| LDR | | 04966cmm u2200481Ma 4500 |
| 001 | | 000000316279 |
| 003 | | OCoLC |
| 005 | | 20230525180120 |
| 006 | | m d |
| 007 | | cr cnu---unuuu |
| 008 | | 190615s2019 enk o 000 0 eng d |
| 020 | |
▼a 1789802083 |
| 020 | |
▼a 9781789802085
▼q (electronic bk.) |
| 035 | |
▼a 2153721
▼b (N$T) |
| 035 | |
▼a (OCoLC)1104078460 |
| 040 | |
▼a EBLCP
▼b eng
▼c EBLCP
▼d N$T
▼d 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a QA76.9.D343 |
| 072 | 7 |
▼a COM
▼x 000000
▼2 bisacsh |
| 082 | 04 |
▼a 006.3/12
▼2 23 |
| 100 | 1 |
▼a Datar, Radhika. |
| 245 | 10 |
▼a Hands-On Exploratory Data Analysis with R
▼h [electronic resource] :
▼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 p.) |
| 500 | |
▼a Description based upon print version of record. |
| 500 | |
▼a Summary |
| 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 |
| 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. |
| 590 | |
▼a Master record variable field(s) change: 050, 072, 082, 650 |
| 650 | 0 |
▼a Data mining
▼x Computer programs. |
| 650 | 0 |
▼a R (Computer program language) |
| 650 | 7 |
▼a COMPUTERS / General.
▼2 bisacsh |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Garg, Harish. |
| 776 | 08 |
▼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,c2019
▼z 9781789804379 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2153721 |
| 938 | |
▼a EBL - Ebook Library
▼b EBLB
▼n EBL5784233 |
| 938 | |
▼a EBSCOhost
▼b EBSC
▼n 2153721 |
| 990 | |
▼a 관리자 |
| 994 | |
▼a 92
▼b N$T |