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.
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:
to install the stable version from CRAN use:
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.
set_email("[email protected]")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()
## [1] 2 4Note 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 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.
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 9Similar 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
Alternatively, use import_rounds for selected ones.
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:
By default it saves the data as 'stata' files.
Alternatively you can use 'spss' or 'sas'.
This will save the data to ./myfolder/ESS_Turkey and
inside that folder there will be the ESS2 folder that
contains the data.
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…
..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.
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.