Data and setting up your workflow

Installation of associated packages

install.packages(drone)
install.packages(tidyverse)

Data set use in geom Encyclopedia

library(drone)
library(tibble)
data(worldbankdata)
worldbankdata
# A tibble: 7,937 × 7
   Country Code  Region                     Year Cooking Electricity Income
   <fct>   <fct> <fct>                     <dbl>   <dbl>       <dbl> <fct> 
 1 Aruba   ABW   Latin America & Caribbean  1990      NA       100   H     
 2 Aruba   ABW   Latin America & Caribbean  2000      NA        91.7 H     
 3 Aruba   ABW   Latin America & Caribbean  2013      NA       100   H     
 4 Aruba   ABW   Latin America & Caribbean  2014      NA       100   H     
 5 Aruba   ABW   Latin America & Caribbean  2015      NA       100   H     
 6 Aruba   ABW   Latin America & Caribbean  2016      NA       100   H     
 7 Aruba   ABW   Latin America & Caribbean  2017      NA       100   H     
 8 Aruba   ABW   Latin America & Caribbean  2018      NA       100   H     
 9 Aruba   ABW   Latin America & Caribbean  2019      NA       100   H     
10 Aruba   ABW   Latin America & Caribbean  2020      NA       100   H     
# ℹ 7,927 more rows

worldbankdata: Data Profiling

library(skimr)
worldbankdata |> 
  skim()
Data summary
Name worldbankdata
Number of rows 7937
Number of columns 7
_______________________
Column type frequency:
factor 4
numeric 3
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
Country 0 1.00 FALSE 227 Afg: 36, Alb: 36, Alg: 36, Ame: 36
Code 0 1.00 FALSE 218 CIV: 48, CUW: 47, CZE: 47, FRO: 47
Region 199 0.97 FALSE 7 Eur: 2038, Sub: 1693, Lat: 1512, Eas: 1343
Income 559 0.93 FALSE 4 H: 2177, LM: 1978, L: 1704, UM: 1519

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Year 1 1.00 2004.59 10.41 1987.00 1996.00 2005.00 2014 2022 ▇▇▇▇▇
Cooking 6047 0.24 65.48 38.46 0.00 27.30 84.75 100 100 ▃▁▁▁▇
Electricity 5693 0.28 84.40 26.45 2.11 79.86 99.80 100 100 ▁▁▁▁▇

worldbankdata: Data Quality Analysis

library(tidyverse)
library(visdat)
vis_dat(worldbankdata) + 
  scale_fill_brewer(palette = "Dark2")

library(naniar)
gg_miss_upset(worldbankdata)

R packages with geom implementation

  1. ggplot2 (Wickham 2016)

  2. ggpattern (FC, Davis, and ggplot2 authors 2023)

  3. ggforce (Pedersen 2022)

  4. ggalluvial (ggalluvial?)

  5. ggbump (Sjoberg 2020)

  6. ggridges (Wilke 2023)

  7. ggalt (Rudis, Bolker, and Schulz 2017)

WIP - adding..

FC, Mike, Trevor L Davis, and ggplot2 authors. 2023. Ggpattern: ’Ggplot2’ Pattern Geoms.
Pedersen, Thomas Lin. 2022. Ggforce: Accelerating ’Ggplot2’. https://CRAN.R-project.org/package=ggforce.
Rudis, Bob, Ben Bolker, and Jan Schulz. 2017. Ggalt: Extra Coordinate Systems, ’Geoms’, Statistical Transformations, Scales and Fonts for ’Ggplot2’. https://CRAN.R-project.org/package=ggalt.
Sjoberg, David. 2020. Ggbump: Bump Chart and Sigmoid Curves. https://CRAN.R-project.org/package=ggbump.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
Wilke, Claus O. 2023. Ggridges: Ridgeline Plots in ’Ggplot2’. https://CRAN.R-project.org/package=ggridges.