Publications by Sebastien Parker
Tutorial 8 (252)
Introduction The tutorial is adapted from a blog post by Andrew Heiss, which I strongly recommend to read: https://www.andrewheiss.com/blog/2022/05/20/marginalia/#other-marginal-slope-things Setup environment First, let’s set up our environment by loading the necessary packages: What are Marginal Effects? Marginal effects represent the change i...
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Tutorial 7 (SOC252)
This tutorial covers advanced regression techniques and post-estimation analysis using R. We’ll explore various aspects of regression analysis, including diagnostics, non-linear relationships, generalized additive models (GAMs), interactions, and techniques for making the most of regression results. Setup environment First, let’s set up our en...
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Tutorial 6 (252)
Setting Up R for Analysis First, let’s install and load the necessary packages. This step ensures that we have all the tools we need for our analysis. Data Preparation and Exploration Let’s create a binary outcome variable for Clinton votes in the 2016 election: # Load the States data data(USStates) States <- USStates States$Clinton <- abs(as...
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Tutorial 6 (252)
Setting Up R for Analysis First, let’s install and load the necessary packages. This step ensures that we have all the tools we need for our analysis. Data Preparation and Exploration Let’s create a binary outcome variable for Clinton votes in the 2016 election: # Load the States data data(USStates) States <- USStates States$Clinton <- abs(as...
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Tutorial 5 (SOC252)
Introduction This tutorial covers how to perform Multiple Linear Regression (MLR) and interactions in R, including how to produce and interpret regression tables using sjPlot. We’ll use two datasets to start: HappyPlanetIndex (for global data) and States (for US state-level data). Setting Up R for Analysis First, let’s install and load the nec...
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Tutorial 4 (SOC252)
# List of packages packages <- c("tidyverse", "fst", "modelsummary", "broom", "sjPlot", "ggplot2", "car", "Lock5Data", "mosaic") # add any you need here # Install packages if they aren't installed already new_packages <- packages[!(packages %in% installed.packages()[,"Package"])] if(length(new_packages)) install.packages(new_packages) # Load the ...
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Tutorial 3 (252)
# List of packages packages <- c("tidyverse", "fst", "modelsummary", "viridis", "kableExtra", "flextable", "officer") # add any you need here # Install packages if they aren't installed already new_packages <- packages[!(packages %in% installed.packages()[,"Package"])] if(length(new_packages)) install.packages(new_packages) # Load the packages la...
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Second Tutorial (252)
Visualizing and Exploring First, let’s load the packages we will use. You can always add more as needed # List of packages packages <- c("tidyverse", "gapminder", "fst", "viridis", "ggridges", "modelsummary", "RColorBrewer", "PrettyCols") # Install packages if they aren't installed already new_packages <- packages[!(packages %in% installed.packa...
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First Tutorial (252)
Introduction to R and Exploratory Data Analysis (EDA) Welcome future data analysts! R is a powerful tool for statistical analysis and data visualization. This comprehensive tutorial will guide you through the basics of R, with a focus on Exploratory Data Analysis (EDA), answering simple descriptive questions, and visualizing the results. Setting U...
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Research Report
# List of packages packages <- c("tidyverse", "infer", "fst") # add any you need here # Install packages if they aren't installed already new_packages <- packages[!(packages %in% installed.packages()[,"Package"])] if(length(new_packages)) install.packages(new_packages) # Load the packages lapply(packages, library, character.only = TRUE) ## ...
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