Lab 2 Data Exploration and Analysis
WeiQi Huang
10/6/2021
#Develop four questions or ideas about climate change from your visualizations.
#Identify what information interests you about climate change.
This analysis is intested in “What is warming our earth?” According to NASA Goddard Institute for Space Studies, the earth has been warming up each year. Global warming was evident in the 1940s and escalated even more starting in the 1970s.
The first dataset is from NASA GISS database that is a Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies (Land-Ocean Temperature Index, L-OTI). The second dataset is from Bloomberg which shows information regarding natural and human causes of climate change. By comparing the temperature anomalies from the year 1880 to 2005 and contrast them with another data set providing information on natural and human causes of climate change to make it plainly visible what is causing the increase in temperature of our earth. Once the source is identified further research can be conducted and climate policies can be made to ensure we take steps to protect our environment.
#Find, collect, organize, and summarize the data necessary to create your data exploration plan.
data = read.csv('/Users/mo_more/2021 HU506 analytics/final-/global_temperature_anomalies.csv')
data.forcings = read.csv('/Users/mo_more/2021 HU506 analytics/final-/forcings.csv')summary(data)## hemisphere year jan feb
## Length:423 Min. :1880 Min. :-1.52000 Min. :-0.97000
## Class :character 1st Qu.:1915 1st Qu.:-0.24000 1st Qu.:-0.23000
## Mode :character Median :1950 Median :-0.03000 Median :-0.04000
## Mean :1950 Mean : 0.04929 Mean : 0.05643
## 3rd Qu.:1985 3rd Qu.: 0.30500 3rd Qu.: 0.36000
## Max. :2020 Max. : 1.60000 Max. : 1.95000
##
## mar apr may jun
## Min. :-0.78 Min. :-0.64000 Min. :-0.72000 Min. :-0.65000
## 1st Qu.:-0.22 1st Qu.:-0.25000 1st Qu.:-0.24000 1st Qu.:-0.26000
## Median :-0.02 Median :-0.03000 Median :-0.04000 Median :-0.05000
## Mean : 0.07 Mean : 0.04875 Mean : 0.03913 Mean : 0.02513
## 3rd Qu.: 0.32 3rd Qu.: 0.28000 3rd Qu.: 0.24000 3rd Qu.: 0.23000
## Max. : 1.91 Max. : 1.49000 Max. : 1.28000 Max. : 1.20000
##
## jul aug sep oct
## Min. :-0.58000 Min. :-0.76000 Min. :-0.79000 Min. :-0.83000
## 1st Qu.:-0.20000 1st Qu.:-0.21000 1st Qu.:-0.21000 1st Qu.:-0.20500
## Median :-0.03000 Median :-0.05000 Median :-0.04000 Median : 0.00000
## Mean : 0.04799 Mean : 0.04648 Mean : 0.04924 Mean : 0.07463
## 3rd Qu.: 0.23500 3rd Qu.: 0.26000 3rd Qu.: 0.25000 3rd Qu.: 0.27500
## Max. : 1.11000 Max. : 1.14000 Max. : 1.23000 Max. : 1.32000
##
## nov dec j_d d_n
## Min. :-0.82000 Min. :-1.13000 Min. :-0.57000 Min. :-0.57000
## 1st Qu.:-0.20000 1st Qu.:-0.22000 1st Qu.:-0.21000 1st Qu.:-0.21000
## Median : 0.01000 Median :-0.04000 Median :-0.03000 Median :-0.04500
## Mean : 0.07113 Mean : 0.04423 Mean : 0.05184 Mean : 0.05293
## 3rd Qu.: 0.27000 3rd Qu.: 0.29500 3rd Qu.: 0.25500 3rd Qu.: 0.27000
## Max. : 1.62000 Max. : 1.54000 Max. : 1.36000 Max. : 1.38000
## NA's :3
## djf mam jja son
## Min. :-1.05000 Min. :-0.70000 Min. :-0.52000 Min. :-0.71000
## 1st Qu.:-0.22000 1st Qu.:-0.24000 1st Qu.:-0.22000 1st Qu.:-0.19500
## Median :-0.04000 Median :-0.05000 Median :-0.05000 Median :-0.02000
## Mean : 0.04955 Mean : 0.05277 Mean : 0.03991 Mean : 0.06511
## 3rd Qu.: 0.29250 3rd Qu.: 0.27500 3rd Qu.: 0.22000 3rd Qu.: 0.26000
## Max. : 1.68000 Max. : 1.51000 Max. : 1.13000 Max. : 1.35000
## NA's :3summary(data.forcings)## Year All.forcings Human Natural
## Min. :1850 Min. :287.1 Min. :287.4 Min. :287.1
## 1st Qu.:1889 1st Qu.:287.5 1st Qu.:287.5 1st Qu.:287.4
## Median :1928 Median :287.6 Median :287.6 Median :287.5
## Mean :1928 Mean :287.7 Mean :287.7 Mean :287.4
## 3rd Qu.:1966 3rd Qu.:287.8 3rd Qu.:287.7 3rd Qu.:287.5
## Max. :2005 Max. :288.4 Max. :288.3 Max. :287.6
## Anthropogenic.tropospheric.aerosol Greenhouse.gases Land.use
## Min. :287.0 Min. :287.4 Min. :287.3
## 1st Qu.:287.2 1st Qu.:287.5 1st Qu.:287.4
## Median :287.4 Median :287.7 Median :287.4
## Mean :287.3 Mean :287.8 Mean :287.4
## 3rd Qu.:287.4 3rd Qu.:287.9 3rd Qu.:287.5
## Max. :287.5 Max. :288.7 Max. :287.5
## Orbital.changes Ozone Solar Volcanic
## Min. :287.4 Min. :287.4 Min. :287.4 Min. :287.1
## 1st Qu.:287.4 1st Qu.:287.4 1st Qu.:287.4 1st Qu.:287.4
## Median :287.4 Median :287.5 Median :287.5 Median :287.5
## Mean :287.4 Mean :287.5 Mean :287.5 Mean :287.4
## 3rd Qu.:287.5 3rd Qu.:287.5 3rd Qu.:287.5 3rd Qu.:287.5
## Max. :287.5 Max. :287.6 Max. :287.6 Max. :287.6#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.
Data visualizaiton1 :
From the fisrt climate change dataset from 1880 to 2020, which covers global temperature changes throughout the year. It is ovious that the global watming is true.
ggplot(data, aes(x=year, y=jan)) +
geom_line(color="Light Blue") +
geom_point(size=0.3, colour=" Dark blue") +
geom_hline(yintercept = mean(data$jan), color="blue") +
labs(y="January temperature",
x="Year",
title="Temperature in January since 1880")+
theme_gray()+ # Default theme
theme(plot.title = element_text(size=22),axis.text.x= element_text(size=15),
axis.text.y= element_text(size=15), axis.title=element_text(size=18))
Data visualizaiton2:
I analyzed the trends and causes of climate warming by detecting global climate change data from 1880 to 2020, which covers global temperature changes throughout the year. Human factors (combined) has more closer trend as the sphere temperature, comparet to the natural factor (combined).
g = ggplot(data.forcings, aes(x=Year, y=Human))
g + geom_line(color="dark red") +
geom_point(size=0.5, colour="orange") +
labs(title = "Temparature changes over years by Human forcings",
x = "Year from 1880 to 2005",
y = "Huamn forcings") +
theme(title=element_text(size=14))
g2 = ggplot(data.forcings, aes(x=Year, y=Natural))
g2 + geom_line(colour="dark green") +
geom_point(size=0.5, color="green") +
labs(title = "Temparature changes over years by Natural forcings",
x = "Year from 1880 to 2005",
y = "Natural forcings") +
theme(title=element_text(size=14))
Data visualizaiton3 :
As human factors combined are more likely to be the cause of global warming, I want to break down which factor, or how many factors should be responsible for it. Among for factors from the dataser, aerosoll, land use, greenhouse gasem ozone, and based on the charts for each, we can conclude that the Greenhouse Gases Temperature line has the closest pattern as the earth’s temperature increasing trend.
g3 = ggplot(data.forcings, aes(x=Year, y=Anthropogenic.tropospheric.aerosol))
g3 + geom_line(color="orange") +
geom_point(size=1, colour="orange") +
geom_smooth(se =FALSE, method = lm) +
labs(title = "Temparature changes over years by Aerosoll",
x = "Year from 1880 to 2005",
y = "Aerosol") +
theme(title=element_text(size=14))## `geom_smooth()` using formula 'y ~ x'
g4 = ggplot(data.forcings, aes(x=Year, y=Greenhouse.gases))
g4 + geom_line(colour="orange") +
geom_point(size=1, color="orange") +
geom_smooth(se =FALSE, method = lm) +
labs(title = "Temparature changes over years by Greenhouse Gases",
x = "Year from 1880 to 2005",
y = "Greenhouse Gases") +
theme(title=element_text(size=14))## `geom_smooth()` using formula 'y ~ x'
g5 = ggplot(data.forcings, aes(x=Year, y= Land.use))
g5 + geom_line(colour="orange") +
geom_point(size=1, color="orange") +
geom_smooth(se =FALSE, method = lm) +
labs(title = "Temparature changes over years by Land.use",
x = "Year from 1880 to 2005",
y = " Land.use") +
theme(title=element_text(size=14))## `geom_smooth()` using formula 'y ~ x'
g6 = ggplot(data.forcings, aes(x=Year, y=Ozone))
g6 + geom_line(colour="orange") +
geom_point(size=1, color="orange") +
geom_smooth(se =FALSE, method = lm) +
labs(title = "Temparature changes over years by Ozone",
x = "Year from 1880 to 2005",
y = "Ozone ") +
theme(title=element_text(size=14))## `geom_smooth()` using formula 'y ~ x'