Nothing
## ----setup, include = FALSE----------------------------------------------
library("knitr")
opts_chunk$set(
collapse = TRUE,
eval = !(Sys.getenv("NASS_KEY") == ""),
comment = "#>"
)
## ----start, warning=FALSE, message=FALSE---------------------------------
library("usdarnass")
library("dplyr") # Helpful package
ohio_rent <- nass_data(commodity_desc = "RENT", agg_level_desc = "COUNTY",
state_name = "OHIO")
glimpse(ohio_rent)
## ----rent_nass_param-----------------------------------------------------
nass_param("short_desc", commodity_desc = "RENT", agg_level_desc = "COUNTY", state_name = "OHIO")
## ----rent_nass_param_alt-------------------------------------------------
table(ohio_rent$short_desc)
## ----non_irrigated-------------------------------------------------------
non_irrigated <- ohio_rent %>%
filter(grepl("NON-IRRIGATED", short_desc))
table(non_irrigated$year) # Observation per year
## ----counties------------------------------------------------------------
table(non_irrigated$county_name)
# nass_param("county_name", state_name = "OHIO")
## ----asd-----------------------------------------------------------------
non_irrigated %>%
filter(county_name == "OTHER (COMBINED) COUNTIES") %>%
pull(asd_code) %>%
table()
## ----ag_census-----------------------------------------------------------
farms <- nass_data(source_desc = "CENSUS", year = 2012, state_name = "OHIO", agg_level_desc = "COUNTY", domain_desc = "TOTAL", short_desc = "FARM OPERATIONS - NUMBER OF OPERATIONS")
## ----combined------------------------------------------------------------
library("tidyr")
base_rent <- farms %>%
select(state_fips_code, county_code, county_name, asd_code, asd_desc) %>%
expand(year = unique(non_irrigated$year), nesting(state_fips_code, county_code, county_name, asd_code)) %>%
full_join(non_irrigated)
# Correct for missing values in the "other"
base_rent <- base_rent %>%
arrange(year, asd_code, county_code) %>%
group_by(year, asd_code) %>%
mutate(Value = ifelse(is.na(Value), Value[county_code == "998"], Value)) %>%
filter(county_code != "998")
# Finally, select only the relevant variables are rename
base_rent <- base_rent %>%
select(year, state_fips_code, county_code, county_name, asd_code, rent = Value) %>%
mutate(rent = as.numeric(rent),
fips = as.numeric(paste0(state_fips_code, county_code)))
glimpse(base_rent)
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