Requirements

Identify what information interests you about climate change. Find and collect the data necessary to create your data exploration plan. Organize and summarize the collected data. Design and create the most appropriate visualizations to explore the data and present that information. Finally organize the layout of those visualizations in a way that shows your path of data exploration. Develop three questions or ideas about climate change from your visualizations.

Background: This study is to see what is the temperature change in United States from 1999 to 2019. Washington, California, Florida and Maine are the 4 example states to present the climate change. And the temperature are the January average temperature in all these years. The data is from https://www.ncdc.noaa.gov/cag/statewide

  1. Let’s look at how average January temperature look like in the above 4 states.
library(readxl)
Washington <- read_excel("C:/Users/Maple/Desktop/HU/Second sem/Data Visualization/Weather.xlsx", 
                                 sheet= "Washington")
## readxl works best with a newer version of the tibble package.
## You currently have tibble v1.4.2.
## Falling back to column name repair from tibble <= v1.4.2.
## Message displays once per session.
View(Washington)

T_W <- c(Washington$Temperature)
barplot(T_W,
        main = "Janauray Temperature in Washington from 1999 - 2019 (°F)", col = "green",
xlab = "Year",
ylab = "Temperature")

library(readxl)
California <- read_excel("C:/Users/Maple/Desktop/HU/Second sem/Data Visualization/Weather.xlsx", 
                                 sheet= "California")


T_C <- c(California$Temperature)
barplot(T_C,
        main = "Janauray Temperature in California from 1999 - 2019(°F)", col = "red",
xlab = "Year",
ylab = "Temperature")

library(readxl)
Florida <- read_excel("C:/Users/Maple/Desktop/HU/Second sem/Data Visualization/Weather.xlsx", 
                                 sheet= "Florida")


T_F <- c(Florida$Temperature)
barplot(T_F,
        main = "Janauray Temperature in Florida from 1999 - 2019(°F)", col = "yellow",
xlab = "Year",
ylab = "Temperature")

library(readxl)
Maine <- read_excel("C:/Users/Maple/Desktop/HU/Second sem/Data Visualization/Weather.xlsx", 
                                 sheet= "Maine")


T_M <- c(Maine$Temperature)
barplot(T_M,
        main = "Janauray Temperature in Maine from 1999 - 2019(°F)", col = "blue",
xlab = "Year",
ylab = "Temperature")

2. Let’s see what is the weather distribution in the 4 states

boxplot(T_W, col = "green", main = "Temperature Distribution in Washington" )

boxplot(T_C, col = "red", main = "Temperature Distribution in California" )

boxplot(T_F, col = "yellow", main = "Temperature Distribution in Florida" )

boxplot(T_M, col = "blue", main = "Temperature Distribution in Maine" )

  1. Let’s see the trend of the 4 states over these 20 years
plot(T_W,
        main = "Temerature Trend in Washington", type = "o", col = "green",
xlab = "Year",
ylab = "Temperature")

plot(T_C,
        main = "Temerature Trend in Califronia", type = "o", col = "red",
xlab = "Year",
ylab = "Temperature")

plot(T_F,
        main = "Temerature Trend", type = "o", col = "yellow",
xlab = "Year",
ylab = "Temperature Trend in Florida")

plot(T_M,
        main = "Temerature Trend", type = "o", col = "blue",
xlab = "Year",
ylab = "Temperature Trend in Maine")