This workshop provides a brief introduction to key concepts for statistical analysis in R. We will start by presenting key features of the dplyr package in R – this will include data frame filtering, variable manipulation, merging of data frames, and handling of missing data. Subsequently, we will delve into two classic statistical models: the linear regression model and the logistic regression model. We will analyze the assumptions underlying each model and explore their limitations. Finally, we will explore statistical testing and experimental design concepts.
- Data Wrangling
- Linear Regression
- Logistic Regression
- Statistical Testing
- Study Design
- previous experience with R
- previous experience with probability
- tidyverse & gapminder packages
install.packages(c("tidyverse","gapminder"))
The material used in this workshop is adapted from past QLS-MiCM workshops and course notes from the following individuals:
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Gerardo Martinez (HGEN, McGill University)
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Alex Diaz-Papkovich (Brown University)
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Larisa Morales Soto (Harvard Medical School)
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Lisa Sullivan (Boston University School of Public Health)