2025BusinessScience.R

###多维度展示上市公司的PE,PB,PS,PEG,
##chatGPT在对话机器人中的应用；
library(ggradar)
library(ggdensity)
library(modelDown)
library(trelliscopejs)
library(radiant)
#install.packages("radiant", repos="https://radiant-rstats.github.io/minicran/"，type = "binary")
library(ggiraph)
library(ggradar)
#install.packages("devtools")
#devtools::install_github("ricardo-bion/ggradar", dependencies = TRUE)
library(dplyr)
library(scales)
library(tibble)
mtcars_radar <- mtcars %>% as_tibble(rownames = "group") %>% 
                 mutate_at(vars(-group), rescale) %>% 
                 tail(4) %>% 
                 select(1:10)
ggradar(mtcars_radar)


#Make a Dumbbell Plot to Visualize Change in ggplot2
library(ggalt)                
#ggforce: Make a Hull Plot to Visualize Clusters in ggplot2
library(ggforce)
#ggdist: Make a Raincloud Plot to Visualize Distribution in ggplot2
library(ggdist)
#grafify: Make 5 powerful ggplot2 graphs quickly with R
library(grafify)
#gghalves: Make Half Boxplot | Half Dotplot Visualizations with ggplot2
library(gghalves)
#DataEditR: The GUI for Interactive Dataframe Editing in R
library(DataEditR)
#ggside: A new R package for plotting distributions in side-plots
library(ggside)
#patchwork: ggplot2 plot combiner
library(patchwork)

#ggplot2 Extension: corrmorrant for Flexible Correlation Plots in R

#Webscraping Tables in R: Datapasta Copy-and-Paster
library(Datapasta)
#Not data.table vs dplyr... data.table + dplyr!
library(data.table)
library(dplyr)
#Assess Your DATA QUALITY in R with skimr
library(skimr)
#DataExplorer: Exploratory Data Analysis in R
library(DataExplorer)

#Interactive Principal Component Analysis in R


#Build and Evaluate A Logistic Regression Classifier


#Predictive Power Score vs CorrelationFunnel


#Full Feature Engineering Tutorial with Max Kuhn

#Make Awesome Statistical Plots in R

#How to Write SQL From R

#How to Handle Missing Data in R with simputation

#Learn how to make AMAZING 3D Plots in R by combining ggplot2 and rayshader.






#Hierarchical Time Series Forecasting [Full Code Tutorial]



#Introducing Modeltime Recursive: Tidy Autoregressive Forecasting with Lags

#Introducing Modeltime H2O: Automatic Forecasting with H2O AutoML

#mmtable2: ggplot2 for tables
library(mmtable2)

#easystats: Quickly investigate model performance
library(easystats)
#How To Make Geographic Map Visualizations In R

#Docker for Data Science: An Important Skill for 2021 

#Analyzing Solar Power Energy (IoT Analysis)

#10 Must-Know Tidyverse Functions: #3 - Pivot Wider and Longer

#10 Must-Know Tidyverse Functions: #2 - across()
###The across() function was released in dplyr 1.0.0. It's a new tidyverse function that extends group_by and summarize for multiple column and function summaries.



#No 1: Forecasting Time Series ARIMA Models 

#No 2: Python and R - Part 2: Visualizing Data with Plotnine 

#No. 3: Detect Relationships With Linear Regression 

#No. 4: Time Series Demand Forecasting

#No. 5: A/B Testing with Machine Learning - A Step-by-Step Tutorial 

#Hyperparameter Tuning Forecasts in Parallel with Modeltime


#R is for Research, Python is for Production

#10 Must-Know Tidyverse Functions: #1 - relocate()

#relocate() is like arrange() for columns. It keeps all of the columns, but provides much more flexibility for reordering. Notice how all of the columns are returned.

#How to Set Up TensorFlow 2 in R in 5 Minutes (BONUS Image Recognition Tutorial)

#Shiny Real Estate with Zillow API (Free Course)

#Tidy Discounted Cash Flow Analysis in R (for Company Valuation)
tidyverse - An ecosystem for wrangling and visualizing data in R
tabulizer - PDF Scraping
fuzzyjoin - Joining data with inexact matching
rvest - Web Scraping
tidyxl - Importing non-tabular (non-tidy) Excel Data



#Part 1 - Data Sources: Collect DCF input data with PDF Scraping, Web Scraping, API’s, and tidy the data into a single DCF Inputs that can be used for Part 2.

#Part 2 - DCF Company Valuation: Model Saudi Aramco’s Company Valuation. Perform sensitivity analysis given various risks to our model.

#tidyquant v1.0.0: Pivot Tables, VLOOKUPs in R The NEW tidyquant package (v1.0.0) makes popular Excel functions like Pivot Tables, VLOOKUP(), SUMIFS(), and much more possible in R.

#Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial

#Part 5 - Five Reasons to Learn H2O for High-Performance Machine Learning

#NEW BOOK - The Shiny Production with AWS Book

#Part 4 - Git for Data Science Applications (A Top Skill for 2020)

#Part 1 - Five Full Stack Data Science Technologies for 2020 (and Beyond)

#Part 3 - Docker for Data Scientists (A Top Skill for 2020)

#Customer Churn Modeling using Machine Learning with parsnip

#Part 2 - Data Science with AWS (A Top Skill for 2020)

#Apply Data Science to Improve Addiction Treatment

#Expert Shiny Developer with AWS - Course Launch Today!

#Course Launch - Expert Shiny Developer with AWS in 7 Days!

#Web Scraping Product Data in R with rvest and purrr

#Cleaning Anomalies to Reduce Forecast Error by 9% with anomalize

#PDF Scraping in R with tabulizer

#Big Data: Wrangling 4.6M Rows with dtplyr (the NEW data.table backend for dplyr)

#Introducing correlationfunnel v0.1.0 - Speed Up Exploratory Data Analysis by 100X

#Unlocking Blue Oceans with Data Science

#How I Started My Data Science Business

#Excel to R, Part 2 - Speed Up Exploratory Data Analysis 100X (R Code!)

#2 Critical Factors to Learn Data Science

#Data Science Workflow - The Process for Solving Data Problems

#Build A R Shiny App (Tutorial) - Wedding Risk Model

#Introducing the Ultimate R Cheat Sheet Version 2.0: The Shinyverse
####The Ultimate R Cheat Sheet now covers the Shinyverse - An Ecosystem of R Packages 
###for Shiny Web Application Development, Deployment, and putting Machine Learning into Production. 
###Download the Cheat Sheet for Free!


#Building APIs with plumber - New Learning Lab 1-Hour Course

#How To Become A Financial Data Scientist (Or A Data Scientist In Any Domain)
###Becoming a data scientist in Finance can be a lofty challenge... unless you know how to streamline the path.

#Predictive Web Applications for Business with R Shiny - Course is Open!!!

#Finance in R with tidyquant - New Learning Lab 1-Hour Course

#Customer Segmentation and RFM Analysis with K-Means Clustering - Part 2

#3 Simple Rules For Getting Results With Data Science (A Story About Learning From Failure)

#New Learning Lab 1-Hour Course - Build A Strategic Database With Web Scraping

#Strategies To Improve Your Business Forecasting 50% (or more) | Time Series Learning Lab

#A/B Testing with Machine Learning - A Step-by-Step Tutorial

#Targeted Marketing with Customer Segmentation and RFM Analysis - Part 1

#New Cheat Sheet - Customer Segmentation and Clustering Workflow

#Excel to R, Part 1 - The 10X Productivity Boost

#Python Integration in RStudio - Data Science IDE Review

#Data Science In R - The Ultimate R Cheat Sheet - The Ultimateness Just Doubled!

#Data Science Course Launch Today - Business Analysis With R Now Open!

#Course Launch - Business Analysis With R in 8 Days!

#Marketing Analytics - Fast-Food Promotion Effectiveness

#Time Series Analysis for Business Forecasting with Artificial Neural Networks
###This article demonstrates a real-world case study for business forecasting with regression models 
###including artificial neural networks (ANNs) with Keras

#Ultimate Python Cheat Sheet: Data Science Workflow with Python

# R Cheat Sheet: Data Science Workflow with R

# Marketing Analytics and Data Science

#  R and Python: How to Integrate the Best of Both into Your Data Science Workflow

#Case Study: How To Build A High Performance Data Science Team


#Agile Framework For Creating An ROI-Driven Data Science Practice

# IML: Machine Learning Model Interpretability And Feature Explanation with IML and H2O
####Model interpretability is critical to businesses. 
###If you want to use high performance models (GLM, RF, GBM, Deep Learning, H2O, Keras, xgboost, etc), 
###you need to learn how to explain them. With machine learning interpretability growing in importance, 
###several R packages designed to provide this capability are gaining in popularity. 
###We analyze the IML package in this article.


# DALEX: Interpretable Machine Learning Algorithms with Dalex and H2O

# New Course Content: DS4B 201 Chapter 7, The Expected Value Framework For Modeling Churn With H2O

# Data Science for Business: 3 Reasons Why You Need the Expected Value Framework

# Time Series Analysis: KERAS LSTM Deep Learning - Part 2


# LIME: Machine Learning Model Interpretability with LIME

# Information Security: Anomaly Detection and Threat Hunting with Anomalize


# Algorithmic Trading: Using Quantopian's Zipline Python Library 
#In R And Backtest Optimizations By Grid Search And Parallel Processing
###tibbletime (time-based extension of tibble) + furrr (a parallel-processing compliment to purrr) + 
##flyingfox (Zipline in R) to develop a backtested trading algorithm that can be optimized via grid search and parallel processing. 
###We are releasing this article as a compliment to the R/Finance Conference

# Time Series Analysis: KERAS LSTM Deep Learning - Part 1

# Anomaly Detection Using Tidy and Anomalize

# How To Learn R, Part 1: Learn From A Master Data Scientist's Code

# The Tidy Time Series Platform: tibbletime 0.1.0

# Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn
###Predict customer churn using deep Learning Keras in R, with a 82% model accuracy.

# EARL Presentation on HR Analytics: Using ML to Predict Employee Turnover
### HR Analytics presentation 

# Demo Week: Time Series Machine Learning with h2o and timetk

# Demo Week: Tidy Time Series Analysis with tibbletime

# LIVE DataTalk on HR Analytics Tonight: Using Machine Learning to Predict Employee Turnover
####new lime package that enables breakdown of complex, black-box machine learning models into variable importance plots. 
###h2o package’s new FREE automatic machine learning algorithm, h2o.automl()


# Demo Week: Tidy Forecasting with sweep
##tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! 

# Demo Week: Time Series Machine Learning with timetk

# Demo Week: class(Monday) <- tidyquant

# Predictive Sales Analytics: Use Machine Learning to Predict and Optimize Product Backorders

# It's tibbletime v0.0.2: Time-Aware Tibbles, New Functions, Weather Analysis and More

# HR Analytics: Using Machine Learning to Predict Employee Turnover

# It's tibbletime: Time-Aware Tibbles

# alphavantager: An R interface to the Free Alpha Vantage Financial Data API

# Tidy Time Series Analysis, Part 4: Lags and Autocorrelation

# Tidy Time Series Analysis, Part 3: The Rolling Correlation

# Tidy Time Series Analysis, Part 2: Rolling Functions

# sweep: Extending broom for time series forecasting
###The sweep package has tidiers that convert the output from forecast modeling and forecasting functions to “tidy” data frames

# Tidy Time Series Analysis, Part 1

#  Business Science EARL SF 2017 Presentation: tidyquant, timekit, and more!

# tidyquant: R/Finance 2017 Presentation

# timekit: New Documentation, Function Improvements, Forecasting Vignette
###you can use machine learning to forecast using the time series signature as an engineered feature space

# tidyquant: New Tools for Performing Financial Analysis within the Tidy Ecosystem

# timekit: Time Series Forecast Applications Using Data Mining


# tidyquant 0.5.0: select, rollapply, and Quandl

# tidyquant Integrates Quandl: Getting Data Just Got Easier

# tidyquant 0.4.0: PerformanceAnalytics, Improved Documentation, ggplot2 Themes and More

# Recreating RView's ''Reproducible Finance With R: Sector Correlations''

# tidyquant 0.3.0: ggplot2 Enhancements, Real-Time Data, and More

# Speed Up Your Code Part 2: Parallel Processing Financial Data with multidplyr + tidyquant

# tidyquant 0.2.0: Added Functionality for Financial Engineers and Business Analysts

# tidyquant: Bringing Quantitative Financial Analysis to the tidyverse

# Speed Up Your Code: Parallel Processing with multidplyr

# Russell 2000 Quantitative Stock Analysis in R: Six Stocks with Amazing, Consistent Growth

# Quantitative Stock Analysis Tutorial: Screening the Returns for Every S&P500 Stock in Less than 5 Minutes

# Customer Segmentation Part 3: Network Visualization

# Customer Segmentation Part 2: PCA for Segment Visualization

# Customer Segmentation Part 1: K Means Clustering

# orderSimulatoR: Simulate Orders for Business Analytics
###which enables fast and easy R order simulation for customer and product learning

# Marketing Strategy: Why MBAs Can Benefit from Learning Analytics

# A Data Scientist's Resources




























































































#sklearn: Make your first linear regression model in Python

#SweetViz: Automated Exploratory Data Analysis (EDA) in Python

#plotnine: Make great-looking correlation plots in Python

#Siuba: Data wrangling with dplyr in Python

#Pandas Profiling: Make Exploratory Data Analysis Reports

#Web Scraping and Analyzing Home Rentals (Python + R)

#Python and R - Part 1: Exploring Data with Datatable