The aim of this series of blog is to **predict monthly admissions** to Singapore public acute adult hospitals. EDA for the dataset was explored in past posts ( part 1 ; part 2 ).

```
library(tidyverse)
library(tidymodels)
library(timetk)
library(modeltime)
library(modeltime.ensemble)
url_datasets<-url("https://github.com/notast/hierarchical-forecasting/blob/main/4Dataset_ML.rds?raw=true")
load(url_datasets)
close(url_datasets)
head(to_train,10)
```

....
**link:** https://notast.netlify.app/post/2021-06-14-hierarchical-forecasting-of-hospital-admissions-ml-approach-modeltime-package/

by Rama Ramakrishnan from https://rviews.rstudio.com/2021/04/15/an-alternative-to-the-correlation-coefficient-that-works-for-numeric-and-categorical-variables/

…

When starting to work with a new dataset, it is useful to quickly pinpoint which pairs of variables appear to be *strongly related*. It helps you spot data issues, make better modeling decisions, and ultimately arrive at better answers.

The *correlation coefficient* is used widely for this purpose, but it is well-known that it can’t detect non-linear relationships. Take a look at this scatterplot of two variables xx and yy.

From https://datageeek.com/

Those who follow my articles know that trying to predict gold prices has become an obsession for me these days. And I am also wondering which factors affect the prices. For the gold prices per gram in Turkey, are told that two factors determine the results: USA prices per ounce and exchange rate for the dollar and the Turkish lira. Let’s check this perception, but first, we need an algorithm for this.

link

by Andrea Luciani

*Andrea Luciani is a Technical Advisor for the Directorate General for Economics, Statistics and Research at the Bank of Italy, and co-author of the bimets package.*

Structural Equation Models (SEM), which are common in many economic modeling efforts, require fitting and simulating whole system of equations where each equation may depend on the results of other equations. Moreover, they often require combining time series and regression equations in ways that are well beyond what the `ts()`

and `lm()`

functions were designed to do. For example, one might want to account for an error auto-correlation of some degree in the regression, or force linear restrictions modeling coefficients.

https://rviews.rstudio.com/2021/01/22/sem-time-series-modeling/

The Covid19 pandemic had, and unfortunately still have, a significant impact on most of the major industries. While for some sectors, the impact was positive (such as online retails, internet and steaming providers, etc.), it was negative for others (such as transportation, tourism, entertainment, etc.). In both cases, we can leverage time series modeling to quantify the effect of the Covid19 on the sector.

Link: https://ramikrispin.github.io/2021/01/covid19-effect/

This is the website for *Tidy Modeling with R*. This book is a guide to using a new collection of software in the R programming language for model building, and it has two main goals:

- First and foremost, this book provides an introduction to
**how to use** our software to create models. We focus on a dialect of R called *the tidyverse* that is designed to be a better interface for common tasks using R. If you’ve never heard of or used the tidyverse, Chapter 2 provides an introduction. In this book, we demonstrate how the tidyverse can be used to produce high quality models. The tools used to do this are referred to as the *tidymodels packages*.
- Second, we use the tidymodels packages to
**encourage good methodology and statistical practice**. Many models, especially complex predictive or machine learning models, can work very well on the data at hand but may fail when exposed to new data. Often, this issue is due to poor choices made during the development and/or selection of the models. Whenever possible, our software, documentation, and other materials attempt to prevent these and other pitfalls.

**New business and financial analysts are finding **`R`

every day. Most of these new *userRs* (R users) are coming from a non-programming background. They have ample domain experience in functions like finance, marketing, and business, but their tool of choice is **Excel (or more recently Tableau & PowerBI)**….

https://www.business-science.io/finance/2020/02/26/r-for-excel-users.html

University students in other disciplines without prior knowledge in statistics and/or programming language are introduced to the statistical method of decision trees in the programming language R during a 45‐minute teaching and practice session. Statistics and programming skills are now frequently required within a wide variety of research fields and private industries. However, students unfamiliar with these subjects may be reluctant to join a full course because of time or student workloads or other commitments or a belief it is not for them. The proposed session is short and can be used as an ice‐breaker to let students have a basic understanding of running statistical models in programming language….

https://onlinelibrary.wiley.com/doi/full/10.1111/test.12210?campaign=wolearlyview

Did you know, that you can transform plain old static `ggplot`

graphs to animated ones? Well you can with the help of the package `gganimate`

by RStudio’s Thomas Lin Pedersen and David Robinson and the results are amazing! My STATWORX colleagues and I are very impressed how effortless all kind of geoms are transformed to suuuper smooth animations. That’s why in this post I will provide a short overview over some of the wonderful functionalities of `gganimate`

, I hope you’ll enjoy them as much as we do!

https://www.statworx.com/de/blog/animated-plots-using-ggplot-and-gganimate/

The first review was about the main ideas and the approximations itself. This time its a review about the spatial models, see the new arxiv’ed report.