Package 'MSG'

Modern Statistical Graphics

Description

Datasets and functions for the Chinese book “Modern Statistical Graphics”.

Author(s)

Yihui Xie <https://yihui.org>

---

Draw Andrew's Curve

Description

This function evaluates the transformation of the original data matrix for t from -pi to pi, and uses matplot to draw the curves.

Usage

andrews_curve(
  x,
  n = 101,
  type = "l",
  lty = 1,
  lwd = 1,
  pch = NA,
  xlab = "t",
  ylab = "f(t)",
  ...
)

Arguments

x	a data frame or matrix
n	number of x-axis values at which f(t) is evaluated
type, lty, lwd, pch, xlab, ylab, ...	passed to matplot

Value

a matrix of coefficients for each observation at different t values

Author(s)

Yihui Xie <https://yihui.org>

References

https://en.wikipedia.org/wiki/Andrews_plot

See Also

matplot

Examples

andrews_curve(iris[, -5], col = as.integer(iris[, 5]))

---

Assists between players in CLE and LAL

Description

The players in the rows assisted the ones in the columns.

References

http://www.basketballgeek.com/data/

Examples

data(assists)

if (require("sna")) {
    set.seed(2011)
    gplot(assists, displaylabels = TRUE, label.cex = 0.7)
}

---

Random numbers containing a “circle”

Description

The data was generated from two independent random varialbes (standard Normal distribution) and further points on a circle were added to the data. The order of the data was randomized.

Format

A data frame with 20000 observations on the following 2 variables.

V1

the first random variable with the x-axis coordinate of the circle

V2

the second random variable with the y-axis coordinate of the circle

Details

See the example section for the code to generate the data.

Source

https://yihui.org/en/2008/09/to-see-a-circle-in-a-pile-of-sand/

Examples

data(BinormCircle)

## original plot: cannot see anything
plot(BinormCircle)

## transparent colors (alpha = 0.1)
plot(BinormCircle, col = rgb(0, 0, 0, 0.1))

## set axes lmits
plot(BinormCircle, xlim = c(-1, 1), ylim = c(-1, 1))

## small symbols
plot(BinormCircle, pch = ".")

## subset
plot(BinormCircle[sample(nrow(BinormCircle), 1000), ])

## 2D density estimation
library(KernSmooth)
fit = bkde2D(as.matrix(BinormCircle), dpik(as.matrix(BinormCircle)))
# perspective plot by persp()
persp(fit$x1, fit$x2, fit$fhat)

if (interactive() && require("rgl")) {
    # perspective plot by OpenGL
    rgl.surface(fit$x1, fit$x2, fit$fhat)
    # animation
    M = par3d("userMatrix")
    play3d(par3dinterp(userMatrix = list(M, rotate3d(M, pi/2, 1, 0, 0), rotate3d(M,
        pi/2, 0, 1, 0), rotate3d(M, pi, 0, 0, 1))), duration = 20)
}

## data generation
x1 = rnorm(10000)
y1 = rnorm(10000)
x2 = rep(0.5 * cos(seq(0, 2 * pi, length = 500)), 20)
y2 = rep(0.5 * sin(seq(0, 2 * pi, length = 500)), 20)
x = cbind(c(x1, x2), c(y1, y2))
BinormCircle = as.data.frame(round(x[sample(20000), ], 3))

---

The scores of the game Canabalt from Twitter

Description

The scores of the game Canabalt from Twitter

References

‘⁠http://www.neilkodner.com/2011/02/visualizations-of-canabalt-scores-scraped-from-twitter/⁠’ (the URL is not longer accessible)

Examples

library(ggplot2)
data(canabalt)
print(qplot(device, score, data = canabalt))
print(qplot(reorder(death, score, median), score, data = canabalt, geom = "boxplot") +
    coord_flip())

---

Generate a matrix of similar characters

Description

This function prints a matrix of characters which are very similar to each other.

Usage

char_gen(x = c("V", "W"), n = 300, nrow = 10)

Arguments

x	a character vector of length 2 (usually two similar characters)
n	the total number of characters in the matrix
nrow	the number of rows

Value

a character matrix on the screen

Author(s)

Yihui Xie <https://yihui.org>

Examples

char_gen()

char_gen(c("O", "Q"))

---

Life Expectancy and the Number of People with Higher Education in China (2005)

Description

This data contains the life expectancy and number of people with higher education in the 31 provinces and districts in China (2005).

Format

A data frame with 31 observations on the following 2 variables.

Life.Expectancy

Life expectancy

High.Edu.NO

Number of people with higher education

Source

China Statistical Yearbook 2005. National Bureau of Statistics.

Examples

data(ChinaLifeEdu)
x = ChinaLifeEdu
plot(x, type = "n", xlim = range(x[, 1]), ylim = range(x[, 2]))
u = par("usr")
rect(u[1], u[3], u[2], u[4], col = "antiquewhite", border = "red")
library(KernSmooth)
est = bkde2D(x, apply(x, 2, dpik))
contour(est$x1, est$x2, est$fhat, nlevels = 15, col = "darkgreen", add = TRUE,
    vfont = c("sans serif", "plain"))

---

Cut the points in a scatter plot into groups according to x-axis

Description

This function can categorize the variable on the x-axis into groups and plot the mean values of y. The purpose is to show the arbitrariness of the discretization of data.

Usage

cut_plot(x, y, breaks, ..., pch.cut = 20)

Arguments

x	the x variable
y	the y variable
breaks	the breaks to cut the x variable
...	other arguments to be passed to plot.default
pch.cut	the point symbol to denote the mean values of y

Author(s)

Yihui Xie <https://yihui.org>

Examples

x = rnorm(100)
y = rnorm(100)
cut_plot(x, y, seq(min(x), max(x), length = 5))

---

Export of US and China from 1999 to 2004 in US dollars

Description

Export of US and China from 1999 to 2004 in US dollars

Format

A data frame with 13 observations on the following 3 variables.

Export

amount of export

Year

year from 1999 to 2004

Country

country: US or China

Source

https://www.wto.org/english/res_e/statis_e/statis_e.htm

Examples

data(Export.USCN)
par(mar = c(4, 4.5, 1, 4.5))
plot(1:13, Export.USCN$Export, xlab = "Year / Country", ylab = "US Dollars ($10^16)",
    axes = FALSE, type = "h", lwd = 10, col = c(rep(2, 6), NA, rep(4, 6)), lend = 1,
    panel.first = grid())
xlabel = paste(Export.USCN$Year, "\n", Export.USCN$Country)
xlabel[7] = ""
xlabel
abline(v = 7, lty = 2)
axis(1, at = 1:13, labels = xlabel, tick = FALSE, cex.axis = 0.75)
axis(2)
(ylabel = pretty(Export.USCN$Export * 8.27))
axis(4, at = ylabel/8.27, labels = ylabel)
mtext("Chinese RMB", side = 4, line = 2)
box()

---

Percentage data in some government websites

Description

This data was collected from Google by searching for percentages in some goverment websites.

Format

A data frame with 10000 observations on the following 4 variables.

percentage

a numeric vector: the percentages

count

a numeric vector: the number of webpages corresponding to a certain percentage

round0

a logical vector: rounded to integers?

round1

a logical vector: rounded to the 1st decimal place?

Details

We can specify the domain when searching in Google. For this data, we used ‘⁠site:gov.cn⁠’, e.g. to search for ‘⁠87.53% site:gov.cn⁠’.

Source

Google (date: 2009/12/17)

Examples

data(gov.cn.pct)
pct.lowess = function(cond) {
    with(gov.cn.pct, {
        plot(count ~ percentage, pch = ifelse(cond, 4, 20), col = rgb(0:1,
            0, 0, c(0.04, 0.5))[cond + 1], log = "y")
        lines(lowess(gov.cn.pct[cond, 1:2], f = 1/3), col = 2, lwd = 2)
        lines(lowess(gov.cn.pct[!cond, 1:2], f = 1/3), col = 1, lwd = 2)
    })
}
par(mar = c(3.5, 3.5, 1, 0.2), mfrow = c(2, 2))
with(gov.cn.pct, {
    plot(percentage, count, type = "l", panel.first = grid())
    plot(percentage, count, type = "l", xlim = c(10, 11), panel.first = grid())
    pct.lowess(round0)
    pct.lowess(round1)
})
if (interactive()) {
    devAskNewPage(ask = TRUE)

    with(gov.cn.pct, {
        plot(count ~ percentage, type = "l")
        grid()

        devAskNewPage(ask = FALSE)

        for (i in 0:99) {
            plot(count ~ percentage, type = "l", xlim = i + c(0, 1),
                panel.first = grid())
        }

        devAskNewPage(ask = TRUE)

        plot(count ~ percentage, pch = 20, col = rgb(0:1, 0, 0, c(0.07,
            1))[round0 + 1], log = "y")
        lines(lowess(gov.cn.pct[round0, 1:2], f = 1/3), col = "red",
            lwd = 2)
        lines(lowess(gov.cn.pct[!round0, 1:2], f = 1/3), col = "black",
            lwd = 2)

        plot(count ~ percentage, pch = 20, col = rgb(0:1, 0, 0, c(0.07,
            1))[round1 + 1], log = "y")
        lines(lowess(gov.cn.pct[round1, 1:2], f = 1/3), col = "red",
            lwd = 2)
        lines(lowess(gov.cn.pct[!round1, 1:2], f = 1/3), col = "black",
            lwd = 2)
    })
}

---

Draw a heart curve

Description

Calculate the coordinates of a heart shape and draw it with a polygon.

Usage

heart_curve(n = 101, ...)

Arguments

n	the number of points to use when calculating the coordinates of the heart shape
...	other arguments to be passed to polygon, e.g. the color of the polygon (usually red)

Author(s)

Yihui Xie <https://yihui.org>

Examples

heart_curve()
heart_curve(col = "red")
heart_curve(col = "pink", border = "red")

---

Plot a graph with a pre-installed R script

Description

Plot a graph with a pre-installed R script

Usage

msg(fig = "3.6", show_code = TRUE, print_plot = TRUE, filter = 0)

Arguments

fig	Character. The figure number or the R script name, which is given in the book.
show_code	Logical. TRUE means the codes are shown in the console.
print_plot	Logical. TRUE means the graph is printed.
filter	Integer. The line numbers indicating which lines in the code are displayed (when positive) or hidden (when negative).

Value

A graph and the source code

Examples

# msg('3.6') msg('ChinaPop')

---

Composition of Soil from Murcia Province, Spain

Description

The proportions of sand, silt and clay in soil samples are given for 8 contiguous sites. The sites extended over the crest and flank of a low rise in a valley underlain by marl near Albudeite in the province of Murcia, Spain. The sites were small areas of ground surface of uniform shape internally and delimited by relative discontinuities externally. Soil samples were obtained for each site at 11 random points within a 10m by 10m area centred on the mid-point of the site. All samples were taken from the same depth. The data give the sand, silt and clay content of each sample, expressed as a percentage of the total sand, silt and clay content.

References

http://www.statsci.org/data/general/murcia.html

Examples

data(murcia)
boxplot(sand ~ site, data = murcia)

---

Attributes of some music clips

Description

Attributes of some music clips

References

Cook D, Swayne DF (2007). Interactive and Dynamic Graphics for Data Analysis With R and GGobi. Springer. ISBN 978-0-387-71761-6.

Examples

data(music)

---

Number of plants corresponding to altitude

Description

For each altitude, the number of plants is recorded.

Format

A data frame with 600 observations on the following 2 variables.

altitude

altitude of the area

counts

number of plants

Source

https://cosx.org/2008/11/lowess-to-explore-bivariate-correlation-by-yihui

Examples

## different span for LOWESS
data(PlantCounts)
par(las = 1, mar = c(4, 4, 0.1, 0.1), mgp = c(2.2, 0.9, 0))
with(PlantCounts, {
    plot(altitude, counts, pch = 20, col = rgb(0, 0, 0, 0.5), panel.first = grid())
    for (i in seq(0.01, 1, length = 70)) {
        lines(lowess(altitude, counts, f = i), col = rgb(0, i, 0), lwd = 1.5)
    }
})

---

Earth quakes from 1973 to 2010

Description

The time, location and magnitude of all the earth quakes with magnitude being greater than 6 since 1973.

References

https://d.cosx.org/d/101510

Examples

data(quake6)
library(ggplot2)
qplot(year, month, data = quake6) + stat_sum(aes(size = ..n..)) + scale_size(range = c(1,
    10))

---

The differences of P-values in t test assuming equal or unequal variances

Description

Given that the variances of two groups are unequal, we compute the difference of P-values assuming equal or unequal variances respectively by simulation.

Format

A data frame with 1000 rows and 99 columns.

Details

See the Examples section for the generation of this data.

Source

By simulation.

References

Welch B (1947). “The generalization of Student's problem when several different population variances are involved.” Biometrika, 34(1/2), 28–35.

Examples

data(t.diff)
boxplot(t.diff, axes = FALSE, xlab = expression(n[1]))
axis(1)
axis(2)
box()

## reproducing the data
if (interactive()) {
    set.seed(123)
    t.diff = NULL
    for (n1 in 2:100) {
        t.diff = rbind(t.diff, replicate(1000, {
            x1 = rnorm(n1, mean = 0, sd = runif(1, 0.5, 1))
            x2 = rnorm(30, mean = 1, sd = runif(1, 2, 5))
            t.test(x1, x2, var.equal = TRUE)$p.value - t.test(x1, x2,
                var.equal = FALSE)$p.value
        }))
    }
    t.diff = as.data.frame(t(t.diff))
    colnames(t.diff) = 2:100
}

---

Results of a Simulation to Tukey's Fast Test

Description

For the test of means of two samples, we calculated the P-values and recorded the counts of Tukey's rule of thumb.

Format

A data frame with 10000 observations on the following 3 variables.

pvalue.t

P-values of t test

pvalue.w

P-values of Wilcoxon test

count

Tukey's counts

Details

See the reference for details.

Source

Simulation; see the Examples section below.

References

D. Daryl Basler and Robert B. Smawley. Tukey's Compact versus Classic Tests. The Journal of Experimental Education, Vol. 36, No. 3 (Spring, 1968), pp. 86-88

Examples

data(tukeyCount)

## does Tukey's rule of thumb agree with t test and Wilcoxon test?
with(tukeyCount, {
    ucount = unique(count)
    stripchart(pvalue.t ~ count, method = "jitter", jitter = 0.2, pch = 19,
        cex = 0.7, vertical = TRUE, at = ucount - 0.2, col = rgb(1, 0, 0, 0.2),
        xlim = c(min(count) - 1, max(count) + 1), xaxt = "n", xlab = "Tukey Count",
        ylab = "P-values")
    stripchart(pvalue.w ~ count, method = "jitter", jitter = 0.2, pch = 21,
        cex = 0.7, vertical = TRUE, at = ucount + 0.2, add = TRUE, col = rgb(0,
            0, 1, 0.2), xaxt = "n")
    axis(1, unique(count))
    lines(sort(ucount), tapply(pvalue.t, count, median), type = "o", pch = 19,
        cex = 1.3, col = "red")
    lines(sort(ucount), tapply(pvalue.w, count, median), type = "o", pch = 21,
        cex = 1.3, col = "blue", lty = 2)
    legend("topright", c("t test", "Wilcoxon test"), col = c("red", "blue"),
        pch = c(19, 21), lty = 1:2, bty = "n", cex = 0.8)
})

if (interactive()) {

    ## this is how the data was generated
    set.seed(402)
    n = 30
    tukeyCount = data.frame(t(replicate(10000, {
        x1 = rweibull(n, runif(1, 0.5, 4))
        x2 = rweibull(n, runif(1, 1, 5))
        c(t.test(x1, x2)$p.value, wilcox.test(x1, x2)$p.value, with(rle(rep(0:1,
            each = n)[order(c(x1, x2))]), ifelse(head(values, 1) == tail(values,
            1), 0, sum(lengths[c(1, length(lengths))]))))
    })))
    colnames(tukeyCount) = c("pvalue.t", "pvalue.w", "count")

}

---

Top TV earners

Description

The pay per episode for actors as well as other information.

References

https://flowingdata.com/2011/02/15/visualize-this-tvs-top-earners/

Examples

data(tvearn)
plot(pay ~ rating, data = tvearn)
library(ggplot2)
qplot(pay, data = tvearn, geom = "histogram", facets = gender ~ ., binwidth = 20000)
qplot(rating, pay, data = tvearn, geom = c("jitter", "smooth"), color = type)

---

Generate colors from a vector

Description

This functions generates a color vector from an input vector, which can be of the class numeric or factor.

Usage

vec2col(vec, n, name)

## Default S3 method:
vec2col(vec, n, name)

## S3 method for class 'factor'
vec2col(vec, n, name)

Arguments

vec	the numeric or factor vector
n	the number of colors to be generated from the palette
name	the name of the palette

Value

a vector of colors corresponding to the input vector

Author(s)

Yihui Xie <https://yihui.org>

Examples

## convert factor to colors
with(iris, plot(Petal.Length, Petal.Width, col = vec2col(Species), pch = 19))

# another palette
with(iris, plot(Petal.Length, Petal.Width, col = vec2col(Species, name = "Dark2"),
    pch = 19))

## turn numeric values to colors
with(iris, plot(Petal.Length, Petal.Width, col = vec2col(Petal.Width), pch = 19))

---