Weĭon’t need to restore labels on subsetted or sorted ame. These methods preserve labels during common operations. There are special methods for subsetting and concatenating labelled Labels are preserved during common operations on the data Return immediately to defaults to avoid issues: expss_enable_value_labels_support() # 2 2 4 0 # weird 'unique'! There is a value 99 which is absent in 'nps' # Detractors Neutralists Promoters Hard to say # now we see "Hard to say" with zero counts expss_enable_value_labels_support_extreme() # 2 2 4 unique(nps) # LABEL: Net promoter scoreĪnd now extreme value labels support - we see “Hard to say” with zeroĬounts. # table with labels but there are no label "Hard to say" Results with default value labels support - three labels are here but Table(nps) # there is no labels in the result # nps Here we disable labels support and get results without labels: expss_disable_value_labels_support() We have label ‘Hard to say’ for which there are no values in That’s why it is recommended to turn off this option immediately Labelled variables: labels without values will be added to unique However, unique will give weird result for Option factor/ as.factor will take into accountĮmpty levels. We have an option for extreme values lables support:Įxpss_enable_value_labels_support_extreme(). Geom_point(aes(y = mpg, x = wt, color = qsec)) + # '.data' is shortcut for all 'mtcars' ame inside expression Note that with ggplot2 version 3.2.0 and higher you need toĮxplicitly convert labelled variables to factors in theįacet_grid formula: library(ggplot2, nflicts = FALSE) # F-statistic: 47.15 on 3 and 28 DF, p-value: 4.506e-11 # boxplot with variable labelsĪnd, finally, ggplot2 graphics with variables and value # Residual standard error: 2.578 on 28 degrees of freedom # lm(formula = `Miles/(US) gallon` ~ `Weight (1000 lbs)` + `Gross horsepower` + Use_labels(mtcars, lm(mpg ~ wt + hp + qsec)) %>% summary # Use_labels(mtcars, table(am, vs)) # Engine By now variables labels support available onlyįor expression which will be evaluated inside ame. There is a special function for variables labels support. There is also prepend_names function but it can beīase table and plotting with value labels: with(mtcars, table(am, vs)) # vs # 1 Promoters prepend_values(nps) # LABEL: Net promoter score # 99 Hard to say unlab(nps) # -1 0 1 1 0 1 1 -1 drop_unused_labels(nps) # LABEL: Net promoter score drop_val_labs(nps) # LABEL: Net promoter score One variable as well as on the entire dataset. Val_lab(nps) = val_lab(nps) %n_d% "Other"Īdditionaly, there are some utility functions. We can read, add or remove existing labels: var_lab(nps) # get variable label # "Net promoter score" val_lab(nps) # get value labels # Detractors Neutralists Promoters In addition to apply_labels we have SPSS-style Getting and setting variable and value labelsįirst, apply value and variables labels to dataset: library(expss) ( use_labels) which greatly simplify variable labels usage. Labels will be preserved during variables Every function which internally converts variable Integrates value labels support into base R functions and into functionsįrom other packages. It is easy to store labels as variable attributes in R but most Rįunctions cannot use them or even drop them. With labels we can manipulate short variable names and codes when weĪnalyze our data but in the resulting tables and graphs we will see Also, we can’t calculate means or other numeric Label, these integers have to be a count starting at 1 and every value Factors only allow for integers to be mapped to a text However, factors miss important features which the value The usual way to connect numeric data to labels in R is factor We can easily get dataset description and variables summary Labeling values means weĭon’t have to remember if 1=Extremely poor and 7=Excellent or Value labels are similar to variable labels, but value labels areĭescriptions of the values a variable can take. With thisĭescription it is easier to remember what those variable names refer to. Variable label can give a nice, long description of variable. Spaces and punctuation but short variables names make coding easier. Supports rather long variable names and these names can contain even Variable label is human readable description of the variable.
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