Quickly create Codeplans of your (labelled) Data #rstats

The view_df() function from the sjPlot-package creates nice „codeplans“ from your data sets, and also supports labelled data and tagged NA-values. This gives you a comprehensive, yet clear overview of your data set.

To demonstrate this function, we use a (labelled) data set from the European Social Survey. view_df() produces a HTML-file, that is – when you use RStudio – displayed in the viewer pane, or it can be opened in your webbrowser.

R and labelled data: Using quasiquotation to add variable and value labels #rstats

Labelling data is typically a task for end-users and is applied in own scripts or functions rather than in packages. However, sometimes it can be useful for both end-users and package developers to have a flexible way to add variable and value labels to their data. In such cases, quasiquotation is helpful.

This vignette demonstrate how to use quasiquotation in sjlabelled to label your data.

Adding value labels to variables using quasiquotation

Usually, set_labels() can be used to add value labels to variables. The syntax of this function is easy to use, and set_labels()allows to add value labels to multiple variables at once, if these variables share the same value labels.

Tagged NA values and labelled data #rstats

sjmisc-package: Working with labelled data

A major update of my sjmisc-package was just released an CRAN. A major change (see changelog for all changes )is the support of the latest release from the haven-package, a package to import and export SPSS, SAS or Stata files.

The sjmisc-package mainly addresses three domains:

• reading and writing data between other statistical packages and R
• functions to make working with labelled data easier
• frequently applied recoding and variable transformation tasks, also with support for labelled data

In this post, I want to introduce the topic of labelled data and give some examples of what the sjmisc-package can do, with a special focus on tagged NA values.