Protein phosphorylation is a post-translational modification (PTM) that plays an important role in diverse cellular processes. Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has allowed for that measurement of thousands of phosphorylation sites from biological specimens. However, there is a lack of bioinformatics tools to systematically analyse the complex data. Most of the current tools only provide a single function, which cannot be used to perform phosphoproteomics routine analysis effectively. Here, we describe PhosMap, a modular R package used to pre-process and analyse phosphoproteomics data. PhosMap supports data quality control at the phosphorylation site and peptide levels, allowing for mapping of phosphorylation sites to the corresponding protein sequence and normalization of phosphorylation based on protein abundance from proteomics data. The cleaned data can be analysed in four analysis modules: (1) clustering and differential expression analysis, (2) time course analysis, (3) kinase activity prediction and (4) phosphorylation motif enrichment analysis. In addition, a user-friendly and compatible visualization module is embedded in PhosMap for more intuitive data visualization. The PhosMap package is compatible with R 3.6 and above on Windows, Mac OS X and Linux and freely-available on GitHub: https://github.com/ecnuzdd/PhosMap.