R resources

• The R Project Homepage: http://www.r-project.org
• Quick R Homepage: http://www.statmethods.net
• Bioconductor: http://www.bioconductor.org
  
• An Introduction to R (long!): http://cran.r-project.org/doc/manuals/R-intro.html
• R for Data Science: https://r4ds.had.co.nz
• Google - tutorials, guides, demos, packages and more

Running R

• Need to make sure that you have R installed
  • locally or on a server
  • https://www.r-project.org
• Run R from the command line
  • just type R
  • can run it locally as well as on clusters
• Install a R Integrated Development Environment (IDE)
  • RStudio: http://www.rstudio.com
  • Makes working with R much easier, particularly for a new R user
  • Run on Windows, Mac or Linux OS
  • We’re running as a server on the AWS instance

RStudio

RMarkdown

• A great way to embed R code into descriptive files
  • http://kbroman.org/knitr_knutshell/pages/Rmarkdown.html
  • https://bookdown.org/yihui/rmarkdown/
  • https://bookdown.org/yihui/bookdown/
• You can insert R chunks into Rmarkdown documents
• You will be doing more with markdown on Thursday

Assigning Variables

• A better way to do this is to assign variables
• Variables are assigned values using the <- operator.
• Variable names must begin with a letter, but other than that, just about anything goes.
• Do keep in mind that R is case sensitive.

Assigning Variables

x <- 2
x * 3

## [1] 6

y <- x * 3
y - 2

## [1] 4

These do not work

3y <- 3
3*y <- 3

Arithmetic operations on functions

y <- 67
print(y)

## [1] 67

x <- 124
z <- (x * y)^2
print(z)

## [1] 69022864

STRINGS

• Operations can be performed on character variables as well
• Note that “characters” need to be set off by quotation marks to differentiate them from numbers
• The c stands for concatenate
• Note that we are using the same variable names as we did previously, which means that we’re overwriting our previous assignment
• A good rule of thumb is to use new names for each variable, and make them short but still descriptive

STRINGS

x <- "I Love"
print(x)

## [1] "I Love"

y <- "Biostatistics"
print(y)

## [1] "Biostatistics"

z <- c(x, y)
print(z)

## [1] "I Love"        "Biostatistics"

VECTORS

• In general R thinks in terms of vectors
  • a list of characters, factors or numerical values (“I Love”)
  • it will benefit any R user to try to write scripts with that in mind
  • it will simplify most things
• Vectors can be assigned directly using the ‘c()’ function and then entering the exact values.

VECTORS

n <- c(2, 3, 4, 2, 1, 2, 4, 5, 10, 8, 9)
print(n)

##  [1]  2  3  4  2  1  2  4  5 10  8  9

z <- n + 3
print(z)

##  [1]  5  6  7  5  4  5  7  8 13 11 12

FACTORS

• The vector x is now what is called a list of character values (“I Love”).
• Sometimes we would like to treat the characters as if they were units for subsequent calculations.
• These are called factors, and we can redefine our character variables as factors.
• This might seem a bit strange, but it’s important for statistical analyses where we might want to see the mean or variance for two different treatments.

FACTORS

x_factor <- as.factor(x)
print(x_factor)

## [1] I Love
## Levels: I Love

• Note that factor levels are reported alphabetically

str(x)

##  chr "I Love"

class(x)

## [1] "character"

• We can also determine how R “sees” a variable using str() or class() functions.
• This is a useful check when importing datasets or verifying that you assigned a class correctly

Basic Statistics

Many functions exist to operate on vectors.

mean(n)
median(n)
var(n)
log(n)
exp(n)
sqrt(n)
sum(n)
length(n)
sample(n, replace = T)  #has an additional argument (replace=T)

• Arguments modify or direct the function in some way
  • There are many arguments for each function, some of which are defaults
  • Tab complete is helpful to view argument options

Getting Help

• Getting Help on any function is very easy - just type a question mark and the name of the function.
• There are functions for just about anything within R and it is easy enough to write your own functions if none already exist to do what you want to do.
• In general, function calls have a simple structure: a function name, a set of parentheses and an optional set of parameters/arguments to send to the function.
• Help pages exist for all functions that, at a minimum, explain what parameters exist for the function.

Getting Help

-help(mean)
-`?`(mean)
-example(mean)
-help.search("mean")
-apropos("mean")
-args(mean)

Drawing samples from distributions

• You’ve probably figured out that y from the last example is drawing numbers with equal probability.
• What if you want to draw from a distribution?
• Again, play around with the arguments in the parentheses to see what happens.

x <- rnorm (10000, 0, 10)
y <- sample (???, 10000, replace = ???)

Drawing samples from distributions

• dnorm() generates the probability density, which can be plotted using the curve() function.
• Note that is curve is added to the plot using add=TRUE

x <- rnorm(1000, 0, 100)
hist(x, xlim = c(-500, 500))
curve(50000 * dnorm(x, 0, 100), xlim = c(-500, 500), add = TRUE, 
    col = "Red")

Visualizing Data

• So far you’ve been visualizing just the list of output numbers
• Except for the last example where I snuck in a hist function.
• You can also visualize all of the variables that you’ve created using the plot function (as well as a number of more sophisticated plotting functions).
• Each of these is called a high level plotting function, which sets the stage
• Low level plotting functions will tweak the plots and make them beautiful

Putting plots in a single figure

• The first line of the lower script tells R that you are going to create a composite figure that has two rows and two columns (on next slide)
  • Can you tell how?

seq_1 <- seq(0, 10, by = 0.1)
seq_2 <- seq(10, 0, by = -0.1)

par(mfrow = c(2, 2))
plot(seq_1, xlab = "time", ylab = "p in population 1", type = "p", 
    col = "red")
plot(seq_2, xlab = "time", ylab = "p in population 2", type = "p", 
    col = "green")
plot(seq_square, xlab = "time", ylab = "p2 in population 2", 
    type = "p", col = "blue")
plot(seq_square_new, xlab = "time", ylab = "p in population 1", 
    type = "l", col = "yellow")

Reading in Data Frames in R

YourFile <- read.table("yourfile.csv", header = T, row.names = 1, 
    sep = ",")
YourFile <- read.csv("yourfile.csv", header = T, row.names = 1, 
    sep = ",")
YourFile <- read.table("yourfile.txt", header = T, row.names = 1, 
    sep = "\t")

Exporting Data Frames in R

write.csv(YourFile, "yourfile.csv", quote = F, row.names = T, 
    sep = ",")
write.table(YourFile, "yourfile.txt", quote = F, row.names = T, 
    sep = "\t")

• you will get more practice with this during the next R interlude

Indexing in data frames

• Next up - indexing just a subset of the data
• This is a very important idea in R, that you can analyze just a subset of the data.

print(YourFile[, 2])
print(YourFile$temp)
print(YourFile[2, ])
plot(YourFile$temp, YourFile$elevation)

Indexing in data frames

• You can also assign values, or single values, from a data set to a new variable

x <- (YourFile[, 2])
y <- (YourFile$temp)
z <- (YourFile$elevation)
plot(y, z)

Indexing in data frames

• You can perform operations on particular levels of a factor
• Note that the first argument is the numerical column vector, and the second is the factor column vector.
• The third is the operation. Reversing the first two does not work - convince yourself of this by altering the order.
  • Tab complete will tell you the correct order for arguments

tapply(YourFile$temp, YourFile$habitat, mean)
tapply(YourFile$temp, YourFile$habitat, var)

Git and GitHub

https://learngitbranching.js.org/