Why R for golf analytics?

R combines data cleaning, visualization, modeling, and reporting in one ecosystem. With golf analytics with R, every step is scripted—your work is reproducible, auditable, and easy to refresh as new rounds and courses are added.

What you’ll learn

• Clean and prepare golf performance data in R.
• Visualize trends and compare player stats across rounds and seasons.
• Analyze shots, rounds, and course difficulty.
• Apply statistical models to identify strengths and weaknesses.
• Use clustering and regression to forecast outcomes and strategy.
• Build performance dashboards to monitor progress.

End-to-end workflow

1. Ingest: Load shot-, round-, and course-level data into tidy data frames.
2. Clean: Standardize player IDs, timestamps, holes, pars, and surface/course attributes.
3. Explore: Visualize distributions (strokes gained, GIR, fairways hit, putts per GIR).
4. Model: Build interpretable baselines; extend to richer models if needed.
5. Report: Deliver reproducible summaries and dashboards that update after each round.

library(dplyr)

rounds <- read.csv("rounds.csv")
shots  <- read.csv("shots.csv")

shots_clean <- shots %>%
  mutate(
    hole = as.integer(hole),
    putt = shot_type == "Putt"
  ) %>%
  distinct(player_id, round_id, shot_id, .keep_all = TRUE)

Statistical models & forecasting

Start simple and explainable (linear/regression trees), then test improvements. In golf data analysis in R you can forecast scoring, model strokes gained by category, and identify course-strategy patterns.

library(tidymodels)

set.seed(42)
split <- initial_split(rounds, prop=.8, strata = score)
train <- training(split); test <- testing(split)

rec <- recipe(score ~ gir + fairways_hit + putts + scrambling + sg_tee + sg_appr + sg_putt, data=train) %>%
  step_normalize(all_numeric_predictors())

mod <- linear_reg() %>% set_engine("lm")
wf  <- workflow() %>% add_recipe(rec) %>% add_model(mod)

fit <- fit(wf, train)
pred <- predict(fit, test) %>% bind_cols(test)
metrics(pred, truth=score, estimate=.pred)

Dashboards & reporting

Turn round summaries and model outputs into simple dashboards. Keep one metric per chart, add interpretation notes, and close with recommended actions. That’s how R programming for golf translates directly into lower scores and smarter decisions on the course.