This lesson is in the early stages of development (Alpha version)

Starting with Data

Overview

Teaching: 50 min
Exercises: 30 min
Questions
  • What is a data.frame?

  • How can I read a complete csv file into R?

  • How can I get basic summary information about my dataset?

  • How can I change the way R treats strings in my dataset?

  • Why would I want strings to be treated differently?

  • How are dates represented in R and how can I change the format?

Objectives
  • Describe what a data frame is.

  • Load external data from a .csv file into a data frame.

  • Summarize the contents of a data frame.

  • Describe the difference between a factor and a string.

  • Convert between strings and factors.

  • Reorder and rename factors.

  • Change how character strings are handled in a data frame.

  • Examine and change date formats.

Open your Rproj file

First, open your R Project file (library_carpentry.Rproj) created in the Before We Start lesson.

If you did not complete that step, do the following:

Presentation of the data

This data was downloaded from the University of Houston–Clear Lake Integrated Library System in 2018. It is a relatively random sample of books from the catalog. It consists of 10,000 observations of 11 variables.

These variables are:

Getting data into R

Ways to get data into R

In order to use your data in R, you must import it and turn it into an R object. There are many ways to get data into R.

Organizing your working directory

Using a consistent folder structure across your projects will help keep things organized and make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, you might create directories (folders) for scripts, data, and documents. Here are some examples of suggested directories:

You may want additional directories or subdirectories depending on your project needs, but these should form the backbone of your working directory.

Structure of a Working Directory, with folders for data, data output, documents, fig output, and scripts

The working directory

The working directory is an important concept to understand. It is the place on your computer where R will look for and save files. When you write code for your project, your scripts should refer to files in relation to the root of your working directory and only to files within this structure.

Using RStudio projects makes this easy and ensures that your working directory is set up properly. If you need to check it, you can use getwd(). If for some reason your working directory is not what it should be, you can change it in the RStudio interface by navigating in the file browser to where your working directory should be, clicking on the blue gear icon “More”, and selecting “Set As Working Directory”. Alternatively, you can use setwd("/path/to/working/directory") to reset your working directory. However, your scripts should not include this line, because it will fail on someone else’s computer.

Setting your working directory with setwd()

Some points to note about setting your working directory:

The directory must be in quotation marks.

On Windows computers, directories in file paths are separated with a backslash \. However, in R, you must use a forward slash /. You can copy and paste from the Windows Explorer window directly into R and use find/replace (Ctrl/Cmd + F) in R Studio to replace all backslashes with forward slashes.

On Mac computers, open the Finder and navigate to the directory you wish to set as your working directory. Right click on that folder and press the options key on your keyboard. The ‘Copy “Folder Name”’ option will transform into ‘Copy “Folder Name” as Pathname. It will copy the path to the folder to the clipboard. You can then paste this into your setwd() function. You do not need to replace backslashes with forward slashes.

After you set your working directory, you can use ./ to represent it. So if you have a folder in your directory called data, you can use read.csv(“./data”) to represent that sub-directory.

Downloading the data and getting set up

Now that you have set your working directory, we will create our folder structure using the dir.create() function.

For this lesson we will use the following folders in our working directory: data/, data_output/ and fig_output/. Let’s write them all in lowercase to be consistent. We can create them using the RStudio interface by clicking on the “New Folder” button in the file pane (bottom right), or directly from R by typing at console:

dir.create("data")
dir.create("data_output")
dir.create("fig_output")

Go to the Figshare page for this curriculum and download the dataset called “books.csv”. The direct download link is: https://ndownloader.figshare.com/files/22031487. Place this downloaded file in the data/ you just created. Alternatively, you can do this directly from R by copying and pasting this in your terminal (your instructor can place this chunk of code in the Etherpad):

download.file("https://ndownloader.figshare.com/files/22031487",
              "data/books.csv", mode = "wb")

Now if you navigate to your data folder, the books.csv file should be there. We now need to load it into our R session.

tidyverse

R has some base functions for reading a local data file into your R session–namely read.table() and read.csv(), but these have some idiosyncrasies that were improved upon in the readr package, which is installed and loaded with tidyverse.

library(tidyverse)  # loads the core tidyverse, including dplyr, readr, ggplot2, purrr
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.2.1     ✔ purrr   0.3.2
✔ tibble  2.1.3     ✔ dplyr   0.8.3
✔ tidyr   0.8.3     ✔ stringr 1.4.0
✔ ggplot2 3.2.1     ✔ forcats 0.4.0
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

To get our sample data into our R session, we will use the read_csv() function and assign it to the books value.

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

You will see the message Parsed with column specification, followed by each column name and its data type. When you execute read_csv on a data file, it looks through the first 1000 rows of each column and guesses the data type for each column as it reads it into R. For example, in this dataset, it reads SUBJECT as col_character (character), and TOT.CHKOUT as col_double. You have the option to specify the data type for a column manually by using the col_types argument in read_csv.

You should now have an R object called books in the Environment pane: 10000 observations of 12 variables. We will be using this data file in the next module.

Note

read_csv() assumes that fields are delineated by commas, however, in several countries, the comma is used as a decimal separator and the semicolon (;) is used as a field delineator. If you want to read in this type of files in R, you can use the read_csv2 function. It behaves exactly like read_csv but uses different parameters for the decimal and the field separators. If you are working with another format, they can be both specified by the user. Check out the help for read_csv() by typing ?read_csv to learn more. There is also the read_tsv() for tab-separated data files, and read_delim() allows you to specify more details about the structure of your file.

The books CSV loaded as a tibble in your R environment
The books CSV loaded as a tibble in your R environment.

What are data frames and tibbles?

Data frames are the de facto data structure for tabular data in R, and what we use for data processing, statistics, and plotting.

A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

A graphical depiction of a data frame. The first vector (column) is numeric; the second is character, and the third is logical.

A data frame can be created by hand, but most commonly they are generated by the functions read_csv() or read_table(); in other words, when importing spreadsheets from your hard drive (or the web).

A tibble is an extension of R data frames used by the tidyverse. When the data is read using read_csv(), it is stored in an object of class tbl_df, tbl, and data.frame. You can see the class of an object with

Inspecting data frames

When calling a tbl_df object (like interviews here), there is already a lot of information about our data frame being displayed such as the number of rows, the number of columns, the names of the columns, and as we just saw the class of data stored in each column. However, there are functions to extract this information from data frames. Here is a non-exhaustive list of some of these functions. Let’s try them out!

Note: most of these functions are “generic”, they can be used on other types of objects besides data frames.

The map() function from purrr is a useful way of running a function on all variables in a data frame or list. If you loaded the tidyverse at the beginning of the session, you also loaded purrr. Here we call class() on books using map_chr(), which will return a character vector of the classes for each variable.

map_chr(books, class)
CALL...BIBLIO.        X245.ab         X245.c       LOCATION     TOT.CHKOUT 
   "character"    "character"    "character"    "character"      "numeric" 
      LOUTDATE        SUBJECT            ISN   CALL...ITEM.  X008.Date.One 
   "character"    "character"    "character"    "character"    "character" 
        BCODE2         BCODE1 
   "character"    "character" 

Indexing and subsetting data frames

Our books data frame has 2 dimensions: rows (observations) and columns (variables). If we want to extract some specific data from it, we need to specify the “coordinates” we want from it. In the last session, we used square brackets [ ] to subset values from vectors. Here we will do the same thing for data frames, but we can now add a second dimension. Row numbers come first, followed by column numbers. However, note that different ways of specifying these coordinates lead to results with different classes.

## first element in the first column of the data frame (as a vector)
books[1, 1]
## first element in the 6th column (as a vector)
books[1, 6]
## first column of the data frame (as a vector)
books[[1]]
## first column of the data frame (as a data.frame)
books[1]
## first three elements in the 7th column (as a vector)
books[1:3, 7]
## the 3rd row of the data frame (as a data.frame)
books[3, ]
## equivalent to head_books <- head(books)
head_books <- books[1:6, ]

Dollar sign

The dollar sign $ is used to distinguish a specific variable (column, in Excel-speak) in a data frame:

head(books$X245.ab)  # print the first six book titles
[1] "Bermuda Triangle /"                                                                  
[2] "Invaders from outer space :|real-life stories of UFOs /"                             
[3] "Down Cut Shin Creek :|the pack horse librarians of Kentucky /"                       
[4] "The Chinese book of animal powers /"                                                 
[5] "Judge Judy Sheindlin's Win or lose by how you choose! /"                             
[6] "Judge Judy Sheindlin's You can't judge a book by its cover :|cool rules for school /"
# print the mean number of checkouts
mean(books$TOT.CHKOUT)
[1] 2.2847

unique(), table(), and duplicated()

Use unique() to see all the distinct values in a variable:

unique(books$BCODE2)
 [1] "a" "w" "s" "m" "e" "4" "k" "5" "n" "o"

Take that one step further with table() to get quick frequency counts on a variable:

table(books$BCODE2)  # frequency counts on a variable

   4    5    a    e    k    m    n    o    s    w 
   1    3 6983   68    3  109    2   21 1988  822 

You can combine table() with relational operators:

table(books$TOT.CHKOUT > 50)  # how many books have 50 or more checkouts?

FALSE  TRUE 
 9991     9 

duplicated() will give you the a logical vector of duplicated values.

duplicated(books$ISN)  # a TRUE/FALSE vector of duplicated values in the ISN column
!duplicated(books$ISN)  # you can put an exclamation mark before it to get non-duplicated values
table(duplicated(books$ISN))  # run a table of duplicated values
which(duplicated(books$ISN))  # get row numbers of duplicated values

Exploring missing values

You may also need to know the number of missing values:

sum(is.na(books))  # How many total missing values?
[1] 14509
colSums(is.na(books))  # Total missing values per column
CALL...BIBLIO.        X245.ab         X245.c       LOCATION     TOT.CHKOUT 
           561             12           2801              0              0 
      LOUTDATE        SUBJECT            ISN   CALL...ITEM.  X008.Date.One 
             0             63           2934           7980            158 
        BCODE2         BCODE1 
             0              0 
table(is.na(books$ISN))  # use table() and is.na() in combination

FALSE  TRUE 
 7066  2934 
booksNoNA <- na.omit(books)  # Return only observations that have no missing values

Exercise

  1. Call View(books) to examine the data frame. Use the small arrow buttons in the variable name to sort tot_chkout by the highest checkouts. What item has the most checkouts?

  2. What is the class of the TOT.CHKOUT variable?

  3. Use table() and is.na() to find out how many NA values are in the ISN variable.

  4. Call summary(books$TOT.CHKOUT). What can we infer when we compare the mean, median, and max?

  5. hist() will print a rudimentary histogram, which displays frequency counts. Call hist(books$TOT.CHKOUT). What is this telling us?

Solution

  1. Highest checkouts: Click, clack, moo : cows that type.

  2. class(books$TOT.CHKOUT) returns numeric

  3. table(is.na(books$ISN)) returns 2934 TRUE values

  4. The median is 0, indicating that, consistent with all book circulation I have seen, the majority of items have 0 checkouts.

  5. As we saw in summary(), the majority of items have a small number of checkouts

Logical tests

R contains a number of operators you can use to compare values. Use help(Comparison) to read the R help file. Note that two equal signs (==) are used for evaluating equality (because one equals sign (=) is used for assigning variables).

operator function
< Less Than
> Greater Than
== Equal To
<= Less Than or Equal To
>= Greater Than or Equal To
!= Not Equal To
%in% Has a Match In
is.na() Is NA
!is.na() Is Not NA

Sometimes you need to do multiple logical tests (think Boolean logic). Use help(Logic) to read the help file.

operator function
& boolean AND
| boolean OR
! Boolean NOT
any() Are some values true?
all() Are all values true?

Key Points

  • Use read.csv to read tabular data in R.

  • Use factors to represent categorical data in R.