• A point of confusion for many learning R are the errors, messages & warnings
  • We often see them while we're loading data
  • Some are actually helpful messages
• A knowledge of data types is beneficial when using R
  • logical, integer, numeric, character
• We can load xlsx, xls, csv, txt, files + many more

• Things we do to make it "look nice" in Excel can create problems:
  • Blank rows are not fatal, just annoying
  • Cell formatting will not be imported
  • Mixtures of numbers and text
  • Plots will not be imported
  • Deleted cells are sometimes imported as blank rows/columns

First let's get the data for this exercise.

• Data for all three days is on your VM in the folder /home/trainee/data.
• It's dynamically linked to the source, so please don't anything edit in this folder

f <- list.files("~/data/intro_r/", full.names = TRUE)
file.copy(f, "~/R_Training/data/")

• In data you'll see a set of csv and other excel-type files we've copied
• Select toothData.csv and click More > Export...
• Save in your Downloads folder and open with Excel (or Libre Office)

• This is actually the type of format R loves to see
• Simple column structure, with column names
• No blank rows at the top or separating sub-tables
• No blank columns

1. Close Excel and go back to RStudio
2. Make sure you're in the data directory
3. Click on toothData.csv
4. Select View File

This will open a preview in the Script Window (close when you're done)

1. File > New File > R Script (Or Ctrl+Shift+N)
2. Save as DataImport.R
3. Enter this line at the top of the script

library(tidyverse)

library(tidyverse)

• The tidyverse is a collection of connected packages
• A package contains a set of functions for related tasks
• We've already seen min(), max() etc from base
• This command loads a core set:
  • readr, dplyr, tibble, stringr, ggplot2, tidyr and purrr

To import into our R Environment we can either:

1. Click on the file and choose Import Dataset, or
2. Use the button as shown in the Environment Tab

Stop and wait until we're all ready

1. Select and copy all the code in the Code Preview Box
2. Click Import
3. Magic happens…
   • Ignore the red/blue text. This is just helpful information
4. Paste the copied code into your script, directly below library(tidyverse)

The code we copied has 3 lines:

1. library(readr)

• This package contains the function read_csv()
• Is already loaded by default with library(tidyverse)
  • We don't strictly need this line today
  • It won't cause any problems though

The code we copied has 3 lines:

1. library(readr)
2. toothData <- read_csv("data/toothData.csv")

• This is the code which loads the data into your R Environment
• It created an object named toothData by using the file name.

1. library(readr)
2. toothData <- read_csv("data/toothData.csv")
3. View(toothData)
   • Opens a preview in a familiar Excel-like format
   • I never use this

Close the preview by clicking the cross and delete the line View(toothData)

• We have just loaded data using the default settings of read_csv()
• Now we've saved the code in our script, we don't need the GUI for this operation again!

1. In the Environment Tab click the broom icon ()
   • This will delete everything from your R Environment
   • It won't unload the packages
2. Select the code we've just pasted and send it to the console
   • (Reloading the packages won't hurt)
3. Check the Environment Tab again and toothData is back

• RStudio now uses read_csv() from the package readr by default
• Old scripts will often use read.csv() from the package utils
• The newer version is slightly faster and fails less
• It also gives informative messages
• Other functions in utils are read.delim() and read.table()
• readr has the functions read_tsv(), read_delim() and read_table() etc.

• R has it's origins in statistical analysis
  • Categorical variables are very common
  • Original data import functions assume text is categorical data
  • Called factors in R \(\implies\) more memory efficient
• readr import functions do not assume this
  (read_csv(), read_tsv() etc)

• The object toothData is known as a data.frame
• R equivalent to a spreadsheet
• readr uses a variant called a tibble (originally tbl_df)
  • Like a data.frame with pretty bows & ribbons
  • Also named after an SQL table
  • Can only have row numbers as the row names

I will be lazy and call this a data frame as the differences are so trivial

• Some additional ways to inspect this are:

toothData
print(toothData)
head(toothData)
glimpse(toothData)

What were the differences between each method?

• Each column is a vector
  • Must contain data of the same type
• Each column must be the same length
• We can access each column by typing the column name after a $

toothData$len

• We can grab columns and rows using square brackets []
  • rows before the comma, columns after the comma

toothData[1:3, ]
toothData[,"len"]

• All data.frame/tibble objects must have column names.

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

?read_csv

• We have four functions but stick to read_csv()

read_csv(file, col_names = TRUE, col_types = 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), 
  progress = show_progress())

• This function has numerous arguments (file, col_names etc.)
• If only the argument name is given(e.g. file) we need to specify something

read_csv(file, col_names = TRUE, col_types = 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), 
  progress = show_progress())

• Otherwise the argument is given a default value (e.g. col_names = TRUE)
• If specifying in order, we don't need to name arguments
  (I like to name them though)

toothData <- read_csv("data/toothData.csv")

Is equivalent to:

toothData <- read_csv(file = "data/toothData.csv")

All arguments for the function were defined somewhere in the GUI.

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

All arguments for the function were defined somewhere in the GUI.

1. Open the GUI Preview by clicking on the file again
2. Uncheck the First Row as Names checkbox
   • What happened to the code?
   • How did the columns change?

• The bottom three functions are simplified wrappers to read_delim()
• read_csv() calls read_delim() using delim = ","
• read_csv2() calls read_delim() using delim = ";"
• read_tsv() calls read_delim() using delim = "\t"

What function would we call for space-delimited files?

R also has a package for loading .xls and xlsx files.

library(readxl)

The main function is read_excel()

?read_excel

Export RealTimeData.xlsx from your data folder to your local machine and inspect

Try to load each of the sheets from RealTimeData.xslx

(Remember to call the R objects something different)

Do you get any weird behaviour for sheet 3?

How could we load these two separate tables?