Theme Song

1 Setting

1.1 SCSS Setup

# install.packages("remotes")
library('BBmisc', 'rmsfuns')
#remotes::install_github("rstudio/sass")
lib('sass')

## sass 
## TRUE

/* https://stackoverflow.com/a/66029010/3806250 */
h1 { color: #002C54; }
h2 { color: #2F496E; }
h3 { color: #375E97; }
h4 { color: #556DAC; }
h5 { color: #92AAC7; }

/* ----------------------------------------------------------------- */
/* https://gist.github.com/himynameisdave/c7a7ed14500d29e58149#file-broken-gradient-animation-less */
.hover01 {
  /* color: #FFD64D; */
  background: linear-gradient(155deg, #EDAE01 0%, #FFEB94 100%);
  transition: all 0.45s;
  &:hover{
    background: linear-gradient(155deg, #EDAE01 20%, #FFEB94 80%);
    }
  }

.hover02 {
  color: #FFD64D;
  background: linear-gradient(155deg, #002C54 0%, #4CB5F5 100%);
  transition: all 0.45s;
  &:hover{
    background: linear-gradient(155deg, #002C54 20%, #4CB5F5 80%);
    }
  }

.hover03 {
  color: #FFD64D;
  background: linear-gradient(155deg, #A10115 0%, #FF3C5C 100%);
  transition: all 0.45s;
  &:hover{
    background: linear-gradient(155deg, #A10115 20%, #FF3C5C 80%);
    }
  }

## https://stackoverflow.com/a/36846793/3806250
options(width = 999)
knitr::opts_chunk$set(class.source = 'hover01', class.output = 'hover02', class.error = 'hover03')

1.2 Setup

suppressPackageStartupMessages(library('BBmisc'))

pkgs <- c('knitr', 'kableExtra', 'devtools', 'lubridate', 'data.table', 'tidyquant', 'stringr', 'magrittr', 'tidyverse', 'plyr', 'dplyr', 'broom', 'highcharter', 'formattable', 'DT', 'httr', 'openxlsx')
suppressAll(lib(pkgs))

#funs <- c('')
#l_ply(funs, function(x) source(paste0('./function/', x)))

Sys.setenv(TZ='Asia/Tokyo')
options(warn=-1)
rm(pkgs)

2 Introduction

2.1 About this Course

Kindly refer to manual Mastering Software Development in R (web-base). (or Mastering Software Development in R.pdf)

2.2 Syllabus

Week 1 : Welcome to Building Data Visualization Tools
- Welcome
- Video：Welcome to Building Data Visualization Tools
- Reading：Textbook Mastering Software Development in R
- Reading：syllabus
- Basic plotting with ggplot2
  - Reading: Introduction
  - Reading: Initializing a ggplot Object
  - Reading: Plot Aesthetics
  - Reading: Creating a Basic ggplot plot
  - Reading: Geoms
  - Reading: Using Multiple geoms
  - Reading: Constant aesthetics
  - Reading: Example plots
  - Reading: Extentions of ggplot2
- Customizing ggplot2 Plots
  - Reading: Introduction
  - Reading: Guideline for Good Plots
  - Reading: Scales and Color
  - Reading: To Find Out More
- ggplot Basic
  - Quiz: Plotting with ggplot2
Week 2 : Mapping and interactive plots
- Mapping
  - Reading: Introduction
  - Reading: ReadingBasics of Mapping
  - Reading: Readingggmap, Google Maps API
  - Reading: ReadingMapping US counties and states
  - Reading: ReadingMore advanced mapping– Spatial objects
  - Reading: ReadingWhere to find more on mapping with R
- htmlWidgets
  - Reading: Overview of htmlWidgets
  - Reading: plotly Package
  - Reading: Creating Your Own Widget
- Mapping Quiz
  - Mapping and interactive plots
Week 3 : The grid Package
- The grid package
  - Reading: Introduction
  - Reading: Overview of grid graphics
  - Reading: Grobs
  - Reading: Viewports
  - Reading: Grid Graphics Coordinate Systems
  - Reading: The gridExtra package
  - Reading: Where to Find More About grid Graphics
- Grid Package quiz
  - Quiz: Basics of grid graphics
Week 4 : Building New Graphical Elements
- Building a New Theme
  - Reading: Introduction
  - Reading: Why Build a New Theme?
  - Reading: Default Theme
  - Reading: Building a New Theme
  - Reading: Summary
- Build New Graphical Elements
  - Build New Graphical Elements
  - Reading: Introduction
  - Reading: Building a Geom
  - Reading: Example: An Automatic Transparency Geom
  - Reading: Building a Stat
  - Reading: Example: Normal Confidence Intervals
  - Reading: Combining Geoms and Stats
  - Reading: Summary
- Build a New Geom
  - Peer-graded Assignment: Build a New Geom
  - Review Your Peers: Build a New Geom

Kindly refer to Mastering Software Development in R Specialization to know the whole courses for the R specialization.

# 1:  <- ''
# 2:  <- ''
# 3:  <- ''

2.3 Lesson

2.3.1 Week 1 : Welcome to Building Data Visualization Tools

2.3.1.1 Welcome

2.3.1.2 Video：Welcome to Building Data Visualization Tools

2.3.1.3 Reading：Textbook Mastering Software Development in R

2.3.1.4 Reading：syllabus

2.3.1.5 Basic plotting with ggplot2

Reading: Introduction

Reading: Initializing a ggplot Object

Reading: Plot Aesthetics

Reading: Creating a Basic ggplot plot

Reading: Geoms

Reading: Using Multiple geoms

Reading: Constant aesthetics

Reading: Example plots

Reading: Extentions of ggplot2

2.3.1.6 Customizing ggplot2 Plots

Reading: Introduction

Reading: Guideline for Good Plots

Reading: Scales and Color

Reading: To Find Out More

2.3.1.7 ggplot Basic

Quiz: Plotting with ggplot2

2.3.2 Week 2 : Mapping and interactive plots

2.3.2.1 Mapping

Reading: Introduction

Reading: ReadingBasics of Mapping

Reading: Readingggmap, Google Maps API

Reading: ReadingMapping US counties and states

Reading: ReadingMore advanced mapping– Spatial objects

Reading: ReadingWhere to find more on mapping with R

2.3.2.2 htmlWidgets

Reading: Overview of htmlWidgets

Reading: plotly Package

Reading: Creating Your Own Widget

2.3.2.3 Mapping Quiz

Mapping and interactive plots

2.3.3 Week 3 : The grid Package

2.3.3.1 The grid package

Reading: Introduction

Reading: Overview of grid graphics

Reading: Grobs

Reading: Viewports

Reading: Grid Graphics Coordinate Systems

Reading: The gridExtra package

Reading: Where to Find More About grid Graphics

2.3.4 Grid Package quiz

Quiz: Basics of grid graphics

2.3.5 Week 4 : Building New Graphical Elements

2.4 Building a New Theme

Reading: Introduction

Reading: Why Build a New Theme?

Reading: Default Theme

Reading: Building a New Theme

Reading: Summary

2.5 Build New Graphical Elements

2.5.1 Build New Graphical Elements

Reading: Introduction

Reading: Building a Geom

Reading: Example: An Automatic Transparency Geom

Reading: Building a Stat

Reading: Example: Normal Confidence Intervals

Reading: Combining Geoms and Stats

Reading: Summary

3 Quiz

The function ggplot() requires that the value supplied to the ‘data’ argument be a

character vector
list
data frame
geom

You run the following code and get the error printed directly after the code:

library(ggplot2) 
library(faraway)

## Error in library(faraway): there is no package called 'faraway'

data(nepali) 
head(nepali, 3)

## Error in head(nepali, 3): object 'nepali' not found

## id sex wt ht mage lit died alive age 
## 1 120011 1 12.8 91.2 35 0 2 5 41 
## 2 120011 1 12.8 93.9 35 0 2 5 45 
## 3 120011 1 13.1 95.2 35 0 2 5 

ggplot(nepali, aes(x = ht, y = wt))

## Error in ggplot(nepali, aes(x = ht, y = wt)): object 'nepali' not found

+ geom_point()

## Error: Cannot use `+.gg()` with a single argument. Did you accidentally put + on a new line?

## Error in +geom_point(): invalid argument to unary operator

What happened?

Because you put the + at the start of the second line, instead of at the end of the first line, R thought the call was over and tried to run the first line by itself, and then tried to run the second line by itself, without an associated ggplot object.

The nepali dataset lacks one or both of the two columns (ht, wt) that you - used in the aes statement of the call.
You are trying to create a scatterplot for a categorical variable.
You have not yet loaded the nepali data frame.

ans: 3 ans: 2 ans: 1 ans: 4

You have a dataset of observations on study subjects which has height and weight for each subject measured at multiple time points. You want to create a scatterplot of height (x-axis) by weight (y-axis). Since there are multiple measurements per subject, you would like to plot the measurements for each subject in a separate color. There is a column in the data called id that gives the unique id of each study subject; this column currently has the class “numeric”.

Which of the following choices is a good strategy for creating a plot where data points use color to identify the subject?

Convert the id column of the data frame to a factor, so a discrete color palette will be used, then use ggplot to create a scatterplot and specify color = id as the sole argument in the geom_point() call.

Example code:

df %>% 
  mutate(id = factor(id)) %>%
  ggplot(aes(x = height, y = weight)) + 
  geom_point(color = id)

## Error in mutate(., id = factor(id)): is.data.frame(.data) || is.list(.data) || is.environment(.data) is not TRUE

Leave the id column of the data frame as a numeric, so a continuous color palette will be used, and use ggplot to create a scatterplot, then use + to add the element color(aes(id)).

Example code:

df %>%
  ggplot(aes(x = height, y = weight)) + 
  geom_point() + 
  color(aes(id))

## Error:   You're passing a function as global data.
##   Have you misspelled the `data` argument in `ggplot()`

Leave the id column of the data frame as a numeric, so a continuous color palette will be used, then use ggplot2 to create a scatterplot and specify color = id in the aes() section of the ggplot() call.

Example code:

df %>% 
  mutate(id = factor(id)) %>%
  ggplot(aes(x = height, y = weight, color = id)) + 
  geom_point()

## Error in mutate(., id = factor(id)): is.data.frame(.data) || is.list(.data) || is.environment(.data) is not TRUE

Convert the id column of the data frame to a factor, so a discrete color palette will be used, then use ggplot2 to create a scatterplot and specify color = id in the aes() section of the ggplot() call.

Example code:

df %>% 
  mutate(id = factor(id)) %>%
  ggplot(aes(x = height, y = weight, color = id)) + 
  geom_point()

## Error in mutate(., id = factor(id)): is.data.frame(.data) || is.list(.data) || is.environment(.data) is not TRUE

ans: 1

ans: 2

You want to save a figure from R as a pdf file named “MyFig.pdf”. Which of the following describes the proper steps to take?

First run all your code to create the figure, then run pdf("MyFig.pdf") to open a pdf device, then use pdf.off() to close the device.

Example code:

ggplot(df, aes(x = x, y = y)) + 
  geom_point()

## Error:   You're passing a function as global data.
##   Have you misspelled the `data` argument in `ggplot()`

pdf("MyFig.pdf")
dev.off()

## png 
##   2

Wrap all your code to create the figure in a pdf statement.

Example code:

ggplot(df, aes(x = x, y = y)) + 
  geom_point()

## Error:   You're passing a function as global data.
##   Have you misspelled the `data` argument in `ggplot()`

pdf("MyFig.pdf")
dev.off()

## png 
##   2

First run dev.on("MyFig.pdf") to open a pdf device, then run all your code to create the figure, then use dev.off() to close the device.

Example code:

dev.on("MyFig.pdf")

## Error in dev.on("MyFig.pdf"): could not find function "dev.on"

ggplot(df, aes(x = x, y = y)) + 
  geom_point()

## Error:   You're passing a function as global data.
##   Have you misspelled the `data` argument in `ggplot()`

First run pdf("MyFig.pdf") to open a pdf device, then run all your code to create the figure, then use dev.off() to close the device.

Example code:

dev.on("MyFig.pdf")

## Error in dev.on("MyFig.pdf"): could not find function "dev.on"

ggplot(df, aes(x = x, y = y)) + 
  geom_point()

## Error:   You're passing a function as global data.
##   Have you misspelled the `data` argument in `ggplot()`

dev.off()

## null device 
##           1

ans: 1

ans: 2

Consider the following dataset.

library(lattice)
data(barley)
head(barley)

##      yield   variety year            site
## 1 27.00000 Manchuria 1931 University Farm
## 2 48.86667 Manchuria 1931          Waseca
## 3 27.43334 Manchuria 1931          Morris
## 4 39.93333 Manchuria 1931       Crookston
## 5 32.96667 Manchuria 1931    Grand Rapids
## 6 28.96667 Manchuria 1931          Duluth

#     yield   variety year            site
# 1 27.00000 Manchuria 1931 University Farm
# 2 48.86667 Manchuria 1931          Waseca
# 3 27.43334 Manchuria 1931          Morris

Which ggplot2 expression would make a scatterplot of year and yield while coloring each point according to its variety?

ggplot(data = barley, aes(year, yield)) + geom_point(aes(color = variety))
ggplot(data = barley, aes(year, yield)) + geom_point()
ggplot(data = barley, aes(year, yield, color = variety))
ggplot(data = barley, aes(year, yield)) + geom_point(color = variety)

ans: 2

ans: 4

ans: 3

Consider the following dataset.

You want to create a scatter plot showing Girth on the x-axis, Height on the y-axis, and Volume with point size.

Which of the following code would create that plot?

trees %>%
  ggplot() +
  geom_point(aes(x = Girth, y = Height, size = Volume))

trees %>%
  ggplot(aes(x = Girth, y = Height)) +
  geom_point(size = Volume)

## Error in layer(data = data, mapping = mapping, stat = stat, geom = GeomPoint, : object 'Volume' not found

trees %>%
  ggplot(aes(x = Girth, y = Height, size = Volume))

trees %>%
  ggplot(aes(x = Girth, y = Height, size = Volume)) +
  geom_point()

ggplot() +
  geom_point(trees,
             aes(x = Girth, y = Height, size = Volume))

## Error: `mapping` must be created by `aes()`

trees %>%
  ggplot(aes(x = Girth, y = Height)) +
  geom_point(aes(size = Volume))

trees %>%
  ggplot(aes(x = Girth, y = Height)) +
  geom_point(aes(size = Volume))

ggplot() +
  geom_point(data = trees,
             aes(x = Girth, y = Height, size = Volume))

ans:2,3,4,5 (0.13/1 point)

ans:2,4,5 (0.25/1 point)

How can you create small multiples using ggplot2?

Add multiple geoms to the plot using the + syntax
Add facet_grid to the geom, specifying one or two columns to use for faceting
Add a scales element to decrease the range of the plot scales
Add facet_wrap to the geom, specifying the formula to use for faceting

ans:2,3 (0.50 / 1point)

ans:1,2,3 (0.25/1 point)

What does geom_smooth(method = "lm") do when it is added to a ggplot object?

It adds a palette of colors for plotting the points in a scatterplot
It adds transparency to the points to avoid over plotting
It adds a nonlinear smoother to the plot
It adds a fitted linear regression line to the plot

When plotting multiple panels using faceting for a single variable, what controls the order in which the panels are plotted?

Panels are ordered by the number of data points in each panel
Panels are ordered by the alphabetical order or by the order of the levels of the faceting variable
Panels are ordered randomly
The order of panels must be specified by the user

What function would you use to limit the range of the x-axis in a scatterplot?

scale_x_continuous()
scale()
geom_scale()
scale_y_continuous()

4 Assignment

Kindly refer to GitHub wk4package(previous The farsdata R Package) which is my R package repo for the assignment.

5 Conclusion

Awarded on 06 Aug 2021

Final Scores : 85.26/100

Above png file and feel free to download Coursera 04 - Building Data Visualization Tools.pdf.

6 Appendix

6.1 Documenting File Creation

File creation date: 2021-07-27
File latest updated date: 2021-12-08
R version 4.1.2 (2021-11-01)
rmarkdown package version: 2.11
File version: 0.2.1
Author Profile: ®γσ, ξηg Lιαη Ημ
GitHub: Source Code
Additional session information:

suppressMessages(require('formattable', quietly = TRUE))
suppressMessages(require('knitr', quietly = TRUE))
suppressMessages(require('kableExtra', quietly = TRUE))

sys1 <- devtools::session_info()$platform |> 
    unlist() |> 
    {\(.) data.frame(row.names = 1:length(.), 
                     Category = names(.), session_info = .)}()

sys2 <- data.frame(Sys.info()) |> 
    {\(.) data.frame(Category = row.names(.), Sys.info = .[,1])}()

#remarks, dim(sys1), dim(sys2)
if (nrow(sys1) == 11 & nrow(sys2) == 8) {
  sys2 <- sys2 |> 
    {\(.) rbind(., data.frame(
    Category = c('rmarkdown', 'rsconnect', 'Current time'), 
    Sys.info = c(as.character(getwd()), 
                 as.character(packageVersion('rsconnect')), 
                 paste(as.character(lubridate::now('Asia/Tokyo')), 'JST 🌏'))))}()
  
} else if (nrow(sys1) == 10 & nrow(sys2) == 8) {
  sys1 %<>% rbind(., data.frame(Category = '', session_info = ''))
  
  sys2 <- sys2 |> 
    {\(.) rbind(., data.frame(
    Category = c('rmarkdown', 'rsconnect', 'Current time'), 
    Sys.info = c(as.character(getwd()), 
                 as.character(packageVersion('rsconnect')), 
                 paste(as.character(lubridate::now('Asia/Tokyo')), 'JST 🌏'))))}()
}

sys <- cbind(sys1, sys2) |> 
  {\(.) 
    kbl(., caption = 'Additional session information:')}() |> 
  {\(.) 
    kable_styling(., bootstrap_options = c('striped', 'hover', 'condensed', 'responsive'))}() |> 
  {\(.) 
    row_spec(., 0, background = 'DimGrey', color = 'yellow')}() |> 
  {\(.) 
    column_spec(., 1, background = 'CornflowerBlue', color = 'red')}() |> 
  {\(.) 
    column_spec(., 2, background = 'grey', color = 'black')}() |> 
  {\(.) 
    column_spec(., 3, background = 'CornflowerBlue', color = 'blue')}() |> 
  {\(.) 
    column_spec(., 4, background = 'grey', color = 'white')}() |> 
  {\(.) 
    row_spec(., 11, bold = TRUE, color = 'yellow', background = '#D7261E')}()

rm(sys1, sys2)
sys

Additional session information:
Category	session_info	Category	Sys.info
version	R version 4.1.2 (2021-11-01)	sysname	Linux
os	Ubuntu 20.04.3 LTS	release	5.4.0-1037-aws
system	x86_64, linux-gnu	version	#39~18.04.1-Ubuntu SMP Fri Jan 15 02:48:42 UTC 2021
ui	X11	nodename	application-3100168-deployment-10747036-p7mhs
language	(EN)	machine	x86_64
collate	C.UTF-8	login	unknown
ctype	C.UTF-8	user	rstudio-user
tz	Asia/Tokyo	effective_user	rstudio-user
date	2021-12-08	rmarkdown	/cloud/project/04 Building Data Visualization Tools
pandoc	2.14.0.3 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)	rsconnect	0.8.25
		Current time	2021-12-08 18:34:04 JST 🌏

6.2 Reference

Mastering Software Development in R (web-base). (or Mastering Software Development in R.pdf)

Coursera - An Online Learning Platform

Plotting with ggplot2

®γσ, ξηg Lιαη Ημ 🇪🇺® 新

2021-12-08

1 Setting

1.1 SCSS Setup

1.2 Setup

2 Introduction

2.1 About this Course

2.2 Syllabus

2.3 Lesson

2.3.1 Week 1 : Welcome to Building Data Visualization Tools

2.3.1.1 Welcome

2.3.1.2 Video：Welcome to Building Data Visualization Tools

2.3.1.3 Reading：Textbook Mastering Software Development in R

2.3.1.4 Reading：syllabus

2.3.1.5 Basic plotting with ggplot2

2.3.1.6 Customizing ggplot2 Plots

2.3.1.7 ggplot Basic

2.3.2 Week 2 : Mapping and interactive plots

2.3.2.1 Mapping

2.3.2.2 htmlWidgets

2.3.2.3 Mapping Quiz

2.3.3 Week 3 : The grid Package

2.3.3.1 The grid package

2.3.4 Grid Package quiz

2.3.5 Week 4 : Building New Graphical Elements

2.4 Building a New Theme

2.5 Build New Graphical Elements

2.5.1 Build New Graphical Elements

3 Quiz

4 Assignment

5 Conclusion

6 Appendix

6.1 Documenting File Creation

6.2 Reference