Data Structures

Last updated on 2024-03-12 | Edit this page

Estimated time: 95 minutes

Overview

Questions

  • How can I read data in R?
  • What are the basic data types in R?
  • How do I represent categorical information in R?

Objectives

  • To be aware of the different types of data.
  • To begin exploring data frames, and understand how they are related to vectors and factors.
  • To be able to ask questions from R about the type, class, and structure of an object.

One of R’s most powerful features is its ability to deal with tabular data, such as you may already have in a spreadsheet or a CSV file. Let’s start by downloading and reading in a file casco_kelp_urchin.csv. We will save this data as an object named casco_dmr:

R

casco_dmr <- read.csv("data/casco_kelp_urchin.csv")

The read.table function is used for reading in tabular data stored in a text file where the columns of data are separated by punctuation characters such as tabs (tab-delimited, sometimes with .txt or .tsv extensions) or commas (comma-delimited values, often with .csv extensions). For convenience R provides 2 other versions of read.table. These are: read.csv for files where the data are separated with commas and read.delim for files where the data are separated with tabs. Of these three functions read.csv is the most commonly used. If needed it is possible to override the default delimiting punctuation marks for both read.csv and read.delim.

Miscellaneous Tips

  • Files can also be downloaded directly from the Internet into a local folder of your choice onto your computer using the download.file function. The read.csv function can then be executed to read the downloaded file from the download location, for example,

R

download.file("https://cobalt-casco.github.io/r-intro-geospatial/data/casco_kelp_urchin.csv",
              destfile = "data/casco_kelp_urchin.csv")
casco_dmr <- read.csv("data/casco_kelp_urchin.csv")
  • Alternatively, you can also read in files directly into R from the Internet by replacing the file paths with a web address in read.csv. One should note that in doing this no local copy of the csv file is first saved onto your computer. For example,

R

casco_dmr <- read.csv("https://cobalt-casco.github.io/r-intro-geospatial/data/casco_kelp_urchin.csv") 
  • You can read directly from excel spreadsheets without converting them to plain text first by using the readxl package.

We can begin exploring our dataset right away, pulling out columns by specifying them using the $ operator:

R

casco_dmr$year

OUTPUT

 [1] 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002
[16] 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2005 2005 2005
[31] 2005 2005 2005 2005 2005 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006
[46] 2007 2007 2007 2007 2007 2007 2007 2008 2008 2008 2008 2008 2008 2009 2009
[61] 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2011 2011
[76] 2011 2011 2011 2011 2011 2011 2012 2014 2014 2014 2014 2014 2014 2014 2014

R

casco_dmr$kelp

OUTPUT

 [1]  92.5  59.0   7.7  52.5  29.2 100.0   0.8  87.5  13.0  86.5  96.5  65.0
[13]   5.0   0.0 100.0  64.0  81.0 100.0  56.0  19.5  31.5  55.0  80.5  68.5
[25]  43.0  50.0   9.5  30.0  49.0  79.5  44.5  46.5  24.0  50.5  30.5  42.0
[37]  49.5  51.0  39.0   0.5  71.0  11.0  33.5  75.0  82.5   0.5  35.0   8.5
[49]  55.5  26.0  87.0  32.5   5.0  16.5 100.0  22.0  97.0  39.0   1.5  71.0
[61]  10.5  63.0  73.5  70.5  67.5  17.5   7.5   7.0   0.0  69.5  13.5   2.0
[73]  41.5   7.5  74.5  62.5  76.5  74.5   4.5  71.0   9.5   0.5  46.5  41.5
[85]  55.0  11.0  63.5  14.5  25.5  31.0

We can do other operations on the columns. For example, if we discovered that our data were actually collected two years later:

R

casco_dmr$year + 2

OUTPUT

 [1] 2003 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004
[16] 2005 2005 2005 2005 2005 2005 2006 2006 2006 2006 2006 2006 2007 2007 2007
[31] 2007 2007 2007 2007 2007 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008
[46] 2009 2009 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2011 2011
[61] 2011 2011 2011 2011 2011 2012 2012 2012 2012 2012 2012 2012 2012 2013 2013
[76] 2013 2013 2013 2013 2013 2013 2014 2016 2016 2016 2016 2016 2016 2016 2016

As an aside, did this change the original year data? How would you check?

But what about:

R

casco_dmr$year + casco_dmr$region

ERROR

Error in casco_dmr$year + casco_dmr$region: non-numeric argument to binary operator

Understanding what happened here is key to successfully analyzing data in R.

Data Types


  • Learners will work with factors in the following lesson. Be sure to cover this concept.

If you guessed that the last command will return an error because 2008 plus "Casco Bay" is nonsense, you’re right - and you already have some intuition for an important concept in programming called data classes. We can ask what class of data something is:

R

class(casco_dmr$year)

OUTPUT

[1] "integer"

There are 6 main types: numeric, integer, complex, logical, character, and factor.

R

class(3.14)

OUTPUT

[1] "numeric"

R

class(1L) # The L suffix forces the number to be an integer, since by default R uses float numbers

OUTPUT

[1] "integer"

R

class(1+1i)

OUTPUT

[1] "complex"

R

class(TRUE)

OUTPUT

[1] "logical"

R

class('banana')

OUTPUT

[1] "character"

R

class(factor('banana'))

OUTPUT

[1] "factor"

The types numeric, integer, and complex are all numbers, although they are stored differently and have different mathematical properties. logical type data include only TRUE and FALSE values, while character type data can contain any kind of characters. Finally, factor is a special type that was built to help us store categorical variables, variables that have a fixed and known set of possible values. We’ll talk more about them in a little bit.

No matter how complicated our analyses become, all data in R is interpreted a specific data class. This strictness has some really important consequences.

Let’s say that a collaborator sends you an updated data file named data/casco_kelp_urchin_2.csv.

Load the new data file as casco_dmr_2, and check what class of data we find in the year column:

R

casco_dmr_2 <- read.csv("data/casco_kelp_urchin_2.csv")
class(casco_dmr_2$year)

OUTPUT

[1] "character"

Oh no, our year data aren’t the numeric type anymore! If we try to do the same math we did on them before, we run into trouble:

R

casco_dmr_2$year + 2

ERROR

Error in casco_dmr_2$year + 2: non-numeric argument to binary operator

What happened? When R reads a csv file into one of these tables, it insists that everything in a column be the same class; if it can’t understand everything in the column as numeric, then nothing in the column gets to be numeric. The table that R loaded our data into is something called a dataframe, and it is our first example of something called a data structure, that is, a structure which R knows how to build out of the basic data types.

We can see that it is a dataframe by calling the class() function on it:

R

class(casco_dmr)

OUTPUT

[1] "data.frame"

In order to successfully use our data in R, we need to understand what the basic data structures are, and how they behave. Note: in this lesson we will not cover lists, which are a basic data structure in R. You can learn more about them here.

Vectors and Type Coercion


To better understand this behavior, let’s meet another of the data structures: the vector.

R

my_vector <- vector(length = 3)
my_vector

OUTPUT

[1] FALSE FALSE FALSE

A vector in R is essentially an ordered list of things, with the special condition that everything in the vector must be the same basic data type. If you don’t choose the data type, it’ll default to logical; or, you can declare an empty vector of whatever type you like.

R

another_vector <- vector(mode = 'character', length = 3)
another_vector

OUTPUT

[1] "" "" ""

You can check if something is a vector:

R

str(another_vector)

OUTPUT

 chr [1:3] "" "" ""

The somewhat cryptic output from this command indicates the basic data type found in this vector (in this case chr or character), an indication of the number of things in the vector (the indexes of the vector, in this case: [1:3]), and a few examples of what’s actually in the vector (in this case empty character strings). If we similarly do:

R

str(casco_dmr$year)

OUTPUT

 int [1:90] 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 ...

we see that casco_dmr$year is a vector, too! The columns of data we load into R data frames are all vectors, and that’s the root of why R forces everything in a column to be the same basic data type.

Discussion 1

Why is R so opinionated about what we put in our columns of data? How does this help us?

By keeping everything in a column the same, we allow ourselves to make simple assumptions about our data; if you can interpret one entry in the column as a number, then you can interpret all of them as numbers, so we don’t have to check every time. This consistency is what people mean when they talk about clean data; in the long run, strict consistency goes a long way to making our lives easier in R.

You can also make vectors with explicit contents with the combine function:

R

combine_vector <- c(2, 6, 3)
combine_vector

OUTPUT

[1] 2 6 3

We can see what is at a certain index of a vector using the [] notation. For example, what is the second element of combine_vector?

R

combine_vector[2]

OUTPUT

[1] 6

Type Coercion


Given what we’ve learned so far, what do you think the following will produce?

R

quiz_vector <- c(2, 6, '3')

This is something called type coercion, and it is the source of many surprises and the reason why we need to be aware of the basic data types and how R will interpret them. When R encounters a mix of types (here numeric and character) to be combined into a single vector, it will force them all to be the same type. Consider:

R

coercion_vector <- c('a', TRUE)
coercion_vector

OUTPUT

[1] "a"    "TRUE"

R

another_coercion_vector <- c(0, TRUE)
another_coercion_vector

OUTPUT

[1] 0 1

The coercion rules go: logical -> integer -> numeric -> complex -> character, where -> can be read as are transformed into. You can try to force coercion against this flow using the as. functions:

R

character_vector_example <- c('0', '2', '4')
character_vector_example

OUTPUT

[1] "0" "2" "4"

R

character_coerced_to_numeric <- as.numeric(character_vector_example)
character_coerced_to_numeric

OUTPUT

[1] 0 2 4

R

numeric_coerced_to_logical <- as.logical(character_coerced_to_numeric)
numeric_coerced_to_logical

OUTPUT

[1] FALSE  TRUE  TRUE

As you can see, some surprising things can happen when R forces one basic data type into another! Nitty-gritty of type coercion aside, the point is: if your data doesn’t look like what you thought it was going to look like, type coercion may well be to blame; make sure everything is the same type in your vectors and your columns of data frames, or you will get nasty surprises!

Challenge 1

Given what you now know about type conversion, look at the class of data in casco_dmr$year and compare it with casco_dmr_2$year. Why are these columns different classes?

R

str(casco_dmr$year)

OUTPUT

 int [1:90] 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 ...

R

str(casco_dmr_2$year)

OUTPUT

 chr [1:90] "year 2001" "2001" "2001" "2001" "2001" "2001" "2001" "2002" ...

The data in casco_dmr_2$year is stored as a character vector, rather than as a numeric vector. This is because of the “year” character string in the first data point.

The combine function, c(), will also append things to an existing vector:

R

ab_vector <- c('a', 'b')
ab_vector

OUTPUT

[1] "a" "b"

R

combine_example <- c(ab_vector, 'DC')
combine_example

OUTPUT

[1] "a"  "b"  "DC"

You can also make series of numbers:

R

my_series <- 1:10
my_series

OUTPUT

 [1]  1  2  3  4  5  6  7  8  9 10

R

seq(10)

OUTPUT

 [1]  1  2  3  4  5  6  7  8  9 10

R

seq(1,10, by = 0.1)

OUTPUT

 [1]  1.0  1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9  2.0  2.1  2.2  2.3  2.4
[16]  2.5  2.6  2.7  2.8  2.9  3.0  3.1  3.2  3.3  3.4  3.5  3.6  3.7  3.8  3.9
[31]  4.0  4.1  4.2  4.3  4.4  4.5  4.6  4.7  4.8  4.9  5.0  5.1  5.2  5.3  5.4
[46]  5.5  5.6  5.7  5.8  5.9  6.0  6.1  6.2  6.3  6.4  6.5  6.6  6.7  6.8  6.9
[61]  7.0  7.1  7.2  7.3  7.4  7.5  7.6  7.7  7.8  7.9  8.0  8.1  8.2  8.3  8.4
[76]  8.5  8.6  8.7  8.8  8.9  9.0  9.1  9.2  9.3  9.4  9.5  9.6  9.7  9.8  9.9
[91] 10.0

We can ask a few questions about vectors:

R

sequence_example <- seq(10)
head(sequence_example,n = 2)

OUTPUT

[1] 1 2

R

tail(sequence_example, n = 4)

OUTPUT

[1]  7  8  9 10

R

length(sequence_example)

OUTPUT

[1] 10

R

class(sequence_example)

OUTPUT

[1] "integer"

Finally, you can give names to elements in your vector:

R

my_example <- 5:8
names(my_example) <- c("a", "b", "c", "d")
my_example

OUTPUT

a b c d 
5 6 7 8 

R

names(my_example)

OUTPUT

[1] "a" "b" "c" "d"

Challenge 2

Start by making a vector with the numbers 1 through 26. Multiply the vector by 2, and give the resulting vector names A through Z (hint: there is a built in vector called LETTERS)

R

x <- 1:26
x <- x * 2
names(x) <- LETTERS

Factors


We said that columns in data frames were vectors:

R

str(casco_dmr$year)

OUTPUT

 int [1:90] 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 ...

R

str(casco_dmr$kelp)

OUTPUT

 num [1:90] 92.5 59 7.7 52.5 29.2 100 0.8 87.5 13 86.5 ...

R

str(casco_dmr$region)

OUTPUT

 chr [1:90] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" ...

One final important data structure in R is called a “factor” (that special data type we mentioned above). Factors look like character data, but are used to represent data where each element of the vector must be one of a limited number of “levels”. To phrase that another way, factors are an “enumerated” type where there are a finite number of pre-defined values that your vector can have.

For example, let’s make a character vector with all the sampling regions in the DMR kelp data:

R

maine_regions <- c("York", "Casco Bay", "Midcoast", "Penobscot Bay", "MDI", "Downeast")
maine_regions

OUTPUT

[1] "York"          "Casco Bay"     "Midcoast"      "Penobscot Bay"
[5] "MDI"           "Downeast"     

R

class(maine_regions)

OUTPUT

[1] "character"

R

str(maine_regions)

OUTPUT

 chr [1:6] "York" "Casco Bay" "Midcoast" "Penobscot Bay" "MDI" "Downeast"

We can turn a vector into a factor like so:

R

me_region_factor <- factor(maine_regions)
class(me_region_factor)

OUTPUT

[1] "factor"

R

str(me_region_factor)

OUTPUT

 Factor w/ 6 levels "Casco Bay","Downeast",..: 6 1 4 5 3 2

Now R has noticed that there are 6 possible categories in our data, but it also did something surprising. Instead of printing out the strings we gave it, we got a bunch of numbers instead. R has replaced our human-readable categories with numbered indices under the hood! This is necessary as many statistical calculations utilize such numerical representations for categorical data.

Challenge 3

Convert the region column of our casco_dmr data frame to a factor. Then try converting it back to a character vector.

Now try converting year in our casco_dmr data frame to a factor, then back to a numeric vector. What happens if you use as.numeric()?

Remember that you can always reload the casco_dmr data frame using read.csv("data/casco_kelp_urchin.csv") if you accidentally mess up your data!

Converting character vectors to factors can be done using the factor() function:

R

casco_dmr$region <- factor(casco_dmr$region)
casco_dmr$region

OUTPUT

 [1] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
 [8] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[15] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[22] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[29] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[36] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[43] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[50] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[57] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[64] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[71] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[78] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
[85] Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay Casco Bay
Levels: Casco Bay

You can convert these back to character vectors using as.character():

R

casco_dmr$region <- as.character(casco_dmr$region)
casco_dmr$region

OUTPUT

 [1] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
 [7] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[13] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[19] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[25] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[31] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[37] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[43] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[49] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[55] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[61] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[67] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[73] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[79] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"
[85] "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay" "Casco Bay"

You can convert numeric vectors to factors in the exact same way:

R

casco_dmr$year <- factor(casco_dmr$year)
casco_dmr$year

OUTPUT

 [1] 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002
[16] 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2005 2005 2005
[31] 2005 2005 2005 2005 2005 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006
[46] 2007 2007 2007 2007 2007 2007 2007 2008 2008 2008 2008 2008 2008 2009 2009
[61] 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2011 2011
[76] 2011 2011 2011 2011 2011 2011 2012 2014 2014 2014 2014 2014 2014 2014 2014
Levels: 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014

But be careful – you can’t use as.numeric() to convert factors to numerics!

R

as.numeric(casco_dmr$year)

OUTPUT

 [1]  1  1  1  1  1  1  1  2  2  2  2  2  2  2  2  3  3  3  3  3  3  4  4  4  4
[26]  4  4  5  5  5  5  5  5  5  5  6  6  6  6  6  6  6  6  6  6  7  7  7  7  7
[51]  7  7  8  8  8  8  8  8  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 11 11
[76] 11 11 11 11 11 11 12 13 13 13 13 13 13 13 13

Instead, as.numeric() converts factors to those “numbers under the hood” we talked about. To go from a factor to a number, you need to first turn the factor into a character vector, and then turn that into a numeric vector:

R

casco_dmr$year <- as.character(casco_dmr$year)
casco_dmr$year <- as.numeric(casco_dmr$year)
casco_dmr$year

OUTPUT

 [1] 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002
[16] 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2005 2005 2005
[31] 2005 2005 2005 2005 2005 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006
[46] 2007 2007 2007 2007 2007 2007 2007 2008 2008 2008 2008 2008 2008 2009 2009
[61] 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2011 2011
[76] 2011 2011 2011 2011 2011 2011 2012 2014 2014 2014 2014 2014 2014 2014 2014

Note: new students find the help files difficult to understand; make sure to let them know that this is typical, and encourage them to take their best guess based on semantic meaning, even if they aren’t sure.

When doing statistical modelling, it’s important to know what the baseline levels are. This is assumed to be the first factor, but by default factors are labeled in alphabetical order. You can change this by specifying the levels:

R

treatment <- c("case", "control", "control", "case")
factor_ordering_example <- factor(treatment, levels = c("control", "case"))
str(factor_ordering_example)

OUTPUT

 Factor w/ 2 levels "control","case": 2 1 1 2

In this case, we’ve explicitly told R that “control” should represented by 1, and “case” by 2. This designation can be very important for interpreting the results of statistical models!

To know what the levels map to, we can use levels() for factors. To do the same for characters, we can use unique().

R

levels(factor_ordering_example)

OUTPUT

[1] "control" "case"   

R

unique(maine_regions)

OUTPUT

[1] "York"          "Casco Bay"     "Midcoast"      "Penobscot Bay"
[5] "MDI"           "Downeast"     

Note that the order is different! For unique(), it’s based on the order of observation in the vector. For levels, it’s been set. If we want to sort from unique(), which can be very useful, we can try:

R

non_alpha_vector <- c("b", "a", "c")

unique(non_alpha_vector)

OUTPUT

[1] "b" "a" "c"

R

sort(unique(non_alpha_vector) )

OUTPUT

[1] "a" "b" "c"

Key Points

  • Use read.csv to read tabular data in R.
  • The basic data types in R are numeric, integer, complex, logical, character, and factor.
  • Dataframes store columns of the same data type as vectors.
  • Use characters and factors to represent categories in R.