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 MyOpenMath before 9:00am 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. 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 download and 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 each data set using the head() function. Print a summary of each data set using the str() function.

# Load dataset in and store as object "nscc_student_data"
nscc_student_data <- read.csv("nscc_student_data.csv")

# Preview of dataframe
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
# Summary of dataframe with str()
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?

40 by 15

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?

2 pulse rates are missing.

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

Question 3

What is the mean of the pulse rate variable? What is the 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.

They are close to eachother with the mean being at 73.47 and the median being at 70.5. They are a couple numbers away from eachother. I think the mean is better because it displays an accurate middle number for there being an even number of pulse rates.

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

Question 4

What is the sample standard deviation of the pulse rate variable?

12.51

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

Question 5

What is the Five Number Summary and IQR of the pulse rate variable? Based on the definition of outliers being more than 1.5 IQRs above Q3 or below Q1, are there any values in the pulse rate variable that are considered outliers?

# Five 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 IQR of the PulseRate variable is: 83.75-64.25 = 19.5

Any outliers that are below (45) or above (113) would be considered to be outliers. Based on that, there are not any outliers

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

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$PulseRate, 5)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   50.00   60.00   71.00   70.85   80.00   96.00
# Five Number Summary of females subset
summary(NSCC_females$PulseRate, 5)
##    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 Q1:60 Median:71 Q3:80 Max:96

Females: Min:56 Q1:65 Median:70 Q3:88 Max:98

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?

The Medians are about the same for male and female pulse rates. The female’s lowest pulse rate is higher than mens lowest and female’s highest pulse rate is higher than the men’s highest.

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

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?

76.9% of males drink coffee and 74.1% of females drink coffee. The percentage of males that drink coffee compared to females is only slightly more.

# 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
mcoffee <- table(NSCC_males$Coffee)[[2]]
total <- mcoffee + table(NSCC_males$Coffee)[[1]]
round((mcoffee/total)*100,1)
## [1] 76.9
# Percent of Females that Drink Coffee
fmcoffee <- table(NSCC_females$Coffee)[[2]]
total <- fmcoffee + table(NSCC_females$Coffee)[[1]]
round((fmcoffee/total)*100,1)
## [1] 74.1