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Marine Debris Final Code #import libraries library(tidyverse) library(readxl) library(maptools) library(plotly) library(ggthemes) library(gganimate) library(shiny) library(shinythemes) library(readr) library(rgdal) library(dplyr) library(ggplot2) library(leaflet) library(sf) library(rnaturalearth) library(rnaturalearthdata) library(ggspatial) library(sp) library(grid) library(htmltools) library(htmlwidgets) library(html.. 2022. 12. 16.
Interactive Visualization: Marine Debris on the Korean Peninsula - 4 #Before moving on to Fourth Visualization The disadvantages of the former scatter plot were enhanced and overlapping parts were removed. But still, there was a lot of wasted space because it was expressed on the map. To solve this problem, we had to create a simple, space-maximizing graph. At the same time, the graph had to include information about the location. #Process Our process consists of.. 2022. 12. 16.
Interactive Visualization: Marine Debris on the Korean Peninsula - 3 #Data wrangling for the second visualization filter_kg % filter(!unit %in% "EA")%>% rename(kg = quantity) filter_kg % addMinicharts( kg_2019$x, kg_2019$y, chartdata = kg_2019[, c("kg_10")], colorPalette = colors, type = "bar", height = 45, width = 30, popup = popupArgs(html = paste0("Location: ", kg_2019$location, " ", "kg * 10: ", kg_2019$kg_10, " kg", " (real data: ", kg_2019$kg, ")")) )%>% ad.. 2022. 12. 16.
Interactive Visualization: Marine Debris on the Korean Peninsula - 2 #Download Previous Data Frame Based on our data frame, we wanted to see the location of the debris on the map of the Korean Peninsula, how the total quantity differed by region, as well as get an initial grasp of how our raw data was spatialized. #Code for first visualizatoin tag_map_title % addCircleMarkers(lng = ~x, lat = ~y, popup = ~mytext,label = mytext, radius = ~(kg/200), fillOpacity = 0... 2022. 12. 16.

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