Blog Archives

Negative Binomial Regression for Complex Samples (Surveys) #rstats

March 9, 2017
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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… Weiterlesen "Negative Binomial Regression for Complex Samples (Surveys) #rstats"

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Descriptive summary: Proportions of values in a vector #rstats

March 6, 2017
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Descriptive summary: Proportions of values in a vector #rstats

When describing a sample, researchers in my field often show proportions of specific characteristics as description. For instance, proportion of female persons, proportion of persons with higher… Read more "Descriptive summary: Proportions of values in a vector #rstats"

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

March 5, 2017
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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.… Read more "Direct integration of sjPlot-tables in knitr-rmarkdown-documents #rstats"

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Data Transformation in R: The #Tidyverse-Approach of Organizing Data #rstats

February 22, 2017
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Data Transformation in R: The #Tidyverse-Approach of Organizing Data #rstats

Yesterday, I had the pleasure to give a talk at the 8th Hamburg R User-Group meeting. I talked about data wrangling and data transformation, and how the… Read more "Data Transformation in R: The #Tidyverse-Approach of Organizing Data #rstats"

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

February 8, 2017
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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

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sjPlot-update: b&w-Figures for Print Journals and Package Vignettes #rstats #dataviz

February 6, 2017
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sjPlot-update: b&w-Figures for Print Journals and Package Vignettes #rstats #dataviz

My sjPlot-package was just updated on CRAN with some – as I think – useful new features. First, I have added some vignettes to the package (based on the existing online-documentation) that cover some core features and principles of the sjPlot-package, so you have direct access to these manuals within R. The vignettes are also

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

December 22, 2016
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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. Steps of the data analysis process

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Pipe-friendly workflow with sjPlot, sjmisc and sjstats, part 1 #rstats #tidyverse

November 14, 2016
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Pipe-friendly workflow with sjPlot, sjmisc and sjstats, part 1 #rstats #tidyverse

Recent development in R packages are increasingly focussing on the philosophy of tidy data and a common package design and api. Tidy data is an important part of data exploration and analysis, as shown in the following figure: Tidying data not only includes data cleaning, but also data transformation, both being necessary to perform the

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Tagged NA values and labelled data #rstats

September 27, 2016
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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

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Effect-Size Calculation for Meta-Analysis in R #rstats

September 8, 2016
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Effect-Size Calculation for Meta-Analysis in R #rstats

When conducting meta-analysis, you most likely have to calculate or convert effects sizes into an effect size with common measure. There are various tools to do this – one easy to use tool is the Practical Meta-Analysis Effect Size Calculator from David B. Wilson. This online-tool is now implemented as an R-package: esc: Effect Size

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