Packages and Basic Programming

R Packages

  • Commonly used R functions are installed with base R

  • R packages containing more specialized R functions can be installed freely from CRAN servers using function install.packages()

  • After packages are installed, their functions can be loaded into the current R session using the function library()

Finding R Packages

  • How do I locate a package with the desired function?

  • Google (“R project” + search term works well)

  • R website task views to search relevant subjects: http://cran.r-project.org/web/views/

  • ??searchterm will search R help for pages related to the search term

Handy R Packages

  • ggplot2: Statistical graphics

  • tidyverse: Manipulating data structures (includes dplyr, tidyr, purr, tibble, etc packages)

  • lme4: Mixed models

  • knitr: integrate LaTeX, HTML, or Markdown with R for easy reproducible research

  • vegan: Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

  • phyloseq: Handling and analysis of high-throughput microbiome census data

  • ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data

  • caret: The R equivalent to scikit learn: train / test split, cross validation, model performance metrics

List of some Handy R Packages - Definitely NOT comprehensive

Creating Your Own Functions

  • Sometimes you just need to create your own function to tackle your data
    • Especially if you notice yourself duplicating code
  • “Remember that a function is essentially a”black box” into which you add some inputs and then receive some outputs. Building a function is about building that “black box”, and there are several components that go into it.” source

Code Skeleton:

foo <- function(arg1, arg2, ...) {
    # Code goes here
    return(output)
}

Creating Your Own Functions (continued)

Example:

mymean <- function(data) {
    ans <- sum(data) / length(data)
    return(ans)
}

set.seed(4) #to generate the same random numbers each time run sample function
example <- sample(1:200, 15) #return 15 random values between 1 and 200 without replacement. without set.seed, will return different values each time
mymean(example) #mean of random generated data
[1] 110.7333
  • Function Name: mymean
  • Arguments (inputs): data (data you want to calculate mean of)
  • Body: ans <- sum(data) / length(data) (code you run to get desired output)
  • Return Value: ans (the mean of the data)

If/Else Statements

Skeleton:

if (condition) {
    # Some code that runs if condition is TRUE
} else {
    # Some code that runs if condition is FALSE
}

Basic Example:

x <- -4
if(x > 0){
print("Non-negative number")
} else {
print("Negative number")
}
[1] "Negative number"

If/Else Statements (Continued)

Example within a Function:

mymean <- function(data) {
    if (!is.numeric(data)) {
        print("Numeric input is required")
    } else {
        ans <- sum(data) / length(data)
        return(ans)
    }
}

mymean("dog")
[1] "Numeric input is required"
mymean(c(2,4,5,6,7))
[1] 4.8

Looping

  • Reducing the amount of typing we do can be nice

  • If we have a lot of code that is essentially the same we can take advantage of looping.

  • R offers several loops: for, while, repeat.

for (i in 1:3) {
    print(i)
}
[1] 1
[1] 2
[1] 3

For Loops (source)

source

For Loop Example

for(i in 1:5) {# Head of for-loop
  x1 <- i^2 # Code block
  print(x1) # Print results
}
[1] 1
[1] 4
[1] 9
[1] 16
[1] 25

For Loop Example

final_shed <- read.csv("https://unl-statistics.github.io/R-workshops/r-intro/data/daily_shedding.csv")
id <- c("pig_weight", "total_shedding", "daily_shedding")
for(colname in id) {
    print(paste(colname, mymean(final_shed[, colname])))
}
[1] "pig_weight NA"
[1] "total_shedding 17.4232730553356"
[1] "daily_shedding 3.76549250132986"

While Loops

  • Loop until a specific condition is met
i <- 1
while (i < 5) {
    print(i)
    i <- i + 1
}
[1] 1
[1] 2
[1] 3
[1] 4

  • The test_expression is i < 5 which is TRUE since 1 is less than 5. So, the body of the loop is entered and i is printed and incremented (i + 1)

  • This will continue until i takes the value 5. The condition 5 < 5 will give FALSE and the while loop exits.

  • Make sure the loop will eventually meet the condition, otherwise the loop will run infinitely.

Your Turn

  1. Create a function that takes numeric input and provides the mean and a 95% confidence interval for the mean for the data (the t.test function could be useful - example in 4-DataStructures.R - will give you your confidence interval)

  2. Add checks to your function to make sure the data is either numeric or logical. If it is logical convert it to numeric.

  3. The diamonds data set is included in the ggplot2 package and is well known as a convenient data set for examples. It can be read into your environment with the function data("diamonds", package = "ggplot2"). Loop over the columns of the diamonds data set and apply your function to all of the numeric columns.

UNL R Workshops