diff --git a/scripts/plotting/structure_figures/AFFINITY_TEST_prominent_effects.R b/scripts/plotting/structure_figures/AFFINITY_TEST_prominent_effects.R
new file mode 100644
index 0000000..2ac94b8
--- /dev/null
+++ b/scripts/plotting/structure_figures/AFFINITY_TEST_prominent_effects.R
@@ -0,0 +1,138 @@
+
+foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,]
+bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,]
+
+wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity)
+wilcox.test(foo$mmcsm_lig~foo$sensitivity)
+
+wilcox.test(foo$affinity_scaled~foo$sensitivity)
+wilcox.test(foo$ligand_affinity_change~foo$sensitivity)
+
+wilcox.test(bar$mcsm_na_scaled~bar$sensitivity)
+wilcox.test(bar$mcsm_na_affinity~bar$sensitivity)
+
+wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity)
+wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity)
+
+
+# find the most "impactful" effect value
+biggest=max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
+
+abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==biggest
+
+abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==c(,biggest)
+
+max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
+
+
+a2 = (a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])
+a2
+# 
+# biggest = max(abs(a2[1,]))
+# 
+# #hmm
+# #which(abs(a2) == biggest)
+# #names(a2)[apply(a2, 1:4, function(i) which(i == max()))]
+# 
+# # get row max
+# a2$row_maximum = apply(abs(a2[,-1]), 1, max)
+# 
+# # get colname for abs(max_value)
+# #https://stackoverflow.com/questions/36960010/get-column-name-that-matches-specific-row-value-in-dataframe
+# #names(df)[which(df == 1, arr.ind=T)[, "col"]]
+# # yayy
+# names(a2)[which(abs(a2) == biggest, arr.ind=T)[, "col"]]
+# 
+# #another:https://statisticsglobe.com/return-column-name-of-largest-value-for-each-row-in-r
+# colnames(a2)[max.col(abs(a2), ties.method = "first")]  # Apply colnames & max.col functions
+# #################################################
+# # use whole df
+# #gene_aff_cols = c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')
+# 
+# biggest      = max(abs(a[gene_aff_cols]))
+# a$max_es     = biggest
+# a$effect     = names(a[gene_aff_cols])[which(abs(a[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
+# 
+# effect_name  = unique(a$effect)
+# #get index of value of max effect
+# ind = (which(abs(a[effect_name]) == biggest, arr.ind=T))
+# a[effect_name][ind]
+# # extract sign
+# a$effect_sign = sign(a[effect_name][ind])
+########################################################
+# maxn <- function(n) function(x) order(x, decreasing = TRUE)[n]
+# second_big = abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols])]
+# apply(df, 1, function(x)x[maxn(1)(x)])
+# apply(a[gene_aff_cols], 1, function(x) abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols]))])
+#########################################################
+# loop
+a2 = df2[df2$position%in%c(167, 423, 427),]
+test <- a2 %>%
+  dplyr::group_by(position) %>%
+  biggest      = max(abs(a2[gene_aff_cols]))
+  a2$max_es    = max(abs(a2[gene_aff_cols]))
+  a2$effect    = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
+  effect_name  = unique(a2$effect)
+  
+  #get index of value of max effect
+  ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
+  a2[effect_name][ind]
+  # extract sign
+  a2$effect_dir = sign(a2[effect_name][ind])
+#################################
+df2_short = df2[df2$position%in%c(167, 423, 427),]
+  
+for (i in unique(df2_short$position) ){
+  #print(i)
+  #print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
+  #cat(length(unique(df2$position)))
+  a2 = df2_short[df2_short$position==i,]
+  biggest     = max(abs(a2[gene_aff_cols]))
+  a2$max_es   = max(abs(a2[gene_aff_cols]))
+  a2$effect   = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
+  effect_name = unique(a2$effect)
+  
+  #get index of value of max effect
+  ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
+  a2[effect_name][ind]
+  # extract sign
+  a2$effect_sign = sign(a2[effect_name][ind])
+}
+
+#========================
+df2_short = df3[df3$position%in%c(167, 423, 427),]
+df2_short = df3[df3$position%in%c(170, 167, 493, 453, 435, 433, 480, 456, 445),]
+df2_short = df3[df3$position%in%c(435, 480),]
+df2_short = df3[df3$position%in%c(435, 480),]
+
+give_col=function(x,y,df=df2_short){
+  df[df$position==x,y]
+}
+  
+for (i in unique(df2_short$position) ){
+  #print(i)
+  #print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
+  #cat(length(unique(df2$position)))
+  #df2_short[df2_short$position==i,gene_aff_cols]
+  
+  biggest     = max(abs(give_col(i,gene_aff_cols)))
+  
+  df2_short[df2_short$position==i,'abs_max_effect'] = biggest
+  df2_short[df2_short$position==i,'effect_type']= names(
+    give_col(i,gene_aff_cols)[which(
+      abs(
+        give_col(i,gene_aff_cols)
+        ) == biggest, arr.ind=T
+      )[, "col"]])
+  
+  effect_name = df2_short[df2_short$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
+  
+  # get index/rowname for value of max effect, and then use it to get the original sign
+  # here
+  #df2_short[df2_short$position==i,c(effect_name)]
+  #which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
+  ind = rownames(which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
+  df2_short[df2_short$position==i,'effect_sign'] = sign(df2_short[effect_name][ind,])
+}
+
+df2_short$effect_type = sub("\\.[0-9]+", "", df2_short$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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