---
title: "Group Graphs.rmd"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source: embed
    storyboard: true
---

```{r data}
library(flexdashboard)
library(ggplot2)
library(plyr)
library(plotly)
library(dplyr)
setwd("C:/Users/Hannah/Desktop/Assignment/ANLY512/WorldProject")
data<-read.csv("Indicators.csv", sep=",", header=T)
```


### Graph 1. Carbon Dioxide Emissions (kt) across Years

```{r message=FALSE, warning=FALSE}
# Graph 1 CO2 emissions
data1 <- data[ which(data$IndicatorName=="CO2 emissions (kt)"),]

data1$Years[data1$Year >=1960 & data1$Year<1970] <- 1960
data1$Years[data1$Year >=1970 & data1$Year<1980] <- 1970
data1$Years[data1$Year >=1980 & data1$Year<1990] <- 1980
data1$Years[data1$Year >=1990 & data1$Year<2000] <- 1990
data1$Years[data1$Year >=2000 & data1$Year<2010] <- 2000

data1_emi<-as.data.frame(
  data1 %>% 
    group_by(Year) %>%                          
    summarise(mean_co2 = mean(Value)))

# Line Graph
g1<-ggplot(data1_emi, aes(x=Year, y=mean_co2, group=1)) +
  geom_line(color="blue")+
  geom_point(color="blue")+
  ggtitle("Graph 1. Carbon Dioxide Emissions (kt) across Years") +
  scale_x_continuous(breaks=seq(1960,2015,5))+
  xlab("Years") + ylab("Emissions (kt)")+
  theme_light()+
  theme(plot.title = element_text(hjust = 0.5))
ggplotly(g1)
```

*** 
Graph 1 shows the average carbon dioxide emissions (kt) in the world from the year of 1960 to 2011. Overall, the average amount of carbon dioxide emissions among countries increases throughout the years, and reaches its peak at 2011.

### Graph 2. Carbon Dioxide Emissions by Country across Years

```{r message=FALSE, warning=FALSE}
# Graph 2 Heatmap for CO2 emission by country
library(scales)
library(reshape2)

data1_4<-as.data.frame(
  data1 %>% 
    group_by(CountryName, Years) %>%                          
    summarise(mean_co2 = mean(Value)))

# Unmelt data
data1_d<-dcast(data1_4, CountryName ~ Years)
data1_d$"NA"<-NULL
data1_d <- na.omit(data1_d)
data1_d$sum=data1_d$"1960"+data1_d$"1970"+data1_d$"1980"+data1_d$"1990"+data1_d$"2000"
data1_d <- data1_d[order(-data1_d$sum),] 

# Top 20 countries
data1_top<-data1_d[c(8, 13, 17,21,24,26,29,30,31,33,34,35,36,37,38,39,41,42,43,44), ]
data1_top$sum<-NULL

# Melt data
data1_m <- melt(data1_top, id=c("CountryName"))
data1_re <- ddply(data1_m, .(variable), transform, rescale = rescale(value))

# Heatmap
g2<-ggplot(data1_re, aes(variable, CountryName)) + 
  geom_tile(aes(fill = data1_re$rescale), colour = "white") + 
  scale_fill_gradient(low = "white", high = "steelblue", breaks=c(0,0.25, 0.5,0.75, 1),
                      name="Percentile Rank", labels=c("0th", "25th", "50th", "75th", "100th"))+
  ggtitle("Graph 2. Carbon Dioxide Emissions by Country across Years") +
  theme(plot.title = element_text(hjust = 0.5))+
  scale_x_discrete(breaks=c(1960, 1970, 1980, 1990, 2000), labels=c("1960s", "1970s","1980s", "1990s", "2000s"))+
  xlab("Years") + ylab("Country Name")
ggplotly(g2)

```

*** 
Graph 2 shows the Percentile rank of carbon dioxide emissions by country across years. The Top 20 countries were included in the graph. Overall, United States is observed to have the largest amount of carbon dioxide emission across years, followed by China. The emission in China kept increasing from 1960s to 2000s.


### Graph 3. Greenhouse Gas Emissions across Years

```{r message=FALSE, warning=FALSE}
# Graph 3 greenhouse emissions 
data2 <- data[ which(data$IndicatorName=="HFC gas emissions (thousand metric tons of CO2 equivalent)"
                   | data$IndicatorName=="PFC gas emissions (thousand metric tons of CO2 equivalent)"
                   | data$IndicatorName=="SF6 gas emissions (thousand metric tons of CO2 equivalent)"),]

data2_green<-as.data.frame(
  data2 %>% 
    group_by(IndicatorName, Year) %>%                          
    summarise(mean_green = mean(Value)))

data2_green$Gas[data2_green$IndicatorName =="HFC gas emissions (thousand metric tons of CO2 equivalent)"] <- "HFC"
data2_green$Gas[data2_green$IndicatorName =="PFC gas emissions (thousand metric tons of CO2 equivalent)"] <- "PFC"
data2_green$Gas[data2_green$IndicatorName =="SF6 gas emissions (thousand metric tons of CO2 equivalent)"] <- "SF6"

# Line graph
g3<-ggplot(data2_green, aes(x=Year, y=mean_green, group=Gas)) +
  geom_line(aes(color=Gas))+
  geom_point(aes(color=Gas))+
  ggtitle("Graph 3. Greenhouse Gas Emissions across Years") +
  xlab("Years") + ylab("Emissions (Thousand Metric Tons)")+
  theme_light()+
  theme(legend.position="right")+
  theme(plot.title = element_text(hjust = 0.5))+
  scale_colour_manual(name="Gas", values=c("red", "green", "blue"),labels = c("HFC", "PFC", "SF6"))
ggplotly(g3)
```

*** 
Graph 3 reveals the trend of greenhouse gas emissions across years, including HFC, PFC, and SF6. In general, greenhouse gas emissions increased from the year of 1990 to 2005, arriving at the peak at 2005, and then decreased from 2005 to 2010. The amount of gas emission is highest for HFC, followed by PFC, and then SF6.  


### Graph 4. Percentage of Emission from Fuel Consumption across Years

```{r message=FALSE, warning=FALSE}
# Graph 4 Percentage of emission
data3_1 <- data[ which(data$IndicatorName=="CO2 emissions from gaseous fuel consumption (% of total)"),]
data3_1[,c("CountryName","IndicatorCode")] <- list(NULL)

data3_2 <- data[ which(data$IndicatorName=="CO2 emissions from liquid fuel consumption (% of total)"),]
data3_2[,c("CountryName","IndicatorCode")] <- list(NULL)

data3_3 <- data[ which(data$IndicatorName=="CO2 emissions from solid fuel consumption (% of total)"),]
data3_3[,c("CountryName","IndicatorCode")] <- list(NULL)

data3_12 <- merge(data3_1, data3_2, by=c("CountryCode","Year"))
data3_12[,c("IndicatorName.x","IndicatorName.y")] <- list(NULL)
colnames(data3_12)<-c("CountryCode","Year","gaseous","liquid")

data3 <- merge(data3_12, data3_3, by=c("CountryCode","Year"))
data3[,c("IndicatorName")] <- list(NULL)
colnames(data3)<-c("CountryCode","Year","gaseous","liquid","solid")
data3$other<-100-(data3$gaseous + data3$liquid + data3$solid)

data3_percent<-as.data.frame(
  data3 %>% 
    group_by(Year) %>%                          
    summarise(mean_g = mean(gaseous),
              mean_l = mean(liquid),
              mean_s = mean(solid),
              mean_o = mean(other)))

colnames(data3_percent)<-c("Year","Gaseous","Liquid","Solid", "Others")

mdata3 <- melt(data3_percent, id=c("Year"))

g4<-ggplot(mdata3, aes(Year, value, fill = variable))+ 
  geom_bar(stat="identity")+
  scale_y_continuous(limits = c(0, 100),labels = function(x) paste0(abs(x), "%"))+
  labs(x="Years", y="Percentage")+
  ggtitle("Graph 4. Percentage of Emission from Fuel Consumption across Years")+
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))+
  scale_x_continuous(breaks=seq(1960,2015,5))+
  scale_fill_manual(name="Fuel", values=c("#a6cee3", "#1f78b4", "#b2df8a","#33a02c"))
ggplotly(g4)
```

*** 
Graph 4 shows the distribution of emission from fuel consumption across years. The percentage of solid fuel kept stable across years. However, the percentage of liquid fuel decreased from the year of 1960 to 2011, while the percentage of gaseous fuel increased across years.


### Graph 5. Where Are Mammal Species Threatened
```{r}
indicators <-read.csv("Indicators.csv", sep=",", header=T)

library(dplyr)

threatened=filter(indicators, IndicatorCode %in% c("EN.MAM.THRD.NO","EN.BIR.THRD.NO","EN.FSH.THRD.NO","EN.HPT.THRD.NO","NY.GDP.PCAP.CD","ER.MRN.PTMR.ZS"))
#mammal species, bird species, fish species, plant species, GDP per capita, marine protected water

library(countrycode)

threatened$continent <- factor(countrycode(sourcevar = threatened[, "CountryName"],
                                         origin = "country.name",
                                          destination = "continent"))
library(zoo)
library(tidyr)
threatened_wide <- threatened %>%
  spread(IndicatorCode, Value) %>%
  group_by(CountryName) %>%
  mutate_all(funs(na.locf(., na.rm = FALSE, fromLast = FALSE)))%>%filter(row_number()==n())

## after spread()
## CountryName   Var1    Var2    Var3    Var4   Var5
## A             12      11      33      NA      NA
## A             112     115     NA      NA      NA
## reference: Combine rows by group with differing NAs in each row: https://stackoverflow.com/questions/45201654/combine-rows-by-group-with-differing-nas-in-each-row

threatened_wide_matched <- na.omit(threatened_wide)
``` 


```{r echo=F, results='hide'}
library(rworldmap)
##Joining data to a country map
matched <- joinCountryData2Map(as.data.frame(threatened_wide_matched),joinCode = "ISO3",nameJoinColumn = "CountryCode")
##displaying the data
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapCountryData(matched, nameColumnToPlot="EN.MAM.THRD.NO", mapTitle = "Graph 5. Number of Mammal Species Threatened in 2015",catMethod = "pretty",colourPalette = "topo") 
```

*** 
Countries with most threatened mammal species are in Asia. 


### Graph 6. Relationship between GDP and threatened mammal species
```{r} 
library(ggplot2)
library(ggthemes)

ggplot(data =threatened_wide_matched, aes(x=NY.GDP.PCAP.CD, y= EN.MAM.THRD.NO,colour=continent, alpha = 0.5)) +
  geom_point() + 
  geom_smooth(method='lm',formula=y~x) +
  facet_wrap(~continent) +
  theme_bw() +
  labs(x="GDP per capita", y="Number of species threatened", title = "Graph 6. GDP per capita and Mammal Species Threatened")
```

***
Countries with lower GDP generally have more mammal species threatened, except for Oceania.


### Graph 7. Relationship between GDP, fish species and protected water
```{r}
library(ggplot2)
library(ggthemes)

ggplot() +
  geom_point(data = threatened_wide_matched, aes(x=NY.GDP.PCAP.CD, y=ER.MRN.PTMR.ZS, colour=continent, size = EN.FSH.THRD.NO), alpha = 0.5) + 
  geom_smooth(method='lm',formula=y~x) +
  labs(title = "Graph 7. Relationship: Fish Species Threatened and Protected Water", subtitle = "Each point represents one country", x="GDP per Capita in U.S. $", y = "Percentage of Water Protected") +
  theme_bw()
  
```


### Graph 8. Protected water and threatened fish species
```{r}
library(ggplot2)
library(ggthemes)

ggplot(data = threatened_wide_matched, aes(x=ER.MRN.PTMR.ZS, y=EN.FSH.THRD.NO, colour = continent, alpha = 0.5)) +
  geom_point() + 
  geom_smooth(method='lm',formula=y~x) +
  labs(title = "Graph 8. Relationship: Protected Water and Fish Species Threatened", subtitle = "Each point represents one country", x="Percentage of Marine Water Protected", y = "Number of Species Threatened") +
  theme_bw() +
  facet_wrap(~continent)
  
```



### Graph 9. 2015 World fresh drinking water distrition (% of population)

Africa has the lowest level of fresh drinking water globally.

```{r}
#devtools::install_github("rstudio/flexdashboard")
library(flexdashboard)
waterdis=read.csv("API_SH.H2O.SMDW.ZS_DS2_en_csv_v2_9987473.csv",skip=4)

waterdis15 <-data.frame(waterdis$Country.Name,waterdis$X2015)
waterdis15=waterdis15[!is.na(waterdis15$waterdis.X2015),]
colnames(waterdis15)=c("CountryName","X2015")
library(rworldmap)
```

```{r echo=F, results='hide'}
matched= joinCountryData2Map(waterdis15,joinCode = "NAME",nameJoinColumn = "CountryName")
```

```{r}
mapCountryData(matched,nameColumnToPlot = "X2015",mapTitle = "Graph 9. 2015 World Fresh Drinking Water Distribution (% of Pop.)",catMethod = "pretty",colourPalette = "topo")
```


### Graph 10. US fresh drinking water (% of population) trend from 2000 to 2015

```{r}
#plot 2
#install.packages("stringi")

uswater=read.csv("US drinking water.csv")
subus=as.data.frame(cbind(as.Date(uswater$Year,origin="1970-01-01"),uswater$Value))
colnames(subus)=c("Year","Value")

library(dygraphs)
library(dplyr)

dygraph(subus, main = "Graph 10. 2005-2015 US Fresh Drinking Water % of Population") %>%
dyRangeSelector()
#US drinking water % of poulation from 2000 to 2015 (line chart)
```

*** 

This interactive graph reflects the trend of the United State fresh drinking water (% of population) from 2000 and 2015.

Fresh drinking water (% of population) has been increasing over the 15 year period. And The increasing rate is relatively stable, which makes the line chart looks like a straight line with a constant slope.

### Graph 11. 2015 annual freshwater withdraw by region per type

```{r}
water3=read.csv("API_ER.H2O.FWAG.ZS_DS2_en_csv_v2_9985917.csv")

library(ggplot2)
library(plotly)

#water3=water3[order(water3$Type, decreasing = T),]

library(magrittr)
library(dplyr)
library(scales)

o <- water3 %>% filter(Type == "Agriculture") %>% arrange(desc(Value)) %>% extract2("Country")

p=water3 %>% 
  mutate(Country = factor(Country, o))%>%
ggplot(aes(x=Country,y=Value,fill=factor(Type,levels=c("Industry","Domestic","Agriculture"))))+geom_bar(stat="identity")+guides(fill=guide_legend("Type"))+scale_fill_brewer()+theme_minimal()+ggtitle("Graph 11. 2015 Annual Freshwater Withdraw by Region per Type")+xlab("Region")+scale_x_discrete(labels = wrap_format(10))
ggplotly(p)

```

*** 

Agriculture accounts for most refresh water usage in most countries and regions in 2015. The agricultural usage of all regions in the graph are over 50% but Europe and Central Asia, where Industrial usage is the biggest contributor but only 5% higher than agricultural usage.

This interactive graph arranges the order of regions by the percentage of it's agricultural fresh water withsdraw from the largest to the smallest.


```{r message=FALSE, warning=FALSE}
library(readxl)
library(flexdashboard)
library(plyr)
library(ggthemes)
library(ggplot2)
library(memisc)
library(gridExtra)
library(ggmap)
library(maps)
library(mapdata)
library(highcharter)
library(viridis)
library(ggpubr)
```


---

### Graph 12. Global Foreset Coverage Rate by Country  

```{r}
datam <- read_xlsx("map.xlsx")
datai <- read_xlsx("incometrend.xlsx")
data1 <- read_xlsx("statetrend.xlsx")
datab <- read_xlsx("bubble.xlsx")
datab2 <- read_xlsx("bubble2.xlsx")
datap <- read_xlsx("protect.xlsx")
datap2 <- read_xlsx("incomeprotect.xlsx")
```


```{r message=FALSE, warning=FALSE, fig.width = 24, fig.height = 12} 
dfm <-data.frame(
  datam$`Country Name`,
  datam$forest)
colnames(dfm)=c("CountryName","forest")

data("worldgeojson")

n <- 4
colstops <- data.frame(
  q = 0:n/n,
  c = substring(viridis(n + 1,direction=-1), 0, 7)) %>%
  list_parse2()

highchart() %>%
  hc_add_series_map(worldgeojson, dfm, name = "Graph 12. World forest",
                    value = "forest", joinBy <-c("name","CountryName"),
                    dataLabels =  list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(stops= colstops)%>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE)
```


*** 
Global Forest Coverage Rate by Country (% of Land)

- Countries in Europe, Northern Sourth America, Mid Africa are having higher percentage of Forest coverage than countries in other regions.

- Coutntry that has the highest forest coverage rate is Suriname (98.28%), countries that has almost 0% forest coverage rate is Curacao, Gibraltar, Kosovo, Monaco, Nauru, Qatar, San Marino, Sint Maarten (Dutch part), and South Sudan.


### Graph 13 & 14. Global Foreset Coverage Rate by Region & Income Group

```{r}
datai <- read_xlsx("incometrend.xlsx")
```

```{r message=FALSE, warning=FALSE, fig.width = 24, fig.height = 12} 
datafi <- data.frame(
  Year = datai$Year,
  HIC = datai$HIC,
  LIC = datai$LIC,
  LMC = datai$LMC,
  MIC = datai$MIC,
  UMC = datai$UMC
  )

datafii <- ggplot(datafi , aes(Year))+
  geom_line(aes(y = HIC, colour = "High Income"), size = 1.5) +
  geom_line(aes(y = UMC, colour = "Upper Middle Income"), size = 1.5) +
  geom_line(aes(y = MIC, colour = "Middle Income"), size = 1.5) +
  geom_line(aes(y = LMC, colour = "Lower Middle Income"), size = 1.5) +
  geom_line(aes(y = LIC, colour = "Low Income"), size = 1.5) +

theme_hc() +theme(legend.position="bottom", legend.direction="horizontal", legend.title = element_blank()) + scale_y_continuous(breaks=seq(20,40,2)) + ggtitle("Graph 13. Global Foreset Coverage Rate by Income Group") + labs(x="Year", y="Foreast Coverage Rate") + theme(plot.title = element_text(size=22)) +theme(text = element_text(size=15))+ theme(legend.text=element_text(size=15),legend.title=element_text(size=15))

data1 <- read_xlsx("statetrend.xlsx")

dataf1 <- data.frame(
  Year = data1$Year,
  EAS = data1$EAS,
  ECS = data1$ECS,
  LCN = data1$LCN,
  MEA = data1$MEA,
  NAC = data1$NAC,
  SAS = data1$SAS,
  SSF = data1$SSF,
  WLD = data1$WLD
  )

datafp <- ggplot(dataf1 , aes(Year))+
  geom_line(aes(y = WLD, colour = "World"), size = 2) +
  geom_line(aes(y = EAS, colour = "East Asia & Pacific"), size = 1.5) +
  geom_line(aes(y = ECS, colour = "Europe & Central Asia"), size = 1.5) +
  geom_line(aes(y = LCN, colour = "Latin America & Caribbean"), size = 1.5) +
  geom_line(aes(y = MEA, colour = "Middle East & North Africa"), size = 1.5) +
  geom_line(aes(y = NAC, colour = "North America"), size = 1.5) +
  geom_line(aes(y = SAS, colour = "South Asia"), size = 1.5) +
  geom_line(aes(y = SSF, colour = "Sub-Saharan Africa", size = 1.5), size = 1.5) + theme_hc() +theme(legend.position="bottom", legend.direction="horizontal", legend.title = element_blank()) + scale_y_continuous(breaks=seq(0,100,10)) + ggtitle("Graph 14. Global Foreset Coverage Rate by Region") + labs(x="Year", y="Foreast Coverage Rate") + theme(plot.title = element_text(size=22)) +theme(text = element_text(size=15))+ theme(legend.text=element_text(size=15),legend.title=element_text(size=15))

grid.arrange(datafii, datafp, ncol = 2, nrow = 1)
```

*** 
The overall world forest coverage rate decreased over time, it lost 0.97% of the total rate from 1990 (31.80%) to 2015 (30.83%).

- From the aspect of different income groups, only high income countries have an increase forest coverage rate over time, however the difference is negligible (27.30% in 1990 and 27.55% in 2015). All other income groups are having different levels of decrease in forest coverage rate. Among them, low income countries are having the greatest decline in their forest coverage rate (29.8% in 1990 and 25.87% in 2015).

- From the aspect of different regions, regions like East Asia & Pacific, Europe & Central Asia, Middle East & North Africa, Middle East & North Africa, South Asia, and North America are having higher forest coverage rate in 2015 than 1990. Among them, Sourth Asia has the highest increment as 0.97%. Latin America & Caribbean and Sub-Saharan Africa are having lower rate in 2015 compare to 1990, their forest coverage rate decreased by 4.76% and 3.82% correspondingly.


### Graph 15 & 16. Forest & Total Protected Land Area in terms of Countries Land Area & Population

```{r message=FALSE, warning=FALSE, fig.width = 24, fig.height = 12}
datab <- read_xlsx("bubble.xlsx")

options(scipen=999)
pb <- ggplot(datab, aes(x=population, y=land,size=`Forest Coverage`, fill=`Total Protected Area Coverage`))+geom_point(shape = 21) + ggtitle("") +  scale_size_area(max_size = 20) + 
            labs(x = "Population(k)", y = "Land Area(sq.km)")+ theme_bw() + theme(legend.text=element_text(size=15),legend.title=element_text(size=15)) +
    scale_fill_continuous(high = "#132B43", low = "#56B1F7")+theme(legend.position="none", legend.direction="horizontal") +ggtitle("Graph 15. All Countries")+theme(plot.title = element_text(size=22))

datab2 <- read_xlsx("bubble2.xlsx")

pb2 <- ggplot(datab2, aes(x=population, y=land,size=`Forest Coverage`, fill=`Total Protected Area Coverage`))+geom_point(shape = 21) + ggtitle("") +  scale_size_area(max_size = 20) + 
            labs(x = "Population(k)", y = "Land Area(sq.km)")+ theme_bw() + theme(legend.text=element_text(size=15),legend.title=element_text(size=15)) +
    scale_fill_continuous(high = "#132B43", low = "#56B1F7")+theme(legend.position="none", legend.direction="horizontal")+ggtitle("Graph 16. Countries have Land Area below 100k & Population below 500k")+theme(plot.title = element_text(size=22))

#pb3 <- grid.arrange(pb, pb2, ncol = 2, nrow = 1) 
ggarrange(pb, pb2, ncol=2, nrow=1, common.legend = TRUE, legend="bottom")
```

*** 
- Protected area coverage and forest coverage does not change as population or total land area increase.

- Average forest coverage is 35%, average protected area coverage is 0.4%.


### Graph 17 & 18. Change in Global Forest and Protected Land(Sea) Area

```{r message=FALSE, warning=FALSE, fig.width = 24, fig.height = 12}
datap <- read_xlsx("protect.xlsx")

datapp <- ggplot(datap,aes(x=region,y=value,fill=factor(type)))+
  geom_bar(stat="identity",position="dodge")  + theme_bw() + ggtitle("Graph 17. Change in Global Forest & Protected Land Area by Region(1990-2015)") + labs(x="region", y="change(%)") +theme(legend.position="bottom", legend.direction="horizontal", legend.title = element_blank())+theme(plot.title = element_text(size=22))+theme(text = element_text(size=15))

datap2 <- read_xlsx("incomeprotect.xlsx")

datapp2 <- ggplot(datap2,aes(x=incomegroup,y=value,fill=factor(type)))+
  geom_bar(stat="identity",position="dodge")  + theme_bw() + ggtitle("Graph 18. Change in Global Forest & Protected Land Area by Income Group(1990-2015)") + labs(x="Income Group", y="change(%)") +theme(legend.position="bottom", legend.direction="horizontal", legend.title = element_blank()) +theme(plot.title = element_text(size=22))+theme(text = element_text(size=17))
  
grid.arrange(datapp, datapp2, ncol = 2, nrow = 1)  

```

*** 
The overall world is losing its forest(-0.97%) and gaining protected land and sea area(0.75% and 0.72%).

- From the aspect of region, LCN(Latin America & Caribbean) is having the highest protected land coverage rate increment(1.62%) and NAC(North America) is having the lowest increment(0.17%). MEA(Middle East & North Africa) is having the highest protected seat coverage rate increment(1.66%) and SSF(Sub-Saharan Africa) is having the lowest increment(-1.00%). 

- From the aspect of income group,LMC(Lower Middle Income), MIC(Middle Income),and LIC(Low Income) countries are having similar protected land coverage rate increment(0.92%,0.98%,1.00%). HIC(High Income) countries are having the highest protected sea coverage rate increment(0.50%) and LIC(Low Income) countries are having the lowest rate increment(-1.00%).

  WLD: World
  EAS: East Asia & Pacific
  ECS: Europe & Central Asia
  LCN: Latin America & Caribbean
  MEA: Middle East & North Africa
  NAC: North America
  SAS: South Asia
  SSF: Sub-Saharan Africa

  HIC: High Income
  UMC: Upper Middle Income
  MIC: Middle Income
  LMC: Lower Middle Income
  LIC: Low Income