Political Science // D-Lab // UC Berkeley
- 3 main facilities for producing graphics in R: base, lattice, and ggplot2
- In practice, these facilities are grouped into two camps: "basic" and "advanced"
- A better formulation: quick/dirty v. involved/fancy
- Recall that R is an object-oriented programming language
tips <- reshape2::tips # Load dataset on tipping behavior included with reshape2 package
attributes(tips) # Check attributes of the tips dataset (names, row.names, class)## $names
## [1] "total_bill" "tip" "sex" "smoker" "day"
## [6] "time" "size"
##
## $row.names
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11"
## [12] "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22"
## [23] "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33"
## [34] "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44"
## [45] "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55"
## [56] "56" "57" "58" "59" "60" "61" "62" "63" "64" "65" "66"
## [67] "67" "68" "69" "70" "71" "72" "73" "74" "75" "76" "77"
## [78] "78" "79" "80" "81" "82" "83" "84" "85" "86" "87" "88"
## [89] "89" "90" "91" "92" "93" "94" "95" "96" "97" "98" "99"
## [100] "100" "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120" "121"
## [122] "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143"
## [144] "144" "145" "146" "147" "148" "149" "150" "151" "152" "153" "154"
## [155] "155" "156" "157" "158" "159" "160" "161" "162" "163" "164" "165"
## [166] "166" "167" "168" "169" "170" "171" "172" "173" "174" "175" "176"
## [177] "177" "178" "179" "180" "181" "182" "183" "184" "185" "186" "187"
## [188] "188" "189" "190" "191" "192" "193" "194" "195" "196" "197" "198"
## [199] "199" "200" "201" "202" "203" "204" "205" "206" "207" "208" "209"
## [210] "210" "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
## [221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230" "231"
## [232] "232" "233" "234" "235" "236" "237" "238" "239" "240" "241" "242"
## [243] "243" "244"
##
## $class
## [1] "data.frame"
# Create an object of class 'lm' (linear model), regressing tip on some
# covariates
tips.reg <- lm(formula = tip ~ total_bill + sex + smoker + day + time + size,
data = tips)
attributes(tips.reg) # Check attributes of the tips.reg object (names, class)## $names
## [1] "coefficients" "residuals" "effects" "rank"
## [5] "fitted.values" "assign" "qr" "df.residual"
## [9] "contrasts" "xlevels" "call" "terms"
## [13] "model"
##
## $class
## [1] "lm"
- Base graphics often recognizes the object type and will implement specific plot methods
plot(tips) # Calls plotting method for class of tips dataset ('data.frame')
plot(tips.reg, which = 1:2) # Calls plotting method for class of tips.reg objects ('lm'), print first two plots only
- lattice and ggplot2 generally don't exhibit this sort of behavior
xyplot(tips) # Attempt in lattice to automatically plot objects of class 'data.frame'## Error: no applicable method for 'xyplot' applied to an object of class
## "data.frame"
ggplot(data = tips) + geom_point() # Attempt in ggplot to automatically plot objects of class 'data.frame'## Error: 'where' is missing
xyplot(tips.reg) # Attempt in lattice to automatically plot objects of class 'lm'## Error: no applicable method for 'xyplot' applied to an object of class
## "lm"
ggplot(data = tips.reg) + geom_point() # Attempt in ggplot to automatically plot objects of class 'lm'## Error: 'where' is missing
- Easiest to cover base graphics on its own, but lattice and ggplot2 in tandem
- Comparative Political Data Set I (Armingeon et al. 2012)
- Cases: 23 industrialized democracies, 1960-2012
- Variables: Government composition (L-R); state structure (federalism, presidentialism, bicameralism, etc.); macroeconomic indicators (output, inflation, unemployment, deficit/debt, etc.); demographics (population, elderly)
For more info: http://www.ipw.unibe.ch/content/team/klaus_armingeon/comparative_political_data_sets/index_eng.html
A copy of the dataset is available in the file "cpds.csv", which can be read in as:
data <- read.csv(file = "cpds.csv")attributes(data)[1:2] # Only show the first two attributes of the dataset (column names and object class)## $names
## [1] "year" "country" "countryn" "gov_right"
## [5] "gov_cent" "gov_left" "govparty" "gov_new"
## [9] "gov_gap" "gov_type" "gov_chan" "elect"
## [13] "vturn" "social1" "social2" "social3"
## [17] "social4" "leftsoc1" "leftsoc2" "leftsoc3"
## [21] "comm1" "comm2" "comm3" "comm4"
## [25] "agrarian" "conserv1" "conserv2" "conserv3"
## [29] "conserv4" "conserv5" "relig1" "relig2"
## [33] "relig3" "relig4" "relig5" "relig6"
## [37] "relig7" "liberal1" "liberal2" "liberal3"
## [41] "liberal4" "liberal5" "liberal6" "ultrar1"
## [45] "ultrar2" "protest1" "protest2" "protest3"
## [49] "protest4" "green1" "green2" "green3"
## [53] "ethnic1" "ethnic2" "ethnic3" "ethnic4"
## [57] "leftall" "centall" "rightall" "others"
## [61] "VAR00017" "VAR00019" "ssocial1" "ssocial2"
## [65] "ssocial3" "ssocial4" "sleftso1" "sleftso2"
## [69] "sleftso3" "scomm1" "scomm2" "scomm3"
## [73] "scomm4" "sagraria" "sconser1" "sconser2"
## [77] "sconser3" "sconser4" "sconser5" "srelig1"
## [81] "srelig2" "srelig3" "srelig4" "srelig5"
## [85] "srelig6" "srelig7" "slibera1" "slibera2"
## [89] "slibera3" "slibera4" "slibera5" "slibera6"
## [93] "sultrar1" "sultrar2" "sprotes1" "sprotes2"
## [97] "sprotes3" "sprotes4" "sgreen1" "sgreen2"
## [101] "sgreen3" "sethnic1" "sethnic2" "sethnic3"
## [105] "sethnic4" "sleftal" "scental" "srightal"
## [109] "sothers" "VAR00018" "VAR00020" "womenpar"
## [113] "rae_ele" "rae_leg" "effpar_ele" "effpar_leg"
## [117] "dis_abso" "dis_rel" "dis_gall" "var00001"
## [121] "var00002" "lfirst1" "lfirst2" "lsec1"
## [125] "lsec2" "leff1" "leff2" "lmin1"
## [129] "lmin2" "lexe1" "lexe2" "ldis1"
## [133] "ldis2" "lint1" "lint2" "lfed1"
## [137] "lfed2" "lbic1" "lbic2" "lrid1"
## [141] "lrid2" "ljud1" "ljud2" "lbank1"
## [145] "lbank2" "lfirstp" "lfirstpi" "lfirstpb"
## [149] "var00003" "var00008" "instcons" "plural"
## [153] "structur" "integr" "fed" "pres"
## [157] "singmemd" "strbic" "referen" "judrev"
## [161] "VAR00015" "VAR00016" "kaopen" "openc"
## [165] "outlays" "receipts" "realgdpgr" "nomgdpgr"
## [169] "inflation" "debt" "deficit" "adjustdef"
## [173] "interest" "VAR00013" "VAR00014" "ttl_labf"
## [177] "civ_labf" "emp_civ" "labfopar" "empratio"
## [181] "emp_ag" "emp_ind" "emp_serv" "emp_un"
## [185] "unemp" "st_unemp" "var00005" "var00004"
## [189] "nld" "wi" "wdlost" "strike"
## [193] "grossu" "netu" "ud" "adjcov"
## [197] "VAR00027" "VAR00028" "sstran" "socexp_t_pmp"
## [201] "socexp_c_pmp" "socexp_k_pmp" "oldage_pmp" "survivor_pmp"
## [205] "incapben_pmp" "health_pmp" "family_pmp" "almp_pmp"
## [209] "unemp_pmp" "housing_pmp" "othsocx_pmp" "var00007"
## [213] "var00006" "fallow_pmp" "mpleave_pmp" "othfam_c_pmp"
## [217] "daycare_pmp" "othfam_k_pmp" "VAR00047" "VAR00048"
## [221] "servadmi_pmp" "training_pmp" "jobrot_pmp" "incent_pmp"
## [225] "disabled_pmp" "jobcrea_pmp" "startup_pmp" "compen_pmp"
## [229] "earretir_pmp" "emprot" "VAR00009" "VAR00010"
## [233] "ilo_tot" "pop" "pop15_64" "pop65"
## [237] "elderly"
##
## $class
## [1] "data.frame"
# There are many variables, so it can be helpful to extract their classes
# (mostly to check for factors) via a quick for-loop
classes <- NULL # Create a placeholder for the class output
# for-loop
for (i in 1:ncol(data)) {
classes <- c(classes, class(data[, i]))
}
sort(classes) # Sort the results alphabetically (appears to be some factors, a few integers, and many numerics)## [1] "factor" "factor" "factor" "factor" "factor" "factor" "factor"
## [8] "factor" "factor" "factor" "factor" "factor" "factor" "integer"
## [15] "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## [22] "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## [29] "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## [36] "integer" "integer" "integer" "integer" "integer" "integer" "integer"
## [43] "integer" "integer" "integer" "integer" "numeric" "numeric" "numeric"
## [50] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [57] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [64] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [71] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [78] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [85] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [92] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [99] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [106] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [113] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [120] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [127] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [134] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [141] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [148] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [155] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [162] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [169] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [176] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [183] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [190] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [197] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [204] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [211] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [218] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [225] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [232] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
- Minimal call takes the following form
plot(x = )- Note that data-bearing arguments of length creater than 1 ("x" in this case) must be vectorized in some manner (usually using the '$' column vector method)
plot(x = vturn) # Attempt to plot variable 'vturn' without vectorization## Error: object 'vturn' not found
plot(x = data$vturn) # Attempt to plot variable 'vturn' with '$' vectorization
- Basic call takes the following form
plot(x = , y = , type = "")- The "type" argument accepts the following character indicators
- "p" -- point/scatter plots (default plotting behavior)
plot(x = data$year, y = data$realgdpgr, type = "p") # Plot Year on x-axis and Real GDP Growth on y-axis
- "l" -- line graphs
plot(x = data$year, y = data$realgdpgr, type = "l") # Plot Year on x-axis and Real GDP Growth on y-axis
- "b" -- both line and point plots
plot(x = data$year, y = data$realgdpgr, type = "b") # Plot Year on x-axis and Real GDP Growth on y-axis
- Certain plot types require different calls outside of the "type" argument
- Ex) Histograms
hist(x = data$vturn) # Plot histogram of voter turnout 
hist(x = data$vturn, breaks = 2) # Plot histogram of voter turnout, with 2 breaks
hist(x = data$vturn, breaks = 50) # Plot histogram of voter turnout, with 50 breaks
- Ex) Density plots
vturn.density <- density(x = data$vturn, na.rm = T) # Create a density object (NOTE: be sure to remove missing values)
class(vturn.density) # Check the class of the object## [1] "density"
vturn.density # View the contents of the object##
## Call:
## density.default(x = data$vturn, na.rm = T)
##
## Data: data$vturn (1134 obs.); Bandwidth 'bw' = 2.826
##
## x y
## Min. : 26.5 Min. :0.00001
## 1st Qu.: 46.0 1st Qu.:0.00248
## Median : 65.4 Median :0.00626
## Mean : 65.4 Mean :0.01285
## 3rd Qu.: 84.8 3rd Qu.:0.02472
## Max. :104.3 Max. :0.03609
plot(x = vturn.density) # Plot the density object
plot(x = density(x = data$vturn, bw = 2, na.rm = T)) # Plot the density object, bandwidth of 2
plot(x = density(x = data$vturn, bw = 0.5, na.rm = T)) # Plot the density object, bandwidth of 0.5
plot(x = density(x = data$vturn, bw = 6, na.rm = T)) # Plot the density object, bandwidth of 6
- Basic call with popular labeling arguments
plot(x = , y = , type = "", xlab = "", ylab = "", main = "")- From the previous example
plot(x = data$year, y = data$realgdpgr, type = "p", xlab = "Year", ylab = "Real GDP Growth",
main = "This Graph is Great") # Add labels for axes and overall plot
- Basic call with popular scaling arguments
plot(x = , y = , type = "", xlim = , ylim = , cex = )- From the previous example
plot(x = data$year, y = data$realgdpgr, type = "p") # Create a basic plot
# Limit the years (x-axis) to between 1976 and 1991
plot(x = data$year, y = data$realgdpgr, type = "p", xlim = c(1976, 1991))
# Limit the years (x-axis) to between 1976 and 1991, increase point size to
# 2
plot(x = data$year, y = data$realgdpgr, type = "p", xlim = c(1976, 1991), cex = 2)
# Limit the years (x-axis) to between 1976 and 1991, decrease point size to
# 0.5
plot(x = data$year, y = data$realgdpgr, type = "p", xlim = c(1976, 1991), cex = 0.5)
- Basic call with popular scaling arguments
plot(x = , y = , type = "", col = "", pch = , lty = , lwd = )- Colors
colors() # View all elements of the color vector## [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"
colors()[179] # View specific element of the color vector## [1] "gray26"
colors()[179:190] # View a selection of elements from the color vector## [1] "gray26" "gray27" "gray28" "gray29" "gray30" "gray31" "gray32"
## [8] "gray33" "gray34" "gray35" "gray36" "gray37"
Another option: R Color Infographic
plot(x = data$year, y = data$realgdpgr, type = "p", col = colors()[145]) # or col='gold3'
plot(x = data$year, y = data$realgdpgr, type = "p", col = colors()[624]) # or col='tan4'
- Point Styles and Widths
plot(x = data$year, y = data$realgdpgr, type = "p", pch = 3) # Change point style to crosses
plot(x = data$year, y = data$realgdpgr, type = "p", pch = 15) # Change point style to filled squares
# Change point style to filled squares and increase point size to 3
plot(x = data$year, y = data$realgdpgr, type = "p", pch = 15, cex = 3)
plot(x = data$year, y = data$realgdpgr, type = "p", pch = "w") # Change point style to 'w'
# Change point style to '$' and increase point size to 2
plot(x = data$year, y = data$realgdpgr, type = "p", pch = "$", cex = 2)
- Line Styles and Widths
# Create a data.frame containing yearly average Real GDP Growth over all
# countries
library(plyr)
# Split-apply-combine via plyr
mean.growth <- ddply(.data = data, .variables = .(year), summarize, mean = mean(realgdpgr))
head(mean.growth)## year mean
## 1 1960 NA
## 2 1961 5.612
## 3 1962 4.830
## 4 1963 5.310
## 5 1964 6.618
## 6 1965 5.010
# Line plot with solid line
plot(x = mean.growth$year, y = mean.growth$mean, type = "l", lty = 1)
# Line plot with medium dashed line
plot(x = mean.growth$year, y = mean.growth$mean, type = "l", lty = 2)
# Line plot with short dashed line
plot(x = mean.growth$year, y = mean.growth$mean, type = "l", lty = 3)
# Change line width to 2
plot(x = mean.growth$year, y = mean.growth$mean, type = "l", lty = 3, lwd = 2)
# Change line width to 3
plot(x = mean.growth$year, y = mean.growth$mean, type = "l", lty = 3, lwd = 3)
# Change line width to 4
plot(x = mean.growth$year, y = mean.growth$mean, type = "l", lty = 3, lwd = 4)
- Layering is accomplished by plotting succesive commands of "lines()", "points()", etc. after "plot()"
# Subset data to create a few data.frames containing data for a single
# country
france.growth <- data[data$country == "France", ]
italy.growth <- data[data$country == "Italy", ]
spain.growth <- data[data$country == "Spain", ]- Successive calls to "plot()" returns two different plots
# First call to plot
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
# Second call to plot
plot(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
- But using "lines()" for the second and subsequent calls layers the plots
# First call to plot
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
# First call to lines
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
# Second call to lines
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
- The same is true for "points()"
# First call to plot
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
# First call to lines
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
# Second call to lines
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
# First call to points
points(x = 1986, y = 6, pch = 13, col = colors()[116])
# Second call to points
points(x = 1986, y = -1, pch = 13, col = colors()[24])
# Third call to points
points(x = 1966, y = 2, pch = 13, col = colors()[391])
# Fourth call to points
points(x = 2008, y = 4, pch = 13, col = colors()[8])
- Text
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
points(x = 1986, y = 6, pch = 13, col = colors()[116])
points(x = 1986, y = -1, pch = 13, col = colors()[24])
points(x = 1966, y = 2, pch = 13, col = colors()[391])
points(x = 2008, y = 4, pch = 13, col = colors()[8])
# First call to text
text(x = 1967, y = -1, labels = "No dot here")
- Reference Lines
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
points(x = 1986, y = 6, pch = 13, col = colors()[116])
points(x = 1986, y = -1, pch = 13, col = colors()[24])
points(x = 1966, y = 2, pch = 13, col = colors()[391])
points(x = 2008, y = 4, pch = 13, col = colors()[8])
text(x = 1967, y = -1, labels = "No dot here")
# First call to abline
abline(v = 1980, h = 0)
- Legends
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
points(x = 1986, y = 6, pch = 13, col = colors()[116])
points(x = 1986, y = -1, pch = 13, col = colors()[24])
points(x = 1966, y = 2, pch = 13, col = colors()[391])
points(x = 2008, y = 4, pch = 13, col = colors()[8])
text(x = 1967, y = -1, labels = "No dot here")
abline(v = 1980, h = 0)
# First call to legend (note the vector position correspondence between each
# of the argument values)
legend("topright", legend = c("France", "Italy", "Spain"), col = c("red", "blue",
"darkgreen"), lty = c(1, 1, 1), lwd = c(2, 2, 2), cex = 0.8)
- Can form tables of graphs using the "par()" call like so:
par(mrow = c(ncols, nrows))# STEP 1: Call 'par() for a 2x2 table'
par(mfrow = c(2, 2))
# STEP 2: Plot #1
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
points(x = 1986, y = 6, pch = 13, col = colors()[116])
points(x = 1986, y = -1, pch = 13, col = colors()[24])
points(x = 1966, y = 2, pch = 13, col = colors()[391])
points(x = 2008, y = 4, pch = 13, col = colors()[8])
# STEP 3: Plot #2
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
points(x = 1986, y = 6, pch = 13, col = colors()[116])
points(x = 1986, y = -1, pch = 13, col = colors()[24])
points(x = 1966, y = 2, pch = 13, col = colors()[391])
points(x = 2008, y = 4, pch = 13, col = colors()[8])
text(x = 1967, y = -1, labels = "No dot here")
# STEP 4: Plot #3
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
points(x = 1986, y = 6, pch = 13, col = colors()[116])
points(x = 1986, y = -1, pch = 13, col = colors()[24])
points(x = 1966, y = 2, pch = 13, col = colors()[391])
points(x = 2008, y = 4, pch = 13, col = colors()[8])
text(x = 1967, y = -1, labels = "No dot here")
abline(v = 1980, h = 0)
# STEP 5: Plot #4
plot(x = france.growth$year, y = france.growth$realgdpgr, type = "l", col = "red",
lwd = 2)
lines(x = italy.growth$year, y = italy.growth$realgdpgr, type = "l", col = "blue",
lwd = 2)
lines(x = spain.growth$year, y = spain.growth$realgdpgr, type = "l", col = "darkgreen",
lwd = 2)
points(x = 1986, y = 6, pch = 13, col = colors()[116])
points(x = 1986, y = -1, pch = 13, col = colors()[24])
points(x = 1966, y = 2, pch = 13, col = colors()[391])
points(x = 2008, y = 4, pch = 13, col = colors()[8])
text(x = 1967, y = -1, labels = "No dot here")
abline(v = 1980, h = 0)
legend("topright", legend = c("France", "Italy", "Spain"), col = c("red", "blue",
"darkgreen"), lty = c(1, 1, 1), lwd = c(2, 2, 2), cex = 0.8)
- Base graphics are really great, but they're not like this (created with a single line of code)
-
lattice (Deepayan Sarkar, ISI, Delhi)
-
ggplot2 (Hadley Wickham, again)
-
Both are built on "grid", both are really huge improvements over base R graphics
-
Both also have entire books written about them (~200-300 pp. each)
- lattice is
a) faster (though only noticeable over many and large plots)
b) simpler (at first)
c) better at trellis graphs
d) able to do 3d graphs
- ggplot2 is
a) generally more elegant
b) more syntactically logical (and therefore simpler, once you learn it)
c) better at grouping
d) able to interface with maps
The general call for lattice graphics looks something like this:
graph_type(formula, data=, [options])The specifics of the formula differ for each graph type, but the general format is straightforward
y # Show the distribution of y
y ~ x # Show the relationship between x and y
y ~ x | A # Show the relationship between x and y conditional on the values of A
y ~ x | A * B # Show the relationship between x and y conditional on the combinations of A and B
z ~ y * x # Show the 3D relationship between x, y, and zThe general call for ggplot2 graphics looks something like this:
ggplot(data=, aes(x=,y=, [options]))+geom_xxxx()+...+...+...Note that ggplot2 graphs in layers in a continuing call (hence the endless +...+...+...), which really makes the extra layer part of the call
...+geom_xxxx(data=, aes(x=,y=,[options]),[options])+...+...+...You can see the layering effect by comparing the same graph with different colors for each layer
ggplot(data = data, aes(x = vturn, y = realgdpgr)) + geom_point(color = "black") +
geom_point(aes(x = vturn, y = unemp), color = "red")
ggplot(data = data, aes(x = vturn, y = realgdpgr)) + geom_point(color = "red") +
geom_point(aes(x = vturn, y = unemp), color = "black")
- Density Plots
- Scatter Plots
- Line Plots
- Bar plots
- Box plots
- Trellis Plots
- Contour Plots
- Tile/Image Plots
- 3D Plots (lattice)
- Panel Plots (ggplot2)
densityplot(~vturn, data = data) # lattice
ggplot(data = data, aes(x = vturn)) + geom_density() # ggplot2
xyplot(outlays ~ vturn, data = data) # lattice
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point() # ggplot2
xyplot(mean ~ year, data = mean.growth, type = "l") # lattice
ggplot(data = mean.growth, aes(x = year, y = mean)) + geom_line() # ggplot2 
# Create data.frame of average growth rates by country over time
growth <- ddply(.data = data, .variables = .(country), summarize, mean = mean(realgdpgr,
na.rm = T))
barchart(mean ~ country, data = growth) # lattice
ggplot(data = growth, aes(x = country, y = mean)) + geom_bar() # ggplot2
bwplot(vturn ~ country, data = data) # lattice
ggplot(data = data, aes(x = country, y = vturn)) + geom_boxplot() # ggplot2
xyplot(vturn ~ year | country, data = data) # lattice
ggplot(data = data, aes(x = year, y = vturn)) + geom_point() + facet_wrap(~country) # ggplot2
data(volcano) # Load volcano contour data
volcano[1:10, 1:10] # Examine volcano dataset (first 10 rows and columns)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 100 100 101 101 101 101 101 100 100 100
## [2,] 101 101 102 102 102 102 102 101 101 101
## [3,] 102 102 103 103 103 103 103 102 102 102
## [4,] 103 103 104 104 104 104 104 103 103 103
## [5,] 104 104 105 105 105 105 105 104 104 103
## [6,] 105 105 105 106 106 106 106 105 105 104
## [7,] 105 106 106 107 107 107 107 106 106 105
## [8,] 106 107 107 108 108 108 108 107 107 106
## [9,] 107 108 108 109 109 109 109 108 108 107
## [10,] 108 109 109 110 110 110 110 109 109 108
volcano3d <- melt(volcano) # Use reshape2 package to melt the data
head(volcano3d) # Examine volcano3d dataset (head)## Var1 Var2 value
## 1 1 1 100
## 2 2 1 101
## 3 3 1 102
## 4 4 1 103
## 5 5 1 104
## 6 6 1 105
names(volcano3d) <- c("xvar", "yvar", "zvar") # Rename volcano3d columns
contourplot(zvar ~ xvar + yvar, data = volcano3d) # lattice
ggplot(data = volcano3d, aes(x = xvar, y = yvar, z = zvar)) + geom_contour() # ggplot2
levelplot(zvar ~ xvar + yvar, data = volcano3d) # lattice
ggplot(data = volcano3d, aes(x = xvar, y = yvar, z = zvar)) + geom_tile(aes(fill = zvar)) # ggplot2
# Create a subset of the dataset containing only data for France
france.data <- data[data$country == "France", ]
cloud(outlays ~ year * vturn, data = france.data)
# Create a subset of the dataset containing only data for Greece, Portugal,
# Ireland, and Spain
pigs.data <- data[data$country %in% c("Greece", "Portugal", "Ireland", "Spain"),
]
cloud(outlays ~ year * vturn | country, data = pigs.data)
ggplot(data = pigs.data, aes(x = year, y = vturn, color = country)) + geom_line()
xyplot(outlays ~ vturn, data = data, xlab = "Voter Turnout (%)", ylab = "Government Outlays",
main = "This Graph is Also Great") # lattice
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point() + xlab(label = "Voter Turnout (%)") +
ylab(label = "Government Outlays") + ggtitle(label = "This Graph is Also Also Great") # ggplot2
xyplot(outlays ~ vturn, data = data, xlim = c(80, 100)) # lattice
xyplot(outlays ~ vturn, data = data, xlim = c(80, 100), cex = 2) # lattice
xyplot(outlays ~ vturn, data = data, xlim = c(80, 100), cex = 0.5) # lattice
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point() + xlim(80, 100) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(size = 3) + xlim(80,
100) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(size = 1) + xlim(80,
100) # ggplot2
- Colors
xyplot(outlays ~ vturn, data = data, col = colors()[145]) #lattice
xyplot(outlays ~ vturn, data = data, col = "red") #lattice
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(color = colors()[145]) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(color = "red") # ggplot2
- Point Styles and Widths
xyplot(outlays ~ vturn, data = data, pch = 3) # lattice
xyplot(outlays ~ vturn, data = data, pch = 15) # lattice
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(shape = 3) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(shape = 15) # ggplot2
- Point Styles and Widths
xyplot(outlays ~ vturn, data = data, pch = 3) # lattice
xyplot(outlays ~ vturn, data = data, pch = 15) # lattice
xyplot(outlays ~ vturn, data = data, pch = "w") # lattice
xyplot(outlays ~ vturn, data = data, pch = "$", cex = 2) # lattice
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(shape = 3) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(shape = 15) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(shape = "w") # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_point(shape = "$", size = 5) # ggplot2
- Line Styles and Widths
xyplot(outlays ~ vturn, data = data, type = "l", lty = 1) # lattice
xyplot(outlays ~ vturn, data = data, type = "l", lty = 2) # lattice
xyplot(outlays ~ vturn, data = data, type = "l", lty = 3) # lattice
xyplot(outlays ~ vturn, data = data, type = "l", lty = 3, lwd = 2) # lattice
xyplot(outlays ~ vturn, data = data, type = "l", lty = 3, lwd = 3) # lattice
xyplot(outlays ~ vturn, data = data, type = "l", lty = 3, lwd = 4) # lattice
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_line(linetype = 1) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_line(linetype = 2) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_line(linetype = 3) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_line(linetype = 3, size = 1) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_line(linetype = 3, size = 1.5) # ggplot2
ggplot(data = data, aes(x = vturn, y = outlays)) + geom_line(linetype = 3, size = 2) # ggplot2
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By now, you might be noticing some trends in how these two packages approach graphics
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lattice tends to focus on a particular type of graph and how to represent cross-sectional variation by splitting it up into smaller chunks
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Becoming a proficient user of lattice requires learning a huge array of graph-specific formulas and options
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ggplot2 tries to represent much more of the cross-sectional variation by making use of various "aesthetics"; general approach is based on The Grammar of Graphics
- Basic idea is that the visualization of all data requires four items
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One or more statistics conveying information about the data (identities, means, medians, etc.)
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A coordinate system that differentiates between the intersections of statistics (at most two for ggplot, three for lattice)
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Geometries that differentiate between off-coordinate variation in kind
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Scales that differentiate between off-coordinate variation in degree
- ggplot2 allows the user to manipulate all four of these items
ggplot(data = , aes(x = , y = , color = , linetype = , shape = , size = ))ggplot2 is optimized for showing variation on all four aesthetic types
# Differences in kind using color
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_line(aes(color = country))
Note what happens when we specify the color parameter outside of the aesthetic operator. ggplot2 views these specifications as invalid graphical parameters.
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_line(color = country)## Error: object 'country' not found
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_line(color = "country")## Error: invalid color name 'country'
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_line(color = "red")
# Differences in kind using line types
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_line(aes(linetype = country))
# Differences in kind using point shapes
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point(aes(shape = country))
# Differences in degree using color
ggplot(data = pigs.data, aes(x = year, y = realgdpgr)) + geom_point(aes(color = vturn))
# Differences in degree using point size
ggplot(data = pigs.data, aes(x = year, y = realgdpgr)) + geom_point(aes(size = vturn))
# Multiple non-cartesian aesthetics (differences in kind using color, degree
# using point size)
ggplot(data = pigs.data, aes(x = year, y = realgdpgr)) + geom_point(aes(color = country,
size = vturn))
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point()
# Add linear model (lm) smoother
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point() + geom_smooth(method = "lm")
# Add local linear model (loess) smoother, span of 0.75
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point() + geom_smooth(method = "loess",
span = 0.75)
# Add local linear model (loess) smoother, span of 0.25
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point() + geom_smooth(method = "loess",
span = 0.25)
# Add linear model (lm) smoother, no standard error shading
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point() + geom_smooth(method = "lm",
se = F)
# Add local linear model (loess) smoother, no standard error shading
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point() + geom_smooth(method = "loess",
se = F)
# Add a local linear (loess) smoother for each country
ggplot(data = pigs.data, aes(x = year, y = vturn)) + geom_point(aes(color = country)) +
geom_smooth(aes(color = country))
# Add a local linear (loess) smoother for each country, no standard error
# shading
ggplot(data = pigs.data, aes(x = year, y = realgdpgr)) + geom_point(aes(color = country,
size = vturn)) + geom_smooth(aes(color = country), se = F)
- Both lattice and ggplot2 graphs can be combined using the grid.arrange() function in the gridExtra package
# Initialize gridExtra library
library(gridExtra)
# Create 3 plots to combine in a table
plot1 <- ggplot(data = pigs.data, aes(x = year, y = vturn, color = )) + geom_line(aes(color = country))
plot2 <- ggplot(data = pigs.data, aes(x = year, y = vturn, linetype = )) + geom_line(aes(linetype = country))
plot3 <- ggplot(data = pigs.data, aes(x = year, y = vturn, shape = )) + geom_point(aes(shape = country))
# Call grid.arrange
grid.arrange(plot1, plot2, plot3, nrow = 3, ncol = 1)
Two basic image types
- Raster/Bitmap (.png, .jpeg)
Every pixel of a plot contains its own separate coding; not so great if you want to resize the image
jpeg(filename = "example.png", width = , height = )
plot(x, y)
dev.off()- Vector (.pdf, .ps)
Every element of a plot is encoded with a function that gives its coding conditional on several factors; great for resizing
pdf(filename = "example.pdf", width = , height = )
plot(x, y)
dev.off()# Assume we saved our plot is an object called example.plot
# lattice
trellis.device(device = "pdf", filename = "example.pdf")
print(example.plot)
dev.off()
# ggplot2
ggsave(filename = "example.pdf", plot = example.plot, scale = , width = , height = ) # ggplot2