Package 'SciViews'

'SciViews::R' Dialect for Data Processing and Visualization

Description

The SciViews::R dialect is base R + tidyverse + a series of additional SciViews packages like data.io, svBase, svFlow, tabularise or chart.

Important functions

• R() for loading the 'SciViews::R“ packages,

• pcomp() for a PCA analysis (unifying various methods),

• correlation() to calculate and plot a correlation matrix,

• panel_reg() and others to plot panels in pairs or coplot graphs,

• panel_boxplot() and others for univariate panels in pairs plots.

• rwb_colors() and others to generate color palettes.

• enum() to enumerate items in a vector,

• timing() to determine the time required to run an R expression,

• nr() and co as convenient shorthand to columns and rows,

• ln() and others for natural logarithm.

Author(s)

Maintainer: Philippe Grosjean [email protected] (ORCID)

See Also

Useful links:

• https://github.com/SciViews/SciViews

• https://www.sciviews.org/SciViews/

• Report bugs at https://github.com/SciViews/SciViews/issues

---

Ask a question to a chatbot

Description

The question is sent to the chatbot server, and the answer is returned. The default chatbot server is ollama running locally, and the default model is codestral:latest. It was also tested with mistral:7b-instruct-v0.2-q6_K for an even smaller model.

Usage

ai_ask(
  question,
  context = NULL,
  max_tokens = getOption("SciViews.chatbot.max_tokens",
    Sys.getenv("SCIVIEWS_CHATBOT_MAX_TOKENS", 1000L)),
  lang = getOption("data.io_lang", "en"),
  url = getOption("SciViews.chatbot.url", Sys.getenv("SCIVIEWS_CHATBOT_URL",
    "http://localhost:11434/api/chat")),
  model = getOption("SciViews.chatbot.model", Sys.getenv("SCIVIEWS_CHATBOT_MODEL",
    "codestral:latest")),
  api_key = Sys.getenv("CONNECT_API_KEY", ""),
  verbose = FALSE
)

ai_explain_term(term, lang = getOption("data.io_lang", "en"), ...)

ai_explain_function(
  fun,
  package = NULL,
  lang = getOption("data.io_lang", "en"),
  ...
)

ai_explain_code(code, lang = getOption("data.io_lang", "en"), ...)

ai_explain_error(
  code = NULL,
  error = NULL,
  lang = getOption("data.io_lang", "en"),
  ...
)

Arguments

question	A character string with the question to ask.
context	An R object used as context (usually a data frame). This is not used yet, but it should be implemented in the future.
max_tokens	The maximum number of tokens to return in the answer. By default, it is 1000.
lang	The language to use for the answer. Default is "en". You can also use "fr" for instance.
url	The URL of the chatbot server. Default is http://localhost:11434/api/chat
model	The LLM (large language model) to use. Default is codestral. Make sure you complies to its license (see https://mistral.ai/news/mistral-ai-non-production-license-mnpl/), or switch to another model that better suits your requirements.
api_key	The API key to use for connecting to the chatbot server (optional, see your server administrator).
verbose	Should more information be printed? FALSE by default.
term	The term to describe.
...	Further arguments passed to ai_ask().
fun	The R function to explain.
package	The R package that provides the function.
code	A small chunk of R code to explain.
error	The error message that R returns.

Value

The answer is returned invisibly. The function is used for its side-effect of displaying the chatbot help page with the question, answer and examples

Examples

## Not run: 


# Basic questions
ai_ask("Who are you?")
ai_ask("What is a chatbot?")
ai_ask("Qui es-tu ?")
ai_ask("Qu'est-ce que R ?")
ai_ask("Qu'est-ce que RStudio ?")
ai_ask("What is GitHub?")
ai_ask("Qu'est-ce que le R Markdown ?")
ai_ask("What is data science?")

# Inappropriate questions
ai_ask("Qu'est ce qu'un Acanthurus sp ?")
ai_ask("Raconte-moi une bonne blague.")
ai_ask("Va te faire voir !")

# Now, more complex questions
ai_ask("Comment filtrer un data frame en R?")
ai_ask("Write R code to filter a data frame.")
ai_ask("Que fait AIC()? Donne un exemple.")
ai_ask("Qu'est ce que l'hétéroscédasticite et comment la détecter dans une ANOVA à un facteur ?")
ai_ask("How to determine which model is better using an ANOVA for nested linear models?")

# Explain terms
ai_explain_term("True positive")
ai_explain_term("percentile", lang = "fr")
ai_explain_term("git push")
ai_explain_term("Quarto", lang = "fr")
ai_explain_term("boite à moustaches") # Language mismatch
ai_explain_term("boites à moustaches parallèles", lang = "fr")

# Explain R functions
ai_explain_function("mean")
ai_explain_function("fmean", lang = "fr")
ai_explain_function("collapse::fmean", lang = "fr")
ai_explain_function("glm", package = "stats", lang = "fr")
ai_explain_function("replace_na", "tidyr", lang = "fr") # collapse::replace_na() used instead!
try(ai_explain_function("nonexistingfunction")) # Error
try(ai_explain_function("apropos", package = "stats")) # Wrong package
try(ai_explain_function("apropos", package = "unknownpkg")) # Unknown package

# Explain R code
ai_explain_code("y <- c(1, 5, 7, NA, -Inf, 8)")
ai_explain_code(r"-[
mtcars |>
  filter(cyl == 4) |>
  summarise(mean_hp = mean(hp), median_disp = median(disp))]-")
ai_explain_code(r"-[
  mtcars %>.%
    sfilter(., cyl == 4) %>.%
    ssummarise(., mean_hp = fmean(hp), median_disp = fmedian(disp))
]-")
ai_explain_code(r"-[
  chart(data = trees, Volume ~ Girth) +
    geom_point() +
    geom_smooth()
]-")
ai_explain_error(error = "longer object length is not a multiple of shorter object length")
ai_explain_error(error = "Error: object 'mydata' not found")
ai_explain_error(error = "Error in lenght(1:10) : could not find function \"lenght\"", lang = "fr")
ai_explain_error(code = r"-[y <- c(1, 5, 7, NA, -Inf, 8, )]-", lang = "fr")
ai_explain_error(code = r"-[trees %>.% filter(Girth > 10))]-", lang = "fr")
ai_explain_error(code = r"-[
urchin <- read("urchin", package = "data.io")
  ]-", error = r"-[
Error in read("urchin", package = "data.io") :
  dataset 'urchin' not found in package 'data.io'
  ]-", lang = "fr")

## End(Not run)

---

SciViews chatbot

Description

A chatbot does not always provide reliable results. Take this with a grain of salt!

Question: No question

Answer

No answer

Examples

# No example

---

SciViews chatbot

Description

A chatbot does not always provide reliable results. Take this with a grain of salt!

Question: No question

Answer

No answer

Examples

# No example

---

SciViews chatbot

Description

A chatbot does not always provide reliable results. Take this with a grain of salt!

Question: No question

Answer

No answer

Examples

# No example

---

Various color palettes

Description

Create vectors of n colors.

Usage

rwb_colors(n, alpha = 1, s = 0.9, v = 0.9)

rwb.colors(n, alpha = 1, s = 0.9, v = 0.9)

rwg_colors(n, alpha = 1, s = 0.9, v = 0.9)

rwg.colors(n, alpha = 1, s = 0.9, v = 0.9)

ryg_colors(n, alpha = 1, s = 0.9, v = 0.9)

ryg.colors(n, alpha = 1, s = 0.9, v = 0.9)

cwm_colors(n, alpha = 1, s = 0.9, v = 0.9)

cwm.colors(n, alpha = 1, s = 0.9, v = 0.9)

Arguments

n	The number of colors (>= 1) to be in the palette.
alpha	The alpha transparency, a number in ⁠[0, 1]⁠, see argument alpha in hsv().
s	The 'saturation' to be used to complete the HSV color descriptions.
v	The 'value' to use for the HSV color descriptions.

Details

cwm_colors(s = 0.5, v = 1) gives very similar colors to cm.colors(). ryg_colors() is similar to rainbow(start = 0, end = 2/6). The xxx_colors() (tidyverse name-compatible) and xxx.colors() (grDevices name-compatible) functions are synonyms.

See Also

cm.colors(), colorRampPalette()

Examples

# Draw color wheels with various palettes
opar <- par(mfrow = c(2, 2))
pie(rep(1, 11), col = cwm.colors(11), main = "Cyan - white - magenta")
pie(rep(1, 11), col = rwb.colors(11), main = "Red - white - blue")
pie(rep(1, 11), col = rwg.colors(11), main = "Red - white - green")
pie(rep(1, 11), col = ryg.colors(11), main = "Red - yellow - green")
par(opar)

---

Correlation matrices

Description

Compute the correlation matrix between all columns of a matrix or data frame.

Usage

correlation(x, ...)

Correlation(x, ...)

## S3 method for class 'formula'
correlation(formula, data = NULL, subset, na.action, ...)

## Default S3 method:
correlation(
  x,
  y = NULL,
  use = "everything",
  method = c("pearson", "kendall", "spearman"),
  ...
)

is.Correlation(x)

is.correlation(x)

as.Correlation(x)

as.correlation(x)

## S3 method for class 'Correlation'
print(x, digits = 3, cutoff = 0, ...)

## S3 method for class 'Correlation'
summary(
  object,
  cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95),
  symbols = c(" ", ".", ",", "+", "*", "B"),
  ...
)

## S3 method for class 'summary.Correlation'
print(x, ...)

## S3 method for class 'Correlation'
plot(
  x,
  y = NULL,
  outline = TRUE,
  cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95),
  palette = rwb.colors,
  col = NULL,
  numbers = TRUE,
  digits = 2,
  type = c("full", "lower", "upper"),
  diag = (type == "full"),
  cex.lab = par("cex.lab"),
  cex = 0.75 * par("cex"),
  ...
)

## S3 method for class 'Correlation'
lines(
  x,
  choices = 1L:2L,
  col = par("col"),
  lty = 2,
  ar.length = 0.1,
  pos = NULL,
  cex = par("cex"),
  labels = rownames(x),
  ...
)

Arguments

x	A numeric vector, matrix or data frame (or any object for is.Correlation() or as.Correlation()).
...	Further arguments passed to functions.
formula	A formula with no response variable, referring only to numeric variables.
data	An optional data frame (or similar, see model.frame()) containing the variables in the formula. By default the variables are taken from environment(formula).
subset	An optional vector used to select rows (observations) of the data matrix x.
na.action	A function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options() and na.fail() is used if that is not set. The 'factory-fresh' default is na.omit().
y	NULL (default), or a vector, matrix or data frame with compatible dimensions to x for Correlation(). The default is equivalent to x = y, but more efficient.
use	An optional character string giving a method for computing correlations in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs".
method	A character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman", can be abbreviated.
digits	Digits to print after the decimal separator.
cutoff	Correlation coefficients lower than this (in absolute value) are suppressed.
object	A 'Correlation' object.
cutpoints	The cut points to use for categories. Specify only positive values (absolute value of correlation coefficients are summarized, or negative equivalents are automatically computed for the graph. Do not include 0 or 1 in the cutpoints).
symbols	The symbols to use to summarize the correlation matrix.
outline	Do we draw the outline of the ellipse?
palette	A function that can produce a palette of colors.
col	Color of the ellipse. If NULL (default), the colors will be computed using cutpoints and palette.
numbers	Do we print correlation values in the center of the ellipses?
type	Do we plot a complete matrix, or only lower or upper triangle?
diag	Do we plot items on the diagonal? They have always a correlation of one.
cex.lab	The expansion factor for labels.
cex	The expansion factor for text.
choices	The items to select.
lty	The line type to draw.
ar.length	The length of the arrow head.
pos	The position relative to arrows.
labels	The label to draw near the arrows.

Value

Correlation() and as.Correlation() create a 'Correlation' object, while is.Correlation() tests for it.

There are print() and summary() methods for the 'Correlation' object that differ in the symbolic encoding of the correlations, (using symnum() for summary()), which makes large correlation matrices more readable.

The plot() method draws ellipses on a graph to represent the correlation matrix visually. This is essentially the plotcorr() function from package ellipse, with slightly different default arguments and with default cutpoints equivalent to those used in the summary() method.

Author(s)

Philippe Grosjean [email protected], wrapping code in package ellipse, function plotcorr() for the plot.Correlation() method.

See Also

cov(), cov2cor(), cov.wt(), symnum(), plotcorr() and look also at panel_cor()

Examples

# This is a simple correlation coefficient
cor(rnorm(10), runif(10))
Correlation(rnorm(10), runif(10))

# 'Correlation' objects allow better inspection of the correlation matrices
# than the output of default R cor() function
(longley.cor <- Correlation(longley))
summary(longley.cor) # Synthetic view of the correlation matrix
plot(longley.cor)    # Graphical representation

# Use of the formula interface
(mtcars.cor <- Correlation(~ mpg + cyl + disp + hp, data = mtcars,
  method = "spearman", na.action = "na.omit"))

mtcars.cor2 <- Correlation(mtcars, method = "spearman")
print(mtcars.cor2, cutoff = 0.6)
summary(mtcars.cor2)
plot(mtcars.cor2, type = "lower")

mtcars.cor2["mpg", "cyl"] # Extract a correlation from the correlation matrix

---

Enumerate items in an object

Description

enum() creates a vector of integers from 1 to length of the object (it enumerates items in the object), except if the object is empty. It is particularly useful in the ⁠for(i in enum(object))⁠ construct.

Usage

enum(x)

Arguments

x	Any object.

Note

The pattern ⁠for(i in 1:length(object))⁠ is often found, but it fails in case length(object) == 0! enum() is indeed a synonym of seq_along(), but the later one is less expressive in the context.

See Also

seq_along()

Examples

enum(letters)
enum(numeric(0))
# Compare with:
1:length(numeric(0))
enum(NULL)
letters5 <- letters[1:5]
for (i in enum(letters5)) cat("letter", i, "=", letters5[i], "\n")

---

Logarithmic and exponential functions

Description

ln() computes natural logarithm, lg() computes base 10 logarithm, and lb() computes binary (base 2) logarithm.

ln1p() and lg1p() computes ln(x + 1) and lg(x + 1) accurately also for ⁠|x| << 1⁠.

E is the Euler constant and is equal to exp(1).

Usage

ln(x)

lg(x)

lb(x)

ln1p(x)

lg1p(x)

E

Arguments

x	A numeric or complex vector.

Format

An object of class numeric of length 1.

Details

Those functions are synonyms of log(), log10(), log2(), log1p() for those who prefer the shorter notation. Beginners sometimes make confusion between log() and log10(). Using ln() for natural logarithms instead of log() eliminates this confusion. E is provided for convenience as exp(1), although the use of exp() is usually familiar enough to everyone.

See Also

log()

Examples

ln(exp(3))              # Same as log(exp(3))
lg(10^3)                # Same as log10(10^3)
lb(1:3)                 # Wrapper for log2()

ln1p(c(0, 1, 10, 100))  # Wrapper for log1p()
lg1p(c(0, 1, 10, 100))  # log10(x + 1), but optimized for x << 1

E^4                     # Similar to exp(4), but different calculation!

---

Convenience functions for rows or columns manipulations

Description

nr() and nc() are synonyms of the ugly NROW() or NCOL() that get the number of row and columns in a matrix or data frame, but also in a vector (they return a value even if the dim attribute of the object is not set, on the contrary to nrow()or ncol()).

ROWS and COLS are constants that makes call to apply() more expressive. ROWS = 1L and COLS = 2L.

Usage

nr(x)

nc(x)

ROWS

COLS

Arguments

x	Any object.

Format

An object of class integer of length 1.

An object of class integer of length 1.

See Also

nrow()

Examples

mm <- matrix(1:6, nrow = 3)
nr(mm)
nc(mm)

vv <- 1:6
nr(vv)
nc(vv)

# ROWS and COLS constants used with apply()
apply(mm, ROWS, mean) # Idem apply(mm, 1, mean)
apply(mm, COLS, mean) # Idem apply(mm, 2, mean)

---

More panel plots

Description

Several panel plots that can be used with coplot() and pairs().

Usage

panel_reg(
  x,
  y,
  col = par("col"),
  bg = par("bg"),
  pch = par("pch"),
  cex = par("cex"),
  lwd = par("lwd"),
  line.reg = lm,
  line.col = "red",
  line.lwd = lwd,
  untf = TRUE,
  ...
)

panel.reg(
  x,
  y,
  col = par("col"),
  bg = par("bg"),
  pch = par("pch"),
  cex = par("cex"),
  lwd = par("lwd"),
  line.reg = lm,
  line.col = "red",
  line.lwd = lwd,
  untf = TRUE,
  ...
)

panel_ellipse(
  x,
  y,
  col = par("col"),
  bg = par("bg"),
  pch = par("pch"),
  cex = par("cex"),
  el.level = 0.7,
  el.col = "cornsilk",
  el.border = "red",
  major = TRUE,
  ...
)

panel.ellipse(
  x,
  y,
  col = par("col"),
  bg = par("bg"),
  pch = par("pch"),
  cex = par("cex"),
  el.level = 0.7,
  el.col = "cornsilk",
  el.border = "red",
  major = TRUE,
  ...
)

panel_cor(
  x,
  y,
  use = "everything",
  method = c("pearson", "kendall", "spearman"),
  alternative = c("two.sided", "less", "greater"),
  digits = 2,
  prefix = "",
  cex = par("cex"),
  cor.cex = cex,
  stars.col = "red",
  ...
)

panel.cor(
  x,
  y,
  use = "everything",
  method = c("pearson", "kendall", "spearman"),
  alternative = c("two.sided", "less", "greater"),
  digits = 2,
  prefix = "",
  cex = par("cex"),
  cor.cex = cex,
  stars.col = "red",
  ...
)

panel_smooth(
  x,
  y,
  col = par("col"),
  bg = NA,
  pch = par("pch"),
  cex = 1,
  col.smooth = 2,
  span = 2/3,
  iter = 3,
  ...
)

Arguments

x	A numeric vector.
y	A numeric vector of same length as x.
col	The color of the points.
bg	The background color for symbol used for the points.
pch	The symbol used for the points.
cex	The expansion factor used for the points.
lwd	The line width.
line.reg	A function that calculates coefficients of a straight line, for instance, lm(), or rlm() for robust linear regression.
line.col	The color of the line.
line.lwd	The width of the line.
untf	Logical asking whether to untransform the straight line in case one or both axis are in log scale.
...	Further arguments to plot functions.
el.level	The confidence level for the bivariate normal ellipse around data; the default value of 0.7 draws an ellipse of roughly +/-1 sd.
el.col	The color used to fill the ellipse.
el.border	The color used to draw the border of the ellipse and the standardized major axis.
major	If TRUE, the standardized major axis is also drawn.
use	One of "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs" (can be abbreviated). Defines how the cor() function behaves with missing observations.
method	One of the three correlation coefficients "pearson" (default), "kendall", or "spearman". Can be abbreviated.
alternative	The alternative hypothesis in correlation test, see cor.test().
digits	The number of decimal digits to print when the correlation coefficient is printed in the graph.
prefix	A prefix (character string) to use before the correlation coefficient printed in the graph.
cor.cex	Expansion coefficient for text in printing correlation coefficients.
stars.col	The color used for significance stars (with: *** p < 0.001, ** p < 0.1, * p < 0.05, . p < 0.1.
col.smooth	Color to be used by lines for drawing the smooths.
span	Smoothing parameter f for lowess(), see there.
iter	Number of robustness iterations for lowess().

Details

Theses functions should be used outside of the diagonal in pairs(), or with coplot(), as they are bivariate plots.

Value

These functions return nothing and are used for their side effect of plotting in panels of composite plots.

Author(s)

Philippe Grosjean [email protected], but code inspired from panel.smooth() in graphics and panel.car() in package car.

See Also

coplot(), pairs(), panel.smooth(), lm(), ellipse(), cor() and cor.test()

Examples

# Smooth lines in lower graphs and straight lines in upper graphs
pairs(trees, lower.panel = panel_smooth, upper.panel = panel_reg)
# Robust regression lines
library(MASS)  # For rlm()
pairs(trees, panel = panel_reg, diag.panel = panel_boxplot,
  reg.line = rlm, line.col = "blue", line.lwd = 2)
# A Double log graph
pairs(trees, lower.panel = panel_smooth, upper.panel = panel_reg, log = "xy")

# Graph suitables to explore correlations (take care there are potentially
# many simultaneous tests done here... So, you loose much power in the whole
# analysis... use it just as an indication!)
# Pearson's r
pairs(trees, lower.panel = panel_ellipse, upper.panel = panel_cor)
# Spearman's rho (ellipse and straight lines not suitable here!)
pairs(trees, lower.panel = panel_smooth, upper.panel = panel_cor,
  method = "spearman", span = 1)
# Several groups (visualize how bad it is to consider the whole set at once!)
pairs(iris[, -5], lower.panel = panel_smooth, upper.panel = panel_cor,
  method = "kendall", span = 1,
  col = c("red3", "blue3", "green3")[iris$Species])
# Now analyze correlation for one species only
pairs(iris[iris$Species == "virginica", -5], lower.panel = panel_ellipse,
  upper.panel = panel_cor)

# A coplot with custom panes
coplot(Petal.Length ~ Sepal.Length | Species, data = iris,
  panel = panel_ellipse)

---

More univariate panel plots

Description

Several panel plots that can be used with pairs().

Usage

panel_boxplot(x, col = par("col"), box.col = "cornsilk", ...)

panel.boxplot(x, col = par("col"), box.col = "cornsilk", ...)

panel_density(
  x,
  adjust = 1,
  rug = TRUE,
  col = par("col"),
  lwd = par("lwd"),
  line.col = col,
  line.lwd = lwd,
  ...
)

panel.density(
  x,
  adjust = 1,
  rug = TRUE,
  col = par("col"),
  lwd = par("lwd"),
  line.col = col,
  line.lwd = lwd,
  ...
)

panel_hist(
  x,
  breaks = "Sturges",
  hist.col = "cornsilk",
  hist.border = NULL,
  hist.density = NULL,
  hist.angle = 45,
  ...
)

panel.hist(
  x,
  breaks = "Sturges",
  hist.col = "cornsilk",
  hist.border = NULL,
  hist.density = NULL,
  hist.angle = 45,
  ...
)

panel_qqnorm(
  x,
  pch = par("pch"),
  col = par("col"),
  bg = par("bg"),
  cex = par("cex"),
  lwd = par("lwd"),
  qq.pch = pch,
  qq.col = col,
  qq.bg = bg,
  qq.cex = cex,
  qqline.col = qq.col,
  qqline.lwd = lwd,
  ...
)

panel.qqnorm(
  x,
  pch = par("pch"),
  col = par("col"),
  bg = par("bg"),
  cex = par("cex"),
  lwd = par("lwd"),
  qq.pch = pch,
  qq.col = col,
  qq.bg = bg,
  qq.cex = cex,
  qqline.col = qq.col,
  qqline.lwd = lwd,
  ...
)

Arguments

x	A numeric vector.
col	The color of the points.
box.col	The filling color of the boxplots.
...	Further arguments to plot functions, or functions that construct items, like density(), depending on the context.
adjust	The bandwidth adjustment factor, see density().
rug	Do we add a rug representation (1-d plot) of the points too?
lwd	The line width.
line.col	The color of the line.
line.lwd	The width of the line.
breaks	The number of breaks, the name of a break algorithm, a vector of breakpoints, or any other acceptable value for breaks argument of hist().
hist.col	The filling color for the histograms.
hist.border	The border color for the histograms.
hist.density	The density for filling lines in the histograms.
hist.angle	The angle for filling lines in the histograms.
pch	The symbol used for the points.
bg	The background color for symbol used for the points.
cex	The expansion factor used for the points.
qq.pch	The symbol used to plot points in the QQ-plots.
qq.col	The color of the symbol used to plot points in the QQ-plots.
qq.bg	The background color of the symbol used to plot points in the QQ-plots.
qq.cex	The expansion factor for points in the QQ-plots.
qqline.col	The color for the QQ-plot lines.
qqline.lwd	The width for the QQ-plot lines.

Details

Panel functions panel_boxplot(), panel_density(), panel_hist() and panel_qqnorm() should be used only to plot univariate data on the diagonals of pairs() plots (or scatterplot matrix).

Value

These functions return nothing and are used for their side effect of plotting in panels of composite plots.

Author(s)

Philippe Grosjean [email protected], but code inspired from spm() in package car.

See Also

pairs(), boxplot(), hist(), density(), qqnorm()

Examples

# Example of scatterplot matrices with custom plots on the diagonal

# Boxplots
pairs(trees, panel = panel_smooth, diag.panel = panel_boxplot)
pairs(trees, diag.panel = panel_boxplot, box.col = "gray")

# Densities
pairs(trees, panel = panel_smooth, diag.panel = panel_density)
pairs(trees, diag.panel = panel_density, line.col = "red", adjust = 0.5)

# Histograms
pairs(trees, panel = panel_smooth, diag.panel = panel_hist)
pairs(trees, diag.panel = panel_hist, hist.col = "gray", breaks = "Scott")

# QQ-plots against Normal theoretical distribution
pairs(trees, panel = panel_smooth, diag.panel = panel_qqnorm)
pairs(trees, diag.panel = panel_qqnorm, qqline.col = 2, qq.cex = .5, qq.pch = 3)

---

Principal Components Analysis

Description

Perform a principal components analysis (PCA) on a matrix or data frame and return a pcomp object.

Usage

pcomp(x, ...)

## S3 method for class 'formula'
pcomp(formula, data = NULL, subset, na.action, method = c("svd", "eigen"), ...)

## Default S3 method:
pcomp(
  x,
  method = c("svd", "eigen"),
  scores = TRUE,
  center = TRUE,
  scale = TRUE,
  tol = NULL,
  covmat = NULL,
  subset = rep(TRUE, nrow(as.matrix(x))),
  ...
)

## S3 method for class 'pcomp'
print(x, ...)

## S3 method for class 'pcomp'
summary(object, loadings = TRUE, cutoff = 0.1, ...)

## S3 method for class 'summary.pcomp'
print(x, digits = 3, loadings = x$print.loadings, cutoff = x$cutoff, ...)

## S3 method for class 'pcomp'
plot(
  x,
  which = c("screeplot", "loadings", "correlations", "scores"),
  choices = 1L:2L,
  col = par("col"),
  bar.col = "gray",
  circle.col = "gray",
  ar.length = 0.1,
  pos = NULL,
  labels = NULL,
  cex = par("cex"),
  main = paste(deparse(substitute(x)), which, sep = " - "),
  xlab,
  ylab,
  ...
)

## S3 method for class 'pcomp'
screeplot(
  x,
  npcs = min(10, length(x$sdev)),
  type = c("barplot", "lines"),
  col = "cornsilk",
  main = deparse(substitute(x)),
  ...
)

## S3 method for class 'pcomp'
points(
  x,
  choices = 1L:2L,
  type = "p",
  pch = par("pch"),
  col = par("col"),
  bg = par("bg"),
  cex = par("cex"),
  ...
)

## S3 method for class 'pcomp'
lines(
  x,
  choices = 1L:2L,
  groups,
  type = c("p", "e"),
  col = par("col"),
  border = par("fg"),
  level = 0.9,
  ...
)

## S3 method for class 'pcomp'
text(
  x,
  choices = 1L:2L,
  labels = NULL,
  col = par("col"),
  cex = par("cex"),
  pos = NULL,
  ...
)

## S3 method for class 'pcomp'
biplot(x, choices = 1L:2L, scale = 1, pc.biplot = FALSE, ...)

## S3 method for class 'pcomp'
pairs(
  x,
  choices = 1L:3L,
  type = c("loadings", "correlations"),
  col = par("col"),
  circle.col = "gray",
  ar.col = par("col"),
  ar.length = 0.05,
  pos = NULL,
  ar.cex = par("cex"),
  cex = par("cex"),
  ...
)

## S3 method for class 'pcomp'
predict(object, newdata, dim = length(object$sdev), ...)

## S3 method for class 'pcomp'
correlation(x, newvars, dim = length(x$sdev), ...)

scores(x, ...)

## S3 method for class 'pcomp'
scores(x, labels = NULL, dim = length(x$sdev), ...)

Arguments

x	A matrix or data frame with numeric data.
...	Arguments passed to or from other methods. If x is a formula one might specify scale, tol or covmat.
formula	A formula with no response variable, referring only to numeric variables.
data	An optional data frame (or similar, see model.frame()) containing the variables in the formula. By default the variables are taken from environment(formula).
subset	An optional vector used to select rows (observations) of the data matrix x.
na.action	A function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options(), and is na.fail() if that is not set. The 'factory-fresh' default is na.omit().
method	Either "svd" (using prcomp()), "eigen" (using princomp()), or an abbreviation.
scores	A logical value indicating whether the score on each principal component should be calculated.
center	A logical value indicating whether the variables should centered. Alternately, a vector of length equal the number of columns of x can be supplied. The value is passed to scale. Note that this argument is ignored for method = "eigen" and the dataset is always centered in this case.
scale	A logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is TRUE, which in general, is advisable. Alternatively, a vector of length equal the number of columns of x can be supplied. The value is passed to scale().
tol	Only when method = "svd". A value indicating the magnitude below which components should be omitted. (Components are omitted if their standard deviations are less than or equal to tol times the standard deviation of the first component.) With the default null setting, no components are omitted. Other settings for ⁠tol =⁠ could be tol = 0 or tol = sqrt(.Machine$double.eps), which would omit essentially constant components.
covmat	A covariance matrix, or a covariance list as returned by cov.wt() (and cov.mve() or cov.mcd() from package MASS). If supplied, this is used rather than the covariance matrix of x.
object	A 'pcomp' object.
loadings	Do we also summarize the loadings?
cutoff	The cutoff value below which loadings are replaced by white spaces in the table. That way, larger values are easier to spot and to read in large tables.
digits	The number of digits to print.
which	The graph to plot.
choices	Which principal axes to plot. For 2D graphs, specify two integers.
col	The color to use in graphs.
bar.col	The color of bars in the screeplot.
circle.col	The color for the circle in the loadings or correlations plots.
ar.length	The length of the arrows in the loadings and correlations plots.
pos	The position of text relative to arrows in loadings and correlation plots.
labels	The labels to write. If NULL default values are computed.
cex	The factor of expansion for text (labels) in the graphs.
main	The title of the graph.
xlab	The label of the x-axis.
ylab	The label of the y-axis.
npcs	The number of principal components to represent in the screeplot.
type	The type of screeplot ("barplot" or "lines") or pairs plot ("loadings" or "correlations").
pch	The type of symbol to use.
bg	The background color for symbols.
groups	A grouping factor.
border	The color of the border.
level	The probability level to use to draw the ellipse.
pc.biplot	Do we create a Gabriel's biplot (see biplot())?
ar.col	Color of arrows.
ar.cex	Expansion factor for text on arrows.
newdata	New individuals with observations for the same variables as those used for calculating the PCA. You can then plot these additional individuals in the scores plot.
dim	The number of principal components to keep.
newvars	New variables with observations for same individuals as those used for calculating the PCA. Correlation with PCs is calculated. You can then plot these additional variables in the correlation plot.

Details

pcomp() is a generic function with "formula" and "default" methods. It is essentially a wrapper around prcomp() and princomp() to provide a coherent interface and object for both methods.

A 'pcomp' object is created. It inherits from 'pca' (as in labdsv package, but not compatible with the version of 'pca' in ade4) and of 'princomp'.

For more information on algorithms, refer to prcomp() for method = "svd" or princomp() for method = "eigen".

Value

A c("pcomp", "pca", "princomp") object.

Note

The signs of the columns for the loadings and scores are arbitrary. So, they could differ between functions for PCA, and even between different builds of R.

Author(s)

Philippe Grosjean [email protected], but the core code is indeed in package stats.

See Also

prcomp(), princomp(), loadings(), vectorplot(), Correlation()

Examples

# Let's analyze mtcars without the Mercedes data (rows 8:14)
data(mtcars)
cars.pca <- pcomp(~ mpg + cyl + disp + hp + drat + wt + qsec,
  data = mtcars, subset = -(8:14))
cars.pca
summary(cars.pca)
screeplot(cars.pca)

# Loadings are extracted and plotted this way:
(cars.ldg <- loadings(cars.pca))
plot(cars.pca, which = "loadings") # Equivalent to vectorplot(cars.ldg)

# Similarly, correlations of variables with PCs are extracted and plotted:
(cars.cor <- Correlation(cars.pca))
plot(cars.pca, which = "correlations") # Equivalent to vectorplot(cars.cor)
# One can add supplementary variables on this graph
lines(Correlation(cars.pca,
  newvars = mtcars[-(8:14), c("vs", "am", "gear", "carb")]))

# Plot the scores:
plot(cars.pca, which = "scores", cex = 0.8) # Similar to plot(scores(x)[, 1:2])
# Add supplementary individuals to this plot (labels), also points() or lines()
text(predict(cars.pca, newdata = mtcars[8:14, ]),
  labels = rownames(mtcars[8:14, ]), col = "gray", cex = 0.8)

# Pairs plot for 3 PCs
iris.pca <- pcomp(iris[, -5])
pairs(iris.pca, col = (2:4)[iris$Species])

---

Give the list of SciViews::R packages and check for conflicts

Description

List required packages or conflicting functions. These functions are inspired by tidyverse::tidyverse_packages() and tidyverse::tidyverse_conflicts(), but adapted to the SciViews::R context.

Usage

SciViews_packages(..., all = FALSE)

SciViews_packages_topics(all = FALSE)

SciViews_conflicts(all = TRUE)

## S3 method for class 'SciViews_conflicts'
print(x, ..., startup = FALSE)

Arguments

...	Further topics to consider in SciViews::R. Currently, "infer", "model", "explore", "ml", "ts" or "spatial".
all	Should all packages be listed (TRUE) or only those that are attached to the search path (FALSE).
x	A SciViews_conflicts object
startup	Should the message be printed at startup?

Value

A list of packages for SciViews_packages(), or a SciViews_conflicted object with a print() method for SciViews_conflicts().

Examples

# List of packages attached to the search path with SciViews::R
SciViews_packages()
# More complete list of packages used by SciViews::R
SciViews_packages(all = TRUE)
# Even more packages, by adding also 'model' and 'ml' topics
SciViews_packages("model", "ml", all = TRUE)
# Conflicts
SciViews_conflicts()

---

Configure R for the SciViews::R dialect

Description

Load required packages like data.table, collapse, ggplot2, dplyr, svMisc, ... to get a fully functional SciViews::R dialect environment.

Usage

R(..., lang = NULL, dtx = NULL, threads.percent = 75, silent = TRUE)

## S3 method for class 'SciViews_R'
print(x, ...)

Arguments

...	Further topics to include to configure R (load more packages). Currently, "infer", "model", "explore", "ml", "ts" or "spatial"
lang	What is the default natural language to use, e.g., "en" or "fr", with uppercase versions "EN" or "FR" convert even more strings, for instance, data.io::read() does not convert factor levels in the corresponding language for supported data sets unless the uppercase version is specified. If NULL (by default), current configuration is not changed.
dtx	Which dtx object is to be used be default? "dtt" or "data.table" for data.table, "dtf" or "data.frame" for data.frame, "dtbl", "tibble" or "tbl_df" for tibble's tbl_df, the name of a function to use to convert a data.frame object, or NULL (by default) to keep current settings.
threads.percent	The percentage of threads to use for {data.table} and {collapse} parallel code (number of threads depend on how many are available, and the value is rounded towards zero).
silent	If TRUE (by default), no report is printed about loaded packages and conflicts.
x	An object to print.

Note

Use SciViews::R instruction in the beginning of an R script, or in the setup or first chunk of an R Markdown/Notebook to ensure the SciViews::R dialect is correctly installed. The report indicating attached packages and conflicts is largely inspired by the corresponding tidyverse code, written by Hadley Wickham.

See Also

library(), utils::install.packages()

Examples

## Not run: 
SciViews::R

## End(Not run)

---

Timing of R expressions

Description

Similar to system.time() but returns a more convenient 'difftime' object with the overall timing (details are stored in the details attribute).

Usage

timing(expr, gc.first = TRUE)

Arguments

expr	Valid R expression to be timed. If missing, proc.time() is used instead and the function returns the time the currently running R process has already taken.
gc.first	Logical - should a garbage collection be performed immediately before the timing? Default is TRUE.

See Also

system.time(), proc.time()

Examples

test <- timing(Sys.sleep(0.5))
test
attr(test, "details")

---

Plot vectors inside a unit circle (PCA loadings or correlations plots).

Description

Plots vectors with 0 < norms < 1 inside a circle. These plots are mainly designed to represent variables in principal components space for PCAs.

Usage

vectorplot(x, ...)

## Default S3 method:
vectorplot(
  x,
  y,
  col = par("col"),
  circle.col = "gray",
  ar.length = 0.1,
  pos = NULL,
  cex = par("cex"),
  labels = NULL,
  ...
)

## S3 method for class 'loadings'
vectorplot(
  x,
  choices = 1L:2L,
  col = par("col"),
  circle.col = "gray",
  ar.length = 0.1,
  pos = NULL,
  cex = par("cex"),
  labels = rownames(x),
  main = deparse(substitute(x)),
  ...
)

## S3 method for class 'Correlation'
vectorplot(
  x,
  choices = 1L:2L,
  col = par("col"),
  circle.col = "gray",
  ar.length = 0.1,
  pos = NULL,
  cex = par("cex"),
  labels = rownames(x),
  main = deparse(substitute(x)),
  ...
)

Arguments

x	An object that has a vectorplot() method, like 'loadings' or 'correlation', or a numeric vector with 0 < values < 1.
...	Further arguments passed to plot functions.
y	A numeric vector with 0 < values < 1 of same length as 'x.
col	Color of the arrows and labels.
circle.col	The color for the circle around the vector plot.
ar.length	The length of the arrows.
pos	The position of text relative to arrows. If NULL, a suitable position is calculated according to the direction where the arrows are pointing.
cex	The factor of expansion for labels in the graph.
labels	The labels to draw near the arrows.
choices	A vector of two integers indicating the axes to plot.
main	The title of the plot.

Value

The object 'x' is returned invisibly. These functions are called for their side-effect of drawing a vector plot.

See Also

pcomp(), loadings(), Correlation()

Examples

# Create a PCA and plot loadings and correlations
iris.pca <- pcomp(iris[, -5])
vectorplot(loadings(iris.pca))
vectorplot(Correlation(iris.pca))
# Note: on screen devices, change aspect ratio of the graph by resizing
# the window to reveal cropped labels...

---