Day 1: Basics of R

Topics included: data.frame, reading and writing data.frames, data types, accessing data by row and columns, summary statistics (sums, means, and standard deviations), creating new datasets (subset and join).

Day 2

• Adding and removing columns in a data.frame
• Creating new data.frames in R
• Data visualization in R: ggplot2

Day 3

• Aggregations in R using the plyr package
• Outlier Cleaning

Day 4

• Review of Aggregations in R
• Custom NMIS Aggregation functions
• Data conversion (type conversion, string searching, string manipulation)
• Writing out data

Day 5

• NMIS Pipeline

Day 6

• Complex Calculations (rowSums)
• NA handling (is.na)
• Data Pipeline

Dplyr Overview

• Aggregations using the dplyr package
• Why dplyr, tbl_df, select, mutate and group_by / summarize.