使用循环在 R 中绘图答案

【问题标题】：Plots in R using Loops使用循环在 R 中绘图
【发布时间】：2017-08-28 14:25:25
【问题描述】：

我不是程序员，但我已经学习了使用 R 进行统计的基础知识。我会尽力描述我的问题：

我有一个 120 列的表格，其中每一列代表一个小通道在两个时间点的横截面坐标，x 或 y：2017 年和 1920 年。表格的第一行是十字架的名称-sections 坐标，例如 "7X" 和 "7Y" 是 2017 年名为 "7" 的部分的 (x,y) 坐标，而 "7BX" 和 "7BY" 是 1920 年同一部分 "7" 的坐标. 我设法使用 R 制作了它的线图，并将其中 4 个图容纳在一个图形中，以便使用以下代码打印，其中 R 中的表名为“SEC”，我使用包 ggpubr 将单独的图放在一起：

library(ggpubr)

g <- ggplot(SEC, aes(x=`7X`, y = `7Y`, colour = "Observed"))+geom_line()+
  geom_line(aes(x = `7BX`,y = `7BY`, colour = "1974"), linetype = "dashed") +
  labs(x = "Distance [cm]", y = "Depth [cm]") + coord_equal() + 
  scale_x_continuous(position = "top", limits = c(0,750)) + 
  scale_y_continuous(limits = c(-280,0)) + 
  scale_colour_manual("", breaks = c("Observed", "1974"), values = c("Observed"="black", "1974"="blue"))
g2 <- ggplot(SEC, aes(x=`10X`, y = `10Y`, colour = "Observed"))+geom_line()+
  geom_line(aes(x = `10BX`,y = `10BY`, colour = "1974"), linetype = "dashed") +
  labs(x = "Distance [cm]", y = "Depth [cm]") + coord_equal() + 
  scale_x_continuous(position = "top", limits = c(0,750)) + 
  scale_y_continuous(limits = c(-280,0)) + 
  scale_colour_manual("", breaks = c("Observed", "1974"), values = c("Observed"="black", "1974"="blue"))
g3 <- ggplot(SEC, aes(x=`13X`, y = `13Y`, colour = "Observed"))+geom_line()+
  geom_line(aes(x = `13BX`,y = `13BY`, colour = "1974"), linetype = "dashed") +
  labs(x = "Distance [cm]", y = "Depth [cm]") + coord_equal() + 
  scale_x_continuous(position = "top", limits = c(0,750)) + 
  scale_y_continuous(limits = c(-280,0)) + 
  scale_colour_manual("", breaks = c("Observed", "1974"), values = c("Observed"="black", "1974"="blue"))
g4 <- ggplot(SEC, aes(x=`14X`, y = `14Y`, colour = "Observed"))+geom_line()+
  geom_line(aes(x = `14BX`,y = `14BY`, colour = "1974"), linetype = "dashed") +
  labs(x = "Distance [cm]", y = "Depth [cm]") + coord_equal() + 
  scale_x_continuous(position = "top", limits = c(0,750)) + 
  scale_y_continuous(limits = c(-280,0)) + 
  scale_colour_manual("", breaks = c("Observed", "1974"), values = c("Observed"="black", "1974"="blue"))


ggarrange(g, g2, g3, g4, ncol = 2, nrow = 2, common.legend = TRUE, legend = "bottom")

上面的代码生成如下图（请注意，我在R studio环境中使用了缩放，我右键单击->在缩放后的图片上复制图像并粘贴到油漆上，因为我仍然不知道如何将其保存为图片具有正确的“缩放”级别）： Plot generated with the above code

到目前为止，一切都很完美。我的问题是如何在我的代码中添加一个循环，以每 4 列执行一次此图并将其保存为 png、jpg 或类似格式。

我使用的数据（修改为共享）是：

  SEC <- structure(list(`7X` = c(7.5, 15, 22.5, 30, 37.5, 45, 52.5, 60, 
67.5, 75, 82.5, 90, 97.5, 105, 112.5, 120, 127.5, 135, 142.5, 
150, 157.5, 165, 172.5, 180, 187.5, 195, 202.5, 210, 217.5, 225, 
232.5, 240, 247.5, 255, 262.5, 270, 277.5, 285, 292.5, 300, 307.5, 
315, 322.5, 330, 337.5, 345, 352.5, 360, 367.5, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), `7Y` = c(-25.9671090715505, -47.4607397762232, 
-53.7559172609319, -63.3665293310876, -66.6777325668064, -73.7850158514536, 
-75.8786077662389, -78.4717300522204, -86.6122602392833, -86.5085656086825, 
-99.7082525346791, -106.066956054077, -104.893267727827, -103.768964560977, 
-101.143312965043, -103.962172334764, -104.758547162389, -102.136349931386, 
-110.815517978626, -111.363366631309, -111.050166912353, -105.649062617965, 
-105.910377967987, -104.4320913694, -113.768783085737, -119.518754325158, 
-131.902196495777, -132.44782879906, -135.956263880875, -133.892807725805, 
-133.693311165822, -136.954487539369, -136.880936445156, -136.861399724998, 
-137.24878640853, -139.889844889866, -140.123989192931, -139.964791362668, 
-142.767842490807, -139.984728213883, -139.514265170192, -133.47785217087, 
-82.7273919344385, -75.020643340269, -61.9680666387492, -53.2860080778223, 
-51.0896682486046, -44.6102547614017, -35.7014461630998, NA, 
NA, NA, NA, NA, NA, NA, NA, NA), `7BX` = c(0, 440, 640, 1080, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA), `7BY` = c(0, -210, -210, 0, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA), `10X` = c(32, 40, 48, 56, 64, 72, 80, 88, 96, 
104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 
208, 216, 224, 232, 240, 248, 256, 264, 272, 280, 288, 296, 304, 
312, 320, 328, 336, 344, 352, 360, 368, 376, 384, 392, 400, 408, 
416, 424, 432, 440, 448, 456, 464, 472, 480, 488), `10Y` = c(-94.5966356796394, 
-98.9763004291606, -103.076968535357, -106.962218988179, -110.617820502447, 
-114.115499665262, -116.110479384182, -120.384670012772, -135.012443220999, 
-140.641277783522, -149.397077818365, -152.23251255149, -154.594844651231, 
-161.870765592212, -169.050648283188, -168.468938070109, -178.406458075646, 
-189.60326884302, -185.215711843659, -192.652302594034, -204.420567844116, 
-214.802445709178, -262.006760906245, -269.627846515966, -271.928416747414, 
-280.842869544577, -286.192359059652, -286.393432557465, -287.096960178529, 
-286.681850224408, -286.247209161192, -283.325346268317, -280.952049206594, 
-275.950384188228, -258.70613971596, -259.410546763113, -245.655256400078, 
-236.838966940681, -228.287891246208, -225.674662960305, -225.790568242069, 
-226.182932581986, -226.575239267478, -227.964898636738, -226.343652570147, 
-200.896351276318, -191.905220163245, -175.399533006979, -168.597240169831, 
-163.1128036503, -157.861050484961, -155.229423199991, -139.207319012034, 
-127.927733637759, -120.782994141792, -113.149068161756, -109.895475650145, 
-94.4163178937629), `10BX` = c(0, 55, 300, 380, 550, 740, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), `10BY` = c(0, -20, -155, -155, -30, 0, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA), `13X` = c(30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 
330, 360, 390, 420, 450, 480, 510, 540, 570, 600, 630, 660, 690, 
720, 750, 780, 810, 840, 870, 900, 930, 960, 990, 1020, 1050, 
1080, 1110, 1140, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA), `13Y` = c(-130.280130140096, 
-133.314602565698, -155.755735588693, -165.349822633039, -163.527278504803, 
-164.127092741566, -168.544964800923, -168.010043126269, -172.859848036266, 
-182.767172542781, -172.116768890092, -172.5868812035, -173.634903800562, 
-176.611077660323, -179.665100040058, -176.989870773949, -180.77134156612, 
-183.742221306137, -183.799677917615, -180.703438314547, -195.745531287296, 
-207.31260678753, -222.757679568742, -225.343317270965, -230.478091545319, 
-232.25420677185, -224.230717742185, -217.685383613481, -213.890519933422, 
-203.152992365013, -200.464974159305, -195.833697602067, -175.547017122402, 
-172.802992846061, -160.173459133272, -159.843210575388, -155.227573251256, 
-130.275570551425, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA), `13BX` = c(0, 308, 378, 
700, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA), `13BY` = c(0, -70.5943380693977, 
-142.827413298, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), `14X` = c(40, 80, 
120, 160, 200, 240, 280, 320, 360, 400, 440, 480, 520, 560, 600, 
640, 680, 720, 760, 800, 840, 880, 920, 960, 1000, 1040, 1080, 
1120, 1160, 1200, 1240, 1280, 1320, 1360, 1400, 1440, 1480, 1520, 
1560, 1600, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), `14Y` = c(-145.990632758813, -150.826188851428, 
-163.682940701739, -172.043833955967, -182.53083213644, -191.353726599893, 
-197.584471071481, -200.834572726043, -207.495959099511, -210.65543163322, 
-209.939464279794, -216.671860614474, -225.310844045373, -232.206404957882, 
-234.306313434513, -243.524048340371, -245.209549795867, -249.902953463223, 
-255.057143558744, -245.369858504693, -220.664700874663, -206.676224685967, 
-205.23664115722, -200.759982388337, -200.092376111362, -200.431526555313, 
-200.338637172383, -200.111899718351, -203.759654556748, -206.71146837615, 
-204.674270849751, -201.336543870959, -200.845407082769, -197.435021642656, 
-192.266899943151, -191.237294125464, -173.518399500314, -166.786712970063, 
-165.921143424977, -145.856527067335, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), `14BX` = c(0, 
360, 460, 800, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), `14BY` = c(0, -43.3291743105714, 
-118.074399602666, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("7X", 
"7Y", "7BX", "7BY", "10X", "10Y", "10BX", "10BY", "13X", "13Y", 
"13BX", "13BY", "14X", "14Y", "14BX", "14BY"), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -58L))

感谢您的任何建议，如果缺少某些内容或问题/问题未正确设置，我们深表歉意。

【问题讨论】：

参见?by——您可能不必编写显式循环。然后，您可以在结果列表中调用do.call(ggarrange, ...)。但您确实需要提供一个可重现的数据集让我们回答您的问题。
另外，如果您想为两个分组变量的每个组合创建单独的图，您应该查看facet_grid()，这可以消除对多个绘图语句的需要和ggarrange()
嗨@C8H10N4O2 我添加了我使用的数据集。感谢 cmets。

标签： r loops plot

【解决方案1】：

1。很抱歉告诉你这个，但你的数据集是一团糟。

是的，您可以遍历列索引并使用get() 或类似的东西以编程方式访问列。但是，这段代码在任何其他情况下都非常难以阅读且毫无用处。

最佳做法是认识到您实际上只有两个观察值 - X 和 Y - 在不同的时间和位置。您当前的行并没有真正表示除按组观察 ID 之外的任何内容。这就像有人在电子表格上并排粘贴了一堆 XY 对。

您理想的数据集应该有四列：

部分 ID（例如 7）
时间点（即观察到的或 B/1974）
X
是

首先让我们将您的数据放入这种“长”形式。您应该查看tidyverse 以了解此问题以及有关 SO 的许多相关问题。我们还将使用regular expressions 解析出关键信息。

#----- These functions help you parse your section/time codes ------
#----- To understand them, use the regex tutorial linked above.  ---
keyStartingDigits <- function(s) as.integer(regmatches(s ,regexpr('^\\d+',s) ))
keyEndingXorY     <- function(s) regmatches(s ,regexpr('[XY]$',s) )
#----- This function helps you parse observed/historical  ----------
keyTime <- function(s) {
  factor(
    ifelse(substr(s,nchar(s)-1,nchar(s)-1)=='B',
           '1974',
           'Observed'
    ), levels=c('1974','Observed')
  )
}

library(dplyr) # \  popular libaries for data manipulation
library(tidyr) # /  part of the 'tidyverse'
df <- 
  SEC %>% 
    mutate(obs_id = 1:n()) %>%
    gather(key=key, value=measurement,-obs_id, na.rm=TRUE) %>% # produces key-value pairs, e.g. [7X, 7.5]
    mutate(section=keyStartingDigits(key), # section ID, e.g. 7
           time = keyTime(key),            # 1974 or Observed
           dimension = keyEndingXorY(key)  # X or Y
           ) %>%
    select(-key) %>%
    spread(dimension,measurement) %>%
    select(-obs_id)

现在你有一个很好的数据集，看起来像这样。

# > head(df)
# # A tibble: 6 x 4
#   section     time     X          Y
#     <chr>   <fctr> <dbl>      <dbl>
# 1      10     1974     0    0.00000
# 2      10 Observed    32  -94.59664
# 3      13     1974     0    0.00000
# 4      13 Observed    30 -130.28013
# 5      14     1974     0    0.00000
# 6      14 Observed    40 -145.99063

现在您在列值（而不是列名！）中拥有分组变量，分组更为自然。

2。不要重复自己（DRY）

您需要一个带有一个几何图形的绘图函数，而不是每个带有两个几何图形的四个绘图。当您的数据采用适当的范式时，很容易使用相同的几何图形创建观察线和历史线，而不是绘制两个子集。

library(ggplot2)
myPlot <- function(section){
  line_cols <- c('Observed'='black', '1974'='blue')
  line_types <- c('Observed'='solid', '1974'='dashed')
  
  ggplot(df[df$section==section,], aes(x=X, y = Y))+
    geom_line(aes(colour = time, linetype=time)) +
    labs(x = "Distance [cm]", y = "Depth [cm]", caption=paste('Section',section)) + 
    coord_equal() + 
    scale_x_continuous(position = "top", limits = c(0,750)) + 
    scale_y_continuous(limits = c(-280,0)) + 
    scale_colour_manual(values = line_cols, guide=FALSE) +
    scale_linetype_manual(values=line_types, guide=FALSE) +
    theme(plot.caption = element_text(hjust=0.5,size=rel(1.5)))
}
myPlot(7) # example

3。现在可以很容易地按部分创建图

有很多选项可以将绘图排列成网格。您可以将绘图列表传递给您的排列功能。此图列表可以是已创建的myPlot 函数的lapply。

library(gridExtra) # could also use ggarrange instead of marrangeGrob, or grid.arrange
pl <- lapply(sort(unique(df$section)), 
         function(i) myPlot(i)) # list of 4 plots
ggsave("plotgrid.png", 
       plot = marrangeGrob(pl, nrow=2, ncol=2), 
       device='png')

【讨论】：

谢谢。您对我的数据集所做的事情令人印象深刻，并且使用您的代码分别生成每个部分的图非常方便快捷，但我试图实现的是放置其中的 4 个图（比如说，第 7 节、第 10 节, 13 和 14) 在同一个图中 (2x2 的网格) 用于打印...但从这里开始应该更容易。
@Daniel 您可以 ggsave 由 gridExtra::grid.arrange 或您的 ggarrange 生成的绘图网格。或者，请参阅 ?png 和 dev.off。打开 png 设备，打印到设备，然后关闭它。我会更新
C8H10N4O2 非常感谢您的帮助。现在我将能够处理它。谢谢。