| Type: | Package |
| Title: | Data and Functions for the Book Modern Statistical Graphics |
| Version: | 0.8 |
| Description: | A companion to the Chinese book “Modern Statistical Graphics”. |
| License: | GPL-2 |GPL-3 [expanded from: GPL] |
| Depends: | R (≥ 3.5.0) |
| Imports: | RColorBrewer (≥ 1.1.2) |
| Suggests: | animation (≥ 2.6), igraph (≥ 1.2.4.1), KernSmooth (≥2.23.15), maps (≥ 3.3.0), mvtnorm (≥ 1.0.11), rgl (≥0.100.30), RgoogleMaps (≥ 1.4.4), plotrix (≥ 3.7.6), ggplot2(≥ 3.2.1), grid (≥ 3.6.0), sna (≥ 2.4) |
| URL: | https://github.com/yihui/MSG |
| BugReports: | https://github.com/yihui/MSG/issues |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2021-07-21 19:38:02 UTC; yihui |
| Author: | Yihui Xie [aut, cre], Peng Zhao [aut], Lijia Yu [ctb], Xiangyun Huang [ctb] |
| Maintainer: | Yihui Xie <xie@yihui.name> |
| Repository: | CRAN |
| Date/Publication: | 2021-07-21 19:50:05 UTC |
Modern Statistical Graphics
Description
Datasets and functions for the Chinese book “Modern Statistical Graphics”.
Author(s)
Yihui Xie <https://yihui.org>
Random numbers containing a “circle”
Description
The data was generated from two independent random varialbes (standard Normaldistribution) and further points on a circle were added to the data. Theorder 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 coordinateof the circle
- V2
the second random variable with the y-axiscoordinate 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 anythingplot(BinormCircle)## transparent colors (alpha = 0.1)plot(BinormCircle, col = rgb(0, 0, 0, 0.1))## set axes lmitsplot(BinormCircle, xlim = c(-1, 1), ylim = c(-1, 1))## small symbolsplot(BinormCircle, pch = ".")## subsetplot(BinormCircle[sample(nrow(BinormCircle), 1000), ])## 2D density estimationlibrary(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 generationx1 = 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))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 highereducation 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 = ChinaLifeEduplot(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$fhat, nlevels = 15, col = "darkgreen", add = TRUE, vfont = c("sans serif", "plain"))Export of US and China from 1999 to 2004 in US dollars
Description
Export of US and China from 1999 to 2004 in US dollars
Format
A data frame with 13 observations on the following 3 variables.
- Export
amount of export
- Year
year from 1999 to2004
- Country
country: US or China
Source
https://www.wto.org/english/res_e/statis_e/statis_e.htm
Examples
data(Export.USCN)par(mar = c(4, 4.5, 1, 4.5))plot(1:13, Export.USCN$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$Country)xlabel[7] = ""xlabelabline(v = 7, lty = 2)axis(1, at = 1:13, labels = xlabel, tick = FALSE, cex.axis = 0.75)axis(2)(ylabel = pretty(Export.USCN$Export * 8.27))axis(4, at = ylabel/8.27, labels = ylabel)mtext("Chinese RMB", side = 4, line = 2)box()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 ofplants
Source
https://cosx.org/2008/11/lowess-to-explore-bivariate-correlation-by-yihui
Examples
## different span for LOWESSdata(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) }})Draw Andrew's Curve
Description
This function evaluates the transformation of the original data matrix fort from-pi topi, and usesmatplot to draw thecurves.
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 |
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
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)}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 eachother.
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"))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 plotthe mean values of y. The purpose is to show the arbitrariness of thediscretization 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 |
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))Percentage data in some government websites
Description
This data was collected from Google by searching for percentages in somegoverment 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 acertain 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 theheart shape |
... | other arguments to be passed to |
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 8contiguous sites. The sites extended over the crest and flank of a low risein a valley underlain by marl near Albudeite in the province of Murcia,Spain. The sites were small areas of ground surface of uniform shapeinternally and delimited by relative discontinuities externally. Soil sampleswere obtained for each site at 11 random points within a 10m by 10m areacentred on the mid-point of the site. All samples were taken from the samedepth. 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 forData Analysis With R and GGobi. Springer. ISBN 978-0-387-71761-6.
Examples
data(music)Earth quakes from 1973 to 2010
Description
The time, location and magnitude of all the earth quakes with magnitude beinggreater than 6 since 1973.
References
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 differenceof 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 whenseveral 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 dataif (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 recordedthe 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 ofWilcoxon 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 versusClassic 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 ofthe 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 colorswith(iris, plot(Petal.Length, Petal.Width, col = vec2col(Species), pch = 19))# another palettewith(iris, plot(Petal.Length, Petal.Width, col = vec2col(Species, name = "Dark2"), pch = 19))## turn numeric values to colorswith(iris, plot(Petal.Length, Petal.Width, col = vec2col(Petal.Width), pch = 19))