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Best R Packages for Machine Learning
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Best R Packages for Machine Learning

Last Updated : 16 Apr, 2025
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Machine Learning is a subset of artificial intelligence that focuses on the development of computer software or programs that access data to learn from them and make predictions.

7-Best-R-Packages-for-Machine-Learning-1

R language is being used in building machine learning models due to its flexibility, efficient packages and the ability to perform deep learning models with integration to the cloud. Being an open-source language, it offers multiple packages. Following are some famous R packages widely used in industry.

1. data.table

data.table package is a enhanced version of data.frame package and is designed for high-performance. It is known for its memory efficiency and ability to perform complex data manipulations at high speed. Some key features of data.table are:

  • Fast file reading and writing
  • Scalable data aggregation with parallelism support
  • Feature-rich data reshaping
  • Simplified syntax for subsetting and merging data
R 
install.packages("data.table") library(data.table)  iris_dt <- as.data.table(iris)  result <- iris_dt[Species == "setosa" & Sepal.Length > 5][1:5] result

Output:

data_table
Data Table

2. Dplyr

Dplyr package is one of the most widely used data manipulation tools in R. It provides easy to implement and consistent set of functions to perform data transformations. The key functions in dplyr are:

  • select(): Choose columns by name
  • filter(): Subset rows based on conditions
  • arrange(): Sort rows by column values
  • mutate(): Add new variables

Select and Mutate Functions :

R 
install.packages("dplyr")  # Run only once library(dplyr)  data("mtcars")  cat("---- Select ----\n") selected <- dplyr::select(mtcars, mpg, cyl) head(selected) cat("\n------------------\n")  cat("---- Mutate ----\n") mutated <- dplyr::mutate(mtcars, power_to_weight = hp / wt) head(mutated) cat("\n------------------\n")

Output:

select-and-filter
Select and Mutate

Filter and Arrange Functions :

Python 
cat("---- Filter ----\n") filtered <- dplyr::filter(mtcars, cyl == 6) head(filtered) cat("\n------------------\n")  cat("---- Arrange ----\n") arranged <- dplyr::arrange(mtcars, desc(mpg)) head(arranged) cat("\n------------------\n")

Output:

Filter-
Filter and Arrange

3. ggplot2

ggplot2 is an open-source visualization package based on the Grammar of Graphics. It is widely regarded as one of the most famous and flexible visualization libraries in R. With ggplot2 users can create a wide range of static and interactive visualizations including:

  • Bar charts
  • Scatter plots
  • Line graphs
  • Histograms
  • Boxplots

The syntax is easy and visualizations are highly customizable making it go-to package for data visualization in R.

R 
install.packages("dplyr")  install.packages("ggplot2") library(dplyr)  library(ggplot2)  ggplot(data = mtcars,          aes(x = hp, y = mpg,             col = disp)) + geom_point()

Output:

Output

4. caret

caret package (Classification and Regression Training) provides a comprehensive framework for building machine learning models in R. It includes tools for:

  • Data splitting
  • Preprocessing
  • Feature selection
  • Model training
  • Model evaluation

caret supports numerous machine learning algorithms and is commonly used in industry due to its ease of use and flexibility.

R 
install.packages("e1071") install.packages("caTools") install.packages("caret")  library(e1071) library(caTools) library(caret)  data(iris)  split <- sample.split(iris, SplitRatio = 0.7) train_cl <- subset(iris, split == TRUE) test_cl <- subset(iris, split == FALSE)  train_scale <- scale(train_cl[, 1:4]) test_scale <- scale(test_cl[, 1:4])  set.seed(120) classifier_cl <- naiveBayes(Species ~ ., data = train_cl)  y_pred <- predict(classifier_cl, newdata = test_cl)  cm <- table(test_cl$Species, y_pred) print(classifier_cl) print(cm)

Output:

  • Model classifier_cl:
nb
Navie Bayers Model
  • Confusion Matrix
caret_cm
caret_cm

5. e1071

e1071 package is known for its implementation of various machine learning algorithms including support vector machines (SVM), clustering algorithms and K-Nearest Neighbors (KNN). It is widely used for classification, regression and clustering tasks.

R 
install.packages("e1071") install.packages("caTools") install.packages("class")  library(e1071) library(caTools) library(class)  data(iris)  split <- sample.split(iris, SplitRatio = 0.7) train_cl <- subset(iris, split == TRUE) test_cl <- subset(iris, split == FALSE)  train_scale <- scale(train_cl[, 1:4]) test_scale <- scale(test_cl[, 1:4])  classifier_knn <- knn(train = train_scale,                       test = test_scale,                       cl = train_cl$Species,                       k = 1)  cm <- table(test_cl$Species, classifier_knn) print(cm)  misClassError <- mean(classifier_knn != test_cl$Species) print(paste('Accuracy =', 1 - misClassError))

Outputs: 

KNN
KNN

6. XGBoost

XGBoost is a implementation of gradient boosting algorithms and is useful for large datasets. It is widely used in machine learning due to its performance and scalability. XGBoost works by bagging and boosting techniques to improve model accuracy.

R 
install.packages("data.table")  install.packages("dplyr")  install.packages("ggplot2")  install.packages("caret")  install.packages("xgboost")  install.packages("e1071")  install.packages("cowplot")   library(data.table)  library(dplyr)  library(ggplot2)  library(caret)  library(xgboost)  library(e1071)  library(cowplot)   test[, Item_Outlet_Sales := NA]   combi = rbind(train, test)   missing_index = which(is.na(combi$Item_Weight))  for(i in missing_index){    item = combi$Item_Identifier[i]    combi$Item_Weight[i] = mean(combi$Item_Weight[combi$Item_Identifier == item], na.rm = T)  }   zero_index = which(combi$Item_Visibility == 0)  for(i in zero_index){    item = combi$Item_Identifier[i]    combi$Item_Visibility[i] = mean(combi$Item_Visibility[combi$Item_Identifier == item], na.rm = T)  }   combi[, Outlet_Size_num := ifelse(Outlet_Size == "Small", 0, ifelse(Outlet_Size == "Medium", 1, 2))]  combi[, Outlet_Location_Type_num := ifelse(Outlet_Location_Type == "Tier 3", 0, ifelse(Outlet_Location_Type == "Tier 2", 1, 2))]  combi[, c("Outlet_Size", "Outlet_Location_Type") := NULL]   ohe_1 = dummyVars("~.", data = combi[, -c("Item_Identifier", "Outlet_Establishment_Year", "Item_Type")], fullRank = T)  ohe_df = data.table(predict(ohe_1, combi[, -c("Item_Identifier", "Outlet_Establishment_Year", "Item_Type")]))  combi = cbind(combi[, "Item_Identifier"], ohe_df)   skewness(combi$Item_Visibility)   skewness(combi$price_per_unit_wt)   combi[, Item_Visibility := log(Item_Visibility + 1)]    num_vars = which(sapply(combi, is.numeric))   num_vars_names = names(num_vars)  combi_numeric = combi[, setdiff(num_vars_names, "Item_Outlet_Sales"), with = F]  prep_num = preProcess(combi_numeric, method = c("center", "scale"))  combi_numeric_norm = predict(prep_num, combi_numeric)  combi[, setdiff(num_vars_names, "Item_Outlet_Sales") := NULL]   combi = cbind(combi, combi_numeric_norm)   train = combi[1:nrow(train)]  test = combi[(nrow(train) + 1):nrow(combi)]  test[, Item_Outlet_Sales := NULL]    param_list = list(    objective = "reg:linear",    eta = 0.01,    gamma = 1,    max_depth = 6,    subsample = 0.8,    colsample_bytree = 0.5  )   Dtrain = xgb.DMatrix(data = as.matrix(train[, -c("Item_Identifier", "Item_Outlet_Sales")]), label = train$Item_Outlet_Sales)  Dtest = xgb.DMatrix(data = as.matrix(test[, -c("Item_Identifier")]))   set.seed(112)  xgbcv = xgb.cv(params = param_list, data = Dtrain, nrounds = 1000, nfold = 5, print_every_n = 10, early_stopping_rounds = 30, maximize = F)   xgb_model = xgb.train(data = Dtrain, params = param_list, nrounds = 428)  xgb_model

Output:

xgb
XGBoost Model

7. randomForest

Random Forest in R Programming is an ensemble learning method that builds multiple decision trees and combines them to provide more accurate predictions. It is especially useful for classification and regression tasks. Each decision tree is trained on a subset of the data and predictions are made by aggregating the results of all trees.

R 
install.packages("caTools") install.packages("randomForest")  library(caTools)  library(randomForest)   data(iris)   split <- sample.split(iris, SplitRatio = 0.7)  train <- subset(iris, split == "TRUE")  test <- subset(iris, split == "FALSE")   set.seed(120)  classifier_RF = randomForest(x = train[-5], y = train$Species, ntree = 500)   classifier_RF   y_pred = predict(classifier_RF, newdata = test[-5])  cm = table(test[, 5], y_pred)  cm

Outputs:

  • Model classifier_RF:
Screenshot-2025-04-16-164956
Random Forest
  • Confusion Matrix:
rfcom
Confusion Matrix

R provides many packages that are widely used for data manipulation, visualization and machine learning. These packages discussed in this article are just few packages available in R for data scientists and researchers.


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Best R Packages for Machine Learning

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Article Tags :
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  • Machine Learning
  • R Language
  • AI-ML-DS Blogs
  • AI-ML-DS
  • R Machine-Learning
Practice Tags :
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