• Test ability to create PDF
• Refresh R skills with 2 exercises
• Develop skills to create and format 1-page PDF document
• Build your own PDF

• Create a 1-page PDF document
  • Include a title
  • Include at least 1 chart
  • Include at least 1 table
  • Submit the 1-page PDF to me by email on or before February 16, 2018

• Markdown is ‘an authoring framework for data science’
• Multiple markdown languages exist
• We will use R Markdown
  
• R Markdown files integrate well with HTML, CSS, & Pandoc Markdown
• We will use a little of each when it makes development of your PDF easier
• For our purposes, knitr is a button at the top of the R Studio window that compiles your document
  • knitr is magical and so much more than just a button
• Resources
  • Template
  • My 1-pager
  • R Markdown Cheatsheet

• Open a new .Rmd file (File > New File > R Markdown)
  • Select PDF from ‘Document’
• Save the file you just created
• Click ‘Knit’ to create PDF at the top of the R Studio window (look for the blue yarn)
• The document should render in the R Studio viewer
• The PDF file should appear in the same location you saved your .Rmd file

Did you run into problems? If so…

1. Confirm you installed a Tex compiler (MacTex on iOS and MikTex on Windows). Install now if you haven’t already.
   
2. If prompted, install Tex packages
   
3. Try reinstalling the packages below
   
4. Ask for additional help

install.packages(c('rmarkdown', 'knitr'))

donor %>% 
    filter(contributor_employer_name %in% c('MICROSOFT', 'THE COCA-COLA COMPANY'
                                            , 'NUCOR STEEL SEATTLE, INC.'
                                            , 'FARMERS INSURANCE' 
                                            , 'UNIVERSITY OF WASHINGTON')) %>% 
    group_by(contributor_employer_name) %>% 
    summarise(avg_amount = mean(amount), max_amount = max(amount))

donor %>% 
    filter(contributor_employer_name %in% c('MICROSOFT', 'THE COCA-COLA COMPANY'
                                            , 'NUCOR STEEL SEATTLE, INC.'
                                            , 'FARMERS INSURANCE' 
                                            , 'UNIVERSITY OF WASHINGTON')) %>% 
    group_by(contributor_employer_name) %>% 
    summarise(avg_amount = mean(amount), max_amount = max(amount))
## # A tibble: 5 x 3
##   contributor_employer_name avg_amount max_amount
##   <fctr>                         <dbl>      <dbl>
## 1 FARMERS INSURANCE              27.1       182  
## 2 MICROSOFT                     387        1400  
## 3 NUCOR STEEL SEATTLE, INC.       1.49       10.0
## 4 THE COCA-COLA COMPANY           9.75       40.4
## 5 UNIVERSITY OF WASHINGTON      144         500

• Answer one or both of the questions below
  • In the subset of contributors data analyzed in Exercise 1, which party received the most contributions?
  • To what extent is there a relationship between between party and a geography variable (location, city, county, region, etc.) for the subset of contributors from Exercise 1?
• Create a visualization that helps answer the question(s)
• To subset the data, use filter
  • Use the same filter code to subset the data as in Exercise 1
  • You can create a new data object with <- if it helps you build your data visualization
• Update NA values so that they are Unknown
  • Use ifelse() in the mutate() function

• Answer one or both of the questions below
  • In the subset of contributors data analyzed in Exercise 1, which party received the most contributions?
  • To what extent is there a relationship between between party and a geography variable (location, city, county, region, etc.) for the subset of contributors from Exercise 1?
• Create a visualization that helps answer the question(s)

Hint

donor_sds <- donor %>% 
    filter(contributor_employer_name %in% c('MICROSOFT', 'THE COCA-COLA COMPANY'
                                            , 'NUCOR STEEL SEATTLE, INC.'
                                            , 'FARMERS INSURANCE' 
                                            , 'UNIVERSITY OF WASHINGTON')) 

• Answer one or both of the questions below
  • In the subset of contributors data analyzed in Exercise 1, which party received the most contributions?
  • To what extent is there a relationship between between party and a geography variable (location, city, county, region, etc.) for the subset of contributors from Exercise 1?
• Create a visualization that helps answer the question(s)

Hint

donor_sds <- donor %>% 
    filter(contributor_employer_name %in% c('MICROSOFT', 'THE COCA-COLA COMPANY'
                                            , 'NUCOR STEEL SEATTLE, INC.'
                                            , 'FARMERS INSURANCE' 
                                            , 'UNIVERSITY OF WASHINGTON')) 
                                        
donor_sds %>% ggplot(aes()) + 

In the subset of contributors data analyzed in Exercise 1, which party received the most contributions?

donor_sds <- donor %>% 
    filter(contributor_employer_name %in% c('MICROSOFT', 'THE COCA-COLA COMPANY'
                                            , 'NUCOR STEEL SEATTLE, INC.'
                                            , 'FARMERS INSURANCE' 
                                            , 'UNIVERSITY OF WASHINGTON')) %>%
    mutate(party = ifelse(party %in% NA, 'Unknown', as.character(party)))

donor_sds %>% ggplot(aes(party)) + geom_bar() + theme_economist() + 
  labs(x = '', y = 'Donations (#)')

In the subset of contributors data analyzed in Exercise 1, which party received the most contributions?

To what extent is there a relationship between between party and a geography variable (location, city, county, region, etc.) for the subset of contributors from Exercise 1?

donor_sds <- donor %>% 
    filter(contributor_employer_name %in% c('MICROSOFT', 'THE COCA-COLA COMPANY'
                                            , 'NUCOR STEEL SEATTLE, INC.'
                                            , 'FARMERS INSURANCE' 
                                            , 'UNIVERSITY OF WASHINGTON')) %>%
    mutate(party = ifelse(party %in% NA, 'Unknown', as.character(party))) %>%
    filter(! party %in% 'Unknown')

donor_sds %>% ggplot(aes(x= contributor_state, fill = party)) + 
  geom_bar() + theme_economist() + labs(x = 'State', y = '')

To what extent is there a relationship between between party and a geography variable (location, city, county, region, etc.) for the subset of contributors from Exercise 1?

• YAML or front-matter
  
• Code chunks
• Code for formatting (Markdown and LaTex)
  
• Text

• YAML or front-matter
  • Use YAML to make universal configurations
  • Very top of the document between two sets of dashes (---)
• Code chunks
• Code for formatting (Markdown and LaTex)
  
• Text

YAML

---
title: "What a great title!"
output: pdf_document
---

• YAML or front-matter
  
• Code chunks
  • Transform data and build tables and visualizations within code chunks
  • Remember to use the 3 backward apostrophes ``` and { } in chunk header
• Code for formatting (Markdown and LaTex)
  
• Text

Code chunks

```{r, warning=FALSE, message=FALSE}  
library(tidyverse) # load libraries
police <- read.csv('https://goo.gl/nNAuDy') # read your data
```

• YAML or front-matter
  
• Code chunks
  
• Code for formatting (Markdown and LaTex)
  • Format text with simple code
    
• Text

Code for formatting (Markdown and LaTex)

# Use hashes for headers

**List (bolded)**
- Bullet 1
- Bullet 2

• Header
  
• Body

• Header
  • Write headers with hashes
    
  • Fewer hashes produces a larger header
    
• Body

# Header
## Header
### Header
#### Header
##### Header
###### Header

• Header
  
• Body
  • Add fontsize to the YAML
    
  • Font size options are limited when developing PDF docs
    
  • Only recognizes inputs of 10pt, 11pt, and 12pt
    
  • fontsize variable affects all text elements

---
title: "Foo"
output: pdf_document
fontsize: 12pt
---

• Use LaTex code to change colors
• LaTex code is required when creating a PDF
  • Other options are available when creating other file types (i.e. HTML)
• Predefined colors
  

black, blue, brown, cyan, darkgray, gray, green, lightgray, lime, magenta,  
olive, orange, pink, purple, red, teal, violet, white, yellow

• To change text colors use the following convention

Look at the \textcolor{red}{balloons}!

Look at the balloons!

• Use LaTex code to align text
  
• To align text use the following convention
• Aligment options

flushleft, flushright, flushcenter

• To change text alignment use the following convention

\begin{flushcenter}Look at the \textcolor{red}{balloons}!\end{flushcenter}

Look at the balloons!

Add a hyperlink

[Add a hyperlink](https://www.a.url.com)

Bold, italicize, and underline words

**Bold words**
*Italicize words*
\underline{Underline words}

• Use -, +, or * to create an un-ordered list
• Use numbers followed by a period to create an ordered list (1.)
• Indent for sub-items

- Item 1
- Item 2
  - Sub-item 1
  - Sub-item 2

• Item 1
• Item 2
  • Sub-item 1
  • Sub-item 2

• ‘Off the shelf’ options are limited for font style
• Three options
  • Roman font: \textrm{...}
  • Sans serif font: \textsf{...}
  • Teletype font: \texttt{...}

Look at the \textcolor{red}{balloons}!  
\textsf{Look at the \textcolor{red}{balloons}!}  
\textrm{Look at the \textcolor{red}{balloons}!}  
\texttt{Look at the \textcolor{red}{balloons}!}  

• Update the YAML
  • Note header-includes text and \usepackage text
• Indicate the color of the page just below the YAML

---
title: "Foo"
output: pdf_document
fontsize: 12pt
header-includes:
- \usepackage{pagecolor}
---
\pagecolor{yellow}

• Create and format charts in code chunks just like in R Notebook
  • Following r in the braces is the name of the code chunk ({r plot})
  • Names must be unique
  • Also include echo=FALSE in braces. This hides your code in the output
  • You can divide code between two or more chunks if it is necessary

```{r plot, echo=FALSE}  
library(tidyverse)
mpg %>% ggplot() + geom_point(aes(displ, hwy))
```

• Create and format charts in code chunks just like in R Notebook
  • Following r in the braces is the name of the code chunk ({r plot})
  • Names must be unique
  • Also include echo=FALSE in braces. This hides your code in the output
  • You can divide code between two or more chunks if it is necessary

• Between braces use fig.width and fig.height
• Start with values close to 4 and adjust accordingly
• Use fig.align to align to move the chart to the left, right, or center

```{r plot, echo=FALSE, fig.width=7, fig.height=4, fig.align='right'}  
library(tidyverse)
mpg %>% ggplot() + geom_point(aes(displ, hwy))
```

• Between braces use fig.width and fig.height
• Start with values close to 4 and adjust accordingly
• Use fig.align to move the chart to the left, right, or center

• Use kable() from knitr to create tables
  
• Not all kable() functionality is available when creating PDFs
• Include results = 'asis' in the chunk header or else the table will not appear
  
• Create a table by calling a dataframe in the kable() function

```{r, echo = FALSE, warning=FALSE, message=FALSE, results = 'asis'}
police %>% group_by(event_clearance_ampm) %>% summarise(n = n()) %>% kable(format = 'markdown')
```

• Use kable() from knitr to create tables
  
• Not all kable() functionality is available when creating PDFs
• Include results = 'asis' in the chunk header or else the table will not appear
  
• Create a table by calling a dataframe in the kable() function

• Create a 1-page PDF document
  • Include a title
  • Include at least 1 chart
  • Include at least 1 table
  • Submit the 1-page PDF to me by email on or before February 16, 2018