Overview: Basic and Advanced Data Manipulation

Materials adapted from Adrien Osakwe, Larisa M. Soto and Xiaoqi Xie.

Basic Data Manipulation

By the end of this module, you will be able to:

Import and Export Data: Read from and write to common file formats (e.g., .csv, .tsv, and .txt) using functions like read.csv() and write.table().
Navigate Data Frames: Utilize foundational functions (dim(), str(), head()) to assess the dimensions and structure of any dataset.
Master Base R Subsetting: Pinpoint specific data using the coordinate system [row, column], the $ operator, and logical indexing with which().
Modify Columns: Add, rename, or transform individual columns using standard assignment logic.
Merge Datasets: Combine tables horizontally and vertically using cbind() and rbind().

By the end of this module, you will be able to:

Apply the dplyr Grammar: Efficiently manipulate data using core “verbs” such as filter(), select(), mutate(), arrange(), and summarize().
Construct Pipelines: Use the pipe operator (%>%) to chain multiple operations into a single, readable workflow.
Perform Grouped Analyses: Use group_by() in combination with summarize() to calculate statistics (like mean or standard deviation) across different experimental cohorts.
Reshape Data: Convert between “wide” and “long” formats using tidyr functions (pivot_longer, pivot_wider) to prepare data for visualization.
Integrate External Packages: Install and load specialized libraries to extend R’s data manipulation capabilities.