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
# 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/