geom_abline多重斜坡和拦截 [英] geom_abline multiple slopes and intercepts

问题描述

考虑到这个初始数据帧（yld_sum）：

  coef pred se ci.lb ci.ub cr.lb cr。 ub Yld_class 
 b0 3164.226 114.256 2940.289 3388.164 2142.724 4185.728 1Low 
 b1 -20.698 3.511 -27.580 -13.816 -50.520 9.124 1Low 
 b0 3985.287 133.220 3724.180 4246.394 2954.998 5015.576 2Low 
 b1 -14.371 4.185 -22.573 -6.168 -44.525 15.784 2Low 
  

如何简化我的语法来绘制两条估计回归线与他们各自的CI，并获得以下情节？

这是我详细的代码：

  library（tidyverse）
 
 yld_sum_est<  -  yld_sum ％>％select（Yld_class，coef，pred）％>％
 spread（coef，pred）
 
 yld_sum _low < -  yld_sum％>％select（Yld_class，coef，ci.lb）％>％
 spread（coef，ci.lb）
 
 yld_sum_up < -  yld_sum％> ;％select（Yld_class，coef，ci.ub）％>％
 spread（coef，ci.ub）
 
 ggplot（）+ 
 geom_abline（data = yld_sum_est， aes（intercept = b0，slope = b1））+ 
 geom_abline（data = yld_sum_low，aes（截距= b0，slope = b1），linetype =dashed）+ 
 geom_abline（data = yld_sum_up， aes（intercept = b0，slope = b1），linetype =dashed）+ 
 scale_x_continuous（limits = c（0,60），name =x）+ 
 scale_y_continuous（limits = c 1000，4200），name =y）
  

解决方案

这是一个'数据形状'问题。如果您希望 ggplot 在单个调用中绘制多个对象，则对象参数（如截取和 slope ）需要是数据框的列，并且对象实例在数据进入时为行 ggplot 。

特别是在你的情况下，你需要一个有6行的数据框 - 每行一个，每行都有该行的标识和参数，如下所示：

  library（tidyverse）
 
数据块来自问题在剪贴板
 read.table（ 剪贴板，标题= T） - > yld_sum 
 
 yld_sum％>％
 select（-starts_with（cr），-se）％>％
 gather（metric，value，-coef，-Yld_class ）％>％
 spread（coef，value）％>％
 ggplot（）+ 
 geom_abline（aes（
截距= b0，
斜率= b1 ，
 linetype = if_else（metric ==pred，，dashed）），
）+ 
 scale_x_continuous（limits = c（0,60），name =x ）+ 
 scale_y_continuous（限制= c（1000,4200），name =y）+ 
 guides（linetype = F）
  

通过在连续步骤之后放置RStudio的 View >调用来探索数据整形过程（例如％>％View ）。

数据的最终形状， ） call）：

pre $ Yld_class度量b0 b1
1 1小ci.lb 2940.289 - 27.580
2 1低ci.ub 3388.164 -13.816
3 1低预计3164.226 -20.698
4 2低ci.lb 3724.180 -22.573
5 2Low ci.ub 4246.394 -6.168
6 2Low pred 3985.287 -14.371


Considering this initial data frame (yld_sum):

  coef     pred      se    ci.lb    ci.ub    cr.lb    cr.ub Yld_class
   b0 3164.226 114.256 2940.289 3388.164 2142.724 4185.728      1Low
   b1  -20.698   3.511  -27.580  -13.816  -50.520    9.124      1Low
   b0 3985.287 133.220 3724.180 4246.394 2954.998 5015.576      2Low
   b1  -14.371   4.185  -22.573   -6.168  -44.525   15.784      2Low

How can I simplify my syntax to plot the two estimated regression lines with their respective CI, and obtain the following plot?

This is my verbose code:

library(tidyverse)

yld_sum_est <- yld_sum %>% select(Yld_class, coef, pred) %>% 
  spread(coef, pred)  

yld_sum_low <- yld_sum %>% select(Yld_class, coef, ci.lb) %>% 
  spread(coef, ci.lb)

yld_sum_up <- yld_sum %>% select(Yld_class, coef, ci.ub) %>% 
  spread(coef, ci.ub)

ggplot() + 
  geom_abline(data = yld_sum_est, aes(intercept = b0, slope = b1)) +
  geom_abline(data = yld_sum_low, aes(intercept = b0, slope = b1), linetype= "dashed") +
  geom_abline(data = yld_sum_up, aes(intercept = b0, slope = b1), linetype= "dashed") +
  scale_x_continuous(limits=c(0,60), name="x") +
  scale_y_continuous(limits=c(1000, 4200), name="y") 

解决方案

It is a 'data shape' problem. If you want ggplot to draw multiple objects within a single call, the object parameters (like intercept and slope) need to be the columns of your data frame and the object instances to be the rows by the time the data enters ggplot.

Particularly in your case you need a data frame with 6 rows - one for each line, each holding the identity of the line and it's parameters, like this:

library(tidyverse)

# data block from the question is in clipboard
read.table("clipboard", header = T) -> yld_sum

yld_sum %>%
  select(-starts_with("cr"), -se) %>%
  gather(metric, value, -coef, -Yld_class) %>%
  spread(coef, value) %>% 
  ggplot() +
  geom_abline(aes(
    intercept = b0, 
    slope = b1,
    linetype = if_else(metric == "pred", "", "dashed")),
    ) +
  scale_x_continuous(limits=c(0,60), name="x") +
  scale_y_continuous(limits=c(1000, 4200), name="y") +
  guides(linetype = F)

Feel free to explore the data reshaping process by putting RStudio's View call after successive steps (like %>% View).

The final shape of the data, for illustration (after the spread() call):

  Yld_class metric       b0      b1
1      1Low  ci.lb 2940.289 -27.580
2      1Low  ci.ub 3388.164 -13.816
3      1Low   pred 3164.226 -20.698
4      2Low  ci.lb 3724.180 -22.573
5      2Low  ci.ub 4246.394  -6.168
6      2Low   pred 3985.287 -14.371

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