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OCoLC |
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20231017213018.0 |
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m o d |
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cr unu|||||||| |
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151023s2015 enka o 001 0 eng d |
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|a UMI
|b eng
|e rda
|e pn
|c UMI
|d OCLCF
|d CEF
|d OCLCQ
|d WYU
|d UAB
|d RDF
|d OCLCO
|d OCLCQ
|d OCLCO
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| 020 |
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|a 9781783982035
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| 020 |
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|a 1783982039
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| 020 |
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|z 9781783982028
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1 |
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|a GBVCP
|b 897168585
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| 035 |
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|a (OCoLC)926118236
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| 037 |
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|a CL0500000663
|b Safari Books Online
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| 050 |
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|a QA76.9.D343
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| 082 |
0 |
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|a 519.5
|q OCoLC
|2 23/eng/20230216
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| 049 |
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|a UAMI
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| 100 |
1 |
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|a Daróczi, Gergely,
|e author.
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| 245 |
1 |
0 |
|a Mastering data analysis with R :
|b gain clear insights into your data and solve real-world data science problems with R--from data munging to modeling and visualization /
|c Gergely Daróczi.
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| 246 |
3 |
0 |
|a Gain clear insights into your data and solve real-world data science problems with R--from data munging to modeling and visualization
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| 264 |
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1 |
|a Birmingham, UK :
|b Packt Publishing,
|c 2015.
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| 300 |
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|a 1 online resource (1 volume) :
|b illustrations.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Community experience distilled
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| 588 |
0 |
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|a Online resource; title from cover page (Safari, viewed October 21, 2015).
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| 500 |
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|a Includes index.
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| 505 |
0 |
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|a Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Hello, Data!; Loading text files of a reasonable size; Data files larger than the physical memory; Benchmarking text file parsers; Loading a subset of text files; Filtering flat files before loading to R; Loading data from databases; Setting up the test environment; MySQL and MariaDB; PostgreSQL; Oracle database; ODBC database access; Using a graphical user interface to connect to databases; Other database backends; Importing data from other statistical systems
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| 505 |
8 |
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|a Loading Excel spreadsheetsSummary; Chapter 2: Getting Data from the Web; Loading datasets from the Internet; Other popular online data formats; Reading data from HTML tables; Reading tabular data from static Web pages; Scraping data from other online sources; R packages to interact with data source APIs; Socrata Open Data API; Finance APIs; Fetching time series with Quandl; Google documents and analytics; Online search trends; Historical weather data; Other online data sources; Summary; Chapter 3: Filtering and Summarizing Data; Drop needless data; Drop needless data in an efficient way
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| 505 |
8 |
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|a Drop needless data in another efficient wayAggregation; Quicker aggregation with base R commands; Convenient helper functions; High-performance helper functions; Aggregate with data.table; Running benchmarks; Summary functions; Adding up the number of cases in subgroups; Summary; Chapter 4: Restructuring Data; Transposing matrices; Filtering data by string matching; Rearranging data; dplyr versus data.table; Computing new variables; Memory profiling; Creating multiple variables at a time; Computing new variables with dplyr; Merging datasets; Reshaping data in a flexible way
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| 505 |
8 |
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|a Converting wide tables to the long table formatConverting long tables to the wide table format; Tweaking performance; The evolution of the reshape packages; Summary; Chapter 5: Building Models (authored by Renata Nemeth and Gergely Toth); The motivation behind multivariate models; Linear regression with continuous predictors; Model interpretation; Multiple predictors; Model assumptions; How well does the line fit in the data?; Discrete predictors; Summary; Chapter 6: Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth); The modeling workflow; Logistic regression
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| 505 |
8 |
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|a Data considerationsGoodness of model fit; Model comparison; Models for count data; Poisson regression; Negative binomial regression; Multivariate non-linear models; Summary; Chapter 7: Unstructured Data; Importing the corpus; Cleaning the corpus; Visualizing the most frequent words in the corpus; Further cleanup; Stemming words; Lemmatisation; Analyzing the associations among terms; Some other metrics; The segmentation of documents; Summary; Chapter 8: Polishing Data; The types and origins of missing data; Identifying missing data; By-passing missing values
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| 590 |
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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| 650 |
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0 |
|a Data mining.
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| 650 |
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0 |
|a R (Computer program language)
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| 650 |
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0 |
|a Information visualization.
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| 650 |
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2 |
|a Data Mining
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| 650 |
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6 |
|a Exploration de données (Informatique)
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| 650 |
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6 |
|a R (Langage de programmation)
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| 650 |
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6 |
|a Visualisation de l'information.
|
| 650 |
|
7 |
|a Data mining
|2 fast
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| 650 |
|
7 |
|a Information visualization
|2 fast
|
| 650 |
|
7 |
|a R (Computer program language)
|2 fast
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| 830 |
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0 |
|a Community experience distilled.
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781783982028/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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| 994 |
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|a 92
|b IZTAP
|
