Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages.: Become an Expert in Exploratory Data Analysis Using R Packages. (2019)

• Record Type: Book
• Other Names: Datar, Radhika; Garg, Harish
• 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
• Publisher Details: Birmingham : Packt Publishing, Limited
• Publication Date: 2019
• Extent: 1 online resource (254 pages)
• Subjects: 006.3/12; Data mining -- Computer programs; R (Computer program language); COMPUTERS -- General; R (Computer program language); Electronic books
• Languages: English
• ISBNs: 1789802083; 9781789802085
• Notes: Note: Print version record.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.