Climate Change Awareness: A Eurobarometer Survey Harmonization Case Study
Daniel Antal, CFA
3/4/2021
Retrospective survey harmonization comes with many challenges. In this tutorial we cannot show all of them, but we can highlight the ones that are almost certain to show up:
- Neither the number of observations or the number of variables (which represents the survey questions and their translation to coded data) is the same. Certain data may be only present in one survey and not the other. This means that you will likely to run loops on lists and not data.frames.
- The original files identifiers may not be unique. Weighting may not be straightforward.
- Similar questions may be imported from a non-native R format, in our case, from an SPSS files, in an inconsistent manner. SPSS’s variable formats cannot be translated unambiguously to R classes.
retroharmonizeintroduced a new S3 class system that handles this problem, but eventually you will have to choose if you want to see a numeric or character coding of each categorical variable. - There are likely to be various types of
missing values. Working with missing values is probably where most human judgment is needed. Why are some answers missing: was the question not asked in some questionnaires? Is there a coding error? Did the respondent refuse the question, or sad that she did not have an answer?retroharmonizehas a special labeled vector type that retains this information from the raw data, if it is present, but you have to make the judgment yourself – in R, eventually you will either create a missing category, or useNA_character_orNA_real_. - Same variables may come with dissimilar variable names and variable labels. It may be a challenge to match age with age. We need to harmonize the names of variables.
- The harmonized variables may have different labeling. One may call refused answers as
declinedand the otherrefusal. On a simple choice, climate change may be ‘Climate change’ orProblem: Climate change. Binary choices may have survey-specific coding conventions. Value labels must be harmonized. - The harmonized surveys, with harmonized variable names and harmonized value labels, must be brought to consistent R representations (most statistical functions will only work on numeric, factor or character data) and carefully joined into a single data table for analysis.
That’s a lot to put on your plate. It is unlikely that you will be able to work with completely unfamiliar survey programs if you do not have a strong intermediate level of R. Our package comes with tutorials for Eurobarometer, Afrobarometer and Arab Barometer, highlighting some peculiar issues with these survey programs, that we hope to give a head start for less experienced R users.
In this example, we will work with Eurobarometer’s data.
Get the Data
retroharmonize is not associated with Eurobarometer, or its creators, Kantar, or its archivists, GESIS. We assume that you have acquired the necessary files from GESIS after carefully reading their terms and you placed it on a path that you call gesis_dir.
#Not run in the vignette. You need to create eb_waves on your computer.
climate_change_files <- c("ZA5877_v2-0-0.sav", "ZA6595_v3-0-0.sav", "ZA6861_v1-2-0.sav",
"ZA7488_v1-0-0.sav", "ZA7572_v1-0-0.sav")
eb_waves <- read_surveys(file.path(gesis_dir, climate_change_files), .f='read_spss')
save ( eb_waves, file = file.path("not_included/eb_climate_change_waves.rda") )devtools::load_all() # until we are in development mode
library(retroharmonize)
library(dplyr) # this is necessary for the example
library(lubridate) # easier date conversion
library(stringr) # You can also use base R string processing functions
if ( file.exists( file.path("not_incldued", "eb_climate_change_waves.rda") )) {
load (file.path( "not_included", "eb_climate_change_waves.rda" ) )
} else {
load (file.path("..", "..", "not_included", "eb_climate_change_waves.rda") )
}The eb_waves nested list contains five surveys imported from SPSS to the survey class of retroharmonize. The survey class is a data.frame that retains important metadata for further harmonization.
document_waves (eb_waves)## # A tibble: 5 x 5
## id filename ncol nrow object_size
## <chr> <chr> <int> <int> <dbl>
## 1 ZA5877_v2-0-0 ZA5877_v2-0-0.sav 604 27919 139352456
## 2 ZA6595_v3-0-0 ZA6595_v3-0-0.sav 519 27718 119370440
## 3 ZA6861_v1-2-0 ZA6861_v1-2-0.sav 657 27901 151397528
## 4 ZA7488_v1-0-0 ZA7488_v1-0-0.sav 752 27339 169465928
## 5 ZA7572_v1-0-0 ZA7572_v1-0-0.sav 348 27655 80562432Beware the object sizes. If you work with many surveys, memory-efficient programming becomes imperative. We will be subsetting whenever possible.
Metadata analysis
As noted before, prepare to work with nested lists. Each imported survey is nested as a data frame in the eb_waves list.
Protocol
Eurobarometer calls certain metadata elements, like interviewee cooperation level or the date of a survey interview as protocol variable. Let’s start here. This will be our template to harmonize more and more aspects of the five surveys (which are, in fact, already harmonizations of about 30 surveys conducted in a single ‘wave’ in multiple countries.)
# select variables of interest from the metadata
eb_protocol_metadata <- eb_climate_metadata %>%
filter ( .data$label_orig %in% c("date of interview") |
.data$var_name_orig == "rowid") %>%
suggest_var_names( survey_program = "eurobarometer" )
# subset and harmonize these variables in all nested list items of 'waves' of surveys
interview_dates <- harmonize_var_names(eb_waves,
eb_protocol_metadata )
# apply similar data processing rules to same variables
interview_dates <- lapply (interview_dates,
function (x) x %>% mutate ( date_of_interview = as_character(.data$date_of_interview) )
)
# join the individual survey tables into a single table
interview_dates <- as_tibble ( Reduce (rbind, interview_dates) )
# Check the variable classes.
vapply(interview_dates, function(x) class(x)[1], character(1))## rowid date_of_interview
## "character" "character"This is our sample workflow for each block of variables. Get an unique identifier, add other values, harmonize the variable names and subset by leaving out not harmonized variables. Apply some normalization in a nested list. When the variables are harmonized to same name, class, merge them into a data.table-like tibble object.
Now finish the harmonization. Wednesday, 31st October 2018 should become a Date type 2018-10-31.
require(lubridate)
harmonize_date <- function(x) {
x <- tolower(as.character(x))
x <- gsub("monday|tuesday|wednesday|thursday|friday|saturday|sunday|\\,|th|nd|rd|st", "", x)
x <- gsub("decemberber", "december", x) # all those annoying real-life data problems!
x <- stringr::str_trim (x, "both")
x <- gsub("^0", "", x )
x <- gsub("\\s\\s", "\\s", x)
lubridate::dmy(x)
}
interview_dates <- interview_dates %>%
mutate ( date_of_interview = harmonize_date(.data$date_of_interview) )
vapply(interview_dates, function(x) class(x)[1], character(1))## rowid date_of_interview
## "character" "Date"To avoid duplication of row IDs in surveys that may not be unique in different surveys, we created a simple, sequential ID for each survey, including the ID of the original file.
set.seed(2021)
sample_n(interview_dates, 6)## # A tibble: 6 x 2
## rowid date_of_interview
## <chr> <date>
## 1 ZA7488_v1-0-0_7016 2018-10-28
## 2 ZA7488_v1-0-0_19187 2018-11-02
## 3 ZA6861_v1-2-0_1218 2017-03-18
## 4 ZA6861_v1-2-0_4142 2017-03-21
## 5 ZA7572_v1-0-0_12363 2019-04-17
## 6 ZA7572_v1-0-0_8071 2019-04-18After this type-conversion problem let’s see an issue when an original SPSS variable can have two meaningful R representations.
Geography
Let’s continue with harmonizing geographical information in the files. In this example, var_name_suggested will contain the harmonized variable name. It is likely that you have to make this call, after carefully reading the original questonnaires and codebooks.
eb_regional_metadata <- eb_climate_metadata %>%
filter ( grepl( "rowid|isocntry|^nuts$", .data$var_name_orig)) %>%
suggest_var_names( survey_program = "eurobarometer" ) %>%
mutate ( var_name_suggested = case_when (
var_name_suggested == "region_nuts_codes" ~ "geo",
TRUE ~ var_name_suggested ))The harmonize_var_names() takes all variables in the subsetted, geographical metadata table, and brings them to the harmonized var_name_suggested name. The function subsets the surveys to avoid the presence of non-harmonized variables. All regional NUTS codes become geo in our case:
geography <- harmonize_var_names(eb_waves,
eb_regional_metadata)If you are used to work with single survey files, you are likely to work in a tabular format, which easily converts into a data.frame like object, for example, to tidyverse’s tibble. However, when working with longitudinal data, it is far more simpler to work with nested lists, because the tables usually have different dimensions (neither the rows corresponding to observations or the columns are the same across all survey files.)
In the nested list, each list element is a single, tabular format survey. The regional information in the Eurobarometer files is contained in the nuts variable. We want to keep both the original labels and values. The original values are the region’s codes, and the labels are the names. The easiest and fastest solution is the base R lapply loop.
geography <- lapply ( geography,
function (x) x %>% mutate ( region = as_character (geo),
geo = as.character(geo) )
)Because each table has exactly the same columns, we can simply use rbind() and reduce the list to a modern data.frame, i.e. a tibble.
geography <- as_tibble ( Reduce (rbind, geography) )Let’s see a dozen cases:
set.seed(2021)
sample_n(geography, 12)## # A tibble: 12 x 4
## rowid isocntry geo region
## <chr> <chr> <chr> <chr>
## 1 ZA7488_v1-0-0_7016 SI SI012 Podravska
## 2 ZA7488_v1-0-0_19187 PL PL63 Pomorskie
## 3 ZA6861_v1-2-0_1218 DK DK02 Sjaelland
## 4 ZA6861_v1-2-0_4142 FI FI1B Helsinki-Uusimaa
## 5 ZA7572_v1-0-0_12363 SE SE12 Oestra Mellansverige
## 6 ZA7572_v1-0-0_8071 IT ITH Nord-Est [IT]
## 7 ZA6861_v1-2-0_6145 IE IE021 Dublin
## 8 ZA6861_v1-2-0_24638 RO RO31 South [RO]
## 9 ZA7488_v1-0-0_11315 CY CY REPUBLIC OF CYPRUS
## 10 ZA6595_v3-0-0_27568 HR HR041 Grad Zagreb
## 11 ZA7572_v1-0-0_17397 CZ CZ06 Jihovychod
## 12 ZA6861_v1-2-0_10993 PT PT17 LisboaThe idea is that we go similar variable block by block, and eventually we will join them togehter. Next step: socio-demography and weights.
Demography
We will harmonize the socio-demographic variables and weight variables next. There are a few peculiar issues to look out for. This example shows that survey harmonization requires plenty of expert judgment, and you cannot fully automate the process.
The Eurobarometer archives do not use all weight and demographic variable names consistently. For example, the wex variable, which is a projected weight for the country’s 15 years old or older population is sometimes called wex, sometimes wextra.
The suggest_var_names() function has a parameter for survey_program = "eurobaromater" which normalizes a bit the most used variables. For example, all variations of wex, wextra wil be noramlized to wex. You can ignore this parameter and use your own names, too.
eb_demography_metadata <- eb_climate_metadata %>%
filter ( grepl( "rowid|isocntry|^d8$|^d7$|^wex|^w1$|d25|^d15a|^d11$", .data$var_name_orig) ) %>%
suggest_var_names( survey_program = "eurobarometer")As you can see, using the original labels would not help, because they also contain various alterations.
eb_demography_metadata %>%
select ( filename, var_name_orig, label_orig, var_name_suggested ) %>%
filter (var_name_orig %in% c("wex", "wextra") )## filename var_name_orig label_orig
## 1 ZA5877_v2-0-0.sav wextra weight extrapolated population 15 plus
## 2 ZA6595_v3-0-0.sav wextra weight extrapolated population 15 plus
## 3 ZA6861_v1-2-0.sav wex weight extrapolated population aged 15 plus
## 4 ZA7488_v1-0-0.sav wex weight extrapolated population aged 15 plus
## 5 ZA7572_v1-0-0.sav wex weight extrapolated population aged 15 plus
## var_name_suggested
## 1 wex
## 2 wex
## 3 wex
## 4 wex
## 5 wexdemography <- harmonize_var_names ( waves = eb_waves,
metadata = eb_demography_metadata ) Socio-demographic variables like level of highest education or occupation are rather country-specific. Eurobarometer uses standardized occupation and marital status scales, and a proxy for education levels, age of education leave. This is a particularly tricky variable, because it’s coding in fact contains three different variables - school leaving age, except for students, and except for people who did not finish their compulsory primary school.
example <- demography[[1]] %>%
mutate ( across ( -any_of(c("rowid", "w1", "wex")), as_character) ) %>%
mutate ( across (any_of(c("w1", "wex")), as_numeric) )
unique ( example$age_education )## [1] "22" "25" "17"
## [4] "19" "12" "23"
## [7] "18" "20" "21"
## [10] "14" "24" "16"
## [13] "26" "15" "Still studying"
## [16] "DK" "31" "29"
## [19] "27" "13" "32"
## [22] "28" "30" "53"
## [25] "42" "62" "40"
## [28] "No full-time education" "Refusal" "37"
## [31] "39" "34" "35"
## [34] "47" "36" "45"
## [37] "51" "33" "43"
## [40] "38" "49" "46"
## [43] "41" "57" "7"
## [46] "48" "44" "50"
## [49] "56" "8" "11"
## [52] "10" "9" "75 years"
## [55] "6" "3" "54"
## [58] "55" "60" "64"
## [61] "2 years" "58" "52"
## [64] "72" "61" "4"
## [67] "63"unique ( example$age_exact)## [1] "54" "66" "56" "53" "33" "72"
## [7] "83" "62" "86" "77" "64" "46"
## [13] "44" "59" "60" "67" "63" "20"
## [19] "43" "37" "78" "49" "90" "45"
## [25] "28" "29" "30" "39" "51" "38"
## [31] "41" "71" "25" "48" "79" "88"
## [37] "61" "85" "70" "35" "81" "52"
## [43] "57" "27" "47" "15 years" "21" "42"
## [49] "32" "68" "36" "34" "19" "31"
## [55] "26" "23" "24" "22" "16" "84"
## [61] "65" "18" "55" "40" "50" "73"
## [67] "69" "87" "89" "74" "75" "98 years"
## [73] "76" "80" "58" "82" "17" "93"
## [79] "91" "92" "95" "94" "97"There are a few strange values here. We’ll take care of them in a minute. Let’s see all the strange labels attached to age-type variables.
collect_val_labels(metadata = eb_demography_metadata %>%
filter ( var_name_suggested %in% c("age_exact", "age_education")) )## [1] "2 years" "75 years"
## [3] "No full-time education" "Still studying"
## [5] "15 years" "98 years"
## [7] "96 years" "[NOT CLEARLY DOCUMENTED]"
## [9] "74 years" "99 and older"
## [11] "Refusal" "87 years"
## [13] "DK" "88 years"There are many exceptions to handle, so we create a function for this purpose.
remove_years <- function(x) {
x <- gsub("years|and\\solder", "", tolower(x))
stringr::str_trim (x, "both")}
process_demography <- function (x) {
x %>% mutate ( across ( -any_of(c("rowid", "w1", "wex")), as_character) ) %>%
mutate ( across (any_of(c("w1", "wex")), as_numeric) ) %>%
mutate ( across (contains("age"), remove_years)) %>%
mutate ( age_exact = as.numeric (age_exact)) %>%
mutate ( is_student = ifelse ( tolower(age_education) == "still studying",
1, 0),
no_education = ifelse ( tolower(age_education) == "no full-time education", 1, 0)) %>%
mutate ( education = case_when (
grepl("studying", age_education) ~ age_exact,
grepl ("education", age_education) ~ 14,
grepl ("refus|document|dk", tolower(age_education)) ~ NA_real_,
TRUE ~ as.numeric(age_education)
)) %>%
mutate ( education = case_when (
education < 14 ~ NA_real_,
education > 30 ~ 30,
TRUE ~ education ))
}
demography <- lapply ( demography, process_demography )## Warning in eval_tidy(pair$rhs, env = default_env): NAs introduced by coercion## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion## Warning in eval_tidy(pair$rhs, env = default_env): NAs introduced by coercion
## Warning in eval_tidy(pair$rhs, env = default_env): NAs introduced by coercion
## Warning in eval_tidy(pair$rhs, env = default_env): NAs introduced by coercion
## Warning in eval_tidy(pair$rhs, env = default_env): NAs introduced by coercion## WE'll full join and not use rbind, because we have different variables in different waves.
demography <- Reduce ( full_join, demography )## Joining, by = c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")
## Joining, by = c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")
## Joining, by = c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")
## Joining, by = c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")set.seed(2021)
sample_n(demography, 12)## # A tibble: 12 x 14
## rowid isocntry w1 wex marital_status age_education age_exact
## <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 ZA7488_~ SI 0.828 1428. (Re-)Married: withou~ 19 43
## 2 ZA7488_~ PL 1.01 32830. (Re-)Married: withou~ 19 64
## 3 ZA6861_~ DK 0.641 3100. (Re-)Married: withou~ 22 78
## 4 ZA6861_~ FI 1.83 8601. (Re-)Married: childr~ 30 38
## 5 ZA7572_~ SE 0.342 2645. (Re-)Married: withou~ 17 68
## 6 ZA7572_~ IT 0.630 32287. (Re-)Married: childr~ 20 40
## 7 ZA6861_~ IE 0.868 3054. (Re-)Married: childr~ 32 42
## 8 ZA6861_~ RO 0.724 11805. (Re-)Married: withou~ 14 59
## 9 ZA7488_~ CY 0.691 1013. (Re-)Married: childr~ 18 67
## 10 ZA6595_~ HR 0.580 2098. Single living w part~ 27 30
## 11 ZA7572_~ CZ 1.86 16908. Single: without chil~ still studying 20
## 12 ZA6861_~ PT 0.932 7448. Widow: with children no full-time ~ 84
## # ... with 7 more variables: occupation_of_respondent <chr>,
## # occupation_of_respondent_recoded <chr>,
## # respondent_occupation_scale_c_14 <chr>, type_of_community <chr>,
## # is_student <dbl>, no_education <dbl>, education <dbl>Harmonizing Variable Labels
And now let’s turn to most important and most challenging bit of the problem. You are not harmonizing surveys to put together protocol or weighting variables. You want to see harmonized answers from respondents in various surveys.
climate_awareness_metadata <- eb_climate_metadata %>%
suggest_var_names( survey_program = "eurobarometer" ) %>%
filter ( .data$var_name_suggested %in% c("rowid",
"serious_world_problems_first",
"serious_world_problems_climate_change")
) hw <- harmonize_var_names ( waves = eb_waves,
metadata = climate_awareness_metadata )The retroharmoinze package comes with a generic harmonize_values function that will change the value labels of categorical variables (including binary ones) to a single format. It will also take care of various types of missing values.
collect_val_labels(climate_awareness_metadata)## [1] "Climate change"
## [2] "International terrorism"
## [3] "Poverty, hunger and lack of drinking water"
## [4] "Spread of infectious diseases"
## [5] "The economic situation"
## [6] "Proliferation of nuclear weapons"
## [7] "Armed conflicts"
## [8] "The increasing global population"
## [9] "Other (SPONTANEOUS)"
## [10] "None (SPONTANEOUS)"
## [11] "Not mentioned"
## [12] "Mentioned"
## [13] "DK"In this case, we want to select Climate change as the mentioned most serious problem, and Climate change taken from a list of three serious problems. In the single choice case, when Climate change is mentioned, the alternative answer is labeled as Not mentioned. In the multiple choice case, the alternative may be Spread of infectious diseases, as we all well know.
We want to see who thought Climate change was the most serious problem, or one of the most serious problems, so we label each mentions of Climate change as mentioned and we pair it with a numeric value of 1. All other cases are labeled as not_mentioned, with the exceptions of various missing observations, which in this cases are Do not know answers, Declined to answer cases, and Inappropriate cases. This is Eurobarometer’s label for questions that were for one reason or other not asked from a particular interviewee – for example, because the Turkish Cypriot community received a different questionnaire.
# positive cases
label_1 = c("^Climate\\schange", "^Mentioned")
# missing cases
na_labels <- collect_na_labels( climate_awareness_metadata)
na_labels## [1] "DK" "Inap. (10 or 11 in qa1a)"
## [3] "Inap. (coded 10 or 11 in qc1a)" "Inap. (coded 10 or 11 in qb1a)"# negative cases
label_0 <- collect_val_labels( climate_awareness_metadata)
label_0 <- label_0[! label_0 %in% label_1 ]The harmonize_serious_problems() function harmonized the labels within the special labelled class of retroharmonize. This class retains all information to give categorical variables a character or numeric representation, and various processing metadata for documentation purposes. While this class is very reach (it contains whatever was imported from SPSS’s proprietary data format and the history), it is not suitable for statistical analysis.
harmonize_serious_problems <- function(x) {
label_list <- list(
from = c(label_0, label_1, na_labels),
to = c( rep ( "not_mentioned", length(label_0) ), # use the same order as in from!
rep ( "mentioned", length(label_1) ),
"do_not_know", "inap", "inap", "inap"),
numeric_values = c(rep ( 0, length(label_0) ), # use the same order as in from!
rep ( 1, length(label_1) ),
99997,99999,99999,99999)
)
harmonize_values(x,
harmonize_labels = label_list,
na_values = c("do_not_know"=99997,
"declined"=99998,
"inap"=99999),
remove = "\\(|\\)|\\[|\\]|\\%"
)
}First, let’s remove the surveys that do not contain these world problem variables. In this cases, the subsetted and harmonized surveys in the nested list have only one columns, i.e. the rowid.
hw <- hw[unlist ( lapply ( hw, ncol)) > 1 ]Now we have a smaller problem to deal with. With many surveys, it is easy to fill up your computer’s memory, so let’s start building up our joined panel data from a smaller set of nested, subsetted surveys.
hw <- lapply ( hw, function (x) x %>% mutate ( across ( contains("problem"), harmonize_serious_problems) ) )We reduced our single and multiple choice questions to binary choice variables. We can now give them a numeric representation. Be mindful that retroharmonize has special methods for its special labelled class that retains metadata from SPSS. This means that as_character and as_numeric knows how to handle various types of missing values, whereas the base R as.character and as.numeric may coerce special values to unwanted results. This is particularly dangerous with numeric variables.
We will ignore the differences between various forms of missingness, i.e. the person said that she did not know, or did not want to answer, or for some reason was not asked in the survey. In a more descriptive, non-harmonized analysis you would probably want to explore them as various ‘categories’ and use a character representation.
hw <- lapply ( hw, function(x) x %>% mutate ( across ( contains("problem"), as_numeric) ))
hw <- Reduce ( full_join, hw)Let’s see what we have:
set.seed(2021)
sample_n (hw, 12)## # A tibble: 12 x 3
## rowid serious_world_problems_fi~ serious_world_problems_climate_~
## <chr> <dbl> <dbl>
## 1 ZA6595_v3-0-0_23~ 0 NA
## 2 ZA7572_v1-0-0_70~ 0 0
## 3 ZA6595_v3-0-0_18~ 0 NA
## 4 ZA6861_v1-2-0_27~ 0 0
## 5 ZA6595_v3-0-0_26~ 0 NA
## 6 ZA7572_v1-0-0_19~ 0 1
## 7 ZA5877_v2-0-0_16~ 0 0
## 8 ZA6861_v1-2-0_12~ 0 0
## 9 ZA7572_v1-0-0_17~ 0 0
## 10 ZA5877_v2-0-0_17~ 0 1
## 11 ZA6861_v1-2-0_41~ 0 0
## 12 ZA6861_v1-2-0_61~ 0 1Creating the Longitudional Table
#start from the smallest (we removed the survey that had no relevant questionnaire item)
panel <- hw %>%
left_join ( geography, by = 'rowid' )
panel <- panel %>%
left_join ( demography, by = c("rowid", "isocntry") )
panel <- panel %>%
left_join ( interview_dates, by = 'rowid' )And let’s see a small sample:
sample_n(panel, 12)## # A tibble: 12 x 19
## rowid serious_world_pr~ serious_world_pr~ isocntry geo region w1 wex
## <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 ZA686~ 0 0 ES ES41 Casti~ 1.21 46787.
## 2 ZA686~ 0 0 RO RO31 South~ 0.724 11805.
## 3 ZA686~ 0 0 SK SK02 Zapad~ 0.774 3499.
## 4 ZA757~ 0 1 PT PT16 Centr~ 1.11 9336.
## 5 ZA659~ 1 NA HR HR041 Grad ~ 0.580 2098.
## 6 ZA659~ 1 NA RO RO21 North~ 1.21 20160.
## 7 ZA686~ 0 0 PT PT17 Lisboa 0.932 7448.
## 8 ZA659~ 0 NA GB-GBN UKI London 0.994 50133.
## 9 ZA757~ 0 0 CY CY REPUB~ 0.594 874.
## 10 ZA686~ 0 0 LT LT003 Klaip~ 0.623 1564.
## 11 ZA757~ 0 0 IE IE013 West ~ 0.490 1651.
## 12 ZA659~ 0 NA LT LT003 Klaip~ 1.16 2917.
## # ... with 11 more variables: marital_status <chr>, age_education <chr>,
## # age_exact <dbl>, occupation_of_respondent <chr>,
## # occupation_of_respondent_recoded <chr>,
## # respondent_occupation_scale_c_14 <chr>, type_of_community <chr>,
## # is_student <dbl>, no_education <dbl>, education <dbl>,
## # date_of_interview <date>saveRDS ( panel, file.path(tempdir(), "climate_panel.rds"), version = 2)# not evaluated
saveRDS( panel, file = file.path("data-raw", "climate-panel.rds"))Putting It on a Map
This is not the end of the story. If you put all this on a map, the results are a bit disappointing.
Why? Because sub-national (provincial, state, county, district, parish) borders are changing all the time - within the EU and everywhere. The next step is to harmonize the geographical information. We have another CRAN released package to help you with. See the next post: Regional Climate Change Awareness Dataset.