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---
title: "[<span style='color:goldenrod'>雷欧 ∪ 瑞欧，黄联富</span>](https://beta.rstudioconnect.com/content/3091/ryo-eng.html)"
subtitle: '个人简历'
author: "[雷欧 ∪ 瑞欧，黄联富](https://englianhu.github.io) <img src='文艺坊图库/ENG.png'
height='16' align='center' valign='middle'> <img src='文艺坊图库/me light mode.jpg'
height='16' align='center' valign='middle'>®"
tags: [计量经济学, 量化圈, 娱乐圈, ®, Python, ®Studio]
date: '22-Oct-1984'
output:
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- 文艺坊图库/weibo.jpg
- 文艺坊图库/weibo_36x36.jpg
- 文艺坊图库/wordpress.jpg
- 文艺坊图库/wordpress_36x36.jpg
- 文艺坊图库/Wechat1.png
- 文艺坊图库/Wechat2.png
- 文艺坊图库/xerox.jpg
- 文艺坊图库/xueba.jpg
- 文艺坊图库/xueba1.jpg
- 文艺坊图库/xueba2.jpg
- 文艺坊图库/xueba2.png
- 文艺坊图库/xueba3.jpg
- 文艺坊图库/Yandex.png
- 文艺坊图库/YouTube.jpeg
- 文艺坊图库/zeef.jpg
- 文艺坊图库/zeef_36x36.jpg
- 文艺坊图库/ZhiHu.png
- 文艺坊图库/zoom.jpeg
- 文艺坊图库/βεst Jοεγ 7.jpg
- 文艺坊图库/βεst Jοεγ 8.jpg
- 文艺坊图库/βεst Jοεγ 胸有大痣.png
- 文艺坊图库/βεst Jοεγ.jpg
- 文艺坊图库/中国区划.gif
- 文艺坊图库/美国省区旗.png
- 文艺坊图库/βεst Jοεγ 胸有大痣.png
- 文艺数据库/dfm.csv
- 文艺数据库/kellym.txt
- 文艺数据库/shinyData.rds
- 文艺数据库/worldcities.csv
- 函数/buildFund.R
- 函数/libs.R
- 函数/plotChart.R
- 文艺坊曲库/bigmoney.mp3
- 文艺坊图库/b4TDbiz-.pdf
- 文艺坊图库/韩国情信.mp4
- 博物院/Coursera Illinous - Data Mining - 01 Data Visualization.pdf
- 博物院/Coursera Illinous - Data Mining - 02 Text Retrieval and Search Engines.pdf
- 博物院/Coursera Illinous - Data Mining - 03 Text Mining and Analytics.pdf
- 博物院/Coursera Illinous - Improving Business Finances and Operations - 02 Managerial
Accounting - Tools for Facilitating and Guiding Business Decisions.pdf
- 博物院/Coursera Illinous - Improving Business Finances and Operations - 04 Financial
Evaluation and Strategy - Corporate Finance.pdf
- 博物院/Coursera Illinous - Improving Business Finances and Operations - 05 Operations
Management.pdf
- 博物院/Coursera Johns Hopkins - 01 Data Science Toolbox 2014.pdf
- 博物院/Coursera Johns Hopkins - 02 R Programming.pdf
- 博物院/Coursera Johns Hopkins - 03 Getting and Cleaning Data 2014.pdf
- 博物院/Coursera Johns Hopkins - 04 Exploratory Data Analysis.pdf
- 博物院/Coursera Johns Hopkins - 05 Reproducible Research.pdf
- 博物院/Coursera Johns Hopkins - 06 Statistical Inference.pdf
- 博物院/Coursera Johns Hopkins - 07 Regression Models.pdf
- 博物院/Coursera Johns Hopkins - 08 Pratical Machine Learning.pdf
- 博物院/Coursera Johns Hopkins - 09 Develeoping Data Products.pdf
- 博物院/Coursera Johns Hopkins - 10 Data Science Capstone.pdf
- 博物院/Coursera Johns Hopkins - 11 Completed 10 Courses in Data Science.pdf
- 博物院/Coursera Michigan - Programming for Everybody (Python).pdf
- 博物院/DataCamp - Data Analysis and Statistical Inference.pdf
- 博物院/DataCamp - Intro to Computational Finance with R.pdf
- 博物院/DataCamp - Introduction to R.pdf
- 博物院/DataCamp - Kaggle R Tutorial on Machine Learning.pdf
- 博物院/Google Analytics.pdf
- 博物院/JLPT Certificate.jpg
- 博物院/Scicom Appreciation Certificate.jpg
- 博物院/Scicom CS Workshop.jpg
- 博物院/SPM Certificate.jpg
- 博物院/Ryo Eng_Introduction to Machine Learning for Trading_certificate.pdf
- 博物院/Ryo Eng_Quantitative Trading Strategies and Models_certificate.pdf
- 博物院/Ryo, Eng Lian Hu_Trading using Options Sentiment Indicators_certificate.pdf
- 博物院/Coursera JHU 01 - The R Programming Environment.pdf
- 博物院/Coursera JHU 02 - Advanced R Programming.pdf
- 博物院/Coursera JHU 03 - Building R Packages.pdf
- 博物院/Coursera JHU 04 - Building Data Visualization Tools.pdf
- 博物院/Coursera JHU 05 - Mastering Software Development in R Capstone.pdf
- 博物院/Coursera NYU - Overview of Advanced Methods of Reinforcement Learning in Finance.pdf
- 博物院/Coursera Peking - Bioinformatics - Hidden Markov Models (生物信息学 - 隐马尔科夫链).pdf
- 博物院/Coursera California - Bayesian Statistics Mixture Models.pdf
- 文艺坊曲库/小松拓也 - 不知道方向 Takuya Komatsu (原创) - (Better Ownself).mp3
runtime: shiny
---
# 设定
## SCSS 设置
<style>
pre {
overflow-x: auto;
}
pre code {
word-wrap: normal;
white-space: pre;
}
.table-hover > tbody > tr:hover {
background-color: #8D918D;
}
</style>
```{r load-sass, class.source='bg-success', class.output='bg-primary'}
# install.packages('remotes', dependencies = TRUE, INSTALL_opts = '--no-lock')
library('BBmisc', 'rmsfuns')
#remotes::install_github("rstudio/sass")
lib('sass')
## https://support.rstudio.com/hc/en-us/articles/200532197
## https://community.rstudio.com/t/r-does-not-display-korean-chinese/30889/3?u=englianhu
#Sys.setlocale("LC_CTYPE", "en_US.UTF-8")
#Sys.setlocale("LC_CTYPE", "zh_CN.UTF-8")
#Sys.setlocale(category = "LC_CTYPE", "Chinese (Simplified)_China.936")
#Sys.setlocale(locale = "Chinese")
#Sys.setlocale(locale = "Japanese")
#Sys.setlocale(locale = "English")
# rmarkdown::render('/home/englianhu/Documents/owner/ryo-cn.Rmd', encoding = 'UTF-8')
#Sys.setlocale("LC_CTYPE", "UTF-8")
#Sys.setlocale(locale = "UTF-8")
#Sys.setlocale(category = "LC_ALL", locale = "chs")
#Sys.setlocale(category = "LC_ALL", locale = "UTF-8")
#Sys.setlocale(category = "LC_ALL", locale = "Chinese")
#Sys.setlocale(category = "LC_ALL", locale = "zh_CN.UTF-8")
Sys.setlocale("LC_ALL", "en_US.UTF-8")
```
```{scss set-scss, class.source='bg-success', class.output='bg-primary'}
/* 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%);
}
}
```
```{r gb-opts, class.source='hover01', class.output='hover02'}
## Set the timezone but not change the datetime
Sys.setenv(TZ = 'Asia/Shanghai')
## Setting to omit all warnings
## https://stackoverflow.com/a/36846793/3806250
## Set width
## options(knitr.table.format = 'html') will set all kableExtra tables to be 'html', otherwise need to set the parameter on every single table.
options(warn = -1, width = 999, knitr.table.format = 'html')#, digits.secs = 6)
## https://stackoverflow.com/questions/39417003/long-vectors-not-supported-yet-abnor-in-rmd-but-not-in-r-script
## https://yihui.org/knitr/options
knitr::opts_chunk$set(
class.source = 'hover01', class.output = 'hover02', class.error = 'hover03',
message = FALSE, warning = FALSE, error = TRUE,
autodep = TRUE, aniopts = 'loop',
progress = TRUE, verbose = TRUE,
cache = FALSE, cache.lazy = FALSE, result = 'asis')
```
<br><br>
## 设置
```{r libs, eval=FALSE}
## Setup Options, Loading Required Libraries and Preparing Environment
## Loading the packages and setting adjustment
source('函数/libs.R')
```
```{r set, include=FALSE}
## Setup Options, Loading Required Libraries and Preparing Environment
## Loading the package 'BBmisc'
if(suppressMessages(!require('BBmisc'))){
install.packages('BBmisc', dependencies = TRUE, INSTALL_opts = '--no-lock')
suppressMessages(library('BBmisc'))
}
if (suppressMessages(!require('rmsfuns'))) {
install.packages('rmsfuns', dependencies = TRUE, INSTALL_opts = '--no-lock')
suppressMessages(library('rmsfuns'))
}
if(!require('REmap')) devtools::install_github('lchiffon/REmap')
## Loading multiple packages at once
pkgs <- c('readr', 'plyr', 'dplyr', 'magrittr', 'tidyverse', 'devtools', 'zoo', 'lubridate', 'stringr', 'rvest', 'markdown', 'googleVis', 'knitr', 'rmarkdown', 'htmltools', 'knitr', 'kableExtra', 'formattable', 'echarts4r', 'radarchart', 'MASS', 'htmlwidgets', 'REmap', 'ggmap', 'vembedr', 'maps', 'maptools', 'conflicted')
suppressAll(lib(pkgs))
load_pkg(pkgs)
rm(pkgs)
conflict_prefer('mutate', 'dplyr')
conflict_prefer('rename', 'dplyr')
conflict_prefer('filter', 'dplyr')
conflict_prefer('select', 'dplyr')
conflict_prefer('layout', 'plotly')
## Set the googleVis options first to change the behaviour of plot.gvis, so that
## only the chart component of the HTML file is written into the output file.
op <- options(gvis.plot.tag = 'chart')
## <audio src='文艺坊曲库/bigmoney.mp3' autoplay controls loop></audio>
```
```{r, error = TRUE, results='asis'}
#remotes::install_git("https://gitee.com/JohnCoene/echarts4r")
lib('echarts4r')
mtcars |>
e_charts(disp) |>
e_scatter(mpg, qsec) |>
e_loess(mpg ~ disp)
iris |>
group_by(Species) |>
e_charts(Sepal.Length) |>
e_line(Sepal.Width) |>
e_lm(Sepal.Width ~ Sepal.Length) |>
e_x_axis(min = 4)
df <- data.frame(
x = seq(50),
y = rnorm(50, 10, 3),
z = rnorm(50, 11, 2),
w = rnorm(50, 9, 2)
)
df |>
e_charts(x) |>
e_line(z) |>
e_area(w) |>
e_title("Line and area charts")
df |>
e_charts(x) |>
e_polar() |>
e_angle_axis(x) |> # angle = x
e_radius_axis() |>
e_bar(y, coord_system = "polar") |>
e_scatter(z, coord_system = "polar")
df |>
head(10) |>
e_charts(x) |>
e_polar() |>
e_angle_axis() |>
e_radius_axis(x) |>
e_bar(y, coord_system = "polar") |>
e_scatter(z, coord_system = "polar")
df <- data.frame(
x = LETTERS[1:5],
y = runif(5, 1, 5),
z = runif(5, 3, 7)
)
df |>
e_charts(x) |>
e_radar(y, max = 7, name = "radar") |>
e_radar(z, max = 7, name = "chart") |>
e_tooltip(trigger = "item")
```
```{r skill, error = TRUE, message = FALSE, warning = FALSE, echo = FALSE, result = 'asis'}
require('dplyr', quietly = TRUE, warn.conflicts = FALSE)
require('magrittr', quietly = TRUE, warn.conflicts = FALSE)
require('formattable', quietly = TRUE, warn.conflicts = FALSE)
require('knitr', quietly = TRUE, warn.conflicts = FALSE)
require('kableExtra', quietly = TRUE, warn.conflicts = FALSE)
conflict_prefer('filter', 'dplyr')
conflict_prefer('layout', 'plotly')
conflict_prefer('select', 'dplyr')
skill <- tibble(
'技能' = c('计量经济学', '量化交易', '®编程', '微软办公软件', 'SQL语言', '派森编程语言', '数据分析', '客服工作', '体育博彩行业', '建立®Studio服务器', '统计学', '数据科学', '闪霓应用', '李呢克斯🐧操作系统', '网页应用程序接口', 'Sparklyr大数据分析', '量化分析', '高级®编程', 'modeltime / tidyverts / prophet', 'tidyverse / tidymodels', '张量Tensorflow / Pytorch', '浏览器驱动', 'FrontPage / 部署网站', 'Photoshop / Picsart'),
'程度' = c(9, 9, 9, 8, 3, 4, 9, 9, 7, 7, 6, 8, 8, 7, 6, 2, 8, 6, 7, 6, 2, 4, 3, 5))
skill <- tibble('序列' = 1:nrow(skill), skill)
skill |>
{\(.) dplyr::mutate(
., `技能` = cell_spec(`技能`, italic = TRUE,
color = spec_color(seq(nrow(skill)), option = 'A',
begin = 0.1, end = 0.9),
tooltip = paste0('程度: ', `程度`), angle = 3))}() |>#,
#Level = color_tile('#FFFDDC', 'darkgoldenrod')(Level)) |>
{\(.) mutate_if(., is.numeric, function(x){
cell_spec(x, 'html', color =
spec_color(x, option = 'A', begin = 0.1, end = 0.9,
direction = -1), angle = 20,
font_size = spec_font_size(x), bold = TRUE) })}() |>
{\(.) kbl(., caption = '技能评估（从新兵1至专业10）', escape = FALSE, align = 'c')}() |>
{\(.) 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(2, background = 'grey', color = 'black')
```
```{r skill2B, error = TRUE, message = FALSE, warning = FALSE, echo = FALSE, result = 'asis'}
#radarchart::chartJSRadar(
# skill[,-1], main = 'Skill Rating', width = '100%', height = '100%',
# maxScale = 10, labelSize = 'auto', showToolTipLabel = TRUE)
### ---------------------------
linear_gradient1 <- htmlwidgets::JS(
"new echarts.graphic.LinearGradient(
0, 0, 0, 1,
[
{ offset: 0, color: '#EDAE01' },
{ offset: 1, color: '#FFEB94' }
])")
linear_gradient2 <- htmlwidgets::JS(
"new echarts.graphic.LinearGradient(
0, 0, 0, 1,
[
{ offset: 0, color: '#002C54' },
{ offset: 1, color: '#4CB5F5' }
])")
skill |>
echarts4r::e_charts(`技能`) |>
echarts4r::e_radar(`程度`, max = 10, name = skill$`技能`,
itemStyle = list(
color = linear_gradient1),
areaStyle = list(
color = linear_gradient2)) |>
echarts4r::e_tooltip(trigger = 'item')
```
```{r REmap-set1, echo=FALSE}
## Reinitialized the REmap setting
remap.init()
```
```{r read-data, echo=FALSE}
## Set a data frame format cities and randomly set country code
data = data.frame(country=mapNames('world'), value=5*sample(178)+200)
vec = c('Beijing','Tokyo','Shanghai','New York','Sydney','London','Bangkok',
'Taipei','Moscow','Washington','Lyon','Milan','Rome','Macao',
'Singapore','Hong Kong','Kuala Lumpur','Madrid','Geneva','Paris',
'Melbourne','Boston','Washington','Liverpool','Brighton','Bali',
'San Francisco','Jakarta','Ho Chi Minh','Brasilia','Barcelona',
'Rio de Janeiro','Buenos Aires','Bern','Basel','Zurich','Manila',
'Toronto','Vancouver','Budapest','New Delhi','Thimphu','Mumbai',
'Cairo','Greater Johannesburg','Durban','Alexandria','Zaria','Dubai',
'Istanbul','Mexico City','Sapporo','Osaka','Ulaanbaatar','Vienna')
## Get world city geocode by ggmap
#geodata = suppressAll(as.data.frame(cbind(ggmap::geocode(vec),vec)))
## Get world city geocode from simplemaps.com
## https://simplemaps.com/data/world-cities
geodata <- readr::read_csv('文艺数据库/worldcities.csv', show_col_types = FALSE) |>
dplyr::filter(capital %in% c('primary', 'admin')) |>
dplyr::select(lng, lat, country, city) |>
dplyr::rename(lon = lng) |>
dplyr::mutate(lon = round(lon, 3))
countries <- geodata$country |>
unique()
## randomly set country codes
data <- data.frame(
country = countries,
value = 5 * sample(length(countries)) + 200)
## cities
#vec <- geodata$city
## lon & lat of cities
geodata <- geodata |>
data.frame() |>
dplyr::select(lon, lat, city) |>
dplyr::rename(vec = city)
## Design a line data frame
#markLinedata <- data.frame(
# origin = rep('Beijing', (length(vec) - 1)),
# destination = geodata$vec[!geodata$vec %in% 'Beijing'])
## Design a line data.frame
markLinedata = data.frame(origin = rep('Beijing', 54), destination = vec[-1])
```
```{r plot-map, echo=FALSE}
## Plot the REmap shining
p <- remapC(data, maptype = 'world', color = c('black', 'black'),
theme = get_theme(backgroundColor = 'black'),
markPointData = vec, markLineData = markLinedata, geoData = geodata)
knitrREmap(p, local = FALSE)
```
<iframe width='100%' height='450' scrolling='no' frameborder='no' src="https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/221986532&auto_play=true&hide_related=false&show_comments=true&show_user=true&show_reposts=false&visual=true"></iframe>
```{r REmap-set2, echo=FALSE}
## Reinitialized the REmap setting
remap.init()
geodata <- maps::world.cities |>
dplyr::rename(city = name, country = country.etc, lon = long) |>
dplyr::select(country, city, capital, lon, lat, pop) |>
dplyr::filter(capital == 1)
countries <- geodata$country |>
unique()
## randomly set country codes
data <- data.frame(
country = countries,
value = 5 * sample(length(countries)) + 200)
## cities
#vec <- geodata$city
## lon & lat of cities
geodata <- geodata |>
data.frame() |>
dplyr::select(lon, lat, city) |>
dplyr::rename(vec = city)
## Design a line data frame
#markLinedata <- data.frame(
# origin = rep('Beijing', (length(vec) - 1)),
# destination = geodata$vec[!geodata$vec %in% 'Beijing'])
## Design a line data.frame
markLinedata = data.frame(origin = rep('Beijing', 54), destination = vec[-1])
## Plot the REmap shining
p <- remapC(data, maptype = 'world', color = c('black', 'black'),
theme = get_theme(backgroundColor = 'black'),
markPointData = vec, markLineData = markLinedata, geoData = geodata)
knitrREmap(p, local = FALSE)
```
```{r, error = TRUE, results='asis'}
# Library
lib('fmsb')
# Create data: note in High school for Jonathan:
data <- as.data.frame(matrix(sample(2:20, 10, replace = TRUE), ncol = 10))
colnames(data) <- c('math', 'english', 'biology', 'music', 'R-coding', 'data-viz', 'french', 'physic', 'statistic', 'sport')
# To use the fmsb package, I have to add 2 lines to the dataframe: the max and min of each topic to show on the plot!
data <- rbind(rep(20, 10), rep(0,10), data)
# Check your data, it has to look like this!
# head(data)
# The default radar chart
radarchart(data)
# Create data: note in High school for Jonathan:
data <- as.data.frame(matrix(sample(2:20, 10, replace = TRUE), ncol = 10))
colnames(data) <- c('math', 'english', 'biology', 'music', 'R-coding', 'data-viz', 'french', 'physic', 'statistic', 'sport')
# To use the fmsb package, I have to add 2 lines to the dataframe: the max and min of each topic to show on the plot!
data <- rbind(rep(20, 10), rep(0, 10), data)
# Check your data, it has to look like this!
# head(data)
# Custom the radarChart !
radarchart(data, axistype = 1,
#custom polygon
pcol = rgb(0.2, 0.5, 0.5, 0.9), pfcol = rgb(0.2, 0.5, 0.5, 0.5), plwd = 4,
#custom the grid
cglcol = 'grey', cglty = 1, axislabcol = 'grey', caxislabels = seq(0, 20, 5), cglwd = 0.8,
#custom labels
vlcex = 0.8
)
```
```{r, error = TRUE, results='asis'}
lib('radarchart')
labs <- c('Communicator', 'Data Wangler', 'Programmer', 'Technologist', 'Modeller', 'Visualizer')
scores <- list(
'Rich' = c(9, 7, 4, 5, 3, 7),
'Andy' = c(7, 6, 6, 2, 6, 9),
'Aimee' = c(6, 5, 8, 4, 7, 6))
chartJSRadar(scores = scores, labs = labs, maxScale = 10)
scores <- data.frame(
'Label' = c('Communicator', 'Data Wangler', 'Programmer', 'Technologist', 'Modeller', 'Visualizer'),
'Rich' = c(9, 7, 4, 5, 3, 7),
'Andy' = c(7, 6, 6, 2, 6, 9),
'Aimee' = c(6, 5, 8, 4, 7, 6))
chartJSRadar(scores, maxScale = 10, showToolTipLabel = TRUE)
chartJSRadar(skills, main = 'Data Science Radar')
chartJSRadarOutput('ID', width = '450', height = '300')
#runExampleApp('basic')
```
```{r, error = TRUE, results='asis'}
lib('fmsb', 'scales')
lib(c('ggradar', 'rescale', 'lattice', 'rgl', 'akima', 'metan'))
df_maxmin <- data.frame(
drat = c(1, 0),
wt = c(1, 0),
qsec = c(1, 0),
vs = c(1, 0),
am = c(1, 0))
#load data
mtcars_radar <- mtcars %>%
as_tibble(rownames = "group") %>%
mutate_at(vars(-group), rescale) %>%
tail(2) %>%
dplyr::select(1,6:10)
#check data type with std() function
str(mtcars_radar)
mtcars_radar <- mtcars_radar[,c('drat','wt','qsec','vs','am')]
mtcars_radar <- rbind(df_maxmin, mtcars_radar)
fmsb::radarchart(mtcars_radar)
```
```{r, error = TRUE, results='asis'}
#devtools::install_github('ricardo-bion/ggradar', dependencies = TRUE, force = T)
#devtools::install_github("poissonconsulting/rescale")
lib(c('ggradar', 'rescale', 'lattice', 'rgl', 'akima', 'metan'))
x=runif(1000)
y=runif(1000)
z=rnorm(1000)
s=interp(x,y,z,duplicate="strip")
surface3d(s$x,s$y,s$z,color="blue")
points3d(s)
x <- seq(-10, 10, length.out = 50)
y <- x
rotsinc <- function(x,y) {
sinc <- function(x) { y <- sin(x)/x ; y[is.na(y)] <- 1; y }
10 * sinc( sqrt(x^2+y^2) )
}
z <- outer(x, y, rotsinc)
persp(x, y, z)
surface3d(x, y, z)
# begin generating my 3D shape
b <- seq(from=0, to=20,by=0.5)
s <- seq(from=0, to=20,by=0.5)
payoff <- expand.grid(b=b,s=s)
payoff$payoff <- payoff$b - payoff$s
payoff$payoff[payoff$payoff < -1] <- -1
# end generating my 3D shape
wireframe(payoff ~ s * b, payoff, shade = TRUE, aspect = c(1, 1),
light.source = c(10,10,10), main = "Study 1",
scales = list(z.ticks=5,arrows=FALSE, col="black", font=10, tck=0.5),
screen = list(z = 40, x = -75, y = 0))
```
```{r, error=TRUE, results='asis'}
plot_rgl_model_a <- function(fdata, plot_contour = T, plot_points = T,
verbose = F, colour = "rainbow", smoother = F){
## takes a model in long form, in the format
## 1st column x
## 2nd is y,
## 3rd is z (height)
## and draws an rgl model
## includes a contour plot below and plots the points in blue
## if these are set to TRUE
# note that x has to be ascending, followed by y
if (verbose) print(head(fdata))
fdata <- fdata[order(fdata[, 1], fdata[, 2]), ]
if (verbose) print(head(fdata))
##
require(reshape2)
require(rgl)
orig_names <- colnames(fdata)
colnames(fdata) <- c("x", "y", "z")
fdata <- as.data.frame(fdata)
## work out the min and max of x,y,z
xlimits <- c(min(fdata$x, na.rm = T), max(fdata$x, na.rm = T))
ylimits <- c(min(fdata$y, na.rm = T), max(fdata$y, na.rm = T))
zlimits <- c(min(fdata$z, na.rm = T), max(fdata$z, na.rm = T))
l <- list (x = xlimits, y = ylimits, z = zlimits)
xyz <- do.call(expand.grid, l)
if (verbose) print(xyz)
x_boundaries <- xyz$x
if (verbose) print(class(xyz$x))
y_boundaries <- xyz$y
if (verbose) print(class(xyz$y))
z_boundaries <- xyz$z
if (verbose) print(class(xyz$z))
if (verbose) print(paste(x_boundaries, y_boundaries, z_boundaries, sep = ";"))
# now turn fdata into a wide format for use with the rgl.surface
fdata[, 2] <- as.character(fdata[, 2])
fdata[, 3] <- as.character(fdata[, 3])
#if (verbose) print(class(fdata[, 2]))
wide_form <- dcast(fdata, y ~ x, value_var = "z")
if (verbose) print(head(wide_form))
wide_form_values <- as.matrix(wide_form[, 2:ncol(wide_form)])
if (verbose) print(wide_form_values)
x_values <- as.numeric(colnames(wide_form[2:ncol(wide_form)]))
y_values <- as.numeric(wide_form[, 1])
if (verbose) print(x_values)
if (verbose) print(y_values)
wide_form_values <- wide_form_values[order(y_values), order(x_values)]
wide_form_values <- as.numeric(wide_form_values)
x_values <- x_values[order(x_values)]
y_values <- y_values[order(y_values)]
if (verbose) print(x_values)
if (verbose) print(y_values)
if (verbose) print(dim(wide_form_values))
if (verbose) print(length(x_values))
if (verbose) print(length(y_values))
zlim <- range(wide_form_values)
if (verbose) print(zlim)
zlen <- zlim[2] - zlim[1] + 1
if (verbose) print(zlen)
if (colour == "rainbow"){
colourut <- rainbow(zlen, alpha = 0)
if (verbose) print(colourut)
col <- colourut[ wide_form_values - zlim[1] + 1]
# if (verbose) print(col)
} else {
col <- "grey"
if (verbose) print(table(col2))
}
open3d()
plot3d(x_boundaries, y_boundaries, z_boundaries,
box = T, col = "black", xlab = orig_names[1],
ylab = orig_names[2], zlab = orig_names[3])
rgl.surface(z = x_values, ## these are all different because
x = y_values, ## of the confusing way that
y = wide_form_values, ## rgl.surface works! - y is the height!
coords = c(2,3,1),
color = col,
alpha = 1.0,
lit = F,
smooth = smoother)
if (plot_points){
# plot points in red just to be on the safe side!
points3d(fdata, col = "blue")
}
if (plot_contour){
# plot the plane underneath
flat_matrix <- wide_form_values
if (verbose) print(flat_matrix)
y_intercept <- (zlim[2] - zlim[1]) * (-2/3) # put the flat matrix 1/2 the distance below the lower height
flat_matrix[which(flat_matrix != y_intercept)] <- y_intercept
if (verbose) print(flat_matrix)
rgl.surface(z = x_values, ## these are all different because
x = y_values, ## of the confusing way that
y = flat_matrix, ## rgl.surface works! - y is the height!
coords = c(2,3,1),
color = col,
alpha = 1.0,
smooth = smoother)
}
}
add_rgl_model <- function(fdata){
## takes a model in long form, in the format