Basics

Overgrown Calculator

# Addition and Subtraction
2 + 5 - 1

[1] 6

# Multiplication
109*23452

[1] 2556268

# Division
3/7

[1] 0.4285714

More Calculator Operations

# Integer division
7 %/% 2

[1] 3

# Modulo operator (Remainder)
7 %% 2

[1] 1

# Powers
1.5^3

[1] 3.375

Even More Functions

Exponentiation
- exp(x)
Logarithms
- log(x)
- log(x, base = 10)
Trigonometric functions
- sin(x)
- asin(x)
- cos(x)
- tan(x)

Creating Variables

We can create variables using the assignment operator <-:

x <- 5
MyAge <- 25

We can then perform any of the functions on the variables:

log(x)

[1] 1.609438

MyAge^2

[1] 625

Rules for Variable Creation

Variable names can’t start with a number
Variables in R are case-sensitive
Some common letters are used internally by R and should be avoided as variable names (c, q, t, C, D, F, T, I)
There are reserved words that R won’t let you use for variable names. (for, in, while, if, else, repeat, break, next)
R will let you use the name of a predefined function. Try not to overwrite those though!

Vectors

A variable does not need to be a single value. We can create a vector using the c function (combine - combines several objects into one):

y <- c(1, 5, 3, 2)

Operations will then be done element-wise:

y / 2

[1] 0.5 2.5 1.5 1.0

Getting Help

We will talk MUCH more about vectors in a bit, but for now, let’s talk about a couple ways to get help. The primary function to use is the help function. Just pass in the name of the function you need help with:

help(head)

The ? function also works:

?head

Googling for help can be difficult at first. You might need to search for R + CRAN + <your query> to get good results

Stackoverflow is VERY helpful

Getting Help

R Reference Card

You can download the reference card from:

Here

Having this open or printed off and near you while working is helpful.

Rstudio cheatsheets

The Rstudio cheatsheets are VERY helpful.

Warnings vs. Errors

Routinely beginners to R panic if they see a red message as innocuous as confirming that a library loaded
- Not all red text mean that there is an error!
A warning is a message that does not disturb the program flow but is displayed along with the output
- Not always a cause for concern
An error will terminate a program from being ran
- Google is a beautiful thing

Your Turn

Using the R Reference Card (and the Help pages, if needed), do the following:

Find out how many rows and columns the iris data set has (use data(iris) to load in the dataset). Figure out at least 2 ways to do this.

Hint: “Variable Information” section on the first page of the reference card!

Use the rep function to construct the following vector: 1 1 2 2 3 3 4 4 5 5

Hint: “Data Creation” section of the reference card

Use rep to construct this vector: 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Data Frames: Introduction

final_shed is a data frame.
Data frames hold data sets
Not every column need be the same type - like an Excel spreadsheet
Each column in a data frame is a vector ¹ - so each column needs to have values that are all the same type.
We can access different columns using the $ operator.

shedding <- final_shed$total_shedding
treatment <- final_shed$treatment

.footnote[ [1] A column can also be a list! This is a more advanced topic that will be saved for later.]

More about Vectors

A vector is a list of values that are all the same type. We have seen that we can create them using the c or the rep function. We can also use the : operator if we wish to create consecutive values:

a <- 10:15
a

[1] 10 11 12 13 14 15

We can extract the different elements of the vector like so:

shedding[3]

[1] 59.04973

Indexing Vectors

We saw that we can access individual elements of the vector. But indexing is a lot more powerful than that:

head(shedding)

[1] 37.14022 43.88073 59.04973 44.96963 38.74342 56.12656

shedding[c(1, 3, 5)]

[1] 37.14022 59.04973 38.74342

shedding[1:5]

[1] 37.14022 43.88073 59.04973 44.96963 38.74342

Logical Values

R has built in support for logical values
TRUE and FALSE are built in. T (for TRUE) and F (for FALSE) are supported but can be modified
Logicals can result from a comparison using
- < : “less than”
- > : “greater than”
- <= : “less than or equal to”
- >= : “greater than or equal to”
- == : “is equal to”
- != : “not equal to”

Indexing with Logicals

We can index vectors using logical values as well:

x <- c(2, 3, 5, 7)
x[c(TRUE, FALSE, FALSE, TRUE)]

[1] 2 7

x > 3.5

[1] FALSE FALSE  TRUE  TRUE

x[x > 3.5]

[1] 5 7

Logical Examples

bad_shedder <- shedding > 50
shedding[bad_shedder]

[1] 59.04973 56.12656 66.20657 51.98984 58.53921 64.74017 64.27066 56.06566
[9] 53.76049

Your Turn

Find out how many pigs had a total shedding value of less than 30 log10 CFUs.

Hint: if you use the sum function on a logical vector, it’ll return how many TRUEs are in the vector:

sum(c(TRUE, TRUE, FALSE, TRUE, FALSE))

[1] 3

More Challenging: Calculate the sum of the total shedding log10 CFUs of all pigs with a total shedding value of less than 30 log10 CFUs.

Element-wise Logical Operators

& (elementwise AND)
| (elementwise OR)

c(T, T, F, F) & c(T, F, T, F)

[1]  TRUE FALSE FALSE FALSE

c(T, T, F, F) | c(T, F, T, F)

[1]  TRUE  TRUE  TRUE FALSE

# Which are high shedders in the control group?
id <- (shedding > 50 & treatment == "control")
final_shed[id,]

# A tibble: 4 × 7
  pignum time_point pig_weight daily_shedding treatment total_shedding  gain
   <dbl>      <dbl>      <dbl>          <dbl> <chr>              <dbl> <dbl>
1    122         21       33.9           5.01 control             59.0  16.8
2    224         21       22.9           3.91 control             56.1  11.4
3    337         21       29.5           5.52 control             66.2  16.2
4    419         21       31             6.21 control             52.0  16.8

Modifying Vectors

We can modify vectors using indexing as well:

x <- shedding[1:5]
x

[1] 37.14022 43.88073 59.04973 44.96963 38.74342

x[1] <- 20
x

[1] 20.00000 43.88073 59.04973 44.96963 38.74342

Vector Elements

Elements of a vector must all be the same type:

head(shedding)

[1] 37.14022 43.88073 59.04973 44.96963 38.74342 56.12656

shedding[bad_shedder] <- ":-("
head(shedding)

[1] "37.1402150411922" "43.8807276727966" ":-("              "44.9696314253854"
[5] "38.7434232007542" ":-("

By changing a value to a string, all the other values were also changed.

Data Types in R

Can use mode or class to find out information about variables
str is useful to find information about the structure of your data
Many data types: numeric, integer, character, Date, and factor most common

str(final_shed)

tibble [59 × 7] (S3: tbl_df/tbl/data.frame)
 $ pignum        : num [1:59] 77 87 122 160 191 224 337 345 419 458 ...
 $ time_point    : num [1:59] 21 21 21 21 21 21 21 21 21 21 ...
 $ pig_weight    : num [1:59] 25.4 23.9 33.9 28.4 28.9 ...
 $ daily_shedding: num [1:59] 4.61 3.91 5.01 3.91 3.91 ...
 $ treatment     : chr [1:59] "control" "control" "control" "control" ...
 $ total_shedding: num [1:59] 37.1 43.9 59 45 38.7 ...
 $ gain          : num [1:59] 13.9 11.7 16.8 15.1 14.6 ...

Converting Between Types

We can convert between different types using the as series of functions:

pignum <- head(final_shed$pignum)
pignum

[1]  77  87 122 160 191 224

as.character(pignum)

[1] "77"  "87"  "122" "160" "191" "224"

as.numeric("77")

[1] 77

#as.factor()

More About Data Types

Some useful functions

There are a whole variety of useful functions to operate on vectors.

A couple of the more common ones are length, which returns the length (number of elements) of a vector, and sum, which adds up all the elements of a vector.

pig_weight <- final_shed$pig_weight
x <- pig_weight[1:5]
length(x)

[1] 5

sum(x)

[1] 140.36

Note: Be careful, if you use length with a dataframe, it will return number of variables not number of observations.

class(shed)

[1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

length(shed)

[1] 12

prod(dim(shed))

[1] 3540

Statistical Functions

Using the basic functions we’ve learned, it wouldn’t be hard to compute some basic statistics.

(n <- length(pig_weight))

[1] 59

(meanweight <- sum(pig_weight) / n)

[1] 28.82305

(standdev <- sqrt(sum((pig_weight - meanweight)^2) / (n - 1)))

[1] 4.10429

But we don’t have to.

Built-in Statistical Functions

mean(pig_weight)

[1] 28.82305

sd(pig_weight)

[1] 4.10429

summary(pig_weight)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  19.50   25.79   28.80   28.82   32.24   36.30

quantile(pig_weight, c(.025, .975))

  2.5%  97.5% 
22.279 35.952

Your Turn

Which pigs have a shedding value less than or equal to 30 OR is in the Acid treatment group?
Explore any more calculations in the dataset you may find interesting.