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 url link into the appropriate Course Project assignment in Blackboard 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 Blackboard 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("nscc_student_data.csv")

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

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

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

I think there was a grammatical typo in this question. It reads “How many […] are in […] are missing.” However, if I’ve interpreted this correctly, there are two missing. As ‘NA’ is equal to TRUE and the output for TRUE reads ‘2’, there are therefore two.

Question 3

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

mean(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 73.47368
median(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 70.5
sd(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 12.51105

Question 4

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.

summary(nscc_student_data$PulseRate)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   50.00   64.25   70.50   73.47   83.75   98.00       2
IQR(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 19.5

The five number summary of the PulseRate variable is: 50.00, 64.25, 70.50, 73.47, 83.75, 98.00 The IQR of the PulseRate variable is: 19.5

Any data values that are below 35 or above 113 would be considered to be outliers. Based on that, there are not any outliers.

Question 5

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

table(nscc_student_data$Gender)
## 
## Female   Male 
##     27     13
barplot(table(nscc_student_data$Gender))

Question 6

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

# Create a nonbinary subset
NSCC_enby <- subset(nscc_student_data, nscc_student_data$Gender == "Nonbinary")

Use an r chunk below to generate the information which will give you 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
# Five Number Summary of enby subset
summary(NSCC_enby$PulseRate)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##

The Five Number Summary of each dataset are:
Males: 50.00, 60.00, 71.00, 70.85, 80.00, 96.00 Females: 56.00, 65.00, 70.00, 74.84, 88.00, 98.00 Nonbinary: 0.00, 0.00, 0.00, 0.00, 0.00

Question 7

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

# Create side-by-side boxplots for each subset.
barplot(na.omit(NSCC_males$PulseRate), na.omit(NSCC_females$PulseRate), xlab = "Gender", ylab = "Pulse Rate", col = c("lightblue", "pink"))

legend("right", 
       legend = c("Gender 1", "Gender 2"),
       fill = c("lightblue", "pink"),
       title = "Gender",
)

Question 8

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
sum(NSCC_males$Coffee == "Yes") / sum(NSCC_males$Coffee == "No") + sum(NSCC_males$Coffee == "Yes")
## [1] 13.33333
# Percent of Females that Drink Coffee
sum(NSCC_females$Coffee == "Yes") / sum(NSCC_females$Coffee == "No") + sum(NSCC_females$Coffee == "Yes")
## [1] 22.85714

13.333% of Males responding drink coffee. 22.857% of Females responding drink coffee.