Activity 1: Intro to RStudio

library(tidyverse)
library(openintro)

Exercise 1

The calculations below were done in the documentation for this lab. Note that all calculations occur between three ` marks that open and close the “code chuck”

2 + 2

## [1] 4

2^127 -1

## [1] 1.701412e+38

sqrt(9)

## [1] 3

log(10, 10)

## [1] 1

These are my calculations with code chunk

3*6

## [1] 18

sqrt(98)

## [1] 9.899495

8/4

## [1] 2

5-5

## [1] 0

This is the data set of Cars 93

?cars93

## starting httpd help server ... done

head(cars93)

## # A tibble: 6 × 6
##   type    price mpg_city drive_train passengers weight
##   <fct>   <dbl>    <int> <fct>            <int>  <int>
## 1 small    15.9       25 front                5   2705
## 2 midsize  33.9       18 front                5   3560
## 3 midsize  37.7       19 front                6   3405
## 4 midsize  30         22 rear                 4   3640
## 5 midsize  15.7       22 front                6   2880
## 6 large    20.8       19 front                6   3470

Exercise 2

We’re going to analyze the cars93 data set.

The data set has a handful of variables. How many? List them below along with their classification (eg numerical, categorical, discrete, continuous, etc.). The first one is included for you.

1. type, regular categorical

2. price, continuous numerical

3. mpg_city, continuous numerical

4. drive_train, regular categorical

5. passengers, discrete numerical

6. weight, continuous numerical

Answers 2

There are six variables within this data set. The variables are type, price, mpg city, drive train, and weight. Following that order to classify the variable are regular categorical, numerical Now determine the number of observations in this data set, using the code chunk below: There are 54 observations within the data set.;;

unique(cars93$type)

## [1] small   midsize large  
## Levels: large midsize small

summary(cars93$type)

##   large midsize   small 
##      11      22      21

summary(cars93$type)/length(cars93$type)

##     large   midsize     small 
## 0.2037037 0.4074074 0.3888889

Are observations rows or columns?

Answers 2.5

Observations are rows

Exercise 3

Still using the cars93 data set, answer the following questions:

• What types of drive trains do the cars in this data set have?
• How many cars in this data set are rear wheel drive?
• What proportion of cars in the data set are 4WD.

summary(cars93$drive_train)

##   4WD front  rear 
##     2    43     9

summary(cars93$drive_train)/length(cars93$drive_train)

##        4WD      front       rear 
## 0.03703704 0.79629630 0.16666667

Answers 3

There are rear, front wheel, and four wheel drive trains within the cars of the data set. The amount of cars in the data set that are rear wheel drive is nine cars. The proportion of 4WD cars in the data set is 2/54.

Exercise 4

p <- ggplot(cars93, aes(x=drive_train)) + geom_bar(stat="count", fill="darkgreen")
p

Price Analysis of Cars

mean(cars93$price)

## [1] 19.99259

## [1] 19.99259
sd(cars93$price)

## [1] 11.50645

## [1] 11.50645
summary(cars93$price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    7.40   10.95   17.25   19.99   26.25   61.90

Exercise 5

Compare the mean and median weight of cars in cars93. Is there a difference between these measures of center? If so, why do you think that is the case?

p <- ggplot(cars93, aes(x=weight)) + geom_histogram(bins=17, fill="darkgreen")
p

mean(cars93$weight)

## [1] 3037.407

sd(cars93$weight)

## [1] 657.6643

summary(cars93$weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1695    2452    3198    3037    3522    4105

Answers 5

There is a difference in measures of center. The median is 3,198 ponds while the mean is 3,037 pounds. One possible explanation for the difference in measures of center is an outlier, which can especially skew the mean in a small data set.

Exercise 6 Construct a histogram to visualize the distribution of the weight variable.

p <- ggplot(cars93, aes(x=weight)) + geom_histogram(bins=14, fill="darkgreen")
p

Describe the shape of this distribution here.

Answers 6

The shape of the distribution could be classified as multimodal.

Exercise 7

Is there any evidence of a correlation between the price of vehicle and it’s fuel economy? Construct a scatter plot to answer this question.

p <- ggplot(data=cars93, aes(x = price, y = mpg_city)) + geom_point(color="darkgreen")
p

• If there is a relationship, try to describe it here.
• cars93 gives information from cars from the year 1993. Do you think the relationship between price and mpg_city would be similar for cars from 2022? Write your answer here.

Answers 7

The data displays a negative correlation between price and miles per gallon. As price increases, miles per gallon decreases. Based on current knowledge of price of today’s cars and their miles per gallon one could conclude that high valued cars have a decreased miles per gallon. This reflects the data displayed in the cars93 data set.

Exercise 8

Describe, in your own words, what a violin plot shows. You may want to look this up; feel free to simply search “violin plot” on the web and see what you find. Write your answer here.

Answers 8

A violin plot consists of a box and whisker plot with reflective modal distribution through the middle of the box and whisker. The wider the flares are on the “violin” the greater the frequency of that event.