This publication provides a practical tutorial on how to perform a meta-analysis using the statistical software environment R, specifically focusing on mental health research. The authors, Sara Balduzzi, Gerta Rücker, and Guido Schwarzer, introduce the importance of meta-analysis in evidence synthesis and highlight the increasing use of this method in mental health studies. They detail the steps involved in conducting a meta-analysis, including the installation and use of R packages such as `meta` and `metasens`. The tutorial covers standard meta-analysis, sensitivity analyses for missing binary outcome data, and potential selection bias. Key methods discussed include fixed effect and random effects meta-analysis, subgroup analysis, forest plots, and funnel plots. The authors also explain how to assess and adjust for funnel plot asymmetry, which can indicate publication bias or other sources of bias. The publication concludes by emphasizing R's versatility and flexibility in meta-analysis and provides directions to more advanced methods available in R.This publication provides a practical tutorial on how to perform a meta-analysis using the statistical software environment R, specifically focusing on mental health research. The authors, Sara Balduzzi, Gerta Rücker, and Guido Schwarzer, introduce the importance of meta-analysis in evidence synthesis and highlight the increasing use of this method in mental health studies. They detail the steps involved in conducting a meta-analysis, including the installation and use of R packages such as `meta` and `metasens`. The tutorial covers standard meta-analysis, sensitivity analyses for missing binary outcome data, and potential selection bias. Key methods discussed include fixed effect and random effects meta-analysis, subgroup analysis, forest plots, and funnel plots. The authors also explain how to assess and adjust for funnel plot asymmetry, which can indicate publication bias or other sources of bias. The publication concludes by emphasizing R's versatility and flexibility in meta-analysis and provides directions to more advanced methods available in R.