DSLabs Homework

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(dslabs)
list.files(system.file("script", package = "dslabs"))

 [1] "make-admissions.R"                   
 [2] "make-brca.R"                         
 [3] "make-brexit_polls.R"                 
 [4] "make-calificaciones.R"               
 [5] "make-death_prob.R"                   
 [6] "make-divorce_margarine.R"            
 [7] "make-gapminder-rdas.R"               
 [8] "make-greenhouse_gases.R"             
 [9] "make-historic_co2.R"                 
[10] "make-mice_weights.R"                 
[11] "make-mnist_127.R"                    
[12] "make-mnist_27.R"                     
[13] "make-movielens.R"                    
[14] "make-murders-rda.R"                  
[15] "make-na_example-rda.R"               
[16] "make-nyc_regents_scores.R"           
[17] "make-olive.R"                        
[18] "make-outlier_example.R"              
[19] "make-polls_2008.R"                   
[20] "make-polls_us_election_2016.R"       
[21] "make-pr_death_counts.R"              
[22] "make-reported_heights-rda.R"         
[23] "make-research_funding_rates.R"       
[24] "make-stars.R"                        
[25] "make-temp_carbon.R"                  
[26] "make-tissue-gene-expression.R"       
[27] "make-trump_tweets.R"                 
[28] "make-weekly_us_contagious_diseases.R"
[29] "save-gapminder-example-csv.R"        

Greenhouse Gases

This dataset includes historical records of various greenhouse gas concentrations, measured in parts per million (ppm), across many years. Each observation corresponds to a certain year and the concentrations of various gases, such as CO2, CH4, and N2O.

data("greenhouse_gases")
#view(greenhouse_gases)
#write_csv(greenhouse_gases, "greenhouse_gases.csv", na="")

head(greenhouse_gases)

  year gas concentration
1   20 CO2         277.7
2   40 CO2         277.8
3   60 CO2         277.3
4   80 CO2         277.3
5  100 CO2         277.5
6  120 CO2         277.6

Scatter Plot for Greenhouse Gases

ggplot(greenhouse_gases, aes(x = year, y = concentration, color = gas)) +
  geom_point() + 
  labs(
    title = "Greenhouse Gas Concentrations Over Time",  
    x = "Year",                                         
    y = "Concentration (ppm)"                           
  ) +
  theme_minimal() +          
  theme(
    plot.title = element_text(size = 14, face = "bold"), 
    axis.title = element_text(size = 12)                 
  ) +
  scale_color_brewer(palette = "Set3") +  
  guides(color = guide_legend(title = "Type of Gas"))  

Observation

CO2: Often displays a large increase, showing its major role as a greenhouse gas and its strong relationship with human activities such as the usage of fossil fuels. CH4: Has increased fluctuation and can show sharp increases at select places, reflecting changes in sources like as agriculture or landfills. N2O: Usually shows a modest but consistent increase over time.