Example of how I use MS Excel's VLOOKUP() function to filter my data.

With this add in it is possible to create map templates from GIS files in KML format, and create choropleths with them. Providing you have access to KML format map boundary files, it is possible to create your own quick and easy choropleth maps in Excel. The KML format files can be converted from 'shape' files. Many shape files are available to download for free from the web, including from Ordnance Survey and the London Datastore. Standard mapping packages such as QGIS (free to download) and ArcGIS can convert the files to KML format. A sample of a KML file (London wards) can be downloaded from this page, so that users can easily test the tool out. Macros must be enabled for the tool to function. When creating the map using the Excel tool, the 'unique ID' should normally be the area code, the 'Name' should be the area name and then if required and there is additional data in the KML file, further 'data' fields can be added. These columns will appear below and to the right of the map. If not, data can be added later on next to the codes and names. In the add-in version of the tool the final control, 'Scale (% window)' should not normally be changed. With the default value 0.5, the height of the map is set to be half the total size of the user's Excel window. To run a choropleth, select the menu option 'Run Choropleth' to get this form. To specify the colour ramp for the choropleth, the user needs to enter the number of boxes into which the range is to be divided, and the colours for the high and low ends of the range, which is done by selecting coloured option boxes as appropriate. If wished, hit the 'Swap' button to change which colours are for the different ends of the range. Then hit the 'Choropleth' button. The default options for the colours of the ends of the choropleth colour range are saved in the add in, but different values can be selected but setting up a column range of up to twelve cells, anywhere in Excel, filled with the option colours wanted. Then use the 'Colour range' control to select this range, and hit apply, having selected high or low values as wished. The button 'Copy' sets up a sheet 'ColourRamp' in the active workbook with the default colours, which can just be extended or deleted with just a few cells, so saving the user time. The add-in was developed entirely within the Excel VBA IDE by Tim Lund. He is kindly distributing the tool for free on the Datastore but suggests that users who find the tool useful make a donation to the Shelter charity. It is not intended to keep the actively maintained, but if any users or developers would like to add more features, email the author. Acknowledgments Calculation of Excel freeform shapes from latitudes and longitudes is done using calculations from the Ordnance Survey.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

CCRI Selected Published Tables Data Files: For each census from 1911-1951, a series of published volumes and tables were produced by the Dominion of Canada’s statistical agency. From those published books, the CCRI made a selection of 23 tables which contain information regarding particular topics such as: population (male and female counts), number of dwellings, households and families, as well as religion and origin of the people. For 1921, selected tables from published volumes (1) included: Population, Canadian, British and Foreign born, classified by sex for counties or census divisions, 1921 Population classified according to principal origins of the people by counties or census divisions, 1921 Population classified according to principal religions of the people by counties or census divisions, 1921 Dwellings and households, classified as rural and urban, for counties or census divisions, 1921

This provides a link to the Washington Secretary of State's Corporations Search tool. The Corporations Data Extract feature is no longer available. Customers needing a list of multiple businesses can use our advanced search to create a list of businesses under specific parameters. You can export this information to an Excel spreadsheet to sort and search more extensively. Below are the steps to perform this type of search. The more specified parameter searches provide narrower search results. Please visit our Corporations and Charities Filing System by following this link https://ccfs.sos.wa.gov/ Scroll down to the “Corporation Search” section and click the “Advanced Search” button on the right. Under the first section, specify how you would like the business name searched. Only use this for single business lookups unless all the businesses you are searching have a common name (use the “contains” selection). Select the appropriate business type from the dropdown if you are looking for a list of a specific business type. For a list of a particular business type with a specific status, select that status under “Business Status.” You can also search by expiration date in this section. Under the “Date of Incorporation/Formation/Registration,” you can search by start or end date. Under the “Registered Agent/Governor Search” section, you can search all businesses with the same registered agent on record or governor listed. Once you have made all your search selections, click the green “Search” button at the bottom right of the page. A list will populate; scroll to the bottom and select the green Excel document icon with CSV. An Excel document should automatically download. If you have popups blocked, please unblock our site, and try again. Once you have opened the downloaded Excel spreadsheet, you can adjust the width of each column and sort the data using the data tab. You can also search by pressing CTRL+F on a Windows keyboard.

About this dataset

Context

The dataset tabulates the data for the Excel Township, Minnesota population pyramid, which represents the Excel township population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Excel Township, Minnesota, is 25.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Excel Township, Minnesota, is 37.7.
• Total dependency ratio for Excel Township, Minnesota is 63.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Excel Township, Minnesota is 2.7.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Excel township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Excel township for the selected age group is shown in the following column.
• Population (Female): The female population in the Excel township for the selected age group is shown in the following column.
• Total Population: The total population of the Excel township for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel township Population by Age. You can refer the same here

Example of a filtered Microsoft Excel spreadsheet for TaAMY2 single null mutant detection (selected data).

This is the dataset for the paper: Understanding the Issues, Their Causes and Solutions in Microservices Systems: An Empirical Study. The dataset is recorded in an MS Excel file which contains the following Excel sheets, and the description of each sheet is briefly presented below.

(1) Selected Systems

contains the 15 selected open source microservices systems with the color code and URL of each system.

(2) Raw Data

contains the information of initially retrieved 10,222 issues, including issue titles, issue links, issue open date, issue closed date, and the number of participants in each issue discussion.

(3) Screened Issues

contains the issues that meet the initial selection criteria (i.e., 5,115 issues) and the issues that do not meet the initial selection criteria (i.e., 5,107 issues).

(4) Selected Issues (Round 1)

contains the list of 5,115 issues that meet the initial selection criteria.

(5) Selected Issues (Round 2)

contains the issues related to RQs (i.e., 2,641 issues) and the issues not related to RQs (i.e., 2,474 issues).

(6) Selected Issues

contains the list of selected 2,641 issues, which were used to answer the RQs.

(7) Initial Codes

contains the initial codes for identifying the types of issues, causes, and solutions. We used these codes to further generate the subcategories and categories of issues, causes, and solutions.

(8) Interview Questionnaire

contains the interview questions we asked microservices practitioners to identify any missing issues, causes, and solutions, as well as to improve the proposed taxonomies.

(9) Interview Results

contains the results of interviews that we conducted to confirm and improve the developed taxonomies of issues, causes, and solutions.

(10) Survey Questionnaire

contains the survey questions we asked microservices practitioners through a Web-based survey to validate our taxonomies of issues, causes, and solutions.

(11) Issue Taxonomy

contains the detailed issue taxonomy consisting of 19 categories, 54 subcategories, and 402 types of issues.

(12) Cause Taxonomy

contains the detailed cause taxonomy consisting of 8 categories, 26 subcategories, and 228 types of causes.

(13) Solution Taxonomy

contains the detailed solution taxonomy consisting of 8 categories, 32 subcategories, and 177 types of solutions.

About this dataset

Context

The dataset tabulates the data for the Excel, AL population pyramid, which represents the Excel population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Excel, AL, is 40.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Excel, AL, is 25.8.
• Total dependency ratio for Excel, AL is 65.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Excel, AL is 3.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Excel population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Excel for the selected age group is shown in the following column.
• Population (Female): The female population in the Excel for the selected age group is shown in the following column.
• Total Population: The total population of the Excel for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel Population by Age. You can refer the same here

This Excel file provides information about 570 journals' rejection rates and impact factors. This represents about 5% of the 2014 Journal Citation Reports.

The data sources are listed on the right hand side of the last column.

Note: This data is not exhaustive nor is it necessarily representative of Open Access as a whole.

SUPPLEMENTARY EXCEL FILE 1: Unique Probe IDs for the GSE datatsets SUPPLMENTARY EXCEL FILE 2: Top Table gene ranking of the selected PD microarray datasets TABEL S3: Select microarray studies in neurodegenerative diseases with GEO accession TABLE S4: Summary table on CSF proteomics on select markers within the CSF portal

Excel Age-Range creator for Office for National Statistics (ONS) Mid year population estimates (MYE) covering each year between 1999 and 2014

https://londondatastore-upload.s3.amazonaws.com/mye-custom-tool.JPG" alt="" />

These files take into account the revised estimates for 2002-2010 released in April 2013 down to Local Authority level and the post 2011 estimates based on the Census results. Scotland and Northern Ireland data has not been revised, so Great Britain and United Kingdom totals comprise the original data for these plus revised England and Wales figures.

This Excel based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range. Please adhere to the terms and conditions of supply contained within the file.

Tip: You can copy and paste the rows you are interested in to another worksheet by using the filters at the top of the columns and then select all by pressing Ctrl+A. Then simply copy and paste the cells to a new location.

ONS Mid year population estimates

Open Excel tool (London Boroughs, Regions and National, 1999-2014)

Also available is a custom-age tool for all geographies in the UK. Open the tool for all UK geographies (local authority and above) for: 2010, 2011, 2012, 2013, and 2014.

This full MYE dataset by single year of age (SYA) age and gender is available as a Datastore package here.

Ward Level Population estimates

Excel single year of age population tool for 2002 to 2013 for all wards in London.

New 2014 Ward boundary estimates

This data is only for wards in the three London boroughs that changed their ward boundaries in May 2014. The estimates in this spreadsheet have been calculated by the GLA by taking the proportion of a the old ward that falls within the new ward based on the proportion of population living in each area at the 2011 Census. Therefore, these estimates are purely indicative and are not official statistics and not endorsed by ONS.

This data set is perfect for practicing your analytical skills for Power BI, Tableau, Excel, or transform it into a CSV to practice SQL.

This use case mimics transactions for a fictional eCommerce website named EverMart Online. The 3 tables in this data set are all logically connected together with IDs.

My Power BI Use Case Explanation - Using Microsoft Power BI, I made dynamic data visualizations for revenue reporting and customer behavior reporting.

Revenue Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total Sales, Product Sales, or Categorical Sales. - Line Graph Visual that shows Total Revenue by Month of the entire year. This graph also changes to calculate Total Revenue by Month for the Total Sales by Product and Total Sales by Category if selected. - Bar Graph Visual showcasing Total Sales by Product. - Donut Chart Visual showcasing Total Sales by Category of Product.

Customer Behavior Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total or by continent selected on the map. - Interactive Map Visual showing key statistics for the continent selected. - The key statistics are presented on the tool tip when you select a continent, and the following statistics show for that continent: - Continent Name - Customer Total - Percentage of Products Sold - Percentage of Total Customers - Percentage of Total Transactions - Percentage of Total Revenue

Dataset

This dataset was created by Mohanad Hazem Qabil

Contents

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

Excel Age-Range creator for Office for National Statistics (ONS) Mid year population estimates (MYE) covering each year between 1999 and 2016 These files take into account the revised estimates for 2002-2010 released in April 2013 down to Local Authority level and the post 2011 estimates based on the Census results. Scotland and Northern Ireland data has not been revised, so Great Britain and United Kingdom totals comprise the original data for these plus revised England and Wales figures. This Excel based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range. Please adhere to the terms and conditions of supply contained within the file. Tip: You can copy and paste the rows you are interested in to another worksheet by using the filters at the top of the columns and then select all by pressing Ctrl+A. Then simply copy and paste the cells to a new location. ONS Mid year population estimates Open Excel tool (London Boroughs, Regions and National, 1999-2016) Also available is a custom-age tool for all geographies in the UK. This full MYE dataset by single year of age (SYA) age and gender is available as a Datastore package here. Ward Level Population estimates Single year of age population tool for 2002 to 2015 for all wards in London. New 2014 Ward boundary estimates Ward boundary changes in May 2014 only affected three London boroughs - Hackney, Kensington and Chelsea, and Tower Hamlets. The estimates between 2001-2013 have been calculated by the GLA by taking the proportion of a the old ward that falls within the new ward based on the proportion of population living in each area at the 2011 Census. Therefore, these estimates are purely indicative and are not official statistics and not endorsed by ONS. From 2014 onwards, ONS began publishing official estimates for the new ward boundaries. Download here.

Describes methods and procedures, data generation and evaluation, formats of tables, data dissemination, references cited in the documentation and database, and other miscellaneous information about this database. Resource Title: Fluoride Database of Selected Beverages and Foods (Release 2). File Name: F02.xlsResource Description: This file contains the Fluoride Database imported into a Microsoft Excel spreadsheet. You need Excel 2000 or later to use this file.

This publication is a compilation of historical data relating to selected birth and fertility data from 1921-1990 for Canada, the ten provinces, and two territories. Major topics included in this publication relate to: the numbers and rates of live births; total, general, and age-specific fertility rates; births and birth rates by age of mother and order of live birth; and birthweights of newborns. This publication contains the following sections: (a) a narrative description of the historical trends exhibited by Canada's birth and fertility rates, supplemented by charts on these topics; and (b) a set of statistical tables containing historical birth and fertility data since 1921. The statistical data in this publication, along with complete documentation, are available in machine readable form from the Canadian Centre for Health Information. This publication was compiled in the Health Status Section of the Canadian Centre for Health Information and is one of a series of historical publications relating to the vital statistics events of births, marriages, deaths, infant mortality and abortions.

Data on obesity among children and adolescents aged 2-19 years by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time.

SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

Asthma ED Visit Rates by ZIP

Counts and Rates by Age Group in California

By Health [source]

About this dataset

This dataset presents a comprehensive look into the prevalence of asthma among Californian residents in terms of emergency department visits. Using age-adjusted rates and county FIPS codes, it offers an accurate snapshot of the prevalence rates per 10,000 people and provides key insights into how this condition affects certain age groups by ZIP Code. With its easy to use associated map view, this dataset allows users to quickly gain deeper knowledge about this important health issue and craft meaningful solutions to address it

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains counts and rates of asthma related emergency department visits by ZIP Code and age group in California. This data can be useful when doing research on asthma related trends or attempting to find correlations between environmental factors, prevalence of disease and geography.

• Select a year for analysis - the latest year for which data is available is the default selection, but other years are also listed in the dropdown menu.
• Select an Age Group to analyze - use the provided dropdown menus to select one or more age groups (all ages, 0-17, 18+) if you wish to analyze two different age groups in your analysis.
• Define a geographical area by selecting a ZIP code or County Fips code from which you wish to obtain your dataset from based on its availability or importance in your research question .
• View and download relevant data - after selecting all of the desired criteria (year,Age group(s), ZIP code/County FIPS Code) click “View Data” then “Download” at the bottom right corner of window that opens up
  5 Analyze information found - use software such as Microsoft Excel or open source programs like Openoffice Calc to gain insight into your downloaded dataset through statistics calculations, graphs etc.. In particular look out for anomalies that could signify further investigation

Research Ideas

• Identifying the geographic clusters of asthma sufferers by analyzing the rate of emergency department visits with geographic mapping.
• Developing outreach initiatives to areas with a high rate of ED visits for asthma to provide education, interventions and resources designed towards increasing preventive care and reducing preventable complications due to lack of access or knowledge about available services in these communities.
• Assessing disparities in ED visit rates for asthma between age groups as well as between urban and rural areas or different socio-economic groups within counties or ZIP codes in order to identify areas where there is a need for increased interventions, services and other resources related to asthma care in order to reduce the burden or severity of this chronic condition among particularly vulnerable population groups

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Asthma_Emergency_Department_Visit_Rates_by_ZIP_Code.csv | Column name | Description | |:----------------------|:------------------------------------------------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | ZIP code | The ZIP code of the area the data was collected from. (String...