{popgendata}

Datasets for teaching and testing population genetics workflows

Installation

remotes::install_github("chollenbeck/popgendata")

Example usage

Load an example simulation dataset in genind format

library(popgendata)
library(pantomime)

## Registered S3 method overwritten by 'pegas':
##   method      from
##   print.amova ade4

twentygen <- popgendata::twentygen

Plot the trajectory of an allele over 20 generations

twentygen %>%
  get_allele_freqs() %>% # From pantomime
  mutate(generation = as.integer(pop)) %>%
  filter(locus == "SNP_1", allele == "G") %>%
  ggplot(aes(x = generation, y = freq)) +
    geom_line()

Larger datasets

data("reddrum")

gen <- reddrum$geno

Run a quick PCA of the data

pca_tbl <- qpca(gen)

ggplot(pca_tbl, aes(x = Axis1, y = Axis2, col = pop)) +
  geom_point()

Calculate stats by locus and pop and Plot F_{IS}:

stats <- get_locus_stats(gen)

stats

## # A tibble: 16,929 × 9
##    locus        pop   n_alleles prop_missing    maf    ho    he     fis hwe_pval
##    <chr>        <chr>     <int>        <dbl>  <dbl> <dbl> <dbl>   <dbl> <lgl>   
##  1 Contig_10008 APA           3       0      0      0.107 0.104 -0.0253 NA      
##  2 Contig_10013 APA           3       0.0714 0.0769 0.538 0.469 -0.148  NA      
##  3 Contig_10030 APA           2       0      0.214  0.357 0.343 -0.0425 NA      
##  4 Contig_1003… APA           2       0      0.179  0.357 0.298 -0.2    NA      
##  5 Contig_10045 APA           6       0      0      0.607 0.759  0.200  NA      
##  6 Contig_1006… APA           2       0      0.429  0.5   0.499 -0.0027 NA      
##  7 Contig_1007… APA           9       0      0      0.714 0.679 -0.0526 NA      
##  8 Contig_10089 APA           2       0      0.0536 0.107 0.103 -0.0385 NA      
##  9 Contig_1012… APA           2       0.0357 0.315  0.482 0.439 -0.0974 NA      
## 10 Contig_10127 APA           2       0      0.0893 0.179 0.165 -0.08   NA      
## # ℹ 16,919 more rows

ggplot(stats, aes(x = fis, fill = pop)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 94 rows containing non-finite outside the scale range
## (`stat_bin()`).