Lecture 3 — R & Tidyverse package Guide
Touseef Hameed
2025-08-28
1 How to use this file (for students)
This is a self-contained lesson. Read the note under each heading, copy and then run the chunk.
2 0) R as a calculator
R does everyday math. Angles in trig functions are radians (π radians = 180°).
1 / 200 * 30 # divide, then multiply
## [1] 0.15
(59 + 73 + 2) / 3 # average of three numbers
## [1] 44.66667
sin(pi / 2) # sine of 90 degrees (pi/2 radians)
## [1] 1Note: pi is built-in (~3.14159). Computers
approximate decimals, so answers can be very close to (not exactly) the
math value.
3 0A) Environment: list / remove / clear objects + mini plot
Your Environment is R’s working memory. Learn to list, delete, reset — then make a tiny plot.
rm(list = ls()) # start clean: remove everything from the Global Environment
# Create a few objects
x <- 2 + 2 # preferred assignment operator in scripts
y = 2 + 2 # also valid; less common in scripts
z <- 5
ls() # list object names (expect "x","y","z")
## [1] "x" "y" "z"
# Delete ONE object
rm(z)
exists("z") # should print FALSE
## [1] FALSE
ls() # only x and y remain
## [1] "x" "y"
# Delete SEVERAL at once
rm(x, y)
ls() # empty -> character(0)
## character(0)
# Safer bulk removal (won't error if a name is missing)
# rm(list = c("x","y","z"))
# Clear ALL, then keep only some
a <- 1; b <- 2; c <- 3
rm(list = ls()); ls()
## character(0)
a <- 1; b <- 2; c <- 3
rm(list = setdiff(ls(), c("a","c"))) # remove all EXCEPT 'a' and 'c'
ls() # should show 'a' and 'c'
## [1] "a" "c"
# Tiny arithmetic + two mini plots (watch the Plots tab)
a <- 10; b <- 20; c <- a + b; c
## [1] 30
plot(1:5, 1:5)
4 1) Objects and assignment
Objects are named boxes that store values. Use <- to
assign.
5 2) Vectors
A vector is a one-dimensional collection of the same type. Most R functions work element-by-element.
7 4) Packages — install and load
Packages add functions. Install once; load every session.
8 5) Core tidyverse (quick reference)
- ggplot2 — plots
- dplyr — filter/select/mutate/summarise
- tidyr — reshape (pivot_longer/pivot_wider)
- readr — read flat files (CSV/TSV)
- tibble — modern data frame
- stringr / forcats — text & categories
- purrr — map functions over lists/vectors
- lubridate — dates/times
9 6) Importing data — web → save to folder → read local copy
Start from the web (fastest for class), then save a copy for later, then read the saved file. Optional: set a working directory for your lab folder.
# Optional: working directory (edit path for your machine)
# setwd("C:/Users/touseefh/OneDrive - Høgskolen i Innlandet/labs")
# getwd() # check current working directory
# 1) Read directly from the web
url_students <- "https://raw.githubusercontent.com/hadley/r4ds/main/data/students.csv"
students_web <- readr::read_csv(url_students)
head(students_web) # quick check
## # A tibble: 6 × 5
## `Student ID` `Full Name` favourite.food mealPlan AGE
## <dbl> <chr> <chr> <chr> <chr>
## 1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
## 2 2 Barclay Lynn French fries Lunch only 5
## 3 3 Jayendra Lyne N/A Breakfast and lunch 7
## 4 4 Leon Rossini Anchovies Lunch only <NA>
## 5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five
## 6 6 Güvenç Attila Ice cream Lunch only 6
# 2) Save a copy to your OneDrive "labs" folder
labs_dir <- "C:/Users/touseefh/OneDrive - Høgskolen i Innlandet/labs"
labs_csv <- file.path(labs_dir, "students.csv")
# create the folder if needed (harmless if it already exists)
if (!dir.exists(labs_dir)) dir.create(labs_dir, recursive = TRUE)
# 2A) Save the data already in R to disk
readr::write_csv(students_web, labs_csv)
# 2B) OR download straight to disk (alternative; leave commented for class)
# utils::download.file(url_students, destfile = labs_csv, mode = "wb")
file.exists(labs_csv) # should return TRUE
## [1] TRUE
# 3) Read the SAVED LOCAL FILE (from your OneDrive folder)
students_local <- readr::read_csv(labs_csv)
head(students_local)
## # A tibble: 6 × 5
## `Student ID` `Full Name` favourite.food mealPlan AGE
## <dbl> <chr> <chr> <chr> <chr>
## 1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
## 2 2 Barclay Lynn French fries Lunch only 5
## 3 3 Jayendra Lyne N/A Breakfast and lunch 7
## 4 4 Leon Rossini Anchovies Lunch only <NA>
## 5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five
## 6 6 Güvenç Attila Ice cream Lunch only 610 7) Quick data checks
Always glance at the shape, names, and a few rows before analysis.
head(students_web) # first 6 rows
## # A tibble: 6 × 5
## `Student ID` `Full Name` favourite.food mealPlan AGE
## <dbl> <chr> <chr> <chr> <chr>
## 1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
## 2 2 Barclay Lynn French fries Lunch only 5
## 3 3 Jayendra Lyne N/A Breakfast and lunch 7
## 4 4 Leon Rossini Anchovies Lunch only <NA>
## 5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five
## 6 6 Güvenç Attila Ice cream Lunch only 6
nrow(students_web); ncol(students_web)
## [1] 6
## [1] 5
names(students_web)
## [1] "Student ID" "Full Name" "favourite.food" "mealPlan"
## [5] "AGE"
# str(students_web) # structure (types & preview)
# dplyr::glimpse(students_web) # compact structure view
ls() # objects in your Environment
## [1] "a" "b"
## [3] "c" "CamelCaseExample"
## [5] "i_use_snake_case" "labs_csv"
## [7] "labs_dir" "primes"
## [9] "r_rocks" "some.people.use.periods"
## [11] "students_local" "students_web"
## [13] "url_students" "x"
# View(students_web) # spreadsheet view (use in RStudio)11 8) Cleaning: treat “N/A” and blanks as missing on read
Convert common missing markers to NA during import to
avoid surprises later.
students_clean <- readr::read_csv(
"https://raw.githubusercontent.com/hadley/r4ds/main/data/students.csv",
na = c("N/A", "")
)
# Quick missingness check
sum(is.na(students_clean)) # total missing cells
## [1] 2
colSums(is.na(students_clean)) # missing cells by column
## Student ID Full Name favourite.food mealPlan AGE
## 0 0 1 0 112 9) Fixing data types (character → numeric)
Example: if AGE contains the word “five”, change it to 5 and convert the column to numeric. Guard with a name check so the knit won’t fail if AGE is absent.
if ("AGE" %in% names(students_clean)) {
students_clean$AGE <- gsub("five", "5", students_clean$AGE) # replace text with digits
students_clean$AGE <- as.numeric(students_clean$AGE) # convert to numeric
summary(students_clean$AGE) # sanity check
} else {
"AGE column not found; skipping type conversion."
}
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.0 5.0 5.0 5.4 6.0 7.0 113 10) Multiple files at once (tag rows with source)
You can read several files and tag each row with its source. Keep this as a template.
# Remote example (uncomment only if the URLs point to real CSVs)
sales_files <- c(
"https://pos.it/r4ds-01-sales",
"https://pos.it/r4ds-02-sales",
"https://pos.it/r4ds-03-sales"
)
sales_all <- readr::read_csv(sales_files, id = "file") # adds a 'file' column with the source
head(sales_all)
# Local folder example: read all CSVs in a folder
# files <- list.files("C:/path/to/folder", pattern = "\\.csv$", full.names = TRUE)
# sales_all <- purrr::map_dfr(files, readr::read_csv, .id = "source_file")
# head(sales_all)14 11) Other file formats (readr + friends)
Use the reader that matches your data.
# readr::read_csv2("data_semicolon.csv") # semicolon ;
# readr::read_tsv("data.tsv") # tab \t
# readr::read_delim("data_pipe.txt", delim="|") # any delimiter
# readr::read_fwf("data_fwf.txt",
# col_positions = readr::fwf_widths(c(3,5), col_names = c("col1","col2")))
# readr::read_table("data.txt") # whitespace
# readr::read_log("access.log") # web server logs
# Non-readr formats (install as needed)
# install.packages("readxl"); library(readxl)
# excel_df <- readxl::read_excel("data.xlsx", sheet = 1)
# install.packages("jsonlite"); library(jsonlite)
# json_df <- jsonlite::fromJSON("data.json", flatten = TRUE)
# install.packages("arrow"); library(arrow)
# parquet_df <- arrow::read_parquet("data.parquet")
6 3) Comments and naming rules
Use
#for notes. Good names help you later. R is case-sensitive (age≠AGE). Valid names start with a letter; then letters, numbers,_, or..