Publications by Daniel
Data visualization in social sciences – what’s new in the sjPlot-package? #rstats
My sjPlot package just reached version 2.0 and got many updates during the couple of last months. The focus was less on adding new functions; rather, I improved existing functions by adding new smaller and bigger features to make working with the package easier and more reliable. In this blog post, I will report some of the new features. Consiste...
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Pipe-friendly bootstrapping with list-variables in #rstats
A few days ago, my package sjstats was updated on CRAN. Most functions of this package are convenient functions for common statistical computations, especially for (mixed) regression models. This latest update introduces some pipe-friendly bootstrapping-methods, namely bootstrap(), boot_ci(), boot_se() and boot_p(). In this post, I just wanted to...
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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 Computation...
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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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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: (Source: http://r4ds.had.co.nz/explore-intro.html) Tidying data not only includes data cleaning, but also data transforma...
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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 Let’s recall ...
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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 online on CRAN. ...
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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 d...
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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 philosophy behind the tidyverse makes these tasks easier. If you like, you can download the slides. Feel free to add your comments to the slide here. Tagged: R, rstats, tidyverse Related To l...
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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. 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: `r sjt.df...
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