Correlations

Code 
options(scipen = 999)

pacman::p_load(
  dplyr,
  htmltools,
  stringr,
  tidyr,
  reactable,
  purrr,
  broom,
  knitr,
  kableExtra,
  ggplot2,
  ggpubr,
  psych,
  tibble,
  Hmisc,
  reshape2,
  viridisLite,
  conflicted,
  readr
)

# GitHub packages
pacman::p_load_gh(
  'ChrisDonovan307/projecter',
  'Food-Systems-Research-Institute/SMdata'
)

# Functions
source('dev/get_reactable.R')
source('dev/get_missing.R')
source('dev/data_pipeline_functions.R')

conflicts_prefer(
  dplyr::select(),
  dplyr::filter(),
  dplyr::pull(),
  dplyr::summarize(),
  stats::lag(),
  base::setdiff(),
  .quiet = TRUE
)

This page explores correlations among metrics.

1 Wrangling

Code 
# metric crosswalk
crosswalk <- data_paper_meta %>% 
  select(variable_name, metric)

# Get df ready
get_str(metrics)
df <- metrics %>% 
  filter(variable_name %in% data_paper_meta$variable_name) %>% 
  get_latest_year() %>% 
  filter_fips('new') %>% 
  mutate(variable_name = str_split_i(variable_name, '_', 1)) %>% 
  left_join(crosswalk) %>% 
  select(-variable_name) %>% 
  pivot_wider(
    id_cols = fips,
    values_from = value,
    names_from = metric
  )
get_str(df)

# Get an ordered framework from which we will pull metrics in alphabetical order
# and get the counts of metrics per dimension that we will use to draw lines
# between dimensions
get_str(data_paper_meta)
ordered_framework <- data_paper_meta %>% 
  arrange(dimension, metric) %>% 
  filter(metric %in% names(df))
ordered_framework

# Get line placements to divide dimensions
# Reverse them to match alphabetical order of dimensions top to bottom
# Get cumulative sums to space them out across graph
# Then add 0.5 to each to put them in between cells
line_placements <- ordered_framework %>% 
  group_by(dimension) %>% 
  summarize(count = n()) %>% 
  pull(count) %>% 
  rev() %>% 
  cumsum() %>% 
  {. + 0.5}
line_placements

# Reorder our df in proper metric order
metric_order <- ordered_framework %>% 
  pull(metric)
df <- df %>% 
  select(fips, any_of(metric_order)) %>% 
  select(rev(everything()))
get_str(df)

# Create matrix of values
mat <- df %>% 
  na.omit() %>% 
  select(-fips) %>% 
  as.matrix()

# Get correlations
cor <- rcorr(mat, type = 'pearson')

# Melt correlation values and rename columns
cor_r <- reshape::melt(cor$r) %>% 
  setNames(c('var_1', 'var_2', 'value'))

# Save p values
cor_p <- melt(cor$P)
p.value <- cor_p$value

# Make heatmap
corplot <- cor_r %>% 
  ggplot(aes(var_1, var_2, fill = value)) + 
  geom_tile() + 
  scale_fill_gradient2(
    low = "#762a83", 
    mid = "white", 
    high = "#1b7837", 
    midpoint = 0
  ) +
  geom_hline(yintercept = line_placements[1]) +
  geom_hline(yintercept = line_placements[2]) +
  geom_hline(yintercept = line_placements[3]) +
  geom_hline(yintercept = line_placements[4]) +
  geom_vline(xintercept = line_placements[1]) +
  geom_vline(xintercept = line_placements[2]) +
  geom_vline(xintercept = line_placements[3]) +
  geom_vline(xintercept = line_placements[4]) +
  theme(
    axis.text.x = element_text(
      hjust = 1, 
      angle = 45
    )
    # axis.text.x = element_blank(),
    # axis.ticks.x = element_blank()
  ) +
  labs(
    x = NULL,
    y = NULL,
    fill = NULL
  )
ggsave(
  plot = corplot,
  filename = 'outputs/fig_corplot.png',
  height = 8,
  width = 9.5,
  dpi = 300
)

2 Figure

3 Table

Large and unwieldy correlation matrix. Added another interactive one below that is probably easier to work with.

Code 
# Latex table
df <- cor$r %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column('var') %>% 
  mutate(var = str_sub(var, end = 6)) %>% 
  setNames(c(names(.) %>% str_sub(end = 6)))
get_str(df)
'data.frame':   44 obs. of  45 variables:
 $ var   : chr  "Social" "Ratio " "Racial" "Produc" "Firear" "Annual" "Acres "..
 $ Social: num  1 0.2403 -0.24705 0.02889 0.35885 -0.50126 0.28993 -0.0401 0.2..
 $ Ratio : num  0.2403 1 -0.00425 -0.28254 0.22364 -0.17571 0.29737 -0.36149 0..
 $ Racial: num  -0.24705 -0.00425 1 0.14245 -0.44591 0.36141 -0.47392 0.0185 -..
 $ Produc: num  0.02889 -0.28254 0.14245 1 -0.08424 0.27511 -0.23707 0.08431 0..
 $ Firear: num  0.35885 0.22364 -0.44591 -0.08424 1 -0.09665 0.53279 0.05923 0..
 $ Annual: num  -0.501257 -0.175707 0.361411 0.275108 -0.096648 1 -0.163404 -0..
 $ Acres : num  0.2899 0.2974 -0.4739 -0.2371 0.5328 -0.1634 1 0.0978 0.3692 0..
 $ Total : num  -0.0401 -0.3615 0.0185 0.0843 0.0592 -0.133 0.0978 1 0.0989 0...
 $ Propor: num  0.2918 0.1527 -0.2077 0.0862 0.4192 -0.0154 0.3692 0.0989 1 -0..
 $ Propor: num  -0.15653 -0.2332 0.00835 -0.05659 -0.14087 -0.00425 0.02816 0...
 $ Crop d: num  -0.189 -0.1056 0.426 0.3636 -0.5843 0.1826 -0.4921 -0.0113 -0...
 $ Rental: num  -0.07237 0.00767 -0.43612 -0.03304 0.47264 0.0039 0.12365 -0.2..
 $ Propor: num  0.042 -0.0928 -0.0651 -0.0264 0.5459 0.1579 -0.1183 -0.1079 0...
 $ Price : num  -0.04524 0.18111 -0.12628 0.03086 -0.08468 -0.22502 0.20636 -0..
 $ Poor m: num  0.53005 0.23827 -0.23958 0.08226 0.37336 -0.2326 0.40697 0.069..
 $ Mobili: num  -0.03712 0.01721 -0.04195 -0.00274 0.0761 0.03819 -0.19338 -0...
 $ Limite: num  0.04309 -0.28885 0.06682 0.30974 -0.36643 -0.2862 -0.35577 -0...
 $ Lackin: num  0.1858 -0.5034 0.0256 -0.045 -0.1466 -0.0537 -0.2611 0.026 -0...
 $ High s: num  0.0666 -0.05356 -0.16039 -0.05997 0.3194 0.07422 0.03824 0.130..
 $ Heart : num  0.014266 0.120252 -0.288339 0.002642 0.314113 -0.000869 0.0281..
 $ Freque: num  0.22728 0.01309 -0.07722 0.05232 0.00903 -0.06496 -0.17607 -0...
 $ Food i: num  0.4017 0.2096 -0.2402 0.0155 0.5141 -0.1046 0.2217 -0.0413 0.2..
 $ Diabet: num  -0.15937 -0.20595 0.33838 -0.11721 -0.39384 0.01822 -0.52207 0..
 $ Weeks : num  -0.28893 -0.27989 0.35194 0.1947 -0.68505 0.10635 -0.49366 0.0..
 $ Tree s: num  0.3549 0.194 -0.4361 0.0193 0.4768 -0.3256 0.4304 0.011 0.2335..
 $ Tree s: num  0.15891 0.16449 -0.3007 -0.05053 -0.17194 -0.1806 0.23523 -0.1..
 $ Total : num  0.4467 0.2984 -0.3646 -0.0406 0.3118 -0.3379 0.7091 -0.0524 0...
 $ Soil o: num  0.56395 0.49479 -0.30076 -0.08992 0.56856 -0.31767 0.64634 -0...
 $ Rarefi: num  0.12662 0.19654 0.00342 0.23801 0.08376 0.08603 0.38444 0.1531..
 $ Rarefi: num  0.30286 0.14656 -0.05094 -0.06177 0.20429 -0.00826 0.40105 0.0..
 $ Propor: num  -0.31282 -0.08265 0.36501 -0.149 -0.31513 0.01802 -0.28255 -0...
 $ Land U: num  -0.4369 -0.4057 0.4279 0.1716 -0.5004 0.3068 -0.6883 0.2929 -0..
 $ Fuel e: num  0.01579 -0.13765 -0.32017 0.34802 0.25056 -0.00172 0.03887 0.0..
 $ Availa: num  0.51552 0.24335 -0.18187 0.16248 0.4856 -0.21215 0.54338 -0.01..
 $ Annual: num  0.1803 0.4033 0.0262 -0.2234 0.4482 -0.0703 0.0632 -0.0678 0.1..
 $ Total : num  -0.00821 -0.23446 0.03665 0.11213 0.03612 -0.06879 -0.13172 0...
 $ Sales : num  -0.0251 0.0894 0.3242 0.2622 0.0815 0.3199 -0.3348 -0.0665 -0...
 $ Percen: num  -0.1497 -0.1187 -0.1596 0.07 0.2095 -0.0178 0.1828 -0.0779 0.0..
 $ Market: num  -0.2958 -0.3499 0.0983 -0.1151 -0.3112 0.0791 -0.2588 -0.1364 ..
 $ Land a: num  -0.5459 -0.3088 0.3205 0.0932 -0.7836 0.2316 -0.6297 -0.1348 -..
 $ Gini I: num  -0.16631 0.25991 0.004 -0.38622 -0.19424 -0.08773 0.0393 -0.26..
 $ Expens: num  -0.22014 -0.54788 0.10397 0.10944 -0.18598 0.01693 -0.19555 0...
 $ Change: num  0.12939 0.24946 -0.16454 -0.06042 0.22254 -0.11503 0.47954 0.0..
 $ Averag: num  -0.16942 0.1466 -0.27879 -0.3921 -0.22891 -0.18117 0.10661 -0...
Code 
df %>% 
  kbl(
    digits = 3, 
    format = "latex",
    caption = 'Correlation matrix of metrics using latest available time point',
    label = 'tab_correlations',
    booktabs = TRUE
    # longtable = TRUE
  ) %>%
  kable_styling(
    font_size = 5,
    bootstrap_options = c(
      "condensed"
    )
  ) %>% 
  save_kable(
    file = 'outputs/tab_correlations.tex'
  )

# HTML for website
cor$r %>% 
  kbl(
    digits = 3, 
    format = "html"
  ) %>%
  column_spec(1, width = '500px') %>% 
  kable_styling(
    full_width = FALSE, 
    bootstrap_options = c(
      "striped", 
      "hover", 
      "condensed"
    )
  )
Social isolation Ratio of female to male producers Racial diversity of producers Producer age skew Firearm fatalities Annual population change Acres in conservation easements Total animal and crop sales Proportion of organic operations Proportion of operations using precision agriculture Crop diversity Rental vacancy rate Proportion uninsured Price of a meal Poor mental health days Mobility disability Limited access to healthy foods Lacking social support High school graduation rate Heart disease prevalence Frequent physical distress Food insecurity rate, overall Diabetes prevelance Weeks of extreme drought Tree species diversity Tree size diversity Total ecosystem carbon per acre Soil organic carbon Rarefied richness of plants Rarefied richness of animals Proportion of invasive species Land Use Diversity Fuel expenses Available water storage Annual precipitation Total indemnities Sales from agritourism and recreation Percent change in wages Market channel ratio Land and building value per acre Gini Index Expenses per operation Change in agricultural establishments Average weekly wages
Social isolation 1.000 0.240 -0.247 0.029 0.359 -0.501 0.290 -0.040 0.292 -0.157 -0.189 -0.072 0.042 -0.045 0.530 -0.037 0.043 0.186 0.067 0.014 0.227 0.402 -0.159 -0.289 0.355 0.159 0.447 0.564 0.127 0.303 -0.313 -0.437 0.016 0.516 0.180 -0.008 -0.025 -0.150 -0.296 -0.546 -0.166 -0.220 0.129 -0.169
Ratio of female to male producers 0.240 1.000 -0.004 -0.283 0.224 -0.176 0.297 -0.361 0.153 -0.233 -0.106 0.008 -0.093 0.181 0.238 0.017 -0.289 -0.503 -0.054 0.120 0.013 0.210 -0.206 -0.280 0.194 0.164 0.298 0.495 0.197 0.147 -0.083 -0.406 -0.138 0.243 0.403 -0.234 0.089 -0.119 -0.350 -0.309 0.260 -0.548 0.249 0.147
Racial diversity of producers -0.247 -0.004 1.000 0.142 -0.446 0.361 -0.474 0.019 -0.208 0.008 0.426 -0.436 -0.065 -0.126 -0.240 -0.042 0.067 0.026 -0.160 -0.288 -0.077 -0.240 0.338 0.352 -0.436 -0.301 -0.365 -0.301 0.003 -0.051 0.365 0.428 -0.320 -0.182 0.026 0.037 0.324 -0.160 0.098 0.321 0.004 0.104 -0.165 -0.279
Producer age skew 0.029 -0.283 0.142 1.000 -0.084 0.275 -0.237 0.084 0.086 -0.057 0.364 -0.033 -0.026 0.031 0.082 -0.003 0.310 -0.045 -0.060 0.003 0.052 0.016 -0.117 0.195 0.019 -0.051 -0.041 -0.090 0.238 -0.062 -0.149 0.172 0.348 0.162 -0.223 0.112 0.262 0.070 -0.115 0.093 -0.386 0.109 -0.060 -0.392
Firearm fatalities 0.359 0.224 -0.446 -0.084 1.000 -0.097 0.533 0.059 0.419 -0.141 -0.584 0.473 0.546 -0.085 0.373 0.076 -0.366 -0.147 0.319 0.314 0.009 0.514 -0.394 -0.685 0.477 -0.172 0.312 0.569 0.084 0.204 -0.315 -0.500 0.251 0.486 0.448 0.036 0.082 0.209 -0.311 -0.784 -0.194 -0.186 0.223 -0.229
Annual population change -0.501 -0.176 0.361 0.275 -0.097 1.000 -0.163 -0.133 -0.015 -0.004 0.183 0.004 0.158 -0.225 -0.233 0.038 -0.286 -0.054 0.074 -0.001 -0.065 -0.105 0.018 0.106 -0.326 -0.181 -0.338 -0.318 0.086 -0.008 0.018 0.307 -0.002 -0.212 -0.070 -0.069 0.320 -0.018 0.079 0.232 -0.088 0.017 -0.115 -0.181
Acres in conservation easements 0.290 0.297 -0.474 -0.237 0.533 -0.163 1.000 0.098 0.369 0.028 -0.492 0.124 -0.118 0.206 0.407 -0.193 -0.356 -0.261 0.038 0.028 -0.176 0.222 -0.522 -0.494 0.430 0.235 0.709 0.646 0.384 0.401 -0.283 -0.688 0.039 0.543 0.063 -0.132 -0.335 0.183 -0.259 -0.630 0.039 -0.196 0.480 0.107
Total animal and crop sales -0.040 -0.361 0.019 0.084 0.059 -0.133 0.098 1.000 0.099 0.616 -0.011 -0.216 -0.108 -0.118 0.070 -0.208 -0.044 0.026 0.131 -0.267 -0.073 -0.041 0.034 0.070 0.011 -0.161 -0.052 -0.045 0.153 0.035 -0.254 0.293 0.084 -0.013 -0.068 0.345 -0.067 -0.078 -0.136 -0.135 -0.262 0.791 0.060 -0.057
Proportion of organic operations 0.292 0.153 -0.208 0.086 0.419 -0.015 0.369 0.099 1.000 -0.050 -0.568 0.267 0.123 0.445 0.135 -0.251 -0.370 -0.447 -0.277 0.048 -0.208 0.270 -0.626 -0.596 0.233 -0.072 0.292 0.383 0.156 0.228 -0.116 -0.264 0.073 0.307 0.107 -0.071 -0.221 0.049 -0.245 -0.483 -0.082 -0.054 -0.038 -0.163
Proportion of operations using precision agriculture -0.157 -0.233 0.008 -0.057 -0.141 -0.004 0.028 0.616 -0.050 1.000 -0.117 -0.257 -0.351 0.004 0.111 0.067 -0.101 -0.145 0.047 -0.036 0.076 -0.158 0.006 0.051 -0.097 0.133 -0.039 -0.078 -0.005 0.015 -0.310 0.218 0.046 -0.135 0.138 -0.052 0.077 -0.254 -0.152 -0.095 -0.062 0.325 -0.059 -0.032
Crop diversity -0.189 -0.106 0.426 0.364 -0.584 0.183 -0.492 -0.011 -0.568 -0.117 1.000 -0.348 -0.255 -0.198 -0.201 -0.093 0.324 0.240 -0.032 -0.317 -0.077 -0.303 0.378 0.747 -0.434 0.010 -0.275 -0.351 0.195 -0.273 0.116 0.407 -0.103 -0.249 -0.392 0.067 0.056 -0.206 0.047 0.533 -0.116 0.167 -0.065 0.043
Rental vacancy rate -0.072 0.008 -0.436 -0.033 0.473 0.004 0.124 -0.216 0.267 -0.257 -0.348 1.000 0.495 0.238 0.199 0.435 0.042 -0.141 0.227 0.711 0.140 0.498 -0.228 -0.243 0.260 -0.256 -0.031 0.191 0.030 0.138 -0.065 -0.308 0.257 -0.018 0.239 0.038 -0.035 0.230 -0.255 -0.337 0.239 -0.220 -0.354 -0.101
Proportion uninsured 0.042 -0.093 -0.065 -0.026 0.546 0.158 -0.118 -0.108 0.123 -0.351 -0.255 0.495 1.000 -0.406 -0.017 0.305 -0.141 0.196 0.344 0.363 0.080 0.431 0.167 -0.277 0.186 -0.529 -0.360 0.031 -0.270 -0.199 -0.099 0.054 0.095 0.024 0.452 0.282 0.299 0.086 0.124 -0.251 -0.153 0.018 -0.202 -0.417
Price of a meal -0.045 0.181 -0.126 0.031 -0.085 -0.225 0.206 -0.118 0.445 0.004 -0.198 0.238 -0.406 1.000 -0.113 -0.305 -0.053 -0.601 -0.648 -0.076 -0.349 -0.115 -0.645 -0.210 0.095 0.225 0.367 0.179 0.117 0.142 0.165 -0.313 -0.004 0.029 -0.130 -0.233 -0.300 0.173 -0.170 -0.098 0.404 -0.251 0.068 0.169
Poor mental health days 0.530 0.238 -0.240 0.082 0.373 -0.233 0.407 0.070 0.135 0.111 -0.201 0.199 -0.017 -0.113 1.000 0.461 0.197 -0.144 0.564 0.487 0.574 0.687 -0.021 -0.190 0.373 -0.005 0.383 0.617 0.404 0.352 -0.430 -0.450 0.095 0.485 0.345 0.069 0.053 0.071 -0.577 -0.673 0.048 -0.188 0.032 -0.168
Mobility disability -0.037 0.017 -0.042 -0.003 0.076 0.038 -0.193 -0.208 -0.251 0.067 -0.093 0.435 0.305 -0.305 0.461 1.000 0.376 0.079 0.654 0.855 0.812 0.487 0.507 0.134 0.100 -0.335 -0.355 0.097 -0.142 0.055 -0.108 0.141 0.023 -0.211 0.349 0.188 0.375 -0.062 -0.315 -0.123 0.251 -0.191 -0.494 -0.121
Limited access to healthy foods 0.043 -0.289 0.067 0.310 -0.366 -0.286 -0.356 -0.044 -0.370 -0.101 0.324 0.042 -0.141 -0.053 0.197 0.376 1.000 0.326 0.129 0.187 0.496 -0.039 0.436 0.493 -0.049 -0.003 -0.241 -0.220 0.086 0.086 0.011 0.190 0.126 -0.236 -0.203 0.226 -0.051 0.202 -0.124 0.267 0.033 0.073 -0.315 -0.022
Lacking social support 0.186 -0.503 0.026 -0.045 -0.147 -0.054 -0.261 0.026 -0.447 -0.145 0.240 -0.141 0.196 -0.601 -0.144 0.079 0.326 1.000 0.274 -0.109 0.155 -0.130 0.495 0.441 -0.194 -0.015 -0.192 -0.238 -0.194 -0.032 0.068 0.157 -0.185 -0.080 -0.315 0.353 -0.048 -0.124 0.327 0.286 -0.236 0.315 -0.096 0.023
High school graduation rate 0.067 -0.054 -0.160 -0.060 0.319 0.074 0.038 0.131 -0.277 0.047 -0.032 0.227 0.344 -0.648 0.564 0.654 0.129 0.274 1.000 0.583 0.635 0.588 0.463 0.085 0.136 -0.235 -0.160 0.207 0.072 0.007 -0.289 0.038 0.039 0.110 0.240 0.269 0.271 0.078 -0.201 -0.236 -0.120 0.131 -0.158 -0.045
Heart disease prevalence 0.014 0.120 -0.288 0.003 0.314 -0.001 0.028 -0.267 0.048 -0.036 -0.317 0.711 0.363 -0.076 0.487 0.855 0.187 -0.109 0.583 1.000 0.601 0.614 0.125 -0.131 0.218 -0.302 -0.133 0.224 -0.057 0.115 -0.209 -0.101 0.155 -0.094 0.368 0.039 0.278 0.020 -0.335 -0.292 0.232 -0.287 -0.431 -0.084
Frequent physical distress 0.227 0.013 -0.077 0.052 0.009 -0.065 -0.176 -0.073 -0.208 0.076 -0.077 0.140 0.080 -0.349 0.574 0.812 0.496 0.155 0.635 0.601 1.000 0.499 0.550 0.119 0.158 -0.161 -0.241 0.178 -0.059 0.122 -0.183 0.132 0.029 -0.067 0.274 0.190 0.348 -0.097 -0.310 -0.108 0.171 -0.051 -0.340 -0.035
Food insecurity rate, overall 0.402 0.210 -0.240 0.016 0.514 -0.105 0.222 -0.041 0.270 -0.158 -0.303 0.498 0.431 -0.115 0.687 0.487 -0.039 -0.130 0.588 0.614 0.499 1.000 0.023 -0.350 0.312 -0.301 0.139 0.540 0.141 0.079 -0.315 -0.358 0.109 0.418 0.533 0.208 0.318 0.124 -0.251 -0.592 0.212 -0.091 -0.150 -0.235
Diabetes prevelance -0.159 -0.206 0.338 -0.117 -0.394 0.018 -0.522 0.034 -0.626 0.006 0.378 -0.228 0.167 -0.645 -0.021 0.507 0.436 0.495 0.463 0.125 0.550 0.023 1.000 0.520 -0.375 -0.287 -0.601 -0.254 -0.245 -0.157 0.129 0.573 -0.296 -0.358 0.017 0.389 0.273 -0.160 0.243 0.469 0.075 0.316 -0.283 0.079
Weeks of extreme drought -0.289 -0.280 0.352 0.195 -0.685 0.106 -0.494 0.070 -0.596 0.051 0.747 -0.243 -0.277 -0.210 -0.190 0.134 0.493 0.441 0.085 -0.131 0.119 -0.350 0.520 1.000 -0.424 0.016 -0.353 -0.389 0.168 -0.122 0.131 0.464 -0.268 -0.337 -0.464 0.279 -0.028 -0.183 0.087 0.588 -0.005 0.284 -0.247 0.053
Tree species diversity 0.355 0.194 -0.436 0.019 0.477 -0.326 0.430 0.011 0.233 -0.097 -0.434 0.260 0.186 0.095 0.373 0.100 -0.049 -0.194 0.136 0.218 0.158 0.312 -0.375 -0.424 1.000 0.205 0.458 0.296 0.223 0.025 -0.110 -0.468 0.510 0.338 0.169 -0.127 -0.131 0.205 -0.262 -0.616 -0.198 -0.301 0.170 -0.251
Tree size diversity 0.159 0.164 -0.301 -0.051 -0.172 -0.181 0.235 -0.161 -0.072 0.133 0.010 -0.256 -0.529 0.225 -0.005 -0.335 -0.003 -0.015 -0.235 -0.302 -0.161 -0.301 -0.287 0.016 0.205 1.000 0.570 0.129 0.269 -0.039 -0.217 -0.413 0.160 0.239 -0.244 -0.365 -0.298 0.151 0.111 0.073 -0.140 -0.357 0.280 0.292
Total ecosystem carbon per acre 0.447 0.298 -0.365 -0.041 0.312 -0.338 0.709 -0.052 0.292 -0.039 -0.275 -0.031 -0.360 0.367 0.383 -0.355 -0.241 -0.192 -0.160 -0.133 -0.241 0.139 -0.601 -0.353 0.458 0.570 1.000 0.592 0.483 0.201 -0.189 -0.792 0.062 0.674 -0.123 -0.253 -0.369 0.146 -0.066 -0.533 -0.016 -0.311 0.526 0.081
Soil organic carbon 0.564 0.495 -0.301 -0.090 0.569 -0.318 0.646 -0.045 0.383 -0.078 -0.351 0.191 0.031 0.179 0.617 0.097 -0.220 -0.238 0.207 0.224 0.178 0.540 -0.254 -0.389 0.296 0.129 0.592 1.000 0.269 0.297 -0.283 -0.648 -0.235 0.736 0.353 -0.003 -0.093 -0.013 -0.364 -0.663 0.088 -0.261 0.266 0.052
Rarefied richness of plants 0.127 0.197 0.003 0.238 0.084 0.086 0.384 0.153 0.156 -0.005 0.195 0.030 -0.270 0.117 0.404 -0.142 0.086 -0.194 0.072 -0.057 -0.059 0.141 -0.245 0.168 0.223 0.269 0.483 0.269 1.000 0.317 -0.253 -0.330 0.107 0.301 -0.291 -0.011 -0.180 0.294 -0.317 -0.348 -0.198 -0.091 0.205 -0.142
Rarefied richness of animals 0.303 0.147 -0.051 -0.062 0.204 -0.008 0.401 0.035 0.228 0.015 -0.273 0.138 -0.199 0.142 0.352 0.055 0.086 -0.032 0.007 0.115 0.122 0.079 -0.157 -0.122 0.025 -0.039 0.201 0.297 0.317 1.000 0.005 -0.249 0.029 0.152 -0.073 0.065 -0.225 0.058 -0.505 -0.344 0.150 -0.058 -0.004 -0.083
Proportion of invasive species -0.313 -0.083 0.365 -0.149 -0.315 0.018 -0.283 -0.254 -0.116 -0.310 0.116 -0.065 -0.099 0.165 -0.430 -0.108 0.011 0.068 -0.289 -0.209 -0.183 -0.315 0.129 0.131 -0.110 -0.217 -0.189 -0.283 -0.253 0.005 1.000 0.201 -0.179 -0.206 -0.286 -0.077 -0.297 -0.048 0.253 0.329 0.081 0.003 -0.173 0.087
Land Use Diversity -0.437 -0.406 0.428 0.172 -0.500 0.307 -0.688 0.293 -0.264 0.218 0.407 -0.308 0.054 -0.313 -0.450 0.141 0.190 0.157 0.038 -0.101 0.132 -0.358 0.573 0.464 -0.468 -0.413 -0.792 -0.648 -0.330 -0.249 0.201 1.000 -0.046 -0.710 -0.213 0.255 0.224 -0.329 0.164 0.612 -0.153 0.519 -0.418 -0.033
Fuel expenses 0.016 -0.138 -0.320 0.348 0.251 -0.002 0.039 0.084 0.073 0.046 -0.103 0.257 0.095 -0.004 0.095 0.023 0.126 -0.185 0.039 0.155 0.029 0.109 -0.296 -0.268 0.510 0.160 0.062 -0.235 0.107 0.029 -0.179 -0.046 1.000 0.002 0.089 -0.161 0.024 0.185 -0.212 -0.214 -0.296 -0.130 -0.110 -0.363
Available water storage 0.516 0.243 -0.182 0.162 0.486 -0.212 0.543 -0.013 0.307 -0.135 -0.249 -0.018 0.024 0.029 0.485 -0.211 -0.236 -0.080 0.110 -0.094 -0.067 0.418 -0.358 -0.337 0.338 0.239 0.674 0.736 0.301 0.152 -0.206 -0.710 0.002 1.000 0.252 -0.089 -0.083 0.130 -0.049 -0.572 -0.176 -0.175 0.390 -0.284
Annual precipitation 0.180 0.403 0.026 -0.223 0.448 -0.070 0.063 -0.068 0.107 0.138 -0.392 0.239 0.452 -0.130 0.345 0.349 -0.203 -0.315 0.240 0.368 0.274 0.533 0.017 -0.464 0.169 -0.244 -0.123 0.353 -0.291 -0.073 -0.286 -0.213 0.089 0.252 1.000 -0.117 0.421 -0.088 -0.203 -0.459 0.173 -0.261 -0.099 -0.278
Total indemnities -0.008 -0.234 0.037 0.112 0.036 -0.069 -0.132 0.345 -0.071 -0.052 0.067 0.038 0.282 -0.233 0.069 0.188 0.226 0.353 0.269 0.039 0.190 0.208 0.389 0.279 -0.127 -0.365 -0.253 -0.003 -0.011 0.065 -0.077 0.255 -0.161 -0.089 -0.117 1.000 0.137 0.068 -0.063 0.047 0.190 0.539 0.091 -0.005
Sales from agritourism and recreation -0.025 0.089 0.324 0.262 0.082 0.320 -0.335 -0.067 -0.221 0.077 0.056 -0.035 0.299 -0.300 0.053 0.375 -0.051 -0.048 0.271 0.278 0.348 0.318 0.273 -0.028 -0.131 -0.298 -0.369 -0.093 -0.180 -0.225 -0.297 0.224 0.024 -0.083 0.421 0.137 1.000 -0.057 0.032 0.054 0.098 -0.088 -0.095 -0.313
Percent change in wages -0.150 -0.119 -0.160 0.070 0.209 -0.018 0.183 -0.078 0.049 -0.254 -0.206 0.230 0.086 0.173 0.071 -0.062 0.202 -0.124 0.078 0.020 -0.097 0.124 -0.160 -0.183 0.205 0.151 0.146 -0.013 0.294 0.058 -0.048 -0.329 0.185 0.130 -0.088 0.068 -0.057 1.000 0.088 -0.089 -0.038 -0.157 0.220 0.110
Market channel ratio -0.296 -0.350 0.098 -0.115 -0.311 0.079 -0.259 -0.136 -0.245 -0.152 0.047 -0.255 0.124 -0.170 -0.577 -0.315 -0.124 0.327 -0.201 -0.335 -0.310 -0.251 0.243 0.087 -0.262 0.111 -0.066 -0.364 -0.317 -0.505 0.253 0.164 -0.212 -0.049 -0.203 -0.063 0.032 0.088 1.000 0.528 -0.064 0.220 0.071 0.097
Land and building value per acre -0.546 -0.309 0.321 0.093 -0.784 0.232 -0.630 -0.135 -0.483 -0.095 0.533 -0.337 -0.251 -0.098 -0.673 -0.123 0.267 0.286 -0.236 -0.292 -0.108 -0.592 0.469 0.588 -0.616 0.073 -0.533 -0.663 -0.348 -0.344 0.329 0.612 -0.214 -0.572 -0.459 0.047 0.054 -0.089 0.528 1.000 0.069 0.260 -0.179 0.375
Gini Index -0.166 0.260 0.004 -0.386 -0.194 -0.088 0.039 -0.262 -0.082 -0.062 -0.116 0.239 -0.153 0.404 0.048 0.251 0.033 -0.236 -0.120 0.232 0.171 0.212 0.075 -0.005 -0.198 -0.140 -0.016 0.088 -0.198 0.150 0.081 -0.153 -0.296 -0.176 0.173 0.190 0.098 -0.038 -0.064 0.069 1.000 -0.116 0.014 0.261
Expenses per operation -0.220 -0.548 0.104 0.109 -0.186 0.017 -0.196 0.791 -0.054 0.325 0.167 -0.220 0.018 -0.251 -0.188 -0.191 0.073 0.315 0.131 -0.287 -0.051 -0.091 0.316 0.284 -0.301 -0.357 -0.311 -0.261 -0.091 -0.058 0.003 0.519 -0.130 -0.175 -0.261 0.539 -0.088 -0.157 0.220 0.260 -0.116 1.000 -0.044 0.055
Change in agricultural establishments 0.129 0.249 -0.165 -0.060 0.223 -0.115 0.480 0.060 -0.038 -0.059 -0.065 -0.354 -0.202 0.068 0.032 -0.494 -0.315 -0.096 -0.158 -0.431 -0.340 -0.150 -0.283 -0.247 0.170 0.280 0.526 0.266 0.205 -0.004 -0.173 -0.418 -0.110 0.390 -0.099 0.091 -0.095 0.220 0.071 -0.179 0.014 -0.044 1.000 0.266
Average weekly wages -0.169 0.147 -0.279 -0.392 -0.229 -0.181 0.107 -0.057 -0.163 -0.032 0.043 -0.101 -0.417 0.169 -0.168 -0.121 -0.022 0.023 -0.045 -0.084 -0.035 -0.235 0.079 0.053 -0.251 0.292 0.081 0.052 -0.142 -0.083 0.087 -0.033 -0.363 -0.284 -0.278 -0.005 -0.313 0.110 0.097 0.375 0.261 0.055 0.266 1.000

4 Long Table

Longer format table that is interactive and shows p values.

Code 
# Create long table with p values and stars
tab <- cor_r %>% 
  rename(cor = value) %>% 
  inner_join(cor_p, by = join_by(var_1 == Var1, var_2 == Var2)) %>% 
  rename(p = value) %>% 
  mutate(sig = ifelse(p < 0.05, '*', ''))

# Save to outputs
write_csv(
  tab,
  'outputs/table_metric_correlations_long.csv'
)

tab %>% 
  get_reactable(
    defaultColDef = colDef(
      format = colFormat(digits = 3)
    )
  )
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