The Mad Hatter’s Guide to Data Viz and Stats in R
  1. Data Viz and Stats
  2. Case Studies
  3. Movie Profits
  • Data Viz and Stats
    • Tools
      • Introduction to R and RStudio
    • Descriptive Analytics
      • Data
      • Inspect Data
      • Graphs
      • Summaries
      • Counts
      • Quantities
      • Groups
      • Distributions
      • Groups and Distributions
      • Change
      • Proportions
      • Parts of a Whole
      • Evolution and Flow
      • Ratings and Rankings
      • Surveys
      • Time
      • Space
      • Networks
      • Miscellaneous Graphing Tools, and References
    • Inference
      • Basics of Statistical Inference
      • 🎲 Samples, Populations, Statistics and Inference
      • Basics of Randomization Tests
      • Inference for a Single Mean
      • Inference for Two Independent Means
      • Inference for Comparing Two Paired Means
      • Comparing Multiple Means with ANOVA
      • Inference for Correlation
      • Testing a Single Proportion
      • Inference Test for Two Proportions
    • Modelling
      • Modelling with Linear Regression
      • Modelling with Logistic Regression
      • 🕔 Modelling and Predicting Time Series
    • Workflow
      • Facing the Abyss
      • I Publish, therefore I Am
      • Data Carpentry
    • Arts
      • Colours
      • Fonts in ggplot
      • Annotating Plots: Text, Labels, and Boxes
      • Annotations: Drawing Attention to Parts of the Graph
      • Highlighting parts of the Chart
      • Changing Scales on Charts
      • Assembling a Collage of Plots
      • Making Diagrams in R
    • AI Tools
      • Using gander and ellmer
      • Using Github Copilot and other AI tools to generate R code
      • Using LLMs to Explain Stat models
    • Case Studies
      • Demo:Product Packaging and Elderly People
      • Ikea Furniture
      • Movie Profits
      • Gender at the Work Place
      • Heptathlon
      • School Scores
      • Children's Games
      • Valentine’s Day Spending
      • Women Live Longer?
      • Hearing Loss in Children
      • California Transit Payments
      • Seaweed Nutrients
      • Coffee Flavours
      • Legionnaire’s Disease in the USA
      • Antarctic Sea ice
      • William Farr's Observations on Cholera in London
    • Projects
      • Project: Basics of EDA #1
      • Project: Basics of EDA #2
      • Experiments

On this page

  • 1 Setting up R Packages
  • 2 Introduction
  • 3 Data
  • 4 Download the Modified data
  • 5 Data Dictionary
  • 6 Plot the Data
  • 7 Task and Discussion
  1. Data Viz and Stats
  2. Case Studies
  3. Movie Profits

Movie Profits

1 Setting up R Packages

library(tidyverse)
library(mosaic)
library(skimr)
library(ggformula)

Plot Fonts and Theme

Show the Code
library(systemfonts)
library(showtext)
## Clean the slate
systemfonts::clear_local_fonts()
systemfonts::clear_registry()
##
showtext_opts(dpi = 96) # set DPI for showtext
sysfonts::font_add(
  family = "Alegreya",
  regular = "../../../../../../fonts/Alegreya-Regular.ttf",
  bold = "../../../../../../fonts/Alegreya-Bold.ttf",
  italic = "../../../../../../fonts/Alegreya-Italic.ttf",
  bolditalic = "../../../../../../fonts/Alegreya-BoldItalic.ttf"
)

sysfonts::font_add(
  family = "Roboto Condensed",
  regular = "../../../../../../fonts/RobotoCondensed-Regular.ttf",
  bold = "../../../../../../fonts/RobotoCondensed-Bold.ttf",
  italic = "../../../../../../fonts/RobotoCondensed-Italic.ttf",
  bolditalic = "../../../../../../fonts/RobotoCondensed-BoldItalic.ttf"
)
showtext_auto(enable = TRUE) # enable showtext
##
theme_custom <- function() {
  font <- "Alegreya" # assign font family up front
  "%+replace%" <- ggplot2::"%+replace%" # nolint

  theme_classic(base_size = 14, base_family = font) %+replace% # replace elements we want to change

    theme(
      text = element_text(family = font), # set base font family

      # text elements
      plot.title = element_text( # title
        family = font, # set font family
        size = 24, # set font size
        face = "bold", # bold typeface
        hjust = 0, # left align
        margin = margin(t = 5, r = 0, b = 5, l = 0)
      ), # margin
      plot.title.position = "plot",
      plot.subtitle = element_text( # subtitle
        family = font, # font family
        size = 14, # font size
        hjust = 0, # left align
        margin = margin(t = 5, r = 0, b = 10, l = 0)
      ), # margin

      plot.caption = element_text( # caption
        family = font, # font family
        size = 9, # font size
        hjust = 1
      ), # right align

      plot.caption.position = "plot", # right align

      axis.title = element_text( # axis titles
        family = "Roboto Condensed", # font family
        size = 12
      ), # font size

      axis.text = element_text( # axis text
        family = "Roboto Condensed", # font family
        size = 9
      ), # font size

      axis.text.x = element_text( # margin for axis text
        margin = margin(5, b = 10)
      )

      # since the legend often requires manual tweaking
      # based on plot content, don't define it here
    )
}

## Use available fonts in ggplot text geoms too!
ggplot2::update_geom_defaults(geom = "text", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))

## Set the theme
ggplot2::theme_set(new = theme_custom())

2 Introduction

This is a dataset pertaining to movies and genres, modified for ease of analysis and plotting.

3 Data

4 Download the Modified data

5 Data Dictionary

NoteQuantitative Variables

Write in.

NoteQualitative Variables

Write in.

NoteObservations

Write in.

6 Plot the Data

7 Task and Discussion

Complete the Data Dictionary. Create the graph shown and discuss the following questions:

  • Identify the type of plot
  • What are the variables used to plot this graph?
  • If you were to invest in movie production ventures, which are the two best genres that you might decide to invest in?
  • Which R command might have been used to obtain the separate plots for each distributor?
  • If the original dataset had BUDGETS and PROFITS in separate columns, what preprocessing might have been done to achieve this plot?
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Ikea Furniture
Gender at the Work Place

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