📎 This article includes 2 downloadable practice files ↓
📘 R Beginner Course · Lesson 3 of 6
“`r
library(readr)
sales <- read_csv("sales.csv")
head(sales); nrow(sales); summary(sales$qty)
library(readxl)
x <- read_excel("file.xlsx", sheet = "Data")
write_csv(sales, "sales_copy.csv")
```
read_csv shows the type it guessed for each column; fix wrong guesses with col_types. Missing values are NA; most functions need na.rm = TRUE to ignore them (e.g. mean(x, na.rm = TRUE)).
💡 For dd-mm-yyyy dates use lubridate::dmy(); for yyyy-mm-dd use ymd().
Code for this course follows standard, current syntax; we test JavaScript and PowerShell scripts before publishing, and R/Java/Rust examples are kept to core language features. Tell us if anything fails on your setup.
Practice
Read sales.csv and check its structure.
← R Lesson 2: Vectors and Data Frames R Lesson 4: dplyr — filter, mutate, summarise →
📎 Practice files for this article Free to use for learning. Files with macros (.bas) are plain text — import them with Alt +F11 → File → Import File, and always test on a copy.
Written by Atul Vij Atul (AJ) builds office-automation and local-first AI tools as Atulya AI, and works with Excel, SAP and Oracle EPM every day. He writes about the shortcuts, macros and automations that actually save time — plus the occasional wacky fact. Videos on YouTube: Lazy Automator AI (@LazyAutomatorAI).
More posts by Atul → ← Previous R Lesson 2: Vectors and Data FramesNext → R Lesson 4: dplyr — filter, mutate, summarise
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