An alternative for downloading various USDA data from https://quickstats.nass.usda.gov/ through R. You must sign up for an API token from the mentioned website in order for this package to work.

Install

Development version (needs devtools installed):

devtools::install_github("rdinter/nassR", upgrade = "never")

Basic Usage

If a query works on the https://quickstats.nass.usda.gov/ interface, then it will work with the nassR package.

There are three basic functions for this package with the first as the workhorse:

1. nass_data this will return a data.frame to the specifications of the query from all of the arguments set in the function call. This mimics the simple "GET DATA" command off of https://quickstats.nass.usda.gov/ and requires an API token. There is a 50,000 limit for each call.
2. nass_param returns all of the possible values for a parameter in a query. Helpful to understand how to subset a query if it runs into the 50,000 limit.
3. nass_count returns the number of records for a query. Very useful in conjunction with nass_param to determine what queries can return data with a nass_data call.

Key Install

library(nassR)
set_nass_key("YOUR_KEY_IN_QUOTATIONS")
# First time, reload your enviornment so you can use the key without restarting R.
readRenviron("~/.Renviron")
# You can check it with:
Sys.getenv("NASS_KEY")

The above script will add a line to your .Renviron file to be re-used whenever you are using the package. If you are not comfortable with that, you can add the following line to your .Renviron file manually to produce the same result.

NASS_KEY='YOUR_KEY_IN_SINGLE_QUOTES'

Sample Code

Example 1

Find the number of observations for county level variables in Wake County North Carolina.

nass_count(state_name = "NORTH CAROLINA", county_name = "WAKE")

## [1] 7012

[1] 6997

Example 2

View all of the parameter values for the group_desc category when the sector is CROPS.

nass_param("group_desc", sector_desc = "CROPS")

## [1] "CROP TOTALS"       "FIELD CROPS"       "FRUIT & TREE NUTS"
## [4] "HORTICULTURE"      "VEGETABLES"

[1] "CROP TOTALS"       "FIELD CROPS"       "FRUIT & TREE NUTS" "HORTICULTURE"      "VEGETABLES"       

Example 3

Download the data pertaining to Wake County in 2012 for its value of agricultural land. Then look at the structure of the object returned by the function call.

j5 <- nass_data(year = 2012, agg_level_desc = "COUNTY", short_desc = "AG LAND, INCL BUILDINGS - ASSET VALUE, MEASURED IN $", county_name = "WAKE")
str(j5)

## 'data.frame':    1 obs. of  39 variables:
##  $ week_ending          : chr ""
##  $ state_name           : chr "NORTH CAROLINA"
##  $ country_code         : chr "9000"
##  $ location_desc        : chr "NORTH CAROLINA, CENTRAL PIEDMONT, WAKE"
##  $ begin_code           : chr "12"
##  $ zip_5                : chr ""
##  $ county_ansi          : chr "183"
##  $ state_alpha          : chr "NC"
##  $ util_practice_desc   : chr "ALL UTILIZATION PRACTICES"
##  $ domain_desc          : chr "TOTAL"
##  $ asd_desc             : chr "CENTRAL PIEDMONT"
##  $ freq_desc            : chr "POINT IN TIME"
##  $ prodn_practice_desc  : chr "ALL PRODUCTION PRACTICES"
##  $ end_code             : chr "12"
##  $ sector_desc          : chr "ECONOMICS"
##  $ short_desc           : chr "AG LAND, INCL BUILDINGS - ASSET VALUE, MEASURED IN $"
##  $ country_name         : chr "UNITED STATES"
##  $ Value                : chr "806,464,000"
##  $ reference_period_desc: chr "END OF DEC"
##  $ CV (%)               : chr "7.9"
##  $ class_desc           : chr "INCL BUILDINGS"
##  $ asd_code             : chr "50"
##  $ agg_level_desc       : chr "COUNTY"
##  $ county_name          : chr "WAKE"
##  $ region_desc          : chr ""
##  $ watershed_desc       : chr ""
##  $ state_ansi           : chr "37"
##  $ congr_district_code  : chr ""
##  $ domaincat_desc       : chr "NOT SPECIFIED"
##  $ state_fips_code      : chr "37"
##  $ group_desc           : chr "FARMS & LAND & ASSETS"
##  $ watershed_code       : chr "00000000"
##  $ unit_desc            : chr "$"
##  $ source_desc          : chr "CENSUS"
##  $ load_time            : chr "2012-12-31 00:00:00"
##  $ county_code          : chr "183"
##  $ statisticcat_desc    : chr "ASSET VALUE"
##  $ commodity_desc       : chr "AG LAND"
##  $ year                 : chr "2012"

'data.frame':   1 obs. of  39 variables:
 $ week_ending          : chr ""
 $ state_name           : chr "NORTH CAROLINA"
 $ country_code         : chr "9000"
 $ location_desc        : chr "NORTH CAROLINA, CENTRAL PIEDMONT, WAKE"
 $ begin_code           : chr "12"
 $ zip_5                : chr ""
 $ county_ansi          : chr "183"
 $ state_alpha          : chr "NC"
 $ util_practice_desc   : chr "ALL UTILIZATION PRACTICES"
 $ domain_desc          : chr "TOTAL"
 $ asd_desc             : chr "CENTRAL PIEDMONT"
 $ freq_desc            : chr "POINT IN TIME"
 $ prodn_practice_desc  : chr "ALL PRODUCTION PRACTICES"
 $ end_code             : chr "12"
 $ sector_desc          : chr "ECONOMICS"
 $ short_desc           : chr "AG LAND, INCL BUILDINGS - ASSET VALUE, MEASURED IN $"
 $ country_name         : chr "UNITED STATES"
 $ Value                : chr "806,464,000"
 $ reference_period_desc: chr "END OF DEC"
 $ CV (%)               : chr "7.9"
 $ class_desc           : chr "INCL BUILDINGS"
 $ asd_code             : chr "50"
 $ agg_level_desc       : chr "COUNTY"
 $ county_name          : chr "WAKE"
 $ region_desc          : chr ""
 $ watershed_desc       : chr ""
 $ state_ansi           : chr "37"
 $ congr_district_code  : chr ""
 $ domaincat_desc       : chr "NOT SPECIFIED"
 $ state_fips_code      : chr "37"
 $ group_desc           : chr "FARMS & LAND & ASSETS"
 $ watershed_code       : chr "00000000"
 $ unit_desc            : chr "$"
 $ source_desc          : chr "CENSUS"
 $ load_time            : chr "2012-12-31 00:00:00"
 $ county_code          : chr "183"
 $ statisticcat_desc    : chr "ASSET VALUE"
 $ commodity_desc       : chr "AG LAND"
 $ year                 : chr "2012"