When working with survey data there are several issues / strategies
to clean and prepare the data that are useful and worth being
incorporated to the routines and workflow. This vignette uses the
CEOdata package to present several examples.
It uses primarily the data retrieved by default using the
CEOdata() function in its default form, which retrieves the
compiled “Barometers” from 2014 onwards.
Once you have retrieved the data of the surveys, it is easy to accommodate them to your regular workflow. For instance, to get the overall number of males and females surveyed:
## # A tibble: 2 × 2 ## SEXE n ## <fct> <int> ## 1 Masculí 21122 ## 2 Femení 22716
Or to trace the proportion of females surveyed over time, across barometers:
The metadata also provides the option of examining the time periods where there has been fieldwork in quantitative studies, since 2018. In addition, we can distinguish between studies that provide microdata and surveys that don’t.
CEOmeta() |> filter(`Dia inici treball de camp` > "2018-01-01") |> ggplot(aes(xmin = `Dia inici treball de camp`, xmax = `Dia final treball de camp`, y = reorder(REO, `Dia final treball de camp`), color = microdata_available)) + geom_linerange() + xlab("Date") + ylab("Surveys with fieldwork") + theme(axis.ticks.y = element_blank(), axis.text.y = element_blank())
Once a dataset has been retrieved from the CEO servers, it is important to clean it and arrange it to one’s individual preferences, and store the result in an R object.
The following example, for instance, process several variables of the survey, picks them and stores the resulting object in a workspace (RData) format.
survey.data <- d |> mutate(Female = ifelse(SEXE == "Dona", 1, 0), Age = EDAT, # Pass NA correctly Income = ifelse(INGRESSOS_1_15 %in% c("No ho sap", "No contesta"), NA, INGRESSOS_1_15), Date = Data, # Reorganize factor labels `Place of birth` = factor(case_when( LLOC_NAIX == "Catalunya" ~ "Catalonia", LLOC_NAIX %in% c("No ho sap", "No contesta") ~ as.character(NA), TRUE ~ "Outside Catalonia")), # Convert into numerical (integer) `Interest in politics` = case_when( INTERES_POL == "Gens" ~ 0L, INTERES_POL == "Poc" ~ 1L, INTERES_POL == "Bastant" ~ 2L, INTERES_POL == "Molt" ~ 3L, TRUE ~ as.integer(NA)), # Convert into numeric (double) and properly address missing values `Satisfaction with democracy` = ifelse( SATIS_DEMOCRACIA %in% c("No ho sap", "No contesta"), NA, as.numeric(SATIS_DEMOCRACIA))) |> # Center income to the median mutate(Income = Income - median(Income, na.rm = TRUE)) |> # Pick only specific variables select(Date, Female, Age, Income, `Place of birth`, `Interest in politics`, `Satisfaction with democracy`)
Finally, this can be stored for further analysis (hence, without the need to download and arrange the data again) in an R’s native format:
There are several packages that construct convenient tables with the
descriptive summary of a dataset. For example, using the
vtable package to produce a table with descriptive
|Variable||N||Mean||Std. Dev.||Min||Pctl. 25||Pctl. 75||Max|
|Place of birth||43836|
|… Outside Catalonia||13085||30%|
|Interest in politics||33736||1.5||0.98||0||1||2||3|
|Satisfaction with democracy||42987||3||0.74||1||3||4||4|
compareGroups that allows to flexibly produce
tables that compare descriptive statistics for different groups of
## ## --------Summary descriptives table by 'Female'--------- ## ## ___________________________________________________ ## 0 p.overall ## N=43838 ## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ ## Edat 50.5 (17.8) . ## Income -0.03 (2.81) . ## Place of birth: . ## Catalonia 30751 (70.2%) ## Outside Catalonia 13085 (29.8%) ## Interest in politics 1.46 (0.98) . ## Satisfaction with democracy 3.02 (0.74) . ## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
The development of
CEOdata (track changes, propose
improvements, report bugs) can be followed at github.
If using the data and the package, please cite and acknowledge properly the CEO and the package, respectively.