Data Structures
Last updated on 2024-03-12 | Edit this page
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.filefunction. Theread.csvfunction 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$year
OUTPUT
[1] 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002
[16] 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2005 2005 2005
[31] 2005 2005 2005 2005 2005 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006
[46] 2007 2007 2007 2007 2007 2007 2007 2008 2008 2008 2008 2008 2008 2009 2009
[61] 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2011 2011
[76] 2011 2011 2011 2011 2011 2011 2012 2014 2014 2014 2014 2014 2014 2014 2014R
casco_dmr$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 31.0We can do other operations on the columns. For example, if we discovered that our data were actually collected two years later:
R
casco_dmr$year + 2
OUTPUT
[1] 2003 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004
[16] 2005 2005 2005 2005 2005 2005 2006 2006 2006 2006 2006 2006 2007 2007 2007
[31] 2007 2007 2007 2007 2007 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008
[46] 2009 2009 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2011 2011
[61] 2011 2011 2011 2011 2011 2012 2012 2012 2012 2012 2012 2012 2012 2013 2013
[76] 2013 2013 2013 2013 2013 2013 2014 2016 2016 2016 2016 2016 2016 2016 2016As an aside, did this change the original year data? How would you check?
But what about:
R
casco_dmr$year + casco_dmr$region
ERROR
Error in casco_dmr$year + casco_dmr$region: non-numeric argument to binary operatorUnderstanding what happened here is key to successfully analyzing data in R.
Data Types
If you guessed that the last command will return an error because
2008 plus "Casco Bay" is nonsense, you’re
right - and you already have some intuition for an important concept in
programming called data classes. We can ask what class of data
something is:
R
class(casco_dmr$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$year + 2: non-numeric argument to binary operatorWhat happened? When R reads a csv file into one of these tables, it insists that everything in a column be the same class; if it can’t understand everything in the column as numeric, then nothing in the column gets to be numeric. The table that R loaded our data into is something called a dataframe, and it is our first example of something called a data structure, that is, a structure which R knows how to build out of the basic data types.
We can see that it is a dataframe by calling the class()
function on it:
R
class(casco_dmr)
OUTPUT
[1] "data.frame"In order to successfully use our data in R, we need to understand what the basic data structures are, and how they behave. Note: in this lesson we will not cover lists, which are a basic data structure in R. You can learn more about them here.
Vectors and Type Coercion
To better understand this behavior, let’s meet another of the data structures: the vector.
R
my_vector <- vector(length = 3)
my_vector
OUTPUT
[1] FALSE FALSE FALSEA vector in R is essentially an ordered list of things, with the
special condition that everything in the vector must be the same basic
data type. If you don’t choose the data type, it’ll default to
logical; or, you can declare an empty vector of whatever
type you like.
R
another_vector <- vector(mode = 'character', length = 3)
another_vector
OUTPUT
[1] "" "" ""You can check if something is a vector:
R
str(another_vector)
OUTPUT
chr [1:3] "" "" ""The somewhat cryptic output from this command indicates the basic
data type found in this vector (in this case chr or
character), an indication of the number of things in the vector (the
indexes of the vector, in this case: [1:3]), and a few
examples of what’s actually in the vector (in this case empty character
strings). If we similarly do:
R
str(casco_dmr$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 3We 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] 6Type 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 1The 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 4R
numeric_coerced_to_logical <- as.logical(character_coerced_to_numeric)
numeric_coerced_to_logical
OUTPUT
[1] FALSE TRUE TRUEAs 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!
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 10R
seq(10)
OUTPUT
[1] 1 2 3 4 5 6 7 8 9 10R
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] 10.0We can ask a few questions about vectors:
R
sequence_example <- seq(10)
head(sequence_example,n = 2)
OUTPUT
[1] 1 2R
tail(sequence_example, n = 4)
OUTPUT
[1] 7 8 9 10R
length(sequence_example)
OUTPUT
[1] 10R
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"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 2Now 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 BayYou 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 2014But be careful – you can’t use as.numeric() to convert
factors to numerics!
R
as.numeric(casco_dmr$year)
OUTPUT
[1] 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4
[26] 4 4 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7
[51] 7 7 8 8 8 8 8 8 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 11 11
[76] 11 11 11 11 11 11 12 13 13 13 13 13 13 13 13Instead, as.numeric() converts factors to those “numbers
under the hood” we talked about. To go from a factor to a number, you
need to first turn the factor into a character vector, and then
turn that into a numeric vector:
R
casco_dmr$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 2014Note: 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 2In 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"