Facing the Abyss

Abstract

A complete EDA Workflow

A Data Analytics Process

So you have your shiny new R skills and you’ve successfully loaded a cool dataframe into R… Now what?

The best charts come from understanding your data, asking good questions from it, and displaying the answers to those questions as clearly as possible.

Setting up R Packages

1. Install packages using install.packages() in your Console.
2. Load up your libraries in a setup chunk:

knitr::opts_chunk$set(dev = "ragg_png")

library(tidyverse)
library(mosaic)
library(palmerpenguins)
library(ggformula)
library(ggridges)
library(skimr)
##
library(GGally)
library(corrplot)
library(corrgram)

Go to https://fonts.google.com/ and choose some professional looking, or funky looking, fonts.

library(extrafont)
extrafont::loadfonts(quiet = TRUE)
##
library(showtext)
## Loading Google fonts (https://fonts.google.com/)
font_add_google(name = "Fira Sans Condensed", family = "fira")
font_add_google("Gochi Hand", "gochi")
font_add_google("Schoolbell", "bell")
font_add_google("Montserrat Alternates", "montserrat")
font_add_google("Roboto Condensed", "roboto")
### Automatically use showtext to render text
showtext_auto()

Read Data

• Use readr::read_csv()

Examine Data

• Use dplyr::glimpse()
• Use mosaic::inspect() or skimr::skim()
• Use dplyr::summarise() and crosstable::crosstable()
• Format your tables with knitr::kable()
• Highlight any interesting summary stats or data imbalances

Data Dictionary and Experiment Description

• A table containing the variable names, their interpretation, and their nature(Qual/Quant/Ord…)
• If there are wrongly coded variables in the original data, state them in their correct form, so you can munge the in the next step
• Declare what might be target and predictor variables, based on available information of the experiment, or a description of the data

Data Munging

• Convert variables to factors as needed
• Reformat / Rename other variables as needed
• Clean badly formatted columns (e.g. text + numbers) using tidyr::separate_**_**()
• Save the data as a modified file
• Do not mess up the original data file

Form Hypotheses

Question-1

• State the Question or Hypothesis
• (Temporarily) Drop variables using dplyr::select()
• Create new variables if needed with dplyr::mutate()
• Filter the data set using dplyr::filter()
• Reformat data if needed with tidyr::pivot_longer() or tidyr::pivot_wider()
• Answer the Question with a Table, a Chart, a Test, using an appropriate Model for Statistical Inference
• Use title, subtitle, legend and scales appropriately in your chart
• Prefer ggformula unless you are using a chart that is not yet supported therein (eg. ggbump() or plot_likert())

## Set graph theme
## Idiotic that we have to repeat this every chunk
## Open issue in Quarto

penguins %>%
  drop_na() %>%
  gf_point(body_mass_g ~ flipper_length_mm,
    colour = ~species
  ) %>%
  gf_labs(
    title = "My First Penguins Plot",
    subtitle = "Using ggformula with fonts",
    x = "Flipper Length mm", y = "Body Mass gms",
    caption = "I love penguins, and R"
  ) %>%
  gf_theme(theme_classic()) %>%
  gf_theme(theme(
    panel.grid.minor = element_blank(),
    ###
    text = element_text(family = "fira", size = 14),
    ###
    plot.title = element_text(
      family = "roboto",
      face = "bold",
      size = 28, hjust = 0
    ),
    plot.subtitle = element_text(
      family = "montserrat",
      face = "bold",
      size = 18, hjust = 0
    ),
    plot.margin = margin(2, 2, 2, 2, unit = "pt"),
    axis.title = element_text(size = 20),
    plot.caption = element_text(family = "gochi", size = 14),
    legend.title = element_text(
      family = "bell",
      face = "bold",
      size = 20
    ),
    legend.text = element_text(
      family = "fira",
      size = 12
    ),
    legend.background = element_rect(
      fill = "cornsilk",
      colour = "black"
    ),
    legend.margin = margin(
      t = 2,
      r = 2,
      b = 2,
      l = 2,
      unit = "pt"
    )
  ))

Inference-1

. . . .

Question-n

….

Inference-n

….

One Most Interesting Graph

Conclusion

Describe what the graph shows and why it so interesting. What could be done next?

References

1. https://shancarter.github.io/ucb-dataviz-fall-2013/classes/facing-the-abyss/