Introduction to Data Management 2600-MSdz2IDMen

• Introduction to R and R data structures, Execute R commands, understand R objects and data structures, use R functions, Data investigation and manipulation using Base R
• Investigate R object type and structure, isolate elements using Base R subset operators and the subset () function, Data investigation and manipulation using tidyverse
• Select, filter, and sort data using tidyverse functions, chain functions together using pipes (%>%), create new variables conditionally using if_else(), recode(), and case_when()
• Calculate aggregate statistics from multiple rows of data, Group rows of data using group_by(), create aggregate statistics using summarize()
• Work with strings and date/datetime objects, Understand string basics, manipulate strings using stringr functions, work with dates and times using the lubridate package
• Understand the class and attributes of R objects, Investigate R object class and attributes, work with factor variables, label variables and values of a data frame using the labelled package
• Tools and guidelines for exploratory data analysis (EDA) Learn common approaches for exploring and analyzing data, understand skip patterns in survey data
• Understand tidy data structure and reshaping data, Define tidy data and how to reshape untidy data into tidy form, reshape data from wide to long using pivot_longer(), reshape data from long to wide using pivot_wider(), handle missing values during reshaping
• Combine data from multiple datasets using joins, Merge datasets using mutating joins, Check quality of merge using filtering joins, append datasets by stacking rows