Part 1

How many cases were included in the data?

By using the this function, I can see the n samples of the data set

library(psych)
describe(iris)
##              vars   n mean   sd median trimmed  mad min max range  skew
## Sepal.Length    1 150 5.84 0.83   5.80    5.81 1.04 4.3 7.9   3.6  0.31
## Sepal.Width     2 150 3.06 0.44   3.00    3.04 0.44 2.0 4.4   2.4  0.31
## Petal.Length    3 150 3.76 1.77   4.35    3.76 1.85 1.0 6.9   5.9 -0.27
## Petal.Width     4 150 1.20 0.76   1.30    1.18 1.04 0.1 2.5   2.4 -0.10
## Species*        5 150 2.00 0.82   2.00    2.00 1.48 1.0 3.0   2.0  0.00
##              kurtosis   se
## Sepal.Length    -0.61 0.07
## Sepal.Width      0.14 0.04
## Petal.Length    -1.42 0.14
## Petal.Width     -1.36 0.06
## Species*        -1.52 0.07

As we can see, there are n = 150 of Irish samples in the data set.

How many numerical variables are included in the data? Indicate what they are, and if they are continuous or discrete.

Using str(iris), we can see that there is 5 variables in the data set.

str(iris)
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

For numerical variables, we will filter it using sapply() then we will have:

sapply(iris, is.numeric)
## Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
##         TRUE         TRUE         TRUE         TRUE        FALSE

In these 5 variables, there is only Species which return the values False meaning it is the only variable that is not numeric. In addition to that, all 4 variables is continuous

How many categorical variables are included in the data, and what are they? List the corresponding levels (categories).

We know from our last questions that there should be 1 categorical variables included in the data, and we can see this more clearly when using the command summary(iris)

Sepal.Length    Sepal.Width     Petal.Length    Petal.Width          Species  
Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100   setosa    :50  
1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300   versicolor:50  
Median :5.800   Median :3.000   Median :4.350   Median :1.300   virginica :50  
Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199                  
3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800                  
Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500

Based on that, we can see the 3 values in the Species variables are: “setosa”, “versicolor”, “virginica”.

Part 2

The data set I will be using is cars which measures speed and stopping distance of cars. The data set is cross sectional data types.

describe(cars)
##       vars  n  mean    sd median trimmed   mad min max range  skew kurtosis
## speed    1 50 15.40  5.29     15   15.47  5.93   4  25    21 -0.11    -0.67
## dist     2 50 42.98 25.77     36   40.88 23.72   2 120   118  0.76     0.12
##         se
## speed 0.75
## dist  3.64
summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00
View(cars)

Before plotting a scatter plot I will have to download 2 libraries:

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggformula)
## Loading required package: ggplot2
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
## Loading required package: scales
## 
## Attaching package: 'scales'
## The following objects are masked from 'package:psych':
## 
##     alpha, rescale
## Loading required package: ggiraph
## Loading required package: ggridges
## 
## New to ggformula?  Try the tutorials: 
##  learnr::run_tutorial("introduction", package = "ggformula")
##  learnr::run_tutorial("refining", package = "ggformula")

After that, I need to run this code and it will give me the scatterplot:

example <- cars #Assigning cars data set to example as a filler then use it with the gf_point command to graph the scatter plot
example %>% 
  gf_point(dist ~ speed, title = "Speed vs Distance")