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 […]

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

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 […]

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

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 […]

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