Help for package puzzle

Type:

Package

Title:

Assembling Data Sets for Non-Linear Mixed Effects Modeling

Version:

0.0.1

Maintainer:

Mario Gonzalez Sales <mario@modelinggreatsolutions.com>

Description:

To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

Imports:

utils, lubridate, stats, readxl, reshape, reshape2, sqldf, kableExtra, plyr, dplyr, tidyverse, readr

Suggests:

rmarkdown, knitr, devtools, testthat

RoxygenNote:

6.1.1

URL:

https://github.com/syneoshealth/puzzle

BugReports:

https://github.com/syneoshealth/puzzle/issues

NeedsCompilation:

Packaged:

2019-11-22 11:26:50 UTC; Juan

Author:

Olivier Barriere [aut], Mario Gonzalez Sales [aut, cre]

Repository:

CRAN

Date/Publication:

2019-11-28 16:10:02 UTC

A covariate data set.

Description

A dataset containing covariate information.

Usage

df_cov

Format

A tibble with 12 rows and 4 variables:

ID: Individual
TIME: Time, in hours
VARIABLE: Variable
VALUE: Value of the variable

Starting covariate data set.

Description

A dataset containing covariate information.

Usage

df_cov_start

Format

A data frame with 4 rows and 3 variables:

ID: Individual
VARIABLE: Variable
VALUE: Value of the variable

A covariate data set to be used with time dependent covariates.

Description

A dataset containing time dependent covariates.

Usage

df_cov_time_dependent_start

Format

A data frame with 6 rows and 4 variables:

ID: Individual
VARIABLE: Variable
VALUE: Value of the variable
TIME: Time, in hours

A dose data set.

Description

A dataset containing dose information.

Usage

df_dose

Format

A data frame with 12 rows and 3 variables:

ID: Individual
TIME: Time, in weeks
AMT: Dose, in mg

A dose data set including datetimes.

Description

A dataset containing dose information in datetime format.

Usage

df_dose_datetime

Format

A data frame with 5 rows and 12 variables:

ID: Individual
TRT: Treatment label
DOSE: Dose, in mg
PERIOD: Period
DAY: Day of adminsitration
AMT: Dose, in mg
DATETIME: Dta ein datetime format
TIMEPOINT: Timepoint
COHORT: Cohort
FORM: Drug form
TREATMENT: Treatment
FOOD: Food status

A dose data set to be used with EVID=4.

Description

A dataset containing dosing information.

Usage

df_dose_evid4

Format

A data frame with 418 rows and 10 variables:

ID: Individual
PERIOD: Period
TIMEPOINT: Timepoint
TIME: Time, in hours
AMT: Dose, in mg
TRT: Treatment label
DAY: Day of adminsitration
SEQUENCE: Sequence
TRT2: Treatment
EVID: Evid value

A dose data set to be used with optional columns.

Description

A dataset containing dosing information.

Usage

df_dose_optional_columns

Format

A data frame with 4 rows and 6 variables:

ID: Individual
TIME: Time, in hours
AMT: Dose, in mg
OCC: Occasion
TIMEPOINT: Timepoint
TRT: Treatment

A dose data set example.

Description

A dataset containing dosing information.

Usage

df_dose_start

Format

A data frame with 4 rows and 3 variables:

ID: Individual
TIME: Time, in hours
AMT: Dose, in mg

An extra times data set example.

Description

A dataset containing extra times.

Usage

df_extra_times

Format

A data frame with 251 rows and 1 variable:

TIME: Time, in hours

An extra times data set example with datetime format.

Description

A dataset containing extra times in datetime format.

Usage

df_extra_times_datetime

Format

A data frame with 20 rows and 1 variable:

ID: Individual
DATETIME: Datetime
TIMEPOINT: Timepoint

An extra times metabolite data set to be used with EVID=4.

Description

A dataset containing extra times for an hypothetical metabolite.

Usage

df_extra_times_metabolite_evid4

Format

A data frame with 770 rows and 3 variable:

PERIOD: Period
TIMEPOINT: Timepoint
TIME: Time, in hours

An extra times parent data set to be used with EVID=4.

Description

A dataset containing extra times for an hypothetical parent drug.

Usage

df_extra_times_parent_evid4

Format

A data frame with 770 rows and 3 variable:

PERIOD: Period
TIMEPOINT: Timepoint
TIME: Time, in hours

An extra times data set example.

Description

A dataset containing extra times.

Usage

df_extra_times_time

Format

A data frame with 1040 rows and 3 variable:

ID: Individual
TIME: Time, in hours
TIMEPOINT: Timepoint

A pharmacokinetic metabolite data set to be used with EVID=4.

Description

A dataset containing pharmacokinetic information for an hypothetical metabolite.

Usage

df_metabolite_evid4

Format

A data frame with 1359 rows and 7 variables:

ID: Individual
PERIOD: Period
TIMEPOINT: Timepoint
TIME: Time, in hours
DV: Drug concentration, in mg/L
TIMEDAY: Timeday
DAY: Day of adminsitration

A pharmacokinetic parent data set to be used with EVID=4.

Description

A dataset containing pharmacokinetic information for an hypothetical parent drug.

Usage

df_parent_evid4

Format

A data frame with 1359 rows and 7 variables:

ID: Individual
PERIOD: Period
TIMEPOINT: Timepoint
TIME: Time, in hours
DV: Drug concentration, in mg/L
TIMEDAY: Timeday
DAY: Day of adminsitration

An starting pharmacoynamic data set example.

Description

A dataset containing pharmacodynamic observations.

Usage

df_pd_start

Format

A tibble with 6 rows and 3 variable:

ID: Individual
TIME: Time, in hours
DV: Response, ng/mL

A pharmacokinetic data set.

Description

A dataset containing pharmacokinetic information.

Usage

df_pk

Format

A tibble with 132 rows and 4 variable:

ID: Individual
TIMEPOINT: Timepoint
TIME: Time, in hours
DV: Drug concentration, ng/mL

A pharmacokinetic data set example in datetime format.

Description

A dataset containing pharmacokinetic information.

Usage

df_pk_datetime

Format

A data frame with 65 rows and 7 variable:

ID: Individual
DV: Response, ng/mL
DATETIME: Datetime
TIMEPOINT: Timepoint
DAY: Day
PERIOD: Period
BLQ: I a BLQ?
LLOQ: Lower limit of quantification, ng/mL

A pharmacokinetic data set of metabolite data.

Description

A dataset containing pharmacokinetic information for an hypothetical metabolite.

Usage

df_pk_metabolite

Format

A data frame with 10 rows and 3 variable:

ID: Individual
TIME: Time, in hours
DV: Drug concentration, ng/mL

A pharmacokinetic data set to be used with optional columns.

Description

A dataset containing pharmacokinetic information.

Usage

df_pk_optional_columns

Format

A data frame with 12 rows and 5 variable:

ID: Individual
TIME: Time, in hours
DV: Drug concentration, ng/mL
OCC: Occasion
TIMEPOINT: Timepoint

A pharmacokinetic data set for an hypothetical parent drug.

Description

A dataset containing pharmacokinetic information.

Usage

df_pk_parent

Format

A data frame with 12 rows and 3 variable:

ID: Individual
TIME: Time, in hours
DV: Drug concentration, ng/mL

A pharmacokinetic data set example.

Description

A dataset containing pharmacokinetic information.

Usage

df_pk_start

df_pk_start

Format

A tibble with 12 rows and 3 variable:

ID: Individual
TIME: Time, in hours
DV: Response, ng/mL

A pharmacodynamic data set.

Description

A dataset containing pharmacodynamic information for response 1.

Usage

df_response1

Format

A data frame with 6 rows and 3 variable:

ID: Individual
TIME: Time, in hours
DV: Response, ng/mL

A pharmacodynamic data set.

Description

A dataset containing pharmacodynamic information for response 2.

Usage

df_response2

Format

A data frame with 6 rows and 3 variable:

ID: Individual
TIME: Time, in hours
DV: Response, seconds

A pharmacodynamic data set.

Description

A dataset containing pharmacodynamic information for response 3.

Usage

df_response3

Format

A data frame with 6 rows and 3 variable:

ID: Individual
TIME: Time, in hours
DV: Response, in hours

puzzle

Description

Build pharmacometric data sets from basic tabulated files

Usage

puzzle(directory = NULL, order, coercion = list(name = NULL, sep =
  ","), optionalcolumns = NULL, pk = list(name = NULL, data = NULL),
  dose = list(name = NULL, data = NULL), cov = list(name = NULL, data =
  NULL), pd = list(name = NULL, data = NULL), extratimes = list(name =
  NULL, data = NULL), nm = list(name = NULL), fillcolumns = NULL,
  nocoercioncolumns = NULL, norepeatcolumns = NULL, initialindex = 0,
  na.strings = "N/A", arrange = "ID,TIME,CMT,desc(EVID)",
  datetimeformat = "%Y-%m-%d %H:%M:%S", timeunits = "hours",
  timezone = Sys.timezone(), ignore = "C", missingvalues = ".",
  parallel = TRUE, verbose = FALSE, username = NULL)

Arguments

directory

path to your directory

order

define the absorption order, can be 0, 1, c(0,1), or c(1,1)

coercion

define name for coercion file

optionalcolumns

define optional columns

pk

define the required file containing the pk information. It can be a .csv or an .xlsx file

dose

define the required file containing the dose information. It can be a .csv, an .xlsx file or an R object.

cov

define the optional file containing the covariate information. It can be a .csv, an .xlsx file or an R object.

pd

define the optional file containing the pd information. It can be a .csv, or a .xlsx file.

extratimes

define the optional file containing the additional times. It can be a .csv, or a .xlsx file.

nm

name of output file generated by puzzle

fillcolumns

define columns to be filled

nocoercioncolumns

define columns to be dropped from the coercion file

norepeatcolumns

define columns not to be repeated

initialindex

define the lower category of categorical covariates

na.strings

define value for na

arrange

define how the columns should be arranged

datetimeformat

define format for date times

timeunits

define time units if needed

timezone

define timezone

ignore

define ignore value

missingvalues

define missing value

parallel

define parallel zero + first order absorption

verbose

define verbose

username

define person performing the assembling

Value

a pharmacometrics ready data set

Examples

## Not run: 
nm = list(pk = list(parent=as.data.frame(puzzle::df_pk_start)),
          dose=as.data.frame(puzzle::df_dose_start), 
          cov=as.data.frame(puzzle::df_cov_start))
puzzle(directory=file.path(tempdir()), 
       order=c(0), 
       pk=list(data=nm$pk), 
       dose=list(data=nm$dose), 
       cov=list(data=nm$cov))

## End(Not run)