This lesson covers the use of functions in R, including built-in functions and functions from packages. It also discusses how to import data from CSV text files and Excel documents.
In R, there are two main types of objects: variables and functions. We covered variables in the introductory lesson. A variable is used to create and reference data. The data can be a character, numeric, or logical data type. Variables can reference various "containers" for data, such as a vector, list, or data frame.
Functions are similar to variables in that they are short names that reference something saved in R. In this case, a function is not referencing data but a piece of code. A function is saved code that can be used to do some operation on data.
R has many built-in functions that perform common tasks. When you open RStudio
you can immediately use a function called mean( ). Here is an example of using
the mean( ) function to find the average of a vector of integers. We first
save a vector of integers in the x variable then put the variable inside the
parentheses of the function.
x <- c(4, 8, 1, 14, 34)
mean(x)## [1] 12.2
As you would expect, R has many built-in math functions. Below are a few examples.
log(27) #Natural logarithm## [1] 3.295837
log10(100) #base 10 logarithm## [1] 2
sqrt(225) # Square root ## [1] 15
abs(-5) #Absolute value ## [1] 5
All of the examples show that the general form is function_name( ). The name
of the function should give you some clue as to what it does, and the ( )
is where you provide the data to the function.
Many functions also have additional options you can choose, which are called the
arguments. To see what needs to go inside ( ), type a
question mark in front of the function and run it in the R console.
?mean()In RStudio, you will see the help page for mean() in the bottom right corner panel.
On the help page, under Usage, you see mean(x, ...). This means that the only
thing that necessarily has to go into ( ) is x. On the help page under Arguments
you will find a description of what x needs to be: a numeric or logical vector.
Many built-in functions in R have multiple arguments. This allows you to give the
function some more information to perform calculation you want. The example below
shows how to use the digits argument in the round( ) function. Providing
different values to the digits argument will return different values.
round(12.3456) ## [1] 12
round(12.3456, digits=3) ## [1] 12.346
round(12.3456, digits=1)## [1] 12.3
In the first example, you can see that we did not provide a value for the
digits argument. That's because there is a default value digits = 0 (see
the Usage section on the help page ?round). If there is a default value,
then that argument does not need to be specified inside ( ). If there is no
default value for an argument, then the function will error and tell you that
you forgot to supply a value for the argument.
When you start an R session there are many built-in functions that are immediately available for you to use. Other functions are available in community developed packages, as explained in a later section of this lesson. Below is a list of a few commonly used built-in functions in R.
Returns the sum of a vector of numeric values.
sum(c(2.3, 7.5, 9, -10))## [1] 8.8
Get the minimum value from a numeric vector.
min(c(6, 9, 3, 11, -2))## [1] -2
Get the maximum value from a numeric vector.
max(c(15, 2, 8.3, -10, 21))## [1] 21
Create a numeric vector with a certain sequence. The example below creates a vector of integers from 1 to 5.
seq(from = 1, to = 5, by = 1)## [1] 1 2 3 4 5
Another way to create a sequence of integers is to use the colon.
1:5## [1] 1 2 3 4 5
Concatenate two or more strings.
x <- "Hello"
y <- "world!"
paste(x, y, sep = " ")## [1] "Hello world!"
Any numbers will be converted to strings.
x <- "You're number "
y <- 1
z <- "!"
paste(x, y, z, sep = "")## [1] "You're number 1!"
The substr() function allows you to pull out a section from a string
based on the position of the characters in the string. This is useful for vectors
of dates, addresses, monitor IDs, parameter descriptions, etc.
For example, in AQS data a monitor ID may be written in the following format:
[State code - County code - Site number - Parameter code - POC].
If we only wanted to pull out the site number for this monitor ID we could do the following:
wisconsin_monitor <- c('55-021-0015-44201-2') # Ozone monitor in Columbia County, WI
site_id <- substr(wisconsin_monitor, start = 8, stop = 11) # start and stop position within the character string.
site_id## [1] "0015"
R allows you to place a function inside another function to perform multiple tasks on data in one step.
For instance, if you want to create a sequence of numbers and then take the mean of that sequence, you could either do it in a couple of steps, or all at once.
#Two steps
x <- seq(from=1, to=10, by=3)
mean(x)## [1] 5.5
#One step
mean(seq(from=1, to=10, by=3)) ## [1] 5.5
Note: Typically you don’t want to have too many nested functions because it becomes difficult to read.
Most of the statistical summary functions in R have the argument na.rm. This
stands for NA remove. The NA value is how R represents a missing value, similar
to the NULL value in a SQL database.
For example, there is a built-in data frame in R called airquality with
daily measurements from a monitor in New York from 1973 (see ?airquality).
If we load the data frame using the data() function and take a look at the top
6 rows using the head() function, we can see some missing values represented
as NA.
data("airquality")
head(airquality)## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
The mean() function, for example, has the argument na.rm set to FALSE. This
means that the NA values will not be removed from the vector for which it
is calculating the mean. As a result, it will return an NA because it cannot
properly calculate the average. Here we use the Ozone column from the airquality
data frame.
mean(airquality$Ozone)## [1] NA
To get the mean value, we set na.rm = TRUE.
mean(airquality$Ozone, na.rm = TRUE)## [1] 42.12931
R comes with basic functionality, meaning that some functions will always be available when you start an R session. But anyone can write functions for R that are not part of the base functionality and make it available to other R users in a package.
Packages must be installed on your computer first then loaded before using it. This is similar to a mobile app: you must first install the R package (like first downloading an app) then you must load the package before using its functions (like opening an app to use it).
If base R doesn't have a function you need, the best thing to do is a Google search. Use a search with key words describing what you want the function to do and just add "R package" at the end.
For example, if you wanted to find serial correlation in an environmental data
set, Google would tell you that the R package EnvStats has a function called
serialCorrelationTest().
First, you might try to use the function.
x <- c(1.3, 3.5, 2.6, 3.4, 6.4)
serialCorrelationTest(x)## Error in serialCorrelationTest(x): could not find function "serialCorrelationTest"
It's not available because we need to install the package first (again, like initially downloading an app).
In the bottom right panel of RStudio, click on the "Packages" tab then click "Install Packages" in the tool bar.
A window will pop up. Start typing "EnvStats" into the "Packages" box, select that package, and click "Install".
Now that we've installed the package, we still can't use the function we want.
We need to load the package first (opening the app). We use the library() function
to do this.
library(EnvStats)Now we can use the function we want.
x <- c(1.3, 3.5, 2.6, 3.4, 6.4)
serialCorrelationTest(x)##
## Results of Hypothesis Test
## --------------------------
##
## Null Hypothesis: rho = 0
##
## Alternative Hypothesis: True rho is not equal to 0
##
## Test Name: Rank von Neumann Test for
## Lag-1 Autocorrelation
## (Exact Method)
##
## Estimated Parameter(s): rho = -0.0187589
##
## Estimation Method: Yule-Walker
##
## Data: x
##
## Sample Size: 5
##
## Test Statistic: RVN = 1.8
##
## P-value: 0.7833333
##
## Confidence Interval for: rho
##
## Confidence Interval Method: Normal Approximation
##
## Confidence Interval Type: two-sided
##
## Confidence Level: 95%
##
## Confidence Interval: LCL = -0.8951272
## UCL = 0.8576094
Here is a link to a page that lists many useful packages for environmental data analysis: https://cran.r-project.org/web/views/Environmetrics.html
Remember, when you close down RStudio, then start it up again, you don’t have to
download the package again. But you do have to use the library() function to
load the package before you can use any function that's not in the R core functionality
(this is very easy to forget).
R can import data from just about any format, including
- CSV
- Excel
- Databases
- GIS shapefiles
This section will demonstrate how to import CSV and Excel files.
R has a built-in function called read.csv() for reading .csv files. Download
the chicago_daily.csv file here and save it to your
working directory. If you don't know what your working directory is, run this
code in R and it will tell you.
getwd()Use read.csv() by providing the location and name of the file as the first
argument. If the file is in your working directory, simply supply the name of the
file. Below, the data from the file is read into R and saved as a data frame,
which is the data type for storing tables. The function head() will show the
first few lines.
chicago_daily <- read.csv("chicago_daily.csv")
head(chicago_daily)## date no2 ozone pm25 so2
## 1 2021-02-06 19.1 NA 4.2 1.5
## 2 2021-02-07 28.7 NA 9.0 3.1
## 3 2021-02-08 51.2 NA 34.6 5.1
## 4 2021-02-09 49.3 NA 16.8 4.6
## 5 2021-02-10 46.0 NA 15.6 3.2
## 6 2021-02-11 39.5 NA 13.1 2.0
There are several packages that can be used to import data from an Excel file,
such as xlsx, XLConnect, and readxl. In this example, we'll use the readxl
package. If you do not have the package installed, you can use RStudio to install
as described in the section above on packages. You can also use the function
install.packages( ).
install.packages("readxl")Once you have the package installed, remember to load the package by using
the library() function.
library(readxl)Use the read_excel() function from the readxl package to read emissions data
from this Excel workbook.
Download the file to your working directory and read the first worksheet (named
"UNIT_DATA"), skipping the first 6 rows.
emissions <- read_excel("emissions_IL_2022.xlsx", sheet = "UNIT_DATA", skip = 6)
head(emissions)## # A tibble: 6 × 17
## `Facility Id` `FRS Id` `Facility Name` City State `Primary NAICS Code`
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1003742 110043790804 31st Street Landf… WEST… IL 562212
## 2 1003742 110043790804 31st Street Landf… WEST… IL 562212
## 3 1003742 110043790804 31st Street Landf… WEST… IL 562212
## 4 1003742 110043790804 31st Street Landf… WEST… IL 562212
## 5 1003742 110043790804 31st Street Landf… WEST… IL 562212
## 6 1003742 110043790804 31st Street Landf… WEST… IL 562212
## # ℹ 11 more variables: `Reporting Year` <dbl>,
## # `Industry Type (subparts)` <chr>, `Industry Type (sectors)` <chr>,
## # `Unit Name` <chr>, `Unit Type` <chr>, `Unit Reporting Method` <chr>,
## # `Unit Maximum Rated Heat Input (mmBTU/hr)` <dbl>,
## # `Unit CO2 emissions (non-biogenic)` <dbl>,
## # `Unit Methane (CH4) emissions` <dbl>,
## # `Unit Nitrous Oxide (N2O) emissions` <dbl>, …
The next lesson in this series is on Subsetting, Sorting, and Combining Data Frames.
Try these exercises to test your comprehension of material in this lesson.
Use the seq() function to create a vector from 1 to 20 by 2. For help with the
parameters, run ?seq() in the console and consult the documentation.
Click for Solution
In the
seq()function, setby = 2.
seq(from = 1, to = 20, by = 2)## [1] 1 3 5 7 9 11 13 15 17 19
Use the round( ) function to round the number 13.5678 to two digits after the
decimal point.
Concatenate the strings "Hello" and "R" using the paste() function.
Sum the numbers 1 through 10 using the sum() function.
Click for Solution
Use
:to create the sequence of integers and place it inside thesum()function.
sum(1:10)## [1] 55
Read in the first 10 rows of the chicago_daily.csv file here.
Click for Solution
Save the file to your working directory and use
nrows = 10in theread.csv()function
read.csv("chicago_daily.csv", nrows = 10)## date no2 ozone pm25 so2
## 1 2021-02-06 19.1 NA 4.2 1.5
## 2 2021-02-07 28.7 NA 9.0 3.1
## 3 2021-02-08 51.2 NA 34.6 5.1
## 4 2021-02-09 49.3 NA 16.8 4.6
## 5 2021-02-10 46.0 NA 15.6 3.2
## 6 2021-02-11 39.5 NA 13.1 2.0
## 7 2021-02-12 36.0 NA 9.8 2.1
## 8 2021-02-13 24.9 NA 11.0 1.9
## 9 2021-02-14 17.6 NA 9.0 1.9
## 10 2021-02-15 22.6 NA 3.3 1.7