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3. Data Import & Export

In real-world data analysis with R, most of your time is spent getting data in and out of R efficiently and correctly. R has excellent support for almost every common data format used in statistics, research, business, and academia.

3.1 Reading CSV, Excel, SPSS, SAS, Stata & JSON Files

CSV (Comma-Separated Values) – Most common format

R

# Basic read df <- read.csv("sales_data.csv") # Recommended modern way (faster, better handling) library(readr) df <- read_csv("sales_data.csv", show_col_types = FALSE) # Useful options df <- read_csv("data.csv", col_types = cols( date = col_date("%Y-%m-%d"), price = col_double(), category = col_factor() ), na = c("", "NA", "missing"), skip = 2) # skip first 2 rows

Excel (.xlsx / .xls)

R

# Recommended package library(readxl) df <- read_excel("report.xlsx", sheet = "Sales", skip = 1) # or read specific range df <- read_excel("report.xlsx", range = "B2:F100")

SPSS (.sav), SAS (.sas7bdat), Stata (.dta) – Very common in research

R

library(haven) # SPSS df_spss <- read_sav("survey.sav") # SAS df_sas <- read_sas("clinical.sas7bdat") # Stata df_stata <- read_dta("economics.dta") # All preserve value labels, formats, etc.

JSON (JavaScript Object Notation)

R

library(jsonlite) df_json <- fromJSON("data.json", flatten = TRUE) # or read from URL df_api <- fromJSON("https://api.example.com/data")

Tip (2026 best practice): Always use readr::read_csv() or readxl::read_excel() instead of base R functions — they are 5–10× faster and handle types better.

3.2 Working with Databases (SQL, BigQuery, etc.)

Connecting to SQL databases

R

# SQLite (local) library(DBI) library(RSQLite) con <- dbConnect(RSQLite::SQLite(), "mydatabase.db") df <- dbGetQuery(con, "SELECT * FROM customers WHERE age > 30") dbDisconnect(con)

PostgreSQL / MySQL / MariaDB

R

library(RPostgres) # or RMariaDB con <- dbConnect(RPostgres::Postgres(), dbname = "sales_db", host = "localhost", port = 5432, user = "user", password = Sys.getenv("DB_PASSWORD")) df <- dbGetQuery(con, "SELECT * FROM orders LIMIT 1000")

Google BigQuery (cloud)

R

library(bigrquery) # Authenticate once bq_auth() project <- "my-project-id" dataset <- "sales_data" table <- "2025_transactions" df <- bq_project_query(project, query = "SELECT * FROM `sales_data.2025_transactions` LIMIT 1000") %>% bq_table_download()

Best practice:

• Never hardcode passwords → use Sys.getenv() or .Renviron file

• Use DBI + backend package (standard interface)

3.3 Exporting Data – CSV, Excel, RDS, RData

CSV

R

write.csv(df, "cleaned_data.csv", row.names = FALSE) # Faster & better: write_csv(df, "cleaned_data.csv")

Excel

R

library(openxlsx) write.xlsx(df, "report.xlsx", sheetName = "Analysis", rowNames = FALSE)

RDS (single R object – recommended for saving models/data frames)

R

saveRDS(df, "processed_data.rds") df_loaded <- readRDS("processed_data.rds")

RData / .rda (multiple objects)

R

save(df, model, file = "session_data.rda") load("session_data.rda")

Quick rule (2026):

• Use CSV for sharing with non-R users

• Use RDS for saving R objects (preserves types, factors, dates)

• Use RData when saving multiple objects together

3.4 Handling Large Datasets Efficiently

R can struggle with very large data (> RAM size). Modern solutions (2026) make it possible to work with gigabytes easily.

data.table – Fast alternative to data.frame

R

library(data.table) dt <- fread("very_large_file.csv") # much faster than read.csv # Syntax is similar but faster dt[age > 30, .(mean_salary = mean(salary)), by = city]

arrow + duckdb – Work with data larger than RAM

R

library(arrow) library(duckdb) # Read Parquet (columnar, compressed format) df <- read_parquet("large_data.parquet") # Use duckdb for SQL on large files without loading fully con <- dbConnect(duckdb()) df <- dbGetQuery(con, "SELECT * FROM 'large_data.parquet' WHERE sales > 100000 LIMIT 1000") dbDisconnect(con)

Best practices for big data in R (2026)

• Use Parquet format instead of CSV (faster, smaller)

• Prefer data.table or Polars (R package) for in-memory speed

• Use duckdb or arrow for querying files larger than memory

• Avoid read.csv() on large files → use fread() or read_csv()

• Sample data first for EDA: df_sample <- head(df, 10000)

Mini Summary Project – Import, Clean & Export Pipeline

R

library(tidyverse) library(haven) # 1. Import SPSS file df_raw <- read_sav("survey_data.sav") # 2. Clean & transform df_clean <- df_raw %>% clean_names() %>% filter(age >= 18 & age <= 65) %>% mutate(income_k = income / 1000, income_log = log1p(income)) %>% select(id, age, gender, income_k, income_log, everything()) # 3. Quick summary skim(df_clean) # 4. Export write_csv(df_clean, "cleaned_survey.csv") saveRDS(df_clean, "cleaned_survey.rds") write_parquet(df_clean, "cleaned_survey.parquet")

This completes the full Data Import & Export section — now you can confidently bring any kind of data into R, clean it, and save it efficiently!

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