Quick answer: A data frame is R’s spreadsheet: rows of observations, columns of variables. Create one with df <- data.frame(name = c("Asha", "Rahul"), score = c(91, 84)), inspect it with head(df), str(df) and summary(df), filter rows with df[df$score > 85, ], and compute per-group summaries with aggregate(score ~ city, data = df, FUN = mean). This guide walks through the first session after installing R and RStudio.

Creating and inspecting a data frame

df <- data.frame(
  name  = c("Asha", "Rahul", "Meera", "Arjun"),
  city  = c("Chennai", "Delhi", "Chennai", "Mumbai"),
  score = c(91, 84, 78, 95)
)

head(df)      # first rows
str(df)       # structure: column types
summary(df)   # statistical overview
nrow(df)      # row count

The assignment arrow <- is R’s convention (equals works, but style guides prefer the arrow). str() is the command to run first, always: it reveals whether R read your numbers as numbers (num) or as text (chr) — the root of many mysterious downstream errors.

Everyday operations

df$score                     # one column
df[df$score > 85, ]          # rows where score > 85 (note the comma)
df[df$city == "Chennai", ]   # rows for one city
df[order(-df$score), ]       # sort descending

mean(df$score)               # simple stats
aggregate(score ~ city, data = df, FUN = mean)   # mean per city

The bracket notation df[rows, columns] is the key mental model — filter with a condition before the comma, select columns after it, and leave either side blank to mean “all”. The comma matters: df[df$score > 85] without it is a different (and confusing) operation.

The tidyverse upgrade path

install.packages("tidyverse")   # once
library(dplyr)

df %>%
  filter(score > 80) %>%
  group_by(city) %>%
  summarise(mean_score = mean(score))

The pipe %>% reads left-to-right like a sentence — filter, then group, then summarize — which is why dplyr became the standard for real R work. Learn base R brackets first (they are everywhere in documentation), then let dplyr make it pleasant.

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Official documentation

For the full reference behind this guide, read the R manual page for data frames. It is the source the details above are drawn from, and the first place to check when an option or attribute name changes.

Frequently asked questions

What is a data frame in R?

A data frame is R’s table structure: columns of equal length that can hold different types (numbers, text, factors). It is the standard structure for datasets and the output of read.csv.

What does the arrow <- mean in R?

It is the assignment operator: df <- data.frame(…) stores the result in df. It is the community-preferred style; = also works in most contexts.

How do I filter rows in R?

Base R: df[df$score > 85, ] – condition before the comma inside single brackets. With dplyr: df %>% filter(score > 85).

Why is my numeric column a character in R?

Usually stray text, commas as thousand separators or empty strings in the source. Check with str(), clean the values, and convert with as.numeric().

Should I learn base R or tidyverse first?

Learn base R brackets and core functions first – they appear everywhere – then add dplyr’s pipes for day-to-day analysis. Most curricula, including one-to-one training, teach them in that order.

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