Análise para um par de variáveis
##-----------------------------------------------------------------------------
## Definições da sessão
pkg <- c("latticeExtra", "doBy", "plyr", "reshape", "reshape2",
"alr3", "nlrwr", "faraway", "car")
sapply(pkg, require, character.only=TRUE)
##-----------------------------------------------------------------------------
## ALWINS (pg 708): O número de vitórias de um time de basebol está
## relacionado ao batting?
url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/ALWINS.DAT"
download.file(url=url, destfile=basename(url))
da <- read.fwf(url, widths=c(12,11,4),
colClasses=c("character",NA,NA))
str(da)
## 'data.frame': 14 obs. of 3 variables:
## $ V1: chr "New York " "Toronto " "Baltimore " "Boston " ...
## $ V2: num 103 78 67 93 55 74 55 81 62 94 ...
## $ V3: num 0.275 0.261 0.246 0.277 0.253 0.249 0.248 0.268 0.256 0.272 ...
names(da) <- c("team", "gamesWon","battingAve")
da
## team gamesWon battingAve
## 1 New York 103 0.275
## 2 Toronto 78 0.261
## 3 Baltimore 67 0.246
## 4 Boston 93 0.277
## 5 Tampa Bay 55 0.253
## 6 Cleveland 74 0.249
## 7 Detroit 55 0.248
## 8 Chicago 81 0.268
## 9 Kansas City 62 0.256
## 10 Minnesota 94 0.272
## 11 Anaheim 99 0.282
## 12 Texas 72 0.269
## 13 Seattle 93 0.275
## 14 Oakland 103 0.261
## Remover espaços de strings: gdata::trim(), stringr::str_trim().
da$team <- gsub(x=da$team, pattern="\\s+$", replacement="")
da
## team gamesWon battingAve
## 1 New York 103 0.275
## 2 Toronto 78 0.261
## 3 Baltimore 67 0.246
## 4 Boston 93 0.277
## 5 Tampa Bay 55 0.253
## 6 Cleveland 74 0.249
## 7 Detroit 55 0.248
## 8 Chicago 81 0.268
## 9 Kansas City 62 0.256
## 10 Minnesota 94 0.272
## 11 Anaheim 99 0.282
## 12 Texas 72 0.269
## 13 Seattle 93 0.275
## 14 Oakland 103 0.261
plot(gamesWon~battingAve, data=da,
xlab="Batting Average", ylab="Vitórias")
## Cores do R.
colors()
## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"
plot(gamesWon~battingAve, data=da,
xlab="Batting Average", ylab="Vitórias",
col="tomato3",
pch=8,
cex=1.5,
sub="Fonte: Bussiness and Economics",
main="Diagrama de dispersão")
abline(h=seq(from=60, to=100, by=10), lty=2, col="gray")
abline(v=seq(from=0.245, to=0.280, by=0.005), lty=2, col="gray")
legend("bottomright", legend="Observações", pch=8)
legend(x=0.245, y=112.5, legend="Observações", pch=8, xpd=TRUE)
## locator()
xyplot(gamesWon~battingAve, data=da,
xlab="Batting Average", ylab="Vitórias")
xyplot(gamesWon~battingAve, data=da,
xlab="Batting Average", ylab="Vitórias",
type=c("p","g"),
col="seagreen",
pch=19,
cex=1.2,
sub="Fonte: Bussiness and Economics",
main="Diagrama de dispersão")
xyplot(gamesWon~battingAve, data=da,
type=c("p","smooth","g"),
xlab="Batting Average", ylab="Vitórias")
##-----------------------------------------------------------------------------
## DIAMONDS (pg 707): Dados sobre 308 diamantes à venda. Verifique a
## relação entre preço (dolares) e tamanho (number of carats).
url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/DIAMONDS.DAT"
## da <- read.table(url, header=FALSE)
## str(da)
## Lê apenas as colunas de interesse.
da <- read.table(url, header=FALSE,
colClasses=c("numeric","NULL","NULL","NULL","integer"))
names(da) <- c("carat", "price")
str(da)
## 'data.frame': 308 obs. of 2 variables:
## $ carat: num 0.3 0.3 0.3 0.3 0.31 0.31 0.31 0.31 0.31 0.31 ...
## $ price: int 1302 1510 1510 1260 1641 1555 1427 1427 1126 1126 ...
xlab <- "Tamanho do diamante"
ylab <- "Preço (U$)"
xyplot(price~carat, data=da, xlab=xlab, ylab=ylab)
xyplot(price~carat, data=da, xlab=xlab, ylab=ylab, type=c("p","smooth"))
## Usando o update().
p0 <- xyplot(price~carat, data=da)
update(p0, type=c("p","smooth"))
## Escala log10 para y.
xyplot(price~carat, data=da, xlab=xlab, ylab=ylab,
type=c("p","smooth", "g"),
scales=list(y=list(log=10)))
## Opções: yscale.components.{log,log10.3,logpower}.
xyplot(price~carat, data=da, xlab=xlab, ylab=ylab,
type=c("p","smooth", "g"),
scales=list(y=list(log=10)),
yscale.components=yscale.components.log10.3)
Exercícios
##-----------------------------------------------------------------------------
## OJUICE (pg 709): O índice de docura do suco de laranja está
## relacionado com a concentração de pectina?
url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/OJUICE.DAT"
da <- read.fwf(url, widths=c(-12,12,3))
names(da) <- c("SweetnessIndex", "pectin")
str(da)
xyplot(SweetnessIndex~pectin, data=da, type=c("p","smooth","g"))
xyplot(SweetnessIndex~log(pectin), data=da, type=c("p","smooth","g"))
xyplot(SweetnessIndex~sqrt(pectin), data=da, type=c("p","smooth","g"))
xyplot(SweetnessIndex~pectin^(1/3), data=da, type=c("p","smooth","g"))
xyplot(log(SweetnessIndex)~log(pectin), data=da, type=c("p","smooth","g"))
##-----------------------------------------------------------------------------
## GASOIL (pg 710): Dados sobre o preço da gasolina (cents/gallon) e do
## barril de óleo cru (refiner acquisition cost, U$/bbl) para o período
## de 1980-2001. Explora e relação entre preço da gasolina (y) e do óleo
## cru (x).
url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/GASOIL.DAT"
da <- read.fwf(url, widths=c(16,8,6))
names(da) <- c("year", "gasoline", "crudeOil")
str(da)
xyplot(gasoline~crudeOil, data=da, type=c("p","smooth","g"))
xyplot(gasoline~year, data=da, type=c("p","smooth","g"))
xyplot(crudeOil~year, data=da, type=c("p","smooth","g"))
xyplot(gasoline+crudeOil~year, data=da, type=c("p","smooth","g"),
auto.key=TRUE)
## Dois eixos y.
p1 <- xyplot(gasoline~year, data=da, type=c("p","smooth"))
p2 <- xyplot(crudeOil~year, data=da, type=c("p","smooth"))
doubleYScale(p1, p2)
##-----------------------------------------------------------------------------
## FERTRATE (pg 735): Dados sobre a taxa de fertilidade (y) e
## prevalencia de contraceptivos (x) para 27 Estados dos EUA.
url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/FERTRATE.DAT"
da <- read.fwf(url, widths=c(36,3))
names(da) <- c("contrPrev", "FertRate")
str(da)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
##-----------------------------------------------------------------------------
## Exportando.
getwd()
jpeg(filename="figura1.jpeg", width=800, height=600, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()
jpeg(filename="figura1.jpeg", width=1600, height=1200, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()
png(filename="figura1.png", width=1600, height=1200, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()
pdf(file="figura1.pdf", w=7, h=5)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()
tiff(filename="figura1.tiff", width=1600, height=1200, res=300)
xyplot(FertRate~contrPrev, data=da, type=c("p","smooth","g"))
dev.off()
Mais de um par de variáveis
##-----------------------------------------------------------------------------
## COLLGPA.
url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXERCISE/COLLGPA.DAT"
da <- read.fwf(url, widths=c(4,3,4))
names(da) <- c("verbal", "math", "gpa")
str(da)
## 'data.frame': 40 obs. of 3 variables:
## $ verbal: num 81 68 57 100 54 82 75 58 55 49 ...
## $ math : num 87 99 86 49 83 86 74 98 54 81 ...
## $ gpa : num 3.49 2.89 2.73 1.54 2.56 3.43 3.59 2.86 1.46 2.11 ...
##-----------------------------------------------------------------------------
## FTC.
url <- "http://www.leg.ufpr.br/~walmes/data/business_economics_dataset/EXAMPLES/FTC.DAT"
da <- read.fwf(url, widths=c(8,8,8,8))
names(da) <- c("tar","nicotine","weight","carbonMono")
str(da)
## 'data.frame': 25 obs. of 4 variables:
## $ tar : num 14.1 16 29.8 8 4.1 15 8.8 12.4 16.6 14.9 ...
## $ nicotine : num 0.86 1.06 2.03 0.67 0.4 1.04 0.76 0.95 1.12 1.02 ...
## $ weight : num 0.985 1.094 1.165 0.928 0.946 ...
## $ carbonMono: num 13.6 16.6 23.5 10.2 5.4 15 9 12.3 16.3 15.4 ...
##-----------------------------------------------------------------------------
## rock.
##-----------------------------------------------------------------------------
## Informações da sessão.
Sys.time()
## [1] "2015-04-13 11:59:10 BRT"
sessionInfo()
## R version 3.1.2 (2014-10-31)
## Platform: i686-pc-linux-gnu (32-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=pt_BR.UTF-8
## [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=pt_BR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=pt_BR.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=pt_BR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] methods splines stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] faraway_1.0.6 nlrwr_1.1-0 sandwich_2.3-2 NRAIA_0.9-8
## [5] nlstools_1.0-0 nls2_0.2 proto_0.3-10 NISTnls_0.9-13
## [9] lmtest_0.9-33 zoo_1.7-11 HydroMe_2.0 minpack.lm_1.1-8
## [13] nlme_3.1-119 drc_2.3-96 plotrix_3.5-10 magic_1.5-6
## [17] abind_1.4-0 MASS_7.3-37 gtools_3.4.1 alr3_2.0.5
## [21] car_2.0-22 reshape2_1.4 reshape_0.8.5 plyr_1.8.1
## [25] doBy_4.5-12 survival_2.37-7 latticeExtra_0.6-26 lattice_0.20-29
## [29] RColorBrewer_1.0-5 rmarkdown_0.3.3 knitr_1.8
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.4 evaluate_0.5.5 formatR_1.0 grid_3.1.2 htmltools_0.2.6
## [6] Matrix_1.1-5 nnet_7.3-8 Rcpp_0.11.3 stringr_0.6.2 tools_3.1.2
## [11] yaml_2.1.13