20  Data Manipulation and Reshaping

Once a dataset has been imported and cleaned (see Data Import and Cleaning), the next step is usually to reshape it — selecting the columns that matter, filtering to the rows of interest, and switching between wide and long layouts depending on what a chart or model needs. This topic continues with the clean farm-records data frame built in the previous topic.

20.1 Data Manipulation with dplyr

The dplyr package provides functions for filtering, selecting, modifying, and restructuring data using a small, consistent set of verbs — select(), filter(), arrange(), and others — that chain together with the pipe operator %>%.

20.1.1 Install the dplyr Package

install.packages("dplyr")

20.1.2 Selecting Specific Columns

Try here

20.1.3 Removing Columns

Try here

20.1.4 Filtering Data

Try here

20.1.5 Sorting Data

Try here

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20.2 Reshaping Data with tidyr

tidyr reshapes data between wide format (one column per variable) and long format (one row per observation) — long format is often what plotting and modeling functions expect.

20.2.1 Converting Wide Data to Long Format

Try here

20.2.2 Converting Long Data to Wide Format

Try here

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20.3 Exporting Data from R

20.3.1 Export the Data as a CSV File

The write.csv() function exports a data frame to a Comma-Separated Values (CSV) file, making it easy to save, share, and open the data in other tools like Excel, Python, or SQL.

write.csv(data, "folder path/filename.csv") — provide a path to save the file in a specific folder.

Try here

20.3.2 Export the Data as an Excel File

writexl: Writing Data to Excel Files

The writexl package provides an easy way to export data from R into an Excel file without requiring external dependencies.

Key features:

• Writes .xlsx files quickly.
• Preserves column types and formats.

Install the writexl Package

install writexl package

install.packages("writexl")

Export the cleaned data file as an .xlsx file:

library(writexl)
# Write data to an Excel file
write_xlsx(clean, "clean_farm_data.xlsx")

20.3.3 Check the List of Files in the Directory (or Project)

check the list of files in the directory

list.files()

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Summary

Concept	Description
Data Manipulation with dplyr
Data Manipulation with `dplyr`	dplyr provides a consistent set of verbs — select(), filter(), arrange() — for filtering, selecting, modifying, and restructuring data
Selecting Specific Columns	select() keeps only the named columns
Removing Columns	select(-column) drops a named column
Filtering Data	filter() keeps rows that meet a logical condition
Sorting Data	arrange(desc(column)) sorts rows by a column, highest first
Reshaping Data with tidyr
Reshaping Data with `tidyr`	tidyr reshapes data between wide (one column per variable) and long (one row per observation) formats
Wide to Long Format	pivot_longer() collapses several columns into key-value pairs
Long to Wide Format	pivot_wider() spreads key-value pairs back into separate columns
Exporting Data from R
Exporting Data from R	write.csv() and write_xlsx() save a data frame to a file others can open
Export as CSV	write.csv(data, "file.csv") saves a CSV file
Export as Excel	write_xlsx(data, "file.xlsx") saves an Excel file