My set of packages for (daily) data analysis #rstats

I started writing my first package as collection of various functions that I needed for (almost) daily work. Meanwhile, packages were growing and bit by bit I sourced out functions to put them into new packages. Although this means more work for CRAN members when they have more packages to manage on their network, from […]

ggeffects: Create Tidy Data Frames of Marginal Effects for ‚ggplot‘ from Model Outputs #rstats

Aim of the ggeffects-package The aim of the ggeffects-package is similar to the broom-package: transforming “untidy” input into a tidy data frame, especially for further use with ggplot. However, ggeffects does not return model-summaries; rather, this package computes marginal effects at the mean or average marginal effects from statistical models and returns the result as tidy data […]

Negative Binomial Regression for Complex Samples (Surveys) #rstats

The survey-package from Thomas Lumley is a great toolkit when analyzing complex samples. It provides svyglm(), to fit generalised linear models to data from a complex survey design. svyglm() covers all families that are also provided by R’s glm() – however, the survey-package has no function to fit negative binomial models, which might be useful […]

Direct integration of sjPlot-tables in knitr-rmarkdown-documents #rstats

A new update of my sjPlot-package was just released on CRAN. Thanks to @c_schwemmer, it’s now possible to easily integrate the HTML-ouput of all table-functions into knitr-rmarkdown-documents. Simpel Tables In the past, to integrate table-output in knitr, you needed to set the argument no.output = TRUE and use the return-value $knitr: If you also wanted […]

Data wrangling within the #tidyverse – the design philosophy behind the sjmisc-package #rstats

I’m pleased to announce sjmisc 2.3.0, which was just updated on CRAN. The update might break existing code – however, functions were largely revised to work seamlessly within the tidyverse. In the long run, consistent design makes working with sjmisc more intuitive. Basically, sjmisc covers two domains of functionality: Reading and writing data between R […]

Exploring the European Social Survey (ESS) – pipe-friendly workflow with sjmisc, part 2 #rstats #tidyverse

This is another post of my series about how my packages integrate into a pipe-friendly workflow. The post focusses on my sjmisc-package, which was just updated on CRAN, and highlights some of the new features. Examples are based on data from the European Social Survey, which are freely available. Please note: The statistical analyses at […]

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 […]