ordinering.qmd

---
title: "Ordinering av NiN hovedtyper"
format: html
editor: visual
---

## Bakgrunn

Her ønsker jeg å gruppere NiN-hovedtyper etter noen variabler som er med på å avgjøre hvorden de evt kan overvåkes i et nasjonalt naturovervåkingsprogram. Datasettet hentes fra google sheets.

```{r setup}
library(tidyverse)
library(googlesheets4)
library(vegan)
library(ggord)
library(ggtext)
library(ggrepel)
```

```{r getData}
gs4_user()

dat <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1_xtwyLeKByIObjxrZP2ApkwQB9_oGlLle3SwSFjL_cA/edit?gid=0#gid=0")
```

#### 

```{r prepData}
mat2 <- dat |>
  column_to_rownames("hovedtype_NiN2_kode") |>
  filter(arcticAlpine != "yes") |>
  mutate(
    distributionalRange = case_match(
      distributionalRange,
      "Broad" ~ 1,
      .default = 0
      ),
    dispersed = case_match(
      patchiness,
      "Dispersed" ~ 1,
      .default = 0),
    localDensity = case_match(
      localDensity,
      "High" ~ 1,
      .default = 0),
    successionalStage = case_match(
      successionalStage,
      "yes" ~ 1,
      .default = 0),
    spatiallyDynamic = case_match(
      spatiallyDynamic,
      "yes" ~ 1,
      .default = 0),
    coastal = case_match(
      coastal,
      "yes" ~ 1,
      .default = 0),
    marginal = case_match(
      marginal,
      "yes" ~ 1,
      .default = 0),
    flater = flater/max(flater)
    ) |>
  select(flater, 
         distributionalRange,
         dispersed,
         localDensity,
         successionalStage,
         spatiallyDynamic,
         coastal,
         marginal)
mat <- mat2 |>
  select(-marginal)


```

```{r}
#| fig-width: 10
#| fig-height: 10
#| eval: false

myOrd <- metaMDS(mat, trace = FALSE)
plot(myOrd, type = "n") |>
#points("sites", cex = 0.8, pch=21, col="red", bg="yellow") |>
text("species", cex=0.7, col="blue")
ordilabel(myOrd, dis="sites", cex=0.7, font=3, fill="hotpink", col="blue")
ordilabel(myOrd, dis="sp", font=1.2, priority=colSums(mat))

```

```{r}
pca1 <- stats::prcomp(mat)
ggord(pca1, obslab=T, labcol = "blue", veccol = "grey",
      vec_ext=1.2,
      size=3,
      txt=3,
      exp = c(0.1, 0.1))

ggsave("pca1.jpg")
```

Samme ordinering, bare uten de marginale typene (for å få mindre overlapp mellom punktene)

```{r}
mat2 <- mat2 |> 
  filter(marginal != 1) |>
  select(-marginal)
pca2 <- stats::prcomp(mat2)
ggord(pca2, obslab=T, labcol = "blue", veccol = "grey",
      vec_ext=1.2,
      size=3,
      txt=3,
      exp = c(0.1, 0.1))

ggsave("pca2_margnalRemoved.jpg")
```

Lag en stilistisk figur basert på rank order

```{r}
pca2[["x"]] |>
  as.data.frame() |>
  select(PC1, PC2) |>
  mutate(PC1x = row_number(PC1),
         PC2x = row_number(PC2)) |>
  rownames_to_column(var = "name") |>
  ggplot(aes(PC1x, PC2x, label = name))+
  geom_point(color = "red")+
  geom_label_repel()
  
ggsave("pca-labels.jpg")
```