test.Rmd

---
title: "Trying Rmarkdown"
author: "Ben Best"
date: "August 1, 2014"
output:
  html_document:
    fig_caption: yes
    number_sections: yes
    toc: yes
  word_document:
    fig_caption: yes
  pdf_document:
    fig_caption: yes
    number_sections: yes
    toc: yes
  md_document:
    variant: markdown_github
csl: apa.csl
bibliography: test.bib
---

```{r setup, echo=FALSE, include=TRUE}
suppressPackageStartupMessages({
  require(dplyr)
  require(knitr)
  require(RColorBrewer)
  suppressWarnings(require(ohicore)) # devtools::install_github('ohi-science/ohicore')
})

refresh_data = F

# get data
csv_d = 'data/scores.csv'
if (!file.exists(csv_d) | refresh_data){
  dir.create('data', showWarnings=F)
  
  # get scores
  url_scores = 'https://raw.githubusercontent.com/OHI-Science/ohi-global/master/eez2014/scores.csv'
  tmp_scores = tempfile(fileext='.csv')
  download.file(url_scores, tmp_scores, method='curl')
  scores = read.csv(tmp_scores)
  
  # get labels
  url_labels = 'https://raw.githubusercontent.com/OHI-Science/ohi-global/master/eez2014/layers/rgn_labels.csv'
  tmp_labels = tempfile(fileext='.csv')
  download.file(url_labels, tmp_labels, method='curl')
  labels = read.csv(tmp_labels)
  
  # explore
  # head(scores)
  # head(labels)
  # select(scores, goal, dimension) %>% table()
  
  # merge
  d = scores %>%
    inner_join(
      labels %>%
        select(
          region_id    = rgn_id, 
          region_label = label) %>%
        rbind(data.frame(
          region_id    = 0, 
          region_label = 'GLOBAL')),
      by='region_id') %>%
    filter(dimension=='score')
  
  # write and cleanup
  write.csv(d, csv_d, row.names=F, na='')
  unlink(c(tmp_scores, tmp_labels))
}
d = read.csv(csv_d) %>%
    arrange(desc(score))

# get goals
csv_g = 'data/goals.csv'
if (!file.exists(csv_g) | refresh_data){
  url_goals = 'https://raw.githubusercontent.com/OHI-Science/ohi-global/master/eez2014/conf/goals.csv'
  tmp_goals = tempfile(fileext='.csv')
  download.file(url_goals, tmp_goals, method='curl')
  g = read.csv(tmp_goals)
  g = g %>%
    filter(!goal %in% g$parent) %>%
    select(goal, weight, order_color, name_flower)
  write.csv(g, csv_g, row.names=F, na='')
  unlink(c(tmp_goals))
}
g = read.csv(csv_g) %>%
  arrange(order_color)
```


## Introduction {-}

The Ocean Health Index [@halpern_index_2012; @selig_assessing_2013] derives most of its pressures from Halpern et al. [-@halpern_global_2008]...

## Food Provision: Fisheries

Amount of sustainable wild-caught seafood compared to the max sustainable

$$
x_{FIS} =  (\prod_{g=1}^{6} SS_{i,g}^{C_{i,g}})^\frac{1}{\sum{C_{i,g}}}
$$

Variables:

- $SS$: stock status score, based on B/Bmsy and an underharvest penalty adjustment
- $C$: total catch
- $i$: OHI reporting region
- $g$: level of taxonomic grouping (ISSCAAP)</small>


## Results

Hats off to the top scoring region of **`r filter(d, goal=='Index') %>% head(1) %>% select(region_label)`** with a score of `r filter(d, goal=='Index') %>% head(1) %>% select(score)`! The top 10 scoring regions (of `r n_distinct(d$region_label) - 1 # remove GLOBAL` globally) are largely comprised of unpopulated islands (see Table 1).

```{r top10, echo=FALSE, results='asis'}
kable(
  d %>%
    filter(region_label != 'GLOBAL' & goal=='Index') %>%
    head(10) %>%
    select(
      Region = region_label,
      Score  = score), 
  format='pandoc', caption='Top 10 scoring regions.')
```

The global average of `r round(d %>% filter(region_label=='GLOBAL' & goal=='Index') %>% select(score))` consists of food provision scores being lowest (Mariculture = `r round(d %>% filter(region_label=='GLOBAL' & goal=='MAR') %>% select(score))`; Fisheries = `r round(d %>% filter(region_label=='GLOBAL' & goal=='FIS') %>% select(score))`), but Artisanal Fishing Opportunities highest (`r round(d %>% filter(region_label=='GLOBAL' & goal=='AO') %>% select(score))`) (see Figure 1). 

```{r flower_plot, echo=FALSE, fig.cap='Global average across Oceean Health Index goals.', fig.width=5, fig.height=5}
# combine goals with scores
x = g %>%
  inner_join(
    d %>%
      filter(region_label=='GLOBAL' & goal!='Index') %>%
      select(goal, score),
    by='goal') %>%
  arrange(order_color)
  
# plot
PlotFlower(
  main       = '',
  lengths    = x$score,
  widths     = x$weight,
  fill.col   = colorRampPalette(brewer.pal(10, 'Spectral'), space='Lab')(nrow(x)),
  labels     = paste(gsub('\\\\n','\\\n', x$name_flower), round(x$score), sep='\n'),
  center     = round(d %>% filter(region_label=='GLOBAL' & goal=='Index') %>% select(score)),
  disk       = 0.4, 
  max.length = 100, cex=2, label.cex=0.5, label.offset=0.13)
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

## Github Markdown

To get github friendly Markdown document for cleanly tracking changes to document in Github, put the following output first:

```
output:
  md_document:
    variant: "markdown_github"
```

NOTE: You need to run this **LAST** though, since knitting other formats wipes out the `test_files` directory. To return to the Knit button having other options (HTML, PDF, Word), move this output type below the first option.

## References {-}
<!-- placeholder for References in toc --!>