| Type: | Package |
| Title: | Interactive Interfaces for Results Exploration |
| Version: | 0.3.10 |
| Date: | 2023-04-29 |
| Maintainer: | Julien Barnier <julien.barnier@cnrs.fr> |
| Description: | Shiny interfaces and graphical functions for multivariate analysis results exploration. |
| License: | GPL (≥ 3) |
| VignetteBuilder: | knitr |
| URL: | https://juba.github.io/explor/ |
| BugReports: | https://github.com/juba/explor/issues |
| Encoding: | UTF-8 |
| Imports: | shiny (≥ 1.0), DT, dplyr (≥ 1.0), tidyr (≥ 1.0), ggplot2, highr, formatR, scatterD3 (≥ 1.0.0), RColorBrewer |
| Suggests: | FactoMineR, ade4 (≥ 1.7-13), GDAtools (≥ 2.0), MASS, quanteda, quanteda.textmodels, testthat, knitr, rmarkdown |
| RoxygenNote: | 7.1.2 |
| NeedsCompilation: | no |
| Packaged: | 2023-04-29 16:57:22 UTC; julien |
| Author: | Julien Barnier [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2023-04-29 17:40:02 UTC |
Interactive CA variables plot
Description
This function generates an HTML widget displaying the variables plot of a CA result.
Usage
CA_var_plot(
res,
xax = 1,
yax = 2,
lev_sup = TRUE,
var_sup = TRUE,
var_sup_choice = NULL,
var_hide = "None",
var_lab_min_contrib = 0,
point_size = 64,
col_var = NULL,
symbol_var = NULL,
size_var = NULL,
size_range = c(10, 300),
zoom_callback = NULL,
in_explor = FALSE,
...
)
Arguments
res |
Result of prepare_results() call |
xax |
Horizontal axis number |
yax |
Vertical axis number |
lev_sup |
TRUE to display supplementary levels |
var_sup |
TRUE to display supplementary variables |
var_sup_choice |
list of supplementary variables to display |
var_hide |
elements to hide (rows or columns) |
var_lab_min_contrib |
Contribution threshold to display points labels |
point_size |
base point size |
col_var |
name of the variable for points color |
symbol_var |
name of the variable for points symbol |
size_var |
name of the variable for points size |
size_range |
points size range with format c(minimum, maximum) |
zoom_callback |
scatterD3 zoom callback JavaScript body |
in_explor |
wether the plot is to be displayed in the |
... |
Other arguments passed to scatterD3 |
Interactive MCA biplot
Description
This function generates an HTML widget displaying the variables plot of an MCA result.
Usage
MCA_biplot(
res,
xax = 1,
yax = 2,
col_var,
ind_sup = TRUE,
var_sup = TRUE,
bi_lab_min_contrib = 0,
symbol_var = NULL,
ind_point_size = 16,
var_point_size = 96,
ind_opacity = 0.5,
ind_opacity_var = NULL,
ind_labels = FALSE,
zoom_callback = NULL,
in_explor = FALSE,
...
)
Arguments
res |
Result of prepare_results() call |
xax |
Horizontal axis number |
yax |
Vertical axis number |
col_var |
name of the variable for points color |
ind_sup |
TRUE to display supplementary individuals |
var_sup |
TRUE to display supplementary variables |
bi_lab_min_contrib |
Contribution threshold to display points labels |
symbol_var |
name of the variable for points symbol |
ind_point_size |
base point size for individuals |
var_point_size |
base point size for variable levels |
ind_opacity |
individuals point opacity (constant) |
ind_opacity_var |
individuals point opacity (variable) |
ind_labels |
TRUE to display individuals labels |
zoom_callback |
scatterD3 zoom callback JavaScript body |
in_explor |
wether the plot is to be displayed in the |
... |
Other arguments passed to scatterD3 |
Interactive MCA indivuals plot
Description
This function generates an HTML widget displaying the individuals plot of an MCA result.
Usage
MCA_ind_plot(
res,
xax = 1,
yax = 2,
ind_sup = TRUE,
ind_lab_min_contrib = 0,
lab_var = NULL,
col_var = NULL,
symbol_var = NULL,
opacity_var = NULL,
size_var = NULL,
size_range = c(10, 300),
zoom_callback = NULL,
in_explor = FALSE,
...
)
Arguments
res |
Result of prepare_results() call |
xax |
Horizontal axis number |
yax |
Vertical axis number |
ind_sup |
TRUE to display supplementary individuals |
ind_lab_min_contrib |
Contribution threshold to display points labels |
lab_var |
variable to be used for points names |
col_var |
variable to be used for points color |
symbol_var |
name of the variable for points symbol |
opacity_var |
name of the variable for points opacity |
size_var |
name of the variable for points size |
size_range |
points size range with format c(minimum, maximum) |
zoom_callback |
scatterD3 zoom callback JavaScript body |
in_explor |
wether the plot is to be displayed in the |
... |
Other arguments passed to scatterD3 |
Interactive MCA variables plot
Description
This function generates an HTML widget displaying the variables plot of an MCA result.
Usage
MCA_var_plot(
res,
xax = 1,
yax = 2,
var_sup = TRUE,
var_sup_choice = NULL,
var_lab_min_contrib = 0,
point_size = 64,
labels_prepend_var = FALSE,
col_var = NULL,
symbol_var = NULL,
size_var = NULL,
size_range = c(10, 300),
zoom_callback = NULL,
in_explor = FALSE,
...
)
Arguments
res |
Result of prepare_results() call |
xax |
Horizontal axis number |
yax |
Vertical axis number |
var_sup |
TRUE to display supplementary variables |
var_sup_choice |
list of supplementary variables to display |
var_lab_min_contrib |
Contribution threshold to display points labels |
point_size |
base point size |
labels_prepend_var |
if TRUE, prepend variable names to labels |
col_var |
name of the variable for points color |
symbol_var |
name of the variable for points symbol |
size_var |
name of the variable for points size |
size_range |
points size range with format c(minimum, maximum) |
zoom_callback |
scatterD3 zoom callback JavaScript body |
in_explor |
wether the plot is to be displayed in the |
... |
Other arguments passed to scatterD3 |
Interactive PCA indivuals plot
Description
This function generates an HTML widget displaying the individuals plot of a PCA result.
Usage
PCA_ind_plot(
res,
xax = 1,
yax = 2,
ind_sup = TRUE,
ind_lab_min_contrib = 0,
col_var = NULL,
symbol_var = NULL,
opacity_var = NULL,
size_var = NULL,
size_range = c(10, 300),
lab_var = NULL,
zoom_callback = NULL,
in_explor = FALSE,
...
)
Arguments
res |
Result of prepare_results() call |
xax |
Horizontal axis number |
yax |
Vertical axis number |
ind_sup |
TRUE to display supplementary individuals |
ind_lab_min_contrib |
Contribution threshold to display points labels |
col_var |
variable to be used for points color |
symbol_var |
name of the variable for points symbol |
opacity_var |
name of the variable for points opacity |
size_var |
name of the variable for points size |
size_range |
points size range with format c(minimum, maximum) |
lab_var |
variable to be used for points names |
zoom_callback |
scatterD3 zoom callback JavaScript body |
in_explor |
wether the plot is to be displayed in the |
... |
Other arguments passed to scatterD3 |
Interactive PCA variables plot
Description
This function generates an HTML widget displaying the variables plot of a PCA result.
Usage
PCA_var_plot(
res,
xax = 1,
yax = 2,
var_sup = TRUE,
var_sup_choice = NULL,
var_lab_min_contrib = 0,
scale_unit = FALSE,
col_var = NULL,
size_var = NULL,
zoom_callback = NULL,
in_explor = FALSE,
xlim = NULL,
ylim = NULL,
...
)
Arguments
res |
Result of prepare_results() call |
xax |
Horizontal axis number |
yax |
Vertical axis number |
var_sup |
TRUE to display supplementary variables |
var_sup_choice |
list of supplementary variables to display |
var_lab_min_contrib |
Contribution threshold to display points labels |
scale_unit |
wether the PCA is scaled |
col_var |
name of the variable for points color |
size_var |
name of the variable for points size |
zoom_callback |
scatterD3 zoom callback JavaScript body |
in_explor |
wether the plot is to be displayed in the |
xlim |
custom x axis limits |
ylim |
custom y axis limits |
... |
Other arguments passed to scatterD3 |
Interface for analysis results exploration
Description
This function launches a shiny app in a web browser in order to do interactive visualisation and exploration of an analysis results.
Usage
explor(obj)
## S3 method for class 'CA'
explor(obj)
## S3 method for class 'textmodel_ca'
explor(obj)
## S3 method for class 'coa'
explor(obj)
## S3 method for class 'MCA'
explor(obj)
## S3 method for class 'speMCA'
explor(obj)
## S3 method for class 'mca'
explor(obj)
## S3 method for class 'acm'
explor(obj)
## S3 method for class 'PCA'
explor(obj)
## S3 method for class 'princomp'
explor(obj)
## S3 method for class 'prcomp'
explor(obj)
## S3 method for class 'pca'
explor(obj)
Arguments
obj |
object containing analysis results |
Details
If you want to display supplementary individuals or variables and you're using
the dudi.coa function, you can add the coordinates of
suprow and/or supcol to as supr and/or
supr elements added to your dudi.coa result (See example).
If you want to display supplementary individuals or variables and you're using
the dudi.acm function, you can add the coordinates of
suprow and/or supcol to as supi and/or
supv elements added to your dudi.acm result (See example).
If you want to display supplementary individuals or variables and you're using
the dudi.pca function, you can add the coordinates of
suprow and/or supcol to as supi and/or
supv elements added to your dudi.pca result (See example).
Value
The function launches a shiny app in the system web browser.
Examples
## Not run:
require(FactoMineR)
## FactoMineR::MCA exploration
data(hobbies)
mca <- MCA(hobbies[1:1000,c(1:8,21:23)], quali.sup = 9:10,
quanti.sup = 11, ind.sup = 1:100, graph = FALSE)
explor(mca)
## FactoMineR::PCA exploration
data(decathlon)
d <- decathlon[,1:12]
pca <- PCA(d, quanti.sup = 11:12, graph = FALSE)
explor(pca)
## End(Not run)
## Not run:
library(ade4)
data(bordeaux)
tab <- bordeaux
row_sup <- tab[5,-4]
col_sup <- tab[-5,4]
coa <- dudi.coa(tab[-5,-4], nf = 5, scannf = FALSE)
coa$supr <- suprow(coa, row_sup)
coa$supc <- supcol(coa, col_sup)
explor(coa)
## End(Not run)
## Not run:
library(ade4)
data(banque)
d <- banque[-(1:100),-(19:21)]
ind_sup <- banque[1:100, -(19:21)]
var_sup <- banque[-(1:100),19:21]
acm <- dudi.acm(d, scannf = FALSE, nf = 5)
acm$supv <- supcol(acm, dudi.acm(var_sup, scannf = FALSE, nf = 5)$tab)
colw <- acm$cw*ncol(d)
X <- acm.disjonctif(ind_sup)
X <- data.frame(t(t(X)/colw) - 1)
acm$supi <- suprow(acm, X)
explor(acm)
## End(Not run)
## Not run:
library(ade4)
data(deug)
d <- deug$tab
sup_var <- d[-(1:10), 8:9]
sup_ind <- d[1:10, -(8:9)]
pca <- dudi.pca(d[-(1:10), -(8:9)], scale = TRUE, scannf = FALSE, nf = 5)
supi <- suprow(pca, sup_ind)
pca$supi <- supi
supv <- supcol(pca, dudi.pca(sup_var, scale = TRUE, scannf = FALSE)$tab)
pca$supv <- supv
explor(pca)
## End(Not run)
Graphical representation of indivduals (rows) of a multivariate analysis
Description
This function displays a graphical representation of the individuals (rows) of a multivariate analysis.
This function displays a graphical representation of the individuals
(rows) of a multiple correspondence analysis generated by the MCA
function of the FactoMineR package.
Usage
ggind(obj, ...)
## S3 method for class 'MCA'
ggind(
obj,
xax = 1,
yax = 2,
fac = NA,
label = NULL,
alpha = 0.5,
palette = "Set1",
...
)
Arguments
obj |
a multivariate analysis results object. Currently only MCA is supported |
... |
arguments passed to other methods |
xax |
number of the x axis |
yax |
number of the y axis |
fac |
an optional factor by which points are colored, and confidence ellipses drawn |
label |
legend title |
alpha |
points opacity |
palette |
palette for points coloring, if |
Graphical representation of the variables (columnss) of a multivariate analysis
Description
This function displays a graphical representation of the variables (columns) of a multivariate analysis.
This function displays a graphical representation of the variables
(columns) of a multiple correspondence analysis generated by the MCA
function of the FactoMineR package.
Usage
ggvar(obj, ...)
## S3 method for class 'MCA'
ggvar(obj, xax = 1, yax = 2, size = 4, alpha = 0.5, palette = "Set1", ...)
Arguments
obj |
a multivariate analysis results object. Currently only MCA is supported |
... |
arguments passed to other methods |
xax |
number of the x axis |
yax |
number of the y axis |
size |
text size |
alpha |
points opacity |
palette |
palette for variables coloring |
See Also
Analysis results preparation
Description
This function prepares results to be used by explor. Not to be used directly.
Usage
prepare_results(obj)
## S3 method for class 'CA'
prepare_results(obj)
## S3 method for class 'mca'
prepare_results(obj)
## S3 method for class 'MCA'
prepare_results(obj)
## S3 method for class 'PCA'
prepare_results(obj)
## S3 method for class 'coa'
prepare_results(obj)
## S3 method for class 'acm'
prepare_results(obj)
## S3 method for class 'pca'
prepare_results(obj)
## S3 method for class 'prcomp'
prepare_results(obj)
## S3 method for class 'princomp'
prepare_results(obj)
## S3 method for class 'speMCA'
prepare_results(obj)
## S3 method for class 'textmodel_ca'
prepare_results(obj)
Arguments
obj |
object containing analysis results |
See Also
Compute supplementary variables data for a GDAtools::speMCA result
Description
Compute supplementary variables data for a GDAtools::speMCA result
Usage
speMCA_varsup(mca, df)
Arguments
mca |
result object from speMCA. |
df |
data frame with the supplementary variables data. Must have the same number of rows than the data used with speMCA. |
Value
A list of results suitable to be added as a 'supv' element to the 'mca' object.