Hands-On Exploratory Data Analysis with R :: Become an Expert in Exploratory Data Analysis Using R Packages.
Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visual...
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns. |
| Beschreibung: | Summary |
| Beschreibung: | 1 online resource (254 pages) |
| ISBN: | 1789802083 9781789802085 |
Internformat
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| 100 | 1 | |a Datar, Radhika. | |
| 245 | 1 | 0 | |a Hands-On Exploratory Data Analysis with R : |b Become an Expert in Exploratory Data Analysis Using R Packages. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (254 pages) | ||
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Setting Up Data Analysis Environment; Chapter 1: Setting Up Our Data Analysis Environment; Technical requirements; The benefits of EDA across vertical markets; Manipulating data; Examining, cleaning, and filtering data; Visualizing data; Creating data reports; Installing the required R packages and tools; Installing R packages from the Terminal; Installing R packages from inside RStudio; Summary; Chapter 2: Importing Diverse Datasets; Technical requirements | |
| 505 | 8 | |a Converting rectangular data into R with the readr R packagereadr read functions; read_tsv method; read_delim method; read_fwf method; read_table method; read_log method; Reading in Excel data with the readxl R package; Reading in JSON data with the jsonlite R package; Loading the jsonlite package; Getting data into R from web APIs using the httr R package; Getting data into R by scraping the web using the rvest package; Importing data into R from relational databases using the DBI R package; Summary; Chapter 3: Examining, Cleaning, and Filtering; Technical requirements; About the dataset | |
| 505 | 8 | |a Reshaping and tidying up erroneous dataThe gather() function; The unite() function; The separate() function; The spread() function; Manipulating and mutating data; The mutate() function; The group_by() function; The summarize() function; The arrange() function; The glimpse() function; Selecting and filtering data; The select() function; The filter() function; Cleaning and manipulating time series data; Summary; Chapter 4: Visualizing Data Graphically with ggplot2; Technical requirements; Advanced graphics grammar of ggplot2; Data; Layers; Scales; The coordinate system; Faceting; Theme | |
| 505 | 8 | |a Installing ggplot2Scatter plots; Histogram plots; Density plots; Probability plots; dnorm(); pnorm(); rnorm(); Box plots; Residual plots; Summary; Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown; Technical requirements; Installing R Markdown; Working with R Markdown; Reproducible data analysis reports with knitr; Exporting and customizing reports; Summary; Section 2: Univariate, Time Series, and Multivariate Data; Chapter 6: Univariate and Control Datasets; Technical requirements; Reading the dataset; Cleaning and tidying up the data | |
| 505 | 8 | |a Understanding the structure of the dataHypothesis tests; Statistical hypothesis in R; The t-test in R; Directional hypothesis in R; Correlation in R; Tietjen-Moore test; Parsimonious models; Probability plots; The Shapiro-Wilk test; Summary; Chapter 7: Time Series Datasets; Technical requirements; Introducing and reading the dataset; Cleaning the dataset; Mapping and understanding structure; Hypothesis test; t-test in R; Directional hypothesis in R; Grubbs' test and checking outliers; Parsimonious models; Bartlett's test; Data visualization; Autocorrelation plots; Spectrum plots; Phase plots | |
| 500 | |a Summary | ||
| 520 | |a Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns. | ||
| 650 | 0 | |a Data mining |x Computer programs. | |
| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
| 650 | 6 | |a Exploration de données (Informatique) |x Logiciels. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 700 | 1 | |a Garg, Harish. | |
| 776 | 0 | 8 | |i Print version: |a Datar, Radhika. |t Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781789804379 |
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| contents | Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Setting Up Data Analysis Environment; Chapter 1: Setting Up Our Data Analysis Environment; Technical requirements; The benefits of EDA across vertical markets; Manipulating data; Examining, cleaning, and filtering data; Visualizing data; Creating data reports; Installing the required R packages and tools; Installing R packages from the Terminal; Installing R packages from inside RStudio; Summary; Chapter 2: Importing Diverse Datasets; Technical requirements Converting rectangular data into R with the readr R packagereadr read functions; read_tsv method; read_delim method; read_fwf method; read_table method; read_log method; Reading in Excel data with the readxl R package; Reading in JSON data with the jsonlite R package; Loading the jsonlite package; Getting data into R from web APIs using the httr R package; Getting data into R by scraping the web using the rvest package; Importing data into R from relational databases using the DBI R package; Summary; Chapter 3: Examining, Cleaning, and Filtering; Technical requirements; About the dataset Reshaping and tidying up erroneous dataThe gather() function; The unite() function; The separate() function; The spread() function; Manipulating and mutating data; The mutate() function; The group_by() function; The summarize() function; The arrange() function; The glimpse() function; Selecting and filtering data; The select() function; The filter() function; Cleaning and manipulating time series data; Summary; Chapter 4: Visualizing Data Graphically with ggplot2; Technical requirements; Advanced graphics grammar of ggplot2; Data; Layers; Scales; The coordinate system; Faceting; Theme Installing ggplot2Scatter plots; Histogram plots; Density plots; Probability plots; dnorm(); pnorm(); rnorm(); Box plots; Residual plots; Summary; Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown; Technical requirements; Installing R Markdown; Working with R Markdown; Reproducible data analysis reports with knitr; Exporting and customizing reports; Summary; Section 2: Univariate, Time Series, and Multivariate Data; Chapter 6: Univariate and Control Datasets; Technical requirements; Reading the dataset; Cleaning and tidying up the data Understanding the structure of the dataHypothesis tests; Statistical hypothesis in R; The t-test in R; Directional hypothesis in R; Correlation in R; Tietjen-Moore test; Parsimonious models; Probability plots; The Shapiro-Wilk test; Summary; Chapter 7: Time Series Datasets; Technical requirements; Introducing and reading the dataset; Cleaning the dataset; Mapping and understanding structure; Hypothesis test; t-test in R; Directional hypothesis in R; Grubbs' test and checking outliers; Parsimonious models; Bartlett's test; Data visualization; Autocorrelation plots; Spectrum plots; Phase plots |
| ctrlnum | (OCoLC)1104078460 |
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| discipline | Informatik |
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| id | ZDB-4-EBA-on1104078460 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:30Z |
| institution | BVB |
| isbn | 1789802083 9781789802085 |
| language | English |
| oclc_num | 1104078460 |
| open_access_boolean | |
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| physical | 1 online resource (254 pages) |
| psigel | ZDB-4-EBA |
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| publishDateSearch | 2019 |
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| publisher | Packt Publishing, Limited, |
| record_format | marc |
| spelling | Datar, Radhika. Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. Birmingham : Packt Publishing, Limited, 2019. 1 online resource (254 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Setting Up Data Analysis Environment; Chapter 1: Setting Up Our Data Analysis Environment; Technical requirements; The benefits of EDA across vertical markets; Manipulating data; Examining, cleaning, and filtering data; Visualizing data; Creating data reports; Installing the required R packages and tools; Installing R packages from the Terminal; Installing R packages from inside RStudio; Summary; Chapter 2: Importing Diverse Datasets; Technical requirements Converting rectangular data into R with the readr R packagereadr read functions; read_tsv method; read_delim method; read_fwf method; read_table method; read_log method; Reading in Excel data with the readxl R package; Reading in JSON data with the jsonlite R package; Loading the jsonlite package; Getting data into R from web APIs using the httr R package; Getting data into R by scraping the web using the rvest package; Importing data into R from relational databases using the DBI R package; Summary; Chapter 3: Examining, Cleaning, and Filtering; Technical requirements; About the dataset Reshaping and tidying up erroneous dataThe gather() function; The unite() function; The separate() function; The spread() function; Manipulating and mutating data; The mutate() function; The group_by() function; The summarize() function; The arrange() function; The glimpse() function; Selecting and filtering data; The select() function; The filter() function; Cleaning and manipulating time series data; Summary; Chapter 4: Visualizing Data Graphically with ggplot2; Technical requirements; Advanced graphics grammar of ggplot2; Data; Layers; Scales; The coordinate system; Faceting; Theme Installing ggplot2Scatter plots; Histogram plots; Density plots; Probability plots; dnorm(); pnorm(); rnorm(); Box plots; Residual plots; Summary; Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown; Technical requirements; Installing R Markdown; Working with R Markdown; Reproducible data analysis reports with knitr; Exporting and customizing reports; Summary; Section 2: Univariate, Time Series, and Multivariate Data; Chapter 6: Univariate and Control Datasets; Technical requirements; Reading the dataset; Cleaning and tidying up the data Understanding the structure of the dataHypothesis tests; Statistical hypothesis in R; The t-test in R; Directional hypothesis in R; Correlation in R; Tietjen-Moore test; Parsimonious models; Probability plots; The Shapiro-Wilk test; Summary; Chapter 7: Time Series Datasets; Technical requirements; Introducing and reading the dataset; Cleaning the dataset; Mapping and understanding structure; Hypothesis test; t-test in R; Directional hypothesis in R; Grubbs' test and checking outliers; Parsimonious models; Bartlett's test; Data visualization; Autocorrelation plots; Spectrum plots; Phase plots Summary Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns. Data mining Computer programs. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Exploration de données (Informatique) Logiciels. R (Langage de programmation) COMPUTERS General. bisacsh R (Computer program language) fast Garg, Harish. Print version: Datar, Radhika. Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. Birmingham : Packt Publishing, Limited, ©2019 9781789804379 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2153721 Volltext |
| spellingShingle | Datar, Radhika Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Setting Up Data Analysis Environment; Chapter 1: Setting Up Our Data Analysis Environment; Technical requirements; The benefits of EDA across vertical markets; Manipulating data; Examining, cleaning, and filtering data; Visualizing data; Creating data reports; Installing the required R packages and tools; Installing R packages from the Terminal; Installing R packages from inside RStudio; Summary; Chapter 2: Importing Diverse Datasets; Technical requirements Converting rectangular data into R with the readr R packagereadr read functions; read_tsv method; read_delim method; read_fwf method; read_table method; read_log method; Reading in Excel data with the readxl R package; Reading in JSON data with the jsonlite R package; Loading the jsonlite package; Getting data into R from web APIs using the httr R package; Getting data into R by scraping the web using the rvest package; Importing data into R from relational databases using the DBI R package; Summary; Chapter 3: Examining, Cleaning, and Filtering; Technical requirements; About the dataset Reshaping and tidying up erroneous dataThe gather() function; The unite() function; The separate() function; The spread() function; Manipulating and mutating data; The mutate() function; The group_by() function; The summarize() function; The arrange() function; The glimpse() function; Selecting and filtering data; The select() function; The filter() function; Cleaning and manipulating time series data; Summary; Chapter 4: Visualizing Data Graphically with ggplot2; Technical requirements; Advanced graphics grammar of ggplot2; Data; Layers; Scales; The coordinate system; Faceting; Theme Installing ggplot2Scatter plots; Histogram plots; Density plots; Probability plots; dnorm(); pnorm(); rnorm(); Box plots; Residual plots; Summary; Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown; Technical requirements; Installing R Markdown; Working with R Markdown; Reproducible data analysis reports with knitr; Exporting and customizing reports; Summary; Section 2: Univariate, Time Series, and Multivariate Data; Chapter 6: Univariate and Control Datasets; Technical requirements; Reading the dataset; Cleaning and tidying up the data Understanding the structure of the dataHypothesis tests; Statistical hypothesis in R; The t-test in R; Directional hypothesis in R; Correlation in R; Tietjen-Moore test; Parsimonious models; Probability plots; The Shapiro-Wilk test; Summary; Chapter 7: Time Series Datasets; Technical requirements; Introducing and reading the dataset; Cleaning the dataset; Mapping and understanding structure; Hypothesis test; t-test in R; Directional hypothesis in R; Grubbs' test and checking outliers; Parsimonious models; Bartlett's test; Data visualization; Autocorrelation plots; Spectrum plots; Phase plots Data mining Computer programs. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Exploration de données (Informatique) Logiciels. R (Langage de programmation) COMPUTERS General. bisacsh R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002004407 |
| title | Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. |
| title_auth | Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. |
| title_exact_search | Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. |
| title_full | Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. |
| title_fullStr | Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. |
| title_full_unstemmed | Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages. |
| title_short | Hands-On Exploratory Data Analysis with R : |
| title_sort | hands on exploratory data analysis with r become an expert in exploratory data analysis using r packages |
| title_sub | Become an Expert in Exploratory Data Analysis Using R Packages. |
| topic | Data mining Computer programs. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Exploration de données (Informatique) Logiciels. R (Langage de programmation) COMPUTERS General. bisacsh R (Computer program language) fast |
| topic_facet | Data mining Computer programs. R (Computer program language) Exploration de données (Informatique) Logiciels. R (Langage de programmation) COMPUTERS General. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2153721 |
| work_keys_str_mv | AT datarradhika handsonexploratorydataanalysiswithrbecomeanexpertinexploratorydataanalysisusingrpackages AT gargharish handsonexploratorydataanalysiswithrbecomeanexpertinexploratorydataanalysisusingrpackages |