---
title: "Climate_Dashboard_Hemant"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}

library(flexdashboard)

here::i_am('Climate_Dshbrd.Rmd')

library(here)


```

# **LAB -2 INSTRUCTIONS**

## Column {data-width=520}

### **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.



### **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 ≤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.


## Column

### **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:

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.


### **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.

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.



# **MAXIMUM TEMPERATURE ANALYSIS**

## Column {.tabset}

### **U.S STATEWIDE MAX TEMPERATURE ANALYSIS OVERVIEW**


**OVERVIEW:**

**Climate and Climate Change Overview:**


  **Climate**, refers to the long-term (usually at least 30 years) regional or even global average of temperature, humidity, and
  rainfall patterns over seasons, years, or decades and whereas **Climate change** is a long-term change in the average weather
  patterns that have come to define Earth’s local, regional, and global climates. These changes have a broad range of observed
  effects that are synonymous with the term. 


  Climate data provides evidence of climate change key indicators, such as global land and ocean temperature increases; rising sea
  levels; ice loss at Earth’s poles and in mountain glaciers; frequency and severity changes in extreme weather such as hurricanes,
  heatwaves, wildfires, droughts, floods, and precipitation; and cloud and vegetation cover changes.
  
  
  
  Reference 1: https://www.climate.gov/
  
  Reference 2: (https://www.ncdc.noaa.gov/)



**U.S STATEWIDE MAXIMUM TEMPERATURE OVERVIEW:**

  This page section of the climate report dashboard presents various varieties of U.S Statewide graph plots for the analysis of the
  maximum U.S statewide temperature ranges, and to further depict any of the observed temperature deviation trends in the area of
  temperature increase from the period 1960 till 2022, in addition to the U.S statewide maximum temperature plots, time the series
  plot, and interactive time series plot are also developed and presented under this page section for depicting the maximum
  temperature anomalies across U.S states between this time frame.    
  

  The "Maximum Temperature Analysis summary" tab from this dashboard section presents some of the imperative finding analysis from
  the perspective of U.S. statewide maximum temperature deviations or depiction of U.S. statewide maximum temperature trend analysis
  for the U.S. states between the period of the Years 1960 and 2022.
  
  Reference: (https://www.ncdc.noaa.gov/)



### **MAX TEMPERATURE PLOT- A**

```{r}


library(ggplot2)

library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 9))


library(quantmod)

library(plyr)

library(DT)

library("dplyr")

library("tidyverse")

library("maps")

library("mapproj")

library("usmap")


## Max Temperature:


## Loading/Extracting the map data:

Max_Temp_1960_61 <- read.csv(file="~/Desktop/Max_Temp_1960_61.csv" , header = TRUE)

states<- map_data("state")

Max_Temp_1960_61$region <- tolower(Max_Temp_1960_61$Location)

Max_Temp_1960_61 <- merge(states, Max_Temp_1960_61, by="region", all=T)

## Determination of Data object layer for the ggplot:

Max_Temp_Plot<- ggplot(Max_Temp_1960_61, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Max_Temp_Plot <- Max_Temp_Plot + scale_fill_gradient(name = "Degrees (°F)", low = "#ff9966", high = "#cc3300", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MAXIMUM TEMPERATURE (°F) BETWEEN YEAR:1960-1961", x = "Longitude Scale ", y = "Latitude Scale")

Max_Temp_Plot


```


### **MAX TEMPERATURE PLOT- B**

```{r}


## Max Temperature:

## Loading/Extracting the map data:

Max_Temp_2021_22 <- read.csv(file="~/Desktop/Max_Temp_2021_22.csv" , header = TRUE)

states<- map_data("state")

Max_Temp_2021_22$region <- tolower(Max_Temp_2021_22$Location)

Max_Temp_2021_22 <- merge(states, Max_Temp_2021_22, by="region", all=T)

## Determination of Data object layer for the ggplot:

Max_Temp_Plot_b<- ggplot(Max_Temp_2021_22, aes(x = long, y = lat, group = group, fill = Value))+geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Max_Temp_Plot <- Max_Temp_Plot_b + scale_fill_gradient(name = "Degrees (°F)", low = "#ff6600", high = "#990000", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MAXIMUM TEMPERATURE (°F) BETWEEN YEAR:2021-2022", x = "Longitude Scale ", y = "Latitude Scale")

Max_Temp_Plot




```


### **MAX TEMPERATURE SIDEWISE COMPARISON PLOT**



```{r}

library(cowplot)

library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 8))



## Max Temperature: 1960-1961


## Loading/Extracting the map data:

Max_Temp_1960_61 <- read.csv(file="~/Desktop/Max_Temp_1960_61.csv" , header = TRUE)

states<- map_data("state")

Max_Temp_1960_61$region <- tolower(Max_Temp_1960_61$Location)

Max_Temp_1960_61 <- merge(states, Max_Temp_1960_61, by="region", all=T)

## Determination of Data object layer for the ggplot:

Max_Temp_Plot<- ggplot(Max_Temp_1960_61, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Max_Temp_Plot_A <- Max_Temp_Plot + scale_fill_gradient(name = "Degrees (°F)", low = "#ff9966", high = "#cc3300", guide = "colorbar", na.value="blue") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MAX TEMP (°F): 1960-61", x = "Longitude Scale ", y = "Latitude Scale")




## Max Temperature 2021-2022:

## Loading/Extracting the map data:

Max_Temp_2021_22 <- read.csv(file="~/Desktop/Max_Temp_2021_22.csv" , header = TRUE)

states<- map_data("state")

Max_Temp_2021_22$region <- tolower(Max_Temp_2021_22$Location)

Max_Temp_2021_22 <- merge(states, Max_Temp_2021_22, by="region", all=T)

## Determination of Data object layer for the ggplot:

Max_Temp_Plot_b<- ggplot(Max_Temp_2021_22, aes(x = long, y = lat, group = group, fill = Value))+geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Max_Temp_Plot_B <- Max_Temp_Plot_b + scale_fill_gradient(name = "Degrees (°F)", low = "#ff6600", high = "#990000", guide = "colorbar", na.value="blue") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MAX TEMP (°F): 2021-22", x = "Longitude Scale ", y = "")



plot_grid(Max_Temp_Plot_A, Max_Temp_Plot_B)


```


### **MAX TEMPERATURE ANOMALY PLOT**

```{r}

## Max Temp Anomaly:

Max_Temp_Anmly_1960_22 <- read.csv(file="~/Desktop/Max_Temp_Anmly_1960_22.csv" , header = TRUE)


## Determination of Data object layer for the ggplot:

Max_Temp_Anmly_1960_22_Plot<- ggplot(Max_Temp_Anmly_1960_22, aes(x = Year, y = Anomaly_Value)) + 
  geom_hline(yintercept= mean(Max_Temp_Anmly_1960_22$Anomaly_Value),color="#006633") + geom_line(color = "#ff6600", size=1) + geom_point()

## Addition of subsequent layer to the ggplot:

Max_Temp_Anmly_1960_22_Plot_a<- Max_Temp_Anmly_1960_22_Plot +
  labs(title = "U.S MAX TEMP ANOMALIES BETWEEN YEAR 1960 AND 2022", x = "Year Scale ", y = "Temperature Scale in Fahrenheit degrees") + scale_fill_discrete("Year") 

Max_Temp_Anmly_1960_22_Plot_a



```

### **MAX TEMPERATURE ANOMALY INTERACTIVE TIME SERIES PLOT**

```{r}

library("dygraphs")

Max_Temp_Anmly_1960_22 <- read.csv(file="~/Desktop/Max_Temp_Anmly_1960_22.csv" , header = TRUE)

dygraph(Max_Temp_Anmly_1960_22,
        main = "U.S MAXIMUM TEMPERATURE ANOMALIES PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "TEMPERATURE SCALE IN FAHRENHEIT DEGREES") %>%
  dySeries("Anomaly_Value", label= "Temp Anomaly Value", strokeWidth = 3, color = "#ff3300") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)




```


### **MAX TEMPERATURE FACET GRID PLOT- A**

```{r}

## Max Temp Facet Grid Plot:


Max_Temp = read.csv(file="~/Desktop/Max_Temp_1960_61.csv" , header = TRUE)

data<-Max_Temp


Max_Temp_Plot <- 
  ggplot(data, aes(x= Location,y=Value, fill=Location)) +
  geom_bar(stat = "identity", color="black") +
  labs(title = "U.S STATEWIDE MAXIMUM TEMPERATURE (°F) BETWEEN 1960-1961") +  
  labs(y = "TEMPERATURE (°F)", x = "U.S States") + facet_wrap(~Region, nrow = 4) +
  scale_fill_discrete("U.S STATES") + cleanup

Max_Temp_Plot


```


### **MAX TEMPERATURE FACET GRID PLOT- B**

```{r}

## Max Temp Facet Grid Plot:


Max_Temp = read.csv(file="~/Desktop/Max_Temp_2021_22.csv" , header = TRUE)

data<-Max_Temp


Max_Temp_Plot <- 
  ggplot(data, aes(x= Location,y=Value, fill=Location)) +
  geom_bar(stat = "identity", color="black") +
  labs(title = "U.S STATEWIDE MAXIMUM TEMPERATURE (°F) BETWEEN 2021-2022") +  
  labs(y = "TEMPERATURE (°F)", x = "U.S States") + facet_wrap(~Region, nrow = 4) +
  scale_fill_discrete("U.S STATES") + cleanup

Max_Temp_Plot



```


### **MAX TEMPERATURE ANALYSIS SUMMARY**

  **MAXIMUM TEMPERATURE KEY ANALYSIS FINDINGS: **
  
  **A.** 
  Referring to Max-Temp Plot-A data, The Highest observed U.S Statewide Temperature observed value in
  the 1960-61 Period was 80.59 Fahrenheit Degrees for the state of Florida, followed by the second-highest
  maximum temperature value of 75.87 Fahrenheit Degrees for the state of Louisiana.
  
  **B.** 
  Referring to Plot-B data, The Highest observed U.S Statewide Temperature observed value in the 2021-22
  Period was 82.39 Fahrenheit Degrees for the state of Florida, followed by the second-highest maximum
  temperature value of 78.85 Fahrenheit Degrees for the state of Texas.
         
  **C.** 
  The Maximum U.S. Statewide Temperature observed in the 2021-22 period was observed to be above 1 to 2
  Fahrenheit degrees above on average as compared to the maximum temperature observed in the 1960-61
  period, as also evidenced by the transition of color gradient and higher Fahrenheit Degrees scale (Ref:
  Max Temp Plot-A and B) for the Maximum temperature for 2021-22 period.
  
  **D.** 
  U.S Statewide Maximum Temperature Anomalies between the year 1960 and 2022 Plots (Reference: Max Temp
  Anomaly Plot and Interactive Time Series Plot) also clearly depicted an increase in the maximum
  temperature anomaly trend from the year 1998 onwards and above the national average value of maximum
  temperature anomaly value in Fahrenheit degrees.
  
  **E.** 
  The Highest value of U.S Statewide Maximum Temperature Anomaly value between 1960 to 2022 period was
  observed at 2.6 Fahrenheit Degrees in the Year 2012, and the lowest observed U.S Statewide Maximum
  Temperature Anomaly value between 1960 to 2022 period was observed at -2.41 Fahrenheit Degrees in the
  Year 1993.
  
  **F.** 
  Referring to Max Temp Grid Plots- A and B, it’s evident that the majority of U.S States from the
  Southern and Mid-West regions have on an average higher temperature, and referring to these plots, it’s
  evident that the U.S. different region Temperature was observed to be above 1 to 2 Fahrenheit degrees
  above on average for 2021-22 Period and as compared to the maximum temperature observed in 1960-61
  period.


# **MINIMUM TEMPERATURE ANALYSIS**

## Column {.tabset}

### **U.S STATEWIDE MIN TEMPERATURE ANALYSIS OVERVIEW**


**OVERVIEW:**

**U.S STATEWIDE MINIMUM TEMPERATURE OVERVIEW:**

  This page section of the climate report dashboard presents various varieties of U.S Statewide graph plots for the analysis of the
  minimum U.S statewide temperature ranges, and to further depict any of the observed temperature deviation trends in the area of
  temperature decrease from the period 1960 till 2022, in addition to the U.S statewide minimum temperature plots, time the series
  plot, and interactive time series plot are also developed and presented under this page section for depicting the minimum
  temperature anomalies across U.S states between this time frame.  
  

  The "Minimum Temperature Analysis summary" tab from this dashboard section presents some of the imperative finding analysis from
  the perspective of U.S. statewide minimum temperature deviations or depiction of U.S. statewide minimum temperature trend analysis
  for the U.S. states between the period of the Years 1960 and 2022.

  Reference: (https://www.ncdc.noaa.gov/)

### **MIN TEMPERATURE PLOT- A**

```{r}

## Min Temperature: 1960-61


## Loading/Extracting the map data:

Min_Temp_1960_61 <- read.csv(file="~/Desktop/Min_Temp_1960_61.csv" , header = TRUE)

states<- map_data("state")

Min_Temp_1960_61$region <- tolower(Min_Temp_1960_61$Location)

Min_Temp_1960_61 <- merge(states, Min_Temp_1960_61, by="region", all=T)

## Determination of Data object layer for the ggplot:

Min_Temp_Plot<- ggplot(Min_Temp_1960_61, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Min_Temp_Plot <- Min_Temp_Plot + scale_fill_gradient(name = "Degrees (°F)", low = "#0033cc", high = "lightblue", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MINIMUM TEMPERATURE (°F) BETWEEN YEAR:1960-1961", x = "Longitude Scale ", y = "Latitude Scale")

Min_Temp_Plot


```


### **MIN TEMPERATURE PLOT- B**

```{r}


## Min Temperature:2021-22

## Loading/Extracting the map data:

Min_Temp_2021_22 <- read.csv(file="~/Desktop/Min_Temp_2021_22.csv" , header = TRUE)

states<- map_data("state")

Min_Temp_2021_22$region <- tolower(Min_Temp_2021_22$Location)

Min_Temp_2021_22 <- merge(states, Min_Temp_2021_22, by="region", all=T)

## Determination of Data object layer for the ggplot:

Min_Temp_Plot_b<- ggplot(Min_Temp_2021_22, aes(x = long, y = lat, group = group, fill = Value))+geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Min_Temp_Plot <- Min_Temp_Plot_b + scale_fill_gradient(name = "Degrees (°F)", low = "darkblue", high = "#0099cc", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MINIMUM TEMPERATURE (°F) BETWEEN YEAR:2021-2022", x = "Longitude Scale ", y = "Latitude Scale")

Min_Temp_Plot


```



### **MIN TEMPERATURE SIDEWISE COMPARISON PLOT**


```{r}

library(cowplot)

library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 8))


## Min Temperature: 1960-61


## Loading/Extracting the map data:

Min_Temp_1960_61 <- read.csv(file="~/Desktop/Min_Temp_1960_61.csv" , header = TRUE)

states<- map_data("state")

Min_Temp_1960_61$region <- tolower(Min_Temp_1960_61$Location)

Min_Temp_1960_61 <- merge(states, Min_Temp_1960_61, by="region", all=T)

## Determination of Data object layer for the ggplot:

Min_Temp_Plot<- ggplot(Min_Temp_1960_61, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Min_Temp_Plot_A <- Min_Temp_Plot + scale_fill_gradient(name = "Degrees (°F)", low = "#0033cc", high = "lightblue", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MIN TEMP (°F): 1960-61", x = "Longitude Scale ", y = "Latitude Scale")




## Min Temperature:2021-22

## Loading/Extracting the map data:

Min_Temp_2021_22 <- read.csv(file="~/Desktop/Min_Temp_2021_22.csv" , header = TRUE)

states<- map_data("state")

Min_Temp_2021_22$region <- tolower(Min_Temp_2021_22$Location)

Min_Temp_2021_22 <- merge(states, Min_Temp_2021_22, by="region", all=T)

## Determination of Data object layer for the ggplot:

Min_Temp_Plot_b<- ggplot(Min_Temp_2021_22, aes(x = long, y = lat, group = group, fill = Value))+geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Min_Temp_Plot_B <- Min_Temp_Plot_b + scale_fill_gradient(name = "Degrees (°F)", low = "darkblue", high = "#0099cc", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S MIN TEMP (°F): 2021-22", x = "Longitude Scale ", y = "")



plot_grid(Min_Temp_Plot_A, Min_Temp_Plot_B)


```


### **MIN TEMPERATURE ANOMALY PLOT**


```{r}

## Min Temp Anomaly:



Min_Temp_Anmly_1960_22 <- read.csv(file="~/Desktop/Min_Temp_Anmly_1960_22.csv" , header = TRUE)


## Determination of Data object layer for the ggplot:

Min_Temp_Anmly_1960_22_Plot<- ggplot(Min_Temp_Anmly_1960_22, aes(x = Year, y = Anmly_Value)) + 
  geom_hline(yintercept= mean(Min_Temp_Anmly_1960_22$Anmly_Value),color="#006633") + geom_line(color = "#00AFBB", size=1) + geom_point()

## Addition of subsequent layer to the ggplot:

Min_Temp_Anmly_1960_22_Plot_a<- Min_Temp_Anmly_1960_22_Plot +
  labs(title = "U.S MIN TEMP ANOMALIES BETWEEN YEAR 1960 and 2022", x = "Year Scale ", y = "Temperature Scale in Fahrenheit degrees") + scale_fill_discrete("Year") 

Min_Temp_Anmly_1960_22_Plot_a




```


### **MIN TEMPERATURE ANOMALY INTERACTIVE TIME SERIES PLOT**

```{r}

library("dygraphs")

Min_Temp_Anmly_1960_22 <- read.csv(file="~/Desktop/Min_Temp_Anmly_1960_22.csv" , header = TRUE)

dygraph(Min_Temp_Anmly_1960_22,
        main = "U.S MINIMUM TEMPERATURE ANOMALIES PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "TEMPERATURE SCALE IN FAHRENHEIT DEGREES") %>%
  dySeries("Anmly_Value", label= "Min Temp Anomaly Value", strokeWidth = 3, color = "#0066ff") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```


### **MIN TEMPERATURE FACET GRID PLOT- A**


```{r}

## Max Temp Facet Grid Plot:


Min_Temp = read.csv(file="~/Desktop/Min_Temp_1960_61.csv" , header = TRUE)

data<-Min_Temp


Min_Temp_Plot <- 
  ggplot(data, aes(x= Location,y=Value, fill=Location)) +
  geom_bar(stat = "identity", color="black") +
  labs(title = "U.S STATEWIDE MIN TEMPERATURE (°F) BETWEEN YEAR 1960-1961") +  
  labs(y = "TEMPERATURE (°F)", x = "U.S States") + facet_wrap(~Region, nrow = 4) +
  scale_fill_discrete("U.S STATES") + cleanup

Min_Temp_Plot



```


### **MIN TEMPERATURE FACET GRID PLOT- B**

```{r}


## Min Temp Facet Grid Plot:


Min_Temp = read.csv(file="~/Desktop/Min_Temp_2021_22.csv" , header = TRUE)

data<-Min_Temp


Min_Temp_Plot <- 
  ggplot(data, aes(x= Location,y=Value, fill=Location)) +
  geom_bar(stat = "identity", color="black") +
  labs(title = "U.S STATEWIDE MIN TEMPERATURE (°F) BETWEEN 2021-2022") +  
  labs(y = "TEMPERATURE (°F)", x = "U.S States") + facet_wrap(~Region, nrow = 4) +
  scale_fill_discrete("U.S STATES") + cleanup

Min_Temp_Plot


```



### **MIN TEMPERATURE ANALYSIS SUMMARY**

  **MINIMUM TEMPERATURE KEY ANALYSIS FINDINGS: **
  
  **A.** 
  Referring to Min-Temp Plot-A data, The Lowest observed U.S Statewide Temperature observed value in the 1960-61 Period was 28.03
  Fahrenheit Degrees for the state of North Dakota, followed by the second-lowest minimum temperature value of 28.30 Fahrenheit
  Degrees for the state of Wyoming.
  
  
  **B.** 
  Referring to Plot-B data, The lowest observed U.S Statewide Temperature observed value in the 2021-22 Period was 29.35 Fahrenheit
  Degrees for the state of Wyoming, followed by the second-lowest minimum temperature value of 29.75 Fahrenheit Degrees for the
  state of North Dakota.
  
         
  **C.** 
  The Minimum U.S. Statewide Temperature observed in the 2021-22 period was observed to be above 1 to 2 Fahrenheit degrees above on
  average as compared to the minimum temperature observed in the 1960-61 period, as also evidenced by the transition of color
  gradient and higher Fahrenheit Degrees scale (Ref: Max Temp Plot-A and B) for the Minimum temperature for 2021-22 period.
  
  
  **D.** 
  U.S Statewide Minimum Temperature Anomalies between the year 1960 and 2022 Plots (Reference: Min Temp Anomaly Plot and Interactive
  Time Series Plot) also clearly depicted an increase in the minimum temperature anomaly trend from the year 1997 onwards and above
  the national average value of minimum temperature anomaly value in Fahrenheit degrees.
  
  
  **E.** 
  The Highest value of U.S Statewide Minimum Temperature Anomaly value between 1960 to 2022 period was observed at 1.67 Fahrenheit
  Degrees in the Year 2016, and the lowest observed U.S Statewide Minimum Temperature Anomaly value between 1960 to 2022 period was
  observed at -2.59 Fahrenheit Degrees in the Year 1976.
  
  
  **F.** 
  Referring to Min Temp Grid Plots- A and B, it’s evident that the majority of U.S States from the North-East and West regions have
  on an average minimum temperature, and referring to these plots, it’s evident that the U.S. different region Temperature was
  observed to be above 1 to 2 Fahrenheit degrees ab on average for 2021-22 Period and as compared to the minimum temperature
  observed in 1960-61 period.
  

# **AVERAGE TEMPERATURE ANALYSIS**

## Column {.tabset}

### **U.S STATEWIDE AVG TEMPERATURE ANALYSIS OVERVIEW**


**OVERVIEW:**

**U.S STATEWIDE AVERAGE TEMPERATURE OVERVIEW:**

  This page section of the climate report dashboard presents various varieties of U.S Statewide graph plots for the analysis of the
  average U.S statewide temperature ranges, and to further depict any of the observed temperature deviation trends in the area of
  average temperature increase or decrease from the period 1960 till 2022, in addition to the U.S statewide average temperature
  plots, time the series plot, and interactive time series plot are also developed and presented under this page section for
  depicting the average temperature anomalies across U.S states between this time frame.
  

  The "Average Temperature Analysis summary" tab from this dashboard section presents some of the imperative finding analysis from
  the perspective of U.S. statewide average temperature deviations or depiction of U.S. statewide average temperature trend analysis
  for the U.S. states between the period of the Years 1960 and 2022.
  
  Reference: (https://www.ncdc.noaa.gov/)



### **AVERAGE TEMPERATURE PLOT- A**


```{r}


## Avg Temperature: 1960-61


## Loading/Extracting the map data:

Avg_Temp_1960_61 <- read.csv(file="~/Desktop/Avg_Temp_1960_61.csv" , header = TRUE)

states<- map_data("state")

Avg_Temp_1960_61$region <- tolower(Avg_Temp_1960_61$Location)

Avg_Temp_1960_61 <- merge(states, Avg_Temp_1960_61, by="region", all=T)

## Determination of Data object layer for the ggplot:

Avg_Temp_Plot<- ggplot(Avg_Temp_1960_61, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Avg_Temp_Plot <- Avg_Temp_Plot + scale_fill_gradient(name = "Degrees F", low = "#0000FF", high = "#FF0000", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S AVG TEMPERATURE (°F) BETWEEN YEAR:1960-1961", x = "Longitude Scale ", y = "Latitude Scale")

Avg_Temp_Plot



```


### **AVERAGE TEMPERATURE PLOT- B**

```{r}


## Avg Temperature: 2021-22


## Loading/Extracting the map data:

Avg_Temp_2021_22 <- read.csv(file="~/Desktop/Avg_Temp_2021_22.csv" , header = TRUE)

states<- map_data("state")

Avg_Temp_2021_22$region <- tolower(Avg_Temp_2021_22$Location)

Avg_Temp_2021_22 <- merge(states, Avg_Temp_2021_22, by="region", all=T)

## Determination of Data object layer for the ggplot:

Avg_Temp_Plot<- ggplot(Avg_Temp_2021_22, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Avg_Temp_Plot <- Avg_Temp_Plot + scale_fill_gradient(name = "Degrees F", low = "#0000cc", high = "#cc0000", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S AVG TEMPERATURE (°F) BETWEEN YEAR:2021-2022", x = "Longitude Scale ", y = "Latitude Scale")

Avg_Temp_Plot


```



### **AVERAGE TEMPERATURE SIDEWISE COMPARISON PLOT**


```{r}

library(cowplot)

library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 8))



## Avg Temperature: 1960-61


## Loading/Extracting the map data:

Avg_Temp_1960_61 <- read.csv(file="~/Desktop/Avg_Temp_1960_61.csv" , header = TRUE)

states<- map_data("state")

Avg_Temp_1960_61$region <- tolower(Avg_Temp_1960_61$Location)

Avg_Temp_1960_61 <- merge(states, Avg_Temp_1960_61, by="region", all=T)

## Determination of Data object layer for the ggplot:

Avg_Temp_Plot<- ggplot(Avg_Temp_1960_61, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Avg_Temp_Plot_A <- Avg_Temp_Plot + scale_fill_gradient(name = "Degrees F", low = "#0000FF", high = "#FF0000", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S AVG TEMP (°F): 1960-61", x = "Longitude Scale ", y = "Latitude Scale")



## Avg Temperature: 2021-22


## Loading/Extracting the map data:

Avg_Temp_2021_22 <- read.csv(file="~/Desktop/Avg_Temp_2021_22.csv" , header = TRUE)

states<- map_data("state")

Avg_Temp_2021_22$region <- tolower(Avg_Temp_2021_22$Location)

Avg_Temp_2021_22 <- merge(states, Avg_Temp_2021_22, by="region", all=T)

## Determination of Data object layer for the ggplot:

Avg_Temp_Plot<- ggplot(Avg_Temp_2021_22, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Avg_Temp_Plot_B <- Avg_Temp_Plot + scale_fill_gradient(name = "Degrees F",low = "#0000cc", high = "#cc0000" , guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S AVG TEMP (°F): 2021-22", x = "Longitude Scale ", y = "")



plot_grid(Avg_Temp_Plot_A, Avg_Temp_Plot_B)



```


### **AVERAGE TEMPERATURE ANOMALY PLOT**


```{r}


## Avg Temp Anomaly:

Avg_Temp_Anmly_1960_22 <- read.csv(file="~/Desktop/Avg_Temp_Anmly_1960_22.csv" , header = TRUE)


## Determination of Data object layer for the ggplot:

Avg_Temp_Anmly_1960_22_Plot<- ggplot(Avg_Temp_Anmly_1960_22, aes(x = Year, y = Anmly_Value)) + 
  geom_hline(yintercept= mean(Avg_Temp_Anmly_1960_22$Anmly_Value),color="#006633") + geom_line(color = "#0066cc", size=1) + geom_point()

## Addition of subsequent layer to the ggplot:

Avg_Temp_Anmly_1960_22_Plot_a<- Avg_Temp_Anmly_1960_22_Plot +
  labs(title = "U.S AVG TEMP ANOMALIES BETWEEN YEAR 1960 AND 2022", x = "Year Scale ", y = "Temperature Scale in Fahrenheit degrees") + scale_fill_discrete("Year") 

Avg_Temp_Anmly_1960_22_Plot_a


```

### **AVG TEMPERATURE ANOMALY INTERACTIVE TIME SERIES PLOT**

```{r}

library("dygraphs")

Avg_Temp_Anmly_1960_22 <- read.csv(file="~/Desktop/Avg_Temp_Anmly_1960_22.csv" , header = TRUE)

dygraph(Avg_Temp_Anmly_1960_22,
        main = "U.S AVERAGE TEMPERATURE ANOMALIES PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "TEMPERATURE SCALE IN FAHRENHEIT DEGREES") %>%
  dySeries("Anmly_Value", label= "Avg Temp Anomaly Value", strokeWidth = 3, color = "#ff9933") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```



### **AVERAGE TEMP ANALYSIS SUMMARY**

**AVERAGE TEMPERATURE KEY ANALYSIS FINDINGS: **
  
  **A.** 
  Referring to Avg-Temp Plot-A data, The Average lowest observed U.S Statewide Temperature observed value in the
  1960-61 Period was 40.33 Fahrenheit Degrees for the state of North Dakota, followed by the second-lowest average
  minimum temperature value of 40.44 Fahrenheit Degrees for the state of Minnesota.
  
  
  **B.** 
  Referring to Avg-Temp Plot-A data, the Average Highest observed U.S Statewide Temperature observed value in the
  1960-61 Period was 69.65 Fahrenheit Degrees for the state of Florida, followed by the second-highest average maximum
  temperature value of 65.09 Fahrenheit Degrees for the state of Louisiana.
  
         
  **C.** 
  Referring to Avg-Temp Plot-B data, the Average lowest observed U.S Statewide Temperature observed value in the
  2021-22 Period was 41.59 Fahrenheit Degrees for the state of North Dakota, followed by the second-lowest average
  minimum temperature value of 42.16 Fahrenheit Degrees for the state of Minnesota.
  
  
  
  **D.** 
  Referring to Avg-Temp Plot-B data, the Average highest observed U.S Statewide Temperature observed value in the
  2021-22 Period was 72.28 Fahrenheit Degrees for the state of Florida, followed by the second-highest average maximum
  temperature value of 67.40 Fahrenheit Degrees for the state of Louisiana.
  
  
  **E.** 
  The Minimum U.S. Statewide Temperature observed in the 2021-22 period was observed to be above 1 to 2 Fahrenheit
  degrees above on average as compared to the minimum temperature observed in the 1960-61 period, as also evidenced by
  the transition of color gradient and higher Fahrenheit Degrees scale (Ref: Max Temp Plot-A and B) for the Minimum
  temperature for 2021-22 period.
  
  
  **F.** 
  U.S Statewide Average Temperature Anomalies between the year 1960 and 2022 Plots (Reference: Average Temp Anomaly
  Plot and Interactive Time Series Plot) also clearly depicted an increase in the average temperature anomaly trend
  from the year 1998 onwards and above the national average value of minimum temperature anomaly value in Fahrenheit
  degrees.
  
 
  **G.** 
  The Highest value of U.S Statewide Average Temperature Anomaly value between 1960 to 2022 period was observed at 2.01
  Fahrenheit Degrees in the Year 2012, and the lowest observed U.S Statewide Average Temperature Anomaly value between
  1960 to 2022 period was observed at -2.4 Fahrenheit Degrees in the Year 1979.




# **GLOBAL AND NORTH AMERICA TEMPERATURE ANOMALIES ANALYSIS**


## Column {.tabset}

### **NORTH AMERICA AND GLOBAL TEMPERATURE ANOMALIES ANALYSIS OVERVIEW**


**OVERVIEW:**

**GLOBAL AND NORTH AMERICA TEMPERATURE ANOMALIES OVERVIEW:**

  This page section of the climate report dashboard presents various varieties of U.S Statewide Land Temperature Anomaly graph
  plots, in addition to the inclusion of a variety of Global Land and Ocean Temperature Anomaly plots for the analysis of the U.S
  statewide as well as Global Land and Ocean temperature anomalies, and to further depict any of the observed temperature anomaly
  deviation trends in the area of temperature anomaly from the period 1960 till 2022, in addition to the U.S statewide and
  global land and oceanic temperature anomaly graph plots, various interactive time series plots are also developed and presented
  under this page section for depicting the temperature anomalies across U.S states as well as entire globe between this time frame.  
  

  The "Global and U.S Temperature Anomalies summary" tab from this dashboard section presents some of the imperative finding
  analyses from the perspective of U.S. statewide as well Global Land and Ocean temperature anomalies or depiction of U.S. statewide
  and globe’s land and oceanic temperature anomaly trend analysis for the Globe and U.S. states between the period of the Years 1960
  and 2022.
  
  Reference: (https://www.ncdc.noaa.gov/)



### **GLOBAL LAND AND OCEAN TEMP ANOMALIES PLOT**


```{r}


Global_Temp_Anomalies_1960_2022 <- read.csv(file="~/Desktop/Global_Temp_Anomalies_1960_2022.csv" , header = TRUE)

## Determination of Data object layer for the ggplot:

Global_Anmly_Temp_Plot<- ggplot(Global_Temp_Anomalies_1960_2022, aes(x = Year, y = Value)) + 
  geom_line(color="#0099cc",size=1) + geom_hline(yintercept= mean(Global_Temp_Anomalies_1960_2022$Value),color="#006633") + geom_point()

## Addition of subsequent layer to the ggplot:

Global_Anmly_Temp_Plot_a<- Global_Anmly_Temp_Plot +
  labs(title = "GLOBAL LAND AND OCEAN TEMP ANOMALIES BETWEEN 1960 TO 2022", x = "YEAR", y = "TEMPERATURE IN CELCIUS") + scale_fill_discrete("Year") + cleanup

Global_Anmly_Temp_Plot_a


```

### **GLOBAL TEMP ANOMALY INTERACTIVE TIME SERIES PLOT**

```{r}

library("dygraphs")

Global_Temp_Anomalies_1960_2022 <- read.csv(file="~/Desktop/Global_Temp_Anomalies_1960_2022.csv" , header = TRUE)

dygraph(Global_Temp_Anomalies_1960_2022,
        main = "GLOBAL LAND AND OCEAN TEMPERATURE ANOMALIES BETWEEN YEAR 1960 and 2022",
        xlab = "YEAR", ylab = "TEMPERATURE SCALE IN CELCIUS DEGREES") %>%
  dySeries("Value", label= "Temp Anomaly Value", strokeWidth = 3, color = "#ff9933") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```


### **U.S LAND TEMPERATURE ANOMALIES PLOT**

```{r}

Temp_Anomalies_1960_2022 <- read.csv(file="~/Desktop/Temp_Anomalies_1960_2022.csv" , header = TRUE)

## Determination of Data object layer for the ggplot:

Anmly_Temp_Plot<- ggplot(Temp_Anomalies_1960_2022, aes(x = Year, y = Temp_Anmly_Value_In_Celcius)) + geom_line(color="#cc9933", size=1) + geom_point() + geom_hline(yintercept= mean(Temp_Anomalies_1960_2022$Temp_Anmly_Value_In_Celcius),color="#006633")

## Addition of subsequent layer to the ggplot:

Anmly_Temp_Plot_a<- Anmly_Temp_Plot +
  labs(title = "NORTH AMERICA LAND TEMPERATURE ANOMALIES BETWEEN YEAR 1960 TO 2022", x = "YEAR ", y = "TEMPERATURE IN CELCIUS") + scale_fill_discrete("Year") + cleanup

Anmly_Temp_Plot_a


```


### **U.S LAND TEMP ANOMALY INTERACTIVE TIME SERIES PLOT**

```{r}

library("dygraphs")

Temp_Anomalies_1960_2022 <- read.csv(file="~/Desktop/Temp_Anomalies_1960_2022.csv" , header = TRUE)

dygraph(Temp_Anomalies_1960_2022,
        main = "NORTH AMERICA TEMPERATURE ANOMALIES BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "TEMPERATURE SCALE IN CELCIUS DEGREES") %>%
  dySeries("Temp_Anmly_Value_In_Celcius", label= "Temp Anomaly Value", strokeWidth = 3, color = "#ff6600") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```



### **U.S TEMPERATURE ANOMALIES COMPARISON PLOT**

```{r}

library(cowplot)

library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 8))


## Temperature Anomaly 1960-1961:


## Loading/Extracting the map data:

Temp_Anomaly_1960_61 <- read.csv(file="~/Desktop/Avg_Temp_1960_61.csv" , header = TRUE)

states<- map_data("state")

Temp_Anomaly_1960_61$region <- tolower(Temp_Anomaly_1960_61$Location)

Temp_Anomaly_1960_61 <- merge(states, Temp_Anomaly_1960_61, by="region", all=T)

## Determination of Data object layer for the ggplot:

Temp_Anomaly_1960_61_Plot<- ggplot(Temp_Anomaly_1960_61, aes(x = long, y = lat, group = group, fill = Anomaly..1901.2000.base.period.)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Temp_Anomaly_1960_61_Plot_A <- Temp_Anomaly_1960_61_Plot + scale_fill_gradient(name = "Degrees F", low = "lightblue", high = "#FF6666", guide = "colorbar", na.value="blue") + coord_map() + theme(legend.position = "top") + labs(title = "TEMP (°F) ANOMALY:1960-61", x = "Longitude Scale ", y = "Latitude Scale")




## Temperature Anomaly:2021-2022:


## Loading/Extracting the map data:

Temp_Anomaly_2021_22 <- read.csv(file="~/Desktop/Avg_Temp_2021_22.csv" , header = TRUE)

states<- map_data("state")

Temp_Anomaly_2021_22$region <- tolower(Temp_Anomaly_2021_22$Location)

Temp_Anomaly_2021_22 <- merge(states, Temp_Anomaly_2021_22, by="region", all=T)

## Determination of Data object layer for the ggplot:

Temp_Anomaly_2021_22_Plot<- ggplot(Temp_Anomaly_2021_22, aes(x = long, y = lat, group = group, fill = Anomaly..1901.2000.base.period.)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Temp_Anomaly_2021_22_Plot_B <- Temp_Anomaly_2021_22_Plot + scale_fill_gradient(name = "Degrees F", low = "#336699", high = "#FF6666", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "TEMP (°F) ANOMALY:2021-22", x = "Longitude Scale ", y = "")



plot_grid(Temp_Anomaly_1960_61_Plot_A, Temp_Anomaly_2021_22_Plot_B)



```

### **GLOBAL AND U.S TEMP ANOMALIES SUMMARY**


**GLOBAL LAND AND OCEAN AND NORTH AMERICA LAND TEMPERATURE ANOMALY KEY ANALYSIS FINDINGS: **
  
  **A.** 
  Reference to Global Land and Ocean Temperature Anomaly plots, there seems to be a constant increase trend in the
  Global Land and Oceanic Temperature Anomaly value from the Year 1977 onwards and above baseline.
  
  
  **B.** 
  Reference to Global Land and Ocean Temperature Anomaly plot and interactive time-series plot, the highest Global and
  Oceanic Temperature Anomaly value was observed as 1.03 Celsius degree in the Year 2016 for the period between the
  Year 1960 and 2022. 
  
         
  **C.** 
  Reference to Global Land and Ocean Temperature Anomaly plot and interactive time-series plot, the Lowest Global and
  Oceanic Temperature Anomaly value was observed as -0.15 Celsius degree in the Year 1964 for the period between the
  Years 1960 and 2022.
  
  
  **D.** 
  Reference to U.S North America Land Temperature Anomaly plots, there seems to be a constant increase trend in the
  North America Land Temperature Anomaly value from the Year 1985 onwards and above baseline.
  
  **E.** 
  Reference to the North America Land Temperature Anomaly plot and interactive time-series plot, the highest Land
  Temperature Anomaly value for North America was observed as 1.99 Celsius degree in the Year 2016 for the period
  between the Year 1960 and 2022. 
  
  
  **F.** 
  Reference to the North America Land Temperature Anomaly plot and interactive time-series plot, the Lowest Land
  Temperature Anomaly value for North America was observed as -0.97 Celsius degree in the Year 1972 for the period
  between the Years 1960 and 2022.
  


# **U.S STATEWIDE PRECIPITATION ANALYSIS**


## Column {.tabset}

### **U.S STATEWIDE PRECIPITATION ANALYSIS**


**OVERVIEW:**

**U.S STATEWIDE PRECIPITATION OVERVIEW:**

  This page section of the climate report dashboard presents various varieties of U.S Statewide graph plots for the analysis of the
  U.S statewide Precipitation ranges, and to further depict any of the observed precipitation deviation trends in the area of
  precipitation increase or decrease from the period 1960 till 2022, also, in addition to the U.S statewide precipitation plots,
  various interactive time series plots are also developed and presented under this page section for depicting the precipitation
  trends across U.S states between this time frame. 

  The "Precipitation Analysis summary" tab from this dashboard section presents some of the imperative finding analysis from the
  perspective of U.S. statewide precipitation deviations or depiction of U.S. statewide precipitation trend analysis for the U.S.
  states between the period of the Years 1960 and 2022. 

  Reference: (https://www.ncdc.noaa.gov/)



### **U.S PRECIPITATION ANALYSIS PLOT**

```{r}

Prescptn_1960_2022 <- read.csv(file="~/Desktop/Precptn_1960_2022.csv" , header = TRUE)


## Determination of Data object layer for the ggplot:

Prescptn_1960_2022<- ggplot(Prescptn_1960_2022, aes(x = Year, y = Value)) + geom_line(color="#3399FF", size=1) +
  geom_point() + geom_hline(yintercept= mean(Prescptn_1960_2022$Value),color="#006633")


## Addition of subsequent layer to the ggplot:

Prescptn_1960_2022_a<- Prescptn_1960_2022 +
  labs(title = "U.S PRECIPATION (JAN-DEC PERIOD) BETWEEN YEAR 1960 TO 2022", x = "YEAR ", y = "PRECIPITATION IN INCHES") + scale_fill_discrete("Year") + cleanup

Prescptn_1960_2022_a


```

### **U.S PRECIPITATION INTERACTIVE TIME SERIES PLOT**

```{r}

library("dygraphs")

Prescptn_1960_2022 <- read.csv(file="~/Desktop/Precptn_1960_2022.csv" , header = TRUE)

dygraph(Prescptn_1960_2022,
        main = "U.S PRECIPITATION (JAN-DEC PERIOD) BETWEEN YEAR 1960 AND 2022 PLOT",
        xlab = "YEAR", ylab = "PRECIPITATION VALUE IN INCHES") %>%
  dySeries("Value", label= "Precipitation Value", strokeWidth = 3, color = "#0099cc") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```


### **US STATEWIDE PRECIPITATION ANALYSIS PLOT- A**

```{r}

Precptn_1960_1961 <- read.csv(file="~/Desktop/Precptn_1960_1961.csv" , header = TRUE)



## Precipitation:1960-1961:


## Loading/Extracting the map data:

Precptn_1960_1961 <- read.csv(file="~/Desktop/Precptn_1960_1961.csv" , header = TRUE)

states<- map_data("state")

Precptn_1960_1961$region <- tolower(Precptn_1960_1961$Location)

Precptn_1960_1961 <- merge(states, Precptn_1960_1961, by="region", all=T)

## Determination of Data object layer for the ggplot:

Precptn_1960_1961_Plot<- ggplot(Precptn_1960_1961, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Precptn_1960_1961_Plot <- Precptn_1960_1961_Plot + scale_fill_gradient(name = "Inches", low = "#009966", high = "#00ccff", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S STATEWIDE PRECIPITATION (IN INCHES) PLOT:1960-1961", x = "Longitude Scale ", y = "Latitude Scale")

Precptn_1960_1961_Plot

```


### **US STATEWIDE PRECIPITATION ANALYSIS PLOT- B**

```{r}

Precptn_2021_2022 <- read.csv(file="~/Desktop/Precptn_2021_2022.csv" , header = TRUE)



## Precipitation:2021-2022:


## Loading/Extracting the map data:

Precptn_2021_2022 <- read.csv(file="~/Desktop/Precptn_2021_2022.csv" , header = TRUE)

states<- map_data("state")

Precptn_2021_2022$region <- tolower(Precptn_2021_2022$Location)

Precptn_2021_2022 <- merge(states, Precptn_2021_2022, by="region", all=T)

## Determination of Data object layer for the ggplot:

Precptn_2021_2022_Plot<- ggplot(Precptn_2021_2022, aes(x = long, y = lat, group = group, fill = Value)) + geom_polygon(color = "white")


## Addition of subsequent layer to the ggplot:

Precptn_2021_2022_Plot <- Precptn_2021_2022_Plot + scale_fill_gradient(name = "Inches", low = "#009933", high = "#3399FF", guide = "colorbar", na.value="grey") + coord_map() + theme(legend.position = "top") + labs(title = "U.S STATEWIDE PRECIPITATION (IN INCHES) PLOT:2021-2021", x = "Longitude Scale ", y = "Latitude Scale")

Precptn_2021_2022_Plot


```

### **PRECIPITATION ANALYSIS SUMMARY**


**U.S. STATEWIDE PRECIPITATION KEY ANALYSIS FINDINGS: **
  
  **A.** 
  The trend of heavy or significant precipitation levels across the U.S. different states between the sixty years since the Year
  1960 till the year 2022 to a greater proportion, as also referenced by the multiple “U.S Precipitation Analysis” page plots, and
  where there appears to be a trend of continuous increase in the precipitation value in inches following Year 1970 and to a
  significant proportion.
  
  
   **B.**
  In reference to U.S. Statewide Precipitation Analysis plot-A and the interactive U.S Statewide Precipitation
  time-series plot-B, the highest precipitation value in inches for the period between the Year 1960 and 2022 was
  observed as 34.96 Inches for the Year 1973, followed by the second highest value of 34.82 for the Year 2019.


  
  **C.** 
  In reference to U.S. Statewide Precipitation Analysis plot-A and the interactive U.S Statewide Precipitation
  time-series plot-B, the lowest precipitation value in inches for the period between the Year 1960 and 2022 was
  observed as 25.7 Inches for the Year 1963, followed by the second lowest value of 25.9 for the Year 1988.


  

# **U.S STATEWIDE DROUGHT-FLOOD ANALYSIS**


## Column {.tabset}

### **U.S STATEWIDE DROUGHT-FLOOD SEVERITY ANALYSIS**


  **OVERVIEW:**

  **U.S STATEWIDE DROUGHT-FLOOD ANALYSIS OVERVIEW:**

  This page section of the climate report dashboard presents various varieties of U.S Statewide graph plots for the analysis of
  the maximum U.S statewide drought and flood severities, and to further depict any of the observed drought or flood deviation
  trends in the area of drought intensity increase or decrease from the period 1960 till 2022, in addition to the U.S statewide
  drought severity plots, time the series plot, and interactive time series plot are also developed and presented under this page
  section for depicting the drought and wetness severity anomalies across U.S states between this time frame.  

  The "Drought Analysis summary" tab from this dashboard section presents some of the imperative finding analysis from the
  perspective of U.S. statewide drought deviations or depiction of U.S. statewide drought or wetness severity trend analysis for the   U.S. states between the period of the Years 1960 and 2022. 

  Ref: https://www.drought.gov/



### **U.S EXCEPTIONAL DROUGHT PLOT**


```{r}


library("dygraphs")


Excp_Drght <- read.csv(file="~/Desktop/Exp_Drought_File.csv" , header = TRUE)



dygraph(Excp_Drght,
        main = "U.S EXCEPTIONAL DROUGHT SEVERITY PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "STANDARDIZED PRECIPITATION INDEX VALUE") %>%
  dySeries("Exceptional_Drought_Index_Value", label= "STI Value", strokeWidth = 3, color = "#666633") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```



### **U.S EXTREME DROUGHT PLOT**

```{r}


library("dygraphs")


Extrme_Drght <- read.csv(file="~/Desktop/Extreme_Drght.csv" , header = TRUE)



dygraph(Extrme_Drght,
        main = "U.S EXTREME DROUGHT SEVERITY PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "STANDARDIZED PRECIPITATION INDEX VALUE") %>%
  dySeries("Extreme_Drought", label= "STI Value", strokeWidth = 3, color = "#999933") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)







```


### **U.S SEVERE DROUGHT PLOT**


```{r}

library("dygraphs")


Severe_Drght <- read.csv(file="~/Desktop/Severe_Drght.csv" , header = TRUE)



dygraph(Severe_Drght,
        main = "U.S SEVERE DROUGHT SEVERITY PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "STANDARDIZED PRECIPITATION INDEX VALUE") %>%
  dySeries("Severe_Drought", label= "STI Value", strokeWidth = 3, color = "#009966") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)




```



### **U.S MODERATE DROUGHT PLOT**

```{r}

library("dygraphs")


Moderate_Drght <- read.csv(file="~/Desktop/Moderate_Drght.csv" , header = TRUE)



dygraph(Moderate_Drght,
        main = "U.S MODERATE DROUGHT SEVERITY PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "STANDARDIZED PRECIPITATION INDEX VALUE") %>%
  dySeries("Moderate_Drought", label= "STI Value", strokeWidth = 3, color = "#33cc99") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```


### **U.S EXCEPTIONAL WET PLOT**

```{r}

library("dygraphs")


Excpnl_Wet <- read.csv(file="~/Desktop/Excpnl_Wet.csv" , header = TRUE)



dygraph(Excpnl_Wet,
        main = "U.S EXCEPTIONAL WET SEVERITY PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "STANDARDIZED PRECIPITATION INDEX VALUE") %>%
  dySeries("Exceptional_Wet", label= "STI Value", strokeWidth = 3, color = "#0033cc") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)



```


### **U.S SEVERE WET PLOT**


```{r}


library("dygraphs")


Severe_Wet <- read.csv(file="~/Desktop/Severe_Wet.csv" , header = TRUE)



dygraph(Severe_Wet,
        main = "U.S SEVERE WET SEVERITY PLOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "STANDARDIZED PRECIPITATION INDEX VALUE") %>%
  dySeries("Severe_Wet", label= "STI Value", strokeWidth = 3, color = "#3366ff") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)






```



### **U.S MODERATE WET PLOT**

```{r}


library("dygraphs")


Moderate_Wet <- read.csv(file="~/Desktop/Moderate_Wet.csv" , header = TRUE)



dygraph(Moderate_Wet,
        main = "U.S MODERATE WET SEVERITY PLPOT BETWEEN YEAR 1960 AND 2022",
        xlab = "YEAR", ylab = "STANDARDIZED PRECIPITATION INDEX VALUE") %>%
  dySeries("Moderate_Wet", label= "STI Value", strokeWidth = 3, color = "#6699ff") %>%
  dyOptions(stackedGraph = TRUE) %>%
  dyRangeSelector(height = 20)


```


### **U.S DROUGHT-FLOOD SEVERITY ANALYSIS SUMMARY**



  **U.S DROUGHT SEVERITY ANALYSIS SUMMARY: **
  
  **A.** 
  Reference to the  U.S. Statewide Exceptional Drought Severity Analysis interactive plot, the standardized
  precipitation index (SPI) is generally considered one of the widely leveraged drought indicators for drought
  analysis, and the prediction has been observed as highest in the Year -1977 and with the standardized
  precipitation index (SPI) value of 19.6 for the period between the Year 1960 and 2022, followed by the second
  highest SPI value of 14 for the Year 2021.
  
  
  **B.** 
  About the U.S. Statewide Extreme Drought Severity Analysis interactive plot, the standardized precipitation
  index (SPI) value has been observed as highest in the Year -1977 and with the standardized precipitation index
  (SPI) value of 27.7 for the period between the Year 1960 and 2022, followed by the second highest SPI value of
  20.1 for the Year 1981.
  
  
         
  **C.** 
  In reference to the U.S. Statewide Severe Drought Analysis interactive plot, the standardized precipitation
  index (SPI) value has been observed as highest in the Year -1977 and with the standardized precipitation index
  (SPI) value of 34.7 for the period between the Year 1960 and 2022, followed by the second highest SPI value of
  30 for the Year 1981.
  
  
  
  **D.** 
  Reference to the U.S. Statewide Moderate Drought Severity Analysis interactive plot, the standardized
  precipitation index (SPI) value has been observed as highest in the Year -1981 and with the standardized
  precipitation index (SPI) value of 49.2 for the period between the Year 1960 and 2022, followed by the second
  highest SPI value of 47.3 for the Year 1977.
  
  **U.S WETNESS-FLOOD SEVERITY ANALYSIS SUMMARY:**
    
  
  **A.** 
  In reference to the U.S. Statewide Exceptional Wet Severity Analysis interactive plot, the standardized
  precipitation index (SPI) value has been observed as highest in the Year -2019 and with the standardized
  precipitation index (SPI) value of 14 for the period between the Year 1960 and 2022, followed by the second
  highest SPI value of 11.4 for the Year 2016.
  
  
  **B.** 
  In reference to the U.S. Statewide Severe Wet Severity Analysis interactive plot, the standardized
  precipitation index (SPI) value has been observed as highest in the Year -2016 and with the standardized
  precipitation index (SPI) value of 35.1 for the period between the Year 1960 and 2022, followed by the second
  highest SPI value of 31.3 for the Year 2019.
  

 **C.** 
 In reference to the U.S. Statewide Moderate Wet Severity Analysis interactive plot, the standardized
 precipitation index (SPI) value has been observed as highest in the Year -2016 and with the standardized
 precipitation index (SPI) value of 58.8 for the period between the Year 1960 and 2022, followed by the second
 highest SPI value of 52.2 for the Year 2005.




# **CLIMATE CHANGE QUESTIONS**


## Column {.tabset}


### **CLIMATE CHANGE QUESTION-1:**

**Climate Change Question # 1:**

Is there a long-term and continuous upward increasing trend for the temperature anomalies for the North American region and the Global land and oceanic temperature anomalies between the sixty years since the Year 1960 till the year 2022?


**Response relative to Dashboard Reports Plot Analysis Findings:**

Yes, there appears to be a long-term and continuous upward increasing trend for the temperature anomalies for the North American region in addition to the Global land and oceanic temperature anomalies between the sixty years since the Year 1960 till the year 2022, as also clearly referenced and depicted by various multiple plots under the “Global and North America Temperature Anomalies Analysis” pages, Regional and Global Land and Oceanic Temperature Anomalies Plots and some of the key supporting findings for this argument further referenced under the supporting evidence section as below:


**Supporting Evidence from the Climate Change Dashboard Reports Analysis plots:**

**A.** Reference to Global Land and Ocean Temperature Anomaly plots, there seems to be a constant increase trend in the Global Land and Oceanic Temperature Anomaly value from the Year 1977 onwards and above baseline.


**B.** Reference to Global Land and Ocean Temperature Anomaly plot and interactive time-series plot, the highest Global and Oceanic Temperature Anomaly value was observed as 1.03 Celsius degree in the Year 2016 for the period between the Year 1960 and 2022.


**C.** Reference to U.S North America Land Temperature Anomaly plots, there seems to be a constant increase trend in the North America Land Temperature Anomaly value from the Year 1985 onwards and above baseline.


**D.** Reference to the North America Land Temperature Anomaly plot and interactive time-series plot, the highest Land Temperature Anomaly value for North America was observed as 1.99 Celsius degree in the Year 2016 for the period between the Year 1960 and 2022.


**E.** Reference to the North America Land Temperature Anomaly plot and interactive time-series plot, the Lowest Land Temperature Anomaly value for North America was observed as -0.97 Celsius degree in the Year 1972 for the period between the Years 1960 and 2022.





### **CLIMATE CHANGE QUESTION-2:**

**Climate Change Question # 2:**

Do we have enough long-term and concrete evidence to reflect the existence of severe trends across the U.S. states for some of the climate change-related key indicator factors such as droughts, floods, average temperature increase, or precipitation?


**Response relative to Dashboard Reports Plot Analysis Findings:**

Yes, there appear to be long-term concrete pieces of evidence to reflect the existence of severe trends across the U.S. states for some of the climate change-related key indicator factors such as droughts, floods, average temperature increase, or precipitation between the sixty years since the Year 1960 till the year 2022, as also clearly referenced and depicted in climate trends of various multiple plots under the “U.S Drought-Flood Analysis”, “U.S Statewide Precipitation”, and “Average Temperature Analysis” pages, and some of the key supporting findings for this argument further referenced under the supporting evidence section as below:


**Supporting Evidence from the Climate Change Dashboard Reports Analysis plots:**


**A.** The Average Minimum and Maximum U.S. Statewide Temperature observed in the 2021-22 period were observed to be above 1 to 2 Fahrenheit degrees above on average as compared to the minimum temperature observed in the 1960-61 period, as also evidenced by the plot data and transition of color gradient and higher Fahrenheit Degrees scale (Ref: Max Temp Plot-A and B, Avg Temp Plot) for the Average temperature for 2021-22 period.

**B.** U.S Statewide Maximum Temperature Anomalies between the year 1960 and 2022 Plots (Reference: Max Temp Anomaly Plot and Interactive Time Series Plot) also clearly depicted an increase in the maximum temperature anomaly trend from the year 1998 onwards and above the national average value of maximum temperature anomaly value in Fahrenheit degrees and further, the U.S Minimum Temperature Anomalies between these years also clearly depicted an increase in the minimum temperature anomaly trend from the year 1997 onwards and above the national average value of minimum temperature anomaly value in Fahrenheit degrees.

**D.** Referencing Precipitation Analysis plot-A and the interactive Precipitation time-series plot-B, the highest precipitation value in inches for the period between the Year 1960 and 2022 was observed as 34.96 Inches for the Year 1973, followed by the second highest value of 34.82 for the Year 2019, further according to the U.S. Statewide Precipitation Analysis plot-A and the interactive U.S Statewide Precipitation time-series plot-B, the lowest precipitation value in inches for the period between the Year 1960 and 2022 was observed as 25.7 Inches for the Year 1963, followed by the second lowest value of 25.9 for the Year 1988.
 
**E.** Referencing the U.S. Statewide Exceptional Drought Severity Analysis interactive plot, the standardized precipitation index (SPI)  has been observed as the highest in the Year -1977 and with the SPI value of 19.6 for the period between the Year 1960 and 2022, followed by the second highest SPI value of 14 for the Year 2021 and per the U.S. Statewide Exceptional Wet Severity Analysis interactive plot, the SPI value has been observed as highest in the Year -2019 and with the standardized precipitation index (SPI) value of 14 for the period between the Years 1960 and 2022, followed by the second highest SPI value of 11.4 for the Year 2016.




### **CLIMATE CHANGE QUESTION-3:**

**Climate Change Question # 3:**


Does there exist a trend of heavy or significant precipitation levels across the U.S. different states between the sixty years since the Year 1960 till the year 2022?


**Response relative to Dashboard Reports Plot Analysis Findings:**

Yes, there appears to be a trend of increasing significant precipitation levels across the U.S. different states between the sixty years since the Year 1960 till the year 2022 to a greater proportion, as also referenced by the multiple “U.S Precipitation Analysis” page plots, and where there appears to be a trend of continuous increase in the precipitation value in inches following Year 1970 and to a significant proportion, and where the highest precipitation value in inches for the period between the Year 1960 and 2022 was observed as 34.96 Inches for the Year 1973, followed by the second highest value of 34.82 for the Year 2019, and the lowest precipitation value in inches for the period between the Year 1960 and 2022 was observed as 25.7 Inches for the Year 1963, followed by the second lowest value of 25.9 for the Year 1988.




### **CLIMATE CHANGE QUESTION-4:**

**Climate Change Question # 4:**

Do we have significant evidence suggesting that there does exist a trend of long-term U.S. state-wide regional change or variations in the average weather patterns defining the U.S. regional climatic patterns or conditions?


**Response relative to Dashboard Reports Plot Analysis Findings:**

Yes, referring to or taking into account all the Climate Dashboard reports multiple pages section plots into consideration, for the assessment of U.S state-wide regional long-term change or variations in the average weather patterns changes between the past sixty years since the Year 1960 till the year 2022, there appears changes in the average weather patterns from the perspective of increase and decrease in the maximum and minimum U.S state-wide regional temperature between the year 1960 and 2022, increase in the average U.S state-wide temperature and precipitation values and followed by significant variations of drought and wetness patterns in terms of exceptional, extreme, severe, or moderate observed severities in the drought pattern and flood-wetness patterns as also evident by multiple plots under the Drought-Flood Analysis page and other subsequent pages of this climate report dashboard.