PORTFOLIO

DATA VISUALIZATION WITH R

Choropleth map

Total labor union election counts across U.S. states between 2016 and 2023.

Plotted using geom_sf() from ggplot2 and the rocket color palette from viridis color maps.

You can access an R tutorial for this type of map in my website using the link below.

Tutorial

Spaghetti plot

Labor union election counts across 2016-2023 for a random subset of U.S. states.

Plotted using ggplot2 package with loess smoothing function

Lollipop plot

Liberal (in blue) and Conservative (in red) mean ratings of perceived power for various target groups. Higher scores indicate target group is perceived to be more powerful.

Chord-flipped lollipop plot graphed using ggplot2 package.

You can access a very quick R tutorial for lollipop plots in my website using the link below.

Tutorial

Violin plot

Predicted counts of yearly labor union elections as a function of state policy (open vs close shop states). Model fitted using lmer function from lme4 package.

Plotted using ggplot2 package using the geom_violin() function.

Corrplot

Correlation matrix from García Ferrés & DePalma (2023) depicting magnitude of correlation between counterfactual thinking scales and importance of COVID-19 prevention measures. Colors and values reflect effect size; crossed-out correlations reflect non-significant associations at p > .05.

Plotted using ggcorrplot package and function.

Wordcloud

Graphic representation of word frequency in participants' qualitative responses.

Plotted with wordcloud from wordcloud and wordcloud2 packages.

Label plot

Power ratings of target groups on a speeded task predict whether liberals (vs. conservatives) favor the target group. The Y axis reflects the standardized beta coefficients of Group Warmth predicted by participant political orientation. y > 0 indicates conservative ideology predicts favoring the target group.

Plotted using ggplot2 with geom_label() command.

Linear regression

Liberals' and conservatives' perceptions of the political orientation of powerful and powerless groups.

Slope comparison with standard errors plotted with ggplot2 using the lm smoothing function.

Latent Profile Boxplot

4 class solution of a Latent Profile Analysis (LPA) of participants responses to four items (Labeled SE1, SE2R, MO1R, MO2).

Results plotted with plot_profiles() function from the tidyLPA package.

DATA ANALYSIS SKILLS

Parametric Data Analyses

Structural Equation Modeling (SEM)

Multi-level Modeling

ANOVAs:

Between-Subjects

Repeated measures

Mixed Effects

ANCOVA

T-Tests:

One-sample

Two-sample

Two-sample paired

Logistic regression

Linear and polynomial regression

Longitudinal Data Analysis

Multi-level Modeling (MLM)

Random Intercept Cross-Lagged Panel Model (RI-CLPM)

Cross-Lagged Panel Model (CLPM)

Non-Parametric Analyses

Cross Tabulations

Kruskal Wallis Test

Mann-Whitney U Test

Scale Psychometric Analyses

Confirmatory Factor Analysis (CFA)

Exploratory Factor Analysis (EFA)

Principal Components Analysis (PCA)

Reliability analysis

Natural Language Processing

Sentiment Analysis

Word Embedding

Part-of-Speech (POS) Tagging

Topic Modeling / Latent Variable Analysis in Text

Data Clustering

Latent Profile Analysis (LPA)

K-means clustering

Page updated

Google Sites