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kvdesaiKalpit DesaiHammadTheOne
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Completed AI/ML section and started on Fundamentals Section (plotly#70)
* ml-knn page with make_moons.csv data * Fixing text * Adding ML ROC & PR page draft * Adding R page for PCA * ML page for t-SNE and UMAP * Adding fundamentals/multiple-chart-types page draft * Adding page for Styling in Plotly with R * committing horiz-vert shapes and figure label pages, without Dash * Adding dependencies=TRUE for Anglr * Added Dash code * Build fix with Anglr issue * Build fix2 with Anglr issue * Build fix3 with Anglr issue * Build fix4 for Anglr * Added the Dash code for Figure-Labels page * Fixing the broken data URL * Update r/2021-07-27-ml-pca.rmd Co-authored-by: HammadTheOne <30986043+HammadTheOne@users.noreply.github.com> * Update r/2021-07-27-ml-pca.rmd Co-authored-by: HammadTheOne <30986043+HammadTheOne@users.noreply.github.com> * Update r/2021-07-28-ml-tsne-umap.rmd Co-authored-by: HammadTheOne <30986043+HammadTheOne@users.noreply.github.com> * Update r/2021-07-28-ml-tsne-umap.rmd Co-authored-by: HammadTheOne <30986043+HammadTheOne@users.noreply.github.com> * Update r/2021-08-02-styling-plotly-in-r.rmd Co-authored-by: HammadTheOne <30986043+HammadTheOne@users.noreply.github.com> * Update r/2021-08-04-figure-labels.rmd Co-authored-by: HammadTheOne <30986043+HammadTheOne@users.noreply.github.com> * Improving aesthetics for plot background and grid lines * Adding front-matter tags and cleanup * Renaming Rmd files * Adding explicit tsne install * Added explicit umap install * Explicitly install rsvd * Explicitly install dash * Fixing dependencies * Fixing order and skipping Dash chunk eval * Deleting old figure-labels page Co-authored-by: Kalpit Desai <kalpit@datakalp.com> Co-authored-by: HammadTheOne <30986043+HammadTheOne@users.noreply.github.com> Co-authored-by: Hammad Khan <hammadtheone@gmail.com>
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‎.circleci/config.yml

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name: install application-level dependencies
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command: |
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sudo apt-get install -y pandoc libudunits2-dev libgdal-dev libxt-dev libglu1-mesa-dev libfftw3-dev libglpk40 libxml2-dev libcurl4-openssl-dev apt-transport-https software-properties-common
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sudo R -e 'install.packages(c("curl", "devtools", "mvtnorm", "hexbin", "tidyverse", "tidymodels", "kknn", "kernlab", "pracma", "reshape2", "ggplot2", "datasets", "fastDummies")); devtools::install_github("ropensci/plotly"); devtools::install_github("johannesbjork/LaCroixColoR"); install.packages("BiocManager"); BiocManager::install("EBImage"); devtools::install_deps(dependencies = TRUE) '
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sudo R -e 'install.packages("https://github.com/hypertidy/anglr/archive/refs/tags/v0.7.0.tar.gz", repos=NULL, type="source"); devtools::install_deps(dependencies = TRUE) '
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sudo R -e 'install.packages(c("curl", "devtools", "mvtnorm", "hexbin", "tidyverse", "tidymodels", "kknn", "kernlab", "pracma", "reshape2", "tsne", "umap", "rsvd", "dash", "tidyquant", "ggplot2", "datasets", "fastDummies")); devtools::install_github("ropensci/plotly"); devtools::install_github("johannesbjork/LaCroixColoR"); install.packages("BiocManager"); BiocManager::install("EBImage"); devtools::install_deps(dependencies = TRUE) '
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sudo R -e 'devtools::install_github("hypertidy/anglr@v0.7.0", dependencies = TRUE) '
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sudo R -e 'devtools::install_github("plotly/dash-daq", dependencies = TRUE, upgrade = TRUE) '
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sudo R -e 'devtools::install_deps(dependencies = TRUE) '
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- save_cache:
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key: cache4
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paths:

‎ggplot2/2016-11-29-facet-wrap.Rmd

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library(plotly)
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## read in data set (tolerance data from the ALDA book)
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tolerance <- read.table("https://stats.idre.ucla.edu/wp-content/uploads/2016/02/tolerance1_pp.txt",
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tolerance <- read.table("https://raw.githubusercontent.com/ASKurz/Applied-Longitudinal-Data-Analysis-with-brms-and-the-tidyverse/master/data/tolerance1_pp.txt",
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sep = ",", header = TRUE)
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## change id and male to factor variables

‎r/2015-07-30-dumbbell-plots.Rmd

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name: Dumbbell Plots
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#Reference
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See [https://plotly.com/r/reference/#scatter](https://plotly.com/r/reference/#scatter) for more information and chart attribute options!
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See [https://plotly.com/r/reference/#scatter](https://plotly.com/r/reference/#scatter) for more information and chart attribute options!

‎r/2015-07-30-figure-labels.Rmd

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‎r/2015-07-30-graphing-multiple-chart-types.Rmd

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‎r/2017-05-19-sankey.Rmd

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name: Sankey Diagram
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### Reference
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See [https://plotly.com/r/reference/#sankey](https://plotly.com/r/reference/#sankey) for more information and options!
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See [https://plotly.com/r/reference/#sankey](https://plotly.com/r/reference/#sankey) for more information and options!

‎r/2017-05-25-dot-plots.Rmd

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See [https://plotly.com/r/reference/#scatter](https://plotly.com/r/reference/#scatter) for more information and chart attribute options!
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See [https://plotly.com/r/reference/#scatter](https://plotly.com/r/reference/#scatter) for more information and chart attribute options!

‎r/2018-11-23-table.Rmd

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#Reference
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See [https://plotly.com/r/reference/#table](https://plotly.com/r/reference/#table) for more information and chart attribute options!
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See [https://plotly.com/r/reference/#table](https://plotly.com/r/reference/#table) for more information and chart attribute options!

‎r/2019-04-12-sunburst-chart.Rmd

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See [https://plotly.com/r/reference/#sunburst](https://plotly.com/r/reference/#sunburst) for more information and chart attribute options!
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See [https://plotly.com/r/reference/#sunburst](https://plotly.com/r/reference/#sunburst) for more information and chart attribute options!

‎r/2020-02-25-treemap.Rmd

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See [https://plotly.com/r/reference/#treemap](https://plotly.com/r/reference/#treemap) for more information and chart attribute options!

‎r/2021-07-08-ml-regression.Rmd

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---
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description: Visualize regression in Tidymodels with Plotly
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display_as: ai_ml
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language: r
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layout: base
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name: ML Regression
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order: 1
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output:
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keep_md: true
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permalink: r/ml-regression/
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thumbnail: thumbnail/ml-regression.png
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---
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```{r, echo = FALSE, message=FALSE}
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knitr::opts_chunk$set(message = FALSE, warning = FALSE)
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```
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<!-- #region -->
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This page shows how to use Plotly charts for displaying various types of regression models, starting from simple models like [Linear Regression](https://parsnip.tidymodels.org/reference/linear_reg.html) and progressively move towards models like Decision Tree and Polynomial Features. We highlight various capabilities of plotly, such as comparative analysis of the same model with different parameters, displaying Latex, and [surface plots](https://plotly.com/r/3d-surface-plots/) for 3D data.
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‎r/2021-07-21-ml-knn.rmd renamed to ‎r/2021-07-21-ml-knn.Rmd

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---
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description: Visualize a k-Nearest-Neighbors (kNN) classification in R with Tidymodels.
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display_as: ai_ml
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language: r
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layout: base
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name: kNN Classification
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order: 2
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output:
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keep_md: true
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permalink: r/knn-classification/
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thumbnail: thumbnail/knn-classification.png
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---
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```{r, echo = FALSE, message=FALSE}
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knitr::opts_chunk$set(message = FALSE, warning = FALSE)
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```
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## kNN Classification in R
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Visualize Tidymodels's k-Nearest Neighbors (kNN) classification in R with Plotly.
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Visualize Tidymodels' k-Nearest Neighbors (kNN) classification in R with Plotly.
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## Basic binary classification with kNN

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