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("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 nscc_student_data has 40 rows, or 40 observations, and 15 columns, or 15 variables.

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

There are two values in the variable PulseRate of the dataset nscc_student_data that 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.

#Calculate the mean of the PulseRate variable in dataset nscc_student_data using mean() function.
mean(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 73.47368
#Calculate the median of the PulseRate variable in dataset nscc_student_data using the median() function.
median(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 70.5

The mean and the mendian of variable PulseRate in dataset nscc_student_data do not differ by much; therefore, either number could be used as the “center” or “average”.

Question 4

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

#Calculate the sample standard deviation of the PulseRate variable of dataset nscc_student_data using sd() function. Remove missing values using na.rm.
sd(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 12.51105

The sample standard deviation of the PulseRate variable of the dataset nscc_student_data rounded to the nearest hundredth 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.

#Find Five Number Summary using summary() function.
summary(nscc_student_data$PulseRate, na.rm = TRUE)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   50.00   64.25   70.50   73.47   83.75   98.00       2
#Find IQR of the PulseRate variable using IQR() function.
IQR(nscc_student_data$PulseRate, na.rm = TRUE)
## [1] 19.5
#To find outliers in the dataset nscc_student_data, we have to find 1.5 IQRs below Q1 and 1.5 IQRs above Q3. 
64.25-1.5*19.5
## [1] 35
83.75+1.5*19.5
## [1] 113

The five number summary of the PulseRate variable is: min 50, max 98, median 70.5, Q1 64.25, Q3 83.75. The IQR of the PulseRate variable is: 19.5.

Any data values in the dataset nscc-student_data that are below 35 or above 113 would be considered outliers. Based on that, there are no outliers in this dataset, because the minimum value is 50 and the maximum value is 98.

Question 6

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

#Find the table of the Gender variable of the dataset nscc_student_data.
table(nscc_student_data$Gender)
## 
## Female   Male 
##     27     13
#Create a barplot of the Gender variable of the dataset nscc_student_data.
barplot(table(nscc_student_data$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.

# Find Five Number Summary of males subset; it can be found using summary() function.
summary(NSCC_males$PulseRate)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   50.00   60.00   71.00   70.85   80.00   96.00
# Find Five Number Summary of females subset.
summary(NSCC_females$PulseRate, na.rm = TRUE)
##    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?

# Create side-by-side boxplots for the PulseRate variable for each of the male and female subsets using boxplot() function.
boxplot(NSCC_males$PulseRate,NSCC_females$PulseRate, na.rm = TRUE)

Comparing male and female subsets of the variable PulseRate we can tell that the median of both subsets is about the same, the pulse rate of females is slightly higher than the pulse rate of the males. Both datasets have no outliers.

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
prop.table(table(NSCC_males$Coffee))[2]*100
##      Yes 
## 76.92308
# Percent of Females that Drink Coffee
prop.table(table(NSCC_females$Coffee))[2]*100
##      Yes 
## 74.07407