The API Usage page provides instructions for its use. If you are using Visual Studio, then set the Startup File to the file run_usda_quick_stats.py. the project, but you have to repeat this process for every new project, Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. NASS collects and manages diverse types of agricultural data at the national, state, and county levels. 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. You can first use the function mutate( ) to rename the column to harvested_sweetpotatoes_acres. parameter. Statistics by State Explore Statistics By Subject Citation Request Most of the information available from this site is within the public domain. Data are currently available in the following areas: Pre-defined queries are provided for your convenience. Before using the API, you will need to request a free API key that your program will include with every call using the API. # filter out Sampson county data
Also note that I wrote this program on a Windows PC, which uses back slashes (\) in file names and folder names. Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. Quick Stats Lite provides a more structured approach to get commonly requested statistics from . 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. NASS Regional Field Offices maintain a list of all known operations and use known sources of operations to update their lists. You can check the full Quick Stats Glossary. Say you want to plot the acres of sweetpotatoes harvested by year for each county in North Carolina. .gitignore if youre using github. Also, before running the program, create the folder specified in the self.output_file_path variable in the __init__() function of the c_usda_quick_stats class. . nassqs_auth(key = NASS_API_KEY). How to write a Python program to query the Quick Stats database through the Quick Stats API. This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. 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. The census collects data on all commodities produced on U.S. farms and ranches, as . object generated by the GET call, you can use nassqs_GET to Now that youve cleaned and plotted the data, you can save them for future use or to share with others. 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)). following: Subsetting by geography works similarly, looping over the geography You can then define this filtered data as nc_sweetpotato_data_survey. To browse or use data from this site, no account is necessary! Remember to request your personal Quick Stats API key and paste it into the value for self.api_key in the __init__() function in the c_usda_quick_stats class. 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. 2017 Census of Agriculture. 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. script creates a trail that you can revisit later to see exactly what An official website of the United States government. The QuickStats API offers a bewildering array of fields on which to Feel free to download it and modify it in the Tableaue Public Desktop application to learn how to create and publish Tableau visualizations. Tableau Public is a free version of the commercial Tableau data visualization tool. The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database. Before coding, you have to request an API access key from the NASS. Other References Alig, R.J., and R.G. or the like) in lapply. In this case, the NC sweetpotato data will be saved to a file called nc_sweetpotato_data_query_on_20201001.csv on your desktop. request. This reply is called an API response. Potter N (2022). The example Python program shown in the next section will call the Quick Stats with a series of parameters. Retrieve the data from the Quick Stats server. The API only returns queries that return 50,000 or less records, so any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the year. Data request is limited to 50,000 records per the API. 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. The primary benefit of rnassqs is that users need not download data through repeated . 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. 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). For N.C. Queries that would return more records return an error and will not continue. Corn stocks down, soybean stocks down from year earlier
Providing Central Access to USDAs Open Research Data. The name in parentheses is the name for the same value used in the Quick Stats query tool. Accessed 2023-03-04. to quickly and easily download new data. In this publication we will focus on two large NASS surveys.
More specifically, the list defines whether NASS data are aggregated at the national, state, or county scale. Next, you can define parameters of interest. Now that you have a basic understanding of the data available in the NASS database, you can learn how to reap its benefits in your projects with the NASS Quick Stats API. 'OR'). many different sets of data, and in others your queries may be larger You will need this to make an API request later. Source: National Weather Service, www.nws.noaa.gov Drought Monitor, Valid February 21, 2023. To cite rnassqs in publications, please use: Potter NA (2019). The resulting plot is a bit busy because it shows you all 96 counties that have sweetpotato data. 2019. use nassqs_record_count(). The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. Taken together, R reads this statement as: filter out all rows in the dataset where the source description column is exactly equal to SURVEY and the county name is not equal to OTHER (COMBINED) COUNTIES. What Is the National Agricultural Statistics Service? In registering for the key, for which you must provide a valid email address.
DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Accessed: 01 October 2020. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. Lock rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. You can use the ggplot( ) function along with your nc_sweetpotato_data variable to do this. DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. replicate your results to ensure they have the same data that you 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. These codes explain why data are missing. 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. Contact a specialist. You can also set the environmental variable directly with In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. If you need to access the underlying request If all works well, then it should be completed within a few seconds and it will write the specified CSV file to the output folder. An open-standard file format that uses human-readable text to transmit data objects consisting of attribute-value pairs and array data types. The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). For In this case, the task is to request NASS survey data. In this case, youre wondering about the states with data, so set param = state_alpha. To submit, please register and login first. The last step in cleaning up the data involves the Value column. 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. Once the Secure .gov websites use HTTPSA 2020. Often 'county', 'state', or 'national', but can include other levels as well", #> [2] "source_desc: Data source. Access Quick Stats (searchable database) The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. sampson_sweetpotato_data <- filter(nc_sweetpotato_data, county_name == "SAMPSON")
Writer, photographer, cyclist, nature lover, data analyst, and software developer. The sample Tableau dashboard is called U.S. 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. ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name)
Note: In some cases, the Value column will have letter codes instead of numbers. 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. Journal of Open Source Software , 4(43 . After it receives the data from the server in CSV format, it will write the data to a file with one record per line. 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. organization in the United States. # select the columns of interest
Some parameters, like key, are required if the function is to run properly without errors. The <- character combination means the same as the = (that is, equals) character, and R will recognize this. This example in Section 7.8 represents a path name for a Mac computer, but a PC path to the desktop might look more like C:\Users\your\Desktop\nc_sweetpotato_data_query_on_20201001.csv. 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 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. NASS - Quick Stats. 2020. 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. 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. NASS has also developed Quick Stats Lite search tool to search commodities in its database. Information on the query parameters is found at https://quickstats.nass.usda.gov/api#param_define. I built the queries simply by selecting one or more items from each of a series of dynamic dropdown menus. nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value)
capitalized. County level data are also available via Quick Stats. Any person using products listed in . Each table includes diverse types of data. It allows you to customize your query by commodity, location, or time period. In the beginning it can be more confusing, and potentially take more bind the data into a single data.frame. 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. nc_sweetpotato_data_survey <- filter(nc_sweetpotato_data_sel, source_desc == "SURVEY" & county_name != "OTHER (COMBINED) COUNTIES")
All of these reports were produced by Economic Research Service (ERS. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. 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 . Prior to using the Quick Stats API, you must agree to the NASS Terms of Service and obtain an API key. manually click through the QuickStats tool for each data Skip to 5. Then use the as.numeric( ) function to tell R each row is a number, not a character. Use nass_count to determine number of records in query. some functions that return parameter names and valid values for those You dont need all of these columns, and some of the rows need to be cleaned up a little bit. equal to 2012. This function replaces spaces and special characters in text with escape codes that can be passed, as part of the full URL, to the Quick Stats web server. install.packages("tidyverse")
# check the class of Value column
want say all county cash rents on irrigated land for every year since Next, you can use the select( ) function again to drop the old Value column. 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. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . To submit, please register and login first. However, the NASS also allows programmatic access to these data via an application program interface as described in Section 2. nassqs is a wrapper around the nassqs_GET 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 article will provide you with an overview of the data available on the NASS web pages. Web Page Resources Otherwise the NASS Quick Stats API will not know what you are asking for. 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. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. 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). 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. Sys.setenv(NASSQS_TOKEN =
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