Introduction to Stats 67 and Data

Dr. Mine Dogucu

Getting to Know Each Other

Merhaba

Hello
Private Sub Form_Load() MsgBox "Hello, World!" End Sub
Hallo
مرحبا
print('Hello world')
नमस्ते & السلام عليكم
print("Hello world")
<html> Hello world</html>
¡Hola!
سلام

Meet and Greet Each Other

In groups three or four meet and greet each other.

Include

Your name
Your year
I live …
My favorite thing about UCI is …
I am awesome because …

Find something in common between all of you by expanding the conversation.
Find a difference.

Getting to Know the Course

The most important thing about this course

The course website

Poll Everywhere

PollEv.com/minedogucu800

What is Statistics?

Think 💭 - Pair 👫🏽 - Share 💬

What do you think statistics is about and what will we learn in this course? There is no right or wrong answer.

PollEv.com/minedogucu800

How to be successful in this course

  • Be punctual
  • Be organized
  • Do the work

How to make your professor happy

  • Be kind
  • Be honest

Getting to Know the Toolbox

hello woRld

Object assignment operator

my_apples <- 4
Windows Mac
Shortcut Alt + - Option + -

R is case-sensitive

My_apples
Error in eval(expr, envir, enclos): object 'My_apples' not found

If something comes in quotes, it is not defined in R.

n_apples <- c(7, my_apples, 3)

names <- c("Menglin", "Gloria", "Robert")

data.frame(person = names, apple_count = n_apples)
   person apple_count
1 Menglin           7
2  Gloria           4
3  Robert           3

Vocabulary

do(something)

do() is a function;
something is the argument of the function.

do(something, colorful)

do() is a function;
something is the first argument of the function;
colorful is the second argument of the function.

Getting Help

In order to get any help we can use ? followed by function (or object) name.

?c

tidyverse_style_guide

canyoureadthissentence?

tidyverse_style_guide

n_apples <- c(7, my_apples, 3)

names <- c("Menglin", "Gloria", "Robert")

data.frame(person = names, 
           apple_count = n_apples)

  • After function names do not leave any spaces.

  • Before and after operators (e.g. <-, =) leave spaces.

  • Put a space after a comma, not before.

  • Object names are all lower case, with words separated by an underscore.

Tip

You can let RStudio do the indentation for your code.

RStudio Setup

Quarto

markdown 


_Hello world_ 

__Hello world__

~~Hello world~~

Hello world

Hello world

Hello world

Quarto parts

Quarto parts

Quarto parts

Quarto parts

Quarto parts

Quarto parts

Slides for this course

Slides that you are currently looking at are also written in Quarto. You can take a look at them on GitHub repo for the course website.

Tip

At the beginning of the quarter, we will provide starter .qmd files for you for lectures and discussions. Some of the code in these files may or may not make sense as it might be beyond the scope of this course. As you learn more about R, you’ll sometimes have to start coding from scratch without any starter help. You should download these files and render them and use them to take notes.

Getting to Know Data

Dear Mona, Which State Has the Worst Drivers?

library(fivethirtyeight)
library(tidyverse)
data("bad_drivers")

Data Matrices

Data Matrices

  • The data matrix has 8 variables (state, num_drivers, perc_speeding, perc_not_distracted, perc_no_previous, insurance_premiums, losses).

  • The data matrix has 51 cases or observations. Each case represents a US state (or District of Columbia).

Data documentation

?bad_drivers

state State

num_drivers Number of drivers involved in fatal collisions per billion miles

perc_speeding Percentage of drivers involved in fatal collisions who were speeding

perc_alcohol Percentage of drivers involved in fatal collisions who were alcohol-impaired

perc_not_distracted Percentage of drivers involved in fatal collisions who were not distracted

perc_no_previous Percentage of drivers involved in fatal collisions who had not been involved in any previous accidents

insurance_premiums Car insurance premiums ($)

losses Losses incurred by insurance companies for collisions per insured driver ($)

Source National Highway Traffic Safety Administration 2012, National Highway Traffic Safety Administration 2009 & 2012, National Association of Insurance Commissioners 2010 & 2011.

head(bad_drivers)
# A tibble: 6 × 8
  state      num_drivers perc_speeding perc_alcohol perc_not_distracted
  <chr>            <dbl>         <int>        <int>               <int>
1 Alabama           18.8            39           30                  96
2 Alaska            18.1            41           25                  90
3 Arizona           18.6            35           28                  84
4 Arkansas          22.4            18           26                  94
5 California        12              35           28                  91
6 Colorado          13.6            37           28                  79
# ℹ 3 more variables: perc_no_previous <int>, insurance_premiums <dbl>,
#   losses <dbl>

tail(bad_drivers)
# A tibble: 6 × 8
  state         num_drivers perc_speeding perc_alcohol perc_not_distracted
  <chr>               <dbl>         <int>        <int>               <int>
1 Vermont              13.6            30           30                  96
2 Virginia             12.7            19           27                  87
3 Washington           10.6            42           33                  82
4 West Virginia        23.8            34           28                  97
5 Wisconsin            13.8            36           33                  39
6 Wyoming              17.4            42           32                  81
# ℹ 3 more variables: perc_no_previous <int>, insurance_premiums <dbl>,
#   losses <dbl>

glimpse(bad_drivers)
Rows: 51
Columns: 8
$ state               <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "Calif…
$ num_drivers         <dbl> 18.8, 18.1, 18.6, 22.4, 12.0, 13.6, 10.8, 16.2, 5.…
$ perc_speeding       <int> 39, 41, 35, 18, 35, 37, 46, 38, 34, 21, 19, 54, 36…
$ perc_alcohol        <int> 30, 25, 28, 26, 28, 28, 36, 30, 27, 29, 25, 41, 29…
$ perc_not_distracted <int> 96, 90, 84, 94, 91, 79, 87, 87, 100, 92, 95, 82, 8…
$ perc_no_previous    <int> 80, 94, 96, 95, 89, 95, 82, 99, 100, 94, 93, 87, 9…
$ insurance_premiums  <dbl> 784.55, 1053.48, 899.47, 827.34, 878.41, 835.50, 1…
$ losses              <dbl> 145.08, 133.93, 110.35, 142.39, 165.63, 139.91, 16…

Variables

Variables

Variables sugarpercent, pricepercent, and winpercent are numerical variables.

We can do certain analyses on these variables such as finding an average winpercent or the maximum or minimum winpercent.

Note: Not everything represented by numbers is a numeric variable. e.g. Student ID number is not a numeric variable.

Variables

Variables such as competitorname, chocolate, and fruity are categorical variables.

We cannot take averages or find maximum or minimum of these variables.

Categorical variables have levels. For instance chocolate and fruity both have two levels as TRUE and FALSE.

Categorical Variables

If the levels of the categorical variable has a comparable ordering then it is called an ordinal variable.

e.g. variable scholarship_status might have three levels as no scholarship, partial scholarship and full scholarship. We can order these levels from less to more or vice versa.

If there is no ordering then a categorical variable would be called a nominal variable. e.g. state names.

Numeric Variables

Consider a variable n_kids which represents number of kids somebody has. Then this variable can take values (0, 1, 2, …). Notice that this variable can take only integer values. This variable is said to be discrete since it does not take on infinitely many numbers that we are not able to count.

Numeric variables that can take infinitely many numbers are said to be continuous. Consider somebody’s height in cm. This is a continuous variable. Even though we might say somebody is 173 cm, in reality the height could be 170.612476314631 cm. So height can take infinitely many values.

stats4cs.com