---
title: "ANLY 512 - Data Exploration and Analysis Laboratory"
author: "235208"
date: "`r Sys.Date()`"
output:
    flexdashboard::flex_dashboard:
      orientation: columns
      vertical_layout: fill
      source_code: embed


---


```{r setup, message=FALSE, warning=FALSE}
if (!require("flexdashboard")) {
install.packages("flexdashboard")
library(flexdashboard)
}
if (!require("dygraphs")) {
install.packages("dygraphs")
library(dygraphs)
}
if (!require("plotly")) {
install.packages("plotly")
library(plotly)
}
#knitr::opts_chunk$set(echo = FALSE, source_code = TRUE)
```


Global Temperature
===========================================

Column {data-width=800}
-----------------------------------------------------------------------

### Global Temperature Change 1880~2017

```{r, echo=FALSE}
globalTemp = read.table("https://data.giss.nasa.gov/gistemp/graphs/graph_data/Global_Mean_Estimates_based_on_Land_and_Ocean_Data/graph.txt", header = FALSE, col.names = c("Year","No_Smoothing","Lowess(5)"),skip = 5)

Lowess_smoothing = ts(globalTemp$Lowess.5., frequency = 1, start=c(1880))
Annual_Mean = ts(globalTemp$No_Smoothing, frequency = 1, start=c(1880))
Temperatures <- cbind(Lowess_smoothing, Annual_Mean)

dygraph(Temperatures, main = "Global Land-Ocean Temperature Index", xlab = "Year", ylab="Temperature Anomaly (C)") %>%
dyRangeSelector() %>%
dyLegend(width = 500, show = "onmouseover") %>%
dyOptions(drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(3, "Set1"))
```


Column {data-width=200}
-----------------------------------------------------------------------
### Global Warming Review

First the temperature gradually increased.

Then we know green house gas emission might be the key.

Then the sea level rised too, while sea ice melt away.

Then the extreme weather is the "new normal".


The fact come to us, and there's more behind.
Climate change is proved with numerous records, however the name "global warming" is a bit misleading today, while the weather system is complicated and unpredictable, The extreme weather are more frequently arriving in our life.

Carbon Dioxide Analysis
===========================================

Column {.tabset}
-----------------------------------------------------------------------
### Carbon Dioxide emissions by source


```{r, echo=FALSE}
#https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions
CO2BySource <- read.csv("C:/Users/alan/Downloads/CO2-by-source.csv", col.names = c("Entity","Code","Year","CO2from gas (tonnes)","CO2from solid fuel (tonnes)","CO2from liquid (tonnes)","CO2from cement (tonnes)","CO2from flaring (tonnes)"))
CO2BySource =  subset(CO2BySource, Entity=='World')
CO2BySourceProcessed = CO2BySource[,3:8]

CO2gas <- CO2BySourceProcessed$CO2from.gas..tonnes.
CO2SolidFuel  <- CO2BySourceProcessed$CO2from.solid.fuel..tonnes.
CO2Liquid <- CO2BySourceProcessed$CO2from.liquid..tonnes.
CO2Cement <- CO2BySourceProcessed$CO2from.cement..tonnes.
CO2Flaring <- CO2BySourceProcessed$CO2from.flaring..tonnes.
year <- CO2BySourceProcessed$Year

p <- plot_ly(y = ~CO2gas, x = ~year, type = 'scatter', mode = 'lines', name = 'CO2 from Gas', fill = 'tozeroy') %>%
  add_trace(y = ~CO2SolidFuel, x = ~year, name = 'CO2 from Solid Fuel', fill = 'tozeroy') %>%
  add_trace(y = ~CO2Liquid, x = ~year, name = 'CO2 from Liquid', fill = 'tozeroy') %>%
  add_trace(y = ~CO2Cement, x = ~year, name = 'CO2 from Cement', fill = 'tozeroy') %>%
  add_trace(y = ~CO2Flaring, x = ~year, name = 'CO2 from Flaring', fill = 'tozeroy') %>%
  layout(xaxis = list(title = 'Year'),
         yaxis = list(title = 'Global CO2 emissions by source'),
         hovermode = 'compare')

p
```

### Global CO2 atmospheric concentration

```{r, echo=FALSE}
concentration = read.table("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_annmean_mlo.txt", header = FALSE, col.names = c("year","mean","unc"),skip = 50)
concentration = concentration[,1:2]

dygraph(concentration, main = "Global mean annual concentration of carbon dioxide (CO2) measured in parts per million (ppm).", xlab = "Year", ylab="ppm") %>%
dyRangeSelector() %>%
dyLegend(width = 500, show = "onmouseover") %>%
dyOptions(drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(3, "Set2"))
```

Sidebar {.sidebar}
-----------------------------------------------------------------------
### Carbon Dioxide Emissions
Greenhouse gases are gases that absorb and emit infrared radiation in the wavelength range emitted by Earth*.
In short, gases such H2O CO2 CH4 N2O can reflect the heat transmission flow, by block the radiation of heat. The quantity of H2O are limited on Earth, but the CO2 are basically generated by human activities. Thus, for a long period of time, people are tracking the quantity of CO2 and CO2 atmospheric concentration. CO2 contribute about 9-26% to the greenhouse effect. And for H20, it is at about 36-72%**.

*"IPCC AR4 SYR Appendix Glossary" (PDF). Retrieved 14 December 2008.

**Kiehl, J.T.; Kevin E. Trenberth (1997). "Earth's annual global mean energy budget" (PDF). Bulletin of the American Meteorological Society. 78 (2): 197-208. Bibcode:1997BAMS...78..197K. doi:10.1175/1520-0477(1997)078<0197:EAGMEB>2.0.CO;2. Archived from the original (PDF) on 30 March 2006. Retrieved 1 May 2006.


Relative Sea Level Trend
===========================================

Column
-----------------------------------------------------------------------

### sea level ( 611-010 Quarry Bay/North Point, China)

```{r, echo=FALSE}
seaLevel611 = read.table("https://tidesandcurrents.noaa.gov/sltrends/data/611-010_meantrend.txt", header = TRUE, col.names = c("Year","Month","Linear_Trend","High_Conf","Low_Conf","other","other2"),fill=T)
seaLevel611 = seaLevel611[1:3]
a = data.frame(seaLevel611)
x <- list(title="Year", titlefont=list(family="Courier New",size=16))
y <- list(title="NOAA Sea Level (Meters)", titlefont=list(family="Courier New",size=16))
plot_ly(a, x = ~Year, y = ~Linear_Trend, type="scatter", mode="lines")%>% layout(xaxis=x, yaxis=y)
```

### sea level (290-004 Levkas, Greece)
```{r, echo=FALSE}
seaLevel290 = read.table("https://tidesandcurrents.noaa.gov/sltrends/data/290-004_meantrend.txt", header = TRUE, col.names = c("Year","Month","Linear_Trend","High_Conf","Low_Conf","other","other2"),fill=T)
seaLevel290 = seaLevel290[1:3]
a = data.frame(seaLevel290)
x <- list(title="Year", titlefont=list(family="Courier New",size=16))
y <- list(title="NOAA Sea Level (Meters)", titlefont=list(family="Courier New",size=16))
plot_ly(a, x = ~Year, y = ~Linear_Trend, type="scatter", mode="lines")%>% layout(xaxis=x, yaxis=y)
```

Column
-----------------------------------------------------------------------

### Global Mean Sea Level (GMSL) Data



```{r, echo=FALSE}
seaLevelGlobal = read.table("ftp://podaac.jpl.nasa.gov/allData/merged_alt/L2/TP_J1_OSTM/global_mean_sea_level/GMSL_TPJAOS_4.2_199209_201811.txt", header = TRUE, col.names = c("altimeter","mergedFileCycle","yearWithFraction","numberOfObservations","numberOfWeightedObservations","GMSL","seaHeightVariation","smoothedmean","GMSLwithMean","sdEstimate","other","other2"), skip = 50)
a = data.frame(seaLevelGlobal$yearWithFraction,seaLevelGlobal$GMSL)
x <- list(title="year+fraction of year")
y <- list(title="GMSL variation (mm) ")
plot_ly(a, x = ~ seaLevelGlobal.yearWithFraction, y = ~seaLevelGlobal.GMSL, name = "year+fraction of year", type="scatter", mode="lines", line = list(color = 'rgb(135, 206, 235)'))%>% layout(xaxis=x, yaxis=y)
```

### sea level ( 010-001 Reykjavik, Iceland)
```{r, echo=FALSE}
seaLevel010 = read.table("https://tidesandcurrents.noaa.gov/sltrends/data/010-001_meantrend.txt", header = TRUE, col.names = c("Year","Month","Linear_Trend","High_Conf","Low_Conf","other","other2"),fill=T)
seaLevel010 = seaLevel010[1:3]
a = data.frame(seaLevel010)
x <- list(title="Year", titlefont=list(family="Courier New",size=16))
y <- list(title="NOAA Sea Level (Meters)", titlefont=list(family="Courier New",size=16))
plot_ly(a, x = ~Year, y = ~Linear_Trend, type="scatter", mode="lines")%>% layout(xaxis=x, yaxis=y)
```


Column
-----------------------------------------------------------------------
### sea level ( 8771450 Galveston Pier 21, Texas)
```{r, echo=FALSE}
seaLevel8771450 = read.table("https://tidesandcurrents.noaa.gov/sltrends/data/8771450_meantrend.txt", header = TRUE, col.names = c("Year","Month","Linear_Trend","High_Conf","Low_Conf","other","other2"),fill=T)
a = data.frame(seaLevel8771450)[1:3]
x <- list(title="Year")
y <- list(title="NOAA Sea Level US station")
plot_ly(a, x = ~Year, y = ~Linear_Trend, type="scatter", mode="lines")%>% layout(xaxis=x, yaxis=y)
```

### sea level ( 8443970 Boston, Massachusetts)
```{r, echo=FALSE}
seaLevel8443970 = read.table("https://tidesandcurrents.noaa.gov/sltrends/data/8443970_meantrend.txt", header = TRUE, col.names = c("Year","Month","Linear_Trend","High_Conf","Low_Conf","other","other2"),fill=T)
a = data.frame(seaLevel8443970)[1:3]
x <- list(title="Year")
y <- list(title="NOAA Sea Level US station")
plot_ly(a, x = ~Year, y = ~Linear_Trend, type="scatter", mode="lines")%>% layout(xaxis=x, yaxis=y)
```


Extreme Weather
===========================================

Sidebar {.sidebar data-width=500}
-----------------------------------------------------------------------
### Global Mean Sea Level

With global mean sea level rise, many countries were hit directly, this also forced people to face the reality, some of the news titles are telling a pessimistic future:

####'We don't have a future here': Nauru refugees
####Venice flooding is getting worse - and the city's grand plan won't save it
####The question is not if the Netherlands will disappear below sea level, but when
####Sea Level Rise Could Threaten 90,000 Homes In Mass., Study Finds

### El Nino and La Nina events

The result of Global Warming gradually shaping the Earth's climate systenm into a "new normal" with extreme weather such as drought for years, whiteout and coldwave in the middle of a warm winter, severe tornado season etc..

Consider about El Nino-Southern Oscillation (ENSO), known as El Nino and La Nina, global warming could cause a 20% increase in the areas impacted by temperatures changes and extremes during ENSO events and a 10% increase in the areas impacted by precipitation changes during ENSO events*.

*Perry, S. J., McGregor, S., Gupta, A. S., & England, M. H. (2017). Future changes to El Nino-Southern Oscillation temperature and precipitation teleconnections. Geophysical Research Letters, 44, 10,608-10,616. https://doi.org/10.1002/2017GL074509

### Conclusion
The Global Temperature are increasing, this is the directly result of Global Warming.
The Carbon Dioxide Emissions and the atmospheric concentration of greenhouse gases reach a new high.
Global Mean Sea Level are increasing, this happens across the world and measured by satellites.
The Oceanic Nino Index are increasing, means extreme weather are even stronger and may affect more areas.

Column {data-width=700}
-----------------------------------------------------------------------
### Oceanic Nino Index (ONI) 1950~2018

Region SST 5N-5S, 170W-120W 


Cold & Warm Episodes by Season (3 month running mean)
```{r, echo=FALSE}
if (!require("formattable")) {
install.packages("formattable")
library(formattable)
}
coldNWarm = read.table("https://www.esrl.noaa.gov/psd/data/correlation/oni.data", header = FALSE, col.names = c("Year","DJF","JFM","FMA","MAM","AMJ","MJJ","JJA","JAS","ASO","SON","OND","NDJ"),skip = 1,nrows=69)


tempFormatter <- formatter("span", 
  style = x ~ style(color = ifelse(x > 1, "red", 
    ifelse(x < -1, "blue", "grey"))))
formattable(coldNWarm, list(
  'Year' = formatter("span", style = ~ style(color = "grey",font.weight = "bold")),
  area(col = 2:13) ~ tempFormatter
))

```