ggplot_201805.html

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# ggplot2 blitz
## A xaringan presentation
### Rick Pack
### 2018/05/21

---


# We are going to turn this table of countries' oil production (look left)
![oil production Wiki](Oil_Wiki.png)
--
# https://en.wikipedia.org/wiki/List_of_countries_by_oil_production

---
# Into this ggplot2 (R Package) graph of countries' oil production 
![oil production graph](Oil_ggplot.png)

# http://www.sharpsightlabs.com/blog/map-oil-production-country-r/

---
# This HTML5 presentation was created in RStudio with the Xaringan package by Yihui Xie
![Xaringan pres](Xaringan_pres.png)

--

![Xaringan code](Xaringan_code.png)

# get started with Xaringan using Yihui Xie's presentation at https://slides.yihui.name/xaringan/

---
#Yihui Xie - courtesy of usethis.com
![Yihui Xie image](Yihui_Xie_water.png)

# "This code is available on my Github page: https://github.com/RickPack/TriPASS_DataSci_ggplot2_201805

---

# sharpsightlobs.com R Code (map oil production by country) [slight mod] - page 1 / X

```r
#=============
# LOAD PACKAGES, INSTALL IF NOT INSTALLED
#=============
list.of.packages &lt;- c("tidyverse", "sf", "rvest", "stringr", "scales", "viridis", "extrafont", "maps","formatR")
new.packages &lt;- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages, repos = "http://cran.us.r-project.org")
```

---

```r
#=============
# LOAD PACKAGES
#=============
# user Nate in https://stackoverflow.com/questions/14733732/cant-change-fonts-in-ggplot-geom-text #&lt;&lt;
# "The key for my system was to call extrafont::loadfonts(device="win") prior to library(ggplot2)."
# needed for Windows to eliminate "font family not found in Windows database error"
# in Linux use X11Fonts()
extrafont::font_import("C:/Windows/Fonts/", pattern = "GIL_____")
```

```
## Exiting.
```

```r
extrafont::loadfonts(device="win")
library(tidyverse)
```

```
## -- Attaching packages --------------------------------------------------------------- tidyverse 1.2.1 --
```

```
## v ggplot2 2.2.1     v purrr   0.2.4
## v tibble  1.4.2     v dplyr   0.7.5
## v tidyr   0.8.1     v stringr 1.3.1
## v readr   1.1.1     v forcats 0.3.0
```

```
## -- Conflicts ------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

```r
library(sf)
```

```
## Linking to GEOS 3.6.1, GDAL 2.2.3, proj.4 4.9.3
```

```r
library(rvest)
```

```
## Loading required package: xml2
```

```
## 
## Attaching package: 'rvest'
```

```
## The following object is masked from 'package:purrr':
## 
##     pluck
```

```
## The following object is masked from 'package:readr':
## 
##     guess_encoding
```

```r
library(stringr)
# I love the percent function in the scales package
library(scales) #&lt;&lt;
```

```
## 
## Attaching package: 'scales'
```

```
## The following object is masked from 'package:purrr':
## 
##     discard
```

```
## The following object is masked from 'package:readr':
## 
##     col_factor
```

```r
# for color palettes: #&lt;&lt;
library(viridis) #&lt;&lt;
```

```
## Loading required package: viridisLite
```

```
## 
## Attaching package: 'viridis'
```

```
## The following object is masked from 'package:scales':
## 
##     viridis_pal
```


```r
scales::percent(3/4)
```

[1] "75%"

---
# An easily produced ggplot2 scatterplot

```r
# mpg is a data frame loaded when one loads the ggplot2 package (loaded in
# tidyverse)
ggplot(mpg, aes(displ, hwy, colour = class)) + geom_point() + ggtitle(label = "Fuel economy data from 1999 and 2008 for 38 popular models of car", 
    subtitle = "from the mpg data frame installed with ggplot2")
```

&lt;img src="ggplot_201805_files/figure-html/unnamed-chunk-4-1.png" width="672" /&gt;
![cars_ggplot](cars_ggplot.png)
# from http://ggplot2.tidyverse.org/

---
# sharpsightlobs.com R Code (map oil production by country) [slight mod] - page 2 / X

```r
# ============ SCRAPE DATA ============

df.oil &lt;- read_html("https://en.wikipedia.org/wiki/List_of_countries_by_oil_production") %&gt;% 
    html_nodes("table") %&gt;% .[[1]] %&gt;% html_table()
```
![Wiki html]("Wiki_html.png")

---
# sharpsightlobs.com R Code (map oil production by country) [slight mod] - page 3 / X

```r
# ==================== CHANGE COLUMN NAMES ====================
print(names(df.oil))
```

```
## [1] ""                                 "Country"                         
## [3] "Oil Production(bbl/day, 2016)[1]"
```

```r
colnames(df.oil) &lt;- c("rank", "country", "oil_bbl_per_day")

# ============================= WRANGLE VARIABLES INTO SHAPE
# =============================


#----------------------------------
# COERCE 'rank' VARIABLE TO INTEGER
#----------------------------------

# What is the class of each variable?
lapply(df.oil, class)
```

```
## $rank
## [1] "character"
## 
## $country
## [1] "character"
## 
## $oil_bbl_per_day
## [1] "character"
```

```r
df.oil &lt;- df.oil %&gt;% mutate(rank = as.integer(rank))
```

```
## Warning in evalq(as.integer(rank), &lt;environment&gt;): NAs introduced by
## coercion
```

```r
# glimpse is installed with the tidyverse See class and sampling of values
# Commas in oil_bbl_per_day are a problem, country sometimes shows OPEC
df.oil %&gt;% glimpse()
```

```
## Observations: 99
## Variables: 3
## $ rank            &lt;int&gt; NA, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
## $ country         &lt;chr&gt; "World Production", "Russia", "Saudi Arabia (O...
## $ oil_bbl_per_day &lt;chr&gt; "80,622,000", "10,551,497", "10,460,710", "8,8...
```



```r
#---------------------------------------------------
# WRANGLE FROM CHARACTER TO NUMERIC: oil_bbl_per_day
#---------------------------------------------------

df.oil &lt;- df.oil %&gt;% mutate(oil_bbl_per_day = oil_bbl_per_day %&gt;% str_replace_all(',','') %&gt;% as.integer())


#===========================
#CREATE VARIABLE: 'opec_ind'
#===========================

df.oil &lt;- df.oil %&gt;% mutate(opec_ind = if_else(str_detect(country, 'OPEC'), 1, 0))
df.oil &lt;- df.oil %&gt;% mutate(country = country %&gt;% 
                     str_replace(' \\(OPEC\\)', '') %&gt;% str_replace('\\s{2,}',' '))
# inspect
df.oil %&gt;% glimpse()
```

```
## Observations: 99
## Variables: 4
## $ rank            &lt;int&gt; NA, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
## $ country         &lt;chr&gt; "World Production", "Russia", "Saudi Arabia", ...
## $ oil_bbl_per_day &lt;int&gt; 80622000, 10551497, 10460710, 8875817, 4451516...
## $ opec_ind        &lt;dbl&gt; 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0...
```

```r
#------------------------------------------
# EXAMINE OPEC COUNTRIES
# - here, we'll just visually inspect
#   to make sure that the names are correct
#------------------------------------------

df.oil %&gt;% 
  filter(opec_ind == 1) %&gt;%
  select(country)
```

```
##                 country
## 1          Saudi Arabia
## 2                  Iraq
## 3                  Iran
## 4  United Arab Emirates
## 5                Kuwait
## 6             Venezuela
## 7               Nigeria
## 8                Angola
## 9                 Qatar
## 10              Algeria
## 11                Libya
## 12              Ecuador
## 13    Equatorial Guinea
## 14                Gabon
```

```r
#==================
# REORDER VARIABLES
#==================

df.oil &lt;- df.oil %&gt;% select(rank, country, opec_ind, oil_bbl_per_day)

#========
# GET MAP
#========
# map_data turns data "from the maps package in to a data frame suitable for plotting with ggplot2".
map.world &lt;- map_data('world')
```

```
## 
## Attaching package: 'maps'
```

```
## The following object is masked from 'package:purrr':
## 
##     map
```

```r
#==========================
# CHECK FOR JOIN MISMATCHES
#==========================

mismatch_frm &lt;- anti_join(df.oil, map.world, by = c('country' = 'region'))
print(head(mismatch_frm))
```

```
##   rank                country opec_ind oil_bbl_per_day
## 1   NA       World Production        0        80622000
## 2    3          United States        0         8875817
## 3   21         United Kingdom        0          939760
## 4   31 Congo, Republic of the        0          308363
## 5   35  Sudan and South Sudan        0          255000
## 6   47    Trinidad and Tobago        0           60090
```

```r
#=====================
# RECODE COUNTRY NAMES
#=====================

map.world %&gt;%
  group_by(region) %&gt;%
  summarise() %&gt;%
  print(n = Inf)
```

```
## # A tibble: 252 x 1
##     region                             
##     &lt;chr&gt;                              
##   1 Afghanistan                        
##   2 Albania                            
##   3 Algeria                            
##   4 American Samoa                     
##   5 Andorra                            
##   6 Angola                             
##   7 Anguilla                           
##   8 Antarctica                         
##   9 Antigua                            
##  10 Argentina                          
##  11 Armenia                            
##  12 Aruba                              
##  13 Ascension Island                   
##  14 Australia                          
##  15 Austria                            
##  16 Azerbaijan                         
##  17 Azores                             
##  18 Bahamas                            
##  19 Bahrain                            
##  20 Bangladesh                         
##  21 Barbados                           
##  22 Barbuda                            
##  23 Belarus                            
##  24 Belgium                            
##  25 Belize                             
##  26 Benin                              
##  27 Bermuda                            
##  28 Bhutan                             
##  29 Bolivia                            
##  30 Bonaire                            
##  31 Bosnia and Herzegovina             
##  32 Botswana                           
##  33 Brazil                             
##  34 Brunei                             
##  35 Bulgaria                           
##  36 Burkina Faso                       
##  37 Burundi                            
##  38 Cambodia                           
##  39 Cameroon                           
##  40 Canada                             
##  41 Canary Islands                     
##  42 Cape Verde                         
##  43 Cayman Islands                     
##  44 Central African Republic           
##  45 Chad                               
##  46 Chagos Archipelago                 
##  47 Chile                              
##  48 China                              
##  49 Christmas Island                   
##  50 Cocos Islands                      
##  51 Colombia                           
##  52 Comoros                            
##  53 Cook Islands                       
##  54 Costa Rica                         
##  55 Croatia                            
##  56 Cuba                               
##  57 Curacao                            
##  58 Cyprus                             
##  59 Czech Republic                     
##  60 Democratic Republic of the Congo   
##  61 Denmark                            
##  62 Djibouti                           
##  63 Dominica                           
##  64 Dominican Republic                 
##  65 Ecuador                            
##  66 Egypt                              
##  67 El Salvador                        
##  68 Equatorial Guinea                  
##  69 Eritrea                            
##  70 Estonia                            
##  71 Ethiopia                           
##  72 Falkland Islands                   
##  73 Faroe Islands                      
##  74 Fiji                               
##  75 Finland                            
##  76 France                             
##  77 French Guiana                      
##  78 French Polynesia                   
##  79 French Southern and Antarctic Lands
##  80 Gabon                              
##  81 Gambia                             
##  82 Georgia                            
##  83 Germany                            
##  84 Ghana                              
##  85 Greece                             
##  86 Greenland                          
##  87 Grenada                            
##  88 Grenadines                         
##  89 Guadeloupe                         
##  90 Guam                               
##  91 Guatemala                          
##  92 Guernsey                           
##  93 Guinea                             
##  94 Guinea-Bissau                      
##  95 Guyana                             
##  96 Haiti                              
##  97 Heard Island                       
##  98 Honduras                           
##  99 Hungary                            
## 100 Iceland                            
## 101 India                              
## 102 Indonesia                          
## 103 Iran                               
## 104 Iraq                               
## 105 Ireland                            
## 106 Isle of Man                        
## 107 Israel                             
## 108 Italy                              
## 109 Ivory Coast                        
## 110 Jamaica                            
## 111 Japan                              
## 112 Jersey                             
## 113 Jordan                             
## 114 Kazakhstan                         
## 115 Kenya                              
## 116 Kiribati                           
## 117 Kosovo                             
## 118 Kuwait                             
## 119 Kyrgyzstan                         
## 120 Laos                               
## 121 Latvia                             
## 122 Lebanon                            
## 123 Lesotho                            
## 124 Liberia                            
## 125 Libya                              
## 126 Liechtenstein                      
## 127 Lithuania                          
## 128 Luxembourg                         
## 129 Macedonia                          
## 130 Madagascar                         
## 131 Madeira Islands                    
## 132 Malawi                             
## 133 Malaysia                           
## 134 Maldives                           
## 135 Mali                               
## 136 Malta                              
## 137 Marshall Islands                   
## 138 Martinique                         
## 139 Mauritania                         
## 140 Mauritius                          
## 141 Mayotte                            
## 142 Mexico                             
## 143 Micronesia                         
## 144 Moldova                            
## 145 Monaco                             
## 146 Mongolia                           
## 147 Montenegro                         
## 148 Montserrat                         
## 149 Morocco                            
## 150 Mozambique                         
## 151 Myanmar                            
## 152 Namibia                            
## 153 Nauru                              
## 154 Nepal                              
## 155 Netherlands                        
## 156 Nevis                              
## 157 New Caledonia                      
## 158 New Zealand                        
## 159 Nicaragua                          
## 160 Niger                              
## 161 Nigeria                            
## 162 Niue                               
## 163 Norfolk Island                     
## 164 North Korea                        
## 165 Northern Mariana Islands           
## 166 Norway                             
## 167 Oman                               
## 168 Pakistan                           
## 169 Palau                              
## 170 Palestine                          
## 171 Panama                             
## 172 Papua New Guinea                   
## 173 Paraguay                           
## 174 Peru                               
## 175 Philippines                        
## 176 Pitcairn Islands                   
## 177 Poland                             
## 178 Portugal                           
## 179 Puerto Rico                        
## 180 Qatar                              
## 181 Republic of Congo                  
## 182 Reunion                            
## 183 Romania                            
## 184 Russia                             
## 185 Rwanda                             
## 186 Saba                               
## 187 Saint Barthelemy                   
## 188 Saint Helena                       
## 189 Saint Kitts                        
## 190 Saint Lucia                        
## 191 Saint Martin                       
## 192 Saint Pierre and Miquelon          
## 193 Saint Vincent                      
## 194 Samoa                              
## 195 San Marino                         
## 196 Sao Tome and Principe              
## 197 Saudi Arabia                       
## 198 Senegal                            
## 199 Serbia                             
## 200 Seychelles                         
## 201 Siachen Glacier                    
## 202 Sierra Leone                       
## 203 Singapore                          
## 204 Sint Eustatius                     
## 205 Sint Maarten                       
## 206 Slovakia                           
## 207 Slovenia                           
## 208 Solomon Islands                    
## 209 Somalia                            
## 210 South Africa                       
## 211 South Georgia                      
## 212 South Korea                        
## 213 South Sandwich Islands             
## 214 South Sudan                        
## 215 Spain                              
## 216 Sri Lanka                          
## 217 Sudan                              
## 218 Suriname                           
## 219 Swaziland                          
## 220 Sweden                             
## 221 Switzerland                        
## 222 Syria                              
## 223 Taiwan                             
## 224 Tajikistan                         
## 225 Tanzania                           
## 226 Thailand                           
## 227 Timor-Leste                        
## 228 Tobago                             
## 229 Togo                               
## 230 Tonga                              
## 231 Trinidad                           
## 232 Tunisia                            
## 233 Turkey                             
## 234 Turkmenistan                       
## 235 Turks and Caicos Islands           
## 236 Uganda                             
## 237 UK                                 
## 238 Ukraine                            
## 239 United Arab Emirates               
## 240 Uruguay                            
## 241 USA                                
## 242 Uzbekistan                         
## 243 Vanuatu                            
## 244 Vatican                            
## 245 Venezuela                          
## 246 Vietnam                            
## 247 Virgin Islands                     
## 248 Wallis and Futuna                  
## 249 Western Sahara                     
## 250 Yemen                              
## 251 Zambia                             
## 252 Zimbabwe
```

```r
df.oil &lt;- df.oil %&gt;%  mutate(country = recode(country, 
                                   `United States` = 'USA'
                                 , `United Kingdom` = 'UK'
                                 , `Congo, Democratic Republic of the` = 'Democratic Republic of the Congo'
                                 , `Trinidad and Tobago` = 'Trinidad'
                                 , `Sudan and South Sudan` = 'Sudan'
                                 #, `Sudan and  South Sudan` = 'South Sudan'
                                 , `Congo, Republic of the` = 'Republic of Congo'
                                 )
                   )
#-----------------------
# JOIN DATASETS TOGETHER
#-----------------------

map.oil &lt;- left_join( map.world, df.oil, by = c('region' = 'country')) 

#=====
# PLOT
#=====

# BASIC (this is a first draft)

ggplot(map.oil, aes( x = long, y = lat, group = group )) +
  geom_polygon(aes(fill = oil_bbl_per_day))
```

&lt;img src="ggplot_201805_files/figure-html/unnamed-chunk-7-1.png" width="672" /&gt;

```r
# Top 5 countries 
map.oil %&gt;% distinct(rank, region) %&gt;% top_n(-5, rank) %&gt;% arrange(rank) %&gt;% select(region)
```

```
##         region
## 1       Russia
## 2 Saudi Arabia
## 3          USA
## 4         Iraq
## 5         Iran
```

```r
# PROFESSIONAL ELEGANCE (this is a first draft)
  
gill_map &lt;-
 ggplot(map.oil, aes( x = long, y = lat, group = group )) +
  geom_polygon(aes(fill = oil_bbl_per_day)) +
  scale_fill_gradientn(colours = c('#461863','#404E88','#2A8A8C','#7FD157','#F9E53F')
                       ,values = scales::rescale(c(100,96581,822675,3190373,10000000))
                       ,labels = comma
                       ,breaks = c(100,96581,822675,3190373,10000000)
                       ) +
  guides(fill = guide_legend(reverse = T)) +
  labs(fill = 'bbl/day'
       ,title = 'Oil Production by Country'
       ,subtitle = 'Barrels per day, 2016'
       ,x = NULL
       ,y = NULL) +
  theme(text = element_text(family = 'Gill Sans MT', color = '#EEEEEE')
        ,plot.title = element_text(size = 28)
        ,plot.subtitle = element_text(size = 14)
        ,axis.ticks = element_blank()
        ,axis.text = element_blank()
        ,panel.grid = element_blank()
        ,panel.background = element_rect(fill = '#333333')
        ,plot.background = element_rect(fill = '#333333')
        ,legend.position = c(.18,.36)
        ,legend.background = element_blank()
        ,legend.key = element_blank()
        ) +
  annotate(geom = 'text'
           ,label = 'Source: U.S. Energy Information Administration\nhttps://en.wikipedia.org/wiki/List_of_countries_by_oil_production'
           ,x = 18, y = -55
           ,size = 3
           ,family = 'Gill Sans MT'
           ,color = '#CCCCCC'
           ,hjust = 'left'
           )
orig_map &lt;-
 ggplot(map.oil, aes( x = long, y = lat, group = group )) +
  geom_polygon(aes(fill = oil_bbl_per_day)) +
  scale_fill_gradientn(colours = c('#461863','#404E88','#2A8A8C','#7FD157','#F9E53F')
                       ,values = scales::rescale(c(100,96581,822675,3190373,10000000))
                       ,labels = comma
                       ,breaks = c(100,96581,822675,3190373,10000000)
                       ) +
  guides(fill = guide_legend(reverse = T)) +
  labs(fill = 'bbl/day'
       ,title = 'Oil Production by Country'
       ,subtitle = 'Barrels per day, 2016'
       ,x = NULL
       ,y = NULL) +
  theme(text = element_text(color = '#EEEEEE')
        ,plot.title = element_text(size = 28)
        ,plot.subtitle = element_text(size = 14)
        ,axis.ticks = element_blank()
        ,axis.text = element_blank()
        ,panel.grid = element_blank()
        ,panel.background = element_rect(fill = '#333333')
        ,plot.background = element_rect(fill = '#333333')
        ,legend.position = c(.18,.36)
        ,legend.background = element_blank()
        ,legend.key = element_blank()
        ) +
  annotate(geom = 'text'
           ,label = 'Source: U.S. Energy Information Administration\nhttps://en.wikipedia.org/wiki/List_of_countries_by_oil_production'
           ,x = 18, y = -55
           ,size = 3
           ,color = '#CCCCCC'
           ,hjust = 'left'
           )
gill_map
```

```
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
```

```
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x
## $y, : font family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x
## $y, : font family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x
## $y, : font family not found in Windows font database
```

```
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
```

&lt;img src="ggplot_201805_files/figure-html/unnamed-chunk-7-2.png" width="672" /&gt;

---
# Default [ (theme_get() ] vs. Gill Sans font
## font family not found in Windows font database
![map original font](oilprod_origfont.png)
--
## Gill Sans after 
![map Gill Sans font](oilprod_gillfont.png)

---
# Same but using grid.arrange from the gridExtra package

```r
if (!require("grid")) install.packages("grid", repos = "http://cran.us.r-project.org")
```

```
## Loading required package: grid
```

```r
if (!require("gridExtra")) install.packages("gridExtra", repos = "http://cran.us.r-project.org")
```

```
## Loading required package: gridExtra
```

```
## 
## Attaching package: 'gridExtra'
```

```
## The following object is masked from 'package:dplyr':
## 
##     combine
```

```r
gridExtra::grid.arrange(orig_map, gill_map , nrow = 2, heights = c(2,2)) 
```

```
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
```

```
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x
## $y, : font family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x
## $y, : font family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x
## $y, : font family not found in Windows font database
```

```
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
```

&lt;img src="ggplot_201805_files/figure-html/unnamed-chunk-8-1.png" width="672" /&gt;
---
# also see grid.arrange in the code from the "Applying Data Science fundamentals to Stock Market Data" 
# TriPASS Data Sci presentation by Conrad D'Cruz and Yin-Ting Chou

![Stocks Meetup](stocks_meetup.png)

# https://github.com/choux130/EU_TSClustering_101117/blob/master/TSClustering/code_1024/execute_funs.R
# https://www.meetup.com/tripass/events/244117529/

---
# References
* http://www.sharpsightlabs.com/blog/map-public-debt-ggplot2/
* http://www.sharpsightlabs.com/blog/map-oil-production-country-r/
* https://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf
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