ramongallego

library(tidyverse)
library(gganimate)
library(ggmap)
library(viridis)
library(lubridate)

# We need three things: The data to plot, the map and the location of the samples

# For me,  the data looked like a long table with five columns: Site, Date, Species, prevalence and abundance

ramongallego

library(tidyverse)
library(insect)
library(rentrez)
set_entrez_key (YOUR_KEY_HERE)
library(here)

worlds.taxonomy <- read_rds(here("data", "all.taxonomy.rds")) 

# make your own taxonomy db using the command
# worlds.taxonomy <- taxonomy(), then save it for later use

ramongallego

# First download to your computer the right database from NCBI. You can either go to https://ftp.ncbi.nlm.nih.gov/blast/db/
# and download the right db, or if it is a multi-file one run
# perl <path/to/ncbi/bin>/update_blastdb.pl --decompress <name_of_db>
# I would recommend having one db per folder, as it might rewrite the taxdb.btd/bti files

######
#
## Once you have the downloaded db, the next thing is to extract the FASTA from the dbs 
# I am sure once there is a downloaded db, there is an easy way of generating the krakendb, but here we are