maritime-pirate-attacks/exploration.Rmd

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2021-12-21 04:59:17 +00:00
---
title: "R Notebook"
output:
html_document:
df_print: paged
---
```{r}
library(tidyverse)
library(lubridate)
library(sf)
library(leaflet)
library(dataReporter)
```
```{r}
source("importer_donnees_csv.R")
source("pirateIcon.R")
```
```{r}
glimpse(pirate_attacks_sf, width = 67)
```
```{r}
pirate_attacks_sf_ss <- pirate_attacks_sf %>%
filter(data_source == "imb") %>%
select(
date,
attack_type,
shore_distance,
vessel_type,
vessel_status
) %>%
replace_na(list(vessel_status="Underway")) %>%
mutate(month_event = month(date),
year_event = year(date))
```
## Statistiques descriptives
```{r}
makeDataReport(pirate_attacks_sf_ss, replace = TRUE)
```
## Exporter les types de navires
```{r}
pirate_attacks_sf_ss %>%
group_by(vessel_type) %>%
st_set_geometry(NULL) %>%
count() %>%
arrange(desc(n)) %>%
write_csv("vessel_types.csv")
```
## Traitement manuel
Je crée manuellement des catégories plus larges, puis je réimporte
```{r}
custom_vessel_categories <-
read_csv(
"data/csv/custom_vessel_categories.csv",
col_types = cols(vessel_type = col_character(),
vessel_category = col_character())
)
```
```{r}
pirate_attacks_sf_ss2 <- pirate_attacks_sf_ss %>%
left_join(custom_vessel_categories)
```
```{r}
pirate_attacks_sf_ss2 %>% sf::write_sf("data/geojson/pirate_attacks_sf_ss2.geojson")
```
## Filtrage
```{r}
pirate_attacks_sf_maps <- pirate_attacks_sf_ss2 %>%
filter(year_event == 2015, month_event==1)
```
```{r}
m <- leaflet(pirate_attacks_sf_maps) %>%
addTiles() %>%
addMarkers(
popup = paste0(
"Type: ",
pirate_attacks_sf_maps$vessel_category,
"<br>",
"Statut: ",
pirate_attacks_sf_maps$vessel_status,
"<br>",
"Type d'attaque: ",
pirate_attacks_sf_maps$attack_type,
"<br>",
"Distance de la côte: ",
round(pirate_attacks_sf_maps$shore_distance, 2),
"km",
"<br>",
"Date: ",
pirate_attacks_sf_maps$date
),
icon = pirateIcon,
clusterOptions = markerClusterOptions()
)
```
```{r}
m
```