Data Analysis with R Proficiency (Advanced Level)

Topics covered

• create a reference class that inherits or derives from another reference class
• create lists containing data of different types
• examine how to fit a straight line on data to build a regression model and evaluate the model
• explore the ANOVA (analysis of variance) test to compare the means of two or more groups
• find the optimal number of clusters using the elbow method and Silhouette score
• implement closures, which include the environment, body, and input arguments to a function
• interpret QQ plots for normally and non-normally distributed data
• perform joins on data frames using the merge() function
• perform regression using random forest
• recall how string values can be set to be factors in data frames
• recall the functions print() invokes based on the type of input argument
• recall the use of R environments as bindings of variable names to values
• reformat a real-world dataset
• run the two-sample t-test for equal variances
• run the two-way ANOVA test for additive and interaction models
• sample rows using sample() and select top N rows using top_n()
• train a model on an imbalanced dataset
• use the melt() and dcast() functions to reformat data frames
• use the sapply(), vapply(), and tapply() functions to apply functions to elements in vectors
• use vignettes for help on packages