DS Labs

DS Labs Datasets

Loading the package DSLabs

In this chunk, I am just loading the package into the dataset that I am going to work with.

library("dslabs")
data(package="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"        

Loading the Dataset

In this chunk, I’m going the load the dataset that I picked, “greenhouse_gases”, then I’m going to do some wrangling to check if the Data is clean.

library(ggthemes)
library(ggrepel)

Loading required package: ggplot2

data("greenhouse_gases")

Visualization

Here is where I am going to create a code to show you some visualizations. I created a heatmap visualization to explore trends in greenhouse gas concentrations (CO₂, CH₄, N₂O) over time using the greenhouse_gases dataset from the dslabs package in R. The code aggregates yearly average concentrations, then uses geom_tile() to represent these values as colored tiles, where warmer colors (reds) indicate higher concentrations and cooler colors (blues) show lower values

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
✔ lubridate 1.9.4     ✔ tibble    3.2.1
✔ purrr     1.0.4     ✔ tidyr     1.3.1
── 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

data("greenhouse_gases")

# Summarize data: Average concentration per year per gas
heatmap_data <- greenhouse_gases %>%
  group_by(year, gas) %>%
  summarise(mean_conc = mean(concentration, na.rm = TRUE)) %>%
  ungroup()

`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.

# Create heatmap
ggplot(heatmap_data, aes(x = year, y = gas, fill = mean_conc)) +
  geom_tile(color = "white", linewidth = 0.3) +  # White grid lines
  scale_fill_gradientn(
    colors = c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c"),
    name = "Concentration\n(CO2: ppm | CH4/N2O: ppb)"
  ) +
  labs(
    title = "Greenhouse Gas Concentrations Over Time",
    x = "Year",
    y = "Gas Type",
    caption = "Source: dslabs R package"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1),
    plot.title = element_text(face = "bold", size = 14),
    legend.position = "right"
  ) +
  scale_x_continuous(breaks = seq(0, 2020, by = 100))  # Custom x-axis breaks

## Recognition at Chatgpt for the line 75.
## Recognition at https://www.youtube.com/channel/UCfJyQ3P2k_SuqfxVdqIEQNw : R Programming 101