Proof of climate change

Column

Global Land temperature in degrees Celsius

Land Average temperature by year

Temperature difference in cities worldwide

CO2 trend and high-low temperatures

Column

CO2 trend

Column

High-low average temperature trend

Climate Change consequences

Column

Most affected cities

Observations

Observations

  1. From the CO2 trend visualization we can see the concentration of the green house gas CO2 has risen from 300 parts per million in the 1960s to over 400 in the present day. We see a similar trend in the rise of temperature in both the high temperatures of earth’s surface, as well as the low temperatures.

  2. We can see from the “Global Land temperature in degrees Celsius” visualization in 150+ years, the overall increase is close to 2 degrees celsius. In the second tab, we see that the “Land Average temperature by year” is also steadily increasing. From the “Temperature difference between 1850 and 2012” tab we see that in various locations across the world, the temperatures have risen from anywhere uptp 4 degrees.

---
title: "ANLY-512 Data Visualization"
Author: "Ankit Ravi,Anirudh Gurnani"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
setwd('C:/Users/aravi/Downloads/RFiles')
library(ggplot2)
library(grid)
library(gridExtra)
library(RColorBrewer)
library(ggthemes)
library(dplyr)
library(sp)
library(rworldmap)
library(data.table)
library(Amelia)
library(caret)
library(ggfortify)
library(forecast)
library(imputeTS)

options(width=100)
knitr::opts_chunk$set(out.width='1000px',dpi=200,message=FALSE,warning=FALSE)

```

Proof of climate change {data-orientation=columns}
==========================================================================

Sidebar {.sidebar}
-----------------------------------------------------------------------

Column {.tabset .tabset-fade data-height=500}
-----------------------------------------------------------------------
  
### Global Land temperature in degrees Celsius
```{r}
glob<-read.csv('C:/Users/aravi/Downloads/RFiles/GlobalTemperatures.csv')
glob$date<-as.Date(glob$dt)
glob$year<-as.numeric(format(glob$date,'%Y'))
glob %>% 
  filter(year>=1850) %>% 
  select(LandAverageTemperature, LandAverageTemperatureUncertainty, year) %>% 
  group_by(year) %>% summarise(avgTemp = mean(LandAverageTemperature), avgError = mean(LandAverageTemperatureUncertainty)) %>% 
  ggplot(aes(x=year,y = avgTemp)) + 
  geom_point(aes(size=avgError,color=avgTemp),alpha=.75) + 
  theme(legend.position='top') + 
  theme_fivethirtyeight() + ylim(5,12) + 
  ggtitle('Global Land temperature in degrees Celsius') + 
  geom_smooth(color='black',size=.4) + 
  labs(size = 'Average errors') + scale_color_gradientn(name='Degrees Celsius',colors=rev(brewer.pal(10,'Spectral'))) +
  scale_size(guide = 'none')
```

###  Land Average temperature by year

```{r}
GT = fread("C:/Users/aravi/Downloads/RFiles/GlobalTemperatures.csv")
GT[,dt:=as.Date(GT[,dt])]
GT[,year:=year(GT[,dt])]
GT[,month:=month(GT[,dt])]
GT[,season:=ifelse(month %in% c(6,7,8),"Summer",
                    ifelse(month %in% c(9,10,11),"Fall",
                           ifelse(month %in% c(12,1,2),"Winter","Spring")
                    ))]
ggplot(data = GT, 
    aes(dt,LandAverageTemperature, colour=season)) +
        geom_point(na.rm = T) +
        xlab("Year") +
        ggtitle("Land Average temperature by year")

```

### Temperature difference in cities worldwide

```{r}
convert<-function(x){
    westEast<-substr(x,nchar(x),nchar(x))
    xx<-substr(x,1,nchar(x)-1)
    val<-as.numeric(char2dms(paste0(strsplit(xx,'[.]')[[1]][1],'d',strsplit(xx,'[.]')[[1]][1],"'",westEast)))
    return(val)
}

worldMap <- fortify(map_data("world"), region = "region")
m <- ggplot() +
  geom_map(data = worldMap, map = worldMap,aes(x = long, y = lat, map_id = region, group = group),fill = "white", color = "black", size = 0.1)

allCities<-read.csv('C:/Users/aravi/Downloads/RFiles/GlobalLandTemperaturesByCity.csv')
allCities<-na.omit(allCities)
allCities$date<-as.Date(allCities$dt)
allCities$year<-as.numeric(format(allCities$date,'%Y'))
allCities <-as.data.frame(allCities %>% filter(year>=1850))
allCities$month<-as.numeric(format(allCities$date,'%m'))
allCities$Longitude<-as.character(allCities$Longitude)
allCities$Latitude<-as.character(allCities$Latitude)

start<-allCities %>% filter(year==1850)
start$LONG<-sapply(start$Longitude,convert)
start$LAT<-sapply(start$Latitude,convert)
start<-as.data.frame(start %>% group_by(Country, City) %>% select(AverageTemperature, City ,LAT, LONG, Country) %>% summarise(avgTemp_start = mean(AverageTemperature), long = mean(LONG), lat = mean(LAT)))

end<-as.data.frame(allCities %>% filter(year==2012))
end<-as.data.frame(end %>% group_by(Country,City) %>% select(AverageTemperature,City) %>% summarise(avgTemp_end = mean(AverageTemperature)))

res2<-as.data.frame(merge(start,end, by=c('Country','City')))

m +
  geom_point(data=res2,aes(x=long, y=lat, size=avgTemp_end - avgTemp_start,color=avgTemp_end - avgTemp_start),alpha=.2) +
  theme_fivethirtyeight() +
  ggtitle('Temperature difference between 1850 and 2012') +
  theme(axis.text = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + labs(size = '') +
  scale_color_gradientn(name='Degrees Celsius',colors=rev(brewer.pal(10,'Spectral'))) + scale_size(guide = 'none')

```

CO2 trend and high-low temperatures {data-orientation=columns}
==========================================================================

Sidebar {.sidebar}
-----------------------------------------------------------------------

Column {data-width=400}
------------------------------------------------------------------------------
  
###  CO2 trend
  
```{r}
df<-read.csv('C:/Users/aravi/Downloads/RFiles/archive.csv',sep=',')
colnames(df)<-c('year','month','decimal_date','carbon_dioxide_ppm','carbon_dioxide_ppm_season_adj','carbon_dioxide_ppm_fit','carbon_dioxide_ppm_season_adj_fit')

ggplot() + 
  geom_line(data=df,aes(x=decimal_date,y=carbon_dioxide_ppm,color="carbon_dioxide"),alpha=.75) +
  geom_line(data=df,aes(x=decimal_date,y=carbon_dioxide_ppm_season_adj,color="carbon_dioxide_adjusted"),alpha=.75) +
  geom_line(data=df,aes(x=decimal_date,y=carbon_dioxide_ppm_fit,color="carbon_dioxide_fit"),alpha=.75) + 
  geom_line(data=df,aes(x=decimal_date,y=carbon_dioxide_ppm_season_adj_fit,color="carbon_dioxide_adjusted_fit"),alpha=.75) + 
  scale_colour_manual(name="data",values=c(carbon_dioxide="#E2D200",carbon_dioxide_adjusted="#46ACC8",carbon_dioxide_fit="#E58601",carbon_dioxide_adjusted_fit="#B40F20")) + 
  theme(legend.position="top") +
  xlab('') + ylab('concentrations [ppm]')
```

Column {data-width=400}
------------------------------------------------------------------------------
  
### High-low average temperature trend
```{r}
GT = fread("C:/Users/aravi/Downloads/RFiles/GlobalTemperatures.csv")
GT[,dt:=as.Date(GT[,dt])]
GT[,year:=year(GT[,dt])]
GT[,month:=month(GT[,dt])]
GT[,season:=ifelse(month %in% c(6,7,8),"Summer",
                    ifelse(month %in% c(9,10,11),"Fall",
                           ifelse(month %in% c(12,1,2),"Winter","Spring")
                    ))]
# Average Max
High.GT = aggregate(GT$LandMaxTemperature, 
                by=list(GT$year), 
                FUN=mean)
# Average Min
Low.GT = aggregate(GT$LandMinTemperature, 
                  by=list(GT$year), 
                  FUN=mean)
HighLow.GT = data.table(merge(Low.GT, High.GT, by = "Group.1"))
names(HighLow.GT) <- c("year","Low","High")
MeltedHighLow.GT = melt(HighLow.GT,
                        id = "year",
                        na.rm = T,
                        variable.name = "Temperature")

ggplot(data = MeltedHighLow.GT,
       aes(year, value)) +
        geom_line(na.rm = T) +
        facet_grid(Temperature ~ ., scales = "free_y") +
        xlab("Year") +
        geom_smooth() +
        geom_vline(xintercept = 1900, alpha = 0.5) +
        geom_vline(xintercept = 1975, alpha = 0.5) +
        ggtitle("High and Low average Temperature trend")
```



Climate Change consequences {data-orientation=columns}
==========================================================================

Sidebar {.sidebar}
-----------------------------------------------------------------------


Column {data-width=800}
-----------------------------------------------------------------------
  
### Most affected cities
  
```{r}

convert<-function(x){
    westEast<-substr(x,nchar(x),nchar(x))
    xx<-substr(x,1,nchar(x)-1)
    val<-as.numeric(char2dms(paste0(strsplit(xx,'[.]')[[1]][1],'d',strsplit(xx,'[.]')[[1]][1],"'",westEast)))
    return(val)
}

majorCities<-read.csv('C:/Users/aravi/Downloads/RFiles/GlobalLandTemperaturesByMajorCity.csv')
majorCities<-na.omit(majorCities)
majorCities$date<-as.Date(majorCities$dt)
majorCities$year<-as.numeric(format(majorCities$date,'%Y'))
majorCities <-as.data.frame(majorCities %>% filter(year>=1850))
majorCities$month<-as.numeric(format(majorCities$date,'%m'))
majorCities$Longitude<-as.character(majorCities$Longitude)
majorCities$Latitude<-as.character(majorCities$Latitude)

start<-majorCities %>% filter(year==1850)
start$LONG<-sapply(start$Longitude,convert) 
start$LAT<-sapply(start$Latitude,convert)

start<-as.data.frame(start %>% group_by(Country, City) %>% select(AverageTemperature, City ,LAT, LONG, Country) %>% summarise(avgTemp_start = mean(AverageTemperature), long = mean(LONG), lat = mean(LAT)))

end<-as.data.frame(majorCities %>% filter(year==2012))
end<-as.data.frame(end %>% group_by(Country,City) %>% select(AverageTemperature,City) %>% summarise(avgTemp_end = mean(AverageTemperature)))

res2<-as.data.frame(merge(start,end, by=c('Country','City')))

ggplot(data=res2,aes(x=reorder(City,avgTemp_end - avgTemp_start),y=avgTemp_end - avgTemp_start)) + 
  geom_histogram(aes(fill=avgTemp_end - avgTemp_start),stat='identity',width=.75) + 
  coord_flip() + 
  theme_fivethirtyeight() + 
  ggtitle('World Major Cities') + 
  scale_fill_gradientn(name='temperature\'s difference in degrees Celsius between 1850 and 2012',colours=rev(brewer.pal(10,'Spectral'))) +
  theme(legend.position='top',axis.text = element_text(size=7))
```



Observations
==========================================================================

### **Observations**
1.
From the CO2 trend visualization we can see the concentration of the green house gas CO2 has risen from 300 parts per million in the 1960s to over 400 in the present day. We see a similar trend in the rise of temperature in both the high temperatures of earth's surface, as well as the low temperatures. 

2.
We can see from the "Global Land temperature in degrees Celsius" visualization in 150+ years, the overall increase is close to 2 degrees celsius. In the second tab, we see that the "Land Average temperature by year" is also steadily increasing. From the "Temperature difference between 1850 and 2012" tab we see that in various locations across the world, the temperatures have risen from anywhere uptp 4 degrees.