Lemur catta tutorial

=== Downloading the map of Madagascar

```{r}
country_map <- ne_countries(country = "Madagascar",
                            scale = "medium",
                            returnclass = "sf")

image::data-use::Taxon_key.png[align=center,width=900,height=125]

Похоже, у нас здесь есть подходящее утверждённое имя. `usageKey` — это ключ, который идентифицирует использование названия в базе данных https://www.gbif.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c[GBIF Taxonomy Backbone]. Как только мы определим, какое именно «использование» нам нужно, мы будем использовать этот ключ в качестве `taxonKey` для указания таксона в данных о его встречаемости.

The following code block store the usageKey of _Lemur catta_ in a variable `usageKey`.

```{r} usageKey <- 2436412 # Alternatively, since we know there is only one match, the following line will store the key in the variable, too. # usageKey <- name_backbone(name = "Lemur catta", rank = "species") %>% pull(usageKey) ```

=== Use the taxonKey to generate the occurrence download of the species of interest
The following code block sends a request for download to the GBIF Occurrence API. A successful request will return a download key and initiate the preparing of the requested data. Note that as we've stored the key in the `usageKey` variable, for the required `taxonKey` for occ_download(), we simply supply the variable. Also, we need to supply our GBIF.org login credentials to request a download.

```{r}
gd_key <- occ_download(
    pred("taxonKey", usageKey),
#   pred("hasCoordinate", TRUE),
    format = "SIMPLE_CSV",
    user = "USERNAME",
    pwd = "PASSWORD",
    email = "EMAIL_ADDRESS"
)

gd <- occ_download_get(gd_key, overwrite = TRUE) %>%
    occ_download_import()

head(gd, 10) # view first ten rows ```
image::data-use::download.png[align=center,width=900,height=250]

### Citing GBIF data Always cite the downloaded dataset in your report. The following code helps generate the citation.

```{r} print(gbif_citation(occ_download_meta(gbif_download_key))$download) ```

== Data Visualization
_Lemur catta_ is native to Madagascar; but just to make sure, let's verify the data by plotting occurrences on a map.

```{r, message = FALSE, error = FALSE}
ggplot() +
  geom_sf(data = world_map) +
  geom_point(data = gd,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "red") +
  theme_bw()

```{r} table(gdf$countryCode) ```
image::data-use::countries.png[align=center,width=600,height=75]

It appears that many records have coordinates outside Madagascar. Let's also have a look at the nature of these records.

```{r} gdf %>% distinct(basisOfRecord) gdf %>% distinct(establishmentMeans) ``` Существует 5 различных значений для https://dwc.tdwg.org/terms/#dwc:basisOfRecord[DwC:basisOfRecord]. Мы также рассмотрели https://dwc.tdwg.org/list/#dwc_establishmentMeans[dwc:establishmentMeans], где для некоторых записей указано только значение `NATIVE`.

== Верификация и исправление данных

После предварительного изучения наших данных мы выяснили, что скачанный набор данных может содержать проблемы с качеством. В частности, записи с координатами за пределами Мадагаскара вызывают подозрения. Мы также рассмотрели столбцы basisOfRecord и establishmentMeans, чтобы определить, какие действия могут потребоваться для дальнейшей обработки данных.

**Data cleaning** typically involves procedures to remove unwanted records based on some criteria, or to correct values to achieve overall operable consistency. In the following section we will try to filter data, show the difference, and plot it on the map.

== Шаг 1: basisOfRecord

We would like to evaluate observations and collections, so `FOSSIL_SPECIMEN` and `MATERIAL_SAMPLE` is not our concern here, let's try to exclude them from our download.

```{r}
clean_step1 <- gdf %>%
  as_tibble() %>%
  filter(!basisOfRecord %in% c("FOSSIL_SPECIMEN", "MATERIAL_SAMPLE"))

print(paste0(nrow(gdf)-nrow(clean_step1), " records deleted; ",
             nrow(clean_step1), " records remaining."))

ggplot() +
  geom_sf(data = world_map) +
  geom_point(data = gdf,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "black") +
  geom_point(data = clean_step1,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "red") +
  theme_bw()

ggplot() +
  geom_sf(data = world_map) +
  geom_point(data = gdf,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "black") +
  geom_point(data = clean_step2,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "red") +
  theme_bw()

ggplot() +
  geom_sf(data = country_map) +
  geom_point(data = gdf,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "black") +
  geom_point(data = clean_step2,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "red") +
  coord_sf(xlim = st_bbox(country_map)[c(1,3)],
           ylim = st_bbox(country_map)[c(2,4)]) +
  theme_bw()

ggplot() +
  geom_sf(data = country_map) +
  geom_point(data = gdf,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "black") +
  geom_point(data = clean_step3,
             aes(x = decimalLongitude,
                 y = decimalLatitude),
             shape = "+",
             color = "red") +
  coord_sf(xlim = st_bbox(country_map)[c(1,3)],
           ylim = st_bbox(country_map)[c(2,4)]) +
  theme_bw()