Importing Spreadsheet-Like Data

RAdelaide 2025

Dr Stevie Pederson

Black Ochre Data Labs
The Kids Research Institute Australia

July 8, 2025

Importing Data

Now that we’ve spent some time exploring the penguins dataset, how would we import this into R from a spreadsheet?

Data In R

• Working with data in R is very different to Excel
• Can have complicated structures or be simple (e.g. x <- 1:5)
• Spreadsheet-like data is very common
• Is known as a data.frame
  • A common variant is known as a tibble
  • These were originally called tbl_df objects referring to SQL tables
  • Is just a data.frame with some convenience features

Data In R

• We import the data as an R object
  • All analysis is performed on the R object
  • Almost never modify the source file
• Once data is imported the source file is no longer involved

Common File Types

• Probably worth noting the many European countries use the comma as a decimal point
• Can also separate using a semi-colon
• Windows hiding file extensions as a fundamentally dumb idea

• Excel files (xls/xlsx) have one or more spreadsheets
  • Can import one sheet at a time using library(readxl)
• Comma-separated Values (.csv)
  • Plain text file
  • Column-style data with commas delimiting the columns
• Resemble a single spreadsheet but:
  • No formatting
  • No formulas \(\implies\) only values
  • No graphs/figures

Common File Types

• Tab-separated Values (.tsv)
  • Like a .csv but with tabs separating columns
• Sometimes see files saved as .txt
  • Can be anything \(\implies\) need to check

Importing Data

When importing data into R:

• Cell formatting will be ignored by R
• Plots will also be ignored
• Blank rows/columns are not fatal, just annoying
• Mixtures of numbers and text in a column
  • data.frame columns contain all the same type of value
• Deleted cells are sometimes imported as blank rows/columns
• Comma-separated or tab-separated files are often favoured for R
  • i.e. plain text, or just the data

• Can also import data from Stata, SPSS or other statistical languages

Other Common Excel Issues

If we’re not careful:

1. Excel thinks everything is a date:
   • Septin genes are now officially named SEPTIN2 etc (not SEPT2)
   • Fractions are also not dates…
2. Excel will remove leading zeroes (e.g. phone numbers, catalog ids)
3. No record of any steps we’ve performed by clicking on something

• Very common sources of broken data \(\implies\) may need fixing
• ’000s of publications with dates for gene names in results

Mention my former collaborator who would often have completely different results in the ones I’d send him - To sort by p-value, he’d select the p-value column & sort (just that column) - There was no record of this. Only discovered by sitting down with him

Preparation

1. File > New File > R Script (Or Ctrl+Shift+N)
2. Save as ImportPenguins.R

Preparation

1. Download the file data.zip from the workshop homepage
2. Place in your directory RAdelaide25
3. Extract to here which should create a folder named data

Make sure your files are in data not in data/data

4. This should contain all files for the workshop
5. Navigate to the data directory using the Files pane

(You should see penguins.csv in data)

Import Using the GUI

Importing Data

1. Preview the file penguins.csv by clicking on it (View File)
   • Try in Excel if you prefer, but DO NOT(!!!) save anything from Excel

• This is a common dataset as a simple .csv file
• This type of data is very easy to manage in R
  • Plain text with comma delimiters
  • Simple column structure with column names
  • No blank rows at the top or separating sub-tables
  • No blank columns
  • No rownames

Using the GUI To Load Data

Click on penguins.csv, choose Import Dataset then stop! 🛑

(Click Update if you don’t see this)

The Preview Window

We have a preview of the data

• This is another preview of the data before we import it
• We can see all the expected columns
• Notice the types of values are provided under the column names

The Preview Window

We also have a preview of the code we’re about to execute

The Preview Window

1. Select and copy all the code in the Code Preview Box
   • We’ll paste this somewhere in a minute…

2. Click Import

3. Magic happens!!!
   • Ignore the coloured text. This is just ‘helpful information’

Now paste the copied code at the top of your script

What just happened?

The code we copied has 3 lines:

library(readr)
penguins <- read_csv("data/penguins.csv")
View(penguins)

• library(readr) loads the package readr
  • Packages are collections (i.e. libraries) of related functions
  • All readr functions are about importing data
• readr contains the function read_csv()
• read_csv() tells R what to do with a csv file

What just happened?

The code we copied has 3 lines:

library(readr)
penguins <- read_csv("data/penguins.csv")
View(penguins)

• This line actually loads the data into your R Environment
• It creates an object named penguins by using the file name (penguins.csv)
• Can change this name if we wish

What just happened?

The code we copied has 3 lines:

library(readr)
penguins <- read_csv("data/penguins.csv")
View(penguins)

• Opens a preview in a familiar Excel-like format
  • I personally never use this

Close the preview by clicking the cross

What just happened?

• We have just loaded data using the default settings of read_csv()
• The object penguins is now in our R Environment
  • The original file remains on our HDD without modification!!!
• The code is saved in our script
  \(\implies\) we don’t need the GUI for this operation again!

Let’s Demonstrate

Realistically we only need to preview it the first time. Having that preview open every time actually ends up being really annoying

1. In the Environment Tab click the broom icon 🧹
   • This will delete everything from your R Environment
   • It won’t unload the packages

2. Highlight the code we’ve just pasted and click Run
   • Reloading the packages won’t hurt

3. Check the Environment Tab again and penguins is back

You can delete the line View(penguins)

Our First R Script

• This is now our first R script
  • An R script is a plain text file
  • No objects are stored

• Individual lines can be run using Ctrl/Cmd + Enter
• Can select multiple lines & run

Our First R Script

• Insert a new line before library(readr) then type

## Load packages first

• This is a comment \(\implies\) no executable code
  • Communicates to “future” you what you’re doing
  • Only needs a single # to be a comment

Our First R Script

• Insert a line after library(readr) then enter

## Now load the file "penguins.csv"
## Taken from https://allisonhorst.github.io/palmerpenguins/index.html

Data Frame Objects

Data Frame Objects

• The object penguins is known as a data.frame
  • Very similar to an SQL table
• R equivalent to a spreadsheet
  • Must have column names
  • row names becoming less common (sometimes just the row numbers)
  • Missing values (blank cells) are usually filled with NA

Data Frame Objects

Instead of View() \(\implies\) preview by typing the object name

penguins

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>

Data Frame Objects

Gives a preview up to 10 lines with:

• The object type: A tibble
• The full dimensions: 60 X 3
• Column names: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex and year
• Data types: <chr>, <chr>, <dbl> etc

I personally find this more informative than View()

Tibble Objects

• readr uses a variant called a tbl_df or tbl (pronounced tibble)
  • A data.frame with convenient features
  • Similar to a SQL table
  • Can only have row numbers for row names
  • Is a foundational structure in the tidyverse

The Tidyverse

• The tidyverse is a collection of thematically-linked packages
  • Produced by developers from RStudio/Posit
  • Often referred to as tidy-programming or similar
• Calling library(tidyverse) loads multiple key packages
  • readr is one of these \(\implies\) usually just load the tidyverse
  • Will load dplyr, tidyr, readr, ggplot2 + others

The Tidyverse

1. Replace library(readr) with library(tidyverse)
2. Execute this line

• This will load all core packages from the tidyverse
• Reloading a package has no effect (is ignored)

Previewing Objects

• Some additional ways to inspect tibbles are:

head(penguins)
glimpse(penguins)

• glimpse is loaded with library(tidyverse)

What were the differences between each method?

Functions

Functions in R

head(penguins)
glimpse(penguins)

• Here we have called the functions head() and glimpse()
  • They were both executed on the object penguins

• Call the help page for head()

?head

(if you get multiple options, choose the one from utils)

Functions in R

• The key place to look at is

head(x, ...)
## Default S3 method:
head(x, n = 6L, ...)

• there are two arguments to head() \(\implies\) x and n
  • x has no default value \(\implies\) we need to provide something
  • n = 6L means n has a default value of 6 (L \(\implies\) integer)

Execute head() to show the error!!!

Functions in R

Lower down the page you’ll see

Arguments

x    an object
n    an integer vector of length up to dim(x) (or 1, for non-dimensioned objects). Blah, blah, blah…

• Some of the rest is technical detail (can sometimes be very helpful)

Function Arguments

• head() prints the first part of an object
• Useful for very large objects (e.g. if we had 1000 penguins)

• We can change the number of rows shown to us

head(penguins, 4)

# A tibble: 4 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
# ℹ 2 more variables: sex <chr>, year <dbl>

Function Arguments

• If passing values in order \(\implies\) no need for names

head(penguins, 4)

# A tibble: 4 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
# ℹ 2 more variables: sex <chr>, year <dbl>

head(x = penguins, n = 4)

# A tibble: 4 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
# ℹ 2 more variables: sex <chr>, year <dbl>

Function Arguments

• If we explicitly name arguments \(\implies\) can pass in any order

head(n = 4, x = penguins)

# A tibble: 4 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
# ℹ 2 more variables: sex <chr>, year <dbl>

Function Arguments

• Check the help page for glimpse()
  • This is from the package pillar
• Try changing the width argument to see what happens
  • Default value auto-detects screen width

Understanding read_csv()

• Earlier we called the R function read_csv()
• Check the help page

?read_csv

• We have four functions shown but stick to read_csv()

Understanding read_csv()

read_csv(
  file, 
  col_names = TRUE, col_types = NULL, col_select = NULL,
  id = NULL, locale = default_locale(), 
  na = c("", "NA"), quoted_na = TRUE,
  quote = "\"", comment = "",
  trim_ws = TRUE,
  skip = 0, n_max = Inf,
  guess_max = min(1000, n_max),
  name_repair = "unique",
  num_threads = readr_threads(),
  progress = show_progress(),
  show_col_types = should_show_types(),
  skip_empty_rows = TRUE,
  lazy = should_read_lazy()
)

• This function has way too many arguments (file, col_names etc.)
• Most have default values given
  col_names = TRUE \(\implies\) column names assumed as the first row

• Ask which arguments have default before moving to the fragment…

Understanding read_csv()

All arguments were defined somewhere in the GUI.

1. Open the GUI Preview by clicking on the file again
2. Uncheck the First Row as Names check-box

• What happened to the code?
• How did the columns change?

Try clicking/unclicking a few more & try understand the consequences

Understanding read_csv()

• Column types can also be set using shorthand
  • “ccnnnncn” is a mix of character & number columns

penguins <- read_csv("data/penguins.csv", col_types = "ccnnnncn")

• Changing one of the character columns to “n” will lead to NAs
• Changing the numeric column to character would mean … ?

Closing Comments

read_csv() Vs read.csv()

• RStudio now uses read_csv() from readr by default
• You will often see read.csv() (from utils) in older scripts
• The newer (readr) version is:
  • slightly faster
  • more user-friendly
  • handles large files more efficiently via indexing
  • gives informative messages

• Earlier functions in utils are read.*() (csv, delim etc.)
• readr has the functions read_*() (csv, tsv, delim etc.)
• I always use the newer ones

Loading Excel Files

• The package readxl is for loading .xls and xlsx files.
• Not part of the core tidyverse but very compatible

library(readxl)

• The main function is read_excel()

?read_excel

Loading Excel Files

• This file contains multiple sheets

excel_sheets("data/RealTimeData.xlsx")

[1] "Sheet1" "Sheet2" "Sheet3"

I found this file after a random Google search for RT-PCR and Excel about 10 years ago. I didn’t keep track of who created it…

• Once again we can click on the file \(\implies\) Import Dataset
  • Sheet1 looks pretty simple
  • First column has no name

• Use the drop-down menu to look at the Sheet2 & Sheet3
• We’ll learn how to manage these as challenges later