03-Day3_finish_ggplot.Rmd

---
title: "R Camp | day 3"
output: 
 html_document:
    toc: true
    toc_depth: 2
    toc_float: true
    theme: readable
    highlight: tango
    self_contained: false
    css: css/camp_style.css
fontsize: 14pt
monofont: Source Code Pro
monofontoptions: Scale = 1.1
---

<style> code {color: #535353 !important;} </style>

```{r setup, include = F}
knitr::opts_chunk$set(echo = T, error = T, message = F, warning = F, fig.width = 9)

htmltools::tagList(rmarkdown::html_dependency_font_awesome())

dt_options <- list(scrollX = T, autoWidth = T, searching = F, ordering = F, lengthChange = F, paginate = F, info = F)
```

<br>


# Good morning, Adventurers!

<img src="http://i0.kym-cdn.com/photos/images/original/001/210/794/4e1.jpg" width="308" align="right" style="margin-top: -57px; margin-left: 35px;">


This tutorial is online at 

> https://mpca-air.github.io/RCamp


#### Please _remote_ connect to your desktop computer

1. __Open__ the Start menu  \ \*_Click the Window’s logo on the bottom left of the screen_
1. Select ` Remote Desktop Connection `
1. Enter ` w7-your7digit# `
1. Press _Connect_

<br>

#### Open your _RStudio_ project

1. __Open__ your project folder from last week.
1. Double click the __.Rproj__ file to open RStudio.
1. Ready yourself for an adventure.

<br><br><br><br>


## CAMP schedule

<br>

<div class="toggle"><button class = "btn_code">_Show schedule_</button>
<br>

__Day 3__
<div style = "background-color: #9df1ff; border-radius: 78px; padding: 20px;">

1. Finish plotting
    - Line charts.
    - Add titles and axis labels.
    - Boxplots.
    - Log transform your chart axis. 
    - Bar charts.
    - Save plots in different formats: `ggsave()` 

2. Tidy your data with _tidyr_ 
    - Replace missing values.
    - Flip your table from wide to tall format. 
    - Join tables together.
    
3. Connect to data sources
    -	Excel
    -	SQL databases (e.g. TEMPO / delta / CEDR / EQUIS)
    - GIS Shapefiles
    -	Web data (URL or FTP site)
    -	Access
    - Tableau
  
</div>

<br><br>

</div>


## Day 2 review

> __Reminder:__ Add your packages to the top of each new script.

```{r, eval = F}
library(readr)
library(dplyr)
library(ggplot2)

# Your code starts here.

```


<br>

### 1. Table summary functions

- `summary()`
- `names()`
- `nrow()`
- `ncol()`
- `glimpse()`


### 2. Completed data transformation

- Save your data with `write_csv()`.
- Add a column with `mutate()`.
- Summarize your data with `summarize()` (or _summarise_ if you're from New Zealand).
    - `mean()`
    - `median()`
    - `max()`
    - `min()`
    - `nth()`
    - `sd()`
    - `n()`
    - `quantile(data, 0.05)`
- __Add__ `na.rm = T` to ignore missing values: `mean(data, na.rm = T)`.     
- Summarize by different categories in a group with `group_by()`.
- Connect multiple functions with the __pipe__ operator ` %>% `.


### 3. Made awesome charts

- `ggplot()` from the _ggplot2_ package.
- Different geometries: 
    - `geom_point()`
    - `geom_smooth()`
    - `geom_histogram()`
- Modified histogram bar position: `position = "dodge"`. 
- Set plot colors to vary based on a column in the data.
    - `geom_point(aes(color = content_rating))`

<br>



```{r, eval = F, echo = F}

### 4. Show and tell | <i class="fa  fa-rocket fa-spin fa-2x"></i>

<br> 

> Share some homework plots.

<br>

Connect the dots.
![](X:/Agency_Files/Outcomes/Risk_Eval_Air_Mod/_Air_Risk_Evaluation/R/R_Camp/Homework Examples/Connect the dots.png)

<br>

![](X:/Agency_Files/Outcomes/Risk_Eval_Air_Mod/_Air_Risk_Evaluation/R/R_Camp/Homework Examples/Question44_PCAonly_plot.png)

<br>

![](X:/Agency_Files/Outcomes/Risk_Eval_Air_Mod/_Air_Risk_Evaluation/R/R_Camp/Homework Examples/plot3.png)

<br>

![Cat alien](X:/Agency_Files/Outcomes/Risk_Eval_Air_Mod/_Air_Risk_Evaluation/R/R_Camp/Homework Examples/cat_alien.JPG){width="50%"}

<br>

```


# Plotting with ggplot2 _...continued_


### Let's load the IMDB movie data again.

__Surprise!__ The data is now in an _Excel_ file.


# Excel files

![](https://www.rstudio.com/wp-content/uploads/2017/05/readxl-259x300.png){width="180" align="right" style="margin-top: -46px; margin-left: 20px;"}

_Excel_ files are complicated. Let's install the _readxl_ package!

```{r, eval=F}
install.packages("readxl")
```

<br>

Now we can use the `read_excel()` function to load data from any tab we want in an Excel file. 

As an example, download the movie data as an _XLSX_ file from [HERE](https://github.com/MPCA-air/RCamp/raw/master/data/movies/IMDB%20movie%20data.xlsx) and save it to your `data` folder.

_Now you can use `read_excel()` to load the data into R._
```{r, warning = F, eval = T}
library(readxl)
library(dplyr)
library(ggplot2)

movie_file <- "data/movies/IMDB movie data.xlsx"

# Read Excel file
movies <- read_excel(movie_file)
```

<br>

Show the sheet names in the Excel file.
```{r, warning = F, eval = T}

# Show sheet names in file
excel_sheets(movie_file)

# The default is the first tab/sheet. 
# Pick the second sheet using `sheet = 2`
movies <- read_excel(movie_file, sheet = 2)

# Or, pick the "use_this" tab
movies <- read_excel(movie_file, sheet = "use_this")

# Column names start on the 3rd row
# Skip the top 2 rows of random info
movies <- read_excel(movie_file, sheet = "use_this", skip = 2)

# Rename the 'color' column
movies <- rename(movies, movie_color = color)

# Check column types
glimpse(movies)

# Convert character numbers to numeric()
movies <- mutate(movies, 
                 budget = as.numeric(budget), 
                 aspect_ratio = as.numeric(aspect_ratio))

```



## Line charts

Now let's look at the total `gross` of movies over time in millions of dollars.

```{r}
gross_by_year <- movies %>% 
                         group_by(title_year) %>% 
                         summarize(total_gross = sum(gross_mil, na.rm = T)) %>% 
                         ungroup()

ggplot(gross_by_year, aes(x = title_year, y = total_gross)) + 
  geom_line() 

```


### Let's use a different `color` line for each `content_rating`
```{r}
gross_by_year <- movies %>% 
                   group_by(title_year, content_rating) %>% 
                   summarize(total_gross = sum(gross_mil, na.rm = T)) %>% 
                   ungroup()

ggplot(gross_by_year, aes(x = title_year, y = total_gross)) + 
  geom_line(aes(color = content_rating)) 

```


This would be even better with a title and better axis labels. Let's add some using the `labs()` function.

## Title and axis labels
```{r}
ggplot(gross_by_year, aes(x = title_year, y = total_gross, color = content_rating)) + 
  geom_line() +
  labs(title = "Is the movie industry making more money now?",
       x     = "Year",
       y     = "Gross in millions of U.S. $$$$")
```


### Better! But it would look even better with a subtitle and a caption.
```{r}
ggplot(gross_by_year, aes(x = title_year, y = total_gross, color = content_rating)) + 
  geom_line() +
  labs(title = "The movie industry is making more money over time",
       subtitle = "What happened in 2012? Netflix? YouTube? New books?",
       x        = "Year",
       y        = "Gross in millions of U.S. $$$$",
       caption  = "Awesome chart made by Derek. December 2017.")
```


#### Exercise <i class="fa fa-bicycle" aria-hidden="true" style="color: green"></i> {-}

Make a line chart showing the total `budget` of all the movies each year.


# Boxplots

Use `geom_boxplot()` to make a boxplot.

## Let's compare how much money movies made by content rating. Use `y = gross_mil` for the y-axis.
```{r}
ggplot(movies, aes(x = content_rating, y = gross_mil)) +
  geom_boxplot()

```

## Transform to Log scale.
```{r}
ggplot(movies, aes(x = content_rating, y = gross_mil)) +
  geom_boxplot() +
  scale_y_log10()
```


### Make the outliers stand out. Rotten tomatoes.
```{r}
ggplot(movies, aes(x = content_rating, y = gross_mil)) +
  geom_boxplot(outlier.color = "tomato") + 
  scale_y_log10()
```


### Add another sandwich showing the points on top of the boxplots.
```{r}
ggplot(movies, aes(x = content_rating, y = gross_mil)) +
  geom_boxplot(outlier.color = "tomato") + 
  scale_y_log10() + 
  geom_point(alpha = 0.1)
```


### Use `jitter()` to spread out the points. This makes a snowfall plot.
```{r}
ggplot(movies, aes(x = content_rating, y = gross_mil)) +
  geom_boxplot(outlier.color = NA) + 
  scale_y_log10() + 
  geom_jitter(alpha = 0.1)

```


### Rearrange the layer order to put the boxplots on top.
```{r}
ggplot(movies, aes(x = content_rating, y = gross_mil)) +
  scale_y_log10() + 
  geom_jitter(alpha = 0.1) + 
  geom_boxplot(outlier.color = NA, alpha = 0.8)

```

<br>

#### Exercise 

Make boxplots comparing the range of `gross_mil` between `movie_color` (black and white films vs. color films).

<br>

<details>
<summary class = "btn_code">_Show solution_</summary>
<p>
```{r}
ggplot(movies, aes(x = movie_color, y = gross_mil)) +
  scale_y_log10() + 
  geom_jitter(alpha = 0.1) + 
  geom_boxplot(outlier.color = NA, alpha = 0.8)

```
</p></details>



## Johnny Depp movies

![](http://www.baldingbeards.com/wp-content/uploads/2017/03/Johhny-Depp-Jack-Sparrow-Beard.png){width="25%"}

> We received questions about creating a new column with `mutate()` based on the values of an existing column. 

To learn how to do this, let's make a boxplot of how much movies make that starred Johnny Depp vs. those that didn't (big mistake!). First we need to learn about `ifelse()`.

`ifelse()` makes a comparison and then returns a value depending on the result of the comparison. The comparison below looks to see whether a movie's #1 actor was Johnny Depp, if it was then `ifelse()` returns _Yes_, if not it returns _No_.

```{r}
movies <- movies %>%
          mutate(stars_johnny_depp = ifelse(actor_1_name == "Johnny Depp", "Yes", "No"))

ggplot(movies, aes(x = stars_johnny_depp, y = gross_mil)) +
  geom_boxplot() +
  scale_y_log10()

```


# Bar charts

### Use a bar chart to compare `imdb_score` by `content_rating`
```{r}
imdb_score_by_content <- movies %>% 
                           group_by(content_rating) %>% 
                           summarize(mean_score = mean(imdb_score, na.rm = T))

ggplot(imdb_score_by_content, aes(x = content_rating, y = mean_score)) + geom_col() 
```

_Note:_ _By default `geom_bar()` simply counts the number of values in each category. If you want to show the height of each bar at a specific value in the data, use `geom_bar(stat = "identity")`. The function `geom_col()` is equivalent to `geom_bar(stat = "identity")`._


### Labels for each bar

There's a `geom_label` we can use to label each column.

```{r}

ggplot(imdb_score_by_content, aes(x = content_rating, y = mean_score)) +
  geom_col() +
  geom_label(aes(label = mean_score))

```

_Hint: Update to `label = round(mean_score, digits = 1))` to make labels legible._

<br>


#### Exercise _(extra credit)_ <i class="fa fa-bicycle" aria-hidden="true" style="color: green"></i> {-}
Create a summarized data set and bar chart of the mean budget by `content_type`.

<details>
<summary class = "btn_code">_Show solution_</summary>
<p>

```{r}
budget_by_content <- movies %>% 
                     group_by(content_rating) %>% 
                     summarize(mean_budget         = mean(budget, na.rm = T) / 1000000,
                               mean_budget_round   = round(mean_budget, digits = 1)) 

ggplot(budget_by_content, aes(x = content_rating, y = mean_budget_round)) +
  geom_bar(stat = "identity") +
  geom_label(aes(label = mean_budget_round))

```

</p></details>


<br>

## Just for fun...

#### the BIG profit chart _(try_ _after class)_

<details>
<summary class = "btn_code">_Show BIG profit chart_</summary>
<p>

### Add a profit column
```{r}
movies <- mutate(movies, 
                 profit     = gross - budget,
                 profit_mil = profit / 1000000)

```

### Plot the profit of every film
```{r}
ggplot(movies, aes(x = title_year,  y = profit)) + geom_jitter(alpha = 0.5)
```


### Label the top and bottom 1/2 percentile of movies
```{r}
ggplot(movies, aes(x = title_year,  y = profit)) +
  geom_jitter(alpha = 0.5) + 
  geom_label(aes(x = title_year,  
                 y = profit,
                 label = ifelse(profit > quantile(movies$profit, 0.997, na.rm = T) |
                                profit < quantile(movies$profit, 0.003, na.rm = T),
                                movie_title,
                                NA)),
             alpha = 0.85, 
             nudge_y = 3) +
  labs(title  = "Which movies made and lost the most $$$ ?",
       y      = "Profit in U.S. dollars",
       x      = "year")
```


### Label them all!  
__Warning!__ This adds thousands of labels and may stall your computer for a few minutes. You have been warned.

```{r}
library(hrbrthemes)  #install.packages(hrbrthemes)
library(scales)      # For the $ dollar_format() labels

ggplot(movies, aes(x = title_year,  y = profit)) + 
  geom_label(aes(label = movie_title, 
                 fill  = profit > 0),
                 show.legend = F,
                 alpha = 0.9) + 
  scale_y_continuous(labels = dollar_format()) + 
  labs(title    = "Did your favorite film make or lose $$$ ?",
       subtitle = "Profit of top 5,000 rated films on IMDB",
       y        = "Profit in U.S. dollars",
       x        = "year",
       caption  = "Brought to you by the #R-Cats") +
  theme_ipsum_rc() + 
  theme(plot.title = element_text(size = 24)) # Increase title size

```

</p></details>


# Plot summary

Table of __aesthetics__.

| aes()         |    |  
|:--------------|:---|  
| `x = `        |    |  
| `y = `        |    |  
| `alpha = `    |    | 
| `fill = `     |    |  
| `color = `    |    |   
| `size = `     |    |   
| `linetype = ` |    |    


<br>

Table of __geoms__.

![](images/geoms_1var.png)

![](images/geoms_diagram.png)

<br>

Table of __themes__.

You can customize the look of your plot by adding a `theme()` function.

![](images/ggplot_themes.png)


# Plotting finale!

Choose one of the plots below and create it with your neighbor. Compare your code when you're done.   


### How many movies were made in each content rating?

_Hint: use `+ coord_flip()`_

```{r, echo = F}
ggplot(movies) + 
  geom_bar(aes(x = content_rating, fill = content_rating)) + 
  coord_flip() + 
  labs(title = "Total movies by content rating")

```


### Are the number of movies increasing for G, PG, PG-13 and R rated films?

_Hint: Use %in% to match on multiple values when you `filter()`._
```{r, echo = F}
filter(movies, content_rating %in% c("G", "PG", "PG-13", "R")) %>% 
  ggplot(aes(x = title_year, fill = content_rating)) + 
  geom_bar() + 
  facet_wrap( ~ content_rating) + 
  labs(title = "Which rating of movie is being produced most often?",
       subtitle = "Trends in G, PG, PG-13 and R movies",
       x = "")

```


### Since 2012, have the total Facebook likes for a movie been associated with the number of cast Facebook likes? 

_Hint: We made a similar chart in Day 2._
```{r, echo = F}
filter(movies, title_year > 2011) %>% 
  ggplot(aes(x = cast_total_facebook_likes, y = movie_facebook_likes)) + 
  geom_point(alpha = 0.2) + 
  geom_smooth(method = "lm") + 
  labs(title = "Movie Facebook likes increase with cast likes",
       x = "Cast likes",
       y = "Movie likes")

```


### What is the trend in the total gross of G and PG rated films?

_Hint: Create a summarized table grouped by `title_year` and `content_rating`_
```{r, echo = F}
filter(movies, content_rating %in%  c("G", "PG")) %>%
  group_by(title_year, content_rating) %>% 
    summarize(gross = sum(gross_mil, na.rm = T)) %>% 
     ungroup() %>% 
      ggplot(aes(x = title_year, y = gross)) + 
       geom_line(aes(color = content_rating)) + 
         labs(title = "The total gross of G and PG rated films",
              x = "",
              y = "Movie gross in MILLIONS")
```


### Since 2012, which content rating has had the highest `median` number of Facebook likes?

```{r, echo = F}
filter(movies, title_year >= 2012) %>%
  ggplot(aes(x = content_rating, y = movie_facebook_likes/1000)) +
  geom_boxplot(aes(fill = content_rating)) + 
  labs(title = "Movie Facebook likes by content rating",
       subtitle = "TV-14 steals the show",
       x = "Content rating",
       y = "Facebook likes (thousands)")
```
<br>


#### Bar chart version

_Hint:_ _Summarize the movies by`content_rating`._
```{r, echo = F}
filter(movies, title_year >= 2012) %>%
  group_by(content_rating) %>% 
  summarize(likes_thousands = median(movie_facebook_likes) / 1000) %>% 
  ggplot(aes(x = content_rating, y = likes_thousands)) + 
  geom_bar(stat = "identity", aes(fill = content_rating), position = "dodge") + 
  labs(title = "Movie Facebook likes by content rating",
       subtitle = "TV-14 steals the show",
       x = "Content rating",
       y = "Facebook likes (thousands)")
```


### Which type of movie has the shortest running time: G, PG-13, or R? 
```{r, echo = F}
filter(movies, content_rating %in% c("G", "PG-13", "R")) %>%
  mutate(duration_hrs = duration / 60) %>% 
  ggplot(aes(x = content_rating, y = duration_hrs)) +
           geom_boxplot(aes(fill = content_rating)) + #guides(fill=F) + 
  labs(title    = "Which movies are shorter?",
       subtitle = "The duration of movies by content rating",
       x        = "Content rating",
       y        = "Movie duration in hours",
       caption  = "Movie data downloaded from IMDB, 2020") 
```



### Movie budgets of longer movies.
_Hint: Create a new column called `longer_than_2hrs` and set it equal to `duration > 120`_  
 
_Hint 2: Use `scale_y_log10()`_

```{r, echo = F}
movies %>% 
  mutate(longer_than_2hrs = duration > 120) %>% 
  ggplot(aes(x = duration, y = budget)) +
  geom_point(aes(color = longer_than_2hrs), alpha = 0.25) +
  geom_smooth(method = "lm") +
  scale_y_log10() + 
  labs(title    = "Do longer movies have a bigger budget?",
       subtitle = "Only up to a point.",
       y        = "log10(budget)",
       x        = "Movie duration in minutes")

```


### They say attention spans are dwindling. Have movie durations been decreasing since 1965?

_Hint:_ _Use `geom_jitter()`_

```{r, echo = F}
filter(movies, title_year >= 1965) %>%
  ggplot(aes(x = title_year, y = duration)) +
  geom_jitter(alpha = 0.2) +
  geom_smooth(method = "lm") +
  labs(title = "Are movie durations decreasing?",
       subtitle = "1965 to 2016",
       x = "")
```


#### What about the duration of only black and white films?
```{r, echo = F}
filter(movies, 
       title_year >= 1965, 
       movie_color != "color") %>%
  ggplot(aes(x = title_year, y = duration)) +
    geom_jitter(alpha = 0.2) + 
    #geom_boxplot(aes(group = title_year), alpha = 0.5) 
    geom_smooth(method = "lm") +
  labs(title = "Are Black and white movie durations decreasing?",
       subtitle = "1965 to 2016",
       x = "")
```


#### Use a line plot to show the mean duration by year for Black and white films.

_Hint:_ _After filtering, create a summarized table grouped by `title_year`._  

_Hint 2:_ _Set `linetype = "dashed"` inside `geom_line()`_.
```{r, echo = F}
filter(movies, 
       title_year >= 1965, 
       movie_color != "color") %>%
  group_by(title_year) %>%
  summarize(mean_duration = mean(duration, na.rm = T)) %>%
  ggplot(aes(x = title_year, y = mean_duration)) +
    geom_line(linetype = "dashed")

```


### Since 1990, what was the average IMDB score for each content rating?  

_Hint:_ _Summarize grouped by `content_rating` and `title_year`._  

_Hint 2:_ _Use `geom_tile(aes(fill = mean_score))`._ 

```{r, echo = T}
filter(movies, title_year >= 1990) %>% 
  group_by(content_rating, title_year) %>% 
  summarize(mean_score = mean(imdb_score, na.rm = T)) %>% 
  ggplot(aes(y = content_rating, x = title_year)) +
  geom_tile(aes(fill = mean_score), color = "white") +   
  #theme_minimal() +
  labs(title = "Average IMDB score by content rating")
```

<br>

### Show the distribution of gross for each content rating.
```{r, echo = T}
#install.packages("ggridges")
library(ggridges)

ggplot(movies, aes(x = gross_mil, y = content_rating, fill = content_rating)) + # x/y reveresed from boxplots
  geom_density_ridges() +
  scale_x_log10() + 
  scale_fill_brewer() + 
  labs(title = "Which rating makes more $$$ ?",
       x = "Gross in millions of dollars",
       y = "Content rating")
```


<br>

# Save plots with `ggsave()`

__After creating a ggplot in RStudio, save it to an image with__ 

```{r, eval = F}
ggsave("Boxplot of movie ratings.png")
```

_Note: The default dimensions will match the plot viewer window in RStudio._

<br>

__To set the size__
```{r, eval = F}
# Widescreen plot for your TED talk
ggsave("Boxplot of movie ratings.png", width = 12, height = 4)
```


<br>

__If you assigned your plot a name:__
```{r, eval = F}

myplot <- ggplot(movies, aes(budget, gross)) + geom_point(aes(color = title_year))

ggsave(myplot, file = "Boxplot of movie ratings.png")

myplot
```

<br>

__To save different formats change the file extension: pdf, jpg, png, tiff, eps, svg.__
```{r, eval = F}
ggsave(myplot, file = "Boxplot of movie ratings.pdf")

ggsave(myplot, file = "Boxplot of movie ratings.jpg")

```

<br>

__To double the size__
```{r, eval = F}
ggsave(myplot, file = "Boxplot of movie ratings.png", scale = 2)

```

<br>

__To GADZOOGLE the font size! Use `base_size = `__
```{r, eval = F}

myplot <- ggplot(movies, aes(budget, gross)) + 
              geom_point(aes(color = title_year)) +
                  theme_gray(base_size = 40) + 
                  labs(title = "BIG TITLE!")

ggsave(myplot, file = "Boxplot of movie ratings.png", scale = 2)

myplot

```

<br>

__Rotate the X-axis labels__
```{r, eval = F}

myplot <- ggplot(movies, aes(budget, gross)) + 
              geom_point(aes(color = title_year)) +
                  theme_gray(base_size = 40) + 
                  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) + 
                  labs(title = "BIG TITLE!")

ggsave(myplot, file = "Boxplot of movie ratings.png", scale = 2)

myplot

```

<br>

__Or shrink the labels__
```{r, eval = F}

myplot <- ggplot(movies, aes(budget, gross)) + 
              geom_point(aes(color = title_year)) +
                  theme_gray(base_size = 40) + 
                  theme(axis.text.x = element_text(size = 20)) + 
                  labs(title = "small TITLE!")

ggsave(myplot, file = "Boxplot of movie ratings.png", scale = 2)

myplot

```


<br>

_Ref: [http://stat545.com/](http://stat545.com/block017_write-figure-to-file.html)_

<br>



### Frequent plotting questions
- How to modify the gridlines behind your chart?
    - Try the different themes at the end of this lesson: `theme_light()` or `theme_bw()`.
    - Or modify the color and size with `theme(panel.grid.minor = element_line(colour = "white", size = 0.5))`.
    - There's even `theme_excel()`
- How do you set the x and y scale manually?
    - Here is an example with a scatter plot: `ggplot() + geom_point() + xlim(beginning, end) + ylim(beginning, end)`
    - __Warning:__ Values above or below the limits you set will not be shown. This is another great way to lie with data. 
- How do you get rid of the legend if you don't need it?
    - `geom_point(aes(color = facility_name), show.legend = FALSE)`
    - The R cookbook shows a number of ways to get rid of legends: http://www.cookbook-r.com/Graphs/Legends_(ggplot2)/
- I only like dashed lines. How do you change the linetype to a _dashed_ line? 
    - `geom_line(aes(color = facility_name), linetype = "dashed")`
    - You should also try `"dotted"` and `"dotdash"`, or maybe`"twodash"` if you really want to go wild.
-How many colors are there in R? How did you know `hotpink` was a color?
    - There is an R color cheatsheet: https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf 
    - And some basic color names here: http://www.r-graph-gallery.com/42-colors-names/
    - This web tool will give you palette ideas and color Hex numbers: http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3
    - `color palettes()` There are color palettes for your favorite movies, just fun palettes that you might like `library(viridis)`, color-blind palettes, and topography palettes.
- What are some handy keyboard shortcuts in R? How do I find them?
    - There is a shortcut cheat sheet online here: https://www.rstudio.com/wp-content/uploads/2016/01/rstudio-IDE-cheatsheet.pdf
    - Or go to _Help_ > _Keyboard Shortcuts  Help_

<br>


# Choose a new path...

<div style = "height: 305px; overflow: hidden;">
<img src="https://img00.deviantart.net/c562/i/2012/139/5/b/two_x_three_doors_by_klopmaster-d50awvk.jpg" width="580" style="margin-top: -10px;">
</div>

<br>

1. [Database connections](03-Day3_db_connect.html)
1. [GIS & Shapefiles](03-Day3_GIS.html)
1. [Web data](03-Day3_web_data.html)
1. [Tableau](03-Day3_tableau.html)
1. [Other Stats software](03-Day3_other_stats.html)

### Good luck!

<br><br>

# Return to [RCamp](index.html) {-}

<br>