, Zachari Swiecki
[aut], Wesley Collier [aut], Brendan Eagan [aut], Roman
Woodward [aut], David Williamson Shaffer [aut]| Title: | Epistemic Network Analysis |
|---|---|
| Description: | ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016). |
| Authors: | Cody L Marquart [aut, cre]
, Zachari Swiecki
[aut], Wesley Collier [aut], Brendan Eagan [aut], Roman
Woodward [aut], David Williamson Shaffer [aut] |
| Maintainer: | Cody L Marquart <[email protected]> |
| License: | GPL-3 | file LICENSE |
| Version: | 0.2.7 |
| Built: | 2024-11-20 06:29:56 UTC |
| Source: | CRAN |
Extract from ena.matrix easily using metadata
## S3 method for class 'ena.matrix' x$i## S3 method for class 'ena.matrix' x$i
x |
[TBD] |
i |
[TBD] |
[TBD]
Extract metadata easily
## S3 method for class 'ena.metadata' x$i## S3 method for class 'ena.metadata' x$i
x |
[TBD] |
i |
[TBD] |
[TBD]
Extract points easily
## S3 method for class 'ena.points' x$i## S3 method for class 'ena.points' x$i
x |
[TBD] |
i |
[TBD] |
[TBD]
Extract line.weignts easily
## S3 method for class 'line.weights' x$i## S3 method for class 'line.weights' x$i
x |
[TBD] |
i |
[TBD] |
[TBD]
Add a group mean to an ena.plot
add_group(x, wh = NULL, ...)add_group(x, wh = NULL, ...)
x |
ena.plot object to plot on |
wh |
which points to plot as the trajectory |
... |
additional parameters to pass along |
ena.plot.object
Add a network to an ENA plot
add_network(x, wh = NULL, ..., with.mean = F)add_network(x, wh = NULL, ..., with.mean = F)
x |
ena.plot object to plot wtih |
wh |
network to plot |
... |
Additional parametesr to pass along |
with.mean |
Logical value, if TRUE plots the mean for the points in the network |
ena.plot.object
Title
add_nodes(x, ...)add_nodes(x, ...)
x |
[TBD] |
... |
[TBD] |
[TBD]
Plot points on an ena.plot
add_points(x, wh = NULL, ..., name = "plot", mean = NULL, colors = NULL)add_points(x, wh = NULL, ..., name = "plot", mean = NULL, colors = NULL)
x |
ena.plot to add point on |
wh |
which points to plot |
... |
additional parameters to pass along |
name |
name to give the plot |
mean |
include a mean point for the provided points |
colors |
colors for plotted points |
ena.plot.object
Plot a trajectory on an ena.plot
add_trajectory(x, wh = NULL, ..., name = "plot")add_trajectory(x, wh = NULL, ..., name = "plot")
x |
ena.plot object to plot on |
wh |
which points to plot as the trajectory |
... |
additional parameters to pass along |
name |
Name, as a character vector, to give the plot |
ena.plot.object
Title
as_trajectory( x, by = x$`_function.params`$conversation[1], model = c("AccumulatedTrajectory", "SeperateTrajectory"), ... )as_trajectory( x, by = x$`_function.params`$conversation[1], model = c("AccumulatedTrajectory", "SeperateTrajectory"), ... )
x |
[TBD] |
by |
[TBD] |
model |
[TBD] |
... |
[TBD] |
[TBD]
Re-class vector as ena.co.occurrence
as.ena.co.occurrence(x)as.ena.co.occurrence(x)
x |
Vector to re-class |
re-classed vector
Re-class matrix as ena.matrix
as.ena.matrix(x, new.class = NULL)as.ena.matrix(x, new.class = NULL)
x |
data.frame, data.table, or matrix to extend |
new.class |
Additional class to extend the matrix with, default: NULL |
Object of same st
Re-class matrix as ena.metadata
as.ena.metadata(x)as.ena.metadata(x)
x |
data.frame, data.table, or matrix to extend |
Object of same st
ENA Connections as a matrix
## S3 method for class 'ena.connections' as.matrix(x, ...)## S3 method for class 'ena.connections' as.matrix(x, ...)
x |
ena.connections object |
... |
additional arguments to be passed to or from methods |
If square is FALSE (default), a matrix with all metadata columns removed, otherwise a list with square matrices
ENA line weights as matrix
## S3 method for class 'ena.line.weights' as.matrix(x, ..., square = FALSE)## S3 method for class 'ena.line.weights' as.matrix(x, ..., square = FALSE)
x |
ena.line.weights data.table to covert to matrix |
... |
additional arguments to be passed to or from methods |
square |
[TBD] |
matrix
Matrix without metadata
## S3 method for class 'ena.matrix' as.matrix(x, ...)## S3 method for class 'ena.matrix' as.matrix(x, ...)
x |
Object to convert to a matrix |
... |
additional arguments to be passed to or from methods |
matrix
ENA nodes as matrix
## S3 method for class 'ena.nodes' as.matrix(x, ...)## S3 method for class 'ena.nodes' as.matrix(x, ...)
x |
ena.nodes to convert to matrix |
... |
additional arguments to be passed to or from methods |
matrix
ENA points as matrix
## S3 method for class 'ena.points' as.matrix(x, ...)## S3 method for class 'ena.points' as.matrix(x, ...)
x |
ena.points to convert to a matrix |
... |
additional arguments to be passed to or from methods |
matrix
ENA rotations as matrix
## S3 method for class 'ena.rotation.matrix' as.matrix(x, ...)## S3 method for class 'ena.rotation.matrix' as.matrix(x, ...)
x |
ena.rotation.matrix to conver to matrix |
... |
additional arguments to be passed to or from methods |
matrix
ENA row connections as matrix
## S3 method for class 'row.connections' as.matrix(x, ...)## S3 method for class 'row.connections' as.matrix(x, ...)
x |
ena.row.connections to conver to a matrix |
... |
additional arguments to be passed to or from methods |
matrix
Title
clear(x, wh = seq(x$plots))clear(x, wh = seq(x$plots))
x |
[TBD] |
wh |
[TBD] |
[TBD]
faster combn alternative
combn_c2(n)combn_c2(n)
n |
TBD |
Connection counts as square matrix
connection.matrix(x)connection.matrix(x)
x |
ena.set or ena.connections (i.e. set$connection.counts) |
matrix
Generates an ENA model by constructing a dimensional reduction of adjacency (co-occurrence) vectors as defined by the supplied conversations, units, and codes.
ena( data, codes, units, conversation, metadata = NULL, model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"), weight.by = "binary", window = c("MovingStanzaWindow", "Conversation"), window.size.back = 1, include.meta = TRUE, groupVar = NULL, groups = NULL, runTest = FALSE, points = FALSE, mean = FALSE, network = TRUE, networkMultiplier = 1, subtractionMultiplier = 1, unit = NULL, include.plots = T, print.plots = F, ... )ena( data, codes, units, conversation, metadata = NULL, model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"), weight.by = "binary", window = c("MovingStanzaWindow", "Conversation"), window.size.back = 1, include.meta = TRUE, groupVar = NULL, groups = NULL, runTest = FALSE, points = FALSE, mean = FALSE, network = TRUE, networkMultiplier = 1, subtractionMultiplier = 1, unit = NULL, include.plots = T, print.plots = F, ... )
data |
data.frame with containing metadata and coded columns |
codes |
vector, numeric or character, of columns with codes |
units |
vector, numeric or character, of columns representing units |
conversation |
vector, numeric or character, of columns to segment conversations by |
metadata |
vector, numeric or character, of columns with additional meta information for units |
model |
character: EndPoint (default), AccumulatedTrajectory, SeparateTrajectory |
weight.by |
"binary" is default, can supply a function to call (e.g. sum) |
window |
MovingStanzaWindow (default) or Conversation |
window.size.back |
Number of lines in the stanza window (default: 1) |
include.meta |
[TBD] |
groupVar |
vector, character, of column name containing group identifiers. If column contains at least two unique values, will generate model using a means rotation (a dimensional reduction maximizing the variance between the means of the two groups) |
groups |
vector, character, of values of groupVar column used for means rotation, plotting, or statistical tests |
runTest |
logical, TRUE will run a Student's t-Test and a Wilcoxon test for groups defined by the groups argument |
points |
logical, TRUE will plot points (default: FALSE) |
mean |
logical, TRUE will plot the mean position of the groups defined in the groups argument (default: FALSE) |
network |
logical, TRUE will plot networks (default: TRUE) |
networkMultiplier |
numeric, scaling factor for non-subtracted networks (default: 1) |
subtractionMultiplier |
numeric, scaling factor for subtracted networks (default: 1) |
unit |
vector, character, name of a single unit to plot |
include.plots |
logical, TRUE will generate plots based on the model (default: TRUE) |
print.plots |
logical, TRUE will show plots in the Viewer(default: FALSE) |
... |
Additional parameters passed to set creation and plotting functions |
This function generates an ena.set object given a data.frame, units, conversations, and codes. After accumulating the adjacency (co-occurrence) vectors, computes a dimensional reduction (projection), and calculates node positions in the projected ENA space. Returns location of the units in the projected space, as well as locations for node positions, and normalized adjacency (co-occurrence) vectors to construct network graphs. Includes options for returning statistical tests between groups of units, as well as plots of units, groups, and networks.
ena.set object
data(RS.data) rs = ena( data = RS.data, units = c("UserName","Condition", "GroupName"), conversation = c("Condition","GroupName"), codes = c('Data', 'Technical.Constraints', 'Performance.Parameters', 'Client.and.Consultant.Requests', 'Design.Reasoning', 'Collaboration'), window.size.back = 4, print.plots = FALSE, groupVar = "Condition", groups = c("FirstGame", "SecondGame") )data(RS.data) rs = ena( data = RS.data, units = c("UserName","Condition", "GroupName"), conversation = c("Condition","GroupName"), codes = c('Data', 'Technical.Constraints', 'Performance.Parameters', 'Client.and.Consultant.Requests', 'Design.Reasoning', 'Collaboration'), window.size.back = 4, print.plots = FALSE, groupVar = "Condition", groups = c("FirstGame", "SecondGame") )
Calculate both Pearson correlations for the provided points and centorids
ena_correlation(points, centroids, conf_level = 0.95)ena_correlation(points, centroids, conf_level = 0.95)
points |
TBD |
centroids |
TBD |
conf_level |
TBD |
This function initializes an ENAdata object, processing conversations from coded data to generate adjacency (co-occurrence) vectors
ena.accumulate.data( units = NULL, conversation = NULL, codes = NULL, metadata = NULL, model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"), weight.by = "binary", window = c("MovingStanzaWindow", "Conversation"), window.size.back = 1, window.size.forward = 0, mask = NULL, include.meta = T, as.list = T, ... )ena.accumulate.data( units = NULL, conversation = NULL, codes = NULL, metadata = NULL, model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"), weight.by = "binary", window = c("MovingStanzaWindow", "Conversation"), window.size.back = 1, window.size.forward = 0, mask = NULL, include.meta = T, as.list = T, ... )
units |
A data frame where the columns are the properties by which units will be identified |
conversation |
A data frame where the columns are the properties by which conversations will be identified |
codes |
A data frame where the columns are the codes used to create adjacency (co-occurrence) vectors |
metadata |
(optional) A data frame with additional columns of metadata to be associated with each unit in the data |
model |
A character, choices: EndPoint (or E), AccumulatedTrajectory (or A), or SeparateTrajectory (or S); default: EndPoint. Determines the ENA model to be constructed |
weight.by |
(optional) A function to apply to values after accumulation |
window |
A character, choices are Conversation (or C), MovingStanzaWindow (MSW, MS); default MovingStanzaWindow. Determines how stanzas are constructed, which defines how co-occurrences are modeled |
window.size.back |
A positive integer, Inf, or character (INF or Infinite), default: 1. Determines, for each line in the data frame, the number of previous lines in a conversation to include in the stanza window, which defines how co-occurrences are modeled |
window.size.forward |
(optional) A positive integer, Inf, or character (INF or Infinite), default: 0. Determines, for each line in the data frame, the number of subsequent lines in a conversation to include in the stanza window, which defines how co-occurrences are modeled |
mask |
(optional) A binary matrix of size ncol(codes) x ncol(codes). 0s in the mask matrix row i column j indicates that co-occurrence will not be modeled between code i and code j |
include.meta |
Locigal indicating if unit metadata should be attached to the resulting ENAdata object, default is TRUE |
as.list |
R6 objects will be deprecated, but if this is TRUE, the original R6 object will be returned, otherwise a list with class 'ena.set' |
... |
additional parameters addressed in inner function |
ENAData objects are created using this function. This accumulation receives separate data frames for units, codes, conversation, and optionally, metadata. It iterates through the data to create an adjacency (co-occurrence) vector corresponding to each unit - or in a trajectory model multiple adjacency (co-occurrence) vectors for each unit.
In the default MovingStanzaWindow model, co-occurrences between codes are calculated for each line k in the data between line k and the window.size.back-1 previous lines and window.size.forward-1 subsequent lines in the same conversation as line k.
In the Conversation model, co-occurrences between codes are calculated across all lines in each conversation. Adjacency (co-occurrence) vectors are constructed for each unit u by summing the co-occurrences for the lines that correspond to u.
Options for how the data is accumulated are endpoint, which produces one adjacency (co-occurrence) vector for each until summing the co-occurrences for all lines, and two trajectory models: AccumulatedTrajectory and SeparateTrajectory. Trajectory models produce an adjacency (co-occurrence) model for each conversation for each unit. In a SeparateTrajectory model, each conversation is modeled as a separate network. In an AccumulatedTrajectory model, the adjacency (co-occurrence) vector for the current conversation includes the co-occurrences from all previous conversations in the data.
ENAdata object with data [adjacency (co-occurrence) vectors] accumulated from the provided data frames.
Find rows of conversations by unit
ena.conversations( set, units, units.by = NULL, codes = NULL, conversation.by = NULL, window = 4, conversation.exclude = c() )ena.conversations( set, units, units.by = NULL, codes = NULL, conversation.by = NULL, window = 4, conversation.exclude = c() )
set |
[TBD] |
units |
[TBD] |
units.by |
[TBD] |
codes |
[TBD] |
conversation.by |
[TBD] |
window |
[TBD] |
conversation.exclude |
[TBD] |
[TBD]
list containing row indices representing conversations
data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning', 'Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("Condition","UserName")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre", "CONFIDENCE.Post","C.Change")], codes = RS.data[,codeNames], model = "EndPoint", window.size.back = 4 ); set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list(accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame") ); ena.conversations(set = RS.data, units = c("FirstGame.steven z"), units.by=c("Condition","UserName"), conversation.by = c("Condition","GroupName"), codes=codeNames, window = 4 )data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning', 'Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("Condition","UserName")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre", "CONFIDENCE.Post","C.Change")], codes = RS.data[,codeNames], model = "EndPoint", window.size.back = 4 ); set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list(accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame") ); ena.conversations(set = RS.data, units = c("FirstGame.steven z"), units.by=c("Condition","UserName"), conversation.by = c("Condition","GroupName"), codes=codeNames, window = 4 )
Calculate both Spearman and Pearson correlations for the provided ENAset
ena.correlations(enaset, dims = c(1:2))ena.correlations(enaset, dims = c(1:2))
enaset |
ENAset to run correlations on |
dims |
The dimensions to calculate the correlations for. Default: c(1,2) |
Matrix of 2 columns, one for each correlation method, with the corresponding correlations per dimension as the rows.
Computes summary statistics for groupings (given as vector) of units in ena data using given method (typically, mean); computes summary statistic for point locations and edge weights for each grouping
ena.group( enaset = NULL, by = NULL, method = mean, names = as.vector(unique(by)) )ena.group( enaset = NULL, by = NULL, method = mean, names = as.vector(unique(by)) )
enaset |
An |
by |
A vector of values the same length as units. Uses rotated points for group positions and normed data to get the group edge weights |
method |
A function that is used on grouped points. Default: mean(). If 'enaset' is an ENAset, enaset$points.rotated will be groups using 'mean' regardless of 'method' provided |
names |
A vector of names to use for the results. Default: unique(by) |
A list containing names, points, and edge weights for each of the unique groups formed by the function
data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum ) means = ena.group(set, "Condition")data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum ) means = ena.group(set, "Condition")
Generates an ENA model by constructing a dimensional reduction of adjacency (co-occurrence) vectors in an ENA data object
ena.make.set( enadata, dimensions = 2, norm.by = fun_sphere_norm, rotation.by = ena.svd, rotation.params = NULL, rotation.set = NULL, endpoints.only = TRUE, center.align.to.origin = TRUE, node.position.method = lws.positions.sq, as.list = TRUE, ... )ena.make.set( enadata, dimensions = 2, norm.by = fun_sphere_norm, rotation.by = ena.svd, rotation.params = NULL, rotation.set = NULL, endpoints.only = TRUE, center.align.to.origin = TRUE, node.position.method = lws.positions.sq, as.list = TRUE, ... )
enadata |
|
dimensions |
The number of dimensions to include in the dimensional reduction |
norm.by |
A function to be used to normalize adjacency (co-occurrence) vectors before computing the dimensional reduction, default: sphere_norm_c() |
rotation.by |
A function to be used to compute the dimensional reduction, default: ena.svd() |
rotation.params |
(optional) A character vector containing additional parameters for the function in rotation.by, if needed |
rotation.set |
A previously-constructed ENARotationSet object to use for the dimensional reduction |
endpoints.only |
A logical variable which determines whether to only show endpoints for trajectory models |
center.align.to.origin |
A logical variable when TRUE (default) determines aligns both point center and centroid center to the origin |
node.position.method |
A function to be used to determine node positions based on the dimensional reduction, default: lws.position.es() |
as.list |
R6 objects will be deprecated, but if this is TRUE, the original R6 object will be returned, otherwise a list with class 'ena.set' |
... |
additional parameters addressed in inner function |
This function generates an ENAset object from an ENAdata object. Takes the adjacency (co-occurrence) vectors from enadata, computes a dimensional reduction (projection), and calculates node positions in the projected ENA space. Returns location of the units in the projected space, as well as locations for node positions, and normalized adjacency (co-occurrence) vectors to construct network graphs
ENAset class object that can be further processed for analysis or plotting
data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum ) set.means.rotated = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) )data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum ) set.means.rotated = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) )
Generates an a plot from a given ENA set object
ena.plot( enaset, title = "ENA Plot", dimension.labels = c("", ""), font.size = 10, font.color = "#000000", font.family = c("Arial", "Courier New", "Times New Roman"), scale.to = "network", ... )ena.plot( enaset, title = "ENA Plot", dimension.labels = c("", ""), font.size = 10, font.color = "#000000", font.family = c("Arial", "Courier New", "Times New Roman"), scale.to = "network", ... )
enaset |
The |
title |
A character used for the title of the plot, default: ENA Plot |
dimension.labels |
A character vector containing labels for the axes, default: c(X, Y) |
font.size |
An integer determining the font size for graph labels, default: 10 |
font.color |
A character determining the color of label font, default: black |
font.family |
A character determining the font type, choices: Arial, Courier New, Times New Roman, default: Arial |
scale.to |
"network" (default), "points", or a list with x and y ranges. Network and points both scale to the c(-max, max) of the corresponding data.frame |
... |
additional parameters addressed in inner function |
This function defines the axes and other features of a plot for displaying an ENAset; generates an ENAplot object that can used to plot points, network graphs, and other information from an ENAset
ENAplot used for plotting an ENAset
Plot a point based on a summary statistic computed from a given method (typically, mean) for a set of points in a projected ENA space
ena.plot.group( enaplot, points = NULL, method = "mean", labels = NULL, colors = default.colors[1], shape = c("square", "triangle-up", "diamond", "circle"), confidence.interval = c("none", "crosshairs", "box"), outlier.interval = c("none", "crosshairs", "box"), label.offset = "bottom right", label.font.size = NULL, label.font.color = NULL, label.font.family = NULL, show.legend = T, legend.name = NULL, ... )ena.plot.group( enaplot, points = NULL, method = "mean", labels = NULL, colors = default.colors[1], shape = c("square", "triangle-up", "diamond", "circle"), confidence.interval = c("none", "crosshairs", "box"), outlier.interval = c("none", "crosshairs", "box"), label.offset = "bottom right", label.font.size = NULL, label.font.color = NULL, label.font.family = NULL, show.legend = T, legend.name = NULL, ... )
enaplot |
|
points |
A matrix or data.frame where columns contain coordinates of points in a projected ENA space |
method |
A function for computing a summary statistic for each column of points |
labels |
A character which will be the label for the group's point |
colors |
A character, determines color of the group's point, default: enaplot$color |
shape |
A character, determines shape of the group's point, choices: square, triangle, diamond, circle, default: square |
confidence.interval |
A character that determines how the confidence interval is displayed, choices: none, box, crosshair, default: none |
outlier.interval |
A character that determines how outlier interval is displayed, choices: none, box, crosshair, default: none |
label.offset |
character: top left (default), top center, top right, middle left, middle center, middle right, bottom left, bottom center, bottom right |
label.font.size |
An integer which determines the font size for label, default: enaplot$font.size |
label.font.color |
A character which determines the color of label, default: enaplot$font.color |
label.font.family |
A character which determines font type, choices: Arial, Courier New, Times New Roman, default: enaplot$font.family |
show.legend |
Logical indicating whether to show the point labels in the in legend |
legend.name |
Character indicating the name to show above the plot legend |
... |
Additional parameters |
Plots a point based on a summary statistic for a group (typically, mean)
The ENAplot provided to the function, with its plot updated to include the new group point.
ena.plot, ena.plot.points
data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) ) plot = ena.plot(set) unitNames = set$enadata$units ### Plot Condition 1 Group Mean plot = ena.plot.group(plot, as.matrix(set$points$Condition$FirstGame), labels = "FirstGame", colors = "red", confidence.interval = "box") ### plot Condition 2 Group Mean plot = ena.plot.group(plot, as.matrix(set$points$Condition$SecondGame), labels = "SecondGame", colors = "blue", confidence.interval = "box") ## Not run: print(plot)data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) ) plot = ena.plot(set) unitNames = set$enadata$units ### Plot Condition 1 Group Mean plot = ena.plot.group(plot, as.matrix(set$points$Condition$FirstGame), labels = "FirstGame", colors = "red", confidence.interval = "box") ### plot Condition 2 Group Mean plot = ena.plot.group(plot, as.matrix(set$points$Condition$SecondGame), labels = "SecondGame", colors = "blue", confidence.interval = "box") ## Not run: print(plot)
Plot an ENA network: nodes and edges
ena.plot.network( enaplot = NULL, network = NULL, node.positions = enaplot$enaset$rotation$nodes, adjacency.key = NULL, colors = c(pos = enaplot$palette[1], enaplot$palette[2]), edge_type = "line", show.all.nodes = T, threshold = c(0), thin.lines.in.front = T, layers = c("nodes", "edges"), thickness = c(min(abs(network)), max(abs(network))), opacity = thickness, saturation = thickness, scale.range = c(ifelse(min(network) == 0, 0, 0.1), 1), node.size = c(3, 10), labels = NULL, label.offset = "middle right", label.font.size = enaplot$get("font.size"), label.font.color = enaplot$get("font.color"), label.font.family = enaplot$get("font.family"), legend.name = NULL, legend.include.edges = F, scale.weights = F, ... )ena.plot.network( enaplot = NULL, network = NULL, node.positions = enaplot$enaset$rotation$nodes, adjacency.key = NULL, colors = c(pos = enaplot$palette[1], enaplot$palette[2]), edge_type = "line", show.all.nodes = T, threshold = c(0), thin.lines.in.front = T, layers = c("nodes", "edges"), thickness = c(min(abs(network)), max(abs(network))), opacity = thickness, saturation = thickness, scale.range = c(ifelse(min(network) == 0, 0, 0.1), 1), node.size = c(3, 10), labels = NULL, label.offset = "middle right", label.font.size = enaplot$get("font.size"), label.font.color = enaplot$get("font.color"), label.font.family = enaplot$get("font.family"), legend.name = NULL, legend.include.edges = F, scale.weights = F, ... )
enaplot |
|
network |
dataframe or matrix containing the edge weights for the network graph; typically comes from ENAset$line.weights |
node.positions |
matrix containing the positiions of the nodes. Defaults to enaplot$enaset$node.positions |
adjacency.key |
matrix containing the adjacency key for looking up the names and positions |
colors |
A String or vector of colors for positive and negative line weights. E.g. red or c(pos= red, neg = blue), default: c(pos= red, neg = blue) |
edge_type |
A String representing the type of line to draw, either "line", "dash", or "dot" |
show.all.nodes |
A Logical variable, default: true |
threshold |
A vector of numeric min/max values, default: c(0,Inf) plotting . Edge weights below the min value will not be displayed; edge weights above the max value will be shown at the max value. |
thin.lines.in.front |
A logical, default: true |
layers |
ordering of layers, default: c("nodes", "edges") |
thickness |
A vector of numeric min/max values for thickness, default: c(min(abs(network)), max(abs(network))) |
opacity |
A vector of numeric min/max values for opacity, default: thickness |
saturation |
A vector of numeric min/max values for saturation, default: thickness |
scale.range |
A vector of numeric min/max to scale from, default: c(0.1,1) or if min(network) is 0, c(0,1) |
node.size |
A lower and upper bound used for scaling the size of the nodes, default c(0, 20) |
labels |
A character vector of node labels, default: code names |
label.offset |
A character vector of representing the positional offset relative to the respective node. Defaults to "middle right" for all nodes. If a single values is provided, it is used for all positions, else the length of the |
label.font.size |
An integer which determines the font size for graph labels, default: enaplot$font.size |
label.font.color |
A character which determines the color of label font, default: enaplot$font.color |
label.font.family |
A character which determines font type, choices: Arial, Courier New, Times New Roman, default: enaplot$font.family |
legend.name |
A character name used in the plot legend. Not included in legend when NULL (Default), if legend.include.edges is TRUE will always be "Nodes" |
legend.include.edges |
Logical value indicating if the edge names should be included in the plot legend. Forces legend.name to be "Nodes" |
scale.weights |
Logical indicating to scale the supplied network |
... |
Additional parameters |
lots a network graph, including nodes (taken from codes in the ENAplot) and the edges (provided in network)
The ENAplot provided to the function, with its plot updated to include the nodes and provided connecting lines.
data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) ) plot = ena.plot(set) ### Subset rotated points and plot Condition 1 Group Mean as.matrix(set$points$Condition$FirstGame) first.game.points = as.matrix(set$points$Condition$FirstGame) plot = ena.plot.group(plot, first.game.points, labels = "FirstGame", colors = "red", confidence.interval = "box") ### Subset rotated points and plot Condition 2 Group Mean second.game.points = as.matrix(set$points$Condition$SecondGame) plot = ena.plot.group(plot, second.game.points, labels = "SecondGame", colors = "blue", confidence.interval = "box") ### get mean network plots first.game.lineweights = as.matrix(set$line.weights$Condition$FirstGame) first.game.mean = colMeans(first.game.lineweights) second.game.lineweights = as.matrix(set$line.weights$Condition$SecondGame) second.game.mean = colMeans(second.game.lineweights) subtracted.network = first.game.mean - second.game.mean plot = ena.plot.network(plot, network = subtracted.network) ## Not run: print(plot)data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) ) plot = ena.plot(set) ### Subset rotated points and plot Condition 1 Group Mean as.matrix(set$points$Condition$FirstGame) first.game.points = as.matrix(set$points$Condition$FirstGame) plot = ena.plot.group(plot, first.game.points, labels = "FirstGame", colors = "red", confidence.interval = "box") ### Subset rotated points and plot Condition 2 Group Mean second.game.points = as.matrix(set$points$Condition$SecondGame) plot = ena.plot.group(plot, second.game.points, labels = "SecondGame", colors = "blue", confidence.interval = "box") ### get mean network plots first.game.lineweights = as.matrix(set$line.weights$Condition$FirstGame) first.game.mean = colMeans(first.game.lineweights) second.game.lineweights = as.matrix(set$line.weights$Condition$SecondGame) second.game.mean = colMeans(second.game.lineweights) subtracted.network = first.game.mean - second.game.mean plot = ena.plot.network(plot, network = subtracted.network) ## Not run: print(plot)
Plot all or a subset of the points of an ENAplot using the plotly plotting library
ena.plot.points( enaplot, points = NULL, point.size = enaplot$point$size, labels = NULL, label.offset = "top left", label.group = NULL, label.font.size = NULL, label.font.color = NULL, label.font.family = NULL, shape = "circle", colors = NULL, confidence.interval.values = NULL, confidence.interval = c("none", "crosshairs", "box"), outlier.interval.values = NULL, outlier.interval = c("none", "crosshairs", "box"), show.legend = T, legend.name = "Points", texts = NULL, ... )ena.plot.points( enaplot, points = NULL, point.size = enaplot$point$size, labels = NULL, label.offset = "top left", label.group = NULL, label.font.size = NULL, label.font.color = NULL, label.font.family = NULL, shape = "circle", colors = NULL, confidence.interval.values = NULL, confidence.interval = c("none", "crosshairs", "box"), outlier.interval.values = NULL, outlier.interval = c("none", "crosshairs", "box"), show.legend = T, legend.name = "Points", texts = NULL, ... )
enaplot |
|
points |
A dataframe of matrix where the first two column are X and Y coordinates |
point.size |
A data.frame or matrix where the first two column are X and Y coordinates of points to plot in a projected ENA space defined in ENAplot |
labels |
A character vector of point labels, length nrow(points); default: NULL |
label.offset |
character: top left (default), top center, top right, middle left, middle center, middle right, bottom left, bottom center, bottom right |
label.group |
A string used to group the labels in the legend. Items plotted with the same label.group will show/hide together when clicked within the legend. |
label.font.size |
An integer which determines the font size for point labels, default: enaplot$font.size |
label.font.color |
A character which determines the color of label font, default: enaplot$font.color |
label.font.family |
A character which determines label font type, choices: Arial, Courier New, Times New Roman, default: enaplot$font.family |
shape |
A character which determines the shape of point markers, choices: square, triangle, diamond, circle, default: circle |
colors |
A character vector of the point marker colors; if one given it is used for all, otherwise must be same length as points; default: black |
confidence.interval.values |
A matrix/dataframe where columns are CI x and y values for each point |
confidence.interval |
A character determining markings to use for confidence intervals, choices: none, box, crosshair, default: none |
outlier.interval.values |
A matrix/dataframe where columns are OI x and y values for each point |
outlier.interval |
A character determining markings to use for outlier interval, choices: none, box, crosshair, default: none |
show.legend |
Logical indicating whether to show the point labels in the in legend |
legend.name |
Character indicating the name to show above the plot legend |
texts |
[TBD] |
... |
additional parameters addressed in inner function |
ENAplot The ENAplot provided to the function, with its plot updated to include the new points.
ena.plot, ENAplot, ena.plot.group
data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) ) plot = ena.plot(set) group1.points = set$points[set$meta.data$Condition == "FirstGame",] group2.points = set$points[set$meta.data$Condition == "SecondGame",] plot = ena.plot.points(plot, points = group1.points); plot = ena.plot.points(plot, points = group2.points); ## Not run: print(plot);data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum, rotation.by = ena.rotate.by.mean, rotation.params = list( accum$meta.data$Condition=="FirstGame", accum$meta.data$Condition=="SecondGame" ) ) plot = ena.plot(set) group1.points = set$points[set$meta.data$Condition == "FirstGame",] group2.points = set$points[set$meta.data$Condition == "SecondGame",] plot = ena.plot.points(plot, points = group1.points); plot = ena.plot.points(plot, points = group2.points); ## Not run: print(plot);
Function used to plot trajectories
ena.plot.trajectory( enaplot, points, by = NULL, labels = NULL, labels.show = c("Always", "Hover", "Both"), names = NULL, label.offset = NULL, label.font.size = enaplot$get("font.size"), label.font.color = enaplot$get("font.color"), label.font.family = c("Arial", "Courier New", "Times New Roman"), shape = c("circle", "square", "triangle-up", "diamond"), colors = NULL, default.hidden = F )ena.plot.trajectory( enaplot, points, by = NULL, labels = NULL, labels.show = c("Always", "Hover", "Both"), names = NULL, label.offset = NULL, label.font.size = enaplot$get("font.size"), label.font.color = enaplot$get("font.color"), label.font.family = c("Arial", "Courier New", "Times New Roman"), shape = c("circle", "square", "triangle-up", "diamond"), colors = NULL, default.hidden = F )
enaplot |
|
points |
dataframe of matrix - first two column are X and Y coordinates, each row is a point in a trajectory |
by |
vector used to subset points into individual trajectories, length nrow(points) |
labels |
character vector - point labels, length nrow(points) |
labels.show |
A character choice: Always, Hover, Both. Default: Both |
names |
character vector - labels for each trajectory of points, length length(unique(by)) |
label.offset |
A numeric vector of an x and y value to offset labels from the coordinates of the points |
label.font.size |
An integer which determines the font size for labels, default: enaplot$font.size |
label.font.color |
A character which determines the color of label font, default: enaplot$font.color |
label.font.family |
A character which determines font type, choices: Arial, Courier New, Times New Roman, default: enaplot$font.family |
shape |
A character which determines the shape of markers, choices: square, triangle, diamond, circle, default: circle |
colors |
A character vector, that determines marker color, default NULL results in alternating random colors. If single color is supplied, it will be used for all trajectories, otherwise the length of the supplied color vector should be equal to the length of the supplied names (i.e a color for each trajectory being plotted) |
|
A logical indicating if the trajectories should start hidden (click on the legend to show them) Default: FALSE |
The ENAplot provided to the function, with its plot updated to include the trajectories
data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("GroupName","ActivityNumber")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post","C.Change")], codes = RS.data[,codeNames], window.size.back = 4, model = "A" ); set = ena.make.set(accum); ### get mean network plots first.game.lineweights = as.matrix(set$line.weights$Condition$FirstGame) first.game.mean = colMeans(first.game.lineweights) second.game.lineweights = as.matrix(set$line.weights$Condition$SecondGame) second.game.mean = colMeans(second.game.lineweights) subtracted.network = first.game.mean - second.game.mean # Plot dimension 1 against ActivityNumber metadata dim.by.activity = cbind( as.matrix(set$points)[,1], set$trajectories$ActivityNumber * .8/14-.4 #scale down to dimension 1 ) plot = ena.plot(set) plot = ena.plot.network(plot, network = subtracted.network, legend.name="Network") plot = ena.plot.trajectory( plot, points = dim.by.activity, names = unique(set$model$unit.label), by = set$trajectories$ENA_UNIT ); ## Not run: print(plot)data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("GroupName","ActivityNumber")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post","C.Change")], codes = RS.data[,codeNames], window.size.back = 4, model = "A" ); set = ena.make.set(accum); ### get mean network plots first.game.lineweights = as.matrix(set$line.weights$Condition$FirstGame) first.game.mean = colMeans(first.game.lineweights) second.game.lineweights = as.matrix(set$line.weights$Condition$SecondGame) second.game.mean = colMeans(second.game.lineweights) subtracted.network = first.game.mean - second.game.mean # Plot dimension 1 against ActivityNumber metadata dim.by.activity = cbind( as.matrix(set$points)[,1], set$trajectories$ActivityNumber * .8/14-.4 #scale down to dimension 1 ) plot = ena.plot(set) plot = ena.plot.network(plot, network = subtracted.network, legend.name="Network") plot = ena.plot.trajectory( plot, points = dim.by.activity, names = unique(set$model$unit.label), by = set$trajectories$ENA_UNIT ); ## Not run: print(plot)
Plots individual units, all units, groups of units, networks, and network subtractions
ena.plotter( set, groupVar = NULL, groups = NULL, points = FALSE, mean = FALSE, network = TRUE, networkMultiplier = 1, subtractionMultiplier = 1, unit = NULL, print.plots = F, ... )ena.plotter( set, groupVar = NULL, groups = NULL, points = FALSE, mean = FALSE, network = TRUE, networkMultiplier = 1, subtractionMultiplier = 1, unit = NULL, print.plots = F, ... )
set |
an ena.set object |
groupVar |
vector, character, of column name containing group identifiers. |
groups |
vector, character, of values of groupVar column you wish to plot. Maxium of two groups allowed. |
points |
logical, TRUE will plot points (default: FALSE) |
mean |
logical, TRUE will plot the mean position of the groups defined in the groups argument (default: FALSE) |
network |
logical, TRUE will plot networks (default: TRUE) |
networkMultiplier |
numeric, scaling factor for non-subtracted networks (default: 1) |
subtractionMultiplier |
numeric, scaling factor for subtracted networks (default: 1) |
unit |
vector, character, name of a single unit to plot |
print.plots |
logical, TRUE will show plots in the Viewer (default: FALSE) |
... |
Additional parameters passed to set creation and plotting functions |
This function includes options to plots individual units, all units, groups of units, networks, and network subtractions, given an ena.set objects. Plots are stored on the supplied ena.set object.
ena.set object
This function allows user to provide a regression formula for rotation on x and optionally on y. If regression formula for y is not provide, svd is applied to the residual data deflated by x to get y coordinates.
ena.rotate.by.hena.regression(enaset, params)ena.rotate.by.hena.regression(enaset, params)
enaset |
An |
params |
list of parameters, may include: x_var: Regression formula for x direction, such as "lm(formula=V ~ Condition + GameHalf + Condition : GameHalf)". y_var: Regression formula for y direction (optional). points: A unit by connection weight matrix for rotation. If not provided, points in enaset are used. fullNames: If true, all independent variable names are included in the x and y names. Otherwise, only first variable name is used. |
Computes a dimensional reduction from a matrix of points such that the first dimension of the projected space passes through the means of two groups in a the original space. Subsequent dimensions of the projected space are computed using ena.svd
ena.rotate.by.mean(enaset, groups)ena.rotate.by.mean(enaset, groups)
enaset |
An |
groups |
A list containing two logical vectors of length |
hENA rotation function.
ena.rotation.h(enaset, params)ena.rotation.h(enaset, params)
enaset |
ena set |
params |
list of parameters |
ena set
Generates an ENA model by constructing a dimensional reduction of adjacency (co-occurrence) vectors as defined by the supplied conversations, units, and codes.
ena.set.creator( data, codes, units, conversation, metadata = NULL, model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"), weight.by = "binary", window = c("MovingStanzaWindow", "Conversation"), window.size.back = 1, include.meta = TRUE, groupVar = NULL, groups = NULL, runTest = FALSE, ... )ena.set.creator( data, codes, units, conversation, metadata = NULL, model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"), weight.by = "binary", window = c("MovingStanzaWindow", "Conversation"), window.size.back = 1, include.meta = TRUE, groupVar = NULL, groups = NULL, runTest = FALSE, ... )
data |
data.frame with containing metadata and coded columns |
codes |
vector, numeric or character, of columns with codes |
units |
vector, numeric or character, of columns representing units |
conversation |
vector, numeric or character, of columns to segment conversations by |
metadata |
vector, numeric or character, of columns with additional meta information for units |
model |
character: EndPoint (default), AccumulatedTrajectory, SeparateTrajectory |
weight.by |
"binary" is default, can supply a function to call (e.g. sum) |
window |
MovingStanzaWindow (default) or Conversation |
window.size.back |
Number of lines in the stanza window (default: 1) |
include.meta |
[TBD] |
groupVar |
vector, character, of column name containing group identifiers. If column contains at least two unique values, will generate model using a means rotation (a dimensional reduction maximizing the variance between the means of the two groups) |
groups |
vector, character, of values of groupVar column used for means rotation or statistical tests |
runTest |
logical, TRUE will run a Student's t-Test and a Wilcoxon test for groups defined by the groups argument |
... |
Additional parameters passed to model generation |
This function generates an ena.set object given a data.frame, units, conversations, and codes. After accumulating the adjacency (co-occurrence) vectors, computes a dimensional reduction (projection), and calculates node positions in the projected ENA space. Returns location of the units in the projected space, as well as locations for node positions, and normalized adjacency (co-occurrence) vectors to construct network graphs. Includes options for returning statistical tests between groups of units.
ena.set object
Calculate both Spearman and Pearson correlations for the provided ENAset
ena.writeup( enaset, tool = "rENA", tool.version = as.character(packageVersion(tool)), comparison = NULL, comparison.groups = NULL, sig.dig = 2, output_dir = getwd(), type = c("file", "stream"), theory = T, methods = T, params = NULL, output_file = NULL, output_format = NULL )ena.writeup( enaset, tool = "rENA", tool.version = as.character(packageVersion(tool)), comparison = NULL, comparison.groups = NULL, sig.dig = 2, output_dir = getwd(), type = c("file", "stream"), theory = T, methods = T, params = NULL, output_file = NULL, output_format = NULL )
enaset |
ENAset to view methods of |
tool |
c("rENA","webENA") |
tool.version |
as.character(packageVersion(tool)) |
comparison |
character string representing the comparison used, c(NULL, "parametric", "non-parametric"). Default NULL |
comparison.groups |
Groups that were used for the comparison |
sig.dig |
Integer for the number of digits to round to |
output_dir |
Where to save the output file |
type |
c("file","stream") File will save to a file in output_dir, Stream returns the contents directly |
theory |
Logical indicating whether to include theory in the writeup |
methods |
Logical indicating whether to include methods in the writeup |
params |
additional parameters for rmarkdown::render |
output_file |
character |
output_format |
character |
String representing the methods used to generate the model
ENAdata R6class
ENAdata R6class
rawA data frame constructed from the unit, convo, code, and metadata parameters of ena.accumulate.data
adjacency.vectorsA data frame of adjacency (co-occurrence) vectors by row
accumulated.adjacency.vectorsA data frame of adjacency (co-occurrence) vectors accumulated per unit
modelThe type of ENA model: EndPoint, Accumulated Trajectory, or Separate Trajectory
unitsA data frame of columns that were combined to make the unique units. Includes column for trajectory selections. (unique)
unit.namesA vector of unique unit values
metadataA data frame of unique metadata for each unit
trajectoriesA list: units - data frame, for a given row tells which trajectory it's a part; step - data frame, where along the trajectory a row sits
adjacency.matrixTBD
adjacency.vectors.rawTBD
codesA vector of code names
function.callThe string representation of function called and parameters provided
function.paramsA list of all parameters sent to function call Construct ENAdata
new()
ENAdata$new( file, units = NULL, units.used = NULL, units.by = NULL, conversations.by = NULL, codes = NULL, model = NULL, weight.by = "binary", window.size.back = 1, window.size.forward = 0, mask = NULL, include.meta = T, ... )
fileTBD
unitsTBD
units.usedTBD
units.byTBD
conversations.byTBD
codesTBD
modelTBD
weight.byTBD
window.size.backTBD
window.size.forwardTBD
maskTBD
include.metaTBD
...TBD
Process accumulation
process()
ENAdata$process()
ENAdata Get property from object
get()
ENAdata$get(x = "data")
xcharacter key to retrieve from object
value from object at x Add metadata
add.metadata()
ENAdata$add.metadata(merge = F)
mergelogical (default: FALSE)
data.frame
clone()
The objects of this class are cloneable with this method.
ENAdata$clone(deep = FALSE)
deepWhether to make a deep clone.
ENAset R6class
ENAset R6class
enaset- The ENAset object from which the ENAplot was constructed
plot- The plotly object used for data visualization
axes- TBD
point- TBD
palette- TBD
plotted- TBD Create ENApolot
new()
ENAplot$new(
enaset = NULL,
title = "ENA Plot",
dimension.labels = c("", ""),
font.size = 14,
font.color = "#000000",
font.family = "Arial",
scale.to = "network",
...
)enasetTBD
titleTBD
dimension.labelsTBD
font.sizeTBD
font.colorTBD
font.familyTBD
scale.toTBD
...TBD
showticklabelsTBD
autosizeTBD
automarginTBD
axispaddingTBD
ENAplot Print ENA plot
print()
ENAplot$print()
Get property from object
get()
ENAplot$get(x)
xcharacter key to retrieve from object
value from object at x
clone()
The objects of this class are cloneable with this method.
ENAplot$clone(deep = FALSE)
deepWhether to make a deep clone.
ENARotationSet R6class
ENARotationSet R6class
rotationTBD
node.positionsTBD
codesTBD
eigenvaluesTBD Create ENARotationSet
new()
ENARotationSet$new(rotation, codes, node.positions, eigenvalues = NULL)
rotationTBD
codesTBD
node.positionsTBD
eigenvaluesTBD
ENARotationsSet
clone()
The objects of this class are cloneable with this method.
ENARotationSet$clone(deep = FALSE)
deepWhether to make a deep clone.
ENAset R6class
ENAset R6class
enadataAn ENAdata object originally used to create the set
points.rawA data frame containing accumulated adjacency (co-occurrence) vectors per unit
points.normed.centeredA data frame of centered normed accumulated adjacency (co-occurrence) vectors for each unit
points.rotatedA data frame of point positions for number of dimensions specified in ena.make.set (i.e., the centered, normed, and rotated data)
line.weightsA data frame of connections strengths per unit (Data frame of normed accumu- lated adjacency (co-occurrence) vectors for each unit)
node.positions- A data frame of positions for each code
codes- A vector of code names
rotation.set- An ENARotationSet object
variance- A vector of variance accounted for by each dimension specified
centroids- A matrix of the calculated centroid positions
function.call- The string representation of function called
function.params- A list of all parameters sent to function call
rotation_distsTBD
points.rotated.scaledTBD
points.rotated.non.zeroTBD
line.weights.unrotatedTBD
line.weights.non.zeroTBD
correlationsA data frame of spearman and pearson correlations for each dimension specified
center.align.to.origin- align point and centroid centers to origin Create ENAset
new()
ENAset$new( enadata, dimensions = 2, norm.by = fun_sphere_norm, rotation.by = ena.svd.R6, rotation.params = NULL, rotation.set = NULL, node.position.method = lws.positions.sq.R6, endpoints.only = TRUE, center.align.to.origin = TRUE, ... )
enadataTBD
dimensionsTBD
norm.byTBD
rotation.byTBD
rotation.paramsTBD
rotation.setTBD
node.position.methodTBD
endpoints.onlyTBD
center.align.to.originTBD
...TBD
ENAset Process ENAset
process()
ENAset$process()
ENASet Get property from object
get()
ENAset$get(x = "enadata")
xcharacter key to retrieve from object
value from object at x
clone()
The objects of this class are cloneable with this method.
ENAset$clone(deep = FALSE)
deepWhether to make a deep clone.
Find code columns
find_code_cols(x)find_code_cols(x)
x |
data.table (or frame) to search for columns of class ena.co.occurrence |
logical vector
Find dimension columns
find_dimension_cols(x)find_dimension_cols(x)
x |
data.table (or frame) to search for columns of class ena.dimension |
logical vector
Find metadata columns
find_meta_cols(x)find_meta_cols(x)
x |
data.table (or frame) to search for columns of class ena.metadata |
logical vector
Calculate Conhen's d
fun_cohens.d(x, y)fun_cohens.d(x, y)
x |
[TBD] |
y |
[TBD] |
Cohen's d calculation
[TBD]
numeric Cohen's d calculation
TBD
fun_skip_sphere_norm(dfM)fun_skip_sphere_norm(dfM)
dfM |
Dataframe |
Non sphere norm
TBD
fun_sphere_norm(dfM)fun_sphere_norm(dfM)
dfM |
Dataframe |
Sphere norm
Title
means_rotate(x, on = NULL)means_rotate(x, on = NULL)
x |
[TBD] |
on |
[TBD] |
[TBD]
TBD
merge_columns_c(df, cols, sep = ".")merge_columns_c(df, cols, sep = ".")
df |
Dataframe |
cols |
Vector |
sep |
Character seperator |
Merge data frame columns
Methods report for rmarkdwon
methods_report( toc = FALSE, toc_depth = 3, fig_width = 5, fig_height = 4, keep_md = FALSE, md_extensions = NULL, pandoc_args = NULL )methods_report( toc = FALSE, toc_depth = 3, fig_width = 5, fig_height = 4, keep_md = FALSE, md_extensions = NULL, pandoc_args = NULL )
toc |
[TBD] |
toc_depth |
[TBD] |
fig_width |
[TBD] |
fig_height |
[TBD] |
keep_md |
[TBD] |
md_extensions |
[TBD] |
pandoc_args |
[TBD] |
Methods report for rmarkdwon
methods_report_stream( toc = FALSE, toc_depth = 3, fig_width = 5, fig_height = 4, keep_md = FALSE, md_extensions = NULL, pandoc_args = NULL )methods_report_stream( toc = FALSE, toc_depth = 3, fig_width = 5, fig_height = 4, keep_md = FALSE, md_extensions = NULL, pandoc_args = NULL )
toc |
[TBD] |
toc_depth |
[TBD] |
fig_width |
[TBD] |
fig_height |
[TBD] |
keep_md |
[TBD] |
md_extensions |
[TBD] |
pandoc_args |
[TBD] |
Title
move_nodes_to_unit_circle(set, dimension_name_1, dimension_name_2)move_nodes_to_unit_circle(set, dimension_name_1, dimension_name_2)
set |
TBD |
dimension_name_1 |
TBD |
dimension_name_2 |
TBD |
TBD
Title
move_nodes_to_unit_circle_with_equal_space( set, dimension_name_1, dimension_name_2 )move_nodes_to_unit_circle_with_equal_space( set, dimension_name_1, dimension_name_2 )
set |
TBD |
dimension_name_1 |
TBD |
dimension_name_2 |
TBD |
TBD
Convert a vector of strings, representing names of a square matrix, to an adjacency
namesToAdjacencyKey(vector, upper_triangle = TRUE)namesToAdjacencyKey(vector, upper_triangle = TRUE)
vector |
Vector representing the names of a square matrix |
upper_triangle |
Not Implemented |
Returns a matrix of 2 rows by choose(length(vector), 2) columns
Plot an ena.set object
## S3 method for class 'ena.set' plot(x, y, ...)## S3 method for class 'ena.set' plot(x, y, ...)
x |
ena.set to plot |
y |
ignored. |
... |
Additional parameters passed along to ena.plot functions |
ena.plot.object
library(magrittr) data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum ) plot(set) %>% add_points(Condition$FirstGame, colors = "blue", with.mean = TRUE) %>% add_points(Condition$SecondGame, colors = "red", with.mean = TRUE) plot(set) %>% add_network(Condition$FirstGame - Condition$SecondGame)library(magrittr) data(RS.data) codeNames = c('Data','Technical.Constraints','Performance.Parameters', 'Client.and.Consultant.Requests','Design.Reasoning','Collaboration'); accum = ena.accumulate.data( units = RS.data[,c("UserName","Condition")], conversation = RS.data[,c("Condition","GroupName")], metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post")], codes = RS.data[,codeNames], window.size.back = 4 ) set = ena.make.set( enadata = accum ) plot(set) %>% add_points(Condition$FirstGame, colors = "blue", with.mean = TRUE) %>% add_points(Condition$SecondGame, colors = "red", with.mean = TRUE) plot(set) %>% add_network(Condition$FirstGame - Condition$SecondGame)
Title
prepare_trajectory_data( x = NULL, by = x$`_function.params`$conversation[1], rotation_matrix = x$rotation.matrix, points = NULL, units = points, units_by = x$`_function.params`$units, steps = NULL )prepare_trajectory_data( x = NULL, by = x$`_function.params`$conversation[1], rotation_matrix = x$rotation.matrix, points = NULL, units = points, units_by = x$`_function.params`$units, steps = NULL )
x |
[TBD] |
by |
[TBD] |
rotation_matrix |
[TBD] |
points |
[TBD] |
units |
[TBD] |
units_by |
[TBD] |
steps |
[TBD] |
[TBD]
Title
## S3 method for class 'ena.set' print(x, ..., plot = FALSE, set = TRUE)## S3 method for class 'ena.set' print(x, ..., plot = FALSE, set = TRUE)
x |
[TBD] |
... |
[TBD] |
plot |
[TBD] |
set |
[TBD] |
[TBD]
Title
project_in(x, by = NULL, ...)project_in(x, by = NULL, ...)
x |
[TBD] |
by |
[TBD] |
... |
[TBD] |
[TBD]
Remove meta columns from data.table
remove_meta_data(x)remove_meta_data(x)
x |
[TBD] |
data.table withe columns of class ena.meta.data removed
rENA is used to create and visualize network models of discourse and other phenomena from coded data using Epistemic Network Analysis (ENA). A more complete description of the methods will be provided with the next release. See also XXXXX
A dataset containing sample chat data from the Rescushell Virtual Internship
RS.dataRS.data
An object of class data.frame with 3824 rows and 20 columns.
Title
## S3 method for class 'ena.set' scale(x, center = TRUE, scale = TRUE)## S3 method for class 'ena.set' scale(x, center = TRUE, scale = TRUE)
x |
[TBD] |
center |
Ignored. |
scale |
[TBD] |
[TBD]
TBD
vector_to_ut(v)vector_to_ut(v)
v |
[TBD] |
Upper Triangle from Vector
Title
with_trajectory( x, ..., by = x$`_function.params`$conversation[1], add_jitter = TRUE, frame = 1100, transition = 1000, easing = "circle-in-out" )with_trajectory( x, ..., by = x$`_function.params`$conversation[1], add_jitter = TRUE, frame = 1100, transition = 1000, easing = "circle-in-out" )
x |
[TBD] |
... |
[TBD] |
by |
[TBD] |
add_jitter |
[TBD] |
frame |
[TBD] |
transition |
[TBD] |
easing |
[TBD] |
[TBD]