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 submit the web 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 normal text does not appear as a header.

Purpose

This first project will introduce you to creating RMarkdown files and doing basic data manipulation with R and RStudio.

Question 1

In a single r chunk, perform two operations: first add the numbers 9 and 23, then divide the number 42 by the sum of 83 and 101. State both answers in complete sentences beneath your r chunk, with your second answer rounded to three decimal places.

9+23
## [1] 32
round(42/(83+101),digits = 3)
## [1] 0.228

First we wrote nine plus twenty three as if it was a simple expression. next, we began with the round function and then put the expression in the parenthesis and specified the digits as three, which allows it to round to the third decimal place.

Question 2

In an r chunk below, store the dataframe mtcars into an object called mtcars and use the head() function to look at the first 6 rows of that object.

mtcars <- mtcars
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

we stored the dataset mtcars into the environment and then used the head function to view the top 6 rows of the dataset

Use the ?mtcars command in the RStudio Console to pull up the help documentation to find out what the qsec variable describes in the mtcars dataset, then write your answer in a complete sentence. (No r chunk is required here).

the variable ‘qsec’ in the dataset ‘mtcars’ refereces the time the car takes to drive a quarter mile.

Question 3

To load a package into R requires the use of the install.packages() and library() functions. Type: install.packages(“openintro”) into the RStudio Console to install that package on to your computer. The install.packages() function requires a character input, so ” ” are needed around the input.

The library() function takes in an object name, so do not use ” ” around the object name.

Use an r chunk below to load the library openintro which contains some datasets we will use going in subsequent problems.

library(openintro)
## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata

With the openintro library loaded, you can now access some datasets from it. In another r chunk, find the dimensions of the email dataset by using the dim() function on it.

dim(email)
## [1] 3921   21

First we installed the openintro package into the console and then we loaded it into the library and then we found the dimensions of the email dataset.

Question 4

The $ sign is used to specify a certain variable of a dataframe. For example, the following will give me the sum of the cylinders variable in the mtcars dataframe.

# Sum of the cyl variable in mtcars dataframe
sum(mtcars$cyl)
## [1] 198

In the email dataframe, what is the sum of the values in the line_breaks variable? State your answer in a complete sentence beneath the chunk.

sum(email$line_breaks)
## [1] 904412

we used the sum function on the variable ‘linebreaks’ on the email dataset, and with this we found that there are 904412 linebreaks in the email dataset

Question 5

In the email dataset, the first variable called spam has a value of 0 if the email was not spam and a value of 1 if the email was spam. You can use the table() function on the spam variable to see how many emails fell into the spam and non-spam categories.

Using an r chunk, calculate what percent of the emails in this dataframe were considered spam? Then state your answer beneath the chunk in a complete sentence, rounded to nearest tenth of a percent.

table(email$spam)
## 
##    0    1 
## 3554  367
sum(3554,367)
## [1] 3921
367/3921
## [1] 0.09359857
round(0.09359857*100,digits = 1)
## [1] 9.4

first we used the table function on spam in order to figure out how many spam emails and total emails there were. then we found out what percent of the total emails were spam, we came to 9.4%.

Question 6

We can use the subset() function to get subsets of a dataframe based on a given characteristic. For example, the code below will store a new dataframe that only contains the observations in which the word “dollar” or a $ sign was found in the email.

# Storing emails with "dollar" in them to an object called money
money <- subset(email, email$dollar > 0)

Use an r chunk to find the percent of emails in the money object that are spam.

table(money$spam)
## 
##   0   1 
## 668  78
sum(668,78)
## [1] 746
78/746
## [1] 0.1045576
0.1045576*100
## [1] 10.45576

using the same method as in question 5, we found that 10.45576% of emails in the money variable are spam.

Comparing your answers from #7 and #8, do dollar signs or the word “dollar” seem to be a key characteristic of emails that are spam? Explain why or why not.

Assuming the previous statement was referring to questions 5 and 6 instead, no it is not a key characteristic of being in the spam folder however it is a little bit more likely.

Question 7

Type View(airquality) into your RStudio console (not in an r chunk) and you will see a tab open up with the dataset in it for viewing. The dataframe gives air quality measurements in NYC over a 153 day period in the year 1973. In the chunk below, I have stored the airquality dataframe into an object called airquality and returned the names of the variables.

# Store airquality dataframe in object called airquality
airquality <- airquality

# Find the names of the variables in the dataframe
names(airquality)
## [1] "Ozone"   "Solar.R" "Wind"    "Temp"    "Month"   "Day"

Type ?airquality into the RStudio console to look at the documentation for the dataset and find out what the variable names in the dataframe represent.
This chunk below tries to find the sum of the solar radiation in langleys (a unit of solar radiation), but there is a problem, it returns NA.

# Find sum of Solar.R variable
sum(airquality$Solar.R)
## [1] NA

If you type View(airquality) into your RStudio console again, you can see that there are some values in the Solar.R variable, but also some NA values, which the sum function cannot handle.

Type ?sum into your RStudio console and look at the help documentation for that function. You’ll see in the Usage section sum(..., na.rm = FALSE). The second argument, na.rm, stands for “remove NAs” and this function defaults to FALSE, or no, do not remove NAs. If we want the sum function to ignore the NAs in a vector, we just need to set na.rm = TRUE as a second argument inside the function.

Use an r chunk to find the sum of the solar radiation (the Solar.R variable) by ignoring NAs.

sum(airquality$Solar.R, na.rm = TRUE)
## [1] 27146

Question 8

Often we will need to load our own data into R for analysis. A common file type that data is stored in is called a “.csv” file or “comma separated values”. The common way to load a .csv in R is with the read.csv() function and storing it to an object.

Example:
new_data <- read.csv("newdata.csv")

For this question:

  • Download the mens_health csv file from the “Additional Datasets” folder in Blackboard and save it your computer
  • Load the mens_health file into your R Environment. One way to do this is to use the Import Dataset button to load the file. You will then need to copy the line of code from the RStudio Console into an r chunk below in this RMarkdown document to ensure it runs in your final knitted project.
mens_health <- read.csv("mens_health.csv")
  • Look at the structure of the dataframe by using the str() function
  • In an r chunk below, find the sum of any numeric variable in that dataset.
str(mens_health)
## 'data.frame':    40 obs. of  14 variables:
##  $ MALE : int  1391 2129 2489 2490 2738 2988 2989 3346 3606 3607 ...
##  $ AGE  : int  58 22 32 31 28 46 41 56 20 54 ...
##  $ HT   : num  70.8 66.2 71.7 68.7 67.6 69.2 66.5 67.2 68.3 65.6 ...
##  $ WT   : num  169 144 179 176 153 ...
##  $ WAIST: num  90.6 78.1 96.5 87.7 87.1 ...
##  $ PULSE: int  68 64 88 72 64 72 60 88 76 60 ...
##  $ SYS  : int  125 107 126 110 110 107 113 126 137 110 ...
##  $ DIAS : int  78 54 81 68 66 83 71 72 85 71 ...
##  $ CHOL : int  522 127 740 49 230 316 590 466 121 578 ...
##  $ BMI  : num  23.8 23.2 24.6 26.2 23.5 24.5 21.5 31.4 26.4 22.7 ...
##  $ LEG  : num  42.5 40.2 44.4 42.8 40 47.3 43.4 40.1 42.1 36 ...
##  $ ELBOW: num  7.7 7.6 7.3 7.5 7.1 7.1 6.5 7.5 7.5 6.9 ...
##  $ WRIST: num  6.4 6.2 5.8 5.9 6 5.8 5.2 5.6 5.5 5.5 ...
##  $ ARM  : num  31.9 31 32.7 33.4 30.1 30.5 27.6 38 32 29.3 ...
sum(mens_health$AGE)
## [1] 1419