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