hw1.Rmd

---
  title: "BIOS 611 HW1 Data visualisation (Chapter 3)"
  author: "Matthew J Rich"
  date: "`r format(Sys.time(), '%m/%d/%Y')`"
  output: html_document
---
  *This homework is due September 5th 6pm.*
  
  (This set of exercise is mostly taken from R for Data Science by Garrett Grolemund and Hadley Wickham.)

# Exercise 1

1.  What's gone wrong with this code? Why are the points not blue?

    ```{r}
    library(ggplot2)
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
    ```
    
    Answer: plot is not blue because manual asctetics must be defined outside AES()

    ```{r}
      library(ggplot2)
       ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
    ```

2.  Which variables in `mpg` are categorical? Which variables are continuous? 
    (Hint: type `?mpg` to read the documentation for the dataset). How
    can you see this information when you run `mpg`?

    Answer: only one column contains real numbers so that is the only continuous data out of the 11 variables present

    ```{r}
       library(ggplot2)
        ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
    ```

3.  Map a continuous variable to `color`, `size`, and `shape`. How do
    these aesthetics behave differently for categorical vs. continuous
    variables? 
    
    Answer: Only color and size can be mapped to a continuous variable 

    ```{r}

    ```

4.  What happens if you map the same variable to multiple aesthetics? 

    Answer: you will generate two legends one for each mapping 

    ```{r}
      library(ggplot2)
      ggplot(data=mpg) +
      lugeom_point(mapping = aes(x = hwy, y = cty, color = displ, size = displ))
    ```

5.  What does the `stroke` aesthetic do? What shapes does it work with?
    (Hint: use `?geom_point`)
    
    Answer: 

    ```{r}

    ```

6.  What happens if you map an aesthetic to something other than a variable 
    name, like `aes(colour = displ < 5)`?  

    Answer: 

    ```{r}

    ```


# Exercise 2

1.  What do the empty cells in plot with `facet_grid(drv ~ cyl)` mean?
    How do they relate to this plot?
    
    ```{r, eval = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = drv, y = cyl))
    ```

    Answer: 

    ```{r}

    ```

2.  What plots does the following code make? What does `.` do?

    ```{r eval = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) +
      facet_grid(drv ~ .)
    
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) +
      facet_grid(. ~ cyl)
    ```

    Answer: 

    ```{r}

    ```

3.  Take the first faceted plot in this section:

    ```{r, eval = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) + 
      facet_wrap(~ class, nrow = 2)
    ```
    
    What are the advantages to using faceting instead of the colour aesthetic?
    What are the disadvantages? How might the balance change if you had a 
    larger dataset?
    
    Answer: 

    ```{r}

    ```

# Exercise 3

1.  What geom would you use to draw a line chart? A boxplot? 
    A histogram? An area chart?

    Answer: 

    ```{r}

    ```

2.  Run this code in your head and predict what the output will look like.
    Then, run the code in R and check your predictions.
    
    ```{r, eval = FALSE}
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
      geom_point() + 
      geom_smooth(se = FALSE)
    ```

    Answer: 

    ```{r}

    ```

3.  What does `show.legend = FALSE` do?  What happens if you remove it?  
    Why do you think I used it earlier in the chapter?

    Answer: 

    ```{r}

    ```

4.  What does the `se` argument to `geom_smooth()` do?


    Answer: 

    ```{r}

    ```

5.  Will these two graphs look different? Why/why not?

    ```{r, eval = FALSE}
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point() + 
      geom_smooth()
    
    ggplot() + 
      geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))
    ```

    Answer: 

    ```{r}

    ```

6.  Recreate the R code necessary to generate the following graphs.
    
    ![Smooth lines for each `drv`](`r "https://github.com/datasci611/datasci_src/blob/master/HW1P0306A.png?raw=true"`)
    
    
    Answer: 
    
    ```{r, echo=TRUE, eval=TRUE}

    ```


    ![A single smooth line, transparency by `year`](`r "https://github.com/datasci611/datasci_src/blob/master/HW1P0306B.png?raw=true"`)
    
    Answer: 
    
    ```{r, echo=TRUE, eval=TRUE}

    ```

    
    ![Layered dots and an additional text information](`r "https://github.com/datasci611/datasci_src/blob/master/HW1P0306C.png?raw=true"`)
    
    Adding texts was not covered in class, but give it a try!
    
    Answer: 
    
    ```{r, echo=TRUE, eval=TRUE}

    ```

# Exercise 4

1.  What is the default geom associated with `stat_summary()`? How could
    you rewrite the previous plot to use that geom function instead of the 
    stat function?

    Answer: 

    ```{r}

    ```

2.  What does `geom_col()` do? How is it different to `geom_bar()`?

    Answer: 

    ```{r}

    ```

3.  What variables does `stat_smooth()` compute? What parameters control
    its behaviour?

    Answer: 

    ```{r}

    ```

# Exercise 5

1.  What is the problem with this plot? How could you improve it?

    ```{r}
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
      geom_point()
    ```

    Answer: 

    ```{r}

    ```

2.  What parameters to `geom_jitter()` control the amount of jittering?

    Answer: 

    ```{r}

    ```

3.  Compare and contrast `geom_jitter()` with `geom_count()`.

    Answer: 

    ```{r}

    ```

# Exercise 6

1.  Turn a stacked bar chart into a pie chart using `coord_polar()`.

    Answer: 

    ```{r}

    ```

2.  What does `labs()` do? Read the documentation.

    Answer: 

    ```{r}

    ```

3.  What does the plot below tell you about the relationship between city
    and highway mpg? Why is `coord_fixed()` important? What does 
    `geom_abline()` do?
    
    ```{r, fig.asp = 1, out.width = "50%"}
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
      geom_point() + 
      geom_abline() +
      coord_fixed()
    ```

    Answer: 

    ```{r}

    ```