As you begin analyzing biological data with R, one of your first tasks is to bring external datasets into your working environment. Most biological data comes in tabular formats, with CSV (Comma Separated Values) and TSV (Tab Separated Values) files being the most common. These formats are widely used because they are simple, human-readable, and compatible with many tools. Importing data correctly is crucial: any errors or misinterpretations at this stage can affect your entire analysis. Whether you are working with gene expression matrices, sample metadata, or protein abundance tables, knowing how to reliably import these files is foundational for any research workflow.

# Import a gene expression dataset from a CSV file
gene_data <- read.csv("gene_expression.csv")

The import process in R typically involves using functions like read.csv(), which reads a CSV file and loads its content into a data frame. A data frame is a structured table where each column represents a variable (such as gene names, sample IDs, or expression levels), and each row represents an observation or sample. The CSV file should have column headers in its first row, and each subsequent row contains the data values. After running read.csv("gene_expression.csv"), you will have a data frame named gene_data in your R environment, ready for further exploration and analysis.

# Explore the imported gene expression data
head(gene_data)
summary(gene_data)
# Check for missing values
any(is.na(gene_data))

Once your data is imported, you need to explore and inspect it to ensure it was read correctly and is suitable for analysis. Using functions like head() lets you quickly view the first few rows of your data frame, making it easy to spot formatting issues or unexpected values. The summary() function provides statistical summaries for each column, such as minimum, maximum, mean, and quartiles—helpful for spotting outliers or unusual distributions. Checking for missing values with is.na() is especially important in biological datasets, where incomplete measurements can bias results or cause errors in downstream analyses. Careful data exploration at this stage helps you catch potential problems early and ensures the quality and reliability of your biological research.