R Programming Language Developer Road Map
Basic Programming Knowledge: Learn basic data types, functions, and structures in R. This covers the basics of defining variables in R programming, assignment operations, mathematical operations, conditional expressions, loops, vectors, arrays, lists, factors, data frames.
Data Analysis and Manipulation: The strongest aspect of the R language is data analysis and manipulation. Therefore, learn the basic functions to access, clean, shape, manipulate, transform, combine and analyze data using the R language. At this stage, you can learn about R packages like tidyverse, dplyr, reshape2, data.table, lubridate, stringr, ggplot2 and many more.
Advanced Data Analysis: Learn to analyze data using various techniques in the R language. This covers topics such as predictive modeling, regression analysis, time series analysis, factor analysis, clustering, machine learning, deep learning, and nonlinear models.
Visualization: Learn to work with various packages such as ggplot2, lattice, plotly, ggvis to visualize your data in R. You can visualize your data in many different ways, such as graphs, drawings, maps, interactive panels.
Package Creation: Learn the tools needed to build your own R packages. This covers topics such as the package creation process, editing code files, preparing documentation, writing tests, deploying packages, troubleshooting and maintenance.
Other Tools: Learn about other tools and technologies in the R language. This covers topics like RStudio IDE, Shiny, RMarkdown, Knitr, SQL, web scraping, Big Data technologies.
This road map covers topics such as basic and advanced programming in R, data analysis, machine learning, visualization, and package development. Of course, this road map can be purely subjective and consist of many different paths, but I tried to present a road map that focuses on data analysis and machine learning in R.
