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Saveti – Statistika

Ukupno: 34, strana 2 od 2

R bloggers, Spelling 1.0, Mapping the largest cities in Asia using R




Spelling 1. 0: quick and effective spell checking in R September 7, 2017 By Jeroen Ooms The new rOpenSci spelling package provides utilities for spell checking common document formats including latex, markdown, manual pages, and DESCRIPTION files. It also includes tools especially for package authors to automate spell checking of R documentation and vignettes. Spell Checking Packages The main purpose of this package is to quickly find spelling errors in R packages. The spell_check_package() function extracts all. . . Read more » Statistical Application Development with R and Python - Second Edition MORE Analysing soil moisture data in NetCDF format with the ncdf4 library September 6, 2017 By Peter Prevos The netCDF format is popular in sciences that analyse sequential spatial data. It is a self-describing, machine-independent data format for creating, accessing and sharing array-oriented information. The netCDF format provides spatial time-series such. . . Read more » Knime 3. 4 connections to Microsoft R, Azure September 6, 2017 By David Smith Version 3. 4 of the Knime Analytics Platform, the open-source data science workflow toolbox, was released back in July. With that release came new integrations with Azure and Microsoft R. . . Read more » Envisioning Data Science Webinar Series and Call for Input September 6, 2017 By Andy Webinar Series: Data Science Undergraduate Education Join the National Academies of Sciences, Engineering, and Medicine for a webinar series on undergraduate data science education. Webinars will take place on Tuesdays. . . Read more » The Ultimate Guide To Partitioning Clustering September 6, 2017 By Easy Guides In this first volume of symplyR, we are excited to share our Practical Guides to Partioning Clustering. The course materials contain 3 chapters organized as follow: K-Means. . . Read more » Mapping the largest cities in Asia using R September 6, 2017 By Sharp Sight "After you've mastered a small number of R functions, visualizations like this become easy (and, they're great practice). " The post Mapping the largest cities in Asia using R appeared first. . . Read more » Beyond the basics of data. table: Smooth data exploration September 5, 2017 By sindri This exercise set provides practice using the fast and concise data. table package. If you are new to the syntax it is recommended that you start by solving the set. . . Read more » Data Science for Fraud Detection September 5, 2017 By Shirin's playgRound I have written the following post about Data Science for Fraud Detection at my company codecentric’s blog: Fraud can be defined as “the crime of getting money by. . . Read more » Readability Redux September 4, 2017 By hrbrmstr I recently posted about using a Python module to convert HTML to usable text. Since then, a new package has hit CRAN dubbed htm2txt that is 100% R and. . . Read more »

R bloggres Sharing Modeling Pipelines in R and more




Scraping NBA game data from basketball-reference. com I’m a casual NBA fan: I don’t have time to watch the games but enjoy viewing the highlights on Instagram/Youtube (especially Shaqtin’ A Fool!); I sometimes read game articles and analyses (e. g.  Blogtable). Apart from the game being an amazing visual spectacle, it’s fun to drink in the deluge of stats that each game brings. I’m not even talking about advanced stats and “ABPRmetrics“: there’s something exciting about seeing how many different statistical records can be broken on a given night. As a data/stats person, I’ve been wanting to get my hands on NBA data and play around with it on my own. However, in my internet searching I didn’t come across any free easy-to-use datasets. The website Basketball-Reference. com is an excellent compendium of all the data I would want, but it was embedded within the webpage, not made available in an analysis-ready format. (Or at least, I couldn’t find it, or it wasn’t free. ) Reading List Faster With parallel, doParallel, and pbapply I have several tables that I would like to load as a sole data frame. Derived functions from read. table () have a lot of convenient features, but it. . . Read more » Sharing Modeling Pipelines in R Reusable modeling pipelines are a practical idea that gets re-developed many times in many contexts. wrapr supplies a particularly powerful pipeline notation, and a pipe-stage re-use system (notes here). . . .  Read more »   R analiza podataka VIŠE O KNJIZI I KORPA ZA NARUČIVANJE How to give money to the R project by Mark Niemann-Ross, an author, educator, and writer who teaches about R and Raspberry Pi at LinkedIn Learning I spend a LOT of time at r-project. org, in particular the. . .  Read more » Christmas starts earlier every year… right? It’s mid-September and you’re wandering around your preferred supermarket when you stumble across the Christmas section. “Already”, you think. “It wasn’t like this back when I was a kid”. . . . Read more » Is Santa Claus predictable? Two years ago, we revealed something sensational: Santa Claus is a data scientist. The secret of his success does not lay in his hundred years of experience as a. . .  Read more »

R i neki novi paketi




Some New R Packages New R packages keep rolling into CRAN at a prodigious rate: 184 in May, 195 in June and July looks like it will continue the trend. I spent some time sorting through them and have picked out a few that that are interesting from a data science point of view. That’s like so random! Monte Carlo for Data Science Another great turnout at the DataPhilly meetup last night. Was great to see all you random data nerds! nzelect 0. 2. 0 on CRAN The nzelect R package which I first introduced in a blog post in April is now available on CRAN. The version number is 0. 2. 0. The difference from version 0. 1. 0 is sizeable – all the 2013 census data has been removed and is now in a companion package, nzcensus. This is for ease of development and maintenance, and to allow organisations that aren’t interested in the election results still to use the census data. It also keeps the package size within CRAN guidelines. I’ll write about nzcensus in a separate post; its coverage has somewhat expanded from when I last blogged about combining census and election data. nzcensus is only available from GitHub due to its size. Notes from the Kölner R meeting, 9 July 2016 Last Thursday the Cologne R user group came together again. This time, our two speakers arrived from Bavaria, to talk about Spark and R Server.

R informator 198 How Happy is Your Country? — Happy Planet Index Visualized




. rprofile: Mara Averick November 9, 2017 By rOpenSci - open tools for open science Mara Averick is a non-profit data nerd, NBA stats junkie, and most recently, tidyverse developer advocate at RStudio. She is the voice behind two very popular Twitter accounts, @dataandme and @batpigandme. Mara and I discussed sports analytics, how attending a cool conference can change the approach to your career, and how she uses Twitter as a mechanism for self-imposed. . . Read more » How Happy is Your Country? — Happy Planet Index Visualized November 9, 2017 By Susan Li The Happy Planet Index (HPI) is an index of human well-being and environmental impact that was introduced by NEF, a UK-based economic think tank promoting social, economic and environmental. . . Read more » Formal ways to compare forecasting models: Rolling windows November 8, 2017 By insightr By Gabriel Vasconcelos Overview When working with time-series forecasting we often have to choose between a few potential models and the best way is to test each model in. . . Read more » Introduction to Visualizing Asset Returns November 8, 2017 By R Views In a previous post, we reviewed how to import daily prices, build a portfolio, and calculate portfolio returns. Today, we will visualize the returns of our individual assets that. . . Read more » Calculating the house edge of a slot machine, with R November 8, 2017 By David Smith Modern slot machines (fruit machine, pokies, or whatever those electronic gambling devices are called in your part of the world) are designed to be addictive. They're also usually quite. . . Read more » Creating Reporting Template with Glue in R November 8, 2017 By Abdul Majed Raja Report Generation is a very important part in any Organization’s Business Intelligence and Analytics Division. The ability to create automated reports out of the given data is one of. . . Read more » R / Finance 2018 Call for Papers November 8, 2017 By Thinking inside the box The tenth (!!) annual annual R/Finance conference will take in Chicago on the UIC campus on June 1 and 2, 2018. Please see the call for papers below (or. . . Read more » solrium 1. 0: Working with Solr from R November 7, 2017 By rOpenSci - open tools for open science Nearly 4 years ago I wrote on this blog about an R package solr for working with the database Solr. Since then we’ve created a refresh of that package. . . Read more »

R INFORMATOR 199, RTutor + Water Pollution and Cancer




visualizing reassortment history using seqcombo December 4, 2017 By R on Guangchuang Yu Reassortment is an important strategy for influenza A viruses to introduce a HA subtype that is new to human populations, which creates the possibilities of pandemic. A diagram showed above (Figure 2 of doi:10. 1038/srep25549) is widely used to illustrate the reassortment events. While such diagrams are mostly manually draw and edit without software tool to automatically generate. Here, I implemented the hybrid_plot function for producing publication. . . Read more » My book ‘Practical Machine Learning with R and Python’ on Amazon December 4, 2017 By Tinniam V Ganesh My book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($9. 99) and kindle ($6. 97/Rs449) versions. In this book I. . . Read more » Magick 1. 6: clipping, geometries, fonts, fuzz, and a bit of history December 4, 2017 By rOpenSci - open tools for open science This week magick 1. 6 appeared on CRAN. This release is a big all-round maintenance update with lots of tweaks and improvements across the package. The NEWS file gives an overview. . . Read more » AI School: Microsoft R and SQL Server ML Services December 4, 2017 By David Smith If you'd like to learn how you use R to develop AI applications, the Microsoft AI School now features a learning path focused on Microsoft R and SQL Server. . . Read more » 5 ways a SAS Health Check can simplify a transition to R December 4, 2017 By Mango Solutions . . . Read more » Outliers Detection and Intervention Analysis December 4, 2017 By Giorgio Garziano In my previous tutorial Arima Models and Intervention Analysis we took advantage of the strucchange package to identify and date time series level shifts structural changes. Based on that,. . . Read more » RTutor: Water Pollution and Cancer December 4, 2017 By Economics and R - R posts One very important benefit of stronger environmental protection is to reduce the damaging effects of pollution on human health. In his very interesting article “The Consequences of Industrialization: Evidence from. . . Read more » When you use inverse probability weighting for estimation, what are the weights actually doing? December 3, 2017 By Keith Goldfeld Towards the end of Part 1 of this short series on confounding, IPW, and (hopefully) marginal structural models, I talked a little bit about the fact that inverse probability. . . Read more »

R, analiza podataka, potpuni vodič




Predgovor za knjigu, R, analiza podataka, drugo izdanje Predgovor Reći ću vam odmah da postoji mnoštvo knjiga o analizi podataka i programskom jeziku R. Pretpostavljam da već znate zašto je izuzetno korisno naučiti R i analizu podataka (ako ne znate, zašto čitate ovaj predgovor?!), ali dozvolite da preporučim ovu knjigu kao vodič na vašem „putovanju“. Prvo, učenje analize podataka je za mene bilo komplikovano. Postoje osobe sa urođenim talentom za statistiku koje mogu odmah da je shvate, ali mislim da ja nisam jedan od njih. Nisam odustajao, jer volim nauku i istraživanje i znao sam da je analiza podataka neophodna, a ne zato što sam je odmah razumeo. Danas volim ovu oblast samu po sebi, a ne zato što mi je potrebna, ali to sam shvatio tek nakon nekoliko meseci truda. Na kraju, nakon što sam pročitao mnogo knjiga, delovi slagalice su počeli da se sklapaju. Nakon toga, počeo sam da podučavam prijatelje koji su bili zainteresovani za ovu temu i video sam da su nailazili na iste prepreke koje sam i ja morao da prevaziđem. Mislim da mi je iskustvo u ovoj oblasti omogućilo da shvatim probleme sa kojima se susreću studenti statistike, pa ih mogu razumeti na način na koji drugi možda neće moći. Uzgred, ne dopustite da vas uplaši to što sam imao poteškoća da shvatim analizu podataka, jer danas sa sigurnošću mogu reći da znam o čemu govorim. Drugo, ova knjiga je nastala zbog frustracije što je većina tekstova o statistici napisana na način koji nije nimalo produktivan. Nasuprot tome, ja sam prihvatio jedan opušten pristup, ali ne i suviše neozbiljan. Treće, ova knjiga sadrži mnogo podataka koje bih voleo da su zastupljeni u više izvora koje sam koristio kada sam učio R analizu podataka. Na primer, čitava poslednja lekcija posebno pokriva teme koje predstavljaju ogroman izazov za R analitičare kada prvi put primenjuju znanje na nesavršene podatke iz stvarnog sveta. Na kraju, dugo sam razmišljao o tome kako da predstavim ovu knjigu i koji redosled tema bi bio najbolji. Napisao sam biblioteku i dizajnirao algoritme za ovaj postupak. Redosled tema u u ovoj knjizi sam pažljivo predstavio na sledeći način: (a) jedna tema se nadgrađuje na drugu, (b) obezbeđena su poglavlja jednostavnijeg sadržaja u skladu sa nivoom složenosti tema (psiholozi ovo nazivaju povremeno nagrađivanje), (c) grupisane su povezane teme i (d) smanjen je broj tema za koje je neophodno znanje iz još nenaučenih oblasti (nažalost, ovo je uobičajeno u statistici). Ako ste zainteresovani, ovaj postupak sam detaljno opisao u tekstu na blogu koji možete pročitati na adresi http://bit. li/teach-stats. Poenta je u tome što je ova knjiga veoma posebna – knjiga u koju sam uložio svo svoje znanje. Međutim, analiza podataka može biti veoma komplikovana oblast i ponekada izgleda kao da ništa nema smisla. Tada zapamtite da su se mnogi drugi (čak i ja) osećali kao da stoje u mestu. Nemojte odustajati, „nagrada“ je sjajna. Takođe zapamtite, ako je smetenjak kao što sam ja mogao da nauči analizu podataka, možete i vi. Samo napred! Kome je namenjena ova knjiga Bilo da prvi put učite analizu podataka ili želite da poboljšate znanje koje već imate, ova knjiga će se pokazati dragocenim izvorom. Ako tražite knjigu koje će vas voditi od osnova do primene naprednih i efikasnih analitičkih metodologija i ako imate neko prethodno iskustvo u programiranju i matematici, onda je ova knjiga za vas. Vikipedija U računarstvu, R je programski jezik i programsko okruženje za statističke izračune i grafike. On je izvedba S programskog jezika sa leksičkom semantikom inspirisanom Scheme -om. R su stvorili Ros Ihaka i Robert Džentlmen[2] na Aukland univerzitetu (University of Auckland), Novi Zeland, a sad ga razvija R Development Core Team. Nazvan je delimično prema imenima autora, a delom kao igra reči na ime S. [3] R jezik je postao standard među statističarima koji razvijaju statistički softver,[4][5] i široko je korišćen za razvoj statističkog softvera i analizu podataka. [5] R je deo GNU projekta. [6] Njegov izvorni kod je slobodan i pod uslovima koje daje GNU-ova opšta javna licenca, a prekompilirane binarne verzije su obezbeđene za različite operativne sisteme. R koristi interfejs komandne linije, kroz više grafičkih korisničkih okruženja.

R, Intro to FFTree Exercise, The use of R in official statistics conference 2018




Intro to FFTree Exercise May 22, 2018 By Biswarup Ghosh In the exercises below, we will work with FFTree pacakge which lets us use fast and frugal decision tree to model the data Please install the package and load the library before starting Answers to these exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please Related exercise sets:Spatial Data. . . Why you should regret not going to eRum 2018? May 22, 2018 By Appsilon Data Science Blog I spent 3 amazing days at eRum conference in Budapest. The conference was a blast and organizers (BIG thanks to them again) did wonderful job compiling such a high-level. . . Read more » The use of R in official statistics conference 2018 May 22, 2018 By mark On September 12-14 the 6th international conference on the use of R in official statistics (#uRos2018) will take place at the Dutch National Statistical Office in Den Haag, the. . . Read more » The myth that AI or Cognitive Analytics will replace data scientists: There is no easy button May 22, 2018 By scottmutchler First, let me say I would love to have an “easy button” for predictive analytics. Unfortunately, predictive analytics is hard work that requires deep subject matter expertise in both. . . Read more » How Has Taylor Swift’s Word Choice Changed Over Time? May 22, 2018 By How Has Taylor Swift's Word Choice Changed Over Time?Sunday night was a big night for Taylor Swift - not only was she nominated for multiple Billboard Music Awards;. . . Read more » Why R? 2018 Conf – CfP ends May 25th May 22, 2018 By Marcin Kosiński We are pleased to announance upcoming Why R? 2018 conference that is going to happen in central-eastern Europe (Poland, Wroclaw) this July (2-5th). It is the last week. . . Read more »

R-bloggers news, Timing Column Indexing in R, How to do simple table manipulations with R using Displayr




Simple Steps to Create Treemap in R The following document details how to create a treemap in R using the treemap package. What are they & when do we use them In the most basic terms a treemap is generally used when we want to visualize proportions. It can be thought of a pie map where the slices are replaced by rectangles. … Continue reading Simple. . .  Read more » R analiza podataka VIŠE O KNJIZI I KORPA ZA NARUČIVANJE   Reproducible development with Rmarkdown and Github I’m pretty sure most readers of this blog are already familiar with Rmarkdown and Github. In this post I don’t pretend to invent the wheel but rather give a. . .  Read more » Timing Column Indexing in R I’ve ended up (almost accidentally) collecting a number of different solutions to the “use a column to choose values from other columns in R” problem. Please read on for. . .  Read more » How to do simple table manipulations with R using Displayr R is not just a tool for the data science elite. It is an immensely powerful tool that can be used by market researchers in. . .  Read more » Using a Column as a Column Index We recently saw a great recurring R question: “how do you use one column to choose a different value for each row?” That is: how do you use a. . .  Read more » Furthest Water Finding the Location Furthest from Water in the Conterminous United States The idea for this post came a few months back when I received an email that started, “I am a. . .  Read more » R from the turn of the century Last week I spent some time reminiscing about my PhD and looking through some old R code. This trip down memory lane led to some of my old R. . .  Read more » Shiny application in production with ShinyProxy, Docker and Debian You created some great Shiny applications, following our advice of Shiny packaging for example, and you want to put them into production, self-hosting, so that others can enjoy them,. . .  Read more » Data Science With R Series – Week 1 Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other. . .  Read more »

R-bloggers novosti i tutorijali




Easy data access: The advantages of a unique database connection with ODBC and DBI Easy data access: The advantages of a unique database connection with ODBC and DBI Every developer has his favorite tools and frameworks to work with when connecting to the database. The number of different front end and back end combinations increases the complexity of the analysis. Any Relational Database Management  Getting a DOI for your code This blog article is an explanation of sharing research code. It focuses on using the version control website Github and academic repository Zenodo. Forget about Excel, Use these R Shiny Packages Instead tl; dr Transferring your Excel sheet to a Shiny app can be the easiest way to create an enterprise ready dashboard. In this post, I  present 6 Shiny alternatives. . . Spatial regression in R part 1: spaMM vs glmmTMB Are you interested in guest posting? Publish at DataScience+ via your editor (i. e. , RStudio). Category Advanced Modeling Tags Data Visualisation GLMM Logistic Regression R Programming spatial model Many datasets these days are collected at different locations over. . R analiza podataka, drugo izdanje VIŠE O KNJIZI I KORPA ZA NARUČIVANJE

R-bloggers R news, Redmonk Language Rankings, June 2018




Redmonk Language Rankings, June 2018 August 10, 2018 By David Smith The latest Redmonk Language Rankings are out. These rankings are published twice a year, and the top three positions in the June 2018 rankings remain unchanged from last time: JavaScript at #1, Java at #2, and Python at #3. The Redmonk rankings are based on Github repositories (as a proxy for developer activity) and StackOverflow activity (as a proxy. . . Read more » Installation of R 3. 5 on Ubuntu 18. 04 LTS and tips for spatial packages August 10, 2018 By Sébastien Rochette Do you plan to upgrade your server installation from Ubuntu 16. 04 to Ubuntu 18. 04 LTS ? It is also the best time to migrate to R 3. 5 ! By. . . Read more » Gender diversity in the TV series industry August 10, 2018 By Caroline Barret In a previous post, I studied gender diversity in the film industry, I did this by focusing on some key behind-the-camera roles and measuring the evolution of the gender diversity. . . Read more » How to (quickly) enrich a map with natural and anthropic details August 9, 2018 By Francesco Bailo In this post I show how to enrich a ggplot map with data obtained from the Open Street Map (OSM) API. After adding elevation details to the map, I. . . Read more » New Course: Fundamentals of Bayesian Data Analysis in R August 9, 2018 By Ryan Sheehy Here is the course link. Course Description Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework. . . Read more » EARL London interviews – Catherine Gamble, Marks and Spencer August 9, 2018 By Mango Solutions For today’s interview, Ruth Thomson, Practice Lead for Strategic Advice spoke to Catherine Gamble, Data Scientist at Marks and Spencer. Catherine is presenting “Using R to Drive Revenue for. . . Read more » Beyond Basic R – Plotting with ggplot2 and Multiple Plots in One Figure August 8, 2018 By The USGS OWI blog R can create almost any plot imaginable and as with most things in R if you don’t know where to start, try Google. The Introduction to R curriculum summarizes. . . Read more » Le Monde puzzle [#1063] August 8, 2018 By xi'an A simple (summertime?!) arithmetic Le Monde mathematical puzzle A “powerful integer” is such that all its prime divisors are at least with multiplicity 2. Are there two powerful integers. . . Read more » Hotels vs Airbnb – Barcelona case study (proof of concept) August 8, 2018 By Nacho Moreno Click here to access the map 1 - Background and motivation During the last few years, the sharing economy has become more and more ubiquitous, from taxi riding applications. . . Read more » IEEE Language Rankings 2018 August 8, 2018 By David Smith Python retains its top spot in the fifth annual IEEE Spectrum top programming language rankings, and also gains a designation as an "embedded language". Data science language R remains. . . Read more »    

R-bloggers R news, Using R to Generate Live World Cup Notifications




Using R to Generate Live World Cup Notifications Here in Belgium, World Cup fever is at fever pitch, but with matches starting during work hours, how is a desk worker supposed to follow along? By leaving the R environment? Blasphemy. Today we show how to use R to generate live desktop notifications for The Beautiful Game. A notification system preview, free of local bias. Overview We break the process of producing a live score. . . Basic Generalised Additive Model In Ecology; Exercise Generalised Additive Models (GAM) are non-parametric models that add smoother to the data. On this exercise, we will look at GAMs using cubic spline using the mgcv package. Dataset. . . Weighting tricks for machine learning with Icarus – Part 1 Calibration in survey sampling is a wonderful tool, and today I want to show you how we can use it in some Machine Learning applications, using the R package. . . hitting a wall Once in a while, or a wee bit more frequently (!), it proves impossible to communicate with a contributor of a question on X validated. A recent instance was. . . R null values: NULL, NA, NaN, Inf R language supports several null-able values and it is relatively important to understand how these values behave, when making data pre-processing and data munging. In general, R supports: NULL. . . The Devil is in the Data is moving Dear readers, I have been consolidating my online presence and the Devil is in the Data blog has moved to the Lucid Manager website. This will be the last. . . Rough looking figures from R A recent blog post regarding data visualization had some barplots I liked the look of (aesthetically…for research purposes, they wouldn’t be suitable). They look as if they’ve be coloured. . . Visualize the World Cup with R! Part 1: Recreating Goals with ggsoccer and ggplot2 After posting a couple of my World Cup viz on Twitter, I thought I'll collate some of them into a blog post. This will be Part 1 of a. . . A brief guide to data visuals in R in 2018 Data visuals 2018 Supplementary notes for CJ Brown’s talks on dataviz in 2018 for Griffith University’s honours students and the UQ Winterschool in Bioinformatics. These notes run through some of the principles I. . . Predict Customer Churn with Gradient Boosting Customer churn is a key predictor of the long term success or failure of a business. But when it comes to all this data, what’s. . .

Sveobuhvatni vodič za R analizu i vizuelizaciju podataka i upravljanje njima




Kome je namenjena ova knjiga Ako prvi put učite analizu podataka ili, pak, želite da poboljšate znanje koje već imate, ova knjiga će se pokazati dragocenim izvorom. Ako tražite knjigu koje će vas voditi od osnova do primene naprednih i efikasnih analitičkih metodologija i ako imate neko prethodno iskustvo u programiranju i matematici, onda je ova knjiga za vas. Šta obuhvata ova knjiga? Poglavlje 1, Osnove programskog jezika R, sadrži pregled aspekata R-a koje ćete morati da upoznate zbog sledećih poglavlja. Ovde učimo osnove R sintakse i osnovne strukture podataka R-a, pišemo funkcije, učitavamo podatke i instaliramo pakete. U Poglavlju 2, Oblik podataka, razmatraju se univarijacioni podaci. Naučićete različite tipove podataka, opisivanje jedinstvenih podataka i vizuelizovanje oblika podataka. Poglavlje 3, Opis veza, obuhvata multivarijacione podatke. Konkretno, naučićete tri osnovne klase bivarijacionih veza i njihov opis. U Poglavlju 4, Verovatnoća, naučićete osnove teorije verovatnoće, Bajesovu teoremu i distribuciju verovatnoće. U Poglavlju 5, Korišćenje podataka za uzorkovanje i procenu, razmatra se teorija uzorkovanja i procene. Pomoću primera naučićete centralnu graničnu teoremu, procenu parametara i intervale poverenja. U Poglavlju 6, Testiranje hipoteza, predstavljeno je testiranje nulte hipoteze (NHST – Null Hypothesis Significance Testing). Naućićete mnogo popularnih testova hipoteza i njihove neparametarske alternative. Možda je najvažnije je da ćete razotkriti zablude o NHST-u. U Poglavlju 7, Bajesove metode, predstavljena je alternativa za NHST koja se zasniva na intuitivnijem prikazu verovatnoće. Takođe ćete upoznati prednosti i nedostatke ovog pristupa. U Poglavlju 8, Bootstrap, opisan je još jedan pristup za NHST pomoću tehnike pod nazivom ponovno uzorkovanje (resampling). Upoznaćete prednosti i nedostatke ovog pristupa. Osim toga, ovo poglavlje služi kao odlična nadgradnja sadržaja iz poglavlja 5 i 6. U Poglavlju 9, Predviđanje kontinualnih promenljivih, započinjemo još jednu novu lekciju o prediktivnoj analitici i detaljno razmatramo linearnu regresiju. Naučićete sve što je potrebno o ovoj tehnici, kada da je koristite i na koje „zamke“ treba da obratite pažnju. U Poglavlju 10, Predviđanje kategorijskih promenljivih, predstavljene su četiri najpopularnije tehnike klasifikacije. Koristićemo sve četiri tehnike u svim primerima, zahvaljujući čemu ćete shvatiti zašto je svaka od njih sjajna. U Poglavlju 11, Predviđanje promena tokom vremena, završavamo lekciju o prediktivnoj analitici i predstavljamo analizu vremenskog niza i prognozu. Na kraju, razmatramo osnove jednog od odličnih metoda prognoze vremenskog niza. Poglavlje 12, Izvori podataka, počinje lekcijom u kojoj je opisana analiza podataka u stvarnom svetu. Razmatra se upotreba različitih izvora podataka u R-u. Konkretno, naučićete kako da se povežete sa bazom podataka i da zahtevate i učitate JSON i XML pomoću primera. U Poglavlju 13, Upravljanje nedostajućim podacima, opisani su nedostajući podaci, način za identifikovanje tipa nedostajućih podataka i dve osnovne metode za upravljanje njima. U Poglavlju 14, Upravljanje neurednim podacima, predstavljeni su neki od problema u vezi sa korišćenjem nesavršenih podataka u praksi. Ovo obuhvata proveru neočekivanih unosa, korišćenje regexa i proveru valjanosti podataka pomoću paketa assertr. U Poglavlju 15, Upravljanje velikim podacima, razmatrane su neke od tehnika koje mogu da se koriste za skupove podataka veće od podataka kojima se može brzo upravljati bez nekog planiranja. Ključni elementi ovog poglavlja su paralelizacija i Rcpp. U Poglavlju 16, Korišćenje popularnih R paketa, potvrđujemo da smo već koristili mnogo popularnih paketa u ovoj lekciji, ali popunjavamo neke praznine i predstavljamo neke od najsavremenijih paketa, zahvaljujući kojima brzina i jednostavnost korišćenja postaju prioritet. U Poglavlju 17, Reproducibilnost i najbolje tehnike, završavamo razmatranje izuzetno važne (ali često ignorisane) teme - kako da R koristite kao profesionalac. Ovo obuhvata učenje alatki, organizacije i reproducibilnosti. Izvucite maksimum iz ove knjige Kompletan kod u ovoj knjizi je napisan za najnoviju verziju R-a 3. 4. 3 u vreme pisanja ove knjige. Najbolje bi bilo da ažurirate R verziju, ali skoro ceo kod bi trebalo da funkcioniše u svakoj skorijoj verziji R-a. Neki R paketi koje ćemo instalirati ipak zahtevaju najnovije verzije. Za druge softvere koji su upotrebljeni u ovoj knjizi uputstva će biti navedena prema potrebi. Međutim, ako želite da budete u startnoj prednosti, instalirajte Rstudio, JAGS i C++ kompajler (ili Rtools ako koristite Windows). R - ANALIZA PODATAKA VIŠE O KNJIZI I KORPA ZA NARUČIVANJE

Velika lista statističkih blogova




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[R-bloggers] Time Series Analysis in ArcGIS (and 5 more aRticles)




Time Series Analysis in ArcGIS In this post I will introduce another toolbox I created to show the functions that can be added to ArcGIS by using R and the R-Bridge technology. In this toolbox I basically implemented the functions I showed in the previous post about time series analysis in R. Once again I prepared a sample dataset that I included in the GitHub archive so that you can reproduce the experiment I’m presenting here. I will start my description from there. Finish line (nearly) We are very close to the finish line $-$ that’s being able to finally submit the BCEA book to the editor (Springer). This has been a rather long journey, but I think the current version (I dread using the word “final” just yet…) is very good, I think. We’ve managed to respond to all the reviewers’ comments, which to be fair were rather helpful and so this should have improved the book.   Most Probable Birth Month In a previous post I showed that the data from www. baseball-reference. com support Malcolm Gladwell’s contention that more professional baseball players are born in August than any other month. Although this might be explained by the 31 July cutoff for admission to baseball leagues, it was suggested that it could also be linked to alarger proportion of babies being born in August. recosystem: Recommender System Using Parallel Matrix Factorization The main task of recommender system is to predict unknown entries in therating matrix based on observed values, as is shown in the table below: New Open Course: Statistical Inference with swirl The DataCamp team is excited to announce another course using swirl. This free course adapts the Statistical Inference curriculum from swirl to the interactive DataCamp in-browser interface. Log-on and get started!  As. Date() Exercises The as. date() function creates objects of the class “Date“, via input of character representations of dates. Answers to the exercises are available here.
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