|
| 1 | +--- |
| 2 | +title: "Website data: MRF" |
| 3 | +output: html_document |
| 4 | +--- |
| 5 | + |
| 6 | +```{r setup, include=FALSE} |
| 7 | +library(tidyverse) |
| 8 | +library(here) |
| 9 | +scores <- read_csv(here("yearly_results/global2021/OHI_final_formatted_scores_2021-10-28.csv")) |
| 10 | +
|
| 11 | +``` |
| 12 | + |
| 13 | +### LSP |
| 14 | +How many countrys have 30% protected areas? |
| 15 | +```{r} |
| 16 | +scores %>% |
| 17 | + filter(scores$dimension == "status") %>% |
| 18 | + filter(scenario == 2021) %>% |
| 19 | + filter(goal == "LSP") %>% |
| 20 | + filter(value >= 100) %>% |
| 21 | + data.frame() |
| 22 | +
|
| 23 | +scores %>% |
| 24 | + filter(scores$dimension == "status") %>% |
| 25 | + filter(scenario == 2012) %>% |
| 26 | + filter(goal == "LSP") %>% |
| 27 | + filter(value >= 100) %>% |
| 28 | + data.frame() |
| 29 | +
|
| 30 | +
|
| 31 | +## has this changed over time? |
| 32 | +
|
| 33 | +tmp <- scores %>% |
| 34 | + filter(scores$dimension == "score") %>% |
| 35 | + filter(value < 50) %>% |
| 36 | + data.frame() |
| 37 | +table(tmp$scenario) |
| 38 | +
|
| 39 | +
|
| 40 | +scores %>% |
| 41 | + filter(scores$dimension == "score") %>% |
| 42 | + filter(region_name == "Puerto Rico and Virgin Islands") %>% |
| 43 | + filter(goal == "TR") %>% |
| 44 | + data.frame() |
| 45 | +``` |
| 46 | + |
| 47 | + |
| 48 | +### TR |
| 49 | +```{r} |
| 50 | +
|
| 51 | +read_csv(here("eez/layers/tr_jobs_pct_tourism.csv")) %>% |
| 52 | + filter(rgn_id=="8") %>% |
| 53 | + data.frame() |
| 54 | +
|
| 55 | +``` |
| 56 | + |
| 57 | +### AO |
| 58 | +```{r} |
| 59 | +
|
| 60 | +
|
| 61 | +tmp <- read_csv(here("eez/layers/ao_need.csv")) %>% |
| 62 | + group_by(year) %>% |
| 63 | + summarize(value = mean(value, na.rm=TRUE)) |
| 64 | +plot(tmp$year, tmp$value, ylab="Rescaled GDPpcPPP") |
| 65 | +
|
| 66 | +read_csv(here("eez/layers/ao_access.csv")) %>% |
| 67 | + group_by(year) %>% |
| 68 | + summarize(value = mean(value, na.rm=TRUE)) |
| 69 | +
|
| 70 | +tmp <- read_csv(here("eez/layers/ao_sust.csv")) %>% |
| 71 | + group_by(year) %>% |
| 72 | + summarize(score = mean(score, na.rm=TRUE)) |
| 73 | +
|
| 74 | +plot(tmp$year, tmp$score, ylab="Avg B/Bmsy score for artisanal species") |
| 75 | +``` |
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