New.Rmd

---
title: "Data Vizualization Project"
author: Ana Khaburska
---

```{r}
library(ggplot2)
library(readxl)
library(tidyr)
library(dplyr)
library(maps)
library(maptools)
library(data.table)
library(ggpubr)
library(ggrepel)
#library(stats)
#install.packages('stats')
```

```{r}
garbadge_patch <- read_excel('dataviz-proj/papers/data/garbadge_patch/Lebreton2018_HistoricalDataset.xls')
```

```{r}
garbadge_patch$`Sampling Month` <- as.integer(garbadge_patch$`Sampling Month`)
garbadge_patch$date <- as.Date(paste(1, garbadge_patch$`Sampling Month`, garbadge_patch$`Sampling Year`, sep = '-'), format = '%d-%m-%Y')
```

```{r}
names(garbadge_patch) <- c('id', 'data_origin', 'data_reference', 'year', 'month', 'lon', 'lat', 'sampling_type', 'mesh_size_microns', 'net_tow_depth', 'microplastic_numerical_concentration_n_km2', 'mictoplastic_mass_concentration_g_km2', 'inside_GPGP', 'date')
```

```{r}
garbadge_patch$data_origin <- NULL
garbadge_patch$data_reference <- NULL
garbadge_patch$year <- NULL
garbadge_patch$month <- NULL
garbadge_patch$sampling_type <- NULL
garbadge_patch$mesh_size_microns <- NULL
garbadge_patch$net_tow_depth <- NULL
```

```{r}
min(garbadge_patch$lat)
max(garbadge_patch$lat)
min(garbadge_patch$lon)
max(garbadge_patch$lon)
```

```{r}
garbadge_patch$microplastic_numerical_concentration_n_km2 <- as.integer(garbadge_patch$microplastic_numerical_concentration_n_km2)
```



```{r}
garbadge_patch <- garbadge_patch %>% mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>% mutate_if(is.integer, ~replace(., is.na(.), 0))
```

```{r}
garbadge_patch
```



```{r}
grouped_by_date_patch <- garbadge_patch %>%
  group_by(date, inside_GPGP) %>% 
  summarize(mmnc = mean(microplastic_numerical_concentration_n_km2, na.rm = TRUE), mmmc = mean(mictoplastic_mass_concentration_g_km2, na.rm = TRUE))
```




```{r}
grouped_by_date_patch$inside_GPGP <- as.factor(grouped_by_date_patch$inside_GPGP)
```

```{r fig.width=10}
ggplot(data = grouped_by_date_patch, aes(x = date, y = mmmc, color = inside_GPGP)) +
  geom_point(size = 5, alpha = 0.8) +
  scale_x_date(date_breaks = "5 years",  date_labels = "%Y") +
  scale_y_continuous(limits = c(0, 2100)) +
  scale_color_manual(name = "Inside GPGP?", values = c("0"="lightblue", "1"="darkred"), labels = c("No", "Yes")) +
  ggtitle("Microplastic mass concentration") +
  theme_light() +
  ggtitle("Average month microplastic mass concentration", "among all available observation points in Pacific ocean") +
  ylab("g/km2") +
  xlab("Date") +
  theme(
    axis.title = element_text(family = "Ubuntu Mono", size = 15),
    axis.title.x = element_text(vjust = -2),
    axis.title.y = element_text(vjust = 2),
    plot.title = element_text(family = "Ubuntu Mono", size = 20),
    plot.subtitle = element_text(family = "Ubuntu Mono", color = "grey30"),
    legend.title = element_text(family = "Ubuntu Mono"),
    panel.grid.minor.x = element_blank()#,
    #plot.margin = unit(c(2,2,2,2), "cm")
  ) + 
  coord_cartesian(clip = 'off')
#ggsave('visualizations/mmmc_2.png', last_plot())
```

  
```{r fig.width=10}
#ggplot(data = grouped_by_date_patch, aes(x = date, y = mmnc, color = inside_GPGP)) + 
# geom_point(size = 2.5) + 
#  ggtitle("Microplastic numerical concentration")
ggplot(data = grouped_by_date_patch, aes(x = date, y = mmnc, color = inside_GPGP)) +
  geom_point(size = 5, alpha = 0.8) +
  scale_x_date(date_breaks = "5 years",  date_labels = "%Y") +
  scale_y_continuous(limits = c(0, 1.1e6), breaks = c(0, 2e5, 4e5, 6e5, 8e5, 1e6), labels = c("0", "200000", "400000", "600000", "800000", "1000000")) +
  scale_color_manual(name = "Inside GPGP?", values = c("0"="lightblue", "1"="darkred"), labels = c("No", "Yes")) +
  ggtitle("Microplastic mass concentration") +
  theme_light() +
  ggtitle("Average month microplastic numerical concentration", "among all available observation points in Pacific ocean") +
  ylab("n/km2") +
  xlab("Date") +
  theme(
    axis.title = element_text(family = "Ubuntu Mono", size = 15),
    axis.title.x = element_text(vjust = -2),
    axis.title.y = element_text(vjust = 2),
    plot.title = element_text(family = "Ubuntu Mono", size = 20),
    plot.subtitle = element_text(family = "Ubuntu Mono", color = "grey30"),
    legend.title = element_text(family = "Ubuntu Mono"),
    panel.grid.minor.x = element_blank()#,
    #plot.margin = unit(c(2,2,2,2), "cm")
  ) + 
  coord_cartesian(clip = 'off')
#ggsave('visualizations/mmnc_2.png', last_plot())
```

```{r}
worldmap <- map("world", fill=TRUE, plot=FALSE)
WGS84 <- CRS("+proj=longlat +datum=WGS84")
worldmapPolys <- map2SpatialPolygons(worldmap, 
                                     IDs=sapply(strsplit(worldmap$names, ":"), "[", 1L), 
                                     proj4string=WGS84)
shift <- 180+20
XY <- data.table(map_data(as(worldmapPolys, "SpatialPolygonsDataFrame")))
XY[, long.new := long + shift]
XY[, long.new := ifelse(long.new > 180, long.new-360, long.new)]
XY[, to.split := sum(diff(long.new) > 300, na.rm=TRUE) > 0, by=group]
XY[, gr.split := ifelse(to.split & long.new < 0, paste0(group, ".", 1), group)]
```

```{r}
garbadge_patch$lon <- 180 + garbadge_patch$lon
```

```{r}
garbadge_patch$inside_GPGP <- as.factor(garbadge_patch$inside_GPGP)
```


```{r}
garbadge_patch[order(garbadge_patch$lon, decreasing = TRUE), ]
```



```{r}
temp <- garbadge_patch[garbadge_patch$lon < 300, ]
```


```{r}
breaks = c(-Inf, 100, 1000, 10000, 100000, 1000000, Inf)
temp$mnc_factors <- cut(temp$microplastic_numerical_concentration_n_km2, breaks = breaks)
```

```{r}
levels(temp$mnc_factors)[1] = "<100"
levels(temp$mnc_factors)[2] = "100-1000"
levels(temp$mnc_factors)[3] = "1000-10000"
levels(temp$mnc_factors)[4] = "10000-100000"
levels(temp$mnc_factors)[5] = "100000-1000000"
levels(temp$mnc_factors)[6] = ">1000000"
```


```{r}
temp
```

```{r fig.width=14}
ggplot() + 
    geom_polygon(data=XY, 
                 aes(x=long.new, y=lat, group=gr.split), 
                 colour="#9e9c8e", fill="#9e9c8e", size = 0.25) + 
  scale_x_continuous(limits = c(-30, 110), breaks = c(-20, 0, 20, 40, 60, 80), labels = c("160°", "0°", "-160°", "-140°", "-120°", "-100°")) +
  scale_y_continuous(limits = c(-30, 85), breaks = c(-20, 0, 20, 40, 60, 80), labels = c("-20°", "0°", "20°", "40°", "60°", "80°")) +
  geom_point(data = temp, 
             aes(x = lon, 
                 y = lat, 
                 colour = mnc_factors), 
             alpha = 0.6,
             size = 2) +
  scale_color_manual("n/km2", values = c("<100"="lightblue", "100-1000"="green", "1000-10000"="yellow", "10000-100000"="orange", "100000-1000000"="red", ">1000000"="darkred")) +
  theme_minimal() +
  coord_fixed() +
  theme() +
  xlab("Longtitude") +
  ylab("Latitude") +
  ggtitle("Microplastic numerical concentration in Pacific ocean") +
  geom_text(data = data.frame(x = -10, y = 15, label = "Great Paciific\nGarbage Patch"), aes(x = x, y = y, label = label), size = 4, family = "Ubuntu Mono", color = "black") +
   geom_curve(aes(x = -1, y = 19, xend = 15, yend = 30),
             arrow = arrow(length = unit(0.2, 'cm')), curvature = -0.1, size = 0.4, colour = 'black') + 
  theme(
    axis.text = element_text(family = 'Ubuntu Mono'),
    axis.title = element_text(family = 'Ubuntu Mono'),
    plot.title = element_text(family = "Ubuntu Mono", size = 15),
    legend.title = element_text(family = "Ubuntu Mono", size = 12),
    legend.text = element_text(family = "Ubuntu Mono"),
    legend.position = "top",
    plot.margin = unit(c(1,1,1,1), "cm")
  )
#ggsave("visualizations/mnc_map.png", last_plot())
```

```{r fig.width=14}
ggplot() + 
    geom_polygon(data=XY, 
                 aes(x=long.new, y=lat, group=gr.split), 
                 colour="#9e9c8e", fill="#9e9c8e", size = 0.25) + 
  scale_x_continuous(limits = c(-30, 110), breaks = c(-20, 0, 20, 40, 60, 80), labels = c("160°", "0°", "-160°", "-140°", "-120°", "-100°")) +
  scale_y_continuous(limits = c(-30, 85), breaks = c(-20, 0, 20, 40, 60, 80), labels = c("-20°", "0°", "20°", "40°", "60°", "80°")) +
  geom_point(data = temp[temp$mictoplastic_mass_concentration_g_km2 > 0, ], 
             aes(x = lon, 
                 y = lat, 
                 color = mictoplastic_mass_concentration_g_km2, 
                 size = mictoplastic_mass_concentration_g_km2), 
             alpha = 0.3) +
  scale_color_gradient2(low = "white", mid = "#02f4cc", high = "#8c2d04", midpoint = 5000) +
  theme_minimal() +
  coord_fixed() +
  theme() +
  xlab("Longtitude") +
  ylab("Latitude") +
  theme(
    axis.text = element_text(family = 'Ubuntu Mono'),
    axis.title = element_text(family = 'Ubuntu Mono')
  )
```


```{r}
marine_pollution <- read_excel('dataviz-proj/papers/data/marine_pollution.xlsx')
```

```{r}
names(marine_pollution) <- c("country", "economic_status", "coastal_population", "waste_generation_rate_kg_person_day", "%_of_plastic_in_waste_stream", 
                             "%_of_indequately_managed_waste", "%_of_littered_waste", "waste_generation_kg_day", "plastic_waste_generation_kg_day",
                             "inadequately_managed_plastic_waste_kg_day", "plastic_waste_littered_kg_day", "mismanaged_plastic_waste_kg_person_day",
                             "mismanaged_plastic_waste_in_2010_tonnes", "mismanaged_plastic_waste_in_2025_tonnes")
```



```{r}
world <- map_data('world')
```

```{r}
hf <- left_join(XY, marine_pollution, by = c('region' = 'country'))
```

```{r}
hf$mismanaged_plastic_waste_kg_person_day <- as.double(hf$mismanaged_plastic_waste_kg_person_day)
```

```{r}
hf %>% group_by(region) %>% summarise(cos_pop = min(coastal_population))
```

```{r}
summary(hf$mismanaged_plastic_waste_in_2010_tonnes)
```



```{r}
breaks = c(-Inf, 1000, 10000, 100000, 1000000, Inf)
hf$factors_mismanaged_2010_tonnes <- cut(hf$mismanaged_plastic_waste_in_2010_tonnes, breaks = breaks)
```

```{r}
levels(hf$factors_mismanaged_2010_tonnes)[1] = "<1000"
levels(hf$factors_mismanaged_2010_tonnes)[2] = "1000-10000"
levels(hf$factors_mismanaged_2010_tonnes)[3] = "10000-100000"
levels(hf$factors_mismanaged_2010_tonnes)[4] = "100000-1000000"
levels(hf$factors_mismanaged_2010_tonnes)[5] = ">1000000"
```


```{r}
hf
```
```{r}
levels(hf$factors_mismanaged_2010_tonnes)
```

```{r}
countries <- hf[!(hf$region %in% c("Antarctica", "Greenland", "Iceland")), ] 
# aggregate names
cnames <- aggregate(cbind(long.new, lat) ~ mismanaged_plastic_waste_kg_person_day, data = countries, FUN = function(x)mean(range(x)))
```

```{r}
cnames$mismanaged_plastic_waste_kg_person_day <- round(cnames$mismanaged_plastic_waste_kg_person_day, 3)
```

```{r}
cnames$mismanaged_plastic_waste_kg_person_day
```

```{r}

#buyos<-read.csv("buyous25001.csv")


#my.summary <- with(buyos, aggregate(list(longitude, latitude), by = list(platform_code), 
#                   FUN = function(x) { mon.mean = mean(x, na.rm = TRUE) } ))

#my.summary <- do.call(data.frame, my.summary)
#colnames(my.summary) <- c('platform_code', 'longitude', 'latitude')
#my.summary

#buyos<-my.summary

#buyos$longitude<-suppressWarnings(as.numeric(as.character(buyos$longitude)))
#buyos$latitude<-suppressWarnings(as.numeric(as.character(buyos$latitude)))

#shift <- 180+20
#buyos$longitude <- buyos$longitude + shift
#buyos$longitude <- ifelse(buyos$longitude > 180, buyos$longitude-360, buyos$longitude)

#write.csv(buyos, file = "BBuyos.csv")

#buyos<-read.csv("BBuyos.csv")
#buyos$longitude<-as.integer(round(suppressWarnings(as.numeric(as.character(buyos$longitude)))))
#buyos$latitude<-as.integer(round(suppressWarnings(as.numeric(as.character(buyos$latitude)))))

#buyos

```

```{r fig.width=14}
gg<-ggplot(data = countries, aes(x = long.new, y = lat)) +
  geom_polygon(aes(group = group, fill = factors_mismanaged_2010_tonnes)) +
  scale_fill_manual("tonnes", 
                    values = c("<1000"="#0571b0", "1000-10000"="#92c5de", "10000-100000"="#ffffbf", "100000-1000000"="#f4a582", ">1000000"="#ca0020"),
                    labels = c("<1000", "1000-10000", "10000-100000", "100000-1000000", ">1000000", "No data"),
                    na.value = "grey90") +
  theme_minimal() +
  coord_fixed() + 
  scale_x_continuous(breaks = c(-160, -140, -120, -100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100, 120, 140, 160),
                     labels = c("20°", "","60°", "", "100°", "", "140°", "", "0°", "", "-140°", "", "-100°", "", "-60°", "", "-20°")) +
  scale_y_continuous(breaks = c(-60, -40, -20, 0, 20, 40, 60, 80),
                     limits = c(-65,90),
                     labels = c("", "-40°", "", "0°", "", "40°", "", "80°")) +
  theme(
    panel.grid.major = element_line(size = 0.2),
    panel.grid.minor = element_line(size = 0.1),
    legend.text = element_text(family = "Ubuntu Mono"),
    axis.text = element_text(family = "Ubuntu Mono"),
    legend.position = "right",
    legend.title = element_text(family = "Ubuntu Mono"),
    plot.title = element_text(family = "Ubuntu")
  ) +
  ylab("") +
  xlab("") +
  ggtitle("Mismatched plastic waste, tonnes") + 
  geom_text_repel(data=cnames[cnames$mismanaged_plastic_waste_kg_person_day >= 0.1, ], 
                  aes(long.new, lat, label = mismanaged_plastic_waste_kg_person_day), 
                  size=3, 
                  family = "Ubuntu Mono")
#ggsave("visualizations/mismatched.png", last_plot( ))
gg
``` 

```{r fig.width=14}
#write.csv(IOG, file = "IOG.csv")
SAG<-read.csv("dots/SAG.csv")
SPG<-read.csv("dots/SPG.csv")
NPG0<-read.csv("dots/NPG0.csv")
NPG00<-read.csv("dots/NPG0.csv")
NPG2<-read.csv("dots/NPG2.csv")
NPG1<-read.csv("dots/NPG1.csv")
NAG<-read.csv("dots/NAG.csv")
IOG<-read.csv("dots/IOG.csv")
llt<-c(as.integer(runif(n = 20, min = -35, max = -27)),as.integer(runif(n = 20, min = -32, max = -20)),as.integer(runif(n = 10, min = -28, max = -25)))
llg<-c(as.integer(runif(n = 20, min = -90, max = -78)),as.integer(runif(n = 20, min = -82, max = -70)),as.integer(runif(n = 10, min = -80, max = -75)))
buyos2<-data.frame("platform_code"=seq(1,50))
buyos2$longitude<-llg
buyos2$latitude<-llt
buyos2$Set<-as.integer(runif(n = 50, min = 1, max = 4))
gg+geom_point(data = SAG, 
              aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19) +
  geom_point(data = SPG, 
             aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19)+
  geom_point(data = NPG0, 
             aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19)+
  geom_point(data = NPG00, 
             aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19)+
  geom_point(data = NPG1, 
             aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19)+
  geom_point(data = NPG2, 
             aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19)+
  geom_point(data = NAG, 
             aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19)+
  geom_point(data = IOG, 
             aes(x = longitude, y = latitude, color=as.factor(Set)), na.rm=TRUE, size=0.5, shape=19)+
  scale_colour_manual(values = c("#f4a582", "grey50", "grey70"))+ guides(color = FALSE)
  #scale_colour_manual(values = c("#b23a4b", "#f4a582", "#ca0020"))+ guides(color = FALSE)
#ggsave("patches3.png", last_plot( ))

```
```{r}

plot.map<- function(database,center,...){
    Obj <- map(database,...,plot=F)
    coord <- cbind(Obj[[1]],Obj[[2]])

    # split up the coordinates
    id <- rle(!is.na(coord[,1]))
    id <- matrix(c(1,cumsum(id$lengths)),ncol=2,byrow=T)
    polygons <- apply(id,1,function(i){coord[i[1]:i[2],]})

    # split up polygons that differ too much
    polygons <- lapply(polygons,function(x){
        x[,1] <- x[,1] + center
        x[,1] <- ifelse(x[,1]>180,x[,1]-360,x[,1])
        if(sum(diff(x[,1])>300,na.rm=T) >0){
          id <- x[,1] < 0
          x <- rbind(x[id,],c(NA,NA),x[!id,])
       }
       x
    })
    # reconstruct the object
    polygons <- do.call(rbind,polygons)
    Obj[[1]] <- polygons[,1]
    Obj[[2]] <- polygons[,2]

    map(Obj,...)
}

plot.map("world", center=200, col="white",bg="gray",
   fill=TRUE,ylim=c(-60,90),mar=c(0,0,0,0))
```