Learning R for geospatial analysis :: leverage the power of R to elegantly manage crucial geospatial analysis tasks /
Annotation
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, England :
Packt Publishing,
2014.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation |
| Beschreibung: | 1 online resource (364 pages) : color illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781783984374 1783984376 |
Internformat
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
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| 100 | 1 | |a Dorman, Michael, |d 1984- |1 https://id.oclc.org/worldcat/entity/E39PBJyh8kgVcB8YYJk886Trbd |0 http://id.loc.gov/authorities/names/no2019007141 | |
| 245 | 1 | 0 | |a Learning R for geospatial analysis : |b leverage the power of R to elegantly manage crucial geospatial analysis tasks / |c Michael Dorman. |
| 264 | 1 | |a Birmingham, England : |b Packt Publishing, |c 2014. | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (364 pages) : |b color illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 490 | 1 | |a Community Experience Distilled | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ebrary, viewed January 14, 2015). | |
| 520 | 8 | |a Annotation |b This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external softwarea working installation of R is all that is necessary to begin. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The R Environment; Installing R and using the command line; Downloading R; Installing R; Using R as a calculator; Coding with R beyond the command line; Approaches to editing R code; Installation of RStudio; Using RStudio; Evaluating expressions; Using arithmetic and logical operators; Using functions; Dealing with warning and error messages; Getting help; Exploring the basic object types in R; Everything is an object; Storing data in data structures | |
| 505 | 8 | |a Calling functions to perform operationsA short sample session; Summary; Chapter 2: Working with Vectors and Time Series; Vectors -- the basic data structures in R; Different types of vectors; Using the assignment operator to save an object; Removing objects from memory; Summarizing vector properties; Element-by-element operations on vectors; The recycling principle; Using functions with several parameters; Supplying more than one argument in a function call; Creating default vectors; Creating repetitive vectors; Substrings; Creating subsets of vectors | |
| 505 | 8 | |a Subsetting with numeric vectors of indicesSubsetting with logical vectors; Dealing with missing values; Missing values and their effect on data; Detecting missing values in vectors; Performing calculations on vectors with missing values; Writing new functions; Defining our own functions; Setting default values for the arguments; Working with dates and time series; Specialized time series classes in R; Reading climatic data from a CSV file; Converting character values to dates; Examining our time series; Creating subsets based on dates; Introducing graphical functions | |
| 505 | 8 | |a Displaying vectors using base graphicsSaving graphical output; The main graphical systems in R; Summary; Chapter 3: Working with Tables; Using the data.frame class to represent tabular data; Creating a table from separate vectors; Creating a table from a CSV file; Examining the structure of a data.frame object; Subsetting data.frame objects; Calculating new data fields; Writing a data.frame object to a CSV file; Controlling code execution; Conditioning execution with conditional statements; Repeatedly executing code sections with loops | |
| 505 | 8 | |a Automated calculations using the apply family of functionsApplying a function on separate parts of a vector; Applying a function on rows or columns of a table; Inference from tables by joining, reshaping, and aggregating; Using contributed packages; Shifting between long and wide formats using melt and dcast; Aggregating with ddply; Joining tables with join; Summary; Chapter 4: Working with Rasters; Using the matrix and array classes; Representing two-dimensional data with a matrix; Representing more than two dimensions with an array; Data structures for rasters in the raster package | |
| 546 | |a English. | ||
| 650 | 0 | |a Geospatial data. |0 http://id.loc.gov/authorities/subjects/sh2006006177 | |
| 650 | 0 | |a Spatial analysis (Statistics) |0 http://id.loc.gov/authorities/subjects/sh85126347 | |
| 650 | 6 | |a Données géospatiales. | |
| 650 | 6 | |a Analyse spatiale (Statistique) | |
| 650 | 7 | |a spatial analysis. |2 aat | |
| 650 | 7 | |a LANGUAGE ARTS & DISCIPLINES |x Library & Information Science |x General. |2 bisacsh | |
| 650 | 7 | |a Geospatial data |2 fast | |
| 650 | 7 | |a Spatial analysis (Statistics) |2 fast | |
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| 758 | |i has work: |a Learning R for geospatial analysis (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGY3WQVpq7yhcvvTttmHP3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Dorman, Michael. |t Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks. |d Birmingham, England : Packt Publishing, ©2014 |h v, 345 pages |k Community experience distilled. |z 9781783984367 |
| 830 | 0 | |a Community experience distilled. |0 http://id.loc.gov/authorities/names/no2011030603 | |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-ocn900886812 |
|---|---|
| _version_ | 1816882301768826880 |
| adam_text | |
| any_adam_object | |
| author | Dorman, Michael, 1984- |
| author_GND | http://id.loc.gov/authorities/names/no2019007141 |
| author_facet | Dorman, Michael, 1984- |
| author_role | |
| author_sort | Dorman, Michael, 1984- |
| author_variant | m d md |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | G - Geography, Anthropology, Recreation |
| callnumber-label | G70 |
| callnumber-raw | G70.217.G46 .D676 2014eb |
| callnumber-search | G70.217.G46 .D676 2014eb |
| callnumber-sort | G 270.217 G46 D676 42014EB |
| callnumber-subject | G - General Geography |
| collection | ZDB-4-EBA |
| contents | Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The R Environment; Installing R and using the command line; Downloading R; Installing R; Using R as a calculator; Coding with R beyond the command line; Approaches to editing R code; Installation of RStudio; Using RStudio; Evaluating expressions; Using arithmetic and logical operators; Using functions; Dealing with warning and error messages; Getting help; Exploring the basic object types in R; Everything is an object; Storing data in data structures Calling functions to perform operationsA short sample session; Summary; Chapter 2: Working with Vectors and Time Series; Vectors -- the basic data structures in R; Different types of vectors; Using the assignment operator to save an object; Removing objects from memory; Summarizing vector properties; Element-by-element operations on vectors; The recycling principle; Using functions with several parameters; Supplying more than one argument in a function call; Creating default vectors; Creating repetitive vectors; Substrings; Creating subsets of vectors Subsetting with numeric vectors of indicesSubsetting with logical vectors; Dealing with missing values; Missing values and their effect on data; Detecting missing values in vectors; Performing calculations on vectors with missing values; Writing new functions; Defining our own functions; Setting default values for the arguments; Working with dates and time series; Specialized time series classes in R; Reading climatic data from a CSV file; Converting character values to dates; Examining our time series; Creating subsets based on dates; Introducing graphical functions Displaying vectors using base graphicsSaving graphical output; The main graphical systems in R; Summary; Chapter 3: Working with Tables; Using the data.frame class to represent tabular data; Creating a table from separate vectors; Creating a table from a CSV file; Examining the structure of a data.frame object; Subsetting data.frame objects; Calculating new data fields; Writing a data.frame object to a CSV file; Controlling code execution; Conditioning execution with conditional statements; Repeatedly executing code sections with loops Automated calculations using the apply family of functionsApplying a function on separate parts of a vector; Applying a function on rows or columns of a table; Inference from tables by joining, reshaping, and aggregating; Using contributed packages; Shifting between long and wide formats using melt and dcast; Aggregating with ddply; Joining tables with join; Summary; Chapter 4: Working with Rasters; Using the matrix and array classes; Representing two-dimensional data with a matrix; Representing more than two dimensions with an array; Data structures for rasters in the raster package |
| ctrlnum | (OCoLC)900886812 |
| dewey-full | 025.06910285 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 025 - Operations of libraries and archives |
| dewey-raw | 025.06910285 |
| dewey-search | 025.06910285 |
| dewey-sort | 225.06910285 |
| dewey-tens | 020 - Library and information sciences |
| discipline | Allgemeines |
| format | Electronic eBook |
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| genre | Geospatial data fast Geospatial data. lcgft http://id.loc.gov/authorities/genreForms/gf2011026297 Données géospatiales. rvmgf |
| genre_facet | Geospatial data Geospatial data. Données géospatiales. |
| id | ZDB-4-EBA-ocn900886812 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:26:27Z |
| institution | BVB |
| isbn | 9781783984374 1783984376 |
| language | English |
| oclc_num | 900886812 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (364 pages) : color illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Community experience distilled. |
| series2 | Community Experience Distilled |
| spelling | Dorman, Michael, 1984- https://id.oclc.org/worldcat/entity/E39PBJyh8kgVcB8YYJk886Trbd http://id.loc.gov/authorities/names/no2019007141 Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / Michael Dorman. Birmingham, England : Packt Publishing, 2014. ©2014 1 online resource (364 pages) : color illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Community Experience Distilled Includes bibliographical references and index. Online resource; title from PDF title page (ebrary, viewed January 14, 2015). Annotation This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external softwarea working installation of R is all that is necessary to begin. Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The R Environment; Installing R and using the command line; Downloading R; Installing R; Using R as a calculator; Coding with R beyond the command line; Approaches to editing R code; Installation of RStudio; Using RStudio; Evaluating expressions; Using arithmetic and logical operators; Using functions; Dealing with warning and error messages; Getting help; Exploring the basic object types in R; Everything is an object; Storing data in data structures Calling functions to perform operationsA short sample session; Summary; Chapter 2: Working with Vectors and Time Series; Vectors -- the basic data structures in R; Different types of vectors; Using the assignment operator to save an object; Removing objects from memory; Summarizing vector properties; Element-by-element operations on vectors; The recycling principle; Using functions with several parameters; Supplying more than one argument in a function call; Creating default vectors; Creating repetitive vectors; Substrings; Creating subsets of vectors Subsetting with numeric vectors of indicesSubsetting with logical vectors; Dealing with missing values; Missing values and their effect on data; Detecting missing values in vectors; Performing calculations on vectors with missing values; Writing new functions; Defining our own functions; Setting default values for the arguments; Working with dates and time series; Specialized time series classes in R; Reading climatic data from a CSV file; Converting character values to dates; Examining our time series; Creating subsets based on dates; Introducing graphical functions Displaying vectors using base graphicsSaving graphical output; The main graphical systems in R; Summary; Chapter 3: Working with Tables; Using the data.frame class to represent tabular data; Creating a table from separate vectors; Creating a table from a CSV file; Examining the structure of a data.frame object; Subsetting data.frame objects; Calculating new data fields; Writing a data.frame object to a CSV file; Controlling code execution; Conditioning execution with conditional statements; Repeatedly executing code sections with loops Automated calculations using the apply family of functionsApplying a function on separate parts of a vector; Applying a function on rows or columns of a table; Inference from tables by joining, reshaping, and aggregating; Using contributed packages; Shifting between long and wide formats using melt and dcast; Aggregating with ddply; Joining tables with join; Summary; Chapter 4: Working with Rasters; Using the matrix and array classes; Representing two-dimensional data with a matrix; Representing more than two dimensions with an array; Data structures for rasters in the raster package English. Geospatial data. http://id.loc.gov/authorities/subjects/sh2006006177 Spatial analysis (Statistics) http://id.loc.gov/authorities/subjects/sh85126347 Données géospatiales. Analyse spatiale (Statistique) spatial analysis. aat LANGUAGE ARTS & DISCIPLINES Library & Information Science General. bisacsh Geospatial data fast Spatial analysis (Statistics) fast Geospatial data. lcgft http://id.loc.gov/authorities/genreForms/gf2011026297 Données géospatiales. rvmgf has work: Learning R for geospatial analysis (Text) https://id.oclc.org/worldcat/entity/E39PCGY3WQVpq7yhcvvTttmHP3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Dorman, Michael. Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks. Birmingham, England : Packt Publishing, ©2014 v, 345 pages Community experience distilled. 9781783984367 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=933799 Volltext |
| spellingShingle | Dorman, Michael, 1984- Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / Community experience distilled. Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The R Environment; Installing R and using the command line; Downloading R; Installing R; Using R as a calculator; Coding with R beyond the command line; Approaches to editing R code; Installation of RStudio; Using RStudio; Evaluating expressions; Using arithmetic and logical operators; Using functions; Dealing with warning and error messages; Getting help; Exploring the basic object types in R; Everything is an object; Storing data in data structures Calling functions to perform operationsA short sample session; Summary; Chapter 2: Working with Vectors and Time Series; Vectors -- the basic data structures in R; Different types of vectors; Using the assignment operator to save an object; Removing objects from memory; Summarizing vector properties; Element-by-element operations on vectors; The recycling principle; Using functions with several parameters; Supplying more than one argument in a function call; Creating default vectors; Creating repetitive vectors; Substrings; Creating subsets of vectors Subsetting with numeric vectors of indicesSubsetting with logical vectors; Dealing with missing values; Missing values and their effect on data; Detecting missing values in vectors; Performing calculations on vectors with missing values; Writing new functions; Defining our own functions; Setting default values for the arguments; Working with dates and time series; Specialized time series classes in R; Reading climatic data from a CSV file; Converting character values to dates; Examining our time series; Creating subsets based on dates; Introducing graphical functions Displaying vectors using base graphicsSaving graphical output; The main graphical systems in R; Summary; Chapter 3: Working with Tables; Using the data.frame class to represent tabular data; Creating a table from separate vectors; Creating a table from a CSV file; Examining the structure of a data.frame object; Subsetting data.frame objects; Calculating new data fields; Writing a data.frame object to a CSV file; Controlling code execution; Conditioning execution with conditional statements; Repeatedly executing code sections with loops Automated calculations using the apply family of functionsApplying a function on separate parts of a vector; Applying a function on rows or columns of a table; Inference from tables by joining, reshaping, and aggregating; Using contributed packages; Shifting between long and wide formats using melt and dcast; Aggregating with ddply; Joining tables with join; Summary; Chapter 4: Working with Rasters; Using the matrix and array classes; Representing two-dimensional data with a matrix; Representing more than two dimensions with an array; Data structures for rasters in the raster package Geospatial data. http://id.loc.gov/authorities/subjects/sh2006006177 Spatial analysis (Statistics) http://id.loc.gov/authorities/subjects/sh85126347 Données géospatiales. Analyse spatiale (Statistique) spatial analysis. aat LANGUAGE ARTS & DISCIPLINES Library & Information Science General. bisacsh Geospatial data fast Spatial analysis (Statistics) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2006006177 http://id.loc.gov/authorities/subjects/sh85126347 http://id.loc.gov/authorities/genreForms/gf2011026297 |
| title | Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / |
| title_auth | Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / |
| title_exact_search | Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / |
| title_full | Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / Michael Dorman. |
| title_fullStr | Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / Michael Dorman. |
| title_full_unstemmed | Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / Michael Dorman. |
| title_short | Learning R for geospatial analysis : |
| title_sort | learning r for geospatial analysis leverage the power of r to elegantly manage crucial geospatial analysis tasks |
| title_sub | leverage the power of R to elegantly manage crucial geospatial analysis tasks / |
| topic | Geospatial data. http://id.loc.gov/authorities/subjects/sh2006006177 Spatial analysis (Statistics) http://id.loc.gov/authorities/subjects/sh85126347 Données géospatiales. Analyse spatiale (Statistique) spatial analysis. aat LANGUAGE ARTS & DISCIPLINES Library & Information Science General. bisacsh Geospatial data fast Spatial analysis (Statistics) fast |
| topic_facet | Geospatial data. Spatial analysis (Statistics) Données géospatiales. Analyse spatiale (Statistique) spatial analysis. LANGUAGE ARTS & DISCIPLINES Library & Information Science General. Geospatial data |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=933799 |
| work_keys_str_mv | AT dormanmichael learningrforgeospatialanalysisleveragethepowerofrtoelegantlymanagecrucialgeospatialanalysistasks |