Instructions

  1. Update the author line at the top to have your name in it.
  2. You must knit this document to an html file and publish it to RPubs. Once you have published your project to the web, you must copy the web url link into the appropriate Course Project assignment in MyOpenMath before 11:59pm on the due date.
  3. Answer all the following questions completely. Some may ask for written responses.
  4. Use R chunks for code to be evaluated where needed and always comment all of your code so the reader can understand what your code aims to accomplish.
  5. Proofread your knitted document before publishing it to ensure it looks the way you want it to. Tip: Use double spaces at the end of a line to create a line break and make sure text does not have a header label that isn’t supposed to.

Purpose

This project will demonstrate your ability to do exploratory data analysis on single variables of data in R and RStudio. The entire project will use the NSCC Student Dataset, which you will need to load into R in question 1.

Question 1

Download the “nscc_student_data.csv” file from MyOpenMath and use the read.csv() function to store it into an object called “nscc_student_data”. Print the first few lines of the dataset using the head() function. Also print the structure of the dataset using the str() function.

# Load dataset in and store as object "nscc_student_data"
nscc_student_data <- read.csv("C:/Users/samura641/Downloads/nscc_student_data.csv")


# Preview first 6 lines of dataset
head(nscc_student_data)
##   Gender PulseRate CoinFlip1 CoinFlip2 Height ShoeLength Age Siblings
## 1 Female        64         5         5     62      11.00  19        4
## 2 Female        75         4         6     62      11.00  21        3
## 3 Female        74         6         1     60      10.00  25        2
## 4 Female        65         4         4     62      10.75  19        1
## 5 Female        NA        NA        NA     66         NA  26        6
## 6 Female        72         6         5     67       9.75  21        1
##   RandomNum HoursWorking Credits    Birthday ProfsAge Coffee VoterReg
## 1       797           35      13      July 5       31     No      Yes
## 2       749           25      12 December 27       30    Yes      Yes
## 3        13           30       6  January 31       29    Yes       No
## 4       613           18       9        6-13       31    Yes      Yes
## 5        53           24      15       02-15       32     No      Yes
## 6       836           15       9    april 14       32     No      Yes
# Structure of dataset
str(nscc_student_data)
## 'data.frame':    40 obs. of  15 variables:
##  $ Gender      : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 1 2 2 1 2 ...
##  $ PulseRate   : int  64 75 74 65 NA 72 72 60 66 60 ...
##  $ CoinFlip1   : int  5 4 6 4 NA 6 6 3 7 6 ...
##  $ CoinFlip2   : int  5 6 1 4 NA 5 6 5 8 5 ...
##  $ Height      : num  62 62 60 62 66 ...
##  $ ShoeLength  : num  11 11 10 10.8 NA ...
##  $ Age         : int  19 21 25 19 26 21 19 24 24 20 ...
##  $ Siblings    : int  4 3 2 1 6 1 2 2 3 1 ...
##  $ RandomNum   : int  797 749 13 613 53 836 423 16 12 543 ...
##  $ HoursWorking: int  35 25 30 18 24 15 20 0 40 30 ...
##  $ Credits     : int  13 12 6 9 15 9 15 15 13 16 ...
##  $ Birthday    : Factor w/ 40 levels "02-15","03.14.1984",..: 32 25 30 18 1 21 19 27 35 31 ...
##  $ ProfsAge    : int  31 30 29 31 32 32 28 28 31 28 ...
##  $ Coffee      : Factor w/ 2 levels "No","Yes": 1 2 2 2 1 1 2 2 2 1 ...
##  $ VoterReg    : Factor w/ 2 levels "No","Yes": 2 2 1 2 2 2 2 2 2 1 ...

Question 2

a.) What are the dimensions of the nscc_student_data dataframe?

# Find the dimensions of the nscc_student_data dataframe
dim(nscc_student_data)
## [1] 40 15

The dimension of the nscc_student_date dateframe 40 by 15

b.) The chunk of code below will tell you how many values in the PulseRate variable exist (FALSE) and how many are NA (TRUE). How many values are in the variable are missing?

# How many values in PulseRate variable are missing
table(is.na(nscc_student_data$PulseRate))
## 
## FALSE  TRUE 
##    38     2

Two values in the PulseRate variable are missing.

Question 3

Use an r chunk to calculate the mean and median of the pulse rate variable. Do they differ by much? If yes, explain why and which would be a better choice as the “center” or “average” of this variable.

# The mean of the pulse rate variable, ignoring the NA variables.
mean(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 73.47368
# The median of the pulse rate variable, ignoring the NA variables.
median(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 70.5

These values do not differ very much. The mean is 73.47 and the median is 70.50 and therefore either value would be a valid “center” or “average” for this variable.

Question 4

Use an r chunk to calculate the sample standard deviation of the pulse rate variable.

# The sample standard deviation of the pulse rate variable.
sd(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 12.51105

The sample standard deviation of the pulse rate variable is 12.51.

Question 5

Use an r chunk to calculate the Five Number Summary and IQR of the pulse rate variable. Clearly state your answer for each below the r chunk. Based on the definition of outliers being more than 1.5 IQRs below Q1 or above Q3, are there any values in the pulse rate variable that are considered outliers? Clearly state below the thresholds for a data to be considered an outlier.

# The five(5) Number Summary of the PulseRate variable
summary(nscc_student_data$PulseRate)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   50.00   64.25   70.50   73.47   83.75   98.00       2

The five number summary of the PulseRate variable is: Min 50.00, 1st Qu. 64.25, Median 70.50, 3rd Qu. 83.75, and Max 98.00.

The IQR of the PulseRate variable is: Q3-Q1, so 83.75-64.25, so the answer is 19.5.

Any data values that are below (64.25) or above (83.75) would be considered to be outliers. Based on that, there (are/are not) any outliers (and those outliers are 50, 56, 60, 61, 62, 64, 87, 88, 89, 85, 98)

Question 6

The Gender variable gives whether students identified as male or female. Create a table and a barplot of that variable.

# Table of results of Gender variable
table(nscc_student_data$Gender)
## 
## Female   Male 
##     27     13
# Barplot of results
barplot(table(nscc_student_data$Gender))

Question 7

Split the dataframe into two subsets – one that has all the males and another that has all the females. Store them into objects called “NSCC_males” and “NSCC_females”. The first one has been done for you as a template.

# Create males subset
NSCC_males <- subset(nscc_student_data, nscc_student_data$Gender == "Male")

# Create females subset
# Create females subset
NSCC_females <- subset(nscc_student_data, nscc_student_data$Gender == "Female")

Question 8

What is the Five Number Summary for the pulse rate variable for each of the male and female subsets.

# Five Number Summary of males subset
summary(NSCC_males$PulseRate)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   50.00   60.00   71.00   70.85   80.00   96.00
# Five Number Summary of females subset
summary(NSCC_females$PulseRate)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   56.00   65.00   70.00   74.84   88.00   98.00       2

The Five Number Summary of each dataset are:

Males:
Min 50.00, 1st Qu. 60.00, Median 71.00, 3rd Qu. 80.00, and Max 96.00

Females: Min 56.00, 1st Qu. 65.00, Median 70.00, 3rd Qu. 88.00, and Max 98.00

Question 9

Create side-by-side boxplots for the pulse rate variable each of the male and female subsets. Is there any noticeable difference between the spread of the variables?

# Create side-by-side boxplots for each subset
boxplot(NSCC_females$PulseRate, NSCC_males$PulseRate)

Yes, there is a noticable difference between the spread of the variables. The medians of the datasets are very close and so are the maximum values of each dataset. However, the minimum number in the male dataset is at least five less than the minimum number of the female dataset, and both the first and the third quartiles differ greatly in each dataset.

Question 10

Create a frequency distribution for how many males and females answered “Yes” or “No” to the variable “Coffee” by using the table() function. What percent of this sample of NSCC students drink coffee? Is there any noticeable difference in coffee drinking based on gender?

# Male Coffee Drinkers
table(NSCC_males$Coffee)
## 
##  No Yes 
##   3  10
# Females Coffee Drinkers
table(NSCC_females$Coffee)
## 
##  No Yes 
##   7  20
# Percent of Males that Drink Coffee
10/(10+3)*100
## [1] 76.92308
# Percent of Females that Drink Coffee
20/(20+7)*100
## [1] 74.07407

77% of the male students in this dataset drink coffee and 74% of female students in the dataset drink coffee. Therefore there is not much of a difference, and definitely not a noticable difference in coffee drinking based on gender.