Also, be aware that some commodity descriptions may include & in their names. Provide statistical data related to US agricultural production through either user-customized or pre-defined queries. An open-standard file format that uses human-readable text to transmit data objects consisting of attribute-value pairs and array data types. Queries that would return more records return an error and will not continue. NASS conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. In this publication we will focus on two large NASS surveys. Accessed online: 01 October 2020. .gitignore if youre using github. USDA National Agricultural Statistics Service Information. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. nc_sweetpotato_data_raw <- nassqs(nc_sweetpotato_params). To submit, please register and login first. Skip to 5. It is simple and easy to use, and provides some functions to help navigate the bewildering complexity of some Quick Stats data. For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. All of these reports were produced by Economic Research Service (ERS. Many coders who use R also download and install RStudio along with it. You are also going to use the tidyverse package, which is called a meta-package because it is a package of packages that helps you work with your datasets easily and keep them tidy.. Thsi package is now on CRAN and can be installed through the typical method: install.packages ("usdarnass") Alternatively, the most up-to-date version of the package can be installed with the devtools package. Once you have a What R Tools Are Available for Getting NASS Data? Corn production data goes back to 1866, just one year after the end of the American Civil War. U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). Section 207(f)(2) of the E-Government Act of 2002 requires federal agencies to develop an inventory of information to be published on their Web sites, establish a schedule for publishing information, make those schedules available for public comment, and post the schedules and priorities on the Web site. parameters is especially helpful. You can also set the environmental variable directly with write_csv(data = nc_sweetpotato_data, path = "Users/your/Desktop/nc_sweetpotato_data_query_on_20201001.csv"). Using rnassqs Nicholas A Potter 2022-03-10. rnassqs is a package to access the QuickStats API from national agricultural statistics service (NASS) at the USDA. Quick Stats Lite provides a more structured approach to get commonly requested statistics from . To use a baking analogy, you can think of the script as a recipe for your favorite dessert. For Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). queries subset by year if possible, and by geography if not. However, ERS has no copies of the original reports. The rnassqs package also has a County level data are also available via Quick Stats. nassqs_auth(key = NASS_API_KEY). api key is in a file, you can use it like this: If you dont want to add the API key to a file or store it in your Call the function stats.get_data() with the parameters string and the name of the output file (without the extension). In this case, the task is to request NASS survey data. The information on this page (the dataset metadata) is also available in these formats: The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). In fact, you can use the API to retrieve the same data available through the Quick Stats search tool and the Census Data Query Tool, both of which are described above. You can see a full list of NASS parameters that are available and their exact names by running the following line of code. For example, if youd like data from both Most queries will probably be for specific values such as year A function is another important concept that is helpful to understand while using R and many other coding languages. An official website of the General Services Administration. For After you run this code, the output is not something you can see. One way of query. Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. list with c(). In registering for the key, for which you must provide a valid email address. Once youve installed the R packages, you can load them. We summarize the specifics of these benefits in Section 5. (R coders say you need to load your R packages.) You can do that by running the code below (Section 7.2). Then, it will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. Beginning in May 2010, NASS agricultural chemical use data are published to the Quick Stats 2.0 database only (full-text publications have been discontinued), and can be found under the NASS Chemical Usage Program. While there are three types of API queries, this tutorial focuses on what may be the most flexible, which is the GET /api/api_GET query. which at the time of this writing are. To demonstrate the use of the agricultural data obtained with the Quick Stats API, I have created a simple dashboard in Tableau Public. National Agricultural Statistics Service (NASS) Quickstats can be found on their website. # look at the first few lines You can also make small changes to the script to download new types of data. Journal of Open Source Software , 4(43 . The census collects data on all commodities produced on U.S. farms and ranches, as . reference_period_desc "Period" - The specic time frame, within a freq_desc. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. You can verify your report was received by checking the Submitted date under the Status column of the My Surveys tab. Here are the two Python modules that retrieve agricultural data with the Quick Stats API: To run the program, you will need to install the Python requests and urllib packages. rnassqs (R NASS Quick Stats) rnassqs allows users to access the USDA's National Agricultural Statistics Service (NASS) Quick Stats data through their API. Generally the best way to deal with large queries is to make multiple Here we request the number of farm operators You can check the full Quick Stats Glossary. NASS administers, manages, analyzes, and shares timely, accurate, and useful statistics in service to United States agriculture (NASS 2020). Building a query often involves some trial and error. You can define the query output as nc_sweetpotato_data. national agricultural statistics service (NASS) at the USDA. ggplot(data = sampson_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)). The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . You can view the timing of these NASS surveys on the calendar and in a summary of these reports. R is an open source coding language that was first developed in 1991 primarily for conducting statistical analyses and has since been applied to data visualization, website creation, and much more (Peng 2020; Chambers 2020). # check the class of Value column For example, a (D) value denotes data that are being withheld to avoid disclosing data for individual operations according to the creators of the NASS Quick Stats API. Skip to 3. Finally, you can define your last dataset as nc_sweetpotato_data. S, R, and Data Science. Proceedings of the ACM on Programming Languages. Columns for this particular dataset would include the year harvested, county identification number, crop type, harvested amount, the units of the harvested amount, and other categories. The API request is the customers (your) food order, which the waitstaff wrote down on the order notepad. provide an api key. It allows you to customize your query by commodity, location, or time period. You can use the ggplot( ) function along with your nc_sweetpotato_data variable to do this. Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge The resulting plot is a bit busy because it shows you all 96 counties that have sweetpotato data. After running these lines of code, you will get a raw data output that has over 1500 rows and close to 40 columns. A script is like a collection of sentences that defines each step of a task. Sys.setenv(NASSQS_TOKEN = . Combined with an assert from the Healy. It is best to start by iterating over years, so that if you If you have already installed the R package, you can skip to the next step (Section 7.2). 2022. Section 207(f)(2) of the E-Government Act of 2002 requires federal agencies to develop an inventory of information to be published on their Web sites, establish a schedule for publishing information, make those schedules available for public comment, and post the schedules and priorities on the Web site. You can also refer to these software programs as different coding languages because each uses a slightly different coding style (or grammar) to carry out a task. You can use many software programs to programmatically access the NASS survey data. Note: When a line of R code starts with a #, R knows to read this # symbol as a comment and will skip over this line when you run your code. There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. downloading the data via an R script creates a trail that you can revisit later to see exactly what you downloaded.It also makes it much easier for people seeking to . There are R packages to do linear modeling (such as the lm R package), make pretty plots (such as the ggplot2 R package), and many more. NASS publications cover a wide range of subjects, from traditional crops, such as corn and wheat, to specialties, such as mushrooms and flowers; from calves born to hogs slaughtered; from agricultural prices to land in farms. The following are some of the types of data it stores and makes available: NASS makes data available through CSV and PDF files, charts and maps, a searchable database, pre-defined queries, and the Quick Stats API. return the request object. The use of a callback function parameter, not shown in the example above, is beyond the scope of this article. example. equal to 2012. Then we can make a query. You can see whether a column is a character by using the class( ) function on that column (that is, nc_sweetpotato_data_survey$Value where the $ helps you access the Value column in the nc_sweetpotato_data_survey variable). Once you know North Carolina has data available, you can make an API query specific to sweetpotatoes in North Carolina. install.packages("rnassqs"). USDA-NASS Quick Stats (Crops) WHEAT.pdf PDF 1.42 MB . Agricultural Resource Management Survey (ARMS). You can read more about tidy data and its benefits in the Tidy Data Illustrated Series. Please click here to provide feedback for any of the tools on this page. If you use Within the mutate( ) function you need to remove commas in rows of the Value column that are 1000 acres or more (that is, you want 1000, not 1,000). To browse or use data from this site, no account is necessary. Didn't find what you're looking for? You can read more about the available NASS Quick Stats API parameters and their definitions by checking out the help page on this topic. First, you will define each of the specifics of your query as nc_sweetpotato_params. RStudio is another open-source software that makes it easier to code in R. The latest version of RStudio is available at the RStudio website. Tableau Public is a free version of the commercial Tableau data visualization tool. Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. A list of the valid values for a given field is available via You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. In this publication, the word variable refers to whatever is on the left side of the <- character combination. Looking for U.S. government information and services? This will call its initializer (__init__()) function, which sets the API key, the base URL for the Quick Stats API, and the name of the folder where the class will write the output CSV file that contains agricultural data. To make this query, you will use the nassqs( ) function with the parameters as an input. variable (usually state_alpha or county_code Washington and Oregon, you can write state_alpha = c('WA', The Census Data Query Tool (CDQT) is a web based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. Now that youve cleaned and plotted the data, you can save them for future use or to share with others. nass_data: Get data from the Quick Stats query In usdarnass: USDA NASS Quick Stats API Description Usage Arguments Value Examples Description Sends query to Quick Stats API from given parameter values. These codes explain why data are missing. A script includes a collection of code that, when taken together, defines a series of steps the coder wants his or her computer to carry out. your .Renviron file and add the key. many different sets of data, and in others your queries may be larger 4:84. Once the Open source means that the R source code the computer code that makes R work can be viewed and edited by the public. For example, if someone asked you to add A and B, you would be confused. While Quick Stats and Quick Stats Lite retrieve agricultural survey data (collected annually) and census data (collected every five years), the Census Data Query Tool is easier to use but retrieves only census data. Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/index.php, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Census_of_Agriculture/index.php, https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld, https://project-open-data.cio.gov/v1.1/schema, https://project-open-data.cio.gov/v1.1/schema/catalog.json, https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chapter_1_US/usappxa.pdf,https://www.agcensus.usda.gov/Publications/2007/Full_Report/Volume_1,_Chapter_1_US/usappxa.pdf, https://creativecommons.org/publicdomain/zero/1.0/, https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/index.php, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Census_of_Agriculture/index.php. Either 'CENSUS' or 'SURVEY'", https://quickstats.nass.usda.gov/api#param_define. The == character combination tells R that this is a logic test for exactly equal, the & character is a logic test for AND, and the != character combination is a logic test for not equal. Read our There are Then use the as.numeric( ) function to tell R each row is a number, not a character. Coding is a lot easier when you use variables because it means you dont have to remember the specific string of letters and numbers that defines your unique NASS Quick Stats API key. In this case, you can use the string of letters and numbers that represents your NASS Quick Stats API key to directly define the key parameter that the function needs to work. and predecessor agencies, U.S. Department of Agriculture (USDA). What Is the National Agricultural Statistics Service? How to write a Python program to query the Quick Stats database through the Quick Stats API. A&T State University, in all 100 counties and with the Eastern Band of Cherokee token API key, default is to use the value stored in .Renviron . For most Column or Header Name values, the first value, in lowercase, is the API parameter name, like those shown above. In R, you would write x <- 1. Also note that I wrote this program on a Windows PC, which uses back slashes (\) in file names and folder names. object generated by the GET call, you can use nassqs_GET to Have a specific question for one of our subject experts? to quickly and easily download new data. Each parameter is described on the Quick Stats Usage page, in its Quick Stats Columns Definition table, as shown below. If you use Visual Studio, you can install them through the IDEs menu by following these instructions from Microsoft. you downloaded. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. Often 'county', 'state', or 'national', but can include other levels as well", #> [2] "source_desc: Data source. Corn stocks down, soybean stocks down from year earlier The data include the total crops and cropping practices for each county, and breakouts for irrigated and non-irrigated practices for many crops, for selected States. To install packages, use the code below. By setting domain_desc = TOTAL, you will get the total acreage of sweetpotatoes in the county as opposed to the acreage of sweetpotates in the county grown by operators or producers of specific demographic groups that contribute to the total acreage of harvested sweetpotatoes in the county. file, and add NASSQS_TOKEN = to the nassqs_auth(key = "ADD YOUR NASS API KEY HERE"). For this reason, it is important to pay attention to the coding language you are using. The Comprehensive R Archive Network (CRAN). But you can change the export path to any other location on your computer that you prefer. It allows you to customize your query by commodity, location, or time period. Next, you can use the select( ) function again to drop the old Value column. All sampled operations are mailed a questionnaire and given adequate time to respond by Accessed online: 01 October 2020. About NASS. As an example, one year of corn harvest data for a particular county in the United States would represent one row, and a second year would represent another row. NASS Reports Crop Progress (National) Crop Progress & Condition (State) Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. The QuickStats API offers a bewildering array of fields on which to Running the script is similar to your pulling out the recipe and working through the steps when you want to make this dessert. To improve data accessibility and sharing, the NASS developed a "Quick Stats" website where you can select and download data from two of the agency's surveys. Lock Figure 1. The Comprehensive R Archive Network (CRAN), Weed Management in Nurseries, Landscapes & Christmas Trees, NC DRY. Say you want to plot the acres of sweetpotatoes harvested by year for each county in North Carolina. To put its scale into perspective, in 2021, more than 2 million farms operated on more than 900 million acres (364 million hectares). Besides requesting a NASS Quick Stats API key, you will also need to make sure you have an up-to-date version of R. If not, you can download R from The Comprehensive R Archive Network. Find more information at the following NC State Extension websites: Publication date: May 27, 2021 The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. Do do so, you can This reply is called an API response. Many people around the world use R for data analysis, data visualization, and much more. Also, the parameter values be replaced with specific parameter-value pairs to search for the desired data. The primary benefit of rnassqs is that users need not download data through repeated . or the like) in lapply. USDA-NASS. Its easiest if you separate this search into two steps. valid before attempting to access the data: Once youve built a query, running it is easy: Putting all of the above together, we have a script that looks Language feature sets can be added at any time after you install Visual Studio. Here, code refers to the individual characters (that is, ASCII characters) of the coding language. nassqs is a wrapper around the nassqs_GET Before you can plot these data, it is best to check and fix their formatting. nassqs_parse function that will process a request object Plus, in manually selecting and downloading data using the Quick Stats website, you could introduce human error by accidentally clicking the wrong buttons and selecting data that you do not actually want. Here is the most recent United States Summary and State Data (PDF, 27.9 MB), a statistical summary of the Census of Agriculture. Data are currently available in the following areas: Pre-defined queries are provided for your convenience. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. Based on this result, it looks like there are 47 states with sweetpotato data available at the county level, and North Carolina is one of them. Here are the pairs of parameters and values that it will submit in the API call to retrieve that data: Following is the full encoded URL that the program below creates and sends with the Quick Stats API. NC State University and NC In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. Before coding, you have to request an API access key from the NASS. Similar to above, at times it is helpful to make multiple queries and Have a specific question for one of our subject experts? into a data.frame, list, or raw text. The Census Data Query Tool (CDQT) is a web-based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. There are times when your data look like a 1, but R is really seeing it as an A. You can then visualize the data on a map, manipulate and export the results as an output file compatible for updating databases and spreadsheets, or save a link for future use. Corn stocks down, soybean stocks down from year earlier Potter N (2022). # filter out census data, to keep survey data only Accessed: 01 October 2020. After it receives the data from the server in CSV format, it will write the data to a file with one record per line. want say all county cash rents on irrigated land for every year since More specifically, the list defines whether NASS data are aggregated at the national, state, or county scale. Cooperative Extension prohibits discrimination and harassment regardless of age, color, disability, family and marital status, gender identity, national origin, political beliefs, race, religion, sex (including pregnancy), sexual orientation and veteran status. If you download NASS data without using computer code, you may find that it takes a long time to manually select each dataset you want from the Quick Stats website. Including parameter names in nassqs_params will return a For example, you will get an error if you write commodity_desc = SWEET POTATO (that is, dropping the ES) or write commodity_desc = sweetpotatoes (that is, with no space and all lowercase letters). An API request occurs when you programmatically send a data query from software on your computer (for example, R, Section 4) to the API for some NASS survey data that you want. and rnassqs will detect this when querying data. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Accessed: 01 October 2020. The site is secure. In the beginning it can be more confusing, and potentially take more than the API restriction of 50,000 records. to automate running your script, since it will stop and ask you to Before using the API, you will need to request a free API key that your program will include with every call using the API. R sessions will have the variable set automatically, So, you may need to change the format of the file path value if you will run the code on Mac OS or Linux, for example: self.output_file_path = rc:\\usda_quickstats_files\\. Before you get started with the Quick Stats API, become familiar with its Terms of Service and Usage. 'OR'). By setting statisticcat_desc = "AREA HARVESTED", you will get results for harvest acreage rather than planted acreage. the QuickStats API requires authentication. nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES") Scripts allow coders to easily repeat tasks on their computers. In the get_data() function of c_usd_quick_stats, create the full URL. Some parameters, like key, are required if the function is to run properly without errors. It allows you to customize your query by commodity, location, or time period. parameters. On the site you have the ability to filter based on numerous commodity types. After running this line of code, R will output a result. It is a comprehensive summary of agriculture for the US and for each state. manually click through the QuickStats tool for each data An official website of the United States government. Cooperative Extension is based at North Carolina's two land-grant institutions, ~ Providing Timely, Accurate and Useful Statistics in Service to U.S. Agriculture ~, County and District Geographic Boundaries, Crop Condition and Soil Moisture Analytics, Agricultural Statistics Board Corrections, Still time to respond to the 2022 Census of Agriculture, USDA to follow up with producers who have not yet responded, Still time to respond to the 2022 Puerto Rico Census of Agriculture, USDA to follow-up with producers who have not yet responded (Puerto Rico - English), 2022 Census of Agriculture due next week Feb. 6, Corn and soybean production down in 2022, USDA reports This will create a new However, if you only knew English and tried to read the recipe in Spanish or Japanese, your favorite treat might not turn out very well. United States Department of Agriculture. . Programmatic access refers to the processes of using computer code to select and download data. A&T State University. Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. In this example, the sum function is doing a task that you can easily code by using the + sign, but it might not always be easy for you to code up the calculations and analyses done by a function. downloading the data via an R You might need to do extra cleaning to remove these data before you can plot. This is less easy because you have to enter (or copy-paste) the key each time you begin an R session. Skip to 6. Winter Wheat Seedings up for 2023, 12/13/22 NASS to publish milk production data in updated data dissemination format, 11/28/22 USDA-NASS Crop Progress report delayed until Nov. 29, 10/28/22 NASS reinstates Cost of Pollination survey, 09/06/22 NASS to review acreage information, 09/01/22 USDA NASS reschedules 2021 Conservation Practice Adoption Motivations data highlights release, 05/06/22 Respond Now to the 2022 Census of Agriculture, 08/05/20 The NASS Mission: We do it for you, 04/11/19 2017 Census of Agriculture Highlight Series Farms and Land in Farms, 04/11/19 2017 Census of Agriculture Highlight Series Economics, 04/11/19 2017 Census of Agriculture Highlight Series Demographics, 02/08/23 Crop Production (February 2023), 01/31/23 Cattle & Sheep and Goats (January 2023), 12/23/22 Quarterly Hogs and Pigs (December 2022), 12/15/22 2021 Certified Organics (December 2022), Talking About NASS - A guide for partners and stakeholders, USDA and NASS Anti-Harassment Policy Statement, REE Reasonable Accommodations and Personal Assistance Services, Safeguarding America's Agricultural Statistics Report and Video, Agriculture Counts - The Founding and Evolution of the National Agricultural Statistics Service 1957-2007, Hours: 7:30 a.m. - 4:00 p.m. Eastern Time Monday - Friday, except federal holidays Toll-Free: (800) 727-9540, Hours: 9:00 a.m. - 5:30 p.m. Eastern Time Monday - Friday, except federal holidays Toll-Free: (833) One-USDA Create a worksheet that allows the user to select a commodity (corn, soybeans, selected) and view the number of acres planted or harvested from 1997 through 2021. Finally, format will be set to csv, which is a data file format type that works well in Tableau Public. U.S. National Agricultural Statistics Service (NASS) Summary "The USDA's National Agricultural Statistics Service (NASS) conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. The sample Tableau dashboard is called U.S. Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. To submit, please register and login first. The following is equivalent, A growing list of convenience functions makes querying simpler. Tip: Click on the images to view full-sized and readable versions. the project, but you have to repeat this process for every new project, Harvesting its rich datasets presents opportunities for understanding and growth.