R is the leading open-source programming language in data science and statistics. It is used by participants, academics and professionals to perform data analysis, and for building data-driven solutions and applications. In this course you will learn how to program in R and how to use R for effective environmental data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Participants will be able to compute basic algebraic calculations, summary statistics such as mean, mode, median and range, confidence intervals, t-test and regression analysis. They will be able to plot histogram, boxplots, stem-and tree, line plots, bar plots and other basic exploratory data analysis. All data examples are from the environmental fields.
Basic computer literacy is such as the efficiency in the use of Microsoft word is required. Knowledge of computer programming is an added advantage, not compulsory. Participants are advised to come with their laptops.