data-raw/mc_test.R
In USEPA/CompTox-ToxCast-tcpl: ToxCast Data Analysis Pipeline
#==============================================================================#
# NOTE: This script is written such that it is run from 'top' to 'bottom'
# or programmatically via the Terminal.
# ('R CMD BATCH --vanilla <script.R>'.)
# Please do not jump around when running this script.
#==============================================================================#
# NOTE: You MUST temporarily update tcplQuery() by adding a line at the top of the
# function: print(query).
# This is because the queries will be captured from output and saved as
# part of the mocking data.
#==============================================================================#
## r packages
devtools::load_all()
library(here)
library(dplyr)
library(stringr)
library(data.table)
#---------------------------#
## code to prepare `mc_test` dataset goes here
# source the user ID, password, host, and database information for connection
# - NOTE: To replicate one will need to save their own 'db_cred.R', including
# the 'userid', 'userpwd', 'host', and DB collection via 'ivtdb'.
source(file = here::here("data-raw/db_cred.R"),verbose = FALSE)
# connect to the DB
tcplConf(user = userid,
pass = userpwd,
host = host,
db = ivtdb,
drvr = "MySQL")
# pick endpoints and ids
# load the number of rows and max hitc per aeid
mc5_counts <- tcplQuery("SELECT DISTINCT aeid,
COUNT( aeid ) as n,
max(hitc) as max_hitc
FROM invitrodb.mc5 GROUP BY aeid")
# filter to only include where at least one sample is active and number of samples == 2
mc5_counts <- mc5_counts %>% filter(max_hitc > 0.9 & n == 2)
# pick one aeid
selected <- mc5_counts[sample(1:nrow(mc5_counts),size = 1,replace = FALSE),aeid]
# obtain the acid for the example dataset
acid <- tcplLoadAcid(fld = 'aeid',val = selected)$acid
# pick one sample/row from each level (lvl 3 contains ids back to lvl 0 and lvl 6 does back to lvl 4)
l3 <- tcplLoadData(lvl = 3, fld = "acid", val = acid)
l3_sample1 <- l3[sample(1:nrow(l3),size = 1,replace = FALSE)]
l3_sample2 <- l3[sample(1:nrow(l3),size = 2,replace = FALSE)]
l5 <- tcplPrepOtpt(tcplLoadData(lvl = 5, fld = "aeid", val = selected, add.fld = FALSE))
l5_sample1 <- l5[sample(1:nrow(l5),size = 1,replace = FALSE)]
l5_sample2 <- l5[sample(1:nrow(l5),size = 2,replace = FALSE)]
l6 <- tcplLoadData(lvl = 6, fld = "aeid", val = selected, add.fld = FALSE)
l6_sample1 <- l6[sample(1:nrow(l6),size = 1,replace = FALSE)]
l6_sample2 <- l6[sample(1:nrow(l6),size = 2,replace = FALSE)]
l7 <- tcplLoadData(lvl = 7, fld = "aeid", val = selected, add.fld = FALSE)
l7_sample1 <- l7[sample(1:nrow(l7),size = 1,replace = FALSE)]
l7_sample2 <- l7[sample(1:nrow(l7),size = 2,replace = FALSE)]
# load level 5 data for aeids which were filtered above
all_mc5 <- tcplPrepOtpt(tcplLoadData(lvl = 5, fld = "aeid", val = mc5_counts$aeid))
wllt_example_aeid <- unique(all_mc5[is.na(chnm) | is.na(dsstox_substance_id)]$aeid)
if(length(wllt_example_aeid) == 0) stop("Endpoint with missing chnm/dtxsid not found for wllt test in dataset. Try broadening which aeids are loaded above in 'all_mc5' line.")
wllt_example_aeid <- wllt_example_aeid[sample(1:length(wllt_example_aeid), size = 1,replace = FALSE)]
# pick compare endpoints and ids
# first determine which aeids have the same chnms tested as the selected aeid
all_mc5 <- all_mc5 |> mutate(match = chnm %in% all_mc5[aeid == selected,chnm]) |>
group_by(aeid) |> filter(all(match) & aeid != selected) |> as.data.table()
mc5_counts <- mc5_counts[aeid %in% unique(all_mc5$aeid)]
if(nrow(mc5_counts) < 3) stop("Not enough endpoints which test the exact same set of chemicals. Try picking a new original aeid ('selected' var above)")
compare.aeid <- mc5_counts[sample(1:nrow(mc5_counts),size = 1,replace = FALSE),aeid]
compare.l5 <- all_mc5[aeid == compare.aeid,]
compare.l5_sample1 <- compare.l5[chnm %in% l5_sample1$chnm]
compare.l5_sample2 <- compare.l5[sample(1:nrow(compare.l5),size = 2,replace = FALSE)]
# pick extra endpoints
mc5_counts <- mc5_counts[aeid != compare.aeid]
extra.aeids <- mc5_counts[sample(1:nrow(mc5_counts),size = 2,replace = FALSE),aeid]
if (any(is.na(extra.aeids))) stop("extra.aeids contain NA aeid.")
aeid <- selected
#to add more functions which load some data from invitro, add more else if conditionals
get_query_data <- function(lvl, fld, val, compare = "m4id", add.fld = TRUE, func = "tcplLoadData") {
if (func == "tcplLoadData") {
# IMPORTANT || MUST ADD TEMPORARY LINE TO TCPLQUERY --------------------------
# add temporary line to top of tcplQuery to get the query string: print(query)
query_strings <- capture.output(result<-tcplLoadData(lvl = lvl, fld = fld, val = val, add.fld = add.fld))
} else if (func == "tcplPlot") {
query_strings <- capture.output(result<-tcplPlot(type = "mc", fld = fld,
val = val, compare = compare,
output = "pdf", multi = TRUE, flags = TRUE,
fileprefix = "temp_tcplPlot"))
file.remove(stringr::str_subset(list.files(), "^temp_tcplPlot")) # clean up
}
query_strings <- unique(gsub("\\\\", "\\\"", gsub("\"", "", gsub("\\\\n", "\\\n", gsub("\\[1\\] ", "", query_strings)))))
# use queries to save data
dat <- lapply(query_strings, function(query_string) {
return(tcplQuery(query_string))
})
names(dat) <- query_strings
# also store fld and val in list object for use in test case
dat[fld] <- val
return(dat)
}
# to add more tests with new/different data for existing or new functions, add lines below and run script
mc_test <- list(
tcplConfQuery = tcplQuery("SHOW VARIABLES LIKE 'max_allowed_packet'"),
mc0_by_m0id = get_query_data(lvl = 0, fld = "m0id", val = l3_sample1$m0id),
mc0_by_acid = get_query_data(lvl = 0, fld = "acid", val = acid),
mc1_by_m1id = get_query_data(lvl = 1, fld = "m1id", val = l3_sample1$m1id),
mc1_by_acid = get_query_data(lvl = 1, fld = "acid", val = acid),
mc2_by_m2id = get_query_data(lvl = 2, fld = "m2id", val = l3_sample1$m2id),
mc2_by_acid = get_query_data(lvl = 2, fld = "acid", val = acid),
mc3_by_m3id = get_query_data(lvl = 3, fld = "m3id", val = l3_sample1$m3id),
mc3_by_aeid = get_query_data(lvl = 3, fld = "aeid", val = aeid),
mc4_by_m4id = get_query_data(lvl = 4, fld = "m4id", val = l5_sample1$m4id),
mc4_by_aeid = get_query_data(lvl = 4, fld = "aeid", val = aeid, add.fld = FALSE),
mc5_by_m5id = get_query_data(lvl = 5, fld = "m5id", val = l5_sample1$m5id),
mc5_by_aeid = get_query_data(lvl = 5, fld = "aeid", val = aeid, add.fld = FALSE),
mc6_by_m6id = get_query_data(lvl = 6, fld = "m6id", val = l6_sample1$m6id),
mc6_by_aeid = get_query_data(lvl = 6, fld = "aeid", val = aeid),
mc7_by_m7id = get_query_data(lvl = 7, fld = "m7id", val = l7_sample1$m7id),
mc7_by_aeid = get_query_data(lvl = 7, fld = "aeid", val = aeid),
mcagg_by_aeid = get_query_data(lvl = "agg", fld = "aeid", val = aeid),
plot_single_m4id = get_query_data(fld = "m4id",
val = l5_sample1$m4id,
func = "tcplPlot"),
plot_multiple_m4id = get_query_data(fld = "m4id",
val = list(l5_sample2$m4id),
func = "tcplPlot"),
plot_single_aeid = get_query_data(fld = "aeid",
val = aeid,
func = "tcplPlot"),
plot_single_aeid_missing_chem = get_query_data(fld = "aeid",
val = wllt_example_aeid,
func = "tcplPlot"),
plot_multiple_aeid = get_query_data(fld = "aeid",
val = list(c(aeid, compare.aeid)),
func = "tcplPlot"),
plot_single_spid = get_query_data(fld = c("spid", "aeid"),
val = list(l5_sample1$spid, aeid),
func = "tcplPlot"),
plot_multiple_spid = get_query_data(fld = c("spid", "aeid"),
val = list(l5_sample2$spid, aeid),
func = "tcplPlot"),
plot_single_m4id_compare = get_query_data(fld = "m4id",
val = list(c(l5_sample1$m4id, compare.l5_sample1$m4id)),
compare = "chnm",
func = "tcplPlot"),
plot_multiple_m4id_compare = get_query_data(fld = "m4id",
val = list(c(l5_sample2$m4id, compare.l5_sample2$m4id)),
compare = "chnm",
func = "tcplPlot"),
plot_single_aeid_compare = get_query_data(fld = "aeid",
val = list(c(aeid, compare.aeid)),
compare = "chnm",
func = "tcplPlot"),
plot_multiple_aeid_compare = get_query_data(fld = "aeid",
val = list(c(aeid, compare.aeid, extra.aeids)),
compare = "chnm",
func = "tcplPlot"),
plot_single_spid_compare = get_query_data(fld = c("spid", "aeid"),
val = list(c(l5_sample1$spid, compare.l5_sample1$spid), c(aeid, compare.aeid)),
compare = "chnm",
func = "tcplPlot"),
plot_multiple_spid_compare = get_query_data(fld = c("spid", "aeid"),
val = list(c(l5_sample2$spid, compare.l5_sample2$spid), c(aeid, compare.aeid)),
compare = "chnm",
func = "tcplPlot")
)
#---------------------------#
## save the data
usethis::use_data(mc_test, overwrite = TRUE)
#---------------------------#
USEPA/CompTox-ToxCast-tcpl documentation built on June 2, 2025, 3:13 a.m.
R Package Documentation
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#==============================================================================#
# NOTE: This script is written such that it is run from 'top' to 'bottom'
# or programmatically via the Terminal.
# ('R CMD BATCH --vanilla <script.R>'.)
# Please do not jump around when running this script.
#==============================================================================#
# NOTE: You MUST temporarily update tcplQuery() by adding a line at the top of the
# function: print(query).
# This is because the queries will be captured from output and saved as
# part of the mocking data.
#==============================================================================#
## r packages
devtools::load_all()
library(here)
library(dplyr)
library(stringr)
library(data.table)
#---------------------------#
## code to prepare `mc_test` dataset goes here
# source the user ID, password, host, and database information for connection
# - NOTE: To replicate one will need to save their own 'db_cred.R', including
# the 'userid', 'userpwd', 'host', and DB collection via 'ivtdb'.
source(file = here::here("data-raw/db_cred.R"),verbose = FALSE)
# connect to the DB
tcplConf(user = userid,
pass = userpwd,
host = host,
db = ivtdb,
drvr = "MySQL")
# pick endpoints and ids
# load the number of rows and max hitc per aeid
mc5_counts <- tcplQuery("SELECT DISTINCT aeid,
COUNT( aeid ) as n,
max(hitc) as max_hitc
FROM invitrodb.mc5 GROUP BY aeid")
# filter to only include where at least one sample is active and number of samples == 2
mc5_counts <- mc5_counts %>% filter(max_hitc > 0.9 & n == 2)
# pick one aeid
selected <- mc5_counts[sample(1:nrow(mc5_counts),size = 1,replace = FALSE),aeid]
# obtain the acid for the example dataset
acid <- tcplLoadAcid(fld = 'aeid',val = selected)$acid
# pick one sample/row from each level (lvl 3 contains ids back to lvl 0 and lvl 6 does back to lvl 4)
l3 <- tcplLoadData(lvl = 3, fld = "acid", val = acid)
l3_sample1 <- l3[sample(1:nrow(l3),size = 1,replace = FALSE)]
l3_sample2 <- l3[sample(1:nrow(l3),size = 2,replace = FALSE)]
l5 <- tcplPrepOtpt(tcplLoadData(lvl = 5, fld = "aeid", val = selected, add.fld = FALSE))
l5_sample1 <- l5[sample(1:nrow(l5),size = 1,replace = FALSE)]
l5_sample2 <- l5[sample(1:nrow(l5),size = 2,replace = FALSE)]
l6 <- tcplLoadData(lvl = 6, fld = "aeid", val = selected, add.fld = FALSE)
l6_sample1 <- l6[sample(1:nrow(l6),size = 1,replace = FALSE)]
l6_sample2 <- l6[sample(1:nrow(l6),size = 2,replace = FALSE)]
l7 <- tcplLoadData(lvl = 7, fld = "aeid", val = selected, add.fld = FALSE)
l7_sample1 <- l7[sample(1:nrow(l7),size = 1,replace = FALSE)]
l7_sample2 <- l7[sample(1:nrow(l7),size = 2,replace = FALSE)]
# load level 5 data for aeids which were filtered above
all_mc5 <- tcplPrepOtpt(tcplLoadData(lvl = 5, fld = "aeid", val = mc5_counts$aeid))
wllt_example_aeid <- unique(all_mc5[is.na(chnm) | is.na(dsstox_substance_id)]$aeid)
if(length(wllt_example_aeid) == 0) stop("Endpoint with missing chnm/dtxsid not found for wllt test in dataset. Try broadening which aeids are loaded above in 'all_mc5' line.")
wllt_example_aeid <- wllt_example_aeid[sample(1:length(wllt_example_aeid), size = 1,replace = FALSE)]
# pick compare endpoints and ids
# first determine which aeids have the same chnms tested as the selected aeid
all_mc5 <- all_mc5 |> mutate(match = chnm %in% all_mc5[aeid == selected,chnm]) |>
group_by(aeid) |> filter(all(match) & aeid != selected) |> as.data.table()
mc5_counts <- mc5_counts[aeid %in% unique(all_mc5$aeid)]
if(nrow(mc5_counts) < 3) stop("Not enough endpoints which test the exact same set of chemicals. Try picking a new original aeid ('selected' var above)")
compare.aeid <- mc5_counts[sample(1:nrow(mc5_counts),size = 1,replace = FALSE),aeid]
compare.l5 <- all_mc5[aeid == compare.aeid,]
compare.l5_sample1 <- compare.l5[chnm %in% l5_sample1$chnm]
compare.l5_sample2 <- compare.l5[sample(1:nrow(compare.l5),size = 2,replace = FALSE)]
# pick extra endpoints
mc5_counts <- mc5_counts[aeid != compare.aeid]
extra.aeids <- mc5_counts[sample(1:nrow(mc5_counts),size = 2,replace = FALSE),aeid]
if (any(is.na(extra.aeids))) stop("extra.aeids contain NA aeid.")
aeid <- selected
#to add more functions which load some data from invitro, add more else if conditionals
get_query_data <- function(lvl, fld, val, compare = "m4id", add.fld = TRUE, func = "tcplLoadData") {
if (func == "tcplLoadData") {
# IMPORTANT || MUST ADD TEMPORARY LINE TO TCPLQUERY --------------------------
# add temporary line to top of tcplQuery to get the query string: print(query)
query_strings <- capture.output(result<-tcplLoadData(lvl = lvl, fld = fld, val = val, add.fld = add.fld))
} else if (func == "tcplPlot") {
query_strings <- capture.output(result<-tcplPlot(type = "mc", fld = fld,
val = val, compare = compare,
output = "pdf", multi = TRUE, flags = TRUE,
fileprefix = "temp_tcplPlot"))
file.remove(stringr::str_subset(list.files(), "^temp_tcplPlot")) # clean up
}
query_strings <- unique(gsub("\\\\", "\\\"", gsub("\"", "", gsub("\\\\n", "\\\n", gsub("\\[1\\] ", "", query_strings)))))
# use queries to save data
dat <- lapply(query_strings, function(query_string) {
return(tcplQuery(query_string))
})
names(dat) <- query_strings
# also store fld and val in list object for use in test case
dat[fld] <- val
return(dat)
}
# to add more tests with new/different data for existing or new functions, add lines below and run script
mc_test <- list(
tcplConfQuery = tcplQuery("SHOW VARIABLES LIKE 'max_allowed_packet'"),
mc0_by_m0id = get_query_data(lvl = 0, fld = "m0id", val = l3_sample1$m0id),
mc0_by_acid = get_query_data(lvl = 0, fld = "acid", val = acid),
mc1_by_m1id = get_query_data(lvl = 1, fld = "m1id", val = l3_sample1$m1id),
mc1_by_acid = get_query_data(lvl = 1, fld = "acid", val = acid),
mc2_by_m2id = get_query_data(lvl = 2, fld = "m2id", val = l3_sample1$m2id),
mc2_by_acid = get_query_data(lvl = 2, fld = "acid", val = acid),
mc3_by_m3id = get_query_data(lvl = 3, fld = "m3id", val = l3_sample1$m3id),
mc3_by_aeid = get_query_data(lvl = 3, fld = "aeid", val = aeid),
mc4_by_m4id = get_query_data(lvl = 4, fld = "m4id", val = l5_sample1$m4id),
mc4_by_aeid = get_query_data(lvl = 4, fld = "aeid", val = aeid, add.fld = FALSE),
mc5_by_m5id = get_query_data(lvl = 5, fld = "m5id", val = l5_sample1$m5id),
mc5_by_aeid = get_query_data(lvl = 5, fld = "aeid", val = aeid, add.fld = FALSE),
mc6_by_m6id = get_query_data(lvl = 6, fld = "m6id", val = l6_sample1$m6id),
mc6_by_aeid = get_query_data(lvl = 6, fld = "aeid", val = aeid),
mc7_by_m7id = get_query_data(lvl = 7, fld = "m7id", val = l7_sample1$m7id),
mc7_by_aeid = get_query_data(lvl = 7, fld = "aeid", val = aeid),
mcagg_by_aeid = get_query_data(lvl = "agg", fld = "aeid", val = aeid),
plot_single_m4id = get_query_data(fld = "m4id",
val = l5_sample1$m4id,
func = "tcplPlot"),
plot_multiple_m4id = get_query_data(fld = "m4id",
val = list(l5_sample2$m4id),
func = "tcplPlot"),
plot_single_aeid = get_query_data(fld = "aeid",
val = aeid,
func = "tcplPlot"),
plot_single_aeid_missing_chem = get_query_data(fld = "aeid",
val = wllt_example_aeid,
func = "tcplPlot"),
plot_multiple_aeid = get_query_data(fld = "aeid",
val = list(c(aeid, compare.aeid)),
func = "tcplPlot"),
plot_single_spid = get_query_data(fld = c("spid", "aeid"),
val = list(l5_sample1$spid, aeid),
func = "tcplPlot"),
plot_multiple_spid = get_query_data(fld = c("spid", "aeid"),
val = list(l5_sample2$spid, aeid),
func = "tcplPlot"),
plot_single_m4id_compare = get_query_data(fld = "m4id",
val = list(c(l5_sample1$m4id, compare.l5_sample1$m4id)),
compare = "chnm",
func = "tcplPlot"),
plot_multiple_m4id_compare = get_query_data(fld = "m4id",
val = list(c(l5_sample2$m4id, compare.l5_sample2$m4id)),
compare = "chnm",
func = "tcplPlot"),
plot_single_aeid_compare = get_query_data(fld = "aeid",
val = list(c(aeid, compare.aeid)),
compare = "chnm",
func = "tcplPlot"),
plot_multiple_aeid_compare = get_query_data(fld = "aeid",
val = list(c(aeid, compare.aeid, extra.aeids)),
compare = "chnm",
func = "tcplPlot"),
plot_single_spid_compare = get_query_data(fld = c("spid", "aeid"),
val = list(c(l5_sample1$spid, compare.l5_sample1$spid), c(aeid, compare.aeid)),
compare = "chnm",
func = "tcplPlot"),
plot_multiple_spid_compare = get_query_data(fld = c("spid", "aeid"),
val = list(c(l5_sample2$spid, compare.l5_sample2$spid), c(aeid, compare.aeid)),
compare = "chnm",
func = "tcplPlot")
)
#---------------------------#
## save the data
usethis::use_data(mc_test, overwrite = TRUE)
#---------------------------#
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