diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084264.1_AR.csv b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084264.1_AR.csv
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--- /dev/null
+++ b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084264.1_AR.csv
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diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084265.1_AR.csv b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084265.1_AR.csv
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+++ b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084265.1_AR.csv
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diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084266.1_AR.csv b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Allelic_richness/CM084266.1_AR.csv
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+"14","F3_Moderate","F2_Moderate",0.004
+"15","F3_Moderate","F3_Low",0.006
diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084275.1_Fst_heatmap.pdf b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084275.1_Fst_heatmap.pdf
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index 0000000..c6a0a18
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diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084276.1_Fst.csv b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084276.1_Fst.csv
new file mode 100644
index 0000000..9bebe62
--- /dev/null
+++ b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084276.1_Fst.csv
@@ -0,0 +1,16 @@
+"","Site1","Site2","Fst"
+"1","F1_Moderate","F1_Low",0.002
+"2","F2_Low","F1_Low",0.004
+"3","F2_Low","F1_Moderate",0.004
+"4","F2_Moderate","F1_Low",0.005
+"5","F2_Moderate","F1_Moderate",0.004
+"6","F2_Moderate","F2_Low",0.004
+"7","F3_Low","F1_Low",0.008
+"8","F3_Low","F1_Moderate",0.007
+"9","F3_Low","F2_Low",0.005
+"10","F3_Low","F2_Moderate",0.005
+"11","F3_Moderate","F1_Low",0.008
+"12","F3_Moderate","F1_Moderate",0.007
+"13","F3_Moderate","F2_Low",0.007
+"14","F3_Moderate","F2_Moderate",0.004
+"15","F3_Moderate","F3_Low",0.006
diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084276.1_Fst_heatmap.pdf b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084276.1_Fst_heatmap.pdf
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diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084277.1_Fst.csv b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084277.1_Fst.csv
new file mode 100644
index 0000000..9bebe62
--- /dev/null
+++ b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084277.1_Fst.csv
@@ -0,0 +1,16 @@
+"","Site1","Site2","Fst"
+"1","F1_Moderate","F1_Low",0.002
+"2","F2_Low","F1_Low",0.004
+"3","F2_Low","F1_Moderate",0.004
+"4","F2_Moderate","F1_Low",0.005
+"5","F2_Moderate","F1_Moderate",0.004
+"6","F2_Moderate","F2_Low",0.004
+"7","F3_Low","F1_Low",0.008
+"8","F3_Low","F1_Moderate",0.007
+"9","F3_Low","F2_Low",0.005
+"10","F3_Low","F2_Moderate",0.005
+"11","F3_Moderate","F1_Low",0.008
+"12","F3_Moderate","F1_Moderate",0.007
+"13","F3_Moderate","F2_Low",0.007
+"14","F3_Moderate","F2_Moderate",0.004
+"15","F3_Moderate","F3_Low",0.006
diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084277.1_Fst_heatmap.pdf b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084277.1_Fst_heatmap.pdf
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diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084278.1_Fst.csv b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084278.1_Fst.csv
new file mode 100644
index 0000000..9bebe62
--- /dev/null
+++ b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084278.1_Fst.csv
@@ -0,0 +1,16 @@
+"","Site1","Site2","Fst"
+"1","F1_Moderate","F1_Low",0.002
+"2","F2_Low","F1_Low",0.004
+"3","F2_Low","F1_Moderate",0.004
+"4","F2_Moderate","F1_Low",0.005
+"5","F2_Moderate","F1_Moderate",0.004
+"6","F2_Moderate","F2_Low",0.004
+"7","F3_Low","F1_Low",0.008
+"8","F3_Low","F1_Moderate",0.007
+"9","F3_Low","F2_Low",0.005
+"10","F3_Low","F2_Moderate",0.005
+"11","F3_Moderate","F1_Low",0.008
+"12","F3_Moderate","F1_Moderate",0.007
+"13","F3_Moderate","F2_Low",0.007
+"14","F3_Moderate","F2_Moderate",0.004
+"15","F3_Moderate","F3_Low",0.006
diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084278.1_Fst_heatmap.pdf b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084278.1_Fst_heatmap.pdf
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diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084279.1_Fst.csv b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084279.1_Fst.csv
new file mode 100644
index 0000000..9bebe62
--- /dev/null
+++ b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084279.1_Fst.csv
@@ -0,0 +1,16 @@
+"","Site1","Site2","Fst"
+"1","F1_Moderate","F1_Low",0.002
+"2","F2_Low","F1_Low",0.004
+"3","F2_Low","F1_Moderate",0.004
+"4","F2_Moderate","F1_Low",0.005
+"5","F2_Moderate","F1_Moderate",0.004
+"6","F2_Moderate","F2_Low",0.004
+"7","F3_Low","F1_Low",0.008
+"8","F3_Low","F1_Moderate",0.007
+"9","F3_Low","F2_Low",0.005
+"10","F3_Low","F2_Moderate",0.005
+"11","F3_Moderate","F1_Low",0.008
+"12","F3_Moderate","F1_Moderate",0.007
+"13","F3_Moderate","F2_Low",0.007
+"14","F3_Moderate","F2_Moderate",0.004
+"15","F3_Moderate","F3_Low",0.006
diff --git a/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084279.1_Fst_heatmap.pdf b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Fst/CM084279.1_Fst_heatmap.pdf
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diff --git a/RAnalysis/Output/Popgen/Low_v_Mod_DAPC.png b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Low_v_Mod_DAPC.png
similarity index 100%
rename from RAnalysis/Output/Popgen/Low_v_Mod_DAPC.png
rename to RAnalysis/Output/Popgen/statistics/low_v_moderate/Low_v_Mod_DAPC.png
diff --git a/RAnalysis/Output/Popgen/Low_v_Mod_Fst.png b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Low_v_Mod_Fst.png
similarity index 100%
rename from RAnalysis/Output/Popgen/Low_v_Mod_Fst.png
rename to RAnalysis/Output/Popgen/statistics/low_v_moderate/Low_v_Mod_Fst.png
diff --git a/RAnalysis/Output/Popgen/Low_v_Mod_PCA.png b/RAnalysis/Output/Popgen/statistics/low_v_moderate/Low_v_Mod_PCA.png
similarity index 100%
rename from RAnalysis/Output/Popgen/Low_v_Mod_PCA.png
rename to RAnalysis/Output/Popgen/statistics/low_v_moderate/Low_v_Mod_PCA.png
diff --git a/RAnalysis/Scripts/Popgen/06_popstats.Rmd b/RAnalysis/Scripts/Popgen/06_popstats.Rmd
index 2e6b8a3..e622822 100644
--- a/RAnalysis/Scripts/Popgen/06_popstats.Rmd
+++ b/RAnalysis/Scripts/Popgen/06_popstats.Rmd
@@ -7,19 +7,17 @@ author: "Samuel Gurr"
 
 * the goal of this script it to compute Fst, Ho, etc between our treatment groups (generations and OA condition) using the R packages ```adagenet``` and ```heirfstat```. TO this end, the final vcf finals must be subset to the desired matrices to build a geneind object. Further, we must subset out master metadat for the indi indivuals present in the matrix before running 
 
-* Fst: 
-
-  - our interest here is to *understand the genetic distance across generations both WITHIN and BETWEEN OA treatments*
-
 * steps: 
   
   (1) subset master vcf file (with all individuals) to those desired for each test
   
   (2) build geneind object from the matrices in #1
   
-  (3) subset the metadata 
+  (3) subset the metadata (complete several checks - **note** none alter our vcf file because we complete these with plink)
   
-  (4) run 
+  (4) run basic stats (Fis, Het, Fst)
+  
+  (5) build a function to execute all the above parsed by chromosome 
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
@@ -108,198 +106,6 @@ F1F2F3_LOW_MOD.metadata <- All.metadata %>% dplyr::filter(id %in%
 ```
 
 
-```{r BasicStats_chromsplit}
-
-chrom_ID     <- c('CM084264.1', 'CM084265.1', 'CM084266.1', 'CM084267.1', 
-                  'CM084268.1', 'CM084269.1', 'CM084270.1', 'CM084271.1', 
-                  'CM084272.1', 'CM084273.1', 'CM084274.1', 'CM084275.1',
-                  'CM084276.1', 'CM084277.1', 'CM084278.1', 'CM084279.1')
-
-chrom_number <- c(1,2,3,4,
-                  5,6,7,8,
-                  9,10,11,12,
-                  13,14,15,16)
-  
-chrom_DF     <- data.frame(chrom_ID, chrom_number)
-
-# Custom theme for ggplot2
-custom_theme = theme(
-                      axis.text.x = element_text(size = 10, angle = 90, vjust = 0.5, face = "bold"),
-                      axis.text.y = element_text(size = 10),
-                      axis.title.y = element_text(size = 12),
-                      axis.title.x = element_blank(),
-                      axis.line.y = element_line(size = 0.5),
-                      legend.title = element_blank(),
-                      legend.text = element_text(size = 12),
-                      panel.grid = element_blank(),
-                      panel.background = element_blank(),
-                      plot.title = element_text(hjust = 0.5, size = 15, face="bold")
-                      )
-
-# run function
-BasicStats_chromsplit <- function(chrom_num) {
-  
-   chrom_id       <- (chrom_DF %>% dplyr::filter(chrom_number == chrom_num))$chrom_ID 
-   chrom.vcf      <- F1F2F3_LOW_MOD.vcf[F1F2F3_LOW_MOD.vcf@fix[, "CHROM"] %in% chrom_id, ] # 300 variants 
-   chrom.metadata <- All.metadata %>% dplyr::filter(id %in% colnames(chrom.vcf@gt[,2:length(colnames(chrom.vcf@gt))]))
-   
-   # prep genind
-   ind      = as.character(chrom.metadata$id) # individual ID
-   gen_pCO2 = as.character(chrom.metadata$gen_treatment) # our metadata to calc pairwise fst
-   strata_df <- data.frame(ID = ind,  Population = gen_pCO2) 
-   
-   # create genind
-   chrom_gen <- vcfR::vcfR2genind(chrom.vcf, sep = "[|/]", pop = strata_df$Population, strata = strata_df)
-   
-   # mean allelic richness per site across all loci
-   AR_df <- as.data.frame(allelic.richness(genind2hierfstat(chrom_gen))$Ar %>%
-                          apply(MARGIN = 2, FUN = mean) %>% 
-                          round(digits = 3)) %>% dplyr::rename(allelicrichness = names(.)[1])
-   
-   # heirfstat basic stats
-   basic_chrom = basic.stats(chrom_gen, diploid = TRUE)
-   
- 
-   # mean observed heterozygosity per site
-   Ho_chrom = apply(basic_chrom$Ho, 
-                     MARGIN = 2, 
-                     FUN = mean, 
-                     na.rm = TRUE) %>%
-               round(digits = 2)
-  
-   # Mean expected heterozygosity per site
-   He_chrom = apply(basic_chrom$Hs, 
-                     MARGIN = 2, 
-                     FUN = mean, 
-                     na.rm = TRUE) %>%
-               round(digits = 2)
-  # Visualize heterozygosity barplot
-  # Create a data.frame of site names, Ho and He and then convert to long format
-  Het_chrom_df = data.frame(Site = names(Ho_chrom), Ho = Ho_chrom, He = He_chrom) %>% melt(id.vars = "Site")
-  hetlab.o = expression(italic("H")[o]) # Italic label
-  hetlab.e = expression(italic("H")[e])# Italic label
-  het_barplot <- ggplot(data = Het_chrom_df, aes(x = Site, y = value, fill = variable))+
-                            geom_bar(stat = "identity", position = position_dodge(width = 0.6), colour = "black")+
-                            scale_y_continuous(expand = c(0,0), limits = c(0,0.50))+
-                            scale_fill_manual(values = c("royalblue", "#bdbdbd"), labels = c(hetlab.o, hetlab.e))+
-                            ylab("Heterozygosity")+
-                            ggtitle(paste0("Chromosome # ",chrom_num, ": Low v Mod multigenerational")) +
-                            custom_theme
-
-
-   # inbreeding coefficient Fis across all loci
-   InbCoeff_df <- as.data.frame(apply(basic_chrom$Fis, 
-                                MARGIN = 2, 
-                                FUN = mean, 
-                                na.rm = TRUE) %>%
-                            round(digits = 3)) %>% dplyr::rename(Fis_inbreeding_coeff = names(.)[1])
-   
-   # calculate Fst
-   chrom_fst = genet.dist(chrom_gen, method = "WC84") %>% round(digits = 3)
-   Fst_df <- as.data.frame(chrom_fst)
-    # visualize Fst
-   lab_order = c('F1_Low', 'F1_Moderate', 'F2_Low', 'F2_Moderate', 'F3_Low', 'F3_Moderate') # Desired order of labels
-   # Change order of rows and cols
-   fst.mat = as.matrix(LowvMod_gen_fst)
-   fst.mat1 = fst.mat[lab_order, ]
-   fst.mat2 = fst.mat1[, lab_order]
-   # Create a data.frame
-   ind = which(upper.tri(fst.mat2), arr.ind = TRUE)
-   fst.df = data.frame(Site1 = dimnames(fst.mat2)[[2]][ind[,2]],
-                      Site2 = dimnames(fst.mat2)[[1]][ind[,1]],
-                      Fst = fst.mat2[ ind ])
-   # Keep the order of the levels in the data.frame for plotting 
-   fst.df$Site1 = factor(fst.df$Site1, levels = unique(fst.df$Site1))
-   fst.df$Site2 = factor(fst.df$Site2, levels = unique(fst.df$Site2))
-   # Convert minus values to zero
-   fst.df$Fst[fst.df$Fst < 0] = 0
-   # Fst italic label
-   fst.label = expression(italic("F")[ST])
-   # Extract middle Fst value for gradient argument
-   mid = max(fst.df$Fst) / 2
-   # Plot heatmap
-   Fst_heatmap <-  ggplot(data = fst.df, 
-                           aes(x = Site1, 
-                               y = Site2, 
-                               fill = Fst))+
-                      geom_tile(colour = "black")+
-                      geom_text(aes(label = Fst), 
-                                color="black", 
-                                size = 3)+
-                      scale_fill_gradient2(low = "blue", 
-                                           mid = "pink", 
-                                           high = "red", 
-                                           midpoint = mid, 
-                                           name = fst.label, 
-                                           limits = c(0, max(fst.df$Fst)), 
-                                           breaks = c(0, 0.05, 0.10, 0.15))+
-                      scale_x_discrete(expand = c(0,0))+
-                      scale_y_discrete(expand = c(0,0), position = "right")+
-                      ggtitle(paste0("Chromosome # ",chrom_num, ": Low v Mod multigenerational")) +
-                      theme(axis.text = element_text(colour = "black", size = 10, face = "bold"),
-                            axis.title = element_blank(),
-                            panel.grid = element_blank(),
-                            panel.background = element_blank(),
-                            legend.position = "right",
-                            legend.title = element_text(size = 14, face = "bold"),
-                            legend.text = element_text(size = 10)
-                            )
-
-
-   
-    # perform DAPC 
-   
-    # Perform cross validation to find the optimal number of PCs to retain in DAPC
-    set.seed(123)
-    x        = tab(chrom_gen, NA.method = "mean")
-    crossval = xvalDapc(x, chrom_gen$pop, result = "groupMean", xval.plot = TRUE) # this takes a LOONG TIME
-    numPCs   = as.numeric(crossval$`Number of PCs Achieving Lowest MSE`)
-    # Run a DAPC using site IDs as priors
-    dapc1 = dapc(chrom_gen, chrom_gen$pop, n.pca = numPCs, n.da = 3)
-    # Analyse how much percent of genetic variance is explained by each axis
-    percent = dapc1$eig/sum(dapc1$eig)*100  
-   
-    # visualize DAPC results
-    
-    ind_coords = as.data.frame(dapc1$ind.coord)
-    colnames(ind_coords) = c("Axis1","Axis2","Axis3") # Rename columns of dataframe
-    ind_coords$Ind = indNames(LowvMod_gen) # Add a column containing individuals
-    ind_coords$Site = LowvMod_gen$pop # Add a column with the site IDs
-    centroid = aggregate(cbind(Axis1, Axis2, Axis3) ~ Site, data = ind_coords, FUN = mean) # Calculate centroid (average) position for each population
-    ind_coords = left_join(ind_coords, centroid, by = "Site", suffix = c("",".cen")) # Add centroid coordinates to ind_coords dataframe
-    cols = brewer.pal(nPop(LowvMod_gen), "Set2") # Define colour palette
-    xlab = paste("Axis 1 (", format(round(percent[1], 1), nsmall=1)," %)", sep="") # Custom x and y labels
-    ylab = paste("Axis 2 (", format(round(percent[2], 1), nsmall=1)," %)", sep="") # Custom x and y labels
-    
-    # Scatter plot axis 1 vs. 2
-    DAPC_plot <- ggplot(data = ind_coords, aes(x = Axis1, y = Axis2))+
-                        geom_hline(yintercept = 0)+
-                        geom_vline(xintercept = 0)+
-                        geom_segment(aes(xend = Axis1.cen, yend = Axis2.cen, colour = Site), show.legend = FALSE)+
-                        geom_point(aes(fill = Site), shape = 21, size = 3, show.legend = FALSE)+
-                        geom_label(data = centroid, aes(label = Site, fill = Site), size = 4, show.legend = FALSE)+
-                        scale_fill_manual(values = cols)+
-                        scale_colour_manual(values = cols)+
-                        labs(x = xlab, y = ylab)+
-                        ggtitle(paste0("Chromosome # ",chrom_num, ": DAPC Low v Mod multigenerational")) +
-                        ggtheme    
-    
-   # return
-   return(list(het_barplot = het_barplot,
-               Fst_heatmap = Fst_heatmap,
-               Allelic_Richness = AR_df,
-               Fis = InbCoeff_df,
-               Fst = Fst_df,
-               DAPC = DAPC_plot))
- 
- 
- }
-
-chrom_1_basic_stats <- BasicStats_chromsplit(1)
-chrom_1_basic_stats$DAPC
-```
-
-
 ### Merge metadata with the raw snp 
 
 Why? we want the metadata to be recognized as the strate /pop when making the genind object - meaning these data need to be the exact order at which the rows appear in the vcf file. To ensure ths goes smoothly, simply add these data to the matrix merging by 'id'
@@ -782,6 +588,241 @@ path_out <- "C:/Users/samuel.gurr/Documents/Github_repositories/EAD-ASEB-Airradi
 ggsave(
   paste0(path_out, "/Low_v_Mod_DAPC.png"), 
          width = 8, height = 8, dpi = 600)
+```
+
+
+## Basic stats for each chromosome (build and execute custom function)
+
+* Build bunction to run all the above split by chromosome!
+
+* execute a for loop to run the function and export all output dataframes and figures
 
+```{r build BasicStats_chromsplit}
+
+chrom_ID     <- c('CM084264.1', 'CM084265.1', 'CM084266.1', 'CM084267.1', 
+                  'CM084268.1', 'CM084269.1', 'CM084270.1', 'CM084271.1', 
+                  'CM084272.1', 'CM084273.1', 'CM084274.1', 'CM084275.1',
+                  'CM084276.1', 'CM084277.1', 'CM084278.1', 'CM084279.1')
+
+chrom_number <- c(1,2,3,4,
+                  5,6,7,8,
+                  9,10,11,12,
+                  13,14,15,16)
+  
+chrom_DF     <- data.frame(chrom_ID, chrom_number)
+
+# Custom theme for ggplot2
+custom_theme = theme(
+                      axis.text.x = element_text(size = 10, angle = 90, vjust = 0.5, face = "bold"),
+                      axis.text.y = element_text(size = 10),
+                      axis.title.y = element_text(size = 12),
+                      axis.title.x = element_blank(),
+                      axis.line.y = element_line(size = 0.5),
+                      legend.title = element_blank(),
+                      legend.text = element_text(size = 12),
+                      panel.grid = element_blank(),
+                      panel.background = element_blank(),
+                      plot.title = element_text(hjust = 0.5, size = 15, face="bold")
+                      )
+
+# run function
+BasicStats_chromsplit <- function(chrom_num) {
+  
+   chrom_id       <- (chrom_DF %>% dplyr::filter(chrom_number == chrom_num))$chrom_ID 
+   chrom.vcf      <- F1F2F3_LOW_MOD.vcf[F1F2F3_LOW_MOD.vcf@fix[, "CHROM"] %in% chrom_id, ] # 300 variants 
+   chrom.metadata <- All.metadata %>% dplyr::filter(id %in% colnames(chrom.vcf@gt[,2:length(colnames(chrom.vcf@gt))]))
+   
+   # prep genind
+   ind      = as.character(chrom.metadata$id) # individual ID
+   gen_pCO2 = as.character(chrom.metadata$gen_treatment) # our metadata to calc pairwise fst
+   strata_df <- data.frame(ID = ind,  Population = gen_pCO2) 
+   
+   # create genind
+   chrom_gen <- vcfR::vcfR2genind(chrom.vcf, sep = "[|/]", pop = strata_df$Population, strata = strata_df)
+   
+   # mean allelic richness per site across all loci
+   AR_df <- as.data.frame(allelic.richness(genind2hierfstat(chrom_gen))$Ar %>%
+                          apply(MARGIN = 2, FUN = mean) %>% 
+                          round(digits = 3)) %>% dplyr::rename(allelicrichness = names(.)[1])
+   
+   # heirfstat basic stats
+   basic_chrom = basic.stats(chrom_gen, diploid = TRUE)
+   
+ 
+   # mean observed heterozygosity per site
+   Ho_chrom = apply(basic_chrom$Ho, 
+                     MARGIN = 2, 
+                     FUN = mean, 
+                     na.rm = TRUE) %>%
+               round(digits = 2)
+  
+   # Mean expected heterozygosity per site
+   He_chrom = apply(basic_chrom$Hs, 
+                     MARGIN = 2, 
+                     FUN = mean, 
+                     na.rm = TRUE) %>%
+               round(digits = 2)
+  # Visualize heterozygosity barplot
+  # Create a data.frame of site names, Ho and He and then convert to long format
+  Het_chrom_df = data.frame(Site = names(Ho_chrom), Ho = Ho_chrom, He = He_chrom) %>% melt(id.vars = "Site")
+  hetlab.o = expression(italic("H")[o]) # Italic label
+  hetlab.e = expression(italic("H")[e])# Italic label
+  het_barplot <- ggplot(data = Het_chrom_df, aes(x = Site, y = value, fill = variable))+
+                            geom_bar(stat = "identity", position = position_dodge(width = 0.6), colour = "black")+
+                            scale_y_continuous(expand = c(0,0), limits = c(0,0.50))+
+                            scale_fill_manual(values = c("royalblue", "#bdbdbd"), labels = c(hetlab.o, hetlab.e))+
+                            ylab("Heterozygosity")+
+                            ggtitle(paste0("Chromosome # ",chrom_num, ": Low v Mod multigenerational")) +
+                            custom_theme
+
+
+   # inbreeding coefficient Fis across all loci
+   InbCoeff_df <- as.data.frame(apply(basic_chrom$Fis, 
+                                MARGIN = 2, 
+                                FUN = mean, 
+                                na.rm = TRUE) %>%
+                            round(digits = 3)) %>% dplyr::rename(Fis_inbreeding_coeff = names(.)[1])
+   
+   # calculate Fst
+   chrom_fst = genet.dist(chrom_gen, method = "WC84") %>% round(digits = 3)
+    # visualize Fst
+   lab_order = c('F1_Low', 'F1_Moderate', 'F2_Low', 'F2_Moderate', 'F3_Low', 'F3_Moderate') # Desired order of labels
+   # Change order of rows and cols
+   fst.mat = as.matrix(LowvMod_gen_fst)
+   fst.mat1 = fst.mat[lab_order, ]
+   fst.mat2 = fst.mat1[, lab_order]
+   # Create a data.frame
+   ind = which(upper.tri(fst.mat2), arr.ind = TRUE)
+   fst.df = data.frame(Site1 = dimnames(fst.mat2)[[2]][ind[,2]],
+                      Site2 = dimnames(fst.mat2)[[1]][ind[,1]],
+                      Fst = fst.mat2[ ind ])
+   # Keep the order of the levels in the data.frame for plotting 
+   fst.df$Site1 = factor(fst.df$Site1, levels = unique(fst.df$Site1))
+   fst.df$Site2 = factor(fst.df$Site2, levels = unique(fst.df$Site2))
+   # Convert minus values to zero
+   fst.df$Fst[fst.df$Fst < 0] = 0
+   # Fst italic label
+   fst.label = expression(italic("F")[ST])
+   # Extract middle Fst value for gradient argument
+   mid = max(fst.df$Fst) / 2
+   # Plot heatmap
+   Fst_heatmap <-  ggplot(data = fst.df, 
+                           aes(x = Site1, 
+                               y = Site2, 
+                               fill = Fst))+
+                      geom_tile(colour = "black")+
+                      geom_text(aes(label = Fst), 
+                                color="black", 
+                                size = 3)+
+                      scale_fill_gradient2(low = "blue", 
+                                           mid = "pink", 
+                                           high = "red", 
+                                           midpoint = mid, 
+                                           name = fst.label, 
+                                           limits = c(0, max(fst.df$Fst)), 
+                                           breaks = c(0, 0.05, 0.10, 0.15))+
+                      scale_x_discrete(expand = c(0,0))+
+                      scale_y_discrete(expand = c(0,0), position = "right")+
+                      ggtitle(paste0("Chromosome # ",chrom_num, ": Low v Mod multigenerational")) +
+                      theme(axis.text = element_text(colour = "black", size = 10, face = "bold"),
+                            axis.title = element_blank(),
+                            panel.grid = element_blank(),
+                            panel.background = element_blank(),
+                            legend.position = "right",
+                            legend.title = element_text(size = 14, face = "bold"),
+                            legend.text = element_text(size = 10)
+                            )
+
+
+   
+    # perform DAPC 
+   
+    # Perform cross validation to find the optimal number of PCs to retain in DAPC
+    set.seed(123)
+    x        = tab(chrom_gen, NA.method = "mean")
+    crossval = xvalDapc(x, chrom_gen$pop, result = "groupMean", xval.plot = TRUE) # this takes a LOONG TIME
+    numPCs   = as.numeric(crossval$`Number of PCs Achieving Lowest MSE`)
+    # Run a DAPC using site IDs as priors
+    dapc1 = dapc(chrom_gen, chrom_gen$pop, n.pca = numPCs, n.da = 3)
+    # Analyse how much percent of genetic variance is explained by each axis
+    percent = dapc1$eig/sum(dapc1$eig)*100  
+   
+    # visualize DAPC results
+    
+    ind_coords = as.data.frame(dapc1$ind.coord)
+    colnames(ind_coords) = c("Axis1","Axis2","Axis3") # Rename columns of dataframe
+    ind_coords$Ind = indNames(LowvMod_gen) # Add a column containing individuals
+    ind_coords$Site = LowvMod_gen$pop # Add a column with the site IDs
+    centroid = aggregate(cbind(Axis1, Axis2, Axis3) ~ Site, data = ind_coords, FUN = mean) # Calculate centroid (average) position for each population
+    ind_coords = left_join(ind_coords, centroid, by = "Site", suffix = c("",".cen")) # Add centroid coordinates to ind_coords dataframe
+    cols = brewer.pal(nPop(LowvMod_gen), "Set2") # Define colour palette
+    xlab = paste("Axis 1 (", format(round(percent[1], 1), nsmall=1)," %)", sep="") # Custom x and y labels
+    ylab = paste("Axis 2 (", format(round(percent[2], 1), nsmall=1)," %)", sep="") # Custom x and y labels
+    
+    # Scatter plot axis 1 vs. 2
+    DAPC_plot <- ggplot(data = ind_coords, aes(x = Axis1, y = Axis2))+
+                        geom_hline(yintercept = 0)+
+                        geom_vline(xintercept = 0)+
+                        geom_segment(aes(xend = Axis1.cen, yend = Axis2.cen, colour = Site), show.legend = FALSE)+
+                        geom_point(aes(fill = Site), shape = 21, size = 3, show.legend = FALSE)+
+                        geom_label(data = centroid, aes(label = Site, fill = Site), size = 4, show.legend = FALSE)+
+                        scale_fill_manual(values = cols)+
+                        scale_colour_manual(values = cols)+
+                        labs(x = xlab, y = ylab)+
+                        ggtitle(paste0("Chromosome # ",chrom_num, ": DAPC Low v Mod multigenerational")) +
+                        ggtheme    
+    
+   # return
+   return(list(het_barplot = het_barplot,
+               Fst_heatmap = Fst_heatmap,
+               Allelic_Richness = AR_df,
+               Fis = InbCoeff_df,
+               Fst = fst.df,
+               DAPC = DAPC_plot))
+ }
+
+```
+
+
+```{r run BasicStats_chromsplit}
+
+# path
+path_out <- "C:/Users/samuel.gurr/Documents/Github_repositories/EAD-ASEB-Airradians_Popgen_OA/RAnalysis/Output/Popgen/"
+
+# for loop each chromosome, output to existing subfolders by chromsome id
+for (i in 1:nrow(chrom_DF)) {
+  # run
+  chrom_basic_stats <- BasicStats_chromsplit(chrom_DF[i,2])
+  
+  # export results
+  
+  # allelic richness dataframe
+  write.csv(chrom_basic_stats$Allelic_Richness, 
+            paste0(path_out, "statistics/low_v_moderate/Allelic_richness/",chrom_DF[i,1], "_AR.csv"))
+  
+  # fis dataframe 
+  write.csv(chrom_basic_stats$Fis, 
+            paste0(path_out, "statistics/low_v_moderate/Fis/",chrom_DF[i,1], "_Fis.csv"))
+  
+  # fst dataframe
+  write.csv(chrom_basic_stats$Fst, 
+            paste0(path_out, "statistics/low_v_moderate/Fst/",chrom_DF[i,1], "_Fst.csv"))
+  
+  # heterzygosity data and barplot 
+  pdf(paste0(path_out, "statistics/low_v_moderate/Het/",chrom_DF[i,1], "_Ho_He_barplot.pdf"), width = 6, height = 5)
+  print(chrom_basic_stats$het_barplot)
+  dev.off()
+  
+  # fst heatmap
+  pdf(paste0(path_out, "statistics/low_v_moderate/Fst/",chrom_DF[i,1], "_Fst_heatmap.pdf"), width = 6, height = 5)
+  print(chrom_basic_stats$Fst_heatmap)
+  dev.off()
+  
+  # DAPC plot
+  pdf(paste0(path_out, "statistics/low_v_moderate/DAPC/",chrom_DF[i,1], "_DAPC.pdf"), width = 6, height = 5)
+  print(chrom_basic_stats$DAPC)
+  dev.off()  
+}
+  
 
 ```
diff --git a/RAnalysis/Scripts/Popgen/NA b/RAnalysis/Scripts/Popgen/NA
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