Demystifying R Part 2
========================================================

You might see a warning message just above this file. Something like...
"R Markdown requires the knitr package (version 1.2 or higher)"
Don't worry about this for now. We'll address it at the end of this file.

1. Run the following command to see what it does.
```{r}
summary(mtcars)
```

If you know about quantiles, then the output should look familiar.
If not, you probably recognize the min (minimum), median, mean, and max (maximum).
We'll go over quantiles in Lesson 3 so don't worry if the output seems overwhelming.

The str() and summary() functions are helpful commands when working with a new data set.
The str() function gives us the variable names and their types.
The summary() function gives us an idea of the values a variable can take on.

2. In 2013, the average mpg (miles per gallon) for a car was 23 mpg.
The car models in the mtcars data set come from the year 1973-1974.
Subset the data so that you create a new data frame that contains
cars that get 23 or more mpg (miles per gallon). Save it to a new data
frame called efficient.
```{r}

```

3. How many cars get more than 23 mpg? Use one of the commands you
learned in the demystifying.R to answer this question.
```{r}

```

4. We can also use logical operators to find out which car(s) get greater
than 30 miles per gallon (mpg) and have more than 100 raw horsepower.
```{r}
subset(mtcars, mpg > 30 & hp > 100)
```

There's only one car that gets more than 30 mpg and 100 hp.

5. What do you think this code does? Scroll down for the answer.
```{r}
subset(mtcars, mpg < 14 | disp > 390)
```

Note: You may be familiar with the || operator in Java. R uses one single & for the logical
operator AND. It also uses one | for the logical operator OR.








The command above creates a data frame of cars that have mpg less than 14
OR a displacement of more than 390. Only one of the conditions for a car
needs to be satisfied so that the car makes it into the subset. Any of the
cars that fit the criteria are printed to the console.

Now you try some.

6. Print the cars that have a 1/4 mile time (qsec) less than or equal to
16.90 seconds to the console.
```{r}

```

7. Save the subset of cars that weigh under 2000 pounds (weight is measured in lb/1000)
to a variable called lightCars. Print the numbers of cars and the subset to the console.
```{r}

```

8. You can also create new variables in a data frame. Let's say you wanted
to have the year of each car's model. We can create the variable
mtcars$year. Here we'll assume that all of the models were from 1974.
Run the code below.
```{r}
mtcars$year <- 1974
```

Notice how the number of variables changed in the work space. You can
also see the result by double clicking on mtcars in the workspace and
examining the data in a table.

To drop a variable, subset the data frame and select the variable you
want to drop with a negative sign in front of it.
```{r}
mtcars <- subset(mtcars, select = -year)
```

Notice, we are back to 11 variables in the data frame.

9. What do you think this code does? Run it to find out.
```{r}
mtcars$year <- c(1973, 1974)
```

Open the table of values to see what values year takes on.

Drop the year variable from the data set.
```{r}

```


10. Now you are going to get a preview of ifelse(). For those new
to programming this example may be confusing. See if you can understand
the code by running the commands one line at a time. Read the output and
make sense of what the code is doing at each step.

If you are having trouble don't worry, we will review the ifelse statement
at the end of Lesson 3. You won't be quizzed on it, and it's not essential
to keep going in this course. We just want you to try to get familiar with
more code.
```{r}
mtcars$wt
cond <- mtcars$wt < 3
cond
mtcars$weight_class <- ifelse(cond, 'light', 'average')
mtcars$weight_class
cond <- mtcars$wt > 3.5
mtcars$weight_class <- ifelse(cond, 'heavy', mtcars$weight_class)
mtcars$weight_class
```

You have some variables in your workspace or environment like 'cond' and
efficient. You want to be careful that you don't bring in too much data
into R at once since R will hold all the data in working memory. We have
nothing to worry about here, but let's delete those variables from the
work space.

```{r}
rm(cond)
rm(efficient)
```

Save this file if you haven't done so yet.


You'll have the opportunity to create one Rmd file for the final project in
this class and submit the Rmd file and knitted output (or HTML file). You'll
need the knitr package to do that so let's install that now. **Uncomment** the
following two lines of code and run them.

```{r}
# install.packages('knitr', dependencies = T)
# library(knitr)
```

Once you've installed knitr, **comment** out the two lines of code above.
When you click the **Knit HTML** button a web page will be generated that
includes both content (text and text formatting from Markdown) as well as
the output of any embedded R code chunks within the document.


You've reached the end of the file so now it's time to write some code to
answer a question to continue on in Lesson 2.

Which car(s) have an mpg (miles per gallon) greater than or equal to 30
OR hp (horsepower) less than 60? Create an R chunk of code to answer the question.



Once you have the answer, go the [Udacity website](https://www.udacity.com/course/viewer#!/c-ud651/l-729069797/e-804129319/m-811719066) to continue with Lesson 2.

Note: You use brackets around text followed by two parentheses to create a link.
There must be no spaces between the brackets and the parentheses. Paste or type
the link into the parentheses. This also works on the discussions!

And if you want to see all of your HARD WORK from this file, click
the **KNIT HTML** button now. (You may or may not need to restart R).

# CONGRATULATIONS
#### You'll be exploring data soon with your new knowledge of R.