Plotting bootrstrapped samples (multiple columns) using ggplot2
Question
I am trying to plot the bootstrap samples (1000 columns) using ggplot but the resulting figure is not correct.
Here's the data:
dat<- structure(list(YY = c(1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L,
1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L, 1961L), MM = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 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, 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, 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, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L), DD = 1:365, RR = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 5.3, 15.7, 3.6, 11.7, 40.1, 0, 0, 34, 9.4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16.3, 15, 0,
0, 0, 4.6, 0, 0, 0, 5.3, 14.7, 7.1, 0, 4.1, 0.5, 0, 1.8, 1.3,
0, 0, 0, 0, 0, 39.4, 1, 25.9, 0, 4.3, 4.6, 2, 9.7, 0, 0, 2, 0,
0, 0.3, 57.9, 31, 2.3, 0, 1.8, 0, 0, 0, 0, 20.8, 11.7, 1, 3.1,
0, 0, 0, 0.3, 8.6, 0, 0.3, 5.1, 0, 0, 0, 10.9, 7.1, 0, 4.1, 1.5,
2.8, 15.8, 109.5, 140, 25.7, 57.2, 9.4, 140.2, 119.6, 31.2, 0.8,
43.9, 2.8, 52.3, 45.5, 0.8, 12.4, 29, 66.3, 116.6, 155.7, 22.6,
6.9, 21.3, 39.1, 21.6, 19.8, 0, 14, 0, 11.2, 0, 14.2, 11.7, 52.8,
74.4, 34, 5.1, 0, 0, 0, 0, 2.5, 0.3, 6.6, 6.1, 31.8, 6.9, 7.6,
1.8, 6.6, 7.4, 8.4, 0, 20.8, 3, 15.5, 8.9, 6.4, 11.9, 11.7, 127,
100.3, 56.4, 6.1, 28.4, 84.6, 33.3, 6.4, 5.1, 30.5, 4.6, 4.1,
19.3, 39.6, 27.2, 7.9, 3.6, 3.3, 9.7, 10.7, 1.3, 59.7, 51.6,
65.8, 28.7, 13.2, 2.8, 32.5, 10.7, 57.2, 2.5, 0, 0, 29.5, 0,
0, 6.6, 31.2, 11.7, 1.5, 0, 0.5, 1.5, 29.7, 13.2, 20.3, 34.3,
9.7, 1, 1.5, 4.1, 13.5, 33, 0, 0, 10.9, 1.5, 7.4, 13, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5.3, 0, 0, 8.9, 13.7, 0, 0, 0, 0,
0, 0, 18.3, 0, 0, 0, 0, 0, 0, 0, 3.6, 14.2, 7.6, 0, 0.5, 0, 0,
0, 0, 0, 0, 0, 0, 0, 6.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), CUMSUM = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.3, 21, 24.6,
36.3, 76.4, 76.4, 76.4, 110.4, 119.8, 119.8, 119.8, 119.8, 119.8,
119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8,
119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8,
119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 119.8,
119.8, 119.8, 119.8, 119.8, 119.8, 119.8, 136.1, 151.1, 151.1,
151.1, 151.1, 155.7, 155.7, 155.7, 155.7, 161, 175.7, 182.8,
182.8, 186.9, 187.4, 187.4, 189.2, 190.5, 190.5, 190.5, 190.5,
190.5, 190.5, 229.9, 230.9, 256.8, 256.8, 261.1, 265.7, 267.7,
277.4, 277.4, 277.4, 279.4, 279.4, 279.4, 279.7, 337.6, 368.6,
370.9, 370.9, 372.7, 372.7, 372.7, 372.7, 372.7, 393.5, 405.2,
406.2, 409.3, 409.3, 409.3, 409.3, 409.6, 418.2, 418.2, 418.5,
423.6, 423.6, 423.6, 423.6, 434.5, 441.6, 441.6, 445.7, 447.2,
450, 465.8, 575.3, 715.3, 741, 798.2, 807.6, 947.8, 1067.4, 1098.6,
1099.4, 1143.3, 1146.1, 1198.4, 1243.9, 1244.7, 1257.1, 1286.1,
1352.4, 1469, 1624.7, 1647.3, 1654.2, 1675.5, 1714.6, 1736.2,
1756, 1756, 1770, 1770, 1781.2, 1781.2, 1795.4, 1807.1, 1859.9,
1934.3, 1968.3, 1973.4, 1973.4, 1973.4, 1973.4, 1973.4, 1975.9,
1976.2, 1982.8, 1988.9, 2020.7, 2027.6, 2035.2, 2037, 2043.6,
2051, 2059.4, 2059.4, 2080.2, 2083.2, 2098.7, 2107.6, 2114, 2125.9,
2137.6, 2264.6, 2364.9, 2421.3, 2427.4, 2455.8, 2540.4, 2573.7,
2580.1, 2585.2, 2615.7, 2620.3, 2624.4, 2643.7, 2683.3, 2710.5,
2718.4, 2722, 2725.3, 2735, 2745.7, 2747, 2806.7, 2858.3, 2924.1,
2952.8, 2966, 2968.8, 3001.3, 3012, 3069.2, 3071.7, 3071.7, 3071.7,
3101.2, 3101.2, 3101.2, 3107.8, 3139, 3150.7, 3152.2, 3152.2,
3152.7, 3154.2, 3183.9, 3197.1, 3217.4, 3251.7, 3261.4, 3262.4,
3263.9, 3268, 3281.5, 3314.5, 3314.5, 3314.5, 3325.4, 3326.9,
3334.3, 3347.3, 3347.3, 3347.3, 3347.3, 3347.3, 3347.3, 3347.3,
3347.3, 3347.3, 3347.3, 3347.3, 3347.3, 3347.3, 3352.6, 3352.6,
3352.6, 3361.5, 3375.2, 3375.2, 3375.2, 3375.2, 3375.2, 3375.2,
3375.2, 3393.5, 3393.5, 3393.5, 3393.5, 3393.5, 3393.5, 3393.5,
3393.5, 3397.1, 3411.3, 3418.9, 3418.9, 3419.4, 3419.4, 3419.4,
3419.4, 3419.4, 3419.4, 3419.4, 3419.4, 3419.4, 3419.4, 3426,
3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426,
3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426, 3426,
3426, 3426, 3426, 3427)), .Names = c("YY", "MM", "DD", "RR",
"CUMSUM"), class = "data.frame", row.names = c(NA, -365L))
Here's my script:
library(reshape2)
library(ggplot2)
library(meboot)
set.seed(1234)
dat[is.na(dat)]<- " "
out <- meboot(x=dat$CUMSUM,reps=1000,trim=0.10,elaps=TRUE)
f<-out$ens
Z<-out$ens
colnames(Z)<-c(seq(1:1000))
ZZ<-cbind(dat[3],Z)
df.melted<-melt(ZZ,id="DD")
p<-ggplot()
p <- p + geom_line(data=df.melted,aes(x=DD,y=value),color="black",alpha=0.1,size=0.5)
p <-p + geom_line(data=dat,aes(x=DD,y=CUMSUM),color="red",size=1)
p<-p + theme(panel.background=element_rect(fill="white"),
plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"),
panel.border=element_rect(colour="black",fill=NA,size=1),
axis.line.x=element_line(colour="black"),
axis.line.y=element_line(colour="black"),
axis.text=element_text(size=15,colour="black",family="serif"),
axis.title=element_text(size=15,colour="black",family="serif"),
legend.position = "none")
p <- p + labs(x="Day of Year",y="Cummulative Rainfall (mm)")
outImg <- paste0("bootrapped",".png")
ggsave(outImg,p,width=8,height=5)
The resulting image is this:
QUESTIONS/PROBLEMS
- I want to plot each column (1000 columns) via ggplot and overlay the original time series(red). However,looking at the graph, the lines (bootstrapped columns) appear to connect to each other. I want the lines to be clearer. I might be using the
meltincorrectly.
Any suggestions on how to do this properly in ggplot? Ill appreciate any help.
1 answers
solution1
5 ACCPTED 2018-06-28 08:16:16
You need to group by variable .
p <- p + geom_line(data=df.melted,aes(x=DD,y=value, group = variable),color="black",alpha=0.1,size=0.5)
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