R Analytics

R language data analysis and visualization toolkit.

Quick Reference

Run R Code

# Run inline code
Rscript -e 'print("Hello R")'

# Run script file
Rscript script.R

# Run with arguments
Rscript script.R arg1 arg2

Package Management

# Update all packages
Rscript -e 'options(repos = c(CRAN = "https://cloud.r-project.org")); update.packages(ask = FALSE, checkBuilt = TRUE)'

# Install package
Rscript -e 'install.packages("tidyverse", repos = "https://cloud.r-project.org")'

# Install multiple packages
Rscript -e 'install.packages(c("ggplot2", "dplyr", "tidyr"), repos = "https://cloud.r-project.org")'

Data Analysis Workflow

1. Load Data

# CSV
df <- read.csv("data.csv")
df <- readr::read_csv("data.csv")  # faster, tibble output

# Excel
df <- readxl::read_excel("data.xlsx", sheet = 1)

# JSON
df <- jsonlite::fromJSON("data.json")

2. Data Manipulation (dplyr)

library(dplyr)

df %>%
  filter(column > 10) %>%           # filter rows
  select(col1, col2) %>%            # select columns
  mutate(new_col = col1 * 2) %>%    # create column
  group_by(category) %>%            # group
  summarise(mean_val = mean(value)) # aggregate

3. Visualization (ggplot2)

library(ggplot2)

# Scatter plot
ggplot(df, aes(x = x_col, y = y_col)) +
  geom_point() +
  labs(title = "Title", x = "X Label", y = "Y Label") +
  theme_minimal()

# Bar chart
ggplot(df, aes(x = category, y = value, fill = category)) +
  geom_bar(stat = "identity") +
  theme_minimal()

# Line chart
ggplot(df, aes(x = date, y = value, color = group)) +
  geom_line() +
  theme_minimal()

# Save plot
ggsave("plot.png", width = 10, height = 6, dpi = 300)
ggsave("plot.pdf", width = 10, height = 6)

4. Statistical Analysis

# Summary statistics
summary(df)

# Correlation
cor(df$x, df$y)
cor.test(df$x, df$y)

# Linear regression
model <- lm(y ~ x1 + x2, data = df)
summary(model)

# T-test
t.test(group1, group2)

# ANOVA
aov_result <- aov(value ~ group, data = df)
summary(aov_result)

Common Packages

Package	Purpose
tidyverse	Meta-package: ggplot2, dplyr, tidyr, readr, etc.
ggplot2	Visualization
dplyr	Data manipulation
tidyr	Data tidying
readr	Fast CSV reading
readxl	Excel files
data.table	Fast data manipulation
plotly	Interactive plots
shiny	Web apps
rmarkdown	Reports

Output Formats

# Save data
write.csv(df, "output.csv", row.names = FALSE)
readr::write_csv(df, "output.csv")

# Save R object
saveRDS(obj, "data.rds")
obj <- readRDS("data.rds")

Resources

Scripts

scripts/update_packages.R - Update all installed packages

References

Quick Reference:

references/packages.md - Common packages quick reference
references/awesome-packages.md - Curated packages by domain (ML, viz, web, finance, etc.)
references/resources-index.md - URLs, cheat sheets, community resources

R for Data Science (2e) - Tidyverse workflow:

File	Topics
references/r4ds-1-visualize.md	ggplot2: geoms, aesthetics, facets, scales, themes
references/r4ds-2-transform.md	dplyr: filter, select, mutate, summarize, group_by
references/r4ds-3-wrangle.md	tidyr (pivot), stringr, forcats, lubridate
references/r4ds-4-import.md	readr, readxl, joins, binding
references/r4ds-5-program.md	Functions, across(), map(), iteration

Advanced R (2e) - Deep R programming:

File	Topics
references/advr-1-foundations.md	Names/values, vectors, subsetting, control flow, functions, environments, conditions
references/advr-2-functional.md	Functionals (map/reduce), function factories, function operators
references/advr-3-oop.md	S3, R6, S4 object-oriented programming
references/advr-4-metaprogramming.md	Expressions, quasiquotation, evaluation, code translation
references/advr-5-techniques.md	Debugging, profiling, performance optimization, Rcpp

R Graphics Cookbook - Visualization recipes:

references/graphics-cookbook.md - Bar graphs, line graphs, scatter plots, distributions, annotations, colors

Ecosystems:

references/tidyverse-ecosystem.md - Core packages, import tools, modeling, patterns
references/bioconductor.md - Installation, GenomicRanges, RNA-seq, annotation

Sub-Skills (Domain-Specific)

Sub-Skill	Description
sub-skills/r-data/	Data manipulation, formats, databases (dplyr, data.table, DBI)
sub-skills/r-viz/	Visualization (ggplot2, plotly, leaflet, HTML widgets)
sub-skills/r-ml/	Machine learning (tidymodels, xgboost, caret, deep learning)
sub-skills/r-nlp/	Natural language processing (tidytext, quanteda, tm)
sub-skills/r-web/	Web technologies & reproducible research (Shiny, rmarkdown)
sub-skills/r-stats/	Bayesian analysis, optimization, finance (Stan, quantmod)
sub-skills/r-bio/	Bioinformatics (Bioconductor, DESeq2, GenomicRanges)
sub-skills/r-network/	Network analysis (igraph, tidygraph, visNetwork)
sub-skills/r-spatial/	Spatial analysis (sf, terra, leaflet, tmap)
sub-skills/r-dev/	R development (devtools, testthat, roxygen2, Rcpp)
sub-skills/r-parallel/	Parallel computing & performance (future, Rcpp, Spark)
sub-skills/r-resources/	Learning resources (books, courses, cheat sheets)

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