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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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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