## 3 Results  
### Selected Data Quality R Packages  
We present the number of packages considered during each phase of the selection process in Figure 1. Our systematic search for packages on CRAN via pkgsearch resulted in a list of 3921 packages. We first filtered this list automatically by keywords (excluding 769 duplicate and 2360 out of scope hits). Then, we manually screened the subset, excluding 669 out of scope hits (i.e., packages addressing none of the domains of the reference DQ framework, packages intended for DQA for a specific type of data or single quality indicators). Combining the remaining 123 search hits from the CRAN search with thelist of 54 previously known to the authors or identified from the literature resulted in 145 distinct packages. We conducted a detailed screening of these. The majority of them did not provide enough functionalities to assess DQ according to our criteria. For example, this applied to packages that were developed specifically for data management, data cleaning, or missing value imputation. Similarly, we excluded packages that solely focused on generating a descriptive data summary or summary tables in R without any graphs. Only 33 packages met the first 4 inclusion criteria. Out of these, xplorerrr[33] had to be excluded, because it could only be partially executed, and we obtained errors when testing it with our example dataset (in the Shiny application for descriptive statistics, the overview for single variables gave the wrong number of observations; additionally, figures could only be partially generated). Likewise, analyzer[34] and mdapack[35] were excluded because they did not properly carry out the analysis of the sample dataset (it was not possible to generate a report for the SHIP data using analyzer as there were multiple issues with different data types causing errors that could not be resolved easily; the package mdapack excluded numeric variables coded as integers from the data overview, and a function for univariate analysis threw an error if less or more than four columns were included). Two additional packages were excluded because they were succeeded by other packages covered in this review: editrules[36] and dataMaid[16].  
Figure 1: PRISMA flow diagram showing the number of packages considered during the three phases of our review process (Identification, Screening, and Inclusion). The Identification step comprises two independent starting points (collection from various sources and CRAN search) because we conducted two searches for packages in parallel. During the Screening phase, we combined the packages identified from both approaches. The main reason for excluding packages after screening was not meeting our inclusion criteria in the scope of functionalities for data quality assessments. In total, 28 packages remained at the end of the search, but we regard one of them (errorlocate) as an addition to another package (validate) and report their assessment together.  
Our final comparison includes the following packages:  
assertable [37] assertive [38] assertr [39] clickR [40] DataExplorer [41] dataquieR [42] dlookr [44] DQAstats [45] explore [48] funModeling [49] MOQA [52] mStats [53] skimr [56] SmartEDA [57] testdat [60] validate [18]  
The package errorlocate builds on the package validate; therefore, we assessed them jointly. The majority of functions of interest were provided by validate. Thus, we report our findings for a total of 27 R packages. The results of our assessment are included in detail in Appendix C.