R Code-R Code for statistical computing.

Example

library(dplyr) # Filter data filtered_data <- filter(data, condition) # Arrange data arranged_data <- arrange(data, column) # Select columns selected_data <- select(data, column1, column2) # Mutate data mutated_data <- mutate(data, new_column = expression) # Summarize data summary_data <- summarize(data, summary_function(column))

Scenario

R Code offers functions for manipulating data, such as filtering, arranging, selecting, mutating, and summarizing data frames. For example, you can filter a data frame to include only rows that meet certain conditions, arrange the rows based on a specific column, select specific columns from a data frame, mutate existing columns or create new ones based on mathematical expressions, and summarize data using various summary functions.

Example

# Perform linear regression lm_model <- lm(dependent_variable ~ independent_variable, data = dataset) # Compute summary statistics summary_statistics <- summary(lm_model) # Generate diagnostic plots plot(lm_model)

Scenario

R Code provides functions for conducting statistical analysis, including regression analysis, hypothesis testing, and descriptive statistics. For instance, you can use R Code to fit linear regression models, compute summary statistics, and generate diagnostic plots to assess the model's assumptions and performance.

Example

library(ggplot2) # Create scatter plot scatter_plot <- ggplot(data, aes(x = x_variable, y = y_variable)) + geom_point() # Create bar chart bar_chart <- ggplot(data, aes(x = categorical_variable)) + geom_bar() # Create line plot line_plot <- ggplot(data, aes(x = x_variable, y = y_variable)) + geom_line()

Scenario

R Code offers powerful tools for data visualization, with the ggplot2 package being one of the most popular choices. You can create a wide range of plots, including scatter plots, bar charts, line plots, and more, with extensive customization options to tailor the visualizations to your specific needs.