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

curl::curl_download(
  "https://raw.githubusercontent.com/heike/rwrks/gh-pages/summerschool/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 can create an object using the assignment operator <-:

x <- 5
todays_date <- 21

We can then perform any of the functions on these objects:

log(x)

## [1] 1.609438

todays_date^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. Try not to overwrite those though!

A variable usually consists of more than a single value. We can 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

We will pass you out a copy, but you can 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.

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

1. Find out how many rows and columns the `iris' data set has. Figure out at least 2 ways to do this. Hint: "Variable Information" section on the first page of the reference card!

2. 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

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

• tips 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.

tip <- tips$tip
bill <- tips$total_bill

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:

bill[3]

## [1] 21.01

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

head(tip)

## [1] 1.01 1.66 3.50 3.31 3.61 4.71

tip[c(1, 3, 5)]

## [1] 1.01 3.50 3.61

tip[1:5]

## [1] 1.01 1.66 3.50 3.31 3.61

• 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

rate <- tips$rate
head(rate)

## [1] 0.05944673 0.16054159 0.16658734 0.13978041 0.14680765 0.18623962

sad_tip <- rate < 0.10
rate[sad_tip]

##  [1] 0.05944673 0.07180385 0.07892660 0.05679667 0.09935739 0.05643341
##  [7] 0.09553024 0.07861635 0.07296137 0.08146640 0.09984301 0.09452888
## [13] 0.07717751 0.07398274 0.06565988 0.09560229 0.09001406 0.07745933
## [19] 0.08364236 0.06653360 0.08527132 0.08329863 0.07936508 0.03563814
## [25] 0.07358352 0.08822232 0.09820426

1. Find out how many people tipped over 20%.

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

1. More Challenging: Calculate the sum of the total bills of anyone who tipped over 20%

We can modify vectors using indexing as well:

x <- bill[1:5]
x

## [1] 16.99 10.34 21.01 23.68 24.59

x[1] <- 20
x

## [1] 20.00 10.34 21.01 23.68 24.59

Elements of a vector must all be the same type:

head(rate)

## [1] 0.05944673 0.16054159 0.16658734 0.13978041 0.14680765 0.18623962

rate[sad_tip] <- ":-("
head(rate)

## [1] ":-("               "0.160541586073501" "0.166587339362208"
## [4] "0.139780405405405" "0.146807645384303" "0.186239620403321"

By changing a value to a string, all the other values got changed as well.

• 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(tips)

## 'data.frame':    244 obs. of  8 variables:
##  $ total_bill: num  17 10.3 21 23.7 24.6 ...
##  $ tip       : num  1.01 1.66 3.5 3.31 3.61 4.71 2 3.12 1.96 3.23 ...
##  $ sex       : Factor w/ 2 levels "Female","Male": 1 2 2 2 1 2 2 2 2 2 ...
##  $ smoker    : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ day       : Factor w/ 4 levels "Fri","Sat","Sun",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ time      : Factor w/ 2 levels "Dinner","Lunch": 1 1 1 1 1 1 1 1 1 1 ...
##  $ size      : int  2 3 3 2 4 4 2 4 2 2 ...
##  $ rate      : num  0.0594 0.1605 0.1666 0.1398 0.1468 ...

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

size <- head(tips$size)
size

## [1] 2 3 3 2 4 4

as.character(size)

## [1] "2" "3" "3" "2" "4" "4"

as.numeric("2")

## [1] 2

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

## [1] 5

sum(x)

## [1] 13.09

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

(n <- length(tip))

## [1] 244

(meantip <- sum(tip) / n)

## [1] 2.998279

(standdev <- sqrt(sum((tip - meantip)^2) / (n - 1)))

## [1] 1.383638

But we don't have to.

mean(tip)

## [1] 2.998279

sd(tip)

## [1] 1.383638

summary(tip)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.000   2.900   2.998   3.562  10.000

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

##   2.5%  97.5% 
## 1.1760 6.4625

• & (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 big bills with a poor tip rate?
id <- (bill > 40 & rate < .10)
tips[id,]

##     total_bill tip    sex smoker day   time size       rate
## 103      44.30 2.5 Female    Yes Sat Dinner    3 0.05643341
## 183      45.35 3.5   Male    Yes Sun Dinner    3 0.07717751
## 185      40.55 3.0   Male    Yes Sun Dinner    2 0.07398274

1. Read up on the dataset (?diamonds)
2. Plot price by carat (use qplot - go back to the motivating example for help with the syntax)
3. Create a variable ppc for price/carat. Store this variable as a column in the diamonds data
4. Make a histogram of all ppc values that exceed $10000 per carat.
5. Explore any other interesting relationships you find