x̄ - > Popular and useful R code

Popular and useful R code snippets that have been widely used in the past. Please note that trends in programming languages can change over time, so it's always a good idea to stay up to date with the latest developments and trends in the R programming community.

1. Data Manipulation with dplyr:

   ```R

   library(dplyr)

   # Filter rows based on a condition

   filtered_data <- filter(data, condition)

   # Select specific columns

   selected_data <- select(data, column1, column2)

   # Arrange rows based on a variable

   arranged_data <- arrange(data, variable)

   # Group by a variable and summarize data

   summarized_data <- data %>% group_by(variable) %>% summarise(avg = mean(value))

   # Join two data frames

   merged_data <- inner_join(data1, data2, by = "key_column")

   ```

2. Data Visualization with ggplot2:

   ```R

   library(ggplot2)

   # Create a scatter plot

   ggplot(data, aes(x = x_variable, y = y_variable)) +

     geom_point()

   # Create a bar plot

   ggplot(data, aes(x = x_variable, y = y_variable)) +

     geom_bar(stat = "identity")

    public static void main(String[] args) {

   # Create a line plot

   ggplot(data, aes(x = x_variable, y = y_variable)) +

     geom_line()

   # Add color or fill based on a variable

   ggplot(data, aes(x = x_variable, y = y_variable, color = variable)) +

     geom_point()

   # Facet the plot based on a variable

   ggplot(data, aes(x = x_variable, y = y_variable)) +

     geom_point() +

     facet_wrap(~ variable)

   ```

3. Machine Learning with caret:

   ```R

   library(caret)

   # Split data into training and testing sets

   train_test_split <- createDataPartition(y = data$target_variable, p = 0.7, list = FALSE)

   training_data <- data[train_test_split, ]

   testing_data <- data[-train_test_split, ]

   # Train a linear regression model

   lm_model <- train(target_variable ~ ., data = training_data, method = "lm")

   # Train a random forest model

   rf_model <- train(target_variable ~ ., data = training_data, method = "rf")

   # Make predictions on new data

   lm_predictions <- predict(lm_model, newdata = testing_data)

   rf_predictions <- predict(rf_model, newdata = testing_data)

   # Evaluate model performance

   lm_rmse <- RMSE(lm_predictions, testing_data$target_variable)

   rf_rmse <- RMSE(rf_predictions, testing_data$target_variable)

   ```

These are just a few examples of trending R code snippets, and there are many more techniques and libraries available in the R ecosystem. It's always a good idea to explore and stay updated with the latest developments in the R programming community.