library(dplyr)
library(ape)
library(metagenomeFeatures)
test_metadata <- list(ACCESSION_DATE = "1/11/1111",
URL = "test-data",
DB_TYPE_NAME = "Test",
DB_TYPE_VALUE = "MgDb",
DB_SCHEMA_VERSION = "2.0")
test_db_file <- "../test_db.sqlite3"
## Test seq data
# Creating DNAStringSet in test - to address build test error on devl branch
test_keys <- as.character(1:10)
set.seed <- 10
rand <- sapply(1:10, function(x) paste(sample(c("A","T","G","C"), 21, replace = T), collapse = ""))
test_seq <- Biostrings::DNAStringSet(rand)
names(test_seq) <- test_keys
test_tree_file <- "../test_tree.rds"
test_tree <- readRDS(test_tree_file)
## Update to work with new class definition
testMgDb <- newMgDb(db_file = test_db_file, tree = test_tree_file, metadata = test_metadata)
## data.frame for testing select methods
tax_names <- matrix(paste0("tax_",0:69), ncol = 7)
colnames(tax_names) <- c("Kingdom","Phylum","Class","Ord",
"Family","Genus","Species")
taxa <- data.frame(Keys = as.character(1:10), tax_names,
stringsAsFactors = FALSE)
taxa <- dplyr::as_data_frame(taxa)
OTU <- c(4,4,4,3,2)
incomplete_query_df <- data.frame(OTU)
Keys_not <- c(1,1,1,1,1)
wrong_query_df <- data.frame(Keys_not, OTU)
Keys <- c(1,2,3,4,5)
query_df <- data.frame(Keys, OTU)
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