---
title: "Lab_02_Data Exploration and Analysis"
author: "Sindhura Arigela"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(maps)
library(ggmap)
library(dplyr)
library(ggplot2)
library(maptools)
library(viridis)
```

### Temperature Observations in the U.S. (Min/Max)

```{r}

maxTempData = read.csv(url("https://www.ncdc.noaa.gov/cag/statewide/mapping/110-tmax-202003-36.csv"), skip=3)

maxTempData$region = tolower(maxTempData$Location)
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") +
scale_fill_gradient(name = "Degrees Fahrenheit", low = "#feceda", high = "#c81f49", guide = "colorbar", na.value="black") +
labs(title="Average Maximum Temperature[April 2017 - March 2020]", x="Longitude", y="Latitude")+
coord_map()


minTempData = read.csv(url("https://www.ncdc.noaa.gov/cag/statewide/mapping/110-tmin-202003-36.csv"),skip=3)

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

ggplot(minTempData, aes(x = long, y = lat, group = group, fill = Value)) + 
geom_polygon(color = "white") +
scale_fill_gradient(name = "Degrees Fahrenheit", na.value="black") +
labs(title="Average Minumum Temperature [April 2017 - March 2020] ", x="Longitude", y="Latitude")+
coord_map()

```

***

The visualizations show the average minimum / maximum temprature accross the country between 3 years peiord. It can be observed from the visualizations that the temperature is rising as we go from the north to south.


### Temperature Observations in the U.S. (Avg)

```{r}
avgTemp = read.csv(url("https://www.ncdc.noaa.gov/cag/national/time-series/110-tavg-1-7-1900-2019.csv?base_prd=true&begbaseyear=1900&endbaseyear=2019"), skip=4) 

avgTemp$Date = substr(avgTemp$Date, 0, 4) 
avgTemp$Date = as.numeric(avgTemp$Date) 

ggplot( avgTemp, aes( x = Date, y = Value, group = 1)) +
geom_line(color = "Blue") +
geom_smooth(method='lm', se=FALSE, color='red') +
labs(title="Average Temperatures in July [1900 - 2019]", x="Year", y="Temperature (F)")

```

***

The plot shows the average temperatures in the month of July over the past 100 years from 1900 - 2019. A steady increase in the average temperatures over the years can be seen in this plot.

### Global Land and Ocean Temperature Anomalies

```{r}
global = read.csv(url("https://www.ncdc.noaa.gov/cag/global/time-series/globe/land_ocean/1/7/1901-2020/data.csv"), skip=4)


names(global)<-c("Year","Anomaly")

ggplot(global, aes(Year, Anomaly))+ 
  geom_point(aes(color = Anomaly)) +                  
  geom_smooth(method ="auto", se = FALSE) +
  scale_color_viridis(discrete = F, option = "D")+
  scale_fill_viridis(discrete = F) +
  labs(title = "Global Land and Ocean Temperature Anomalies",
       x = "Year",     
       y = "Temperature Anomalies in Degree Celsius")
```

***

The graph shows the Global Land and Ocean Temperature anomalies over the past 100 years (1900-2020). It is observed that there is a natural rise in the global temperatures on both land and ocean.

### Annual Lower Tropospheric Global Temperature Anomalies

```{r}
LTGP_data <- read.csv(url("https://www.ncdc.noaa.gov/temp-and-precip/msu/global/lt/dec/ytd/data.csv"),skip=1)

LTGP_data_melted <- reshape2::melt(LTGP_data, id.var='Year')

ggplot(LTGP_data_melted, aes(x=Year, y=value, col=variable)) +
geom_line() +
labs(title="Annual Lower Tropospheric Global Temperature Anomalies", 
      x = "Year",
      y = "Anomalies", 
      color=NULL)
  

```

***

Lower Tropospheric Temperature is the one of the most common indicators to determine the global climate change. The graph shows the clear increase in the lower tropospheric temperature globally between 1979 and 2020 based on two models (UAH and RSS). 

### Sea Ice Extent

```{r}

NHSI_data <- read.csv(url("https://www.ncdc.noaa.gov/snow-and-ice/extent/sea-ice/G/7.csv"),skip=3)

ggplot(NHSI_data,aes(x=Date,y=Value))+
  geom_point(color = "brown")+
  geom_smooth(method = 'lm', color = "yellow")+
  labs(title="July Globe Sea Ice Extent (1979-2020)",x="Year",y="million square km")

```

***

This graph shows the level of Sea Ice Extent globally in the month of July from 1979 - 2020. Changes in the amount of sea ice can disrupt normal ocean circulation, thereby leading to changes in global climate.  Even a small increase in temperature can lead to greater warming over time, making the polar regions the most sensitive areas to climate change on Earth. It can be seen from the graph that there is an evident decrease in the levels of Sea Ice Extent from 1979 - 2020.  

### Conclusion

From the visualizations, the following questions could be developed for further analyses.

1.	Is there a relationship between the trends of global temperatures on land, ocean and lower troposphere?

2.	By analysing the national average temperatures based on the state, can we say that the most greenest/environmental friendly states like Vermont has relatively lower temperature change?
 
3.	Is there a relationship between the rise in the global temperature and the decline in the sea ice extent?

4.	Has the difference between the maximum and minimum temperatures increase over the past 100 years?

**Conclusion**
After analyzing the various datasets spread across the past 100 years, it can be concluded that the global temperatures have been gradually increasing on land, ocean and lower troposphere and calls for an immediate action from the entire humakind to control this climate crisis.