R Data Analysis and Visualization Guide | ggplot2 Plotting, Data Wrangling and Econometrics
How to analyze and visualize research data using R and RStudio? This guide covers data wrangling (dplyr/tidyverse), publication-grade plotting with ggplot2, OLS, and panel data models.
Direct answer for this topic
R is the premier open-source tool for quantitative analysis, using the Tidyverse framework to maximize wrangling efficiency.
- ggplot2 relies on aesthetic mappings. Academic plots generally use theme_bw() or theme_classic() to strip non-essential backgrounds.
- For econometric analysis, the plm package provides robust functions to estimate fixed and random panel data models.
- Always save figures using ggsave() as PDFs to maintain infinite vector scaling. Use TIFF only if bitmapped files are explicitly requested.
- Efficient data wrangling, merging, and cleaning using dplyr and Tidyverse
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Reviewed against the platform’s public data-analysis, Python plotting, and quantitative-method pages, and cross-referenced with the official R Project manuals (CRAN manuals) and Tidyverse/ggplot2 documentations to verify R commands, dplyr pipelines, ggplot2 aesthetics, and plm panel data model syntax.
Related workflows and reference pages
What this page helps you do first
- Efficient data wrangling, merging, and cleaning using dplyr and Tidyverse
- Academic chart beautification using ggplot2 with Times New Roman settings
- Code templates for panel data estimation (fixed and random effects) via plm package
Why choose R for academic papers and econometrics
R is an open-source programming language built specifically for statistical computing and graphics. Its advantages over Stata and SPSS include: powerful visualization ecosystems (led by ggplot2); rapid package updates on CRAN; and strong scraping/text-mining pipelines.
For papers requiring advanced machine learning models, complex non-linear relations, or high-fidelity custom plots, R is an essential asset.
Data wrangling with dplyr and Tidyverse
- [Install and load core packages] install.packages("tidyverse"); library(tidyverse)
- [Import csv data] df <- read_csv("data.csv")
- [Select columns and rename] df_select <- df %>% select(id, year, Y = wage, X1 = educ)
- [Filter rows and arrange] df_filter <- df_select %>% filter(year >= 2018) %>% arrange(desc(Y))
- [Create new variables] df_mutate <- df_filter %>% mutate(log_Y = log(Y), X1_sq = X1^2)
ggplot2 academic plotting and font customization
- [Basic line and scatter plot] ggplot(df, aes(x = X1, y = Y)) + geom_point() + theme_bw()
- [Apply global Times New Roman theme] theme_academic <- theme_classic() + theme(text = element_text(family = "Times New Roman", size = 10))
- [Arrange subplots via facet] p + facet_wrap(~ category, scales = "free_y")
OLS and panel data regressions with plm
- [OLS regression] model_ols <- lm(Y ~ X1 + X2 + X3, data = df); summary(model_ols)
- [Robust standard errors] coeftest(model_ols, vcov = vcovHC(model_ols, type = "HC1"))
- [Panel data setup] pdf <- pdata.frame(df, index = c("id", "year"))
- [Fixed effects model (FE)] model_fe <- plm(Y ~ X1 + X2, data = pdf, model = "within")
- [Hausman test] phtest(model_fe, model_re)
High-quality figure export guidelines
- [Vector PDF export] ggsave("figure_1.pdf", plot = p, width = 6, height = 4.5, units = "in", device = "pdf")
- [High-resolution TIFF export] ggsave("figure_1.tiff", plot = p, dpi = 300, width = 15, height = 11, units = "cm")
- Make sure to match width and height parameter units with the target journal layout constraints to avoid scaling artifacts.
Frequently asked questions
- How do I resolve font errors in ggplot2?
- Install the extrafont package in R, run font_import() to register system fonts, and specify family = "Times New Roman" inside theme() configurations.