Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Make list_vars return a list of vars invisibly #172

Closed
wants to merge 1 commit into from
Closed

Make list_vars return a list of vars invisibly #172

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 4 additions & 3 deletions NEWS.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,13 +2,14 @@
### General
* Improved the detection of modifications to the xpdb the tables through md5 check sum (@sebastianueckert)
* Fix bug in all general plot functions where a qualified package access (e.g. `xpose::dv_vs_ipred`) would result in an error when printing (@rfaelens, #95)
* `list_vars` now invisbly returns a list (@billdenney #124)

### Data import
* Added raw NONMEM output file to the xpose database. Added a new `raw = TRUE/FALSE` argument to `get_code()` (@billdenney #103)
* Improved on the data import code (i.e. performance gains on the data import overhead)
* `read_nm_model()` now accepts `.nmlog` and `.nmctl` extensions for NONMEM
* All source files are now stored along with their md5 check sum. They can be accessed with `get_source_files()` (#140)
* Fixed a bug that prevented the import of table in the format $TABLE FILE=<file> <col-names>
* Fixed a bug that prevented the import of table in the format $TABLE FILE=<file> <col-names>
* The `ID` column is no longer required in the tables (#97)

# xpose 0.4.5
Expand Down Expand Up @@ -85,7 +86,7 @@

### Data import/edit
* Improved `dir` and `file` arguments usage
* Improved error robustness of `xpose_data()`
* Improved error robustness of `xpose_data()`
* Added new dplyr verbs for xpdb editing: `slice()`, `select()`, `rename()`, `distinct()`, `summarize()`, `group_by()` and `ungroup()`
* dplyr verbs can now also be used to edit vpc data
* Added `irep()` function to add simulation counter to any dataset
Expand All @@ -107,7 +108,7 @@
* New internal data structure using nested tibbles
* Improvement of documentation, and testing

### Data import
### Data import
#### `read_nm_tables()`
* Handles NONMEM tables in .csv, .zip format
* Handles multiple $PROB and tables with FIRSTONLY option
Expand Down
136 changes: 92 additions & 44 deletions R/vars_list.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,59 +4,107 @@
#'
#' @param xpdb An \code{xpose_data} object from which the model code will be extracted.
#' @param .problem The problem to be used, by lists all available problems.
#'
#' @return Prints the list of all available variables, and returns that list
#' invisibly. The name of the list is the problem number, the names of the
#' elements of the sub-lists are the variable types, and the values of the
#' sub-lists are the column names.
#' @seealso \code{\link{set_var_types}}
#' @examples
#' list_vars(xpdb_ex_pk)
#' @export
list_vars <- function(xpdb, .problem = NULL) {
# Check input
check_xpdb(xpdb, check = 'data')

#' @importFrom dplyr group_by_at
#' @importFrom purrr map
#' @importFrom stringr str_c
list_vars <- function(xpdb, .problem=NULL) {
name_map <-
c(
"id"="Subject identifier (id)",
"occ"="Occasion flag (occ)",
"na"="Not attributed (na)",
"amt"="Dose amount (amt)",
"idv"="Independent variable (idv)",
"ipred"="Model individual predictions (ipred)",
"pred"="Model typical predictions (pred)",
"res"="Residuals (res)",
"evid"="Event identifier (evid)",
"dv"="Dependent variable (dv)",
"catcov"="Categorical covariates (catcov)",
"contcov"="Continuous covariates (contcov)",
"param"="Model parameter (param)",
"eta"="Eta (eta)",
"a"="Compartment amounts (a)",
"dvid"="DV identifier (dvid)",
"mdv"="Missing dependent variable (mdv)"
)
ret <- list_vars_prep(xpdb, .problem=.problem)
ret_print <-
lapply(
X=ret,
FUN=function(x) {
new_names <- name_map[names(x)]
new_names <-
sprintf(
# left-justified, space-filled with the required number of
# characters
fmt=paste0("%-", max(nchar(new_names)) + 1, "s"),
new_names
)
setNames(object=x, nm=new_names)
}
)
lapply(
X=names(ret_print),
FUN=function(x) {
cat("\nList of available variables for problem no. ", x, "\n", sep="")
cat(
sprintf(
" - %s: %s\n",
names(ret_print[[x]]),
sapply(X=ret_print[[x]], FUN=paste, collapse=", ")
),
sep=""
)
}
)
invisible(ret)
}

#' @importFrom tidyr nest
#' @importFrom dplyr bind_rows
list_vars_prep <- function(xpdb, .problem=NULL) {
check_xpdb(xpdb, check = "data") # Check input

x <- xpdb$data

if (!is.null(.problem)) {
if (!all(.problem %in% x$problem)) {
stop('Problem no.', stringr::str_c(.problem[!.problem %in% x$problem], collapse = ', '),
' not found in the data.', call. = FALSE)
stop(
"Problem no.",
stringr::str_c(.problem[!.problem %in% x$problem], collapse = ", "),
" not found in the data.",
call. = FALSE
)
}
x <- x[x$problem %in% .problem, ]
}

order <- c('id', 'dv', 'idv', 'dvid', 'occ', 'amt', 'evid', 'mdv', 'pred', 'ipred',
'param', 'eta', 'res', 'catcov', 'contcov', 'a', 'na')

x <- x %>%
dplyr::mutate(grouping = as.integer(.$problem)) %>%
dplyr::group_by_at(.vars = 'grouping') %>%
tidyr::nest() %>%
dplyr::ungroup() %>%
{purrr::map(.$data, function(df) {
cat('\nList of available variables for problem no.', df$problem[1], '\n')
df$index[[1]] %>%
dplyr::group_by_at(.vars = 'type') %>%
tidyr::nest() %>%
dplyr::ungroup() %>%
dplyr::mutate(string = purrr::map_chr(.$data, ~stringr::str_c(unique(.$col), collapse = ', ')),
descr = dplyr::case_when(.$type == 'id' ~ 'Subject identifier (id)',
.$type == 'occ' ~ 'Occasion flag (occ)',
.$type == 'na' ~ 'Not attributed (na)',
.$type == 'amt' ~ 'Dose amount (amt)',
.$type == 'idv' ~ 'Independent variable (idv)',
.$type == 'ipred' ~ 'Model individual predictions (ipred)',
.$type == 'pred' ~ 'Model typical predictions (pred)',
.$type == 'res' ~ 'Residuals (res)',
.$type == 'evid' ~ 'Event identifier (evid)',
.$type == 'dv' ~ 'Dependent variable (dv)',
.$type == 'catcov' ~ 'Categorical covariates (catcov)',
.$type == 'contcov' ~ 'Continuous covariates (contcov)',
.$type == 'param' ~ 'Model parameter (param)',
.$type == 'eta' ~ 'Eta (eta)',
.$type == 'a' ~ 'Compartment amounts (a)',
.$type == 'dvid' ~ 'DV identifier (dvid)',
.$type == 'mdv' ~ 'Missing dependent variable (mdv)')) %>%
dplyr::mutate(descr = stringr::str_pad(.$descr, 37, 'right')) %>%
dplyr::slice(order(match(.$type, order))) %>%
{stringr::str_c(' -', .$descr, ':', .$string, sep = ' ')} %>%
cat(sep = '\n')})}
type_order <-
c("id", "dv", "idv", "dvid", "occ", "amt", "evid", "mdv", "pred",
"ipred", "param", "eta", "res", "catcov", "contcov", "a", "na")
ret <-
tidyr::nest(
.data=dplyr::group_by_at(.tbl=x, .vars="problem")
)
ret$list_of_vars <-
purrr::map(
.x=ret$data,
.f=function(y) {
ret <- list()
current_index <- dplyr::bind_rows(y$index)
for (current_type in intersect(type_order, current_index$type)) {
ret[[current_type]] <- unique(current_index$col[current_index$type %in% current_type])
}
ret
}
)
setNames(object=ret$list_of_vars, nm=as.character(ret$problem))
}
6 changes: 6 additions & 0 deletions man/list_vars.Rd
View file
Open in desktop

Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.

2 changes: 1 addition & 1 deletion tests/testthat/test-console_outputs.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ print_text_modified <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0
prm_text_1 <- '\nReporting transformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value RSE\n THETA1 TVCL 26.29 0.03391\n THETA2 TVV 1.348 0.0325\n THETA3 TVKA 4.204 0.1925\n THETA4 LAG 0.208 0.07554\n THETA5 Prop. Err 0.2046 0.1097\n THETA6 Add. Err 0.01055 0.3466\n THETA7 CRCL on CL 0.007172 0.2366\n OMEGA(1,1) IIV CL 0.2701 0.08616\n OMEGA(2,2) IIV V 0.195 0.1643\n OMEGA(3,3) IIV KA 1.381 0.1463\n SIGMA(1,1) 1 fix - '
prm_text_2 <- '\nReporting untransformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as variances for the diagonal elements and as covariances for the off-diagonal elements.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value SE\n THETA1 TVCL 26.29 0.8915\n THETA2 TVV 1.348 0.04381\n THETA3 TVKA 4.204 0.8091\n THETA4 LAG 0.208 0.01571\n THETA5 Prop. Err 0.2046 0.02244\n THETA6 Add. Err 0.01055 0.003658\n THETA7 CRCL on CL 0.007172 0.001697\n OMEGA(1,1) IIV CL 0.07295 0.01257\n OMEGA(2,2) IIV V 0.03802 0.0125\n OMEGA(3,3) IIV KA 1.907 0.5582\n SIGMA(1,1) 1 fix - '
summary_text <- '\nSummary for problem no. 0 [Global information] \n - Software @software : nonmem\n - Software version @version : 7.3.0\n - Run directory @dir : analysis/models/pk/\n - Run file @file : run001.lst\n - Run number @run : run001\n - Reference model @ref : 000\n - Run description @descr : NONMEM PK example for xpose\n - Run start time @timestart : Mon Oct 16 13:34:28 CEST 2017\n - Run stop time @timestop : Mon Oct 16 13:34:35 CEST 2017\n\nSummary for problem no. 1 [Parameter estimation] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - ADVAN @subroutine : 2\n - Estimation method @method : foce-i\n - Termination message @term : MINIMIZATION SUCCESSFUL\n - Estimation runtime @runtime : 00:00:02\n - Objective function value @ofv : -1403.905\n - Number of significant digits @nsig : 3.3\n - Covariance step runtime @covtime : 00:00:03\n - Condition number @condn : 21.5\n - Eta shrinkage @etashk : 9.3 [1], 28.7 [2], 23.7 [3]\n - Epsilon shrinkage @epsshk : 14.9 [1]\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n\nSummary for problem no. 2 [Model simulations] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - Estimation method @method : sim\n - Number of simulations @nsim : 20\n - Simulation seed @simseed : 221287\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n (WARNING 22) WITH $MSFI AND \"SUBPROBS\", \"TRUE=FINAL\" ...'
vars_text <- '\nList of available variables for problem no. 1 \n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model typical predictions (pred) : PRED\n - Model individual predictions (ipred) : IPRED\n - Model parameter (param) : KA, CL, V, ALAG1\n - Eta (eta) : ETA1, ETA2, ETA3\n - Residuals (res) : CWRES, IWRES, RES, WRES\n - Categorical covariates (catcov) : SEX, MED1, MED2\n - Continuous covariates (contcov) : CLCR, AGE, WT\n - Compartment amounts (a) : A1, A2\n - Not attributed (na) : DOSE, SS, II, TAD, CPRED'
vars_text <- '\nList of available variables for problem no. 1\n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model typical predictions (pred) : PRED\n - Model individual predictions (ipred) : IPRED\n - Model parameter (param) : KA, CL, V, ALAG1\n - Eta (eta) : ETA1, ETA2, ETA3\n - Residuals (res) : CWRES, IWRES, RES, WRES\n - Categorical covariates (catcov) : SEX, MED1, MED2\n - Continuous covariates (contcov) : CLCR, AGE, WT\n - Compartment amounts (a) : A1, A2\n - Not attributed (na) : DOSE, SS, II, TAD, CPRED'

# Tests start here --------------------------------------------------------
test_that('Check print.xpose_data returns a proper message', {
Expand Down
2 changes: 1 addition & 1 deletion tests/testthat/test-edits.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ test_xpdb_1 <- vpc_data(xpdb_ex_pk, opt = vpc_opt(n_bins = 2), quiet = TRUE)
ctrl_xpdb_1 <- test_xpdb_1
ctrl_xpdb_1$special$data[[1]]$vpc_dat <- dplyr::filter(.data = ctrl_xpdb_1$special$data[[1]]$vpc_dat, bin == 2)

ctrl_list_vars_1 <- '\nList of available variables for problem no. 1 \n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model typical predictions (pred) : PRED\n - Model individual predictions (ipred) : IPRED\n - Model parameter (param) : KA, CL, V, ALAG1\n - Eta (eta) : ETA1, ETA2, ETA3\n - Residuals (res) : CWRES, IWRES, RES, WRES\n - Categorical covariates (catcov) : SEX, MED1, MED2\n - Continuous covariates (contcov) : CLCR, AGE, WT\n - Compartment amounts (a) : A1, A2\n - Not attributed (na) : DOSE, SS, II, CPRED, DV2\n\nList of available variables for problem no. 2 \n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model individual predictions (ipred) : IPRED\n - Not attributed (na) : DOSE, SEX, CLCR, AGE, WT, DV2'
ctrl_list_vars_1 <- '\nList of available variables for problem no. 1\n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model typical predictions (pred) : PRED\n - Model individual predictions (ipred) : IPRED\n - Model parameter (param) : KA, CL, V, ALAG1\n - Eta (eta) : ETA1, ETA2, ETA3\n - Residuals (res) : CWRES, IWRES, RES, WRES\n - Categorical covariates (catcov) : SEX, MED1, MED2\n - Continuous covariates (contcov) : CLCR, AGE, WT\n - Compartment amounts (a) : A1, A2\n - Not attributed (na) : DOSE, SS, II, CPRED, DV2\n\nList of available variables for problem no. 2\n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model individual predictions (ipred) : IPRED\n - Not attributed (na) : DOSE, SEX, CLCR, AGE, WT, DV2'

# Tests start here --------------------------------------------------------

Expand Down