broom

broom

Convert statistical objects into tidy data frames.

Core Functions

library(broom)

# Fit a model
model <- lm(mpg ~ wt + hp, data = mtcars)

# tidy() - coefficient-level statistics
tidy(model)
# Returns: term, estimate, std.error, statistic, p.value

# glance() - model-level statistics
glance(model)
# Returns: r.squared, adj.r.squared, sigma, statistic, p.value, df, etc.

# augment() - observation-level statistics
augment(model)
# Returns: original data + .fitted, .resid, .hat, .sigma, .cooksd, .std.resid

Linear Models

# lm
model <- lm(y ~ x1 + x2, data = df)
tidy(model, conf.int = TRUE, conf.level = 0.95)

# glm
model <- glm(y ~ x, data = df, family = binomial)
tidy(model, exponentiate = TRUE)  # Odds ratios

# Robust standard errors
library(sandwich)
tidy(model, vcov = vcovHC)

Statistical Tests

# t-test
t_result <- t.test(x, y)
tidy(t_result)

# Correlation
cor_result <- cor.test(x, y)
tidy(cor_result)

# Chi-squared
chisq_result <- chisq.test(table(x, y))
tidy(chisq_result)

# ANOVA
aov_result <- aov(y ~ group, data = df)
tidy(aov_result)

Survival Analysis

library(survival)

# Cox model
cox_model <- coxph(Surv(time, status) ~ age + sex, data = df)
tidy(cox_model, exponentiate = TRUE)  # Hazard ratios
glance(cox_model)

# Kaplan-Meier
km_fit <- survfit(Surv(time, status) ~ group, data = df)
tidy(km_fit)

Machine Learning Models

# Random forest (ranger)
library(ranger)
rf_model <- ranger(y ~ ., data = df, importance = "impurity")
tidy(rf_model)  # Variable importance

# kmeans
km <- kmeans(df, centers = 3)
tidy(km)      # Cluster centers
glance(km)    # Clustering metrics
augment(km, df)  # Cluster assignments

Multiple Models

library(dplyr)
library(purrr)

# Fit models by group
models <- df %>%
  group_by(category) %>%
  nest() %>%
  mutate(
    model = map(data, ~ lm(y ~ x, data = .x)),
    tidied = map(model, tidy),
    glanced = map(model, glance)
  )

# Extract results
models %>%
  unnest(tidied)

models %>%
  unnest(glanced)