Method ConvertFromViSibook-ViSibook

Description

The method ConvertFromViSibook converts a ViSibook in a data.frame object.

Usage

ConvertFromViSibook(x)

## S4 method for signature 'ViSibook'
ConvertFromViSibook(x)

Arguments

Value

a data.frame.

See Also

ViSibook and see plot-ViSigrid-method for examples.

Function ConvertoViSibook

Description

ConvertoViSibook convert a data.frame into a ViSibook object.

Usage

ConvertoViSibook(x)

Arguments

Value

a ViSibook object.

See Also

See visielse for examples.

Class ViSiBook

Description

Class ViSibook defines the structure of the process to be plotted.

Slots

vars

a vector storing names of actions.

label

a vector storing brief description of actions.

typeA

a vector storing type of actions, "l" for long actions ( which have a stating time and an ending time ), "p" for punctual actions.

showorder

a vector storing order in which actions will be plotted. When an action should not be plotted, showorder should be NA.

deb

a vector.

• Long actions deb stores the punctual action names that delimit the long actions beginning.

• Punctual action NA .

fin

a vector.

• Long actions fin stores the punctual action names that delimit the long actions ending.

• Punctual actions NA .

GZDeb

a vector, optional, GZdeb stores punctual actions green zone starting time.

GZFin

a vector, optional, GZFin stores punctual actions green zone ending time.

Repetition

optional a vector, optional, When a green zone is defined, Repetition stores the duration of the time interval between green zones.

BZBeforeDeb

a vector, optional, BZBeforeDeb a vector storing punctual black zone 1 starting time.

BZBeforeFin

a vector, optional, BZBeforeFin storing punctual black zone 1 ending time.

BZAfterDeb

a vector, optional, BZAfterDeb stores punctual black zone 2 starting time .

BZAfterFin

a vector, optional, BZAfterFin stores punctual black zone 2 ending time.

BZLong

a vector, optional, BZLong stores the long action black zone time.

BZLtype

a vector, optional, BZLtype stores the type of the black zone, "time" if the action should be finish at a time, "span" if the action should be finish in a time.

NAMES

a vector storing names of slots that are to be defined.

See Also

visielse for examples.

Function ViSibookfromDATA

Description

ViSibookfromDATA build an object class ViSibook from observational data. The process is the ordered list of punctual actions given by the columns names of X.

Usage

ViSibookfromDATA(X, idsubject = 1)

Arguments

Value

a ViSibook corresponding to the dataset X.

Class ViSigrid

Description

Class ViSigrid defines the structure of the process to be plotted.

Slots

MATp

A "dgCMatrix". It stores the grid for all punctual actions in the book.

MATpsup

A "dgCMatrix". It stores the grid for all punctual actions in the book corresponding to the supplementary times.

idsup

A "vector" It stores individuals id having supplementary times.

colvect

A "matrix" Matrix with colors to use.

L

A "data.frame" It stores the data corresponding to long actions having a showorder.

idsort

A "matrix" For all long actions, it stores the order of individuals in which each actions will be plot.

BZL

A "dgCMatrix" It stores black zones for long actions, calculated for each individuals.

Lsup

A "data.frame" for the long actions having a showorder and supplementary times defined, it stores the data corresponding to those actions.

book

A "ViSibook" it stores the structure of the grid for the plot.

group

A "factor" it stores the group for the each individuals.

vect_tps

A "vector" it stores the times vector mapping the grid.

informers

A "matrix" It stores the statistics (mean, median or NULL) by actions.

testsP

A "vector" Results of tests p.value<threshold.test.

parameters

A "list". It stores the parameters put in the visielse function.

See Also

visielse, plot,ViSigrid-method, ViSibook

Method get for ViSibook object.

Description

Method get for ViSibook object.

Usage

## S4 method for signature 'ViSibook,numeric,missing,ANY'
x[i, j, drop = TRUE]

## S4 method for signature 'ViSibook,missing,numeric,ANY'
x[i, j, drop = TRUE]

## S4 method for signature 'ViSibook,numeric,numeric,ANY'
x[i, j, drop = TRUE]

Arguments

Value

obj.

See Also

ViSibook.

Simulated time data of the coffee process

Description

This dataset shows the actions required to make coffee. The simulated dataset of 10 subjects correspond to the timestamps (in s) of each action. Each value is the time elapse between the beginning of the coffee process and the execution of an action.

Usage

data(coffee)

Format

A data frame with 10 rows and 6 variables:

id

Midwife students ID.

coffee

Time (in s) when the subject takes the coffee capsule.

fill_coffee

Time (in s) when the subject puts the coffee capsule into the machine.

fill_water

Time (in s) when the subject fills the water tank of the coffee machine.

push_B

Time (in s) when the subject pushes the button to start the machine.

drink

Time (in s) when the subject drinks the coffee.

Examples

data(coffee)
head(coffee)

Method dim-ViSibook

Description

Method Dim for ViSibook object.

Usage

## S4 method for signature 'ViSibook'
dim(x)

Arguments

Value

Vector

• [1] The number of actions defined in x.

• [2] The number of characteristics defined in x, its minimum value is 6 and its maximum is 15.

See Also

ViSibook

Method initialize-ViSibook

Description

Method initialize for class ViSibook object.

Usage

## S4 method for signature 'ViSibook'
initialize(.Object, vars, label, typeA, showorder,
  deb, fin, GZDeb, GZFin, Repetition, BZBeforeDeb, BZBeforeFin, BZAfterDeb,
  BZAfterFin, BZLong, BZLtype, NAMES)

Arguments

Value

a ViSibook object

See Also

See plot-ViSigrid-method for examples.

Intubation time data from a simulation of a neonatal resuscitation

Description

Time data from a high-fidelity simulation experiment of a neonatal resuscitation with 37 midwife students. The simulation was video recorded and actions required in the intubation process were tagged. This dataset is the execution time (in seconds) of each action performed by the students.

Usage

data(intubation)

Format

A data frame with 37 rows and 7 variables:

id

Midwife students ID.

deci_intub

Time (in s) when the student decides to intubate the newborn.

stop_ventil

Time (in s) when the student stops the mask ventilation of the newborn.

blade_in

Time (in s) when the student inserts the laryngoscope blade in the newborn mouth.

insert_tube

Time (in s) when the student inserts the endotracheal tube.

blade_out

Time (in s) when the student removes the laryngoscope blade out of the newborn mouth.

restart_ventil

Time (in s) when the student restarts to ventilate the newborn through the tube.

References

Garnier EM, Fouret N, Descoins M (2019) ViSiElse: An innovative R-package to visualize raw behavioral data over time. PeerJ Preprints 10.7287/peerj.preprints.27665v2 ([PeerJ](https://doi.org/10.7287/peerj.preprints.27665v2))

Examples

data(intubation)
head(intubation)

Method plot-ViSibook

Description

Method plot for ViSibook object.

Usage

## S4 method for signature 'ViSibook'
plot(x, ncharmax = 10, ncharmaxdelay = 50)

Arguments

See Also

ViSibook, visielse

Method plot-ViSigrid

Description

Method plot for ViSigrid object. This method provides a graphic of raw data during experimental observations of the realization of a procedure like a medical algorithm. It graphically presents an overview of individuals and group actions usually acquired from timestamps during video recorded sessions.

Usage

## S4 method for signature 'ViSigrid'
plot(x, scal.unit.tps = 10, unit.tps = "s",
  main = " ", ncharlabel = 30, size.main = 12, Fontsize.title = 11,
  Fontsize.label.Action = 11, Fontsize.label.Time = 11,
  Fontsize.label.color = 9, col.main = "black", col.grid = "grey",
  colgreenzone = "green", colblackzone = "black", alphainf = 0.8,
  alphasup = 1, alphaZones = 0.2, vp0h = 0.6, vp0w = 0.6,
  linA = 0.7, rcircle = 15, lwdline = 2, lwd.grid = 1,
  lty.grid = 1)

Arguments

See Also

ViSigrid, ViSibook, visielse.

Method print-ViSibook

Description

Method print for ViSibook object.

Usage

## S4 method for signature 'ViSibook'
print(x)

Arguments

See Also

ViSibook, visielse, and see plot-ViSigrid-method for examples.

Method set for ViSibook object.

Description

Method set for ViSibook object.

Usage

## S4 replacement method for signature 'ViSibook,numeric,numeric,ANY'
x[i, j] <- value

## S4 replacement method for signature 'ViSibook,missing,numeric,ANY'
x[i, j] <- value

## S4 replacement method for signature 'ViSibook,numeric,missing,ANY'
x[i, j] <- value

Arguments

Value

a ViSibook object.

See Also

ViSibook

Simulated online shopping behavior time data

Description

This dataset shows the buying process of consumers over internet based on a 5-steps model: need recognition, information search, evaluation, purchase decision, and post-purchase behavior. This simulated dataset of 100 subjects correspond to the timestamps (in s) of each action of the model (except for the post-purchase behavior) executed by the subjects.

Usage

data(shoppingBehavior)

Format

A data frame with 100 rows and 7 variables:

id

Customer ID.

need

Time (in s) when the customer decides he/she needs an item.

start_search

Time (in s) when the customer starts to search for the item.

stop_search

Time (in s) when the customer stops to search for the item.

start_eval

Time (in s) when the customer starts to evaluate the item.

stop_eval

Time (in s) when the customer stops to evaluate the item.

deci

Time (in s) when the customer decides to buy the item.

References

Garnier EM, Fouret N, Descoins M (2019) ViSiElse: An innovative R-package to visualize raw behavioral data over time. PeerJ Preprints 10.7287/peerj.preprints.27665v2 ([PeerJ](https://doi.org/10.7287/peerj.preprints.27665v2))

Examples

data(shoppingBehavior)
head(shoppingBehavior)

Method show-ViSibook

Description

Method show for ViSibook object.

Usage

## S4 method for signature 'ViSibook'
show(object)

Arguments

See Also

ViSibook.

Method show-ViSigrid

Description

Method show for ViSigrid object.

Usage

## S4 method for signature 'ViSigrid'
show(object)

Arguments

See Also

ViSigrid and see plot-ViSigrid-method for examples.

Method summary-ViSigrid

Description

Method summary for ViSigrid object.

Usage

## S4 method for signature 'ViSigrid'
summary(object)

Arguments

Value

list

• punctual summary of punctual actions (typeA=="p").

• longs summary of long actions (typeA=="p").

See Also

ViSigrid, visielse,ViSibook. and see plot-ViSigrid-method for examples.

Simulated time data of the actions performed in a typical day

Description

This dataset shows the actions usually performed during a typical day. The simulated dataset of 100 subjects correspond to the timestamps (in min) of each action of the day, from midnight to midnight. Each value is the time elapse between the beginning of the day (midnight) and the execution of the action.

Usage

data(typDay)

Format

A data frame with 100 rows and 15 variables:

id

Midwife students ID.

start_sleep

Time (in min) when the subject is sleeping. All subjects are set to 0 (0:00 or midnight) as the dataset shows a day from midnight to midnight

stop_sleep

Time (in min) when the subject stops to sleep.

wake_up

Time (in min) when the subject wakes up (same values as stop_sleep).

shower

Time (in min) when the subject takes a shower.

breakfast

Time (in min) when the subject eats breakfast.

start_work

Time (in min) when the subject starts working.

start_lunch

Time (in min) when the subject starts to eat lunch.

stop_lunch

Time (in min) when the subject finishes his lunch.

stop_work

Time (in min) when the subject stops to work.

pickup_kids

Time (in min) when the subject picks up his kids.

start_cook

Time (in min) when the subject starts cooking.

stop_cook

Time (in min) when the subject stops cooking.

go_sleep

Time (in min) when the subject goes to sleep.

first_coffee

Time (in min) when the subject drinks his first coffee of the day.

References

Garnier EM, Fouret N, Descoins M (2019) ViSiElse: An innovative R-package to visualize raw behavioral data over time. PeerJ Preprints 10.7287/peerj.preprints.27665v2 ([PeerJ](https://doi.org/10.7287/peerj.preprints.27665v2))

Examples

data(typDay)
head(typDay)

Function visielse

Description

visielse plots the graphic from time data and build an object class ViSigrid with, at least, the time data of each punctual action defined in the ViSibook for all subjects.

Usage

visielse(X, book = NULL, is.ViSibook = FALSE, doplot = TRUE,
  Xsup = NULL, method = "global", group = NULL, grwithin = NULL,
  informer = "median", tests = TRUE, threshold.test = 0.01,
  quantity = "N", pixel = 20, t_0 = 0, sorted.line = TRUE,
  decrgr2 = FALSE, max_tps = NULL, colvect = NULL, ncolvect = NULL,
  times = FALSE, timeformat = c("hh:mm:ss"), idsubject = 1)

Arguments

Details

• method

  • global : The plot of the ViSigrid object will not consider the parameter group and plot indistinctly all individuals.

  • cut : In the plot of the ViSigrid object, each group will be plotted separately, one under the other with different colors.

  • join : In the plot of the ViSigrid object, groups are spatially mixed but they are represented by different colors.

  • within : In the plot of the ViSigrid object, all individuals are plotted together then the group specified in grwithin is plotted another time underneath.

• informer

  The parameter informer allows users to choose the statistics to be plotted. informer can take three values:

  • median: Median and quartiles are calculated for each action, using the function quantile from the package stats. This is the default value.

  • mean: Mean and standard deviation are calculated for each action, using the functions mean and var from the package stats.

  • NULL: no indicators are computed.

  When a group is defined, statistics are calculated per group if the method cut or within is chosen.

  When plotting the ViSigrid object, statistics for punctual actions are represented by white circles linked by a line. For long action, only a black line is plotted starting at the median (or mean) value of the punctual action staring times. The line length represents the median (or mean) of the long action duration. Informers are computed directly on the given matrix for punctual action. And for long actions, it is based on the difference between the punctual action defining its beginning and the one defining its ending.

• tests and threshold.test

  As for the parameter informer, tests are computed on the given matrix or data.frame X for a punctual action. And for a long action, it is calculated on its difference between its beginning and its ending punctual actions. In plot-ViSigrid-method, results of the tests are represented by a star only when the resulted p-value is bellow or equal to value defined by the parameter threshold.test.

• pixel

  The parameter pixel represents the number of unit of time under which individuals are aggregated for punctual action in the plot. When the parameter pixel is too small the information represented will be too much aggregated to allow interpretation.

  For punctual actions data are aggregated in a matrix M . The number of row of M is the number of action and its number of columns is [ ( max(X)-t_{0} )/pixel].

  M_{i,j} contains the number of observations of the i-th punctual action (by the order of the ViSibook object) between t_0 + (j-1)pixel included and t_0 + j*pixel excluded.

• t_0

  The origin of the graphic can be set using the parameter t_0. There is two ways to define it:

  • A number: set to 0__. It can be change at convenience, but for long actions black zones will not be drawn, and for punctual actions black and green zones will not be translated.

  • The name of a punctual action: To set the origin of the graphic to the moment when the action was done for each individual. Black and green zones will not be translated as well.

x can also has the columns : GZDebn, GZFin, Repetition, BZBeforeDeb, BZBeforeFin, BZAfterDeb, BZAfterFin, BZLong , BZLtype

Value

a ViSigrid object.

See Also

Classes ViSigrid and ViSibook. The method plot for ViSigrid object plot-ViSigrid-method for examples.

Examples

coffee <- c(  58, 11,  5, 53, 53, 59, 24, 59, 46, 20)
fill_coffee <- c(162,  57,103,154,165,132,  74, 107, 104,  93)
fill_water <- c(  66,  92,54, 78, 74, 114, 91, 129, 71, 56)
push_B <- c( 74, 99, 62, 84, 83, 120, 95, 129, 80, 63 )
drink <- c( 472, 176, 475, 283, 265, 207, 234, 184, 490, 520)
X <- data.frame(id = seq(1,10), coffee, fill_coffee,fill_water,push_B,drink)
library(ViSiElse)
visi1 <- visielse(X)

#### Changing the pixel of time

visi1 <- visielse(X, pixel = 10)
# Plot the mean and standart deviation

visi1 <- visielse(X,informer = "mean")

#### Do not plot indicators
visi1 <- visielse(X,informer = NULL)


# Extraction of the visibook from the data

visi1 <- visielse(X,informer = NULL, doplot = FALSE)
book <- visi1@book
plot(book)

#### Changing labels

book[,2]<- c("Taking the coffee",
             "Fill the machine with coffee",
             "Fill the tank with water",
             "Push the Button",
             "Drink the coffee")
plot(book)
visi1 <- visielse(X, book=book, is.ViSibook = TRUE,informer = NULL)


#### Change the order of Actions in the process

book[,4]<- c(5,1,2,4,3)
plot(book)
visi1 <- visielse(X, book=book, is.ViSibook = TRUE)



#### Adding a long Actions

visi1 <- visielse( X )
book <- ConvertFromViSibook( visi1@book ) # Convert book into data.frame
add_delay <- c( "delay_coffee_push","Preparation","l","6","coffee","push_B")
book[6,] <- add_delay
book


### ViSiElse representation of long actions

visi2 <- visielse( X=X , book=book,informer=NULL)

## Green & Black zones

book$GZDeb <- c(NA,60,NA,NA,NA,NA)
book$GZFin <- c(NA,120,NA,NA,NA,NA)
book$BZBeforeDeb <- c(NA,0,NA,NA,NA,NA)
book$BZBeforeFin <- c(NA,30,NA,NA,NA,NA)
book$BZAfterDeb <- c(NA,180,NA,NA,NA,NA)
book$BZAfterFin <- c(NA,Inf,NA,NA,NA,NA)
book$BZLong <- c(rep(NA,5),150)
book$BZLtype <- c(rep(NA,5),"time")
visi1 <- visielse( X, book=book , informer = NULL)

book$BZLtype <- c(rep(NA,5),"span")
visi1 <- visielse( X, book=book ,informer = NULL)


## Group

### Method : Cut
group <- c( "group2","group1","group2","group1","group1",
             "group2","group1","group1","group1","group2")
visi1 <- visielse( X,group=group, book=book ,informer = NULL, method = "cut")

visi1 <- visielse( X,group=group, book=book ,informer = NULL, method = "join")

visi1 <- visielse( X,group=group, book=book ,informer = NULL, method = "within",grwithin = "group1")