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Añadir al carritoHardcover. Condición: Brand New. 277 pages. 9.00x6.00x1.00 inches. In Stock.
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Añadir al carritoCondición: New. 2021. hardcover. . . . . .
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Añadir al carritoHardback. Condición: New. This book covers some introductory steps in using R programming language as a data science tool. The data science field has evolved so much recently with incredible quantities of generated data. To extract value from those data, one needs to be trained in the proper data science skills like statistical analysis, data cleaning, data visualization, and machine learning. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all the previous skills, but also, has additional packages that was developed by different scientists in diverse fields. These fields include, but are not limited to, business, marketing, microbiology, social science, geography, genomics, environmental science, etc. Furthermore, R is free software and can run on all major platforms: Windows, Mac Os, and UNIX/Linux. The first two chapters involve installing and using R and RStudio. RStudio is an IDE (integrated development environment) that makes R easier to use and is more similar to SPSS or Stata. Chapters 3-8 covers the different R objects and how to manipulate them including the very popular one, dataframes. Chapter 9 is about importing different files into your R working session like text or excel files. Chapters 10 and 11 are dealing with different tidyverse packages that can do interesting summaries of different dataframes including different types of data visualizations. In the last chapter, it introduces how functions are created in R along with some control structures and useful functions. In all these chapters, many examples along with different codes and outputs are given to help your understanding of this powerful programming language. I hope this book will be great addition to your future data analysis projects.
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Añadir al carritoCondición: New.
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Añadir al carritoHardback. Condición: New. This book covers some introductory steps in using R programming language as a data science tool. The data science field has evolved so much recently with incredible quantities of generated data. To extract value from those data, one needs to be trained in the proper data science skills like statistical analysis, data cleaning, data visualization, and machine learning. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all the previous skills, but also, has additional packages that was developed by different scientists in diverse fields. These fields include, but are not limited to, business, marketing, microbiology, social science, geography, genomics, environmental science, etc. Furthermore, R is free software and can run on all major platforms: Windows, Mac Os, and UNIX/Linux. The first two chapters involve installing and using R and RStudio. RStudio is an IDE (integrated development environment) that makes R easier to use and is more similar to SPSS or Stata. Chapters 3-8 covers the different R objects and how to manipulate them including the very popular one, dataframes. Chapter 9 is about importing different files into your R working session like text or excel files. Chapters 10 and 11 are dealing with different tidyverse packages that can do interesting summaries of different dataframes including different types of data visualizations. In the last chapter, it introduces how functions are created in R along with some control structures and useful functions. In all these chapters, many examples along with different codes and outputs are given to help your understanding of this powerful programming language. I hope this book will be great addition to your future data analysis projects.
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Añadir al carritoHardback. Condición: New. This book covers the use of R programming language for data visualization. The large real-world datasets can be quickly visualized to gain several insights from it using R. Whether the data is already clean or needs some preliminary steps before data visualization like data cleaning and wrangling, all these can be done using R to produce elegant, publication-ready, and exciting plots. This book covers different plot types from simple ones like univariate and bivariate plots (histograms, box plots, violin plots, scatter plots, density plots, bar graphs, pie charts, tree maps, beeswarm plots, Cleveland dot charts, line plots, etc) to more complex ones like mapping and visualizing patterns of missing data (upset plots, different Stamen map types with different zoom levels). All these were shown using free real-world data sets to illustrate the diverse capability of R for data visualization.
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 277 pages. 9.00x6.00x9.02 inches. In Stock.
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Añadir al carritoHardback. Condición: New. This book covers the use of R programming language for data visualization. The large real-world datasets can be quickly visualized to gain several insights from it using R. Whether the data is already clean or needs some preliminary steps before data visualization like data cleaning and wrangling, all these can be done using R to produce elegant, publication-ready, and exciting plots. This book covers different plot types from simple ones like univariate and bivariate plots (histograms, box plots, violin plots, scatter plots, density plots, bar graphs, pie charts, tree maps, beeswarm plots, Cleveland dot charts, line plots, etc) to more complex ones like mapping and visualizing patterns of missing data (upset plots, different Stamen map types with different zoom levels). All these were shown using free real-world data sets to illustrate the diverse capability of R for data visualization.
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