Getting Started in R — Part II: Reading Data and Reproducible Reports

Before you begin, take a few minutes to check what you already know and how confident you feel. You will see the same questions again at the end of the module — this helps both you and us measure what you have learned. Click an option for every question and confidence rating, then click Next to continue.

In Part I you built values and vectors by hand with <- and c(). Real clinical data does not arrive one value at a time — it lives in a file. This part takes you from a file on disk to a loaded, checked dataset, and then to a report that computes every number in it from that raw data instead of copying it by hand.

This part of the module covers three foundations you will reach for in every analysis that follows:

• Reading data — loading a .csv file into R with read_csv() and giving the result a name you can work with.
• Checking the load — using the tibble printout, glimpse(), and head() to confirm every column came in as the type you expected.
• Reproducible reports — writing a Quarto document that mixes your prose with runnable code, so one render rebuilds the whole report from the data.

By the end of this part you will be able to read a clinical dataset into R with read_csv(), confirm it loaded correctly with glimpse() and head(), pull a single column out as a vector with $, and explain why a Quarto report is reproducible when a copy-and-pasted number is not.

Try every snippet in the R Scratchpad on the right — the dataset diabetes_clinic.csv is already loaded and waiting for you.

Clinical data usually lives in a .csv file — a plain-text table with one row per patient. You read one into R with read_csv() from the readr package.

A package is a bundle of extra functions; you switch it on for the session with library(). readr is part of the tidyverse, the family of packages this course uses throughout.

The file diabetes_clinic.csv is already in your workspace. Read it in and give it a name:

library(readr)
clinic <- read_csv("diabetes_clinic.csv")
clinic

What you get back is a tibble — the tidyverse's tidy version of a table. A tibble prints cleanly: it shows the first ten rows, and under each column name it tells you the column's type (dbl for a number, chr for text, and so on), so you can spot at a glance whether a column came in as you expected.

Two functions help you confirm the data loaded correctly. glimpse() lists every column down the page with its type and first few values; head() shows the first six rows across.

library(readr)
library(dplyr)
clinic <- read_csv("diabetes_clinic.csv")
glimpse(clinic)
head(clinic)

To pull one column out as a vector, write the dataset name, a dollar sign, and the column name: clinic$age is every patient's age.

library(readr)
clinic <- read_csv("diabetes_clinic.csv")
clinic$age
mean(clinic$age)

Excel files work almost the same way: read_excel("file.xlsx") from the readxl package reads a worksheet into a tibble. Reach for read_csv() when you can, though — a plain .csv is readable on any machine and never hides formatting surprises.

There is one more piece you will use: Quarto. A Quarto document (a .qmd file) mixes your written explanation with runnable R code in the same file. When you render it, R runs every code chunk fresh and drops the results — numbers, tables, plots — straight into a finished HTML, PDF, or Word report.

This matters because it makes your analysis reproducible: the report is generated from the raw data and the code in one click, so no number is ever copied by hand. You will write Quarto reports in RStudio for your labs; the in-browser scratchpad here is just for trying things out.

Before you start on a worked example, let’s try and familiarize yourself with the syntax first.

This Parsons problem gives you the right lines in the wrong order, plus a few lines that do not belong. Drag the correct lines into order and leave the wrong ones in the bank.

The worked example below fades the support as you go: first you study a full solution, then you fill the gaps, then you solve a fresh one on your own.

glucose <- c(5.4, 7.1, 6.8, 9.2, 5.9, 8.3, 6.1, 7.7)
length(glucose)
mean(glucose)

You have reached the end of the module. Try the same questions again — your answers here, paired with your pre-test answers, are how we measure what the module taught you. Answer every question and confidence rating, then click Submit and see results to view your score.

Here is how your post-test answers compare with your pre-test answers. The pre/post pairing is the most reliable way to see what this module actually taught you.