---
title: "ANLY512 Global Warning Dashboard"
author: "Shenxiao Li(236500),Li Li(236090)"
date: "2/12/2019"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
knitr::opts_chunk$set(echo = TRUE)
library(maps)
library(dygraphs)
library(ggplot2)
#install.packages("mapproj")
library(mapproj)
```

Introduction & Questions {data-orientation=row}
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### Global warming brief introduction 

Global warming is the unusually rapid increase in Earth's average surface temperature over the past century. We developed this dashboard to show the changes in the climate and answer three major questions about global warming.


1. How strong is the scientific evidence that Earth is warming?

2. Hasn't Earth warmed and cooled naturally throughout history?

3. What does global warming have to do with rising sea levels?

Data Source: 
[Global Temperature](http://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt)
[Earth System labotory](https://www.esrl.noaa.gov/gmd/dv/data/index.php?parameter_name=Carbon%2BDioxide)

Column {data-height=500}
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Land-Ocean Temperature {data-orientation=row}
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### Land-Ocean Temperature

History of Land-Ocean Temperature from 1880 to 2018

```{r, echo=FALSE}
lot = read.table("https://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt", header = FALSE, col.names = c("Year","Annual_Mean","Five_Year_Mean"),skip = 5, nrows=136)

lot[,3] = as.numeric(as.character(lot[,3]))
gmt=lot
gmt[,2]=scale(gmt$Annual_Mean)
gmt[,3]=scale(gmt$Five_Year_Mean)
scaled_lot_time_series = ts(gmt$Five_Year_Mean, frequency = 1, start=c(1880))
dygraph(scaled_lot_time_series, main = "Time Series of Annual Land-Ocean Temperature 1880-2015", xlab = "Year", ylab="Annual 5-Year Land-Ocean Temperature")%>% 
  dySeries("Series 1", label = "Five Year Mean Temperature") %>%
  dyLegend(show = "always", hideOnMouseOut = TRUE)
```
Row {data-height=800}
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Annual CO2 Growth Rate {data-orientation=row}
==========================================================================

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### Annual CO2 Growth Rate

History of Annual CO2 Growth Rate from 1959 to 2018

```{r, echo=FALSE}
acgr = read.table("co2_gr_mlo.txt", header = FALSE, col.names = c("Year","Ann inc","Unc"), skip = 59)

scaled_acgr = acgr
scaled_acgr_Ann.inc = scale(acgr$Ann.inc)
scaled_acgr_time_series = ts(scaled_acgr$Ann.inc, frequency = 1, start = c(1959,2))

# plot
dygraph(scaled_acgr_time_series, main = "Time Series of Annual Atmospheric Carbon Dioxide Growth Rate 1959-2018", xlab = "Year", ylab="Annual Atmospheric Carbon Dioxide Level") %>% 
  dySeries("V1", label = "Anual CO2 Growth Rate") %>%
  dyLegend(show = "always", hideOnMouseOut = TRUE)

```

Row {data-height=800}
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Sea Level Variation {data-orientation=row}
==========================================================================

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### Sea Level Variation

History of Annual CO2 Growth Rate from 1959 to 2018

```{r, echo=FALSE}
sealevel = read.table("sea level.txt", header = FALSE, col.names = c("Type","Cycle","Year","Observation","weightedOb","GMSL1","SDGMSL1","SmoothedGMSL1","GMSL2","SDGMSL2","SmoothedGMSL2","SmoothedGMSL2Annual"), skip = 50)
sealevel_GMSL2 = scale(sealevel$GMSL2)
sealevel_GMSL2_time_series = ts(sealevel$GMSL2, frequency = 1, start = c(1))

# plot
dygraph(sealevel_GMSL2_time_series, main = "Time Series of Global Mean Sea Level Variation with Respect to 20-Year Mean 1993-2018", xlab = "Time Point from 1993 to 2018", ylab="GMSL Variation with Respect to 20-Year Mean") %>% 
  dySeries("V1", label = "Sea Level") %>%
  dyLegend(show = "always", hideOnMouseOut = TRUE)



```

Row {data-height=800}
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Arctic Sea Ice Conditions {data-orientation=row}
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### Arctic Sea Ice Conditions

```{r, echo=FALSE}
ice = read.table("ice.txt", header = FALSE, col.names = c("Year","Month","Day","IceExtent","Year","Month","Day","IceArea"), skip = 4)
ice_extent = scale(ice$IceExtent)
ice_area=scale(ice$IceArea)
ice_extent_time_series = ts(ice$IceExtent, frequency = 1, start = c(1))
ice_area_time_series = ts(ice$IceArea, frequency = 1, start = c(1))
ice_time_series=cbind(ice_extent_time_series,ice_area_time_series)

# plot
dygraph(ice_time_series, main = "Time Series of Arctic Sea Ice Conditions 1979-2017", xlab = "Year", ylab="Ice") %>%
  dyLegend(width = 190)



```

Row {data-height=800}
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Conclustion {data-orientation=row}
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### Observation & Conclusion
 
The temperature has dramatically increased since 20th century due to the acceleration of human industrialization process, for example, the emission of greenhouse gas CO2 and it continues to be a worldwide issue.
 
Global warming contributes to rising sea levels in two ways. First, the Earth's polar regions are especially vulnerable to global warming because temperatures in the Arctic and Antarctic are rising at twice the rate of the world average. Hotter summers, warmer winters, and earlier springs are causing glaciers and ice sheets to gradually melt. Besides, the natural expansion of water as it heats up, is causing the ocean to take up more space, which also leads to rising sea levels.

Row {data-height=800}
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