Data Structure: Data Frame

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A list of common functions in R for creating, manipulating, and analyzing data frames:

1. Creating Data Frames

• data.frame() – Create a data frame by combining vectors of equal length.
• as.data.frame() – Convert an object to a data frame.
• tibble::tibble() – Create a modern data frame (tibble) with better printing and handling of large datasets (from the tibble package).

2. Accessing and Modifying Data Frames

• $ – Access a column in a data frame by name (e.g., df$column).
• [] – Subset rows, columns, or individual elements in a data frame.
• names() – Get or set column names of a data frame.
• rownames() – Get or set row names.
• colnames() – Get or set column names.

3. Inspecting Data Frames

• str() – Display the structure of a data frame.
• summary() – Get summary statistics for each column.
• head() / tail() – Display the first or last few rows.
• dim() – Get the dimensions (number of rows and columns).
• nrow() – Get the number of rows.
• ncol() – Get the number of columns.
• View() – Open the data frame in a spreadsheet-like viewer (in RStudio).

4. Data Manipulation Functions

• subset() – Subset rows based on conditions.
• transform() – Add or modify columns in a data frame.
• merge() – Merge two data frames by common columns or row names.
• cbind() – Add columns to a data frame.
• rbind() – Add rows to a data frame.

5. dplyr Package Functions

• filter() – Subset rows based on conditions.
• select() – Select specific columns.
• mutate() – Add new columns or modify existing columns.
• arrange() – Sort data frame rows by one or more columns.
• group_by() – Group data by one or more columns (usually used with summarize()).
• summarize() / summarise() – Create summary statistics for grouped data.
• distinct() – Remove duplicate rows.
• rename() – Rename columns in a data frame.

6. Applying Functions

• apply() – Apply a function to rows or columns of a data frame.
• lapply() – Apply a function to each column and return a list.
• sapply() – Apply a function to each column and return a simplified result.

7. Handling Missing Data

• is.na() – Check for missing values in a data frame.
• na.omit() – Remove rows with NA values.
• na.fill() (from the zoo package) – Fill in missing values.

8. Reshaping Data

• reshape() – Reshape data from wide to long format or vice versa.
• reshape2::melt() and reshape2::dcast() – Reshape data using the melt and dcast functions (from the reshape2 package).
• tidyr::pivot_longer() / pivot_wider() – Reshape data from wide to long and vice versa (from the tidyr package).

9. Combining Data Frames

• bind_rows() – Combine data frames by rows (from dplyr).
• bind_cols() – Combine data frames by columns (from dplyr).
• full_join(), left_join(), right_join(), inner_join() – Join data frames based on common columns (from dplyr).

10. Other Useful Functions

• table() – Create a frequency table for one or more columns.
• duplicated() – Identify duplicate rows.
• order() – Get the order of rows based on one or more columns.
• with() – Apply an expression within the context of a data frame.
• attach() / detach() – Temporarily add a data frame to the R search path (use with caution).

These functions cover a range of operations from basic data frame creation and inspection to advanced manipulation and reshaping, providing a robust toolkit for data analysis in R.