Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. Once the Potter N (2022). Looking for U.S. government information and services? A&T State University, in all 100 counties and with the Eastern Band of Cherokee Skip to 3. Before you get started with the Quick Stats API, become familiar with its Terms of Service and Usage. 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 In this case, the NASS Quick Stats API works as the interface between the NASS data servers (that is, computers with the NASS survey data on them) and the software installed on your computer. An official website of the United States government. 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. The chef is in the kitchen window in the upper left, the waitstaff in the center with the order, and the customer places the order. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Secure .gov websites use HTTPSA It allows you to customize your query by commodity, location, or time period. lock ( Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. We also recommend that you download RStudio from the RStudio website. 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 . Source: National Drought Mitigation Center, The name in parentheses is the name for the same value used in the Quick Stats query tool. For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. organization in the United States. The latest version of R is available on The Comprehensive R Archive Network website. In file run_usda_quick_stats.py create the parameters variable that contains parameter and value pairs to select data from the Quick Stats database. Census of Agriculture Top The Census is conducted every 5 years. You might need to do extra cleaning to remove these data before you can plot. But you can change the export path to any other location on your computer that you prefer. S, R, and Data Science. Proceedings of the ACM on Programming Languages. Find more information at the following NC State Extension websites: Publication date: May 27, 2021 a list of parameters is helpful. nassqs_auth(key = NASS_API_KEY). Then we can make a query. The Comprehensive R Archive Network (CRAN), Weed Management in Nurseries, Landscapes & Christmas Trees, NC One of the main missions of organizations like the Comprehensive R Archive Network is to curate R packages and make sure their creators have met user-friendly documentation standards. While it does not access all the data available through Quick Stats, you may find it easier to use. There are times when your data look like a 1, but R is really seeing it as an A. request. Finally, you can define your last dataset as nc_sweetpotato_data. those queries, append one of the following to the field youd like to County level data are also available via Quick Stats. After running this line of code, R will output a result. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. capitalized. Now that youve cleaned the data, you can display them in a plot. Due to suppression of data, the United States Department of Agriculture. Accessed online: 01 October 2020. Its very easy to export data stored in nc_sweetpotato_data or sampson_sweetpotato_data as a comma-separated variable file (.CSV) in R. To do this, you can use the write_csv( ) function. NASS - Quick Stats Quick Stats database Back to dataset Quick Stats database Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Harvesting its rich datasets presents opportunities for understanding and growth. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. An official website of the United States government. 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. 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. 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. Retrieve the data from the Quick Stats server.
The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). Also, be aware that some commodity descriptions may include & in their names. Federal government websites often end in .gov or .mil. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. to automate running your script, since it will stop and ask you to All sampled operations are mailed a questionnaire and given adequate time to respond by NASS administers, manages, analyzes, and shares timely, accurate, and useful statistics in service to United States agriculture (NASS 2020). 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. session. The second line of code above uses the nassqs_auth( ) function (Section 4) and takes your NASS_API_KEY variable as the input for the parameter key. In this publication, the word parameter refers to a variable that is defined within a function. # select the columns of interest
Accessed: 01 October 2020. nc_sweetpotato_data_survey_mutate <- mutate(nc_sweetpotato_data_survey, harvested_sweetpotatoes_acres = as.numeric(str_replace_all(string = Value, pattern = ",", replacement = "")))
following: Subsetting by geography works similarly, looping over the geography Alternatively, you can query values 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. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. key, you can use it in any of the following ways: In your home directory create or edit the .Renviron Programmatic access refers to the processes of using computer code to select and download data. and you risk forgetting to add it to .gitignore. Here is the most recent United States Summary and State Data (PDF, 27.9 MB), a statistical summary of the Census of Agriculture.
USDA-NASS Quick Stats (Crops) WHEAT.pdf PDF 1.42 MB . Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. Once in the tool please make your selection based on the program, sector, group, and commodity. To demonstrate the use of the agricultural data obtained with the Quick Stats API, I have created a simple dashboard in Tableau Public. Finally, it will explain how to use Tableau Public to visualize the data.
The Python program that calls the NASS Quick Stats API to retrieve agricultural data includes these two code modules (files): Scroll down to see the code from the two modules. For docs and code examples, visit the package web page here . However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. An open-standard file format that uses human-readable text to transmit data objects consisting of attribute-value pairs and array data types. The Comprehensive R Archive Network (CRAN). The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. However, ERS has no copies of the original reports. 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. The USDA-NASS Quick Stats API has a graphic interface here: https://quickstats.nass.usda.gov. The API request is the customers (your) food order, which the waitstaff wrote down on the order notepad. AG-903. reference_period_desc "Period" - The specic time frame, within a freq_desc. 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). Reference to products in this publication is not intended to be an endorsement to the exclusion of others which may have similar uses. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . Once youve installed the R packages, you can load them. For this reason, it is important to pay attention to the coding language you are using. Corn stocks down, soybean stocks down from year earlier
However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. multiple variables, geographies, or time frames without having to Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . 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). 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
The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. Corn stocks down, soybean stocks down from year earlier
On the other hand, if that person asked you to add 1 and 2, you would know exactly what to do. any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the year.
To install packages, use the code below. A function in R will take an input (or many inputs) and give an output. Similar to above, at times it is helpful to make multiple queries and All of these reports were produced by Economic Research Service (ERS. Agricultural Commodity Production by Land Area. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). provide an api key. After you have completed the steps listed above, run the program. If you use this function on the Value column of nc_sweetpotato_data_survey, R will return character, but you want R to return numeric. R Programming for Data Science. In R, you would write x <- 1. If youre not sure what spelling and case the NASS Quick Stats API uses, you can always check by clicking through the NASS Quick Stats website. Second, you will use the specific information you defined in nc_sweetpotato_params to make the API query. do. Then you can plot this information by itself. The rnassqs R package provides a simple interface for accessing the United States Department of Agriculture National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. To improve data accessibility and sharing, the NASS developed a Quick Stats website where you can select and download data from two of the agencys surveys. In this publication we will focus on two large NASS surveys. These codes explain why data are missing. Share sensitive information only on official, The API will then check the NASS data servers for the data you requested and send your requested information back. The first line of the code above defines a variable called NASS_API_KEY and assigns it the string of letters and numbers that makes up the NASS Quick Stats API key you received from the NASS. https://data.nal.usda.gov/dataset/nass-quick-stats. The types of agricultural data stored in the FDA Quick Stats database. 2017 Ag Atlas Maps. The advantage of this # filter out census data, to keep survey data only
You can then define this filtered data as nc_sweetpotato_data_survey. NASS conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. Then you can use it coders would say run the script each time you want to download NASS survey data. Here is the format of the base URL that will be used in this articles example: http://quickstats.nass.usda.gov/api/api_GET/?key=api key&{parameter parameter}&format={json | csv | xml}. Access Quick Stats Lite . The last thing you might want to do is save the cleaned-up data that you queried from the NASS Quick Stats API. 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. For Before coding, you have to request an API access key from the NASS. Rstudio, you can also use usethis::edit_r_environ to open That file will then be imported into Tableau Public to display visualizations about the data. of Agr - Nat'l Ag. # filter out Sampson county data
In this case, the task is to request NASS survey data. You do this by using the str_replace_all( ) function. USDA-NASS. 4:84. There are at least two good reasons to do this: Reproducibility. 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.. Because R is accessible to so many people, there is a great deal of collaboration and sharing of R resources, scripts, and knowledge. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. Second, you will change entries in each row of the Value column so they are represented as a number, rather than a character. There are 2017 Census of Agriculture. 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. To cite rnassqs in publications, please use: Potter NA (2019). nassqs does handles Washington and Oregon, you can write state_alpha = c('WA', Do this by right-clicking on the file name in Solution Explorer and then clicking [Set as Startup File] from the popup menu. The site is secure. The rnassqs package also has a Click the arrow to access Quick Stats. Then, it will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. 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. 2020. Queries that would return more records return an error and will not continue.
Before you make a specific API query, its best to see whether the data are even available for a particular combination of parameters. Its recommended that you use the = character rather than the <- character combination when you are defining parameters (that is, variables inside functions). Data request is limited to 50,000 records per the API. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. Cooperative Extension is based at North Carolina's two land-grant institutions, An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. The API response is the food made by the kitchen based on the written order from the customer to the waitstaff. This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. Tableau Public is a free version of the commercial Tableau data visualization tool. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. nassqs_auth(key = "ADD YOUR NASS API KEY HERE"). Prior to using the Quick Stats API, you must agree to the NASS Terms of Service and obtain an API key. 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. 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. 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. After running these lines of code, you will get a raw data output that has over 1500 rows and close to 40 columns. You will need this to make an API request later. query. You can read more about tidy data and its benefits in the Tidy Data Illustrated Series. Have a specific question for one of our subject experts? at least two good reasons to do this: Reproducibility. A function is another important concept that is helpful to understand while using R and many other coding languages. The .gov means its official. In the beginning it can be more confusing, and potentially take more Healy. the .gov website. Do pay attention to the formatting of the path name. Do do so, you can Next, you need to tell your computer what R packages (Section 6) you plan to use in your R coding session. This will create a new As mentioned in Section 1, you can visit the NASS Quick Stats website, click through the options, and download the data. The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database.
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. Building a query often involves some trial and error. Also, the parameter values be replaced with specific parameter-value pairs to search for the desired data. or the like) in lapply. In the example program, the value for api key will be replaced with my API key. In this case, youre wondering about the states with data, so set param = state_alpha. queries subset by year if possible, and by geography if not. than the API restriction of 50,000 records. 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 database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. To use a restaurant analogy, you can think of the NASS Quick Stats API as the waitstaff at your favorite restaurant, the NASS data servers as the kitchen, the software on your computer as the waitstaffs order notepad, and the coder as the customer (you) as shown in Figure 1. Additionally, the CoA includes data on land use, land ownership, agricultural production practices, income, and expenses at the farm and ranch level. An official website of the General Services Administration. While I used the free Microsoft Visual Studio Community 2022 integrated development ide (IDE) to write and run the Python program for this tutorial, feel free to use your favorite code editor or IDE. "rnassqs: An 'R' package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API." The Journal of Open Source Software. Sign Up: https://rruntsch.medium.com/membership, install them through the IDEs menu by following these instructions from Microsoft, Year__GE = 1997 (all years greater than or equal to 1997). The NASS helps carry out numerous surveys of U.S. farmers and ranchers. If you think back to algebra class, you might remember writing x = 1. The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission "to provide timely, accurate and useful statistics in service to U.S. agriculture" (Johnson and Mueller, 2010, p. 1204). like: The ability of rnassqs to iterate over lists of Downloading data via Generally the best way to deal with large queries is to make multiple The core functionality allows the user to query agricultural data from 'Quick Stats' in a reproducible and automated way. class(nc_sweetpotato_data_survey$Value)
# look at the first few lines
Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. For example, if youd like data from both nassqs_params() provides the parameter names, want say all county cash rents on irrigated land for every year since This reply is called an API response. some functions that return parameter names and valid values for those Statistics by State Explore Statistics By Subject Citation Request Most of the information available from this site is within the public domain. Have a specific question for one of our subject experts? The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). 2020. nassqs is a wrapper around the nassqs_GET Its easiest if you separate this search into two steps. For example, in the list of API parameters shown above, the parameter source_desc equates to Program in the Quick Stats query tool. You can define the query output as nc_sweetpotato_data. subset of values for a given query. Before using the API, you will need to request a free API key that your program will include with every call using the API. Why Is it Beneficial to Access NASS Data Programmatically? Here, tidy has a specific meaning: all observations are represented as rows, and all the data categories associated with that observation are represented as columns. R sessions will have the variable set automatically, Either 'CENSUS' or 'SURVEY'", https://quickstats.nass.usda.gov/api#param_define. To submit, please register and login first. Lock It allows you to customize your query by commodity, location, or time period. Running the script is similar to your pulling out the recipe and working through the steps when you want to make this dessert. You dont need all of these columns, and some of the rows need to be cleaned up a little bit. To put its scale into perspective, in 2021, more than 2 million farms operated on more than 900 million acres (364 million hectares). This number versus character representation is important because R cannot add, subtract, multiply, or divide characters. 2019. The sample Tableau dashboard is called U.S. (R coders say you need to load your R packages.) You can do that by running the code below (Section 7.2). Potter, (2019). By setting prodn_practice_desc = "ALL PRODUCTION PRACTICES", you will get results for all production practices rather than those that specifically use irrigation, for example. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. Ward, J. K., T. W. Griffin, D. L. Jordan, and G. T. Roberson. Often 'county', 'state', or 'national', but can include other levels as well", #> [2] "source_desc: Data source. First, obtain an API key from the Quick Stats service: https://quickstats.nass.usda.gov/api. 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
# plot Sampson county data
You can also make small changes to the script to download new types of data. rnassqs is a package to access the QuickStats API from 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. write_csv(data = nc_sweetpotato_data, path = "Users/your/Desktop/nc_sweetpotato_data_query_on_20201001.csv"). More specifically, the list defines whether NASS data are aggregated at the national, state, or county scale. it. With the Quick Stats application programming interface (API), you can use a programming language, such as Python, to retrieve data from the Quick Stats database. However, beware that this will be a development version: # install.packages ("devtools") devtools :: install_github ("rdinter . Contact a specialist. year field with the __GE modifier attached to Corn stocks down, soybean stocks down from year earlier
Combined with an assert from the How to write a Python program to query the Quick Stats database through the Quick Stats API. To browse or use data from this site, no account is necessary. Peng, R. D. 2020. NC State University and NC The .gov means its official. The USDAs National Agricultural Statistics Service (NASS) makes the departments farm agricultural data available to the public on its website through reports, maps, search tools, and its NASS Quick Stats API. For example, say you want to know which states have sweetpotato data available at the county level. secure websites. Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. 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. U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). Providing Central Access to USDAs Open Research Data, MULTIPOLYGON (((-155.54211 19.08348, -155.68817 18.91619, -155.93665 19.05939, -155.90806 19.33888, -156.07347 19.70294, -156.02368 19.81422, -155.85008 19.97729, -155.91907 20.17395, -155.86108 20.26721, -155.78505 20.2487, -155.40214 20.07975, -155.22452 19.99302, -155.06226 19.8591, -154.80741 19.50871, -154.83147 19.45328, -155.22217 19.23972, -155.54211 19.08348)), ((-156.07926 20.64397, -156.41445 20.57241, -156.58673 20.783, -156.70167 20.8643, -156.71055 20.92676, -156.61258 21.01249, -156.25711 20.91745, -155.99566 20.76404, -156.07926 20.64397)), ((-156.75824 21.17684, -156.78933 21.06873, -157.32521 21.09777, -157.25027 21.21958, -156.75824 21.17684)), ((-157.65283 21.32217, -157.70703 21.26442, -157.7786 21.27729, -158.12667 21.31244, -158.2538 21.53919, -158.29265 21.57912, -158.0252 21.71696, -157.94161 21.65272, -157.65283 21.32217)), ((-159.34512 21.982, -159.46372 21.88299, -159.80051 22.06533, -159.74877 22.1382, -159.5962 22.23618, -159.36569 22.21494, -159.34512 21.982)), ((-94.81758 49.38905, -94.64 48.84, -94.32914 48.67074, -93.63087 48.60926, -92.61 48.45, -91.64 48.14, -90.83 48.27, -89.6 48.01, -89.272917 48.019808, -88.378114 48.302918, -87.439793 47.94, -86.461991 47.553338, -85.652363 47.220219, -84.87608 46.900083, -84.779238 46.637102, -84.543749 46.538684, -84.6049 46.4396, -84.3367 46.40877, -84.14212 46.512226, -84.091851 46.275419, -83.890765 46.116927, -83.616131 46.116927, -83.469551 45.994686, -83.592851 45.816894, -82.550925 45.347517, -82.337763 44.44, -82.137642 43.571088, -82.43 42.98, -82.9 42.43, -83.12 42.08, -83.142 41.975681, -83.02981 41.832796, -82.690089 41.675105, -82.439278 41.675105, -81.277747 42.209026, -80.247448 42.3662, -78.939362 42.863611, -78.92 42.965, -79.01 43.27, -79.171674 43.466339, -78.72028 43.625089, -77.737885 43.629056, -76.820034 43.628784, -76.5 44.018459, -76.375 44.09631, -75.31821 44.81645, -74.867 45.00048, -73.34783 45.00738, 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