Read in

df <- read_csv("~/data/gcme/data_received_190325/NewData_wide_CORRECTED2.csv") %>%
  
  # something is wrong in columns ambient_Sd, ambient_Se, elevated...
  mutate( ambient_Sd  = as.numeric(ambient_Sd),  ambient_Se  = as.numeric(ambient_Se), 
          elevated_Sd = as.numeric(elevated_Sd), elevated_Se = as.numeric(elevated_Se) )
  
# save experiments names
df_experiments <- df %>% select(exp_nam, prev_name) %>% distinct()

list_exp_gt1yr <- df %>% 
  filter(!is.na(Year)) %>% 
  group_by(exp_nam) %>% 
  summarise(nyears=max(Year)) %>% 
  filter(nyears>1) %>% 
  select(exp_nam) %>% 
  unlist() %>% 
  unname()

df_c <- df %>%
  
  # ## Take this info from experiments below
  # select(-Fumigation_type, -Vegetation_type) %>% 
  
  # ## Add prev_name back
  # left_join( df_experiments, by = "exp_nam") %>% 
  
  ## filter experiments with only manipulated CO2
  ## (no other factors manipulated, strong reduction of data)
  filter(treatment=="c") %>% 
  
  ## More than 1 year data
  filter(exp_nam %in% list_exp_gt1yr) %>% 
  
  ## Combine with experiments meta info
  left_join( 
    read_csv("~/data/gcme/data_received_190325/table_var_exp_names_experiments.csv") %>% 
      select(prev_name, Fumigation_type=my_fumigation_type, Vegetation_type),
    by = c("prev_name")
  ) %>% 
  
  ## Filter only Fumigation_type OTC or FACE
  filter( Fumigation_type %in% c("OTC", "FACE") ) %>%
  
  {.}

Complement based on Kevin’s comments:

• Experiment ‘MI’: for the year 1997, we can calculate ANPP + BNPP = NPP.

df_c %>% 
  filter(exp_nam == "MI") %>% 
  filter(Data_type %in% c("ANPP", "BNPP"))

## # A tibble: 3 × 53
##   ALIAS exp_nam prev_name factors treatment TT_Nut_Detail co2_a co2_e fert_a
##   <chr> <chr>   <chr>     <chr>   <chr>     <chr>         <dbl> <dbl>  <dbl>
## 1 25213 MI      MI_c      c       c         c               410   760      0
## 2 25214 MI      MI_c      c       c         c               410   760      0
## 3 25215 MI      MI_c      c       c         c               410   760      0
## # … with 44 more variables: fert_e <dbl>, nfert_a <dbl>, nfert_e <dbl>,
## #   nfertQ_a <dbl>, nfertQ_e <dbl>, pfert_a <dbl>, pfert_e <dbl>,
## #   pfertQ_a <dbl>, pfertQ_e <dbl>, kfert_a <dbl>, kfert_e <dbl>,
## #   kfertQ_a <dbl>, kfertQ_e <dbl>, lime_a <dbl>, lime_e <dbl>, foth_a <dbl>,
## #   foth_e <dbl>, warm_a <dbl>, warm_e <dbl>, warmQ_e <dbl>, irr_a <dbl>,
## #   irr_e <dbl>, d_a <dbl>, d_e <dbl>, ozone_a <dbl>, ozone_e <dbl>,
## #   swarm_a <dbl>, swarm_e <dbl>, swarmQ_e <dbl>, Data_type <chr>, …

• Popface_pooled: probably better leave out

df_c <- df_c %>% 
  filter(exp_nam != "POPFACE_pooled")

• Durham_DukeFACE = Duke

df_c %>% 
  pull(exp_nam) %>% 
  unique() %>% 
  sort()

##   [1] "Abisko_subarctic"                   
##   [2] "BangorFACE"                         
##   [3] "BioCON"                             
##   [4] "Birmensdorf"                        
##   [5] "Birmensdorf_c"                      
##   [6] "Birmensdorf_cf"                     
##   [7] "Brandbjerg"                         
##   [8] "Chaux-des-Breuleux"                 
##   [9] "Christchurch_nf"                    
##  [10] "Christchurch_pr"                    
##  [11] "DRI"                                
##  [12] "DUKE"                               
##  [13] "DUKE2"                              
##  [14] "Durham_DukeFACE"                    
##  [15] "Dwingeloo"                          
##  [16] "EucFACE"                            
##  [17] "EUROFACE4_pa"                       
##  [18] "EUROFACE4_pe"                       
##  [19] "EUROFACE4_pn"                       
##  [20] "EUROFACE7_pooled"                   
##  [21] "GiFACE"                             
##  [22] "Glendevon_ag"                       
##  [23] "Glendevon_bp"                       
##  [24] "Glendevon_ps"                       
##  [25] "Glendevon2_ag"                      
##  [26] "Glendevon2_psi"                     
##  [27] "Glendevon3_ag"                      
##  [28] "Glendevon3_psi"                     
##  [29] "Headley_fe"                         
##  [30] "Headley_ps"                         
##  [31] "Headley_qp"                         
##  [32] "HohenheimFACE"                      
##  [33] "Jokioinen"                          
##  [34] "JRBP_FACE3"                         
##  [35] "JRBP_OTC"                           
##  [36] "JRBP_OTC2sand"                      
##  [37] "JRBP_OTC2ser"                       
##  [38] "JRBP_OTCsand"                       
##  [39] "JRBP_OTCser"                        
##  [40] "MBS_pe"                             
##  [41] "MBS_pt"                             
##  [42] "MBS_pt2"                            
##  [43] "MBS_pt3"                            
##  [44] "MBS_pt4_as"                         
##  [45] "MBS_pt4_pooled"                     
##  [46] "MBS_pt4_pt"                         
##  [47] "MI"                                 
##  [48] "ORNERP_acer_ar"                     
##  [49] "ORNERP_acer_as"                     
##  [50] "ORNERP_acer_pooled"                 
##  [51] "ORNERP_grassland"                   
##  [52] "ORNERP_liqui"                       
##  [53] "ORNERP_liqui2"                      
##  [54] "ORNERP_liqui3"                      
##  [55] "ORNERP_liriodendron"                
##  [56] "ORNERP_quercus"                     
##  [57] "OzFACE"                             
##  [58] "PHACE"                              
##  [59] "POPFACE_pa"                         
##  [60] "POPFACE_pe"                         
##  [61] "POPFACE_pn"                         
##  [62] "Rhine-aspenFACE"                    
##  [63] "RiceFACE_China_23N_113E_1"          
##  [64] "RiceFACE_China_30N_112E_Or_1"       
##  [65] "RiceFACE_China_32N_120E_Or"         
##  [66] "RiceFACE_China_32N_120E_Or_5"       
##  [67] "RiceFACE_China_32N_120E_Or_7"       
##  [68] "RiceFACE_China_32N_120E_Or_9"       
##  [69] "RiceFACE_China_32N_120E_Or_A"       
##  [70] "RiceFACE_China_32N_120E_Or_Tr"      
##  [71] "RiceFACE_China_32N_120E_Or_Tr_2"    
##  [72] "RiceFACE_China_32N_120E_Or_Tr_4"    
##  [73] "RiceFACE_China_32N_120E_Or_Tr_9"    
##  [74] "RiceFACE_China_32N_120E_Tr_1"       
##  [75] "RiceFACE_China_33N_120E_Or_11"      
##  [76] "RiceFACE_China_33N_120E_Or_3"       
##  [77] "RiceFACE_China_33N_120E_Or_7"       
##  [78] "RiceFACE_China_33N_120E_Or_Tr_1"    
##  [79] "RiceFACE_Japan_A_1998_39,38_140,57" 
##  [80] "RiceFACE_Japan_A_1998_39,40_141"    
##  [81] "RiceFACE_Japan_A_2003_39,38_140,57" 
##  [82] "RiceFACE_Japan_A6_2010_35,38_139,60"
##  [83] "RiceFACE_Japan_Ki_2004_39,38_140,57"
##  [84] "RiceFACE_Japan_Ko_2011_35,58_139,59"
##  [85] "RiceFACE_Japan_L_1999_39,38_140,57" 
##  [86] "RiceFACE_Japan_L_2000_39,38_140,57" 
##  [87] "RiceFACE_Japan_L_2010_35,58_139,60" 
##  [88] "RiceFACE_Japan_L_2011_35,58_139,60" 
##  [89] "Salmisuo"                           
##  [90] "SCA"                                
##  [91] "SCBG"                               
##  [92] "Suonenjoki2_bp4"                    
##  [93] "Suonenjoki2_bp80"                   
##  [94] "SwissFACE_lolium"                   
##  [95] "SwissFACE_lolium2"                  
##  [96] "SwissFACE_medicago"                 
##  [97] "SwissFACE_trifolium"                
##  [98] "SwissFACE_trifolium2"               
##  [99] "TasFACE"                            
## [100] "UA_OTC"                             
## [101] "USDA_citrus"                        
## [102] "Vielsalm"                           
## [103] "webFACE"

df_c <- df_c %>% 
  mutate(exp_nam = ifelse(exp_nam == "Durham_DukeFACE", "DUKE", exp_nam))

Simplified data selection

Look at available variables. They are a bit inconsistently named. Make a sound selection.

all_selvars <- c()
# df_c$Data_type %>% unique()

Test

selvars <- c("belowground_biomass", "belowground_biomass_C", "root_biomass", "root_mass", 
             "root_C", "fine_roots_C", "coarse_root_biomass", "coarse_roots_biomass", 
             "coarse_roots_C")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c %>% 
  filter(Data_type %in% selvars) %>% 
  mutate(varnam = "test")

selvars <- c("GPP")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c %>% 
  filter(Data_type %in% selvars) %>% 
  mutate(varnam = "gpp")

selvars <- c("total_plant_C", "total_biomass")   # Not: "biomass_C", 
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      filter(!(Unit %in% c("mg_C_"))) %>% 
      mutate(per_plant = ifelse(Unit %in% c("g/plant", "kg/tree"), TRUE, FALSE)) %>% 
      mutate(varnam = "cveg")
  )

selvars <- c("aboveground_biomass", "Aboveground_biomass", "aboveground_biomass_", "aboveground_biomass_(green)", "aboveground_biomass_C", "total_aboveground_biomass")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      filter(!(Unit %in% c("%"))) %>% 
      mutate(per_plant = ifelse(Unit %in% c("g/plant", "kg/tree", "mg/plant"), TRUE, FALSE)) %>% 
      mutate(varnam = "cveg_ag")
  )

selvars <- c("belowground_biomass", "belowground_biomass_C", "root_biomass", "root_mass", 
             "root_C", "fine_roots_C", "coarse_root_biomass", "coarse_roots_biomass", 
             "coarse_roots_C")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      filter(!(Unit %in% c("%"))) %>% 
      mutate(per_soil = ifelse(Unit %in% c("g/g_soil"), TRUE, FALSE)) %>% 
      mutate(per_plant = ifelse(Unit %in% c("g/tree", "kg/plant", "kg/tree"), TRUE, FALSE)) %>% 
      mutate(varnam = "cveg_bg")
  )

selvars <- c("wood_C", "wood_biomass", "stem_biomass", "woody_biomass")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      mutate(varnam = "cveg_wood")
  )

selvars <- c("leaf_C", "leaf_biomass", "green_leaf_biomass")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      mutate(varnam = "cveg_fol")
  )

selvars <- c("NPP")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      mutate(varnam = "npp")
  )

selvars <- c("aboveground_production", "annual_aboveground_biomass_production", "ANPP")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      mutate(varnam = "anpp")
  )

selvars <- c("ANPP_foliage")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      mutate(varnam = "npp_fol")
  )

selvars <- c("NPP_woody", "ANPP_woody")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      mutate(varnam = "npp_wood")
  )

selvars <- c("fine_root_production", "BNPP")
all_selvars <- c(all_selvars, selvars)

df_c_sub <- df_c_sub %>% 
  bind_rows(
    df_c %>% 
      filter(Data_type %in% selvars) %>% 
      mutate(varnam = "bnpp")
  )

# "soil_C" "soil_organic_C" "soil_organic_carbon" "soil_total_C" "SOM"

df_c_sub %>% 
  group_by(varnam) %>% 
  summarise(n_plots = sum(n_plots), n_exp = n()) %>% 
  knitr::kable()

all_vars <- df_c$Data_type %>% unique()
# all_vars[!(all_vars %in% all_selvars)]

Meta analysis

Aggregate by variable and experiment, pooling multiple years, sampling dates, and plots/replicates and calculate log response ratio

df_c_sub <- df_c_sub %>%         
  
  # get standard deviation for all data
  mutate( 
    my_ambient_sd = ambient_Sd, 
    my_elevated_sd = elevated_Sd 
    ) %>%
  rowwise() %>% 
  mutate( 
    my_ambient_sd = ifelse( is.na(my_ambient_sd),  ambient_Se  * sqrt(n_plots), my_ambient_sd ),
    my_elevated_sd  = ifelse( is.na(my_elevated_sd), elevated_Se * sqrt(n_plots), my_elevated_sd )
    ) %>%

  ## Get logarithm of response ratio and its variance
  metafor::escalc( 
    measure = "ROM", 
    m1i = elevated, sd1i = my_elevated_sd, n1i = n_plots, 
    m2i = ambient,  sd2i = my_ambient_sd,  n2i = n_plots, 
    data=., 
    append = TRUE, var.names = c("logr", "logr_var") ) %>% 
  as_tibble() %>% 
  mutate( logr_se = sqrt(logr_var)/sqrt(n_plots) )

## Warning: Some 'yi' and/or 'vi' values equal to +-Inf. Recoded to NAs.

write_csv(df_c_sub, file = "data/df_c_sub.csv")

Pool all measurements (multiple years, sampling dates and plots) by variable and experiment for meta analysis

df_c_agg <- df_c_sub %>% 
  
  filter(!is.na(logr_var) & !is.na(logr)) %>% 
  mutate( id = paste(exp_nam, varnam, sep = "_XXX_")) %>% 
  
  MAd::agg( id = id, es = logr, var = logr_var, n.1 = n_plots, n.2 = n_plots, cor = 1.0, method = "BHHR", data = . ) %>% 
  
  as_tibble() %>% 
  mutate( id = str_split(id, "_XXX_") ) %>% 
  mutate( exp_nam = purrr::map_chr(id, 1),
          varnam = purrr::map_chr(id, 2) ) %>% 
  select(exp_nam, varnam, es, var) %>% 

  ## add number of plots column and varnam
  left_join( df_c_sub %>% 
               group_by( exp_nam, varnam ) %>%
               summarise( n_plots = sum(n_plots) ) %>% 
               select( exp_nam, varnam, n_plots ),
             by = c("exp_nam", "varnam") ) %>% 
  rename( logr = es, logr_var = var ) %>% 
  mutate( logr_se = sqrt(logr_var)/sqrt(n_plots) ) %>% 
  # left_join( df_varnams, by = "varnam" ) %>% 
  
  ## filter NA for exp_nam due to unidentified experiment name in soil decomposition dataset
  filter(exp_nam!="NA" & !is.na(exp_nam))

## `summarise()` has grouped output by 'exp_nam'. You can override using the `.groups` argument.

write_csv(df_c_agg, file = "data/df_c_agg.csv")

Aggregate by variable, doing a meta-analysis of the log response ratios, with experiment as random factor (method=“DL”)

agg_meta <- function(df, groupvar){
  
  # print(groupvar)
  
  out_meta <- df %>% 
    
    dplyr::filter(varnam == eval(parse_character(groupvar))) %>% 
    
    # main meta analysis function call, adjusted step size (see http://www.metafor-project.org/doku.php/tips:convergence_problems_rma)
    # metafor::rma( logr, logr_var, method = "REML", slab = exp_nam, control = list(stepadj=0.3), data = . )
    metafor::rma.mv( logr, logr_var, method = "REML", random = ~ 1 | exp_nam, slab = exp_nam, control = list(stepadj=0.3), data = . )
  
  # transform back
  out_meta_scaled <- predict( out_meta, transf=exp )
  
  df_box <- tibble(
    varnam = groupvar, 
    middle = out_meta$b[1,1], 
    ymin   = out_meta$ci.lb, 
    ymax   = out_meta$ci.ub,
    
    middle_scaled = out_meta_scaled$pred, 
    ymin_scaled   = out_meta_scaled$ci.lb, 
    ymax_scaled   = out_meta_scaled$ci.ub
  )
  
  # print("... done.")
  return(list(df_box=df_box, out_meta=out_meta))
}

# do meta-analysis on all variables
varlist <- unique(df_c_agg$varnam)

## didn't work
varlist <- varlist[-which(varlist == "npp_fol")]
list_meta  <- purrr::map(as.list(varlist), ~agg_meta(df_c_agg, .))
df_metabox <- purrr::map_dfr(list_meta, "df_box")
  # left_join( df_varnams, by = "varnam" )
names(list_meta) <- varlist

df_c_agg %>%
  # arrange(logr) %>% 
  # mutate( my_lab = factor(my_lab, levels=rev(c("Asat", "Leaf Nmass", "Leaf Narea", "ANPP", "N uptake", "N availability", "root:shoot ratio", "BNPP", "Litter", "Soil decomposition rate")))) %>% 
  ggplot( aes(x = varnam, y = logr)) +
  geom_jitter( color = rgb(0,0,0,0.3), aes( size = 1/logr_se ), position = position_jitter(w = 0.2, h = 0) ) +
  geom_crossbar( data = df_metabox, aes(x = varnam, y = middle, ymin = ymin, ymax = ymax), fill = "grey80", alpha = 0.6, width = 0.5 ) +
  geom_hline( yintercept = 0.0, size = 0.5 ) +
  labs(x = "", y = "Log Response Ratio", size = expression(paste("Error"^{-1}))) +
  coord_flip() +
  ylim(-1,1)

## Warning: Removed 2 rows containing missing values (geom_point).

Couplings

## make a wide table with ANPP and BNPP
df_c_wide <- df_c_agg %>% 
  select(exp_nam, varnam, logr) %>%
  tidyr::spread( varnam, logr )

## add standard error
df_c_wide_se <- df_c_agg %>%
  select(exp_nam, varnam, logr_se) %>%
  tidyr::spread( varnam, logr_se)

write_csv(df_c_wide, file = "data/df_c_wide.csv")
write_csv(df_c_wide_se, file = "data/df_c_wide_se.csv")

df_c_wide %>% 
  knitr::kable()

tmp <- df_c_wide %>% 
  filter(!is.na(npp) & !is.na(gpp)) %>% 
  mutate(l_npp_gpp = npp / gpp) %>% 
  left_join(
    df_c_wide_se %>% 
      select(exp_nam, npp_se = npp, gpp_se = gpp) %>% 
      filter(!is.na(npp_se) & !is.na(gpp_se)),
    by = "exp_nam"
  ) %>% 
  mutate(certainty = 1/(gpp_se * npp_se))

tmp %>% 
  ggplot(aes(x = gpp, y = npp, label = exp_nam, size = certainty)) +
  geom_point(color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

tmp %>% 
  ggplot(aes(x = l_npp_gpp, y = exp_nam, size = certainty)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp <- df_c_wide %>% 
  filter(!is.na(anpp) & !is.na(npp)) %>% 
  mutate(l_anpp_npp = anpp / npp)

tmp %>% 
  ggplot(aes(x = npp, y = anpp, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_text_repel).

tmp %>% 
  ggplot(aes(x = l_anpp_npp, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp <- df_c_wide %>% 
  filter(!is.na(bnpp) & !is.na(npp)) %>% 
  mutate(l_bnpp_npp = bnpp / npp)

tmp %>% 
  ggplot(aes(x = npp, y = bnpp, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_text_repel).

tmp %>% 
  ggplot(aes(x = l_bnpp_npp, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp <- df_c_wide %>% 
  filter(!is.na(npp_wood) & !is.na(npp)) %>% 
  mutate(l_npp_wood_npp = npp_wood / npp)

tmp %>% 
  ggplot(aes(x = npp, y = npp_wood, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

tmp %>% 
  ggplot(aes(x = l_npp_wood_npp, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp <- df_c_wide %>% 
  filter(!is.na(cveg) & !is.na(npp)) %>% 
  mutate(l_cveg_npp = cveg / npp)

tmp %>% 
  ggplot(aes(x = npp, y = cveg, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_text_repel).

tmp %>% 
  ggplot(aes(x = l_cveg_npp, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp <- df_c_wide %>% 
  filter(!is.na(cveg_ag) & !is.na(cveg)) %>% 
  mutate(l_cveg_ag_cveg = cveg_ag / cveg)

tmp %>% 
  ggplot(aes(x = cveg, y = cveg_ag, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_text_repel).

tmp %>% 
  ggplot(aes(x = l_cveg_ag_cveg, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp %>% 
  ggplot(aes(x = l_cveg_ag_cveg, y = ..density..)) +
  geom_density() +
  geom_vline(xintercept = 1, linetype = "dotted") +
  xlim(-1, 3)

## Warning: Removed 3 rows containing non-finite values (stat_density).

tmp <- df_c_wide %>% 
  filter(!is.na(cveg_bg) & !is.na(cveg)) %>% 
  mutate(l_cveg_bg_cveg = cveg_bg / cveg)

tmp %>% 
  ggplot(aes(x = cveg, y = cveg_bg, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_text_repel).

tmp %>% 
  ggplot(aes(x = l_cveg_bg_cveg, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp %>% 
  ggplot(aes(x = l_cveg_bg_cveg, y = ..density..)) +
  geom_density() +
  geom_vline(xintercept = 1, linetype = "dotted") +
  xlim(-1, 3)

## Warning: Removed 3 rows containing non-finite values (stat_density).

tmp <- df_c_wide %>% 
  filter(!is.na(cveg_wood) & !is.na(cveg)) %>% 
  mutate(l_cveg_wood_cveg = cveg_wood / cveg)

tmp %>% 
  ggplot(aes(x = cveg, y = cveg_wood, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_text_repel).

tmp %>% 
  ggplot(aes(x = l_cveg_wood_cveg, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")

tmp <- df_c_wide %>% 
  filter(!is.na(cveg_fol) & !is.na(cveg)) %>% 
  mutate(l_cveg_fol_cveg = cveg_fol / cveg)

tmp %>% 
  ggplot(aes(x = cveg, y = cveg_fol, label = exp_nam)) +
  geom_point(size = 2, color = "red") +
  xlim(-0.1, 0.7) + ylim(-0.1, 0.7) +
  geom_abline(a = 0, b = 1, linetype = "dotted") +
  coord_equal() +
  theme_classic() +
  geom_text_repel(size = 2, color = "grey30") +
  labs(subtitle = paste("N = ", nrow(tmp)))

## Warning: Ignoring unknown parameters: a, b

tmp %>% 
  ggplot(aes(x = l_cveg_fol_cveg, y = exp_nam)) +
  geom_jitter(position = position_jitter(w = 0.2, h = 0)) +
  geom_vline(xintercept = 1, linetype = "dotted")