Column

About

Carbon Emission and Temperature Rise: Through the years, CO2 emissions for US and China have been increasing at a much higher pace than Australia which has only a slight rise in emissions. UK has more or less the same emission. Irrespective of the amount of CO2 emission, every country has recorded significant increase in temperatures through the years. This tells us that the effects of CO2 emission are not only felt locally but worldwide and all countries will have to work together and reduce the overall CO2 emissions.
Sea Ice: The area in orange shows how the sea ice has been decreasing over the years with rising global temperatures. The area in blue shows the sea ice in 2009.
Data Sources:
Carbon Emission Data
Sea Ice and Temperature Data

Column

Annual Carbon Emissions

Temperature Rise

Column

Top 3 CO2 emiting countries in 2018 on each continent

Column

North Pole Sea Ice - 1991 to 2009

South Pole Sea Ice - 1991 to 2009

---
title: "Climate Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(rnoaa)
library(ggplot2)
library(dplyr)
library(readxl)
library(forcats)
```


Column {data-width=150}
-----------------------------------------------------------------------

### About
**Carbon Emission and Temperature Rise**: Through the years, CO2 emissions for US and China have been increasing at a much higher pace than Australia which has only a slight rise in emissions. UK has more or less the same emission. Irrespective of the amount of CO2 emission, every country has recorded significant increase in temperatures through the years. This tells us that the effects of CO2 emission are not only felt locally but worldwide and all countries will have to work together and reduce the overall CO2 emissions.  
**Sea Ice**: The area in orange shows how the sea ice has been decreasing over the years with rising global temperatures. The area in blue shows the sea ice in 2009.  
**Data Sources**:  
[Carbon Emission Data](https://github.com/owid/co2-data)  
[Sea Ice and Temperature Data](https://www.ncdc.noaa.gov)


Column {data-width=300}
-----------------------------------------------------------------------

```{r}

#Pittsburgh - locationid = CITY:US420016
# Mumbai -  locationid = CITY:IN000021

ncdcds <- ncdc_datasets(token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt")

#Reading Annual Avg Temp for Beijing
p1 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:CHM00054511', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1948-01-01', enddate = '1957-12-31')
p2 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:CHM00054511', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1958-01-01', enddate = '1967-12-31')
p3 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:CHM00054511', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1968-01-01', enddate = '1977-12-31')
p4 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:CHM00054511', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1978-01-01', enddate = '1987-12-31')
p5 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:CHM00054511', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1988-01-01', enddate = '1997-12-31')
p6 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:CHM00054511', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1998-01-01', enddate = '2007-12-31')
p7 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:CHM00054511', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '2008-01-01', enddate = '2017-12-31')

p1 <- as.data.frame(p1[2])
p2 <- as.data.frame(p2[2])
p3 <- as.data.frame(p3[2])
p4 <- as.data.frame(p4[2])
p5 <- as.data.frame(p5[2])
p6 <- as.data.frame(p6[2])
p7 <- as.data.frame(p7[2])

#Reading Annual Avg Temp for NYC
n1 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:USW00014732', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1948-01-01', enddate = '1957-12-31')
n2 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:USW00014732', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1958-01-01', enddate = '1967-12-31')
n3 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:USW00014732', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1968-01-01', enddate = '1977-12-31')
n4 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:USW00014732', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1978-01-01', enddate = '1987-12-31')
n5 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:USW00014732', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1988-01-01', enddate = '1997-12-31')
n6 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:USW00014732', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1998-01-01', enddate = '2007-12-31')
n7 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:USW00014732', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '2008-01-01', enddate = '2017-12-31')

n1 <- as.data.frame(n1[2])
n2 <- as.data.frame(n2[2])
n3 <- as.data.frame(n3[2])
n4 <- as.data.frame(n4[2])
n5 <- as.data.frame(n5[2])
n6 <- as.data.frame(n6[2])
n7 <- as.data.frame(n7[2])

#Reading Annual Avg Temp for London
l1 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:UKE00156884', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1948-01-01', enddate = '1957-12-31')
l2 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:UKE00107650', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1958-01-01', enddate = '1967-12-31')
l3 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:UKE00107650', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1968-01-01', enddate = '1977-12-31')
l4 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:UKE00107650', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1978-01-01', enddate = '1987-12-31')
l5 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:UKE00107650', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1988-01-01', enddate = '1997-12-31')
l6 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:UKE00107650', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1998-01-01', enddate = '2007-12-31')
l7 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:UKE00107650', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '2008-01-01', enddate = '2017-12-31')

l1 <- as.data.frame(l1[2])
l1$data.station[] <- "GHCND:UKE00107650" 
l2 <- as.data.frame(l2[2])
l3 <- as.data.frame(l3[2])
l4 <- as.data.frame(l4[2])
l5 <- as.data.frame(l5[2])
l6 <- as.data.frame(l6[2])
l7 <- as.data.frame(l7[2])

#Reading Annual Avg Temp for Melbourne
m1 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:ASN00086071', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1948-01-01', enddate = '1957-12-31')
m2 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:ASN00086071', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1958-01-01', enddate = '1967-12-31')
m3 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:ASN00086071', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1968-01-01', enddate = '1977-12-31')
m4 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:ASN00086071', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1978-01-01', enddate = '1987-12-31')
m5 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:ASN00086071', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1988-01-01', enddate = '1997-12-31')
m6 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:ASN00086071', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '1998-01-01', enddate = '2007-12-31')
m7 <- ncdc(datasetid = 'GSOY', stationid = 'GHCND:ASN00086071', datatypeid = "TAVG", token = "UiiKwLAKaNUjyMPQlJQGwPiWuEpBGePt", startdate = '2008-01-01', enddate = '2017-12-31')

m1 <- as.data.frame(m1[2])
m2 <- as.data.frame(m2[2])
m3 <- as.data.frame(m3[2])
m4 <- as.data.frame(m4[2])
m5 <- as.data.frame(m5[2])
m6 <- as.data.frame(m6[2])
m7 <- as.data.frame(m7[2])
```


### Annual Carbon Emissions

```{r fig.width=4, fig.height=3}
co2em <- read_xlsx("owid-co2-data.xlsx")
co2ems <- subset(co2em, country == c("China", "US", "UK", "Australia"))
co2ems <- subset(co2ems, year >= "1925")
co2ems %>%
  mutate(country = factor(country, levels = c("China", "US", "UK", "Australia"))) %>%
  ggplot(aes(year, co2, colour = country)) +
  geom_line(size = 1) + theme_classic() + scale_color_viridis_d() +
  labs(x="Years", y="CO2 emissions (in million tonnes)", colour = "Country")


```


### Temperature Rise

```{r fig.width=4, fig.height=3}
ntemp <- rbind(n1,n2,n3,n4,n5,n6,n7)
ptemp <- rbind(p1,p2,p3,p4,p5,p6,p7)
ltemp <- rbind(l1,l2,l3,l4,l5,l6,l7)
mtemp <- rbind(m1,m2,m3,m4,m5,m6,m7)
ntemp$data.station[] <- "NYC"
ptemp$data.station[] <- "Beijing"
ltemp$data.station[] <- "London"
mtemp$data.station[] <- "Melbourne"
temp <- rbind(ntemp, ptemp, ltemp, mtemp)
temp$data.date <- as.Date(temp$data.date)

temp %>%
  mutate(data.station = factor(data.station, levels = c("Beijing", "NYC", "London", 
                                                   "Melbourne"))) %>%
ggplot(aes(x=data.date, y=data.value, colour = data.station)) +
  geom_line(aes(x=data.date, y=data.value), alpha = 0.3) +
  geom_smooth(data = temp, aes(x=data.date, y=data.value), method = "lm", se = FALSE) +
  labs(x="Years", y="Average Temperature (C)", colour = "City") + 
  scale_colour_viridis_d() + theme_classic()
  # scale_colour_manual(values=c("red", "blue", "darkgreen", "orange"), 
  #                   name="City",
  #                   breaks = c("GHCND:CHM00054511", "GHCND:USW00014732", 
  #                              "GHCND:UKE00107650", "GHCND:ASN00086071"),
  #                   labels=c("Beijing", "NYC", "London", "Melbourn")) +

```


Column {data-width=250}
-----------------------------------------------------------------------

### Top 3 CO2 emiting countries in 2018 on each continent
```{r fig.height=12}
co2em2018 <- subset(co2em, year == "2018")
co2em2018 <- subset(co2em2018, !is.na(continent))
co2em2018$country <- as.factor(co2em2018$country)
co2em2018$continent <- as.factor(co2em2018$continent)

co2em2018$label <- " "
co2em2018$label[10] <- "of total annual global emission"

co2em2018 %>%
  mutate(country = fct_reorder(country, co2)) %>%
  mutate(continent = factor(continent, levels = c("Asia", "North America", "Europe", 
                                                   "Africa","South America", "Australia"))) %>%
#  top_n(5, co2) %>% 
  ggplot(aes(co2, country, label = share_global_co2)) +
  geom_col(aes(fill=continent)) + theme_classic() + xlim(0,12000) +
  geom_label(aes(label = paste(round(share_global_co2, digits = 1), "%", label)), 
             hjust = -0.2, size=5) +
  facet_grid(continent ~ ., scales = "free", space = "free") +
  labs(fill = " ", x = "CO2 emitted (in million tonnes)", y = "Country") +
  scale_fill_viridis_d() +
  theme(strip.text = element_blank(), legend.position = "bottom", legend.box = "horizontal",
        axis.title.y = element_blank(), axis.text.y = element_text(angle = 45),
        text=element_text(size=19))

```


Column {data-width=300}
-----------------------------------------------------------------------

### North Pole Sea Ice - 1991 to 2009

```{r}
northp <- sea_ice(year = c(1991,1993,1995,1997,1999,2001,2003,2005,2007,2009), month = "Jan", pole = "N")

n1990 <- as.data.frame(northp[1])
n1991 <- as.data.frame(northp[2])
n1992 <- as.data.frame(northp[3])
n1993 <- as.data.frame(northp[4])
n1994 <- as.data.frame(northp[5])
n1995 <- as.data.frame(northp[6])
n1996 <- as.data.frame(northp[7])
n1997 <- as.data.frame(northp[8])
n1998 <- as.data.frame(northp[9])
n1999 <- as.data.frame(northp[10])
nyears <- rbind(n1990, n1991)
northpole <- data.frame(long=0, lat=0, label="North Pole", group = 1)

ggplot(data = nyears, aes(long, lat, group = group)) +
  geom_polygon(data = n1990, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1991, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1992, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1993, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1994, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1995, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1996, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1997, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1998, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = n1999, fill = "#99DDFF", alpha = 1) +
  geom_point(data=northpole, aes(x=long, y=lat), color = "black") +
  geom_text(data=northpole, aes(x=long, y=lat, label=label),
            color = "black", size = 5, vjust = -0.75) +
  theme_ice() + theme(panel.background = element_rect(fill = "white"))

```

### South Pole Sea Ice - 1991 to 2009

```{r}
southp <- sea_ice(year = c(1991,1993,1995,1997,1999,2001,2003,2005,2007,2009), month = "Jan", pole = "S")

s1990 <- as.data.frame(southp[1])
s1991 <- as.data.frame(southp[2])
s1992 <- as.data.frame(southp[3])
s1993 <- as.data.frame(southp[4])
s1994 <- as.data.frame(southp[5])
s1995 <- as.data.frame(southp[6])
s1996 <- as.data.frame(southp[7])
s1997 <- as.data.frame(southp[8])
s1998 <- as.data.frame(southp[9])
s1999 <- as.data.frame(southp[10])
syears <- rbind(s1990, s1991)
southpole <- data.frame(long=180, lat=180, label="South Pole", group = 1)

ggplot(data = syears, aes(long, lat, group = group)) +
  geom_polygon(data = s1990, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1991, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1992, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1993, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1994, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1995, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1996, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1997, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1998, fill = "#FF9900", alpha = 0.2) +
  geom_polygon(data = s1999, fill = "#99DDFF", alpha = 1) +
  geom_point(data=southpole, aes(x=long, y=lat), color = "black") +
  geom_text(data=southpole, aes(x=long, y=lat, label=label),
            color = "black", size = 5, vjust = 2) +
  theme_ice() + theme(panel.background = element_rect(fill = "white"))
```