Last updated: 2021-12-14

Purpose

This code explores the distribution and influence of interpolated area data.

Libraries & functions

library(tidyverse)

Load data

area <- read.csv("../output_big/hectares_state_usda_usgs_20200404.csv") %>%
  mutate(interp = ifelse(SOURCE_DESC == "interp", "yes", "no"))  # fix interp column

Calculate summaries

# calculate interpolated area by crop

area_crop_tot <- area %>%
  group_by(USGS_group) %>%
  summarise(ha_tot = sum(ha, na.rm=T))

area_crop_sum <- area %>%
  group_by(USGS_group, interp) %>%
  summarise(ha = sum(ha, na.rm=T)) %>%
  left_join(area_crop_tot) %>%
  pivot_wider(names_from = interp, values_from = ha) %>%
  mutate(perc_interp = (yes/ha_tot)*100)
Joining, by = "USGS_group"
# calculate interpolated area over the whole sample

area_tot <- area %>%
  summarise(ha_tot = sum(ha, na.rm=T))

area_sum <- area %>%
  group_by(interp) %>%
  summarise(ha = sum(ha, na.rm=T)) %>%
  mutate(ha_tot = area_tot$ha_tot) %>%
  pivot_wider(names_from = interp, values_from = ha) %>%
  mutate(perc_interp = (yes/ha_tot)*100)

# repeat calcs for only census years

area_cen <- filter(area, YEAR %in% c(1997, 2002, 2007, 2012, 2017))

area_crop_tot_cen <- area_cen %>%
  group_by(USGS_group) %>%
  summarise(ha_tot = sum(ha, na.rm=T))

area_crop_sum_cen <- area_cen %>%
  group_by(USGS_group, interp) %>%
  summarise(ha = sum(ha, na.rm=T)) %>%
  left_join(area_crop_tot) %>%
  pivot_wider(names_from = interp, values_from = ha) %>%
  mutate(perc_interp = (yes/ha_tot)*100)
Joining, by = "USGS_group"

Session information

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] forcats_0.4.0   stringr_1.4.0   dplyr_0.8.3     purrr_0.3.2    
[5] readr_1.3.1     tidyr_1.1.0     tibble_2.1.3    ggplot2_3.2.0  
[9] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6       cellranger_1.1.0 pillar_1.4.2     compiler_3.6.1  
 [5] tools_3.6.1      digest_0.6.20    lubridate_1.7.4  jsonlite_1.6    
 [9] evaluate_0.14    lifecycle_0.2.0  nlme_3.1-140     gtable_0.3.0    
[13] lattice_0.20-38  pkgconfig_2.0.2  rlang_0.4.7      cli_1.1.0       
[17] rstudioapi_0.10  yaml_2.2.0       haven_2.1.1      xfun_0.8        
[21] withr_2.1.2      xml2_1.2.0       httr_1.4.2       knitr_1.23      
[25] hms_0.5.0        generics_0.0.2   vctrs_0.3.2      grid_3.6.1      
[29] tidyselect_1.1.0 glue_1.3.1       R6_2.4.0         readxl_1.3.1    
[33] rmarkdown_1.14   modelr_0.1.4     magrittr_1.5     ellipsis_0.2.0.1
[37] backports_1.1.4  scales_1.0.0     htmltools_0.3.6  rvest_0.3.4     
[41] assertthat_0.2.1 colorspace_1.4-1 stringi_1.4.3    lazyeval_0.2.2  
[45] munsell_0.5.0    broom_0.5.2      crayon_1.3.4

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