your email. If you haven’t registered, create an account at http://www.europeansocialsurvey.org/user/new. For those unfamiliar with the ESS, this vignette uses the term rounds, here a synonym of waves to denote the same survey in different time points.

Once you register visit your email account to validate the account and you’re ready to access the data.

Given that some essurvey functions require your email address, this vignette will use a fake email but everything should work accordingly if you registered with the ESS.

Downloading country specific rounds

Note: versions less than and including essurvey 1.0.1 returned wrong countries. Please install the latest CRAN/Github version.

To install and load development version of the package use:

# install.packages("devtools")
devtools::install_github("ropensci/essurvey")

to install the stable version from CRAN use:

install.packages("essurvey")

Downloading the ESS data requires validating your email every time you download data. We can set our email as an environment variable with set_email.

Once that’s executed you can delete the previous line and any import_* call will look for the email automatically, stored as an environment variable.

Let’s suppose you don’t know which countries or rounds are available for the ESS. Then the show_* family of functions is your friend.

To find out which countries have participated you can use show_countries()

##  [1] "Albania"            "Austria"            "Belgium"           
##  [4] "Bulgaria"           "Croatia"            "Cyprus"            
##  [7] "Czechia"            "Denmark"            "Estonia"           
## [10] "Finland"            "France"             "Germany"           
## [13] "Greece"             "Hungary"            "Iceland"           
## [16] "Ireland"            "Israel"             "Italy"             
## [19] "Kosovo"             "Latvia"             "Lithuania"         
## [22] "Luxembourg"         "Montenegro"         "Netherlands"       
## [25] "Norway"             "Poland"             "Portugal"          
## [28] "Romania"            "Russian Federation" "Serbia"            
## [31] "Slovakia"           "Slovenia"           "Spain"             
## [34] "Sweden"             "Switzerland"        "Turkey"            
## [37] "Ukraine"            "United Kingdom"

This function actually looks up the countries in the ESS website. If new countries enter, this will automatically grab those countries as well. Let’s check out Turkey. How many rounds has Turkey participated in? We can use show_country_rounds()

tk_rnds <- show_country_rounds("Turkey")
tk_rnds
## [1] 2 4

Note that country names are case sensitive. Use the exact name printed out by show_countries()

Using this information, we can download those specific rounds easily with import_country. Since essurvey 1.0.0 all ess_* functions have been deprecated in favor of the import_* and download_* functions.

turkey <-
  import_country(
    country = "Turkey",
    rounds = c(2, 4)
    )

turkey will now be a list of length(rounds) containing a data frame for each round. If you only specified one round, then all import_* functions return a data frame. import_country is useful for when you want to download specific rounds, but not all. To download all rounds for a country automatically you can use import_all_cntrounds.

The import_* family is concerned with downloading the data and thus always returns a list containing data frames unless only one round is specified, in which it returns a tibble. Conversely, the show_* family grabs information from the ESS website and always returns vectors.

Download complete rounds

Similarly, we can use other functions to download rounds. To see which rounds are currently available, use show_rounds.

## [1] 1 2 3 4 5 6 7 8 9

Similar to show_countries, show_rounds interactively looks up rounds in the ESS website, so any future rounds will automatically be included.

To download all available rounds, use import_all_rounds

all_rounds <- import_all_rounds()

Alternatively, use import_rounds for selected ones.

selected_rounds <- import_rounds(c(1, 3, 6))

Downloading data for Stata, SPSS and SAS users

All import_* functions have an equivalent download_* function that allows the user to save the datasets in a specified folder in 'stata', 'spss' or 'sas' formats.

For example, to save round two from Turkey in a folder called ./my_folder, we use:

download_country("Turkey", 2,
                 output_dir = "./myfolder/")

By default it saves the data as 'stata' files. Alternatively you can use 'spss' or 'sas'.

download_country("Turkey", 2,
                 output_dir = "./myfolder/",
                 format = 'sas')

This will save the data to ./myfolder/ESS_Turkey and inside that folder there will be the ESS2 folder that contains the data.

Correcting for missing values

Whenever you download the ESS data, it comes together with a script that recodes the values 6 = ‘Not applicable’, 7 = ‘Refusal’, 8 = ‘Don’t know’, 9 = ‘No answer’ and 9 = ‘Not available’ as missings. However, that is the case for variables that have a scaling of 1-5. For variables which have a scaling from 1-10 the corresponding missings are 66, 77, and so on. At first glance new users might not know this and start calculating statistics with these variables such as…

sp <- import_country("Spain", 1)
mean(sp$tvtot)
# 4.622406

..but that vector contains numbers such as 66, 77, that shouldn’t be there. recode_missings() removes the corresponding missings for numeric variables as well as for character variables. It accepts the complete tibble and recodes all variables that should be recoded.

new_coding <- recode_missings(sp)
mean(new_coding$tvtot, na.rm = TRUE)
# 4.527504

It also gives you the option of recoding only specific categories. For example…

other_newcoding <- recode_missings(sp, c("Don't know", "Refusal"))
table(other_newcoding$tvpol)
#  0   1   2   3   4   5   6   7  66 
# 167 460 610 252  95  36  26  31  45 

…still has missing values but recoded the ones that were specified. I strongly suggest the user not to recode these categories as missing without looking at the data as there might be substantial differences between people who didn’t and who did answer questions. If the user is decided to do so, use recode_missings to recode everything and the corresponding recode_*_missings functions for numeric and character recodings separately. See the documentation of ?recode_missings for more information.

Analyzing ESS data

Be aware that for analyzing data from the ESS survey you should take into consideration the sampling and weights of the survey. The survey package provides very good support for this. A useful example comes from the work of Anthony Damico and Daniel Oberski here.