---
title: "Anly 512 lab 2"
author: "saipriya kallepalli"
date: "4/6/2-23"
output: 
  flexdashboard::flex_dashboard:
   orientation: columns
   vertical_layout: fill
   social: menu
   source: embed
   html_document:
   df_print: paged
   pdf_document: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

library(flexdashboard)
library(knitr)
library(ggplot2)
library(tidyverse)
library(readxl)
library(dplyr)
library(xts)
library(zoo)
library(ggrepel)
library(maps)
library(ggsn)
library(stats)
library(RColorBrewer)
library(maps)
library(usmap)
```

# Introduction

Row {data-height=230}
-------------------------------------

### **Overview**

The scientific consensus on climate change has been hotly contested for the past decade. Global climate data is now being gathered from a wide variety of locations. The most recent analysis performed on data culled from vast web archives of this data forms the basis for policy decisions. When faced with the task of making a quantitative judgment, many people report feeling overwhelmed by the sheer volume of data at their disposal.

All good data scientists need to be able to visually synthesize massive data sets. Scientists who work with data are also expected to synthesize that data and use it as a springboard for new questions or ideas. 

### **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.


### **Four Questions to answer through this analysis**

1. Which State had Minimum temperature across U.S.?
2. Which State had Maximum extreme temperature across U.S.?
3. What does heating degree of North Carolina indicate.?
4. What's the future trend of maximum temperature in North Carolina.?
5. What does precipitation rate over years indicate and how does it effect us.?
   Source: [NOAA] (https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/statewide/mapping)

# Statewide Maximum and Minimum Tempertaure for year 2022 in USA

## Column{data-height=900 .tabset .tabset-fade}

### Top 10 states in USA with maximum temp {data-width=900}

```{r echo=FALSE, warning=FALSE, message=FALSE}
state_values <- read.csv("/Users/darklord/Downloads/maximum_temp.csv")
top_states <- head(state_values[order(-state_values$Value),], 10)

set1 <- brewer.pal(n = 5, name = "Set3")
set2 <- brewer.pal(n = 5, name = "Set2")

color_palette <- c(set1,set2)
top_states$Color <- color_palette

ggplot(top_states, aes(x=Value, y=Location, fill=Color)) +
  geom_bar(stat="identity", width =0.6, height =6) +
  ggtitle("Top 10 States in USA with the Highest Temperatures in year 2022") +
  ylab("States") + xlab("Value") +
  theme(axis.text.y = element_text(size = 10, hjust = 0)) +
  scale_fill_identity()
```


### Top 10 states in USA with minimum temp

```{r echo=FALSE, warning=FALSE, message=FALSE}
state_values <- read.csv("/Users/darklord/Downloads/minimum_temp.csv")
top_states <- tail(state_values[order(-state_values$Value),], 10)

set1 <- brewer.pal(n = 5, name = "Set3")
set2 <- brewer.pal(n = 5, name = "Set2")

color_palette <- c(set1,set2)
top_states$Color <- color_palette

ggplot(top_states, aes(x=Value, y=Location, fill=Color)) +
  geom_bar(stat="identity", width=0.6, height =6) +
  ggtitle("Top 10 States in USA with the Minimum Temperatures in year 2022") +
  xlab("Value") + ylab("States") +
  theme(axis.text.y = element_text(size = 10, hjust = 0)) +
  scale_fill_identity()

```


# North Carolina Heating degree from 1895-2023

```{r echo=FALSE, message=FALSE, Fig.width=25, Fig.height=25}

# Load data from CSV file
data <- read.csv("/Users/darklord/Downloads/heatingdegree.csv")

# Create a linear scatter plot
ggplot(data, aes(x = year, y = Value)) +
  geom_point(color = "brown") +  # Add points
  geom_smooth(method = "lm", color = "darkgreen") +  # Add linear regression line
  labs(title = "Heating Degree of North Carolina from 1895-2023", x = "Year", y = "Value")

```


# North Carolina Average Temperatures From 1895-2023
```{r echo=FALSE, message=FALSE, Fig.width=25, Fig.height=25}
# Read the data from the CSV file
data <- read.csv("/Users/darklord/Downloads/two.csv")
# Create a scatter plot with points and lines
ggplot(data, aes(x=year, y=Value)) +
  geom_point(color="blue") +  # Add points
  geom_line(color="green") +  # Add line
  labs(title = "North carolina Average temperatures from 1895-2023", x = "Year", y = "Value")

```


# North carolina Precipitation temperature from 1895-2023

```{r echo=FALSE, warning=FALSE, message=FALSE}

# Read data from CSV file
data <- read.csv("/Users/darklord/Downloads/precipitation2.csv")

ggplot(data, aes(x = year, y = Value)) +
  geom_line(color = "orange") +  # Add line
  labs(title = "North Carolina Precipitation from 1895-2023", x = "Year", y = "Value")

```

# Insights

1.Maine, followed by Vermont, has the coldest average temperature of any US state. It stands to reason that when one travels further north, one will experience cooler temperatures.

2.The graphic shows that the state of Florida has the nation's highest temperatures, followed by the state of Texas. The 10 most populous states are clustered in the southern United States. High maximum temperatures may increase heat-related sickness and death, labor productivity. These states may also see changes in plant and animal behavior, flowering and migration time, and other environmental and social effects. Understanding the top 10 states with maximum temperature can help policymakers and individuals prepare for and adapt to extreme heat through heat warning systems, building design and construction improvements, and public health interventions to reduce heat-related illness and death.

3.The North Carolina heating degree rate over time graph shows some volatility, which may be indicative of erratic winter weather in the state. To fully grasp the underlying trends and their potential implications on North Carolina and its residents, it is crucial to keep an eye on the heating degree rate over the years graph.

4.As we see average high temperature is rising in North Carolina, then means it's growing hotter all around. This could be attributed to a number of things, such as global warming or changes in land usage. Changes in evaporation rates, rainfall patterns, and the habits of plants and animals are only some of the potential consequences of a rising trend in the average maximum temperature.

5.Although the annual precipitation rate in North Carolina has varied over the years, the general downward trend suggests that the region is drying out. Changes in global weather patterns, shifts in land use, and human induced climate change are all possible explanations. The agricultural sector, water supplies, and ecological systems can all be severely impacted by a decline in precipitation.

Conclusion: Global warming is happening and will get worse if nothing is done.