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     "href": "use-cases/demographics.html",
     "title": "Demographics",
     "section": "",
-    "text": "This page is still under development - new content may be added and existing content may change at any time without notice\n\n\n\n\n\n\nDepending on what demographics data sources and software you decide to use, the methods needed to combine, overlay, or compare with the data you are interested in may vary. See the Demographics Data section of the Data Collection page for general guidance and things to consider when using demographics data. See below for step by step guidance on how to download and compare demographics data to point, line, and polygon data types programmatically using R or Python, or manually using ESRI tools.\n\n\n\n\n\n\nImportant Reminders Before You Dive In\n\n\n\nData are NOT people - We need to use these data to get a better understanding of what’s going on in our communities, but the data (at best) only represent a sample of the communitie’s population and in no way reflect everyone or their lived experiences.\nThere’s no such thing as “equity data” - how we use data, interpret it, and act on what we learn makes our use equitable (or not). Simply including demographics data in your project’s analysis or data products does not make those resources equitable - to operationalize equity we need to take actions and make decisions in ways to advance equitable outcomes.\nThe data you’re using has limitations, be sure you know what they are before moving forward - as discussed on the Data Collection page, all data have limitations, and that is particularly true for demographics data. Be sure you have a clear and comprehensive understanding of the limitations that apply to the specific datasets you’re using so you can collect and eventually process and analyze those data in ways that are appropriate.\n\n\n\n\nR1 is a free software environment for statistical computing and graphics (Training Resources). RStudio is an integrated development environment (IDE) that includes is a set of tools and user interfaces built to help you be more productive with R and Python.\n\n\nIf you haven’t already, you will need to install R and RStudio. Water Boards staff should be able to do so through the Software Center. Also see step by step installiation instructions for outside of the Software Center environment.\nIf you will be using U.S. Census data regularly and will be accessing and analyzing it programmatically (e.g. using R or Python), you will also need to Request a U.S. Census Data API Key.\n\n\n\nR Packages / Libraries are extensions to the R statistical programming language that contain code, data, and documentation in a standardized collection format that can be installed by users of R.\n\n\nYou must install any packages you will use on your computer before you can load them. You only need to install a package once; if you have already installed the below packages you can skip this step and proceed to the Load Packages step.\n1install.packages(\"here\")\n2install.packages(\"tidyverse\")\n3install.packages(\"ggplot2\")\n4install.packages(\"tidycensus\")\n5install.packages(\"sf\")\n6install.packages(\"patchwork\")\n\n1\n\nThe here package enables easy, shareable and reproducible file referencing in project-oriented workflows. In contrast to using setwd(), which is fragile and dependent on the way you organize your files, here uses the top-level directory of a project to easily build paths to files. Package Documentation\n\n2\n\nThe tidyverse package installs all packages in the tidyverse at once, including: ggplot2, dplyr, tidyr, among others. Package Documentation\n\n3\n\nThe ggplot2 package is used to create graphics and data visualizations. Package Documentation\n\n4\n\nThe tidycensus package allows users to interface with the US Census Bureau’s decennial Census and five-year American Community APIs and return tidyverse-ready data frames. Package Documentation\n\n5\n\nThe sf package provides access to simple features in R so users can work with geographic vectors. Package Documentation\n\n6\n\nThe patchwork package is an extension of the ggplot2 package, designed to simplify the process of combining multiple plots into a single layout. Package Documentation\n\n\n\n\n\nYou must load all packages you will use for an analysis before each use.\nlibrary(here) \nlibrary(ggplot2) \nlibrary(tidyverse) \nlibrary(tidycensus) \nlibrary(sf) \nlibrary(patchwork)\n\n\n\n\n\n\n\n\n\n\n\n\nWarning\n\n\n\nLEFT OFF HERE\npull over content + code from:\nhttps://datamade.github.io/waterboard-coaching/\nhttps://walker-data.com/census-r/census-geographic-data-and-applications-in-r.html\nhttps://daltare.github.io/example-census-race-ethnicity-calculation/example_census_race_ethnicity_calculation.html\n\n\n7census_api_key(\" \")\n\n7\n\nmodify code to pull from api key located in file to increase security\n\n\n\ngeographies\nlast data update\nage categories\n\nTake some time to review the Concept and Labels in the table -\nsummary_var = \"P2_001N\" # Total population\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnalyzing Water Boards and Demographic Data for Equity. Jun 2024. Hannah Cushman Garland. State Water Board Racial Equity Data Subcommittee Webinar. Recording | Download and Use the Code | View Code\nEstimating Demographics of Custom Spatial Features is another detailed example of how to use the R programming language to estimate demographics and other characteristics with U.S. census data to be used for custom spatial features, and can be tailored to programs with the help of a data scientist proficient in R and staff familiar with the program.\n\n\n\n\n\nPython is a….\n\n\n\nESRI is a….",
+    "text": "This page is still under development - new content may be added and existing content may change at any time without notice\n\n\n\n\n\n\nDepending on what demographics data sources and software you decide to use, the methods needed to combine, overlay, or compare with the data you are interested in may vary. See the Demographics Data section of the Data Collection page for general guidance and things to consider when using demographics data. See below for step by step guidance on how to download and compare demographics data to point, line, and polygon data types programmatically using R or Python, or manually using ESRI tools.\n\n\n\n\n\n\nImportant Reminders Before You Dive In\n\n\n\nData are NOT people - We need to use these data to get a better understanding of what’s going on in our communities, but the data (at best) only represent a sample of the community’s population and in no way reflect everyone or their lived experiences.\nThere’s no such thing as “equity data” - how we use data, interpret it, and act on what we learn makes our use equitable (or not). Simply including demographics data in your project’s analysis or data products does not make those resources equitable - to operationalize equity we need to take actions and make decisions in ways to advance equitable outcomes.\nThe data you’re using has limitations, be sure you know what they are before moving forward - as discussed on the Data Collection page, all data have limitations, and that is particularly true for demographics data. Be sure you have a clear and comprehensive understanding of the limitations that apply to the specific datasets you’re using so you can collect and eventually process and analyze those data in ways that are appropriate.\n\n\n\n\nR1 is a free software environment for statistical computing and graphics (Training Resources). RStudio is an integrated development environment (IDE) that includes is a set of tools and user interfaces built to help you be more productive with R and Python.\n\n\nIf you haven’t already, you will need to install R and RStudio. Water Boards staff should be able to do so through the Software Center. Also see step by step installation instructions for outside of the Software Center environment.\nIf you will be using U.S. Census data regularly and will be accessing and analyzing it programmatically (e.g. using R or Python), you will also need to Request a U.S. Census Data API Key.\n\n\n\nR Packages / Libraries are extensions to the R statistical programming language that contain code, data, and documentation in a standardized collection format that can be installed by users of R.\n\n\nYou must install any packages you will use on your computer before you can load them. You only need to install a package once; if you have already installed the below packages you can skip this step and proceed to the Load Packages step.\n1install.packages(\"here\")\n2install.packages(\"tidyverse\")\n3install.packages(\"ggplot2\")\n4install.packages(\"tidycensus\")\n5install.packages(\"sf\")\n6install.packages(\"patchwork\")\n\n1\n\nThe here package enables easy, shareable and reproducible file referencing in project-oriented workflows. In contrast to using setwd(), which is fragile and dependent on the way you organize your files, here uses the top-level directory of a project to easily build paths to files. Package Documentation\n\n2\n\nThe tidyverse package installs all packages in the tidyverse at once, including: ggplot2, dplyr, tidyr, among others. Package Documentation\n\n3\n\nThe ggplot2 package is used to create graphics and data visualizations. Package Documentation\n\n4\n\nThe tidycensus package allows users to interface with the US Census Bureau’s decennial Census and five-year American Community APIs and return tidyverse-ready data frames. Package Documentation\n\n5\n\nThe sf package provides access to simple features in R so users can work with geographic vectors. Package Documentation\n\n6\n\nThe patchwork package is an extension of the ggplot2 package, designed to simplify the process of combining multiple plots into a single layout. Package Documentation\n\n\n\n\n\nYou must load all packages you will use for an analysis before each use.\nlibrary(here) \nlibrary(ggplot2) \nlibrary(tidyverse) \nlibrary(tidycensus) \nlibrary(sf) \nlibrary(patchwork)\n\n\n\n\n\n\n\n\n\n\n\n\nWarning\n\n\n\nLEFT OFF HERE\npull over content + code from:\nhttps://datamade.github.io/waterboard-coaching/\nhttps://walker-data.com/census-r/census-geographic-data-and-applications-in-r.html\nhttps://daltare.github.io/example-census-race-ethnicity-calculation/example_census_race_ethnicity_calculation.html\n\n\n7census_api_key(\" \")\n\n7\n\nmodify code to pull from api key located in file to increase security\n\n\n\ngeographies\nlast data update\nage categories\n\nTake some time to review the Concept and Labels in the table -\nsummary_var = \"P2_001N\" # Total population\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnalyzing Water Boards and Demographic Data for Equity. Jun 2024. Hannah Cushman Garland. State Water Board Racial Equity Data Subcommittee Webinar. Recording | Download and Use the Code | View Code\nEstimating Demographics of Custom Spatial Features is another detailed example of how to use the R programming language to estimate demographics and other characteristics with U.S. census data to be used for custom spatial features, and can be tailored to programs with the help of a data scientist proficient in R and staff familiar with the program.\n\n\n\n\n\nPython is a….\n\n\n\nESRI is a….",
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@@ -399,7 +399,7 @@
     "href": "use-cases/demographics.html#r-data-integration-example",
     "title": "Demographics",
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-    "text": "R1 is a free software environment for statistical computing and graphics (Training Resources). RStudio is an integrated development environment (IDE) that includes is a set of tools and user interfaces built to help you be more productive with R and Python.\n\n\nIf you haven’t already, you will need to install R and RStudio. Water Boards staff should be able to do so through the Software Center. Also see step by step installiation instructions for outside of the Software Center environment.\nIf you will be using U.S. Census data regularly and will be accessing and analyzing it programmatically (e.g. using R or Python), you will also need to Request a U.S. Census Data API Key.\n\n\n\nR Packages / Libraries are extensions to the R statistical programming language that contain code, data, and documentation in a standardized collection format that can be installed by users of R.\n\n\nYou must install any packages you will use on your computer before you can load them. You only need to install a package once; if you have already installed the below packages you can skip this step and proceed to the Load Packages step.\n1install.packages(\"here\")\n2install.packages(\"tidyverse\")\n3install.packages(\"ggplot2\")\n4install.packages(\"tidycensus\")\n5install.packages(\"sf\")\n6install.packages(\"patchwork\")\n\n1\n\nThe here package enables easy, shareable and reproducible file referencing in project-oriented workflows. In contrast to using setwd(), which is fragile and dependent on the way you organize your files, here uses the top-level directory of a project to easily build paths to files. Package Documentation\n\n2\n\nThe tidyverse package installs all packages in the tidyverse at once, including: ggplot2, dplyr, tidyr, among others. Package Documentation\n\n3\n\nThe ggplot2 package is used to create graphics and data visualizations. Package Documentation\n\n4\n\nThe tidycensus package allows users to interface with the US Census Bureau’s decennial Census and five-year American Community APIs and return tidyverse-ready data frames. Package Documentation\n\n5\n\nThe sf package provides access to simple features in R so users can work with geographic vectors. Package Documentation\n\n6\n\nThe patchwork package is an extension of the ggplot2 package, designed to simplify the process of combining multiple plots into a single layout. Package Documentation\n\n\n\n\n\nYou must load all packages you will use for an analysis before each use.\nlibrary(here) \nlibrary(ggplot2) \nlibrary(tidyverse) \nlibrary(tidycensus) \nlibrary(sf) \nlibrary(patchwork)\n\n\n\n\n\n\n\n\n\n\n\n\nWarning\n\n\n\nLEFT OFF HERE\npull over content + code from:\nhttps://datamade.github.io/waterboard-coaching/\nhttps://walker-data.com/census-r/census-geographic-data-and-applications-in-r.html\nhttps://daltare.github.io/example-census-race-ethnicity-calculation/example_census_race_ethnicity_calculation.html\n\n\n7census_api_key(\" \")\n\n7\n\nmodify code to pull from api key located in file to increase security\n\n\n\ngeographies\nlast data update\nage categories\n\nTake some time to review the Concept and Labels in the table -\nsummary_var = \"P2_001N\" # Total population\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnalyzing Water Boards and Demographic Data for Equity. Jun 2024. Hannah Cushman Garland. State Water Board Racial Equity Data Subcommittee Webinar. Recording | Download and Use the Code | View Code\nEstimating Demographics of Custom Spatial Features is another detailed example of how to use the R programming language to estimate demographics and other characteristics with U.S. census data to be used for custom spatial features, and can be tailored to programs with the help of a data scientist proficient in R and staff familiar with the program.",
+    "text": "R1 is a free software environment for statistical computing and graphics (Training Resources). RStudio is an integrated development environment (IDE) that includes is a set of tools and user interfaces built to help you be more productive with R and Python.\n\n\nIf you haven’t already, you will need to install R and RStudio. Water Boards staff should be able to do so through the Software Center. Also see step by step installation instructions for outside of the Software Center environment.\nIf you will be using U.S. Census data regularly and will be accessing and analyzing it programmatically (e.g. using R or Python), you will also need to Request a U.S. Census Data API Key.\n\n\n\nR Packages / Libraries are extensions to the R statistical programming language that contain code, data, and documentation in a standardized collection format that can be installed by users of R.\n\n\nYou must install any packages you will use on your computer before you can load them. You only need to install a package once; if you have already installed the below packages you can skip this step and proceed to the Load Packages step.\n1install.packages(\"here\")\n2install.packages(\"tidyverse\")\n3install.packages(\"ggplot2\")\n4install.packages(\"tidycensus\")\n5install.packages(\"sf\")\n6install.packages(\"patchwork\")\n\n1\n\nThe here package enables easy, shareable and reproducible file referencing in project-oriented workflows. In contrast to using setwd(), which is fragile and dependent on the way you organize your files, here uses the top-level directory of a project to easily build paths to files. Package Documentation\n\n2\n\nThe tidyverse package installs all packages in the tidyverse at once, including: ggplot2, dplyr, tidyr, among others. Package Documentation\n\n3\n\nThe ggplot2 package is used to create graphics and data visualizations. Package Documentation\n\n4\n\nThe tidycensus package allows users to interface with the US Census Bureau’s decennial Census and five-year American Community APIs and return tidyverse-ready data frames. Package Documentation\n\n5\n\nThe sf package provides access to simple features in R so users can work with geographic vectors. Package Documentation\n\n6\n\nThe patchwork package is an extension of the ggplot2 package, designed to simplify the process of combining multiple plots into a single layout. Package Documentation\n\n\n\n\n\nYou must load all packages you will use for an analysis before each use.\nlibrary(here) \nlibrary(ggplot2) \nlibrary(tidyverse) \nlibrary(tidycensus) \nlibrary(sf) \nlibrary(patchwork)\n\n\n\n\n\n\n\n\n\n\n\n\nWarning\n\n\n\nLEFT OFF HERE\npull over content + code from:\nhttps://datamade.github.io/waterboard-coaching/\nhttps://walker-data.com/census-r/census-geographic-data-and-applications-in-r.html\nhttps://daltare.github.io/example-census-race-ethnicity-calculation/example_census_race_ethnicity_calculation.html\n\n\n7census_api_key(\" \")\n\n7\n\nmodify code to pull from api key located in file to increase security\n\n\n\ngeographies\nlast data update\nage categories\n\nTake some time to review the Concept and Labels in the table -\nsummary_var = \"P2_001N\" # Total population\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnalyzing Water Boards and Demographic Data for Equity. Jun 2024. Hannah Cushman Garland. State Water Board Racial Equity Data Subcommittee Webinar. Recording | Download and Use the Code | View Code\nEstimating Demographics of Custom Spatial Features is another detailed example of how to use the R programming language to estimate demographics and other characteristics with U.S. census data to be used for custom spatial features, and can be tailored to programs with the help of a data scientist proficient in R and staff familiar with the program.",
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       "Use Cases",
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