Creating objects in R

weight_kg <- 55

When assigning a value to an object, R does not print anything. You can force R to print the value by using parentheses or by typing the object name:

weight_kg <- 55    
(weight_kg <- 55)  
## [1] 55
weight_kg          
## [1] 55

For instance, we may want to convert this weight into pounds (weight in pounds is 2.2 times the weight in kg):

2.2 * weight_kg
## [1] 121

We can also change an object’s value by assigning it a new one:

weight_kg <- 57.5
2.2 * weight_kg
## [1] 126.5

For example, let’s store the animal’s weight in pounds in a new object, weight_lb:

weight_1b <- 2.2 * weight_kg

and then change weight_kg to 100.

weight_kg <- 100

R executes code in top-down order. So what happens on line 10 occurs before line 11. What do you think is the current content of the object weight_lb? 126.5 or 220?

write your answer here #220

Functions and their arguments

sqrt(4)
## [1] 2

Let’s look into the round function.

round(3.14159)
## [1] 3

We see that if we want a different number of digits, we can type digits = 2 or however many we want.

round(3.14159, digits = 2)
## [1] 3.14

If you provide the arguments in the exact same order as they are defined you don’t have to name them:

round(3.14159, 2)
## [1] 3.14

And if you do name the arguments, you can switch their order:

round(digits = 2, x = 3.14159)
## [1] 3.14

Data Types

R objects come in different data types.

Numbers

im_a_number <- 50
im_a_number*2
## [1] 100

Letters

(im_a_character<-"dog")
## [1] "dog"
im_a_character
## [1] "dog"

Boolean

3>4
## [1] FALSE
sqrt(4)==2
## [1] TRUE

Data Structures

Vectors

For example we can create a vector of animal weights and assign it to a new object weight_g:

(weight_g <- c(50,60,65,82))
## [1] 50 60 65 82

A vector can also contain characters:

(animals <- c("mouse", "rat", "dog"))
## [1] "mouse" "rat"   "dog"

You can use the function class() to see what data type a vector is.

class(weight_g)
## [1] "numeric"
class(animals)
## [1] "character"

If you combine letters and numbers, everything will be treated as characters.

(mix_match <- c(weight_g, animals))
## [1] "50"    "60"    "65"    "82"    "mouse" "rat"   "dog"

Doing math on vectors

You can perform math operations on the elements of a vector such as

weight_KG <- weight_g/1000
weight_KG
## [1] 0.050 0.060 0.065 0.082

When adding two vectors together, the elements in the same position are added to each other. So element 1 in the vector a is added to element 1 in vector b.

a <- c(1,2,3)
b <- c(6,7,8)
a+b
## [1]  7  9 11

More complex calculations can be performed on multiple vectors.

wt_1b <- c(155, 135, 90)
ht_in <-c(72, 64, 50)
bmi <- 703*wt_1b/ht_in^2
bmi
## [1] 21.01948 23.17017 25.30800

All these operations on vectors behave the same way when dealing with variables in a data set (data.frame).

If you want to add the values within a vector, you use functions such as sum(), max() and mean()

sum(a)
## [1] 6
max(b)
## [1] 8
mean(a+b)
## [1] 9

Subsetting vectors

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:

animals <- c("mouse", "rat", "dog", "cat")
animals[2]
## [1] "rat"
animalsanimals <- c("mouse", "rat", "dog", "cat")
animals[c(2, 3)]
## [1] "rat" "dog"

The number in the indices indicates which element to extract. For example we can extract the 3rd element in weight_g by typing

weight_g[3]
## [1] 65

Conditional subsetting

Typically, these logical vectors are not typed by hand, but are the output of other functions or logical tests such as:

weight_g>50
## [1] FALSE  TRUE  TRUE  TRUE

For instance, if you wanted to select only the values where weight in grams is above 50 we would type:

weight_g[weight_g > 50]
## [1] 60 65 82

You can combine multiple tests using & (both conditions are true, AND) or | (at least one of the conditions is true, OR):

Weight is less than 30g or greater than 60g

weight_g[weight_g < 30 | weight_g > 60]
## [1] 65 82

Weight is between 60 and 80lbs

weight_g[weight_g >= 60 & weight_g <= 80]
## [1] 60 65

The function %in% allows you to test if any of the elements of a search vector are found:

animals <- c("mouse", "rat", "dog", "cat")
animals[animals == "cat" | animals == "rat"] # returns both rat and cat
## [1] "rat" "cat"
animals %in% c("rat", "cat", "dog", "duck", "goat")
## [1] FALSE  TRUE  TRUE  TRUE
animals[animals %in% c("rat", "cat", "dog", "duck", "goat")]
## [1] "rat" "dog" "cat"

Order matters.

When considering string or character vectors or data elements, R treats everything in alphabetical order.

"four" > "five"
## [1] TRUE

Data Frames

We can load the diamonds data set into our global environment by typing

diamonds <- ggplot2::diamonds

We can get an idea of the structure of the data frame including variable names and types by using the str function,

str(diamonds)
## tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame)
##  $ carat  : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

Inspecting data.frame objects

Identifying variables

Below is an example of finding the average price for all diamonds in the data set.

mean(diamonds$price)
## [1] 3932.8

Here is an example of finding the average price for Good quality diamonds.

mean(diamonds$price[diamonds$cut=="Good"])
## [1] 3928.864