---
title: "ANLY 512 - Climate Dashboard"
author: "Sneha Yerunkar"
date: "02-18-2023"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    Horizontal_layout: fill
    social: menu
    source: embed
    html_document: default
    df_print: paged
    pdf_document: default
---

# Table of Contents {.sidebar}

* Introduction
  
* US Statewide Temperature Distribution                       
  
* Distribution of Speed of Wind in Chicago

* Annual Residential Energy Demand Temperature Index

* Conclusion & Insights

# **Introduction**

Row {data-height=230}
-------------------------------------
    
### **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.


Row
-------------------------------------
    
### **Objective** 

1. What is the trend of average temperature statewide in U.S?
2. Which area had higher maximum extreme temperature across U.S.?
3. Which regions or states of U.S show lowest average temperature in U.S?
4. How is the hourly speed of wind distributed in Chicago?
5. Has there been any trend in Annual Residential Energy Demand Temperature Index over the years?


```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(ggplot2)
library(maps)
library(ggmap)
library(maptools)
library(rgdal)
library (RCurl)
library(tidyverse)
library(plotly)
library(scatterpie)
library(rnoaa)
library(usmap)
library(mapproj)
library(lubridate)


```



# **U.S Statewide Temperature Distribution**

```{r}
data <- read.csv(url("https://www.ncdc.noaa.gov/cag/statewide/mapping/110-tavg-201906-12.csv"),skip=3)
state = map_data("state")
data$region = tolower(data$Location)
temp = merge(state, data, by="region", all=T)
temp<-temp[-6]
temp<-drop_na(temp)

```



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

### Average temperature

```{r}
mt = ggplot(temp, aes(x = long, y = lat, group = group, fill = Value))+geom_polygon(color = "white")
mt = mt + scale_fill_gradient(name = "degrees F",low = "blue", high = "red" ,na.value="white") + labs(x="Longitude",y="Latitude")
mt + coord_map()

```


### Highest Temperature {data-width=900}
```{r}
mt = ggplot(temp, aes(x = long, y = lat, group = group, fill = Value))+geom_polygon(color = "white")
mt = mt + scale_fill_gradient(name = "degrees F",  high = "red", na.value="white") + labs(x="Longitude",y="Latitude")
mt + coord_map()
```



### Lowest Temperature {data-width=900}
```{r}
mt = ggplot(temp, aes(x = long, y = lat, group = group, fill = Value))+geom_polygon(color = "white")
mt = mt + scale_fill_gradient(name = "degrees F",  low = "blue", na.value="white") + labs(x="Longitude",y="Latitude")
mt + coord_map()

```



# **Distribution of Speed of Wind in Chicago**


```{r}
data_2 <- read.csv("C:/Users/drsne/Downloads/LocalClimate.csv")

# Convert datetime string to datetime object
data_2$datetime <- parse_date_time(data_2$DATE, orders = c("YmdHMS", "YmdHM", "YmdH", "mdYHMS", "Ymd", "Ym", "Y"))

# Extract hour component from datetime object
data_2$hour <- hour(data_2$datetime)

ggplot(data = data_2, aes(x = hour, y = HourlyWindSpeed)) + 
  geom_point(color = "red") +
  labs(title = "Hourly Distribution of Speed of Wind", x = "Hours", y = "Speed of Wind") 














```

# **Annual Residential Energy Demand Temperature Index**


```{r}

data_3 <- read.csv(url("https://www.ncdc.noaa.gov/societal-impacts/redti/USA/jun/1-month/data.csv"),skip=1)

ggplot(data_3,aes(x=Date,y=REDTI))+geom_col()+
  geom_smooth(method='lm',se=FALSE)+
  labs(title="Annual Residential Energy Demand Temperature Index",x="Year",y="REDTI")

```

# **Conclusion**

1. What is the trend of average temperature statewide in U.S??
Answer: The average temperature seems to be lower in northern, northeastern and northwestern states and higher in the southern states. However, it isn't uniform as some of the relatively southern regions show lower temperatures.

2. Which area had higher maximum extreme temperature across U.S.?
Answer: The southern and southeastern regions show higher average temperature compared to the northern regions and some of the states in southwestern regions.  

3. Which regions or states of U.S show lowest average temperature in U.S?
Answer: The northern regions show lower average temperature compared to the southern regions and some of the states in southwestern regions.

4. How is the hourly speed of wind distributed in Chicago?
Answer: The hourly distribution of wind in Chicago shows the speed of wind is higher during early morning hours (0500) and lowest around afternoon (1500). 

5. Has there been any trend in Annual Residential Energy Demand Temperature Index over the years?
Answer: There has been a gradual increase in trend of Residential Energy Demand Temperature Index over the years. This could be due to advanced development in technology, energy needs and surge in use of technology in daily living activities,


Conclusion:
The overall analysis shows that there is a gradual increase in heat wave and shift in the weather trends. The Residential Energy Demand Temperature Index (REDTI) is based on population weighted heating and cooling degree days, and as such, is a valuable tool for explaining year-to-year fluctuations in energy demand for residential heating and cooling. A rise in trend of REDTI shows that due to modernization, deforestation, and increase in use of fuels, there has been increase in demand for energy leading to rise in carbon emissions, depletion of natural resources, thus accentuating the effects of global warming. Hence, there is a strong need to resort to environmental friendly resources to save our planet.











```