Description

The dataset I chose is the carbon emmissions dataset to creat a scatter plot that shows land, air, and sea anomalies. Also, an average line was created to show the averages of each variable over the time period.

#Loading Packages from library
library(dslabs)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
library(dplyr)
library(RColorBrewer)
#Shows First Six Rows of Dataset
head(temp_carbon)
##   year temp_anomaly land_anomaly ocean_anomaly carbon_emissions
## 1 1880        -0.11        -0.48         -0.01              236
## 2 1881        -0.08        -0.40          0.01              243
## 3 1882        -0.10        -0.48          0.00              256
## 4 1883        -0.18        -0.66         -0.04              272
## 5 1884        -0.26        -0.69         -0.14              275
## 6 1885        -0.25        -0.56         -0.17              277
#Loading the Data Set in the Environment
data("temp_carbon")
#Filter the years from 1880 to 2018
temp_carbon <- temp_carbon %>%
  filter(year >= 1880 & year <= 2018)
# Transform the data to long format for easier plotting
temp_long <- temp_carbon %>%
  select(year, temp_anomaly, land_anomaly, ocean_anomaly) %>%
  gather(key = "category", value = "value", -year)
# Define colors for points and lines
category_colors <- c("temp_anomaly" = "blue", "land_anomaly" = "red", "ocean_anomaly" = "green")
# Create the scatter plot
ggplot(temp_long, aes(x = year, y = value, color = category)) +
  # Add scatter plot points
  geom_point(size = 2, alpha = 0.5) +
  # Add a smooth line (optional, remove if not needed)
  geom_smooth(method = "lm", se = FALSE, aes(color = category), size = 1) +
  # Use the defined colors for each category
  scale_color_manual(values = category_colors) +
  # Add meaningful labels
  labs(title = "Temperature Anomalies on Air, Land, and Sea(1880-2018)",
       x = "Year",
       y = "Temperature Anomaly (C)",
       color = "Category") +
  # Customize the theme
  theme_minimal(base_size = 15) +
  theme(legend.position = "right",
        plot.title = element_text(hjust = 0.5, size = 10))  # Center the plot title and decrease size
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `geom_smooth()` using formula = 'y ~ x'