4  R Basics

Before we get started with this analysis we are reviewing some basics.

4.1 Packages

Use install.packages to install the dslabs package.

Tryout the following functions: sessionInfo, installed.packages

4.2 Prebuilt functions

Much of what we do in R is called prebuilt functions. Today we are using: ls, rm, library, search, factor, list, exists, str, typeof, class and maybe more.

You can see the code for a function by typing it without the parenthesis:

Try this:

ls

4.3 Help system

In R you can use ? or help to learn more about functions.

You can learn about function using

help("ls")

or

?ls

4.4 The workspace

Define a variable.

Use ls to see if it’s there. Also take a look at the Environment tab in RStudio.

Use rm to remove the variable you defined.

4.5 Variable name convention

A nice convention to follow is to use meaningful words that describe what is stored, use only lower case, and use underscores as a substitute for spaces.

For more we recommend this guide.

4.6 Data types

The main data types in R are:

  • One dimensional vectors: numeric, integer, logical, complex, characters.

  • Factors

  • Lists: this includes data frames

  • Arrays: Matrices are the most widely used

  • Date and time

  • tibble

  • S4 objects

Many errors in R come from confusing data types. Let’s learn what these data types are and useful tools to help us.

str stands for structure, gives us information about an object.

typeof gives you the basic data type of the object. It reveals the lower-level, more fundamental type of an object in R’s memory.

class This function returns the class attribute of an object. The class of an object is essentially type_of at a higher, often user-facing level.

library(dslabs)
typeof(murders)
[1] "list"
class(murders)
[1] "data.frame"
str(murders)
'data.frame':   51 obs. of  5 variables:
 $ state     : chr  "Alabama" "Alaska" "Arizona" "Arkansas" ...
 $ abb       : chr  "AL" "AK" "AZ" "AR" ...
 $ region    : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
 $ population: num  4779736 710231 6392017 2915918 37253956 ...
 $ total     : num  135 19 232 93 1257 ...

4.7 Data frames

Date frames are the most common class used in data analysis. It is like a spreadsheet. Rows represents observations and columns variables. Each variable can be a different data type.

You can add columns like this:

murders$pop_rank <- rank(murders$population)
head(murders)
       state abb region population total pop_rank
1    Alabama  AL  South    4779736   135       29
2     Alaska  AK   West     710231    19        5
3    Arizona  AZ   West    6392017   232       36
4   Arkansas  AR  South    2915918    93       20
5 California  CA   West   37253956  1257       51
6   Colorado  CO   West    5029196    65       30

You can access columns with the $

murders$population
 [1]  4779736   710231  6392017  2915918 37253956  5029196  3574097   897934
 [9]   601723 19687653  9920000  1360301  1567582 12830632  6483802  3046355
[17]  2853118  4339367  4533372  1328361  5773552  6547629  9883640  5303925
[25]  2967297  5988927   989415  1826341  2700551  1316470  8791894  2059179
[33] 19378102  9535483   672591 11536504  3751351  3831074 12702379  1052567
[41]  4625364   814180  6346105 25145561  2763885   625741  8001024  6724540
[49]  1852994  5686986   563626

but also []

murders[1:5,]
       state abb region population total pop_rank
1    Alabama  AL  South    4779736   135       29
2     Alaska  AK   West     710231    19        5
3    Arizona  AZ   West    6392017   232       36
4   Arkansas  AR  South    2915918    93       20
5 California  CA   West   37253956  1257       51
murders[1:5, 1:2]
       state abb
1    Alabama  AL
2     Alaska  AK
3    Arizona  AZ
4   Arkansas  AR
5 California  CA
murders[1:5, c("state", "abb")]
       state abb
1    Alabama  AL
2     Alaska  AK
3    Arizona  AZ
4   Arkansas  AR
5 California  CA

4.8 with

The function with let’s us use the column names as objects:

with(murders, length(state))
[1] 51

4.9 Vectors

The columns of data frames are one dimensional (atomic) vectors.

Here is an example:

length(murders$population)
[1] 51

How to create vectors:

x <- c("b", "s", "t", " ", "2", "6", "0")

Sequences are particularly useful:

seq(1, 10)
 [1]  1  2  3  4  5  6  7  8  9 10
seq(1, 9, 2)
[1] 1 3 5 7 9
1:10
 [1]  1  2  3  4  5  6  7  8  9 10
seq_along(x)
[1] 1 2 3 4 5 6 7

4.10 Factors

One key data type distinction is factors versus characters:

typeof(murders$state)
[1] "character"
typeof(murders$region)
[1] "integer"

Factors store levels and then the label of each level. They are very useful for categorical data.

x <- murders$region
levels(x)
[1] "Northeast"     "South"         "North Central" "West"

4.10.1 Categories based on strata

The function cut is useful for converting numbers into categories

with(murders, cut(population, 
                  c(0, 10^6, 10^7, Inf)))
 [1] (1e+06,1e+07] (0,1e+06]     (1e+06,1e+07] (1e+06,1e+07] (1e+07,Inf]  
 [6] (1e+06,1e+07] (1e+06,1e+07] (0,1e+06]     (0,1e+06]     (1e+07,Inf]  
[11] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07] (1e+07,Inf]   (1e+06,1e+07]
[16] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07]
[21] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07]
[26] (1e+06,1e+07] (0,1e+06]     (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07]
[31] (1e+06,1e+07] (1e+06,1e+07] (1e+07,Inf]   (1e+06,1e+07] (0,1e+06]    
[36] (1e+07,Inf]   (1e+06,1e+07] (1e+06,1e+07] (1e+07,Inf]   (1e+06,1e+07]
[41] (1e+06,1e+07] (0,1e+06]     (1e+06,1e+07] (1e+07,Inf]   (1e+06,1e+07]
[46] (0,1e+06]     (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07] (1e+06,1e+07]
[51] (0,1e+06]    
Levels: (0,1e+06] (1e+06,1e+07] (1e+07,Inf]
murders$size <- cut(murders$population, c(0, 10^6, 10^7, Inf), 
            labels = c("small", "medium", "large"))
murders[1:6,c("state", "size")]
       state   size
1    Alabama medium
2     Alaska  small
3    Arizona medium
4   Arkansas medium
5 California  large
6   Colorado medium

4.10.2 changing levels

You can change the levels (this will come in handy when we learn linear models)

Order levels alphabetically:

factor(x, levels = sort(levels(murders$region)))
 [1] South         West          West          South         West         
 [6] West          Northeast     South         South         South        
[11] South         West          West          North Central North Central
[16] North Central North Central South         South         Northeast    
[21] South         Northeast     North Central North Central South        
[26] North Central West          North Central West          Northeast    
[31] Northeast     West          Northeast     South         North Central
[36] North Central South         West          Northeast     Northeast    
[41] South         North Central South         South         West         
[46] Northeast     South         West          South         North Central
[51] West         
Levels: North Central Northeast South West

Make west the first level:

x <- relevel(x, ref = "West")

Order levels by population size:

x <- reorder(murders$region, murders$population, sum)

Factors are more efficient:

x <- sample(murders$state[c(5,33,44)], 10^7, replace = TRUE)
y <- factor(x)
object.size(x)
80000232 bytes
object.size(y)
40000648 bytes
system.time({x <- tolower(x)})
   user  system elapsed 
  1.451   0.009   1.460

Exercise: How can we make this go much faster?

system.time({levels(y) <- tolower(levels(y))})
   user  system elapsed 
  0.019   0.003   0.022

Factors can be confusing:

x <- factor(c("3","2","1"), levels = c("3","2","1"))
as.numeric(x)
[1] 1 2 3
x[1]
[1] 3
Levels: 3 2 1
levels(x[1])
[1] "3" "2" "1"
table(x[1])

3 2 1 
1 0 0 
z <- x[1]
z <- droplevels(z)
x[1] <- "4"
Warning in `[<-.factor`(`*tmp*`, 1, value = "4"): invalid factor level, NA
generated
x
[1] <NA> 2    1   
Levels: 3 2 1

4.11 NAs

NA stands for not available. We will see many NAs if we analyze data generally.

x <- as.numeric("a")
Warning: NAs introduced by coercion
is.na(x)
[1] TRUE
is.na("a")
[1] FALSE
1 + 2 + NA
[1] NA

When used with logicals behaves like FALSE

TRUE & NA
[1] NA
TRUE | NA
[1] TRUE

But is is not FALSE. Try this:

if (NA) print(1) else print(0)

A related constant is NaN which stands for not a number. It is a numeric that is not a number.

class(0/0)
[1] "numeric"
sqrt(-1)
Warning in sqrt(-1): NaNs produced
[1] NaN
log(-1)
Warning in log(-1): NaNs produced
[1] NaN
0/0
[1] NaN

4.12 coercing

When you do something nonsensical with data types, R tries to figure out what you mean. This can cause confusion and unnoticed errors. So it’s important to understand how and when it happens. Here are some examples:

typeof(1L)
[1] "integer"
typeof(1)
[1] "double"
typeof(1 + 1L)
[1] "double"
c("a", 1, 2)
[1] "a" "1" "2"
TRUE + FALSE
[1] 1
factor("a") == "a"
[1] TRUE
identical(factor("a"), "a")
[1] FALSE

You want to avoid automatic coercion and instead explicitly do it. Most coercion functions start with as.

x <- factor(c("a","b","b","c"))
as.character(x)
[1] "a" "b" "b" "c"
as.numeric(x)
[1] 1 2 2 3
x <- c("12323", "12,323")
as.numeric(x)
Warning: NAs introduced by coercion
[1] 12323    NA
readr::parse_guess(x)
[1] 12323 12323

4.13 lists

Data frames are a type of list. List permit components of different types and, unlike data frames, length

x <- list(name = "John", id = 112, grades = c(95, 87, 92))

You can access components in different ways:

x$name
[1] "John"
x[[1]]
[1] "John"
x[["name"]]
[1] "John"

4.14 matrics

Matrices are another widely used data type. They are similar to data frames except all entries need to be of the same type.

We will learn more about matrices in the High Dimensional data Analysis part of the class.

4.15 functions

You can define your own function. The form is like this:

f <- function(x, y, z = 0){
  ### do calculations with x, y, z to compute object
  ## return(object)
}

Here is an example of a function that sums \(1,2,\dots,n\)

s <- function(n){
   return(sum(1:n))
}

4.16 Lexical scope

f <- function(x){
  cat("y is", y,"\n")
  y <- x
  cat("y is", y,"\n")
  return(y)
}
y <- 2
f(3)
y is 2 
y is 3 
[1] 3
y <- f(3)
y is 2 
y is 3 
y
[1] 3

4.17 Namespaces

Look at this function.

filter
library(dplyr)
filter

Note this is just the Global Environment.

Use search to see other environments.

Note all the functions in stats

You can explicitly say which you want:

stats::filter
dplyr::filter

Try to understand this example:

exists("murders")
[1] TRUE
library(dslabs)
exists("murders")
[1] TRUE
murders <- murders
murders2 <- murders
rm(murders)
exists("murders")
[1] TRUE
detach("package:dslabs")
exists("murders")
[1] FALSE
exists("murders2")
[1] TRUE

4.18 object oriented programming

R uses object oriented programming. It uses to approaches referred to as S3 and S4. The original S3 is more common.

What does this mean?

class(co2)
[1] "ts"
plot(co2)

plot(as.numeric(co2))

See the difference? The first one actually calls the function

plot.ts

Notice all the plot functions that start with plot.

The function plot will call different functions depending on the class of the arguments:

plot
function (x, y, ...) 
UseMethod("plot")
<bytecode: 0x1157ebb90>
<environment: namespace:base>

4.19 Exercises

  1. What is the sum of the first 100 positive integers? The formula for the sum of integers \(1\) through \(n\) is \(n(n+1)/2\). Define \(n=100\) and then use R to compute the sum of \(1\) through \(100\) using the formula. What is the sum?
n <- 100
n*(n + 1) / 2
[1] 5050
  1. Now use the same formula to compute the sum of the integers from 1 through 1,000.
n <- 1000
n*(n + 1) / 2
[1] 500500
  1. Now use the functions seq and sum to compute the sum with R for any n, rather than a formula.
n <- 100
x <- seq(1, 100)
sum(x)
[1] 5050
  1. In math and programming, we say that we evaluate a function when we replace the argument with a given number. So if we type sqrt(4), we evaluate the sqrt function. In R, you can evaluate a function inside another function. The evaluations happen from the inside out. Use one line of code to compute the log, in base 10, of the square root of 100.
log(sqrt(100), base = 10)
[1] 1
log10(sqrt(100))
[1] 1
  1. Make sure the US murders dataset is loaded. Use the function str to examine the structure of the murders object. What are the column names used by the data frame for these five variables?
library(dslabs)
str(murders)
'data.frame':   51 obs. of  5 variables:
 $ state     : chr  "Alabama" "Alaska" "Arizona" "Arkansas" ...
 $ abb       : chr  "AL" "AK" "AZ" "AR" ...
 $ region    : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
 $ population: num  4779736 710231 6392017 2915918 37253956 ...
 $ total     : num  135 19 232 93 1257 ...