---
title: "ANLY 512 Lab 2 Climate Change "
author: "Sun,Shengxi"
date: "April 11, 2023"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    social: menu
    orientation: columns
    vertical_layout: fill
    
---

```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(dplyr)
library(quantmod)
library(dygraphs)
library(maps)
library(ggmap)
library(maptools)

```


# Climate Change - Lab Overview 
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### **Overview**  

Climate change and science has been an issue for discussion and debate for at least the last decade. Climate data collection is currently being collected for areas all over the world. Policy decisions are based on the most recent analysis conducted on data extracted from huge online repositories of this data. Due to the inherent growth in the electronic production and storage of information, there is often a feeling of “information overload” or inundation when facing the process of quantitative decision making. As an analyst your job will often be to explore large data sets and develop questions or ideas from visualizations of those data sets.

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### **Objective** 

The objective of this laboratory is to survey the available data, plan, design, and create an information dashboard/presentation that not only explores the data but helps you develop questions based on that data exploration. To accomplish this task you will have to complete a number of steps:


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### **The Decision & Rules**

In this lab I would like to 

Identify what information interests you about climate change.
Find, collect, organize, and summarize the data necessary to create your data exploration plan.
Design and create the most appropriate visualizations (no less than 5 visualizations) to explore the data and present that information.
Finally organize the layout of those visualizations into a dashboard (use the flexdashboard package) in a way that shows your path of data exploration.
Develop four questions or ideas about climate change from your visualizations.

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-------------------------------------

### **Methods Help**
##### *Getting data*

There are lots of places we can get climate data to answer your questions. The simplest would be to go to NOAA National Centers for Environmental Information (https://www.ncdc.noaa.gov/). There are all kinds of data here (regional, global, marine). Also, on the front page of the NOAA website there are also other websites that have climate data, such as: (https://www.climate.gov/), (https://www.weather.gov/), (https://www.drought.gov/drought/), and (https://www.globalchange.gov/). Obviously, you don’t have to use all of them but it might be helpful to browse them to get ideas for the development of your questions.

# Global Temperature 

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### Data overview: 

In this section, I would like to compare the global land, land and ocean average, maximum, minimum temperature,from 2000 to 2014. I will split the data into 4 sub-unit and by doing this I am able to make comparison within different month.

Data source:
These data was generated by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory.

Global Land and Ocean-and-Land Temperatures average, maximum and minimum land temperatures and global ocean and land temperatures
Month: March, June, September, and December 


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### Question:

My Question is: Will there be any shape increase in these four types of temperature across 15 years:


  * Land Average Temperature: global average land temperature in celsius (In Yellow)

  * Land Maximum Temperature: global average maximum land temperature in celsius (In Dark Red)

  * Land Minimum Temperature: global average minimum land temperature in celsius (In Skyblue)

  * Land And Ocean Average Temperature: global average land and ocean temperature in celsius (In Darkblue)

## Globel Tempareture - March 2000 - December 2014 {.tabset data-width=1000}

### Globel Tempareture - March 2000 - December 2014

```{r}
library(readr)
global_t<- read_csv("Global_temp.csv")

ggplot(global_t, aes(x=Year)) + 
  geom_line(aes(y = L_Avg), color = "yellow") + 
  geom_line(aes(y = L_Max), color="darkred") +
  geom_line(aes(y = L_Min), color="skyblue") +
  geom_line(aes(y = LOA), color="darkblue") +
  facet_wrap(. ~ Month) +
  labs(x = "Year", y = "Land Average Temp", title = "Globel Tempareture - March 2000 - December 2014") 

```

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------------------------------
### Summary: 

Based on the result from the graph, I feel like the temperature in all 4 categories are slightly increase from 2000 to 2014 in December and September, but not in March and June. Overall, there isn't much of increase in temperature over these 15 years. Future study could increase the time period in order to see a much more significant change. 

In addition, Land and Ocean Average Temperature are always higher than the land average temperature. As we all know, global warming increase the sea level, which then increase the ocean area and decrease the land area. Causing a vicious circle.


# United State Tempreture - July

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### Section overview:

In this page I would like to compare the U.S Average Temperature in July, in 10 years interval, from 1990 to 2020.


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------------------------------
### Question: 

My question for this section is: Will climate change affect July average temperature Nationwide? What is the direction of change? from 1990 to 2022, in the U.S.  
 


## The U.S. Average Tempreture {.tabset data-width=1000}

### The U.S. Average Tempreture, July 1990 

```{r}

maxTempData = read.csv(url("https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/statewide/mapping/110-tavg-199007-1.csv"), skip=3)

maxTempData$region = tolower(maxTempData$Location)
us_states = map_data("state")
maxTempData = merge(us_states, maxTempData, by="region", all=T)

ggplot(maxTempData, aes(x = long, y = lat, group = group, fill = Value)) + 
geom_polygon(color = "white") +
scale_fill_gradient(name = "F", low = "yellow", high = "red", guide = "colorbar", na.value="black") +
labs(title="The U.S Average Temperature July 1990 ")+
coord_map()
```

### The U.S. Average Tempreture, July 2000

```{r}
maxTempData = read.csv(url("https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/statewide/mapping/110-tavg-200007-1.csv"), skip=3)

maxTempData$region = tolower(maxTempData$Location)
us_states = map_data("state")
maxTempData = merge(us_states, maxTempData, by="region", all=T)

ggplot(maxTempData, aes(x = long, y = lat, group = group, fill = Value)) + 
geom_polygon(color = "white") +
scale_fill_gradient(name = "F", low = "yellow", high = "red", guide = "colorbar", na.value="black") +
labs(title="The U.S Average Temperature July 2000", x="Longitude", y="Latitude")+
coord_map()
```

### The U.S. Average Tempreture, July 2010

```{r}
maxTempData = read.csv(url("https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/statewide/mapping/110-tavg-201007-1.csv"), skip=3)

maxTempData$region = tolower(maxTempData$Location)
us_states = map_data("state")
maxTempData = merge(us_states, maxTempData, by="region", all=T)

ggplot(maxTempData, aes(x = long, y = lat, group = group, fill = Value)) + 
geom_polygon(color = "white") +
scale_fill_gradient(name = "F", low = "yellow", high = "red", guide = "colorbar", na.value="black") +
labs(title="The U.S Average Temperature July 2010",x="Longitude", y="Latitude")+
coord_map()
```

### The U.S. Average Tempreture, July 2020

```{r}
maxTempData = read.csv(url("https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/statewide/mapping/110-tavg-202007-1.csv"), skip=3)

maxTempData$region = tolower(maxTempData$Location)
us_states = map_data("state")
maxTempData = merge(us_states, maxTempData, by="region", all=T)

ggplot(maxTempData, aes(x = long, y = lat, group = group, fill = Value)) + 
geom_polygon(color = "white") +
scale_fill_gradient(name = "F", low = "yellow", high = "red", guide = "colorbar", na.value="black") +
labs(title="The U.S Average Temperature July 2020",x="Longitude", y="Latitude")+
coord_map()
```

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------------------------------
### Answer: 

Summary based on these four maps:


 * The maximum temperature in July was increased over time (2000 - 2020). However, The maximum temperature in July 1990 is the highest(over 85F) 
 
 
 * Overtime, the heat wave shift toward the south and southeast. 
 
 
 * Compared to 1990, the average temperature across the country are more even in 2000 and 2010.

# Sea Level
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### Data overview & Question

Sourse: https://climate.nasa.gov/
This data were captured the change in sea level since 1993 as observed by satellites (according to nasa).

Sea level rise is caused primarily by two factors related to global warming: 

* Melting ice sheets 

* The expansion of seawater as it warms. 


Variable used: (Global Isostatic Adjustment (GIA) applied) variation (mm) with respect to 20-year TOPEX/Jason collinear mean reference.

  * Question: Did sea level increase in the past 30 years? How much does it increase. Will this be a result from the climate change?
  
  * The answer is YES. In addition to this, please remember one of the key finding we got from the tab "global temperature" the average temperature of ocean are higher then average temperature of land, which might further increase the overall temperature.


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------------------------------
### Sea level 1993 - 2021

```{r}

library(readr)
sea <- read_csv("sea.csv")

ggplot(sea, aes(x=year)) + 
  geom_line(aes(y = GMSL_GIA_AVE), color = "black") + 
  labs(x = "Year", y = "Average Global Isostatic Adjust (mm)", title = "Sea Level change - 1993-2021") 



```