• 7 Topics

    Data Untangled: Transforming and Cleaning Data with dplyr

    This course builds on the foundation established in the R foundations course by diving deeper into working with data frames. You will gain hands-on practice selecting, filtering, arranging, renaming, and mutating columns to prepare raw data for statistical analysis and visualization.
    Topics covered include:
    • Selecting specific columns or rows of a data frame based on names, positions, or logical conditions
    • Filtering out unwanted observations or variables
    • Creating new variables through mathematical operations or recombination of existing variables
    • Handling missing data by dropping, replacing, or imputing values
    • Grouping rows by a variable and calculating summary statistics for each group
    • Reshaping data between wide and long formats
  • 3 Topics

    Data on display: R plots with ggplot2 (beta)

    This course will teach you how to harness the power of visualization in R to explore data and to present your findings to others. In addition to learning the mechanics of learning to write code for visualizations, you will also learn how to use the art of visualization to tell a story with your data. We will be focusing on learning how to use ggplot2, one of the the most popular R packages, to produce a variety of high quality visualizations.

    Topics include:

    • Building a plot in layers with ggplot2
    • Implementing the Grammar of Graphics in R
    • Visualization strategies for univariate, bivariate, and multivariate data
    • Histograms, boxplots, and kernel density plots for visualizing the distribution of continuous variables
    • Simple, stacked, and grouped barplots for visualizing the distribution of discrete variables
    • Scatterplots and linegraphs for visualizing the relationship between two continuous variables
    • Time series plots to visualize epidemic trends
    • Grouping and faceting plots to visualize relationships among three or more variables
    • Customizing plots with labels, color palettes, and themes

    During the beta release, new lessons will be made public periodically.

  • 7 Topics

    Foundations of data analysis with R

    This introductory course provides a comprehensive overview of the R programming language. Through hands-on practice, you will learn how to set up your R programming environment, import and explore data, perform basic data analysis and data visualization, and create reports to communicate your results.
    Topics include:
    • Installing R and the RStudio IDE
    • Importing data from various formats into R
    • Fundamentals of R programming
    • Organizing your R project and workspace
    • Exploring data through summary statistics and data visualization
    • Creating reproducible reports in R Markdown to communicate your analysis
  • 6 Topics

    Intro to EpiGraphHub (beta)

    This course aims to enable public health students and professionals to engage as advanced users with the EpiGraphHub platform, our data management, exploration and dashboarding platform. It requires minimal technical knowledge of coding or informatics.

    During this course, you will learn:

    • What the EpiGraphHub platform is, and what functionalities it offers to non-programmer users
    • What databases are available and how datasets are organised
    • How to browse available datasets
    • How to use the interactive drag & drop user interface to develop Exploratory analyses
    • How to create powerful data visualizations and interactive dashboards without writing code
    • How to import new data into the Hub from CSV files as well as Excel and Google spreadsheets

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