Nigel Stackhouse
Reputation: 364
Add shaded region to ggplot with unscaled x-axis
Using the data provided at bottom, I would like to add one shaded region to the left and a different shaded region to the right of the vertical line in each facet in the following ggplot. Because the x-axis in each facet is simply a unique label, I'm struggling with this application as I have no clear xmin/xmax to provide.
So my question is, is it possible to add shaded regions to each facet in plots without continuous x-axes?
> dput(WF_postdist_melt)
structure(list(Species = c("ABDU", "AMCO", "AMWI", "BUFF", "BWTE",
"CANG", "CNV", "COGO", "GADW", "GWTE", "HOME", "MALL", "MERG",
"NOPI", "NSHO", "REDH", "RNDU", "SCAUP", "SNGO", "SWAN", "WODU",
"ABDU", "AMCO", "AMWI", "BUFF", "BWTE", "CANG", "CNV", "COGO",
"GADW", "GWTE", "HOME", "MALL", "MERG", "NOPI", "NSHO", "REDH",
"RNDU", "SCAUP", "SNGO", "SWAN", "WODU", "ABDU", "AMCO", "AMWI",
"BUFF", "BWTE", "CANG", "CNV", "COGO", "GADW", "GWTE", "HOME",
"MALL", "MERG", "NOPI", "NSHO", "REDH", "RNDU", "SCAUP", "SNGO",
"SWAN", "WODU", "ABDU", "AMCO", "AMWI", "BUFF", "BWTE", "CANG",
"CNV", "COGO", "GADW", "GWTE", "HOME", "MALL", "MERG", "NOPI",
"NSHO", "REDH", "RNDU", "SCAUP", "SNGO", "SWAN", "WODU", "ABDU",
"AMCO", "AMWI", "BUFF", "BWTE", "CANG", "CNV", "COGO", "GADW",
"GWTE", "HOME", "MALL", "MERG", "NOPI", "NSHO", "REDH", "RNDU",
"SCAUP", "SNGO", "SWAN", "WODU", "ABDU", "AMCO", "AMWI", "BUFF",
"BWTE", "CANG", "CNV", "COGO", "GADW", "GWTE", "HOME", "MALL",
"MERG", "NOPI", "NSHO", "REDH", "RNDU", "SCAUP", "SNGO", "SWAN",
"WODU"), Coefficient = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L), class = "factor", .Label = c("AmeliaIntercept",
"AmeliaWLCoef", "AmeliaRCCoef", "BUGSIntercept", "BUGSWLCoef",
"BUGSRCCoef")), Mean = c(-4802, -4805, -4800, 30050, -4801, 36090,
-4807, -4802, -4803, -4803, -128800, -4802, 43430, -4787, -4801,
-4802, -4802, -4805, -4803, -4801, -4800, 2505, -1437, 20, -1543,
271, -1260, 242, 5609, 778, 465, 1113, 1187, -364, 1469, 754,
376, 573, -1539, 9, 924, 261, 2625, 1440, -242.9, -1268, 34.87,
-217.8, 503.5, 4765, 1026, -185.1, -591.4, -90.39, 273.3, -612.9,
-7.29, 197.2, 16.38, 519.6, 636.3, 116.9, -812.5, 16250, 16260,
16250, -50800, 16260, 2496, 16240, 16250, 16260, 16250, 61630,
16250, 51830, 16270, 16250, 16260, 16250, 16260, 16250, 16250,
16250, 2749, -1690, 157, -336, 183, 347, -54, 5231, 203, -700,
-771, 1074, -677, 987, 663, 227, 321, -948, -740, 703, 81, 2688,
-554.9, -252.8, 1183, -103.9, -1333, 1336, 5160, -498.8, 280.1,
-770, -233.8, -183, -597.4, -146.1, 97.26, 139.7, 295.5, 137.5,
85.01, -82.17), LowerCI = c(-5091, -5046, -5087, 21390, -5089,
25700, -5135, -5089, -5087, -5093, -165800, -5092, 31220, -5066,
-5090, -5088, -5091, -5095, -5102, -5091, -5092, 981, -1444,
-58, -1545, -128, -1260, 235, 3964, 723, 411, 1113, 645, -365,
1461, 307, 34, 52, -1621, -161, 483, 208, 738.3, 1430, -297.2,
-1269, -378.2, -218, 495.4, 2734, 619.1, -245.3, -594.4, -543.9,
268, -627.8, -500.7, -273.3, -680.2, 236.6, 500.6, -426.2, -939,
16010, 15870, 16010, -71570, 16010, -1601, 15960, 16010, 16010,
16020, 49940, 16010, 42920, 16060, 16010, 16010, 16010, 16020,
16010, 16010, 16010, 1200, -1697, 106, -337, -82, 347, -59, 3607,
130, -834, -771, 749, -677, 980, 386, -104, 2, -1038, -915, 358,
23, 741, -561.7, -308.5, 1183, -512.3, -1336, 1316, 3280, -559.5,
216.5, -793.4, -720.1, -185.1, -607.8, -456.5, -287.4, -239.5,
195.7, 46.58, -417.2, -140.6), UpperCI = c(-4502, -4511, -4505,
40620, -4499, 48580, -4494, -4501, -4505, -4499, -97720, -4501,
57960, -4299, -4508, -4501, -4500, -4510, -4509, -4503, -4501,
4051, -1428, 88, -1542, 504, -1259, 251, 7319, 825, 512, 1114,
1727, -364, 1476, 1146, 734, 1134, -1480, 153, 1366, 322, 4470,
1447, -185.6, -1267, 622.4, -217.6, 513.4, 6706, 1619, -117.2,
-585.9, 646.6, 279.7, -603.1, 572.2, 578, 573.9, 772, 743.1,
605.5, -693.7, 16500, 16550, 16510, -33780, 16510, 6005, 16480,
16510, 16510, 16520, 75220, 16500, 62640, 16560, 16510, 16510,
16510, 16510, 16500, 16510, 16500, 4137, -1683, 206, -335, 468,
348, -45, 6914, 273, -569, -770, 1363, -676, 993, 930, 566, 666,
-861, -572, 938, 119, 4485, -549, -209.9, 1185, 265.6, -1330,
1352, 7023, -446.9, 404.1, -764.4, 145.4, -181.7, -586.1, 234.3,
480.2, 590.2, 396.4, 224.7, 706.7, -16.03), Imputation_Method = c("Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia",
"Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "Amelia", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS",
"BUGS", "BUGS", "BUGS", "BUGS", "BUGS", "BUGS"), CoefficientType = c("Intercept",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef",
"WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef",
"WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef",
"RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef",
"RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef",
"RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"Intercept", "Intercept", "Intercept", "Intercept", "Intercept",
"Intercept", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef",
"WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef",
"WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef", "WLCoef",
"WLCoef", "WLCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef",
"RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef",
"RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef", "RCCoef",
"RCCoef", "RCCoef")), .Names = c("Species", "Coefficient", "Mean",
"LowerCI", "UpperCI", "Imputation_Method", "CoefficientType"), row.names = c(NA,
-126L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x00000000001a0788>)
ggplot construction:
ggplot(data=WF_postdist_melt,aes(x=Species,y=Mean,colour=CoefficientType,
group=Coefficient)) +
# geom_rect(aes(xmin=-Inf,xmax=0,ymin=-10000,ymax=10000)) +
geom_errorbar(aes(ymin=LowerCI,ymax=UpperCI),size=1.25,width=1,
position=pd) +
geom_point(position=pd,size=1.5,shape=21,fill="white") +
geom_hline(yintercept=0) +
ylab("Waterfowl Model Posterior Estimate") + xlab(NULL) +
scale_colour_hue(name="Parameter",
breaks=c("AmeliaIntercept","AmeliaWLCoef",
"AmeliaRCCoef","BUGSIntercept","BUGSWLCoef",
"BUGSRCCoef"),
labels=c("Intercept (Amelia)",
"Water Level Coefficient (Amelia)",
"Rice Cover Coefficient (Amelia)",
"Intercept (BUGS)",
"Water Level Coefficient (BUGS)",
"Rice Cover Coefficient (BUGS)"),
l=40) +
facet_wrap(~Species,scales="free") + theme_bw() +
theme(
axis.title=element_text(size=20, face="bold"),
axis.text=element_text(size=10, face="bold"),
legend.title=element_text(face="bold")
)
Upvotes: 0
Views: 954
Answers (1)
aosmith
Reputation: 36076
A categorical x axis is plotted on integer values starting with 1, which you can refer to when adding, e.g., rectangles to denote certain areas.
As you have a single x value per facet, each x axis label is centered at the value 1 and the x axis of each facet goes from .5 to 1.5.
To add rectangles to each facet, one to the left and one to the right of your axis label and grid line, you can use the x axis integer coordinate system and add something like:
geom_rect(xmin = .5, xmax = 1, ymin = -Inf, ymax = Inf) +
geom_rect(xmin = 1, xmax = 1.5, ymin = -Inf, ymax = Inf, fill = "blue")
Upvotes: 1
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