---
title: "Lab-2"
author: "Harish Reddy"
date: "03/26/2025"
output: 
  flexdashboard::flex_dashboard:
    orientation: row
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}

# Load required libraries
library(flexdashboard)
library(ggplot2)
library(dplyr)
library(dygraphs)
library(rnoaa)
library(RColorBrewer)
```

Overview
===================================== 

Inputs {.sidebar}
-------------------------------------
### Project Introduction

**Climate Change Data Analysis**

This dashboard explores critical indicators of global climate change, examining:
- Global Temperature Trends
- Sea Ice Coverage
- Carbon Dioxide Emissions
- Residential Energy Demand

The analysis aims to provide insights into long-term environmental changes and their interconnections.

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

### Project Summary

Climate change is a complex global phenomenon with far-reaching implications. This analysis brings together multiple datasets to paint a comprehensive picture of environmental transformations from 1880 to 2024.

**Key Observations:**
- Global temperatures are rising
- Sea ice extent is changing, especially in the Northern Hemisphere
- CO2 emissions continue to increase
- Energy demand shows significant variations

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

Inputs {.sidebar}
-------------------------------------
### Global Temperature Analysis

The GISS Surface Temperature Analysis provides global temperature anomaly data, estimating surface (land and ocean) temperature changes from 1880 to 2024.

**Key Insights:**
- Highest temperature point around 2021
- Noticeable dip in 2020 (possibly due to COVID-19 pandemic)
- Early 20th century shows significantly lower temperatures

Column
-------------------------------------
    
### Global Temperature Change 1880 - 2024
```{r}
# Fetch global temperature data
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)

# Create time series
smoothing <- ts(globalTemp$Lowess.5., frequency = 1, start=c(1880))
annualMean <- ts(globalTemp$No_Smoothing, frequency = 1, start=c(1880))
temp <- cbind(smoothing, annualMean)

# Generate interactive dygraph
dygraph(temp, main = "Global Temperature Anomaly From 1880 To 2024", 
        xlab = "Year", 
        ylab="Temperature Anomaly") %>%
  dyRangeSelector() %>%
  dyLegend(width = 500, show = "onmouseover") %>%
  dyOptions(drawGrid = FALSE) %>%
  dyOptions(colors = RColorBrewer::brewer.pal(3, "Set1"))
```

Sea Ice Coverage
===================================== 

Inputs {.sidebar}
-------------------------------------
### Sea Ice Extent Analysis

Data from the National Snow & Ice Data Center shows sea ice extent from 1979-2024.

**Key Findings:**
- Northern Hemisphere: Significant decrease
- Southern Hemisphere: Relatively stable
- Approximately 2-3 million square km reduction in the North

Column {data-height=650 .tabset .tabset-fade}
-------------------------------------
    
### Northern Hemisphere Sea Ice {data-width=700}
```{r}
# Fetch Northern Hemisphere sea ice data
northIce <- read.csv(url("https://www.ncdc.noaa.gov/snow-and-ice/extent/sea-ice/N/8.csv"), skip=4)

# Create visualization
ggplot(northIce, aes(x = Date, y = Value)) + 
  geom_line(color = "red", linewidth = 1) +
  geom_point(color = "darkred") +
  scale_y_continuous(breaks = seq(7, 9, by = 0.5)) +
  theme_minimal() + 
  ylab("Sea Ice Extent (million sq km)") +
  xlab("Year") +
  ggtitle("Northern Hemisphere Sea Ice Extent in August (1979-2024)")
```
    
### Southern Hemisphere Sea Ice {data-width=700}
```{r}
# Fetch Southern Hemisphere sea ice data
southIce <- read.csv(url("https://www.ncdc.noaa.gov/snow-and-ice/extent/sea-ice/S/8.csv"), skip=4)

# Create visualization
ggplot(southIce, aes(x = Date, y = Value)) + 
  geom_area(position = "jitter", alpha = 0.2, fill= "blue") +
  scale_y_continuous(breaks=c(0,2,4,6,8,10,12,14)) +
  theme_minimal() + 
  ylab("Extent (in millions of square kilometers)") +
  ggtitle("Southern Hemisphere Sea Ice Extent in Every August (1979-2024)")
```

Global CO2 Emissions
===================================== 

Inputs {.sidebar}
-------------------------------------
### CO2 Emissions Analysis

Data from NOAA/Earth System Research Laboratory tracking global carbon dioxide levels.

**Key Observations:**
- Continuous increase since 1960
- Strong correlation with global temperature trends
- Indicates ongoing human impact on atmospheric composition

Column
-------------------------------------
    
### Global Annual CO2 Emission
```{r}
# Read CO2 data (assuming the CSV is in the same directory)
co2 <- read.csv("~/Assignments/Data Visualization ANLY_512_90/lab-2/co2-annmean-gl.csv")

# Create CO2 emissions visualization
ggplot(co2, aes(Year, Mean)) +
  geom_point(color = "blue") +
  geom_smooth(method = "lm", se = TRUE, color = "red") +
  ggtitle("Global Average Carbon Dioxide Emission from 1960 - 2023") + 
  labs(x = "Year", y = "Mean CO2 Concentration")
```

Residential Energy Demand
===================================== 

Inputs {.sidebar}
-------------------------------------
### REDTI Analysis

Residential Energy Demand Temperature Index (REDTI) provides insights into energy consumption patterns.

**Key Findings:**
- Energy demand fluctuates with heating and cooling degree days
- Provides indication of national energy consumption trends

Column
-------------------------------------
    
### Annual Residential Energy Demand Temperature Index
```{r}
# Fetch REDTI data
REDTI_data <- read.csv(url("https://www.ncei.noaa.gov/access/monitoring/redti/USA/jan/year-to-date/data.csv"), skip=3)

# Create REDTI visualization
ggplot(REDTI_data, aes(x = Date, y = Redti)) +
  geom_area(color = "black", fill = "gray") + 
  scale_y_continuous(limits = c(0, 100)) +
  geom_smooth(method = 'lm', se = FALSE, color = "red") +
  labs(title = "REDTI, Contiguous U.S. (1895 - 2025)", 
       x = "Year", 
       y = "Residential Energy Demand Temperature Index")
```

Conclusions
===================================== 

Inputs {.sidebar}
-------------------------------------
### Research Insights

**Summary of Findings:**
- Global temperatures are rising
- Sea ice, especially in the Northern Hemisphere, is decreasing
- CO2 emissions continue to increase
- Energy demand shows complex patterns

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

### Research Implications

#### Climate Change Indicators

Our analysis reveals interconnected changes in global climate systems:

1. **Temperature Trends**
   - Consistent warming since 1880
   - Potential short-term variations due to global events

2. **Sea Ice Dynamics**
   - Significant reduction in Northern Hemisphere
   - Implications for global climate regulation

3. **Emissions and Energy**
   - Increasing CO2 levels
   - Changing energy consumption patterns

#### Recommendations

- Continue long-term environmental monitoring
- Support research into climate mitigation strategies
- Develop comprehensive predictive models
- Encourage interdisciplinary climate research