• Follow along (copy & paste the code into the console):

curl::curl_download(
  "https://raw.githubusercontent.com/heike/summerschool-2022/master/01-Introduction-to-R/code/2-basics.R",
  "2-basics.R"
)
file.edit("2-basics.R")

# Addition and Subtraction
2 + 5 - 1

## [1] 6

# Multiplication
109*23452

## [1] 2556268

# Division
3/7

## [1] 0.4285714

# Integer division
7 %/% 2

## [1] 3

# Modulo operator (Remainder)
7 %% 2

## [1] 1

# Powers
1.5^3

## [1] 3.375

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

We create an object using the assignment operator <-

x <- 5
y <- 21

We then perform any operations on these objects:

log(x)

## [1] 1.609438

y^2

## [1] 441

• 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 without any warning.

Pro-tip: before introducing a new object, type it in the console to check that it is not yet taken

A variable usually consists of more than a single value. We create a vector using the c (combine) function:

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

Operations will then be done element-wise:

y / 2

## [1] 0.5 2.5 1.5 1.0

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 is a bit hard. Searches of the form R + CRAN + usually get good results

Download the reference card from:

http://cran.r-project.org/doc/contrib/Short-refcard.pdf

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

• penguins is a data frame.
• Data frames hold data sets
• Columns can have different types - like an Excel spreadsheet
• Each column in a data frame is a vector - so each column needs to have values that are all the same type.
• We can access different columns using the $ operator.

penguins <- read.csv("https://raw.githubusercontent.com/heike/summerschool-2022/main/01-Introduction-to-R/data/penguins.csv", stringsAsFactors = TRUE)

species <- penguins$species
bill_length <- penguins$bill_length_mm

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 (note, unlike python indexing starts with 1):

bill_length[3]

## [1] 40.3

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

head(bill_length)

## [1] 39.1 39.5 40.3   NA 36.7 39.3

bill_length[c(1, 3, 5)]

## [1] 39.1 40.3 36.7

bill_length[1:5]

## [1] 39.1 39.5 40.3   NA 36.7

• 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
  • \(<\)
  • \(>\)
  • \(<=\)
  • \(>=\)
  • \(==\)
  • \(!=\)

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

bill_length <- penguins$bill_length
head(bill_length)

## [1] 39.1 39.5 40.3   NA 36.7 39.3

short_bills <- bill_length < 35
species[short_bills]

##  [1] <NA>   Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [11] <NA>  
## Levels: Adelie Chinstrap Gentoo

• 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(penguins)

## 'data.frame':    344 obs. of  8 variables:
##  $ species          : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ island           : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ bill_length_mm   : num  39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
##  $ bill_depth_mm    : num  18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
##  $ flipper_length_mm: int  181 186 195 NA 193 190 181 195 193 190 ...
##  $ body_mass_g      : int  3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
##  $ sex              : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
##  $ year             : int  2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...

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.

x <- bill_length[1:5]
length(x)

## [1] 5

sum(x)

## [1] NA

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

(n <- length(bill_length))

## [1] 344

(meanlength <- sum(bill_length) / n)

## [1] NA

(standdev <- sqrt(sum((bill_length - meanlength)^2) / (n - 1)))

## [1] NA

But we don’t have to.

mean(bill_length)

## [1] NA

sd(bill_length)

## [1] NA

summary(bill_length)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   32.10   39.23   44.45   43.92   48.50   59.60       2

quantile(bill_length, c(.025, .975), na.rm = TRUE)

##   2.5%  97.5% 
## 34.810 53.085

• & (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

# How many of the short billed penguins are male?
id <- (bill_length <35  & penguins$sex == "male")
penguins[id,]

##      species    island bill_length_mm bill_depth_mm flipper_length_mm
## NA      <NA>      <NA>             NA            NA                NA
## NA.1    <NA>      <NA>             NA            NA                NA
## 15    Adelie Torgersen           34.6          21.1               198
## NA.2    <NA>      <NA>             NA            NA                NA
##      body_mass_g  sex year
## NA            NA <NA>   NA
## NA.1          NA <NA>   NA
## 15          4400 male 2007
## NA.2          NA <NA>   NA