rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. 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. While it does not access all the data available through Quick Stats, you may find it easier to use. Census of Agriculture (CoA). You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. query. Provide statistical data related to US agricultural production through either user-customized or pre-defined queries. Also, be aware that some commodity descriptions may include & in their names. 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. Washington and Oregon, you can write state_alpha = c('WA', = 2012, but you may also want to query ranges of values. 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. and predecessor agencies, U.S. Department of Agriculture (USDA). The sample Tableau dashboard is called U.S. You can check the full Quick Stats Glossary. manually click through the QuickStats tool for each data NASS - Quick Stats | Ag Data Commons - USDA 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 . ~ 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 In this case, the NC sweetpotato data will be saved to a file called nc_sweetpotato_data_query_on_20201001.csv on your desktop. 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. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. is needed if subsetting by geography. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. Corn stocks down, soybean stocks down from year earlier 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. Why am I getting National Agricultural Statistics Service (NASS - USDA 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. Instructions for how to use Tableau Public are beyond the scope of this tutorial. Many coders who use R also download and install RStudio along with it. # filter out census data, to keep survey data only The census takes place once every five years, with the next one to be completed in 2022. The .gov means its official. In this case, youre wondering about the states with data, so set param = state_alpha. The census collects data on all commodities produced on U.S. farms and ranches, as . Summary rnassqs Quick Stats Agricultural Database - Catalog to quickly and easily download new data. following: Subsetting by geography works similarly, looping over the geography USDA National Agricultural Statistics Service. For most Column or Header Name values, the first value, in lowercase, is the API parameter name, like those shown above. provide an api key. NASS Regional Field Offices maintain a list of all known operations and use known sources of operations to update their lists. 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. Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. It allows you to customize your query by commodity, location, or time period. It allows you to customize your query by commodity, location, or time period. In both cases iterating over Generally the best way to deal with large queries is to make multiple By setting statisticcat_desc = "AREA HARVESTED", you will get results for harvest acreage rather than planted acreage. 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. Dont repeat yourself. The QuickStats API offers a bewildering array of fields on which to 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.. The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. The site is secure. # filter out Sampson county data and rnassqs will detect this when querying data. To use a baking analogy, you can think of the script as a recipe for your favorite dessert. USDA NASS Quick Stats API | ProgrammableWeb USDA - National Agricultural Statistics Service - Publications - Report Create a worksheet that shows the number of acres harvested for top commodities from 1997 through 2021. One way it collects data is through the Census of Agriculture, which surveys all agricultural operations with $1,000 or more of products raised or sold during the census year. Access Data from the NASS Quick Stats API rnassqs - rOpenSci I built the queries simply by selecting one or more items from each of a series of dynamic dropdown menus. Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . use nassqs_record_count(). To submit, please register and login first. You can get an API Key here. Building a query often involves some trial and error. 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. Next, you need to tell your computer what R packages (Section 6) you plan to use in your R coding session. than the API restriction of 50,000 records. PDF Released March 18, 2021, by the National Agricultural Statistics N.C. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . 2020. It allows you to customize your query by commodity, location, or time period. USDA National Agricultural Statistics Service Information. Then, when you click [Run], it will start running the program with this file first. rnassqs package and the QuickStats database, youll be able NC State University and NC Writer, photographer, cyclist, nature lover, data analyst, and software developer. The latest version of R is available on The Comprehensive R Archive Network website. nassqs_auth(key = NASS_API_KEY). 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. 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. PDF usdarnass: USDA NASS Quick Stats API Now that youve cleaned and plotted the data, you can save them for future use or to share with others. The last step in cleaning up the data involves the Value column. This number versus character representation is important because R cannot add, subtract, multiply, or divide characters. Before you make a specific API query, its best to see whether the data are even available for a particular combination of parameters. 'OR'). United States Dept. Downloading data via In the beginning it can be more confusing, and potentially take more ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name) The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. write_csv(data = nc_sweetpotato_data, path = "Users/your/Desktop/nc_sweetpotato_data_query_on_20201001.csv"). Potter N (2022). class(nc_sweetpotato_data_survey$Value) it. There are at least two good reasons to do this: Reproducibility. Home | NASS NASS Reports Crop Progress (National) Crop Progress & Condition (State) For example, you can write a script to access the NASS Quick Stats API and download data. Now you have a dataset that is easier to work with. Usage 1 2 3 4 5 6 7 8 Quick Stats System Updates provides notification of upcoming modifications. Some parameters, like key, are required if the function is to run properly without errors. Which Software Programs Can Be Used to Programmatically Access NASS Survey Data? An official website of the United States government. Feel free to download it and modify it in the Tableaue Public Desktop application to learn how to create and publish Tableau visualizations. Accessed online: 01 October 2020. PDF Texas Crop Progress and Condition nass_data: Get data from the Quick Stats query in usdarnass: USDA NASS Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Journal of the American Society of Farm Managers and Rural Appraisers, p156-166. 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. Then use the as.numeric( ) function to tell R each row is a number, not a character. You can check by using the nassqs_param_values( ) function. Do do so, you can It also makes it much easier for people seeking to This article will provide you with an overview of the data available on the NASS web pages. modify: In the above parameter list, year__GE is the NASS develops these estimates from data collected through: Dynamic drill-down filtered search by Commodity, Location, and Date range, (dataset) USDA National Agricultural Statistics Service (2017). The waitstaff and restaurant use that number to keep track of your order and bill (Figure 1). at least two good reasons to do this: Reproducibility. It allows you to customize your query by commodity, location, or time period. 4:84. NASS_API_KEY <- "ADD YOUR NASS API KEY HERE" rnassqs (R NASS Quick Stats) rnassqs allows users to access the USDA's National Agricultural Statistics Service (NASS) Quick Stats data through their API. This will create a new It allows you to customize your query by commodity, location, or time period. Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. Agricultural Census since 1997, which you can do with something like. The Comprehensive R Archive Network website, Working for Peanuts: Acquiring, Analyzing, and Visualizing Publicly Available Data. Call the function stats.get_data() with the parameters string and the name of the output file (without the extension). Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. function, which uses httr::GET to make an HTTP GET request token API key, default is to use the value stored in .Renviron . 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. N.C. national agricultural statistics service (NASS) at the USDA. (PDF) USDA-NASS Quick Stats (Crops) WHEAT - ResearchGate Figure 1. You can define this selected data as nc_sweetpotato_data_sel. nassqs_parse function that will process a request object For example, if someone asked you to add A and B, you would be confused. Otherwise the NASS Quick Stats API will not know what you are asking for. Combined with an assert from the 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 That is an average of nearly 450 acres per farm operation. Quick Stats Lite It is simple and easy to use, and provides some functions to help navigate the bewildering complexity of some Quick Stats data. 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)). Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. 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. Also, the parameter values be replaced with specific parameter-value pairs to search for the desired data. First, you will rename the column so it has more meaning to you. functions as follows: # returns a list of fields that you can query, #> [1] "agg_level_desc" "asd_code" "asd_desc", #> [4] "begin_code" "class_desc" "commodity_desc", #> [7] "congr_district_code" "country_code" "country_name", #> [10] "county_ansi" "county_code" "county_name", #> [13] "domaincat_desc" "domain_desc" "end_code", #> [16] "freq_desc" "group_desc" "load_time", #> [19] "location_desc" "prodn_practice_desc" "reference_period_desc", #> [22] "region_desc" "sector_desc" "short_desc", #> [25] "state_alpha" "state_ansi" "state_name", #> [28] "state_fips_code" "statisticcat_desc" "source_desc", #> [31] "unit_desc" "util_practice_desc" "watershed_code", #> [34] "watershed_desc" "week_ending" "year", #> [1] "agg_level_desc: Geographical level of data. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. It allows you to customize your query by commodity, location, or time period. The download data files contain planted and harvested area, yield per acre and production. A script is like a collection of sentences that defines each step of a task. It can return data for the 2012 and 2017 censuses at the national, state, and local level for 77 different tables. Moreover, some data is collected only at specific Next, you can use the select( ) function again to drop the old Value column. 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). 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. Chambers, J. M. 2020. NASS conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. Lock 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. 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. In this case, the task is to request NASS survey data. 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. subset of values for a given query. Retrieve the data from the Quick Stats server. 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. nassqs does handles The Comprehensive R Archive Network (CRAN), Weed Management in Nurseries, Landscapes & Christmas Trees, NC they became available in 2008, you can iterate by doing the object generated by the GET call, you can use nassqs_GET to 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. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. Peng, R. D. 2020. U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). Skip to 5. You can define the query output as nc_sweetpotato_data. # check the class of Value column Have a specific question for one of our subject experts? Next, you can use the filter( ) function to select data that only come from the NASS survey, as opposed to the census, and represents a single county. USDA - National Agricultural Statistics Service - Quick Stats Quickstats is the main public facing database to find the most relevant agriculture statistics. 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. value. "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. 2020. *In this Extension publication, we will only cover how to use the rnassqs R package. 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. We also recommend that you download RStudio from the RStudio website. R is also free to download and use. time, but as you become familiar with the variables and calls of the However, other parameters are optional. 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 USDA-NASS. Do this by right-clicking on the file name in Solution Explorer and then clicking [Set as Startup File] from the popup menu. the project, but you have to repeat this process for every new project, The USDA Economics, Statistics and Market Information System (ESMIS) contains over 2,100 publications from five agencies of the . Multiple values can be queried at once by including them in a simple To demonstrate the use of the agricultural data obtained with the Quick Stats API, I have created a simple dashboard in Tableau Public. file, and add NASSQS_TOKEN = to the Agricultural Commodity Production by Land Area. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. Once the Data request is limited to 50,000 records per the API. Citation Request - USDA - National Agricultural Statistics Service Homepage queries subset by year if possible, and by geography if not. 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\\. 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. Reference to products in this publication is not intended to be an endorsement to the exclusion of others which may have similar uses. rnassqs tries to help navigate query building with The primary benefit of rnassqs is that users need not download data through repeated . The following pseudocode describes how the program works: Note the use of the urllib.parse.quote() function in the creation of the parameters string in step 1. Data by subject gives you additional information for a particular subject area or commodity. Source: National Weather Service, www.nws.noaa.gov Drought Monitor, Valid February 21, 2023. 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. # select the columns of interest Quick Stats. lock ( R Programming for Data Science. The <- character combination means the same as the = (that is, equals) character, and R will recognize this. Its easiest if you separate this search into two steps. The rnassqs package also has a commitment to diversity. Accessed: 01 October 2020. Harvest and Analyze Agricultural Data with the USDA NASS API, Python 2017 Census of Agriculture - Census Data Query Tool, QuickStats Parameter Definitions and Operators, Agricultural Statistics Districts (ASD) zipped (.zip) ESRI shapefile format for download, https://data.nal.usda.gov/dataset/nass-quick-stats, National Agricultural Library Thesaurus Term, hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture, the Census of Agriculture conducted every five years providing state- and county-level aggregates. For more specific information please contact nass@usda.gov or call 1-800-727-9540. .gitignore if youre using github. Alternatively, you can query values nc_sweetpotato_data_raw <- nassqs(nc_sweetpotato_params). Depending on what agency your survey is from, you will need to contact that agency to update your record. 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. Contact a specialist. file. The next thing you might want to do is plot the results. For example, if youd like data from both 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. If you are interested in trying Visual Studio Community, you can install it here. 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. # drop old Value column Looking for U.S. government information and services? 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. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. 2020. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. install.packages("tidyverse") Before using the API, you will need to request a free API key that your program will include with every call using the API.