R ggplot2：使用嵌套循环将多个geom_ribbon对象叠加在一个绘图中

本文介绍了R ggplot2：使用嵌套循环将多个geom_ribbon对象叠加在一个绘图中的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 29岁程序员，3月因学历无情被辞！ 我有五条线图，我想从中输出一个阴影区域，代表它们绘制的上部和下部区域之间的区域。我创建了一个R脚本（见下文），因为我有多个数据集，需要重复这个练习。 然而，我只能打印geom_ribbon from the last i and j pair - 我似乎无法将每个geom_ribbon输出到创建的列表中。 感谢您提供关于如何导入所有geom_ribbon对象放入列表中。只有一个图是用 print（Z）（下面的例子）打印的。如果可能的话，我想要将所有geom_ribbon对象重叠并打印为单个ggplot？ Z allmaxi allmaxj for（i in allmaxi）{ for（j in allmaxj）{l Z [[length（Z）+ 1]] print（i） print b} print（ggplot（）+ Z） （从print（i）和print（j）以脚本形式）从输入一个数据集（decade_maxa）到i列表，并将其他四个数据集输入到j列表： [1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106 [11] 1580.880 1685.253 1653.178 1824.842 [1] 1390.260 1247.700 1263.578 1711.638 1228.326 1762.045 1260.147 1171.914 1697.987 1350.867 [11] 1434.525 1488.818 1610.513 1536.895 ``[1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106 [11] 1580.880 1685.253 1653.178 1824.842 ``[1] 1120.2700 1094.3047 1196.8792 1227.9660 1236.9170 1266.0935 1127.1480 974.6948 947.3365 [10] 1244.3242 1254.2704 1082.3667 1286.9080 1126.1943 ``[1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106 [11] 1580.880 1685.253 1653.178 1824.842 ``[1] 1396.695 1425.073 1382.941 1913.495 1401.754 1499.763 1600.656 1367.043 1413.390 1343.804 [11] 1431.790 1402.292 1329.192 1696.729 ``[1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106 [11] 1580.880 1685.253 1653.178 1824.842 ``[1] 1718.874 1389.134 1501.574 1233.189 1262.480 1508.919 1291.467 1431.869 1505.102 1376.519 [11] 1441.181 1421.552 1326.547 1635.599 `> print（ggplot（）+ Z） ` 这是我的目标。也许有更好的方法与lapply？ 这是如下所提出的那样通过整合中间值来输出图像： median_g median_gm< - 变异（median_g，median = median（value）） p2 geom_point（aes（x = decade， y =中位数）） p2 解决方案以下是一个稍微过度设计的解决方案：查找所有细分市场交集，添加这些abscis ＃某段段交集代码＃http://paulbourke.net/geometry/pointlineplane/ ssi< - 函数（x1，x2，x3，x4，y1，y2，y3，y4）{ denom < - （（y4-y3）*（x2-x1） - （x4-x3）*（y2-y1）） denom [abs（denom）< （（x4-x3）*（y1-y3） - （y4-y3）*（x1-x3））/ denom（1e-10） ua< ub x y< -y1 + ua *（y2-y1） inside< - （ua> = 0）& （ua< = 1）& （ub> = 0）& （ub data.frame（x = ifelse（inside，x，NA），y = ifelse（inside，y，NA）） } ＃用两条多段线（xy dataframes）做 ssi_polyline n1 n2 stopifnot（n1 == n2） x1 x2 x3 x4 ssi（x1，x2，x3，x4，y1，y2，y3，y4）} ＃测试上述 d1 d2 plot（rbind（d1， d2），t =n） lines（d1） lines（d2，col = 2） points（ssi_polyline（d1，d2）） ＃做一个矩阵的所有列（假设常见的xs）＃一般情况下（不同的xs）可以被类似地处理＃例如通过首先对所有唯一的xs进行线性插值 ssi_matrix ＃成对组合 cn test_pair< - function（i）{ l1 p2< ; - ssi_polyline（l1，l2） pts [complete.cases（pts），] } ints< - lapply（seq_len（ncol（cn）），test_pair） $ c $（ $ b）＃在一个矩阵上测试它m< - replicate（5，rnorm（10））x< - seq_len（nrow（m）） matplot（x，m， t =l，lty = 1） test points（test） ＃现在，将其应用于手头的数据集 $ b库（ggplot2）库（reshape2）库（plyr） set.seed（123）数据< - data.frame（十进制= 1：10）n = nrow（数据）数据$ maxa 数据$ maxb data $ maxc data $ maxd data $ maxe newpoints< - setNames（data.frame（ssi_matrix（data $ decade，data [， - 1L]），added），c（decade （data，id = 1L） 插值< - ddply（mdata，variable，function（d ）{ xy }） 全部 rib ymin = min（value），ymax = max（value）） ggplot（mdata，aes（decade））+ geom_ribbon（data = rib，aes（x = decade，ymin = ymin，ymax = ymax）， alpha = 0.40，fill =＃3985ff）+ geom_line（aes（y = value，color = variable）） I have five line plots from which I'd like to output a shaded area that represents the region between their plotted upper and lower regions. I'm creating an R script (see below) as I have multiple datasets for which I need to repeat this exercise.However, I'm only able to print the geom_ribbon from the last i and j pair - I can't seem to output every geom_ribbon into the created list.I'd grateful for any ideas on how to import all of the geom_ribbon objects into the list. Only one plot is printed with print(Z)(example below). I'd like, if possible, all geom_ribbon objects to be overlain and printed as a single ggplot?Z <- list()allmaxi <- list(cahp_max_plot15cb$decade_maxa, cahp_max_plot15cb$decade_maxc,cahp_max_plot15cb$decade_maxd, cahp_max_plot15cb$decade_maxe, cahp_max_plot15cb$decade_maxf)allmaxj <- list(cahp_max_plot15cb$decade_maxa, cahp_max_plot15cb$decade_maxc,cahp_max_plot15cb$decade_maxd, cahp_max_plot15cb$decade_maxe, cahp_max_plot15cb$decade_maxf)for (i in allmaxi) { for (j in allmaxj) { l <- geom_ribbon(data=cahp_max_plot15cb,aes(x=decade,ymin=i, ymax=j)) Z[[length(Z) + 1]] <- l print(i) print(j) }}print(ggplot() + Z)Sample output (from print(i) and print(j) in script) from inputting one dataset (decade_maxa) to i list, and four other data sets to j list: [1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106[11] 1580.880 1685.253 1653.178 1824.842[1] 1390.260 1247.700 1263.578 1711.638 1228.326 1762.045 1260.147 1171.914 1697.987 1350.867[11] 1434.525 1488.818 1610.513 1536.895` `[1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106[11] 1580.880 1685.253 1653.178 1824.842` `[1] 1120.2700 1094.3047 1196.8792 1227.9660 1236.9170 1266.0935 1127.1480 974.6948 947.3365[10] 1244.3242 1254.2704 1082.3667 1286.9080 1126.1943` `[1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106[11] 1580.880 1685.253 1653.178 1824.842` `[1] 1396.695 1425.073 1382.941 1913.495 1401.754 1499.763 1600.656 1367.043 1413.390 1343.804[11] 1431.790 1402.292 1329.192 1696.729` `[1] 2010.811 1723.783 1961.088 1662.909 1587.191 1662.140 1665.415 1602.974 1807.453 1586.106[11] 1580.880 1685.253 1653.178 1824.842` `[1] 1718.874 1389.134 1501.574 1233.189 1262.480 1508.919 1291.467 1431.869 1505.102 1376.519[11] 1441.181 1421.552 1326.547 1635.599`> print(ggplot() + Z)`This is my aim. Maybe there is a better way with lapply?This is the image output by integrating median values, as proposed below:median_g <- group_by(cahp_max_plot15cbm,decade)median_gm <- mutate(median_g, median=median(value))p2 <- ggplot(median_gm) + geom_ribbon(aes(x=decade, ymin=median,ymax=value,group=variable),alpha=0.40,fill="#3985ff") + geom_line(aes(x=decade,y=value,group=variable,color=variable),lwd=1) + geom_point(aes(x=decade,y=median))p2 解决方案 Here's a slightly over-engineered solution: find all segment-segment intersections, add those abscissae to the mix, and for each x locate the min and max values.# some segment-segment intersection code# http://paulbourke.net/geometry/pointlineplane/ssi <- function(x1, x2, x3, x4, y1, y2, y3, y4){ denom <- ((y4 - y3)*(x2 - x1) - (x4 - x3)*(y2 - y1)) denom[abs(denom) < 1e-10] <- NA # parallel lines ua <- ((x4 - x3)*(y1 - y3) - (y4 - y3)*(x1 - x3)) / denom ub <- ((x2 - x1)*(y1 - y3) - (y2 - y1)*(x1 - x3)) / denom x <- x1 + ua * (x2 - x1) y <- y1 + ua * (y2 - y1) inside <- (ua >= 0) & (ua <= 1) & (ub >= 0) & (ub <= 1) data.frame(x = ifelse(inside, x, NA), y = ifelse(inside, y, NA))}# do it with two polylines (xy dataframes)ssi_polyline <- function(l1, l2){ n1 <- nrow(l1) n2 <- nrow(l2) stopifnot(n1==n2) x1 <- l1[-n1,1] ; y1 <- l1[-n1,2] x2 <- l1[-1L,1] ; y2 <- l1[-1L,2] x3 <- l2[-n2,1] ; y3 <- l2[-n2,2] x4 <- l2[-1L,1] ; y4 <- l2[-1L,2] ssi(x1, x2, x3, x4, y1, y2, y3, y4)}# testing the aboved1 <- cbind(seq(1, 10), rnorm(10))d2 <- cbind(seq(1, 10), rnorm(10))plot(rbind(d1, d2), t="n")lines(d1)lines(d2, col=2)points(ssi_polyline(d1, d2))# do it with all columns of a matrix (common xs assumed)# the general case (different xs) could be treated similarly# e.g by doing first a linear interpolation at all unique xsssi_matrix <- function(x, m){ # pairwise combinations cn <- combn(ncol(m), 2) test_pair <- function(i){ l1 <- cbind(x, m[,cn[1,i]]) l2 <- cbind(x, m[,cn[2,i]]) pts <- ssi_polyline(l1, l2) pts[complete.cases(pts),] } ints <- lapply(seq_len(ncol(cn)), test_pair) do.call(rbind, ints)}# testing this on a matrixm <- replicate(5, rnorm(10))x <- seq_len(nrow(m))matplot(x, m, t="l", lty=1)test <- ssi_matrix(x, m)points(test)# now, apply this to the dataset at handlibrary(ggplot2)library(reshape2)library(plyr)set.seed(123)data <- data.frame(decade=1:10)n=nrow(data)data$maxa <- runif(n,1000,2000)data$maxb <- runif(n,1000,2000)data$maxc <- runif(n,1000,2000)data$maxd <- runif(n,1000,2000)data$maxe <- runif(n,1000,2000)newpoints <- setNames(data.frame(ssi_matrix(data$decade, data[,-1L]), "added"), c("decade", "value", "variable"))mdata <- melt(data, id=1L)interpolated <- ddply(mdata, "variable", function(d){ xy <- approx(d$decade, d$value, xout=newpoints[,1]) data.frame(decade = xy$x, value=xy$y, variable = "interpolated")})all <- rbind(mdata, interpolated, newpoints)rib <- ddply(all, "decade", summarise, ymin=min(value), ymax=max(value))ggplot(mdata, aes(decade)) + geom_ribbon(data = rib, aes(x=decade, ymin=ymin, ymax=ymax), alpha=0.40,fill="#3985ff")+ geom_line(aes(y=value, colour=variable)) 这篇关于R ggplot2：使用嵌套循环将多个geom_ribbon对象叠加在一个绘图中的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！