To make this query, you will use the nassqs( ) function with the parameters as an input. nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES") Building a query often involves some trial and error. An official website of the General Services Administration. than the API restriction of 50,000 records. Scripts allow coders to easily repeat tasks on their computers. Corn stocks down, soybean stocks down from year earlier Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. 2017 Ag Atlas Maps. DRY. A locked padlock There are thousands of R packages available online (CRAN 2020). NASS administers, manages, analyzes, and shares timely, accurate, and useful statistics in service to United States agriculture (NASS 2020). The Comprehensive R Archive Network (CRAN). lock ( You know you want commodity_desc = SWEET POTATOES, agg_level_desc = COUNTY, unit_desc = ACRES, domain_desc = TOTAL, statisticcat_desc = "AREA HARVESTED", and prodn_practice_desc = "ALL PRODUCTION PRACTICES". A&T State University, in all 100 counties and with the Eastern Band of Cherokee Tip: Click on the images to view full-sized and readable versions. Peng, R. D. 2020. You can register for a NASS Quick Stats API key at the Quick Stats API website (click on Request API Key). You can check the full Quick Stats Glossary. it. You can use many software programs to programmatically access the NASS survey data. You dont need all of these columns, and some of the rows need to be cleaned up a little bit. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. This is less easy because you have to enter (or copy-paste) the key each For 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. # filter out census data, to keep survey data only The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . This reply is called an API response. Once youve installed the R packages, you can load them. This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. For example, in the list of API parameters shown above, the parameter source_desc equates to Program in the Quick Stats query tool. All sampled operations are mailed a questionnaire and given adequate time to respond by 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. NASS Reports Crop Progress (National) Crop Progress & Condition (State) like: The ability of rnassqs to iterate over lists of Census of Agriculture (CoA). 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). 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. Its main limitations are 1) it can save visualization projects only to the Tableau Public Server, 2) all visualization projects are visible to anyone in the world, and 3) it can handle only a small number of input data types. The API only returns queries that return 50,000 or less records, so Web Page Resources You can read more about the available NASS Quick Stats API parameters and their definitions by checking out the help page on this topic. request. While the Quick Stats database contains more than 52 million records, any call using GET /api/api_GET query is limited to a 50,000-record result set. This work is supported by grant no. The next thing you might want to do is plot the results. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. Some parameters, like key, are required if the function is to run properly without errors. Instructions for how to use Tableau Public are beyond the scope of this tutorial. 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. However, there are three main reasons that its helpful to use a software program like R to download these data: Currently, there are four R packages available to help access the NASS Quick Stats API (see Section 4). 4:84. Also, the parameter values be replaced with specific parameter-value pairs to search for the desired data. Create a worksheet that shows the number of acres harvested for top commodities from 1997 through 2021. 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. Quick Stats Lite query. The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). See the Quick Stats API Usage page for this URL and two others. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. There are at least two good reasons to do this: Reproducibility. Also note that I wrote this program on a Windows PC, which uses back slashes (\) in file names and folder names. which at the time of this writing are. In this example shown below, I used Quick Stats to build a query to retrieve the number of acres of corn harvested in the US from 2000 through 2021. Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Moreover, some data is collected only at specific After you have completed the steps listed above, run the program. Otherwise the NASS Quick Stats API will not know what you are asking for. It allows you to customize your query by commodity, location, or time period. For example, if you wanted to calculate the sum of 2 and 10, you could use code 2 + 10 or you could use the sum( ) function (that is sum(2, 10)). NASS Regional Field Offices maintain a list of all known operations and use known sources of operations to update their lists. Then you can plot this information by itself. It is best to start by iterating over years, so that if you Multiple values can be queried at once by including them in a simple Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. Griffin, T. W., and J. K. Ward. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. 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. In this case, the NC sweetpotato data will be saved to a file called nc_sweetpotato_data_query_on_20201001.csv on your desktop. Healy. nc_sweetpotato_data_raw <- nassqs(nc_sweetpotato_params). Including parameter names in nassqs_params will return a You can view the timing of these NASS surveys on the calendar and in a summary of these reports. Providing Central Access to USDAs Open Research Data. nassqs does handles Share sensitive information only on official, NASS collects and manages diverse types of agricultural data at the national, state, and county levels. 2019. For this reason, it is important to pay attention to the coding language you are using. S, R, and Data Science. Proceedings of the ACM on Programming Languages. However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. or the like) in lapply. rnassqs is a package to access the QuickStats API from NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. To use a baking analogy, you can think of the script as a recipe for your favorite dessert. Federal government websites often end in .gov or .mil. 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. Do pay attention to the formatting of the path name. Secure .gov websites use HTTPSA Usage 1 2 3 4 5 6 7 8 # plot Sampson county data An official website of the United States government. file, and add NASSQS_TOKEN = to the Parameters need not be specified in a list and need not be 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. As an example, you cannot run a non-R script using the R software program. reference_period_desc "Period" - The specic time frame, within a freq_desc. This tool helps users obtain statistics on the database. Corn production data goes back to 1866, just one year after the end of the American Civil War. into a data.frame, list, or raw text. # filter out Sampson county data Before you get started with the Quick Stats API, become familiar with its Terms of Service and Usage. When you are coding, its helpful to add comments so you will remember or so someone you share your script with knows what you were trying to do and why. Each table includes diverse types of data. Then, it will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. 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. 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. Journal of Open Source Software , 4(43 . Say you want to plot the acres of sweetpotatoes harvested by year for each county in North Carolina. method is that you dont have to think about the API key for the rest of Any person using products listed in . 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 . To install packages, use the code below. 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. Code is similar to the characters of the natural language, which can be combined to make a sentence. Your home for data science. rnassqs tries to help navigate query building with Winter Wheat Seedings up for 2023, NASS to publish milk production data in updated data dissemination format, USDA-NASS Crop Progress report delayed until Nov. 29, NASS reinstates Cost of Pollination survey, USDA NASS reschedules 2021 Conservation Practice Adoption Motivations data highlights release, Respond Now to the 2022 Census of Agriculture, 2017 Census of Agriculture Highlight Series Farms and Land in Farms, 2017 Census of Agriculture Highlight Series Economics, 2017 Census of Agriculture Highlight Series Demographics, NASS Climate Adaptation and Resilience Plan, Statement of Commitment to Scientific Integrity, USDA and NASS Civil Rights Policy Statement, Civil Rights Accountability Policy and Procedures, Contact information for NASS Civil Rights Office, International Conference on Agricultural Statistics, Agricultural Statistics: A Historical Timeline, As We Recall: The Growth of Agricultural Estimates, 1933-1961, Safeguarding America's Agricultural Statistics Report, Application Programming Interfaces (APIs), Economics, Statistics and Market Information System (ESMIS). To put its scale into perspective, in 2021, more than 2 million farms operated on more than 900 million acres (364 million hectares). install.packages("tidyverse") You do this by using the str_replace_all( ) function. You can think of a coding language as a natural language like English, Spanish, or Japanese. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. The core functionality allows the user to query agricultural data from 'Quick Stats' in a reproducible and automated way. system environmental variable when you start a new R 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. You can add a file to your project directory and ignore it via Writer, photographer, cyclist, nature lover, data analyst, and software developer. organization in the United States. nassqs_parse function that will process a request object This publication printed on: March 04, 2023, Getting Data from the National Agricultural Statistics Service (NASS) Using R. Skip to 1. If you need to access the underlying request In the example program, the value for api key will be replaced with my API key. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. # look at the first few lines 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. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. Many coders who use R also download and install RStudio along with it. to quickly and easily download new data. Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. developing the query is to use the QuickStats web interface. 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. An official website of the United States government. As an analogy, you can think of R as a plain text editor (such as Notepad), while RStudio is more like Microsoft Word with additional tools and options. to the Quick Stats API. The <- character combination means the same as the = (that is, equals) character, and R will recognize this. 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. Skip to 5. 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. Where available, links to the electronic reports is provided. In some cases you may wish to collect for each field as above and iteratively build your query. The QuickStats API offers a bewildering array of fields on which to For example, say you want to know which states have sweetpotato data available at the county level. Because R is accessible to so many people, there is a great deal of collaboration and sharing of R resources, scripts, and knowledge. It allows you to customize your query by commodity, location, or time period. It also makes it much easier for people seeking to Reference to products in this publication is not intended to be an endorsement to the exclusion of others which may have similar uses. 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. they became available in 2008, you can iterate by doing the downloading the data via an R national agricultural statistics service (NASS) at the USDA. Source: National Weather Service, www.nws.noaa.gov Drought Monitor, Valid February 21, 2023. install.packages("rnassqs"). These collections of R scripts are known as R packages. County level data are also available via Quick Stats. 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. This number versus character representation is important because R cannot add, subtract, multiply, or divide characters. Create an instance called stats of the c_usda_quick_stats class. 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). https://data.nal.usda.gov/dataset/nass-quick-stats. at least two good reasons to do this: Reproducibility. bind the data into a single data.frame. 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. The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database. write_csv(data = nc_sweetpotato_data, path = "Users/your/Desktop/nc_sweetpotato_data_query_on_20201001.csv"). It accepts a combination of what, where, and when parameters to search for and retrieve the data of interest. API makes it easier to download new data as it is released, and to fetch A list of the valid values for a given field is available via You can change the value of the path name as you would like as well. Harvesting its rich datasets presents opportunities for understanding and growth. Lets say you are going to use the rnassqs package, as mentioned in Section 6. Finally, format will be set to csv, which is a data file format type that works well in Tableau Public. However, here are the basic steps to install Tableau Public and build and publish the dashboard: After completing this tutorial, you should have a general understanding of: I can imagine many use cases for projects that would use data from the Quick Stats database. 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. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. your .Renviron file and add the key. head(nc_sweetpotato_data, n = 3). "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. Please note that you will need to fill in your NASS Quick Stats API key surrounded by quotation marks. Special Tabulations and Restricted Microdata, 02/15/23 Still time to respond to the 2022 Census of Agriculture, USDA to follow up with producers who have not yet responded, 02/15/23 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), 01/31/23 United States cattle inventory down 3%, 01/30/23 2022 Census of Agriculture due next week Feb. 6, 01/12/23 Corn and soybean production down in 2022, USDA reports ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name) 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.. is needed if subsetting by geography. to automate running your script, since it will stop and ask you to What R Tools Are Available for Getting NASS Data? The advantage of this Second, you will change entries in each row of the Value column so they are represented as a number, rather than a character. To submit, please register and login first. It allows you to customize your query by commodity, location, or time period. function, which uses httr::GET to make an HTTP GET request example, you can retrieve yields and acres with. Email: askusda@usda.gov Contact a specialist. Finally, it will explain how to use Tableau Public to visualize the data. http://quickstats.nass.usda.gov/api/api_GET/?key=PASTE_YOUR_API_KEY_HERE&source_desc=SURVEY§or_desc%3DFARMS%20%26%20LANDS%20%26%20ASSETS&commodity_desc%3DFARM%20OPERATIONS&statisticcat_desc%3DAREA%20OPERATED&unit_desc=ACRES&freq_desc=ANNUAL&reference_period_desc=YEAR&year__GE=1997&agg_level_desc=NATIONAL&state_name%3DUS%20TOTAL&format=CSV. Visit the NASS website for a full library of past and current reports . To browse or use data from this site, no account is necessary! 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). example. 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. multiple variables, geographies, or time frames without having to Official websites use .govA If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. On the site you have the ability to filter based on numerous commodity types. Contact a specialist. All of these reports were produced by Economic Research Service (ERS. The United States is blessed with fertile soil and a huge agricultural industry. Other References Alig, R.J., and R.G. The report shows that, for the 2017 census, Minnesota had 68,822 farm operations covering 25,516,982 acres. both together, but you can replicate that functionality with low-level While it does not access all the data available through Quick Stats, you may find it easier to use. Call the function stats.get_data() with the parameters string and the name of the output file (without the extension). 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. To cite rnassqs in publications, please use: Potter NA (2019). Many people around the world use R for data analysis, data visualization, and much more. Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. Its recommended that you use the = character rather than the <- character combination when you are defining parameters (that is, variables inside functions). You can define this selected data as nc_sweetpotato_data_sel. You can then define this filtered data as nc_sweetpotato_data_survey. Quick Stats Lite provides a more structured approach to get commonly requested statistics from . Second, you will use the specific information you defined in nc_sweetpotato_params to make the API query. The .gov means its official. nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value) R Programming for Data Science. Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. 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. 2020. You can first use the function mutate( ) to rename the column to harvested_sweetpotatoes_acres. The example Python program shown in the next section will call the Quick Stats with a series of parameters. 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. Accessed online: 01 October 2020. U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). 2020. These codes explain why data are missing. The census takes place once every five years, with the next one to be completed in 2022. The data found via the CDQT may also be accessed in the NASS Quick Stats database. 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. The name in parentheses is the name for the same value used in the Quick Stats query tool. and predecessor agencies, U.S. Department of Agriculture (USDA). However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. On the other hand, if that person asked you to add 1 and 2, you would know exactly what to do. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. In the get_data() function of c_usd_quick_stats, create the full URL. There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. 2020. As mentioned in Section 1, you can visit the NASS Quick Stats website, click through the options, and download the data. As mentioned in Section 4, RStudio provides a user-friendly way to interact with R. If this is your first time using a particular R package or if you have forgotten whether you installed an R package, you first need to install it on your computer by downloading it from the Comprehensive R Archive Network (Section 4). # check the class of new value column USDA National Agricultural Statistics Service. Receive Email Notifications for New Publications. You can also write the two steps above as one step, which is shown below. Production and supplies of food and fiber, prices paid and received by farmers, farm labor and wages, farm finances, chemical use, and changes in the demographics of U.S. producers are only a few examples. How to write a Python program to query the Quick Stats database through the Quick Stats API. Access Quick Stats (searchable database) The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. 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. The USDA Economics, Statistics and Market Information System (ESMIS) contains over 2,100 publications from five agencies of the . In the example below, we describe how you can use the software program R to write and run a script that will download NASS survey data. There are The API will then check the NASS data servers for the data you requested and send your requested information back. Texas Crop Progress and Condition (February 2023) USDA, National Agricultural Statistics Service, Southern Plains Regional Field Office Seven Day Observed Regional Precipitation, February 26, 2023. In registering for the key, for which you must provide a valid email address. But you can change the export path to any other location on your computer that you prefer. What Is the National Agricultural Statistics Service? 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. rnassqs (R NASS Quick Stats) rnassqs allows users to access the USDA's National Agricultural Statistics Service (NASS) Quick Stats data through their API. year field with the __GE modifier attached to Didn't find what you're looking for? Read our The last step in cleaning up the data involves the Value column. A function in R will take an input (or many inputs) and give an output. This image shows how working with the NASS Quick Stats API is analogous to ordering food at a restaurant. If you use it, be sure to install its Python Application support. 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. N.C. parameters. secure websites. An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. You can verify your report was received by checking the Submitted date under the Status column of the My Surveys tab. 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. modify: In the above parameter list, year__GE is the .gitignore if youre using github. 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. For session. Agricultural Resource Management Survey (ARMS). More specifically, the list defines whether NASS data are aggregated at the national, state, or county scale. If the survey is from USDA National Agricultural Statistics Service (NASS), y ou can make a note on the front page and explain that you no longer farm, no longer own the property, or if the property is farmed by someone else.