R Statistical Computing Expert

Expert guidance for R programming, statistical analysis, data visualization, and data science.

Core Concepts

R Fundamentals

• Vectors and data frames

• Factors and lists

• Functions and apply family

• Packages and libraries

• R Markdown

• Tidyverse ecosystem

Statistical Analysis

• Descriptive statistics

• Hypothesis testing

• Regression analysis

• ANOVA

• Time series analysis

• Machine learning

Data Visualization

• ggplot2

• Base R graphics

• Interactive plots (plotly)

• Statistical charts

• Maps and spatial data

R Basics

Vectors

numbers <- c(1, 2, 3, 4, 5) names <- c("Alice", "Bob", "Charlie")

Data frames

df <- data.frame( id = 1:5, name = c("Alice", "Bob", "Charlie", "David", "Eve"), age = c(25, 30, 35, 28, 32), salary = c(50000, 60000, 55000, 52000, 58000) )

Subsetting

df[df$age > 30, ] # Rows where age > 30 df[, c("name", "age")] # Select columns

Functions

calculate_mean <- function(x) { sum(x) / length(x) }

Apply family

sapply(df$age, function(x) x * 2) lapply(list(1:5, 6:10), sum)

Control structures

if (mean(df$age) > 30) { print("Average age is above 30") } else { print("Average age is 30 or below") }

Loops

for (i in 1:nrow(df)) { print(df$name[i]) }

Tidyverse

library(dplyr) library(tidyr) library(stringr)

dplyr operations

df %>% filter(age > 28) %>% select(name, age, salary) %>% mutate( salary_bonus = salary * 1.1, age_group = case_when( age < 30 ~ "Young", age < 35 ~ "Mid-career", TRUE ~ "Senior" ) ) %>% arrange(desc(salary)) %>% group_by(age_group) %>% summarise( count = n(), avg_salary = mean(salary), total_salary = sum(salary) )

Reshaping data

wide_data <- data.frame( id = 1:3, year_2021 = c(100, 200, 150), year_2022 = c(120, 210, 160) )

Wide to long

long_data <- wide_data %>% pivot_longer( cols = starts_with("year"), names_to = "year", values_to = "value", names_prefix = "year_" )

Long to wide

wide_again <- long_data %>% pivot_wider( names_from = year, values_from = value, names_prefix = "year_" )

String operations

df %>% mutate( name_upper = str_to_upper(name), name_length = str_length(name), first_letter = str_sub(name, 1, 1) )

Joining data

df1 <- data.frame(id = 1:3, value1 = c("A", "B", "C")) df2 <- data.frame(id = 2:4, value2 = c("X", "Y", "Z"))

inner_join(df1, df2, by = "id") left_join(df1, df2, by = "id") full_join(df1, df2, by = "id")

ggplot2 Visualization

library(ggplot2)

Basic scatter plot

ggplot(df, aes(x = age, y = salary)) + geom_point(size = 3, color = "blue") + geom_smooth(method = "lm", se = TRUE) + labs( title = "Age vs Salary", x = "Age (years)", y = "Salary ($)" ) + theme_minimal()

Bar plot with facets

ggplot(df, aes(x = name, y = salary, fill = age_group)) + geom_col() + facet_wrap(~ age_group) + theme(axis.text.x = element_text(angle = 45, hjust = 1))

Box plot

ggplot(df, aes(x = age_group, y = salary)) + geom_boxplot(fill = "lightblue") + geom_jitter(width = 0.2, alpha = 0.5)

Histogram with density

ggplot(df, aes(x = salary)) + geom_histogram(aes(y = ..density..), bins = 10, fill = "steelblue") + geom_density(color = "red", size = 1)

Time series

ggplot(time_series_df, aes(x = date, y = value)) + geom_line(color = "darkgreen") + geom_point() + scale_x_date(date_breaks = "1 month", date_labels = "%b %Y") + theme(axis.text.x = element_text(angle = 45, hjust = 1))

Statistical Analysis

Descriptive statistics

summary(df) mean(df$age) median(df$salary) sd(df$age) var(df$salary) quantile(df$age, probs = c(0.25, 0.5, 0.75))

Correlation

cor(df$age, df$salary) cor.test(df$age, df$salary)

T-test

t.test(df$salary ~ df$gender)

ANOVA

model <- aov(salary ~ age_group, data = df) summary(model) TukeyHSD(model)

Linear regression

lm_model <- lm(salary ~ age + experience, data = df) summary(lm_model)

Predictions

new_data <- data.frame(age = c(30, 35), experience = c(5, 8)) predict(lm_model, new_data, interval = "confidence")

Multiple regression

multi_model <- lm(salary ~ age + experience + education, data = df) summary(multi_model)

Check assumptions

par(mfrow = c(2, 2)) plot(multi_model)

Logistic regression

logit_model <- glm(outcome ~ age + salary, data = df, family = binomial(link = "logit")) summary(logit_model)

Time Series Analysis

library(forecast)

Create time series

ts_data <- ts(data, start = c(2020, 1), frequency = 12)

Decomposition

decomposed <- decompose(ts_data) plot(decomposed)

ARIMA model

auto_arima <- auto.arima(ts_data) summary(auto_arima)

Forecasting

forecast_result <- forecast(auto_arima, h = 12) plot(forecast_result)

Accuracy metrics

accuracy(forecast_result)

Machine Learning

library(caret) library(randomForest)

Split data

set.seed(123) train_index <- createDataPartition(df$outcome, p = 0.8, list = FALSE) train_data <- df[train_index, ] test_data <- df[-train_index, ]

Train model

rf_model <- randomForest( outcome ~ ., data = train_data, ntree = 500, importance = TRUE )

Predictions

predictions <- predict(rf_model, test_data)

Confusion matrix

confusionMatrix(predictions, test_data$outcome)

Feature importance

importance(rf_model) varImpPlot(rf_model)

Cross-validation

train_control <- trainControl( method = "cv", number = 10, savePredictions = TRUE )

cv_model <- train( outcome ~ ., data = train_data, method = "rf", trControl = train_control )

print(cv_model)

R Markdown

title: "Analysis Report" author: "Data Scientist" date: "r Sys.Date()" output: html_document: toc: true toc_float: true code_folding: hide

Introduction

This analysis explores the relationship between variables.

knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(tidyverse)

Data Loading

df <- read.csv("data.csv")
head(df)

Visualization

ggplot(df, aes(x = x, y = y)) +
  geom_point() +
  theme_minimal()

Results

The analysis shows that r cor(df$x, df$y)
 correlation.

## Data Import/Export

```r
# CSV
df <- read.csv("data.csv")
write.csv(df, "output.csv", row.names = FALSE)

# Excel
library(readxl)
library(writexl)
df <- read_excel("data.xlsx", sheet = "Sheet1")
write_xlsx(df, "output.xlsx")

# JSON
library(jsonlite)
df <- fromJSON("data.json")
write_json(df, "output.json")

# Database
library(DBI)
library(RSQLite)
con <- dbConnect(SQLite(), "database.db")
df <- dbReadTable(con, "table_name")
dbWriteTable(con, "new_table", df)
dbDisconnect(con)

# Web APIs
library(httr)
response <- GET("https://api.example.com/data")
data <- content(response, as = "parsed")

Best Practices

Code Style

- Use <- for assignment

- Follow tidyverse style guide

- Write functions for repeated code

- Use meaningful variable names

- Comment complex operations

- Use %>% pipe for readability

Data Analysis

- Always explore data first

- Check for missing values

- Validate assumptions

- Use visualization

- Document your analysis

- Make analysis reproducible

Performance

- Vectorize operations

- Use data.table for large data

- Avoid growing objects in loops

- Profile code with Rprof()

- Use parallel processing

- Cache expensive computations

Anti-Patterns

❌ Growing vectors in loops
❌ Not setting random seed
❌ Ignoring NA values
❌ Using attach()
❌ Not documenting code
❌ Hardcoding file paths
❌ Not checking assumptions

Resources

- R Documentation: https://www.r-project.org/

- Tidyverse: https://www.tidyverse.org/

- ggplot2: https://ggplot2.tidyverse.org/

- R for Data Science (book): https://r4ds.had.co.nz/

- CRAN Task Views: https://cran.r-project.org/web/views/