Read Histograms and Use Them in R

Bom tutorial para construir histogramas no R

Bom tutorial para construir histogramas no R

Tutorials,

How to Read Histograms and Use Them in R

By Nathan Yau
The chart type often goes overlooked because people don’t understand them. Maybe this will help.

The histogram is one of my favorite chart types, and for analysis purposes, I probably use them the most. Devised by Karl Pearson (the father of mathematical statistics) in the late 1800s, it’s simple geometrically, robust, and allows you to see the distribution of a dataset.

If you don’t understand what’s driving the chart though, it can be confusing, which is probably why you don’t see it often in general publications.

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IFORS Simulation

Algoritmos e Problemas de Simulação

Algoritmos e Problemas de Simulação

The following 6 pages are in this category, out of 6 total.

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IFORS Education Resources Project

portal com materal sobre Investigação Operacional, otimização e SADs

portal com materal sobre Investigação Operacional, otimização e SADs

Welcome to the International Federation of Operational Research Societies (IFORS) Education Resources Project

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Useful Videos on Information Visualization

Bons videos sobre visualização de dados

Bons videos sobre visualização de dados

Noah Iliinsky – Data Visualizations Done Wrong – A Beautiful Collection of Stories and Tips for Success.

The Four Pillars of Data Visualization

Designing Data Visualizations with Noah Iliinsky

Best Practices for Data Visualization

Designing Data Visualizatins

Seeing the Story in the Data and Learning to Effectively Communicate – Inspired by Stephen Few Principles, Visualization Guru

David McCandless: “The beauty of data visualization” – Data Detective Telling Stories From Visualization of Information

This also has a nice quiz about visualization principles.

As I collect more, I will consolidate this list.

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17 short tutorials all data scientists should read

Excelentes textos fundamentais para cientistas dos dados

Excelentes textos fundamentais para cientistas dos dados

Here’s the list:

Related linkThe Data Science Toolkit

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Little Book of R for Time Series!

Um excelente tutorial sobre modelos de previsão simples no R

Um excelente tutorial sobre modelos de previsão simples no R

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How many statisticians does it take to split a bill?

stas

Bom blogue, bem disposto aborda diferenças entre estatística e ML

Bom blogue, bem disposto aborda diferenças entre estatística e ML

Some thoughts on the Fall term, now that Spring is well under way [edit: added a few more points]:

  • RMarkdown and knitr are amazing. When I next teach a course using R, my students will be turning in homeworks using these tools: The output immediately shows whether the code runs and what its results are. This is much better than students copying and pasting possibly-broken code and unconnected output into a text file or (gasp) Word document.
  • I’m glad my cohort socializes outside the office, taking each other out for birthday lunches or going to see a Pirates game. Some of the older PhD students are so focused on their thesis work that they don’t take time for a social break, and I’d like to avoid getting stuck in that rut.
    However! Our lunches always lead us back to the age old question: How many statisticians does it take to split a bill? Answer: too long. I threw together a Shiny app, DinneR, to help us answer this question.

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Using Dates and Times in R

Excelente sobre a utilização de tempos e datas no R

Excelente sobre a utilização de tempos e datas no R

Using Dates and Times in R

by Bonnie Dixon, 10 February 2014


Today at the Davis R Users’ GroupBonnie Dixon gave a tutorial on the various ways to handle dates and times in R. Bonnie provided this great script which walks through essential classes, functions, and packages. Here it is piped throughknitr::spin. The original R script can be found as a gist here.

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The Dangers of Bling Data Visualizations

Excelente de descrição de erros em visualização

Excelente de descrição de erros em visualização

The Dangers of Bling Data Visualizations

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Given the volume of information that’s pouring into the enterprise from so many disparate sources, knowledge workers need to be able to visualize information in order to analyze it and extrapolate insights effectively.

When business users can visualize information, they’re able to process it more effectively and make faster and better decisions, according to Aberdeen research. Business users are constantly seeking the best ways to understand the data behind the data. If a monthly sales figure is low, what are the reasons the sales team is underperforming? The most effective way to help business users understand the data behind the data is by making it visual for them.

Data visualization has recently made its way into the mainstream by the way of infographics, business intelligence dashboards and, in some cases, statistical graphics. However, today data visualization comes in many forms and more often than not there might be too much “bling” incorporated into these data representations, leaving an audience with nothing more than a pretty picture. In this article, we contrast some good and bad examples of visualizations via examination of the salient features of the graphical displays. We will also demonstrate how poorly designed visualizations can lead to erroneous decisions.

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Learn R interactively with the swirl package

Um pacote R para construir lições interativas

Um pacote R para construir lições interativas

swirl is a software package for the R statistical programming language. Its purpose is to teach users statistics and R simultaneously and interactively.

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