chapter2.Rmd

---
title: 'Lab 2 - Introduction to data'
description: 'Questions for Lab 2'
---

## Question 1

```yaml
type: ExplorableExercise
key: 02d4222b54
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file:</strong>

```Type 'go()' and hit Enter to get started!```

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q1. How many flights are in this `sfo_feb_flights`?</strong>

`@possible_answers`
- 68
- 1345
- 2286
- 3563
- 32735

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 1)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex1.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex1.init.R
# lab2_ex1.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex1.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Great work!"
msg2 <- "Try again."
msg3 <- "Keep trying!"
msg4 <- "Nope."
msg5 <- "Not quite."

ex() %>% check_mc(1, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 2

```yaml
type: ExplorableExercise
key: 5440b387f2
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q2. Which of the following is false?</strong>

`@possible_answers`
- The distribution is unimodal.
- The distribution is right skewed.
- No flight is delayed more than 2 hours.
- The distribution has several extreme values on the right side.
- More than 50% of flights arrive on time or earlier than scheduled.

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 2)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex2.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex2.init.R
# lab2_ex2.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex2.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Not quite."
msg2 <- "Keep trying!"
msg3 <- "Nice job!"
msg4 <- "Nope!"
msg5 <- "Try again!"

ex() %>% check_mc(3, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 3

```yaml
type: ExplorableExercise
key: eb6689ed9c
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q3. Which carrier has the highests IQR of arrival delays?</strong>

`@possible_answers`
- American Airlines
- JetBlue Airways
- Virgin America
- Delta and United Airlines
- Frontier Airlines

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 3)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex3.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex3.init.R
# lab2_ex3.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex3.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
# rdu_flights.RData: https://assets.datacamp.com/production/course_1156/datasets/rdu_flights.RData
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Try again."
msg2 <- "Awesome!"
msg3 <- "Keep trying!"
msg4 <- "Great! That's right."
msg5 <- "Try again."

ex() %>% check_mc(4, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 4

```yaml
type: ExplorableExercise
key: e64497e7f4
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q4. Which month has the highest AVERAGE departure delay from an NYC airport?</strong>

`@possible_answers`
- January
- March
- July
- October
- December

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 4)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex4.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex4.init.R
# lab2_ex4.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex4.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Try again."
msg2 <- "Keep trying!"
msg3 <- "You got it!"
msg4 <- "Try again."
msg5 <- "Try again."
ex() %>% check_mc(3, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 5

```yaml
type: ExplorableExercise
key: b073c8a4d3
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q5. Which month has the highest MEDIAN departure delay from an NYC airport?</strong>

`@possible_answers`
- January
- March
- July
- October
- December

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 5)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex5.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex5.init.R
# lab2_ex5.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex5.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Try again."
msg2 <- "Not quite."
msg3 <- "Keep trying!"
msg4 <- "Try again."
msg5 <- "You are correct!"

ex() %>% check_mc(5, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 6

```yaml
type: ExplorableExercise
key: 8aee5ae535
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q6. Is the mean or the median a more reliable measure for deciding which month(s) to avoid flying if you really dislike delayed flights, and why?</strong>

`@possible_answers`
- Mean would be more reliable as it gives us the true average.
- Mean would be more reliable as the distribution of delays is symmetric.
- Median would be more reliable as the distribution of delays is skewed.
- Median would be more reliable as the distribution of delays is symmetric.
- Both give us useful information.

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 6)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex6.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex6.init.R
# lab2_ex6.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex6.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Keep trying!"
msg2 <- "Try again."
msg3 <- "Great work!"
msg4 <- "Try again."
msg5 <- "Nope, try again."
ex() %>% check_mc(3, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 7

```yaml
type: ExplorableExercise
key: 63abfd6950
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q7. If you were selecting an airport simply based on on time departure percentage, which NYC airport would you choose to fly out of?</strong>

`@possible_answers`
- EWR
- JFK
- LGA

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 7)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex7.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex7.init.R
# lab2_ex7.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex7.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Try again."
msg2 <- "Keep trying!"
msg3 <- "Awesome, you did great!"

ex() %>% check_mc(3, feedback_msgs = c(msg1, msg2, msg3))
```

---

## Question 8

```yaml
type: ExplorableExercise
key: ca476ff87f
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q8. What is the tail number of the plane with the fastest `avg_speed`?</strong>

`@possible_answers`
- N666DN
- N755US
- N779JB
- N947UW
- N959UW

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 8)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex8.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex8.init.R
# lab2_ex8.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex8.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Well done!"
msg2 <- "Try again."
msg3 <- "Keep trying!"
msg4 <- "Try again."
msg5 <- "Nope, not quite"

ex() %>% check_mc(1, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 9

```yaml
type: ExplorableExercise
key: 4099fadc1b
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q9. Which of the following is true about the relationship between average speed and distance?</strong>

`@possible_answers`
- As distance increases the average speed of flights decreases.
- The relationship is linear.
- There is an overall postive association between distance and average speed.
- There are no outliers.
- The distribution of distances are uniform over 0 to 5000 miles.

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 9)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex9.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex9.init.R
# lab2_ex9.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex9.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Try again."
msg2 <- "Try again."
msg3 <- "Great work!"
msg4 <- "Try again."
msg5 <-  "Keep trying!"

ex() %>% check_mc(3, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
```

---

## Question 10

```yaml
type: ExplorableExercise
key: caf40f340b
xp: 50
skills: 1
```

<strong>Follow the directions in the console to open the R Markdown file.</strong>

You can use the RStudio platform just like you would on your home computer.

The markdown file contains the instuctions to answer the multiple choice question below.

<strong>Q10. Determine the on time arrival percentage based on whether the flight departed on time or not. What percent of flights that were "delayed" departing arrive "on time"?</strong>

`@possible_answers`
- Number 1 - 0.15
- Number 2 - 0.17
- Number 3 - 0.18
- Number 4 - 0.20
- Number 5 - 0.21

`@hint`
Check out the instructions in the .Rmd file to answer the multiple choice question.

`@pre_exercise_code`
```{r}
# Import the helper file contained in the image
source('/usr/local/lib/R/site-library/run_rstudio.R')

# Run a new RStudio session and serve it to the user
run_rstudio(2, 10)

# .Rprofile: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex10.Rprofile
# .init.R: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex10.init.R
# lab2_ex10.Rmd: https://assets.datacamp.com/production/course_1156/datasets/lab2_ex10.Rmd
# lab.css: https://assets.datacamp.com/production/course_1156/datasets/lab.css
```

`@sct`
```{r,eval=FALSE}
msg1 <- "Try again."
msg2 <- "Try again."
msg3 <- "Awesome!"
msg4 <- "Try again."
msg5 <- "Keep trying!"

ex() %>% check_mc(3, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))
success_msg("Click [here](https://assets.datacamp.com/production/repositories/302/datasets/1b666c3c765d89a092e0e0ce8899f380cac0f96c/ch2_key.Rmd) to get a complete Rmarkdown file of the exercises in this Lab and click [here](https://assets.datacamp.com/production/repositories/302/datasets/43e95196b97c019a619b7ec8386257659bafe766/ch2_key.html) to get the Knitted HTML output.")
```