how to cite usda nass quick stats how to cite usda nass quick stats

The USDA Economics, Statistics and Market Information System (ESMIS) contains over 2,100 publications from five agencies of the . rnassqs: Access the NASS 'Quick Stats' API. 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. nc_sweetpotato_data_survey_mutate <- mutate(nc_sweetpotato_data_survey, harvested_sweetpotatoes_acres = as.numeric(str_replace_all(string = Value, pattern = ",", replacement = ""))) If you download NASS data without using computer code, you may find that it takes a long time to manually select each dataset you want from the Quick Stats website. list with c(). Peng, R. D. 2020. 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. 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). 1987. Provide statistical data related to US agricultural production through either user-customized or pre-defined queries. 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)). This is why functions are an important part of R packages; they make coding easier for you. For example, you will get an error if you write commodity_desc = SWEET POTATO (that is, dropping the ES) or write commodity_desc = sweetpotatoes (that is, with no space and all lowercase letters). use nassqs_record_count(). ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name) it. NASS collects and manages diverse types of agricultural data at the national, state, and county levels. Federal government websites often end in .gov or .mil. You can also write the two steps above as one step, which is shown below. 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. 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. Next, you can define parameters of interest. You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. The resulting plot is a bit busy because it shows you all 96 counties that have sweetpotato data. These collections of R scripts are known as R packages. 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". Email: askusda@usda.gov Data are currently available in the following areas: Pre-defined queries are provided for your convenience. The CoA is collected every five years and includes demographics data on farms and ranches (CoA, 2020). Then, it will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. Then, when you click [Run], it will start running the program with this file first. Indians. 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. Accessing data with computer code comes in handy when you want to view data from multiple states, years, crops, and other categories. It is simple and easy to use, and provides some functions to help navigate the bewildering complexity of some Quick Stats data. The Comprehensive R Archive Network (CRAN), Weed Management in Nurseries, Landscapes & Christmas Trees, NC A function is another important concept that is helpful to understand while using R and many other coding languages. NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. Once the token API key, default is to use the value stored in .Renviron . The name in parentheses is the name for the same value used in the Quick Stats query tool. Accessed online: 01 October 2020. In the beginning it can be more confusing, and potentially take more 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. You can use the select( ) function to keep the following columns: Value (acres of sweetpotatoes harvested), county_name (the name of the county), source_desc (whether data are coming from the NASS census or NASS survey), and year (the year of the data). nassqs does handles To cite rnassqs in publications, please use: Potter NA (2019). 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. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. A Medium publication sharing concepts, ideas and codes. I built the queries simply by selecting one or more items from each of a series of dynamic dropdown menus. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). Columns for this particular dataset would include the year harvested, county identification number, crop type, harvested amount, the units of the harvested amount, and other categories. The types of agricultural data stored in the FDA Quick Stats database. You can define the query output as nc_sweetpotato_data. Instead, you only have to remember that this information is stored inside the variable that you are calling NASS_API_KEY. How to Develop a Data Analytics Web App in 3 Steps Alan Jones in CodeFile Data Analysis with ChatGPT and Jupyter Notebooks Zach Quinn in Pipeline: A Data Engineering Resource Creating The Dashboard That Got Me A Data Analyst Job Offer Youssef Hosni in Level Up Coding 20 Pandas Functions for 80% of your Data Science Tasks Help Status Writers Blog 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. 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. There are for each field as above and iteratively build your query. Sys.setenv(NASSQS_TOKEN = . You can check by using the nassqs_param_values( ) function. 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. lock ( rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. We also recommend that you download RStudio from the RStudio website. 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. to quickly and easily download new data. For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. nc_sweetpotato_data_sel <- select(nc_sweetpotato_data_raw, county_name, year, source_desc, Value) About NASS. organization in the United States. Based on your experience in algebra class, you may remember that if you replace x with NASS_API_KEY and 1 with a string of letters and numbers that defines your unique NASS Quick Stats API key, this is another way to think about the first line of code. Source: National Drought Mitigation Center, 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. 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. This tool helps users obtain statistics on the database. Additionally, the CoA includes data on land use, land ownership, agricultural production practices, income, and expenses at the farm and ranch level. Griffin, T. W., and J. K. Ward. Receive Email Notifications for New Publications. secure websites. 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