R Notebook

library(tidyverse)
library()
co2_data <- read_csv("~/Desktop/School/Summer_22/JOUR_S-171/JOUR_S_171/owid-co2-data.csv")
climate_change <- read_csv("~/Desktop/School/Summer_22/JOUR_S-171/JOUR_S_171/climate-change.csv")
water_ph <- read_csv("~/Desktop/School/Summer_22/JOUR_S-171/JOUR_S_171/climate-change_ph.csv")

Data Analysis steps 1. Create scatter plots for co2 emissons per capita in the world and the USA over the past 20 years

co2_data %>%
  filter(country == "World", year >= 2000) %>%
  ggplot(aes(x = year, y = co2_per_capita)) +
  geom_point() +
  theme_minimal()

co2_data %>%
  filter(iso_code == "USA", year >= 2000) %>%
  ggplot(aes(x = year, y = co2_per_capita)) +
  geom_point(color = "darkslategray4") +
  theme_minimal()

2. Create table of global co2 emissions per capita and gloabl co2 emissions over time

co2_data %>%
  filter(year >= 2000, country == "World") %>%
  select(year, co2_per_capita, co2) %>%
  arrange(year)

3. Create table of monthly pH measurements

water_ph %>%
  filter(Entity == "World", !is.na(`Monthly pH measurement`)) %>%
  select(Date, `Monthly pH measurement`)

4. Graph pH levels over time

water_ph %>%
  filter(Entity == "World", !is.na(`Monthly pH measurement`)) %>%
  ggplot(aes(x = Date, y = `Monthly pH measurement`)) +
  geom_point(color = "darkslategray4") + 
  theme_minimal() +
  labs(title = "Ocean Acidification: pH levels over time, Aloha station, Hawaii")