DsLabs Assignment 2

Author

Duchelle K

DSLABS PACKAGE

Install package dslabs

library(dslabs)

Warning: package 'dslabs' was built under R version 4.4.1

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"

Choose a dataset and Load additional library packages to explore it

I have chosen the gapminder dataset. This dataset includes health and income outcomes for 184 countries from 1960 to 2016. It also includes two character vectors, OECD and OPEC, with the names of OECD and OPEC countries from 2016.

OPEC, the Organization of the Petroleum Exporting Countries, was formed in 1960 and consists of 13 member states, including Saudi Arabia, Iraq, and Venezuela. These countries collectively hold over 80% of the world’s proven oil reserves and produce about 40% of the world’s crude oil, with their exports accounting for around 60% of global petroleum trade. OPEC aims to coordinate petroleum policies among its members to stabilize oil prices, ensure supply, and provide a fair return on investment.

The OECD (Organization for Economic Co-operation and Development) is made up of industrialized countries, such as the United States and much of Europe. While these economies consume less oil than non-OECD countries, they still have a significant impact on global oil demand.

data("gapminder")
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(leaflet)

Warning: package 'leaflet' was built under R version 4.4.1

library(leaflet.extras)

Warning: package 'leaflet.extras' was built under R version 4.4.1

Upload a dataset that has countries longitude and latitude

setwd("C:/Users/User/Downloads/Data 110 Projects and Assignments")
countries_coordinates <- read_csv("world_country_and_usa_states_latitude_and_longitude_values.csv")

Rows: 245 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): country_code, country, usa_state_code, usa_state
dbl (4): latitude, longitude, usa_state_latitude, usa_state_longitude

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(countries_coordinates)

# A tibble: 6 × 8
  country_code latitude longitude country      usa_state_code usa_state_latitude
  <chr>           <dbl>     <dbl> <chr>        <chr>                       <dbl>
1 AD               42.5      1.60 Andorra      AK                           63.6
2 AE               23.4     53.8  United Arab… AL                           32.3
3 AF               33.9     67.7  Afghanistan  AR                           35.2
4 AG               17.1    -61.8  Antigua and… AZ                           34.0
5 AI               18.2    -63.1  Anguilla     CA                           36.8
6 AL               41.2     20.2  Albania      CO                           39.6
# ℹ 2 more variables: usa_state_longitude <dbl>, usa_state <chr>

Merge the two datasets

I will join this gapminder and some variables of countries_coordinates based on the “country” column

world <- left_join(gapminder,
# Select relevant columns                  
                    select(countries_coordinates,
                          "latitude",
                          "longitude",
                          "country"),
                   by = "country") 

head(world)

              country year infant_mortality life_expectancy fertility
1             Albania 1960           115.40           62.87      6.19
2             Algeria 1960           148.20           47.50      7.65
3              Angola 1960           208.00           35.98      7.32
4 Antigua and Barbuda 1960               NA           62.97      4.43
5           Argentina 1960            59.87           65.39      3.11
6             Armenia 1960               NA           66.86      4.55
  population          gdp continent          region  latitude  longitude
1    1636054           NA    Europe Southern Europe  41.15333  20.168331
2   11124892  13828152297    Africa Northern Africa  28.03389   1.659626
3    5270844           NA    Africa   Middle Africa -11.20269  17.873887
4      54681           NA  Americas       Caribbean  17.06082 -61.796428
5   20619075 108322326649  Americas   South America -38.41610 -63.616672
6    1867396           NA      Asia    Western Asia  40.06910  45.038189

Create a map with leaflet

I will create an interactive map with Leaflet for one year (2010), and add circle markers representing various metrics for each country. The popup will only show up when we click the marker on the map.

# Filter for the year 2010
world_filtered <- world %>%
  filter(year == 2010) 

# Create the leaflet map
p1 <-  leaflet()|>
  addTiles() |>
# Center the map at specified coordinates and zoom level
  setView(lng = 12.43, lat = 42.98, zoom = 5) %>%
# Add circle markers to the map, with popups displaying various metrics
  addCircleMarkers(data = world_filtered, color = "darkblue",
             lat = ~latitude , 
             lng = ~longitude ,
             popup = ~paste("gdp:",gdp, 
                                  "<br>Life Expectancy:",life_expectancy,
                                  "<br>Year:", year, 
                                  "<br>Fertility:",fertility,
                                  "<br>Population:", population,
                                  "<br>Infant Mortality:", infant_mortality))|>
# Add a toolbar to the map for drawing shapes
  addDrawToolbar()

Warning in validateCoords(lng, lat, funcName): Data contains 13 rows with
either missing or invalid lat/lon values and will be ignored

p1

When mousing over this map, we can view various observations for each country. The goal was to display the geographical distribution of GDP, life expectancy, fertility rate, and infant mortality.

However, this method isn’t ideal for visualizing relationships between variables; scatterplots or line charts would be more effective for that.