Midterm Project
Daeshik Park
I will investigate the data temp_carbon obtained from the data package dslabs. The data temp_carbon has 5 columns: year, temp_anomaly, land_anomaly, ocean_anomaly, and carbon_emissions. The anomalies are deviations from the baseline the 20th century average. According to NOAA, the 20th century average temperature of the global land and ocean surface is 14.8°C (58.6°F) and the 20th century average temperature of the global land-only surface is 11.1°C (52.0°F). Temperature anomalies are in degrees Celsius and carbon emissions are in millions of metric tons.
Read the data temp_carbon.
library("dslabs")
#data(package = "dslabs")
data("temp_carbon")
glimpse(temp_carbon)## Rows: 268
## Columns: 5
## $ year <dbl> 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 18...
## $ temp_anomaly <dbl> -0.11, -0.08, -0.10, -0.18, -0.26, -0.25, -0.24, -...
## $ land_anomaly <dbl> -0.48, -0.40, -0.48, -0.66, -0.69, -0.56, -0.51, -...
## $ ocean_anomaly <dbl> -0.01, 0.01, 0.00, -0.04, -0.14, -0.17, -0.17, -0....
## $ carbon_emissions <dbl> 236, 243, 256, 272, 275, 277, 281, 295, 327, 327, ...summary(temp_carbon)## year temp_anomaly land_anomaly ocean_anomaly
## Min. :1751 Min. :-0.450 Min. :-0.69000 Min. :-0.46000
## 1st Qu.:1818 1st Qu.:-0.180 1st Qu.:-0.31500 1st Qu.:-0.17000
## Median :1884 Median :-0.030 Median :-0.05000 Median :-0.01000
## Mean :1884 Mean : 0.060 Mean : 0.07086 Mean : 0.05273
## 3rd Qu.:1951 3rd Qu.: 0.275 3rd Qu.: 0.30500 3rd Qu.: 0.25500
## Max. :2018 Max. : 0.980 Max. : 1.50000 Max. : 0.79000
## NA's :129 NA's :129 NA's :129
## carbon_emissions
## Min. : 3.00
## 1st Qu.: 13.75
## Median : 264.00
## Mean :1522.98
## 3rd Qu.:1431.50
## Max. :9855.00
## NA's :4After looking at the data, there are no data values for anomalies from the years 1751 ~ 1879. Remove those years from the data.
temp_carbon_1880 <- temp_carbon %>% filter(year >= 1880)
summary(temp_carbon_1880)## year temp_anomaly land_anomaly ocean_anomaly
## Min. :1880 Min. :-0.450 Min. :-0.69000 Min. :-0.46000
## 1st Qu.:1914 1st Qu.:-0.180 1st Qu.:-0.31500 1st Qu.:-0.17000
## Median :1949 Median :-0.030 Median :-0.05000 Median :-0.01000
## Mean :1949 Mean : 0.060 Mean : 0.07086 Mean : 0.05273
## 3rd Qu.:1984 3rd Qu.: 0.275 3rd Qu.: 0.30500 3rd Qu.: 0.25500
## Max. :2018 Max. : 0.980 Max. : 1.50000 Max. : 0.79000
##
## carbon_emissions
## Min. : 236
## 1st Qu.: 837
## Median :1392
## Mean :2942
## 3rd Qu.:5116
## Max. :9855
## NA's :4Comparing Anomalies
Visualize the 3 anomaly variables in a plot to compare using highcharts.
highchart() %>%
hc_add_series(data = temp_carbon_1880$temp_anomaly,
type = "column",
name = "Global Temp Anomaly",
color = "gray") %>% # bars for temp_anomaly
hc_add_series(data = temp_carbon_1880$land_anomaly,
type = "line",
name = "Land Temp Anomaly",
color = "green") %>% # lines for land_anomaly
hc_add_series(data = temp_carbon_1880$ocean_anomaly,
type = "line",
name = "Ocean Temp Anomaly",
color = "blue") %>% # lines for ocean_anomaly
hc_title(text = "<b> Annual Temperature Anomalies </b>",
align = "center") %>%
hc_subtitle(text = "Baseline: 20th Century Mean Temperatures",
align = "center") %>%
hc_xAxis(title = list(text = "<b> Year </b>"),
categories = temp_carbon_1880$year,
tickInterval = 10) %>%
hc_yAxis(title = list(text = "<b> Temperature Anomaly (degrees Celsius) </b>")) %>%
hc_legend(align = "right", verticalAlign = "top") %>%
hc_tooltip(shared = TRUE)Summary: During the world war II, there are positive deviations because they made extreme number of weapons of mass destruction. Since 1975, anomalies are positive and in an increasing trend. Industries were developed rapidly, and land anomalies became the main factor of global temperature anomalies.
Animating the Variables temp_anomaly and carbon_emissions
Convert the variable year as an integer class and investigate covariate relation between the Variables temp_anomaly and carbon_emissions.
temp_carbon_1880$year <- as.integer(temp_carbon_1880$year)
# static plot
gg1 <- ggplot(temp_carbon_1880, aes(temp_anomaly, carbon_emissions)) +
geom_point(aes(color = land_anomaly, size = ocean_anomaly)) +
labs(title = "Trends of Carbon Emissions over Temperature Anomaly",
x = "Global Temperature Anomaly (degrees Celsius)",
y = "Carbon Emissions (millions tons)",
color = "Land Anomaly",
size = "Ocean Anomaly") +
scale_color_viridis_c()
# transition of states
gg2 <- gg1 +
transition_time(year) +
labs(subtitle = "Year: {frame_time}") +
shadow_mark(color = "black", alpha = 1, size = 0.5)
# changing themes
gg3 <- gg2 +
theme(plot.title = element_text(size = 13, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 11, face = "bold",
color = "darkblue", hjust = 0.5),
plot.background = element_rect("lightblue"),
plot.margin = unit(c(0.5,1,0.5,1), "cm"),
axis.text.x = element_text(size = 7),
axis.text.y = element_text(size = 7),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.title = element_text(color = "blue", size = 8),
legend.text = element_text(color = "red", size = 7),
legend.background = element_rect("lightgreen"))
animate(gg3, fps=3.5) # transition speed
Summary: Until mid-1940s, global temperature anomalies are negative, and the amounts of carbon emissions are stayed below. Since then, the amounts of carbon emissions keep increasing and most of the global temperature anomalies are positive. During the last 20 years (the 21st century), it looks to me that there are record highs in the two variables almost every year. So, I may project that we will have record highs in the two variables this year (but, there is a counter-factor, COVID-19). Overall, it seems that there is a positive correlation between the variables temp_anomaly and carbon_emissions.