---
title: "Lab 2 Data Exploration & Visualization climate change"
author: " Ramesh Anusha Katta"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=FALSE)
library(httr)
library(mapproj)
library(readr)
library(flexdashboard)
library(dslabs) 
library(tidyverse)
library(dplyr)
library(matrixStats)
library(quantmod)
library(dygraphs)
library(magrittr) 
library(RColorBrewer)
library(plyr)
library(lubridate)
library(dygraphs)
library(ggplot2)
library(broom)
library(ClimInd)
library(maps)
library(maps)
library(raster)
library(ncdf4)
library(RColorBrewer)
library(data.table)
library(readxl)
library(rnaturalearth)
library(rnaturalearthdata)
library(ggthemes)
library(viridis)
library(scales)
library(ggthemes)
```

##  {data-width="700"}

# Table of Contents {.sidebar}

-   Introduction

-   Carbon Dioxide Levels

-   Temperature Analysis

-   Greenhouse Gases

-   Carbon Emission and Temperature Correlation

-   Conclusion

# **Introduction**

## Row {data-height="270"}

### 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.

The ability to synthesize large data sets using visualizations is a skill that all data scientists should have. In addition to this data scientists are called upon to present data syntheses and develop questions or ideas based on their data exploration. This lab should take you through the major steps in data exploration and presentation.

### 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:

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

### Dates & Deliverables

You are responsible for submitting a link to your dashboard hosted on the Rpubs site. The dashboard must include the source_code = embed parameter.

The due date for this project is XX at the start of class. This assignment is worth 75 points, 3x a normal homework, the additional time should allow you to spend the neccessary effort on this assignment.

You are welcome to work in groups of $\leq 2$ people. However, each person in a group must submit their own link to the assignment on Canvas for grading! Each team member can submit the same link to a single rpubs account, however it may be a good idea for each of you to post your own copy to rpubs in case you want to share it to prospective employers.

### Methods Help

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.

Alternatively, and more professionally, there are tons of packages that allow you to access data from R. See here for a great primer on accessing NOAA data with ‘R’. It is also a good introduction to API keys and their use.

##################################################################################################################### 

# **Global Temperature Change in Celcius (1880 - 2024)**

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

### **Trend Global Temperature change**

The dataset, obtained from the GISS, provides the global temperature anomalies from 1880 to 2024. The visualizations show an estimate of global surface temperature change, both on land and in the oceans.

From the graph, we can conclude that the global temperature has been steadily increasing, with a sharp rise as of 1976. We can also note a slight dip in 2020, likely due to the quarantine and lock downs associated with the Covid-19 pandemic.

## Column {data-width="850"}

### **Global Temperature Change in Celcius (1880 - 2024)**

```{r}
# Create time series for smoothing and no smoothing
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)
smoothing <- ts(globalTemp$Lowess.5., frequency = 1, start=c(1880))
annualMean <- ts(globalTemp$No_Smoothing, frequency = 1, start=c(1880))
temp <- cbind(smoothing, annualMean)
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"))

```

##################################################################################################################### 

# **Carbon Emissions**

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

### **Carbon Emissions Trend**

Trend of Global CO₂ Emissions (1959–2024)

The dataset, sourced from the Mauna Loa Observatory and maintained by NOAA, represents the annual global mean carbon dioxide (CO₂) concentrations from 1959 to 2024. The visual shows a consistent and accelerating increase in CO₂ emissions over the past six decades. The red points represent the observed yearly means, while the blue line captures the overall trend. A dashed green line indicates a linear model fit, highlighting the long-term upward trajectory of atmospheric CO₂. This sharp and steady rise reflects the continued global reliance on fossil fuels and industrial activities, underlining the urgency of sustainable environmental policies and climate action. The data and visualization together emphasize how human activities have significantly impacted atmospheric chemistry over time.

## Column {data-width="850"}

### **Carbon Emissions**

```{r}
data <- read.csv(url("https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.csv"), skip = 43)
ggplot(data, aes(x = year, y = mean)) +
  geom_line(color = "#1f77b4", size = 1.2) +
  geom_point(color = "#d62728", size = 2) +
  geom_smooth(method = "lm", se = FALSE, color = "#2ca02c", linetype = "dashed") +
  labs(
    title = "AVG Global Co2 Emissions(NOAA)1959–2024",
    subtitle = "Showing linear trend in annual mean Co2 concentration (ppm)",
    x = "Year",
    y = "Mean CO2 (ppm)",
    caption = "Data Source: NOAA Earth System Research Laboratories"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(color = "gray30"),
    axis.title = element_text(face = "bold"),
    panel.grid.minor = element_blank()
  ) +
  scale_y_continuous(labels = comma)


```

##################################################################################################################### 

# **Trend of Residential Energy Demand Temperature Index (REDTI) – June (USA)**

## Column {data-width="350"}

### **Residential Energy Demand**

The plot visualizes the Residential Energy Demand Temperature Index (REDTI) across June months in the United States. REDTI is a metric used to measure temperature anomalies that influence residential energy demand—higher values typically indicate greater cooling needs due to higher-than-average temperatures.

This area chart, with a fitted linear trend line, reveals a gradual increase in REDTI over time, reflecting the rising summer temperatures and the potential growth in energy demand for air conditioning and cooling systems. The trend underscores the impact of climate change on energy infrastructure and planning, particularly during warmer months. The data emphasizes the need for sustainable energy strategies to manage increasing demand and mitigate environmental impact.

## Column {data-width="850"}

### **Trend of Residential Energy Demand Temperature Index (REDTI) – June (USA)**

```{r}
R_data = read.csv(url("https://www.ncdc.noaa.gov/societal-impacts/redti/USA/jun/1-month/data.csv"), skip = 3)



ggplot(R_data, aes(x = Date, y = Redti)) +
  geom_area(fill = "#a6cee3", color = "#1f78b4", alpha = 0.6) +
  geom_smooth(method = 'lm', se = FALSE, color = "#e31a1c", linetype = "dashed", size = 1) +
  scale_y_continuous(limits = c(0, 100), expand = c(0, 0)) +
  labs(
    title = "Residential Energy Demand Temperature Index (REDTI) - USA",
    subtitle = "Monthly values for June over the years",
    x = "Year",
    y = "REDTI"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", size = 15),
    plot.subtitle = element_text(size = 12),
    axis.title = element_text(face = "bold"),
    panel.grid.minor = element_blank()
  )

```

##################################################################################################################### 

# **Northern Hemisphere**

## Column {data-width="350"}

### **Decline in Northern Hemisphere Sea Ice – August (1979–2024)**

The chart presents the extent of Northern Hemisphere sea ice in August from 1979 to 2024. Represented in million square kilometers, the data clearly shows a downward trend over the past four decades.

This decline is illustrated using individual yearly data points and a linear trend line, highlighting the ongoing reduction in Arctic ice coverage. The diminishing sea ice is a strong indicator of climate change, with profound implications for global weather patterns, sea level rise, and Arctic ecosystems. The significant drop emphasizes the urgency of addressing greenhouse gas emissions and environmental sustainability at a global scale.

## Column {data-width="1650"}

### **Northern Hemisphere Sea Ice – August (1979–2024)**

```{r}
N_data <- read.csv(url("https://www.ncdc.noaa.gov/snow-and-ice/extent/sea-ice/N/8.csv"), skip = 4)


ggplot(N_data, aes(x = Date, y = Value)) +
  geom_point(color = "#8B4513", alpha = 0.7) +
  geom_smooth(method = 'lm', color = "#FF6347", se = FALSE, size = 1) +
  labs(
    title = "Decline in August Northern Hemisphere Sea Ice Extent (1979–2024)",
    subtitle = "Measured in million square kilometers",
    x = "Year",
    y = "Sea Ice Extent (million sq km)"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", size = 15),
    plot.subtitle = element_text(size = 12),
    axis.title = element_text(face = "bold"),
    panel.grid.minor = element_blank()
  )

```

##################################################################################################################### 

# **Min and max temperatures**

## Column {data-width="230"}

### **Min & max Temperatures**

The provided maps visualize the statewide maximum and minimum temperatures across the United States over a five-year period (july 2014 - 2019).

The maximum temperature map uses a color gradient ranging from soft pinks to deep reds and purples, highlighting the variation in temperature across different states. The warmer temperatures are concentrated in the southern and central regions of the U.S., while cooler temperatures are observed in the northern states. This gradient visually represents the significant temperature differences between regions, with higher temperatures in the southern areas and lower temperatures in the north.

Similarly, the minimum temperature map employs a color gradient that ranges from light blue to darker blue shades, indicating the variation in low temperatures. As expected, the cooler states in the northern part of the country experience lower minimum temperatures, while the southern states have relatively higher minimum temperatures, which is visually reflected through the color intensity.

These maps help in understanding the temperature patterns and trends over the 5-year period, allowing for easy comparison of how temperatures vary geographically across the U.S. during this timeframe. The maps also emphasize the contrast between regions, with warmer temperatures in the south and cooler temperatures in the north, showcasing the impact of latitude on temperature fluctuations.

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

### **Min and max Temp**

```{r}
# Fetching dataset for maximum temperature from NCDC

# Loading the dataset for Maximum Temperature
maxTempData = read.csv(url("https://www.ncdc.noaa.gov/cag/statewide/mapping/110-tmax-201906-60.csv"), skip=4)

maxTempData$region = tolower(maxTempData$Name)
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", size = 0.2) +
  scale_fill_gradientn(name = "Degrees Fahrenheit",
                       colors = c("#ffcccb", "#ff5733", "#ff0000", "#8b0000", "#4b0082"), 
                       guide = "colorbar", na.value = "gray90") +
  labs(title = "  Max Temperature [July 2014 - June 2019]", 
       subtitle = "Temperature distribution across the U.S.",
       x = "Longitude", y = "Latitude") +
  theme_minimal() + 
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
        plot.subtitle = element_text(size = 12, hjust = 0.5),
        axis.title = element_text(size = 12, face = "bold"),
        panel.grid = element_blank(),
        legend.position = "right") +
  coord_fixed(ratio=1)


minTempData = read.csv(url("https://www.ncdc.noaa.gov/cag/statewide/mapping/110-tmin-201906-60.csv"), skip=4)

minTempData$region = tolower(minTempData$Name)
minTempData = merge(us_states, minTempData, by="region", all=T)

ggplot(minTempData, aes(x = long, y = lat, group = group, fill = Value)) + 
  geom_polygon(color = "white", size = 0.2) +
  scale_fill_gradientn(name = "Degrees Fahrenheit",
                       colors = c("#add8e6", "#87ceeb", "#4682b4", "#1e3a5f", "#001f3d"),
                       guide = "colorbar", na.value = "gray90") +
   labs(title = "  Min Temperature [July 2014 - June 2019]", 
       subtitle = "Temperature distribution across the U.S.",
       x = "Longitude", y = "Latitude") +
   theme_minimal() + 
   theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
         plot.subtitle = element_text(size = 12, hjust = 0.5),
        axis.title = element_text(size = 12, face = "bold"),
        panel.grid = element_blank(),
        legend.position = "right") +
   coord_fixed(ratio=2)



```




# **Questions and Conclusion**

### Questions

1- Considering the graphs analyzed, is the trend for temperatures to increase or decrease in the coming years?

Answer: Considering the numbers from the past 50 years, the trend is that the temperature keep going up, if no intermission is made.

2- Is the relationship between temperature and CO2 emissions positively or negatively related?

Answer: The relationship between temperature and CO2 emissions is positively related. We see that with the rising carbon emissions, temperatures have also seen a rise.

3-Based on the plot of the Residential Energy Demand Temperature Index (REDTI) across June months in the United States, what trend is observed and what does it suggest about future energy demand?

**Answer:** The plot shows a gradual increase in the REDTI over time, indicating that higher-than-average temperatures in June are becoming more common. This trend suggests that energy demand for cooling, particularly air conditioning, is likely to increase in the future. It highlights the growing impact of climate change on energy infrastructure and the need for sustainable strategies to meet rising demand and reduce environmental impact.

4- What does the decline in Northern Hemisphere sea ice extent from 1979 to 2024 indicate, and why is it significant?

**Answer:** The chart shows a clear downward trend in Northern Hemisphere sea ice extent over the past four decades, indicating a significant reduction in Arctic ice coverage. This decline is a strong indicator of climate change, with serious consequences for global weather patterns, sea level rise, and Arctic ecosystems. The drop emphasizes the urgent need to address greenhouse gas emissions and adopt sustainable environmental practices globally.

5-What do the statewide maximum and minimum temperature maps reveal about temperature patterns across the United States from July 2014 to 2019?

**Answer:** The maps illustrate significant temperature differences across the U.S. over a five-year period. The maximum temperature map shows warmer temperatures in the southern and central regions, with cooler temperatures in the northern states, represented by a color gradient from soft pinks to deep reds and purples. The minimum temperature map reveals cooler temperatures in the northern states and relatively higher minimum temperatures in the southern states, with a color gradient ranging from light blue to dark blue. These maps highlight the regional temperature variations, emphasizing how latitude influences temperature patterns across the country.

### Conclusion

The data presented in these graphs clearly indicates the ongoing reality of climate change, with a significant rise in temperatures across various regions of the world, particularly in the United States. The statewide temperature maps for both maximum and minimum temperatures over the five-year period (July 2014 - 2019) show that the southern and central U.S. states experience much higher temperatures than the northern states. This regional disparity highlights the global nature of climate change, with rising temperatures across the board, although some areas are more severely impacted than others.

In addition, the decline in Northern Hemisphere sea ice over the past decades, along with the increase in residential energy demand related to rising temperatures, further underscores the growing effects of climate change. These trends highlight not only the environmental challenges but also the strain on infrastructure and energy systems that are becoming increasingly vulnerable to the changes in climate patterns.