在每个图中绘制回归线方程（2的阶数）

Question

I have been working on some code to iterate creating scatter plots based on data from a data frame and exporting each scatter plot with an 2nd order regression line to a single PDF file with each page as its own scatter plot. What I would like to do is produce the regression line equation and place it in the top left margin of the scatter plot for each iteration.

library(gridExtra)
library(purrr)
library(tidyverse)

plot_5 <-
    Infil_Data2 %>% 
    split(.$Site_ID) %>% 
    map2(names(.),
         ~ggplot(.x, aes(Sqrt_Time.x, Cal_Vol_cm)) + 
         geom_point() +
         labs(title = paste(.y)) +
         theme(plot.title = element_text(hjust = 0.5)) + 
         stat_smooth(mapping = aes(x = Sqrt_Time.x, y = Cal_Vol_cm),
                     method = "lm", se = FALSE, 
                     formula = y ~ poly(x, 2, raw = TRUE),
                     color = "red") +
         theme(plot.margin = unit(c(1, 5, 1, 1), "cm")))


    pdf("allplots5.pdf", onefile = TRUE)
    walk(plot_5, print)
    dev.off()

Here is a sample of the Infil_Data2 dataframe that I am using:

Infil_Data2 <-
    structure(list(Time = c(0L, 30L, 60L, 90L, 120L, 150L, 180L, 
    210L, 240L, 270L, 300L, 0L, 30L, 60L, 90L, 120L, 150L, 180L, 
    210L, 240L, 270L, 300L, 0L, 30L, 60L, 90L, 120L, 150L, 180L, 
    210L, 240L, 270L, 300L), Site_ID = c("H1", "H1", "H1", "H1", 
    "H1", "H1", "H1", "H1", "H1", "H1", "H1", "H2", "H2", "H2", "H2", 
    "H2", "H2", "H2", "H2", "H2", "H2", "H2", "H3", "H3", "H3", "H3", 
    "H3", "H3", "H3", "H3", "H3", "H3", "H3"), Vol_mL = c(63, 62, 
    60, 59, 58, 56, 54, 52.5, 50, 48.5, 46.5, 82, 77, 73, 68, 65, 
    51, 56, 52, 47.5, 42.5, 37.5, 69, 67, 65, 63, 61, 60, 58, 56, 
    54, 51.5, 49), Sqrt_Time.x = c(0, 5.477225575, 7.745966692, 9.486832981, 
    10.95445115, 12.24744871, 13.41640786, 14.49137675, 15.49193338, 
    16.43167673, 17.32050808, 0, 5.477225575, 7.745966692, 9.486832981, 
    10.95445115, 12.24744871, 13.41640786, 14.49137675, 15.49193338, 
    16.43167673, 17.32050808, 0, 5.477225575, 7.745966692, 9.486832981, 
    10.95445115, 12.24744871, 13.41640786, 14.49137675, 15.49193338, 
    16.43167673, 17.32050808), Cal_Vol_cm = c(0, 0.124339799, 0.373019398, 
    0.497359197, 0.621698996, 0.870378595, 1.119058194, 1.305567893, 
    1.616417391, 1.80292709, 2.051606688, 0, 0.621698996, 1.119058194, 
    1.74075719, 2.113776588, 3.854533778, 3.232834782, 3.730193979, 
    4.289723076, 4.911422072, 5.533121068, 0, 0.248679599, 0.497359197, 
    0.746038796, 0.994718394, 1.119058194, 1.367737792, 1.616417391, 
    1.865096989, 2.175946488, 2.486795986)), row.names = c(NA, 33L
    ), class = "data.frame")

Answer 1

Based off of what you did and the Adding Regression Line Equation and R2 on graph post, the code below produces a pdf with one plot per page, and with the equation in the plots. The equations appear in the same relative place on the plot even though the scales are changing. The original code in the question was very close and the code below only adds the call to stat_smooth_func .

# Input data.
Infil_Data2 <-
structure(list(Time = c(0L, 30L, 60L, 90L, 120L, 150L, 180L, 
210L, 240L, 270L, 300L, 0L, 30L, 60L, 90L, 120L, 150L, 180L, 
210L, 240L, 270L, 300L, 0L, 30L, 60L, 90L, 120L, 150L, 180L, 
210L, 240L, 270L, 300L), Site_ID = c("H1", "H1", "H1", "H1", 
"H1", "H1", "H1", "H1", "H1", "H1", "H1", "H2", "H2", "H2", "H2", 
"H2", "H2", "H2", "H2", "H2", "H2", "H2", "H3", "H3", "H3", "H3", 
"H3", "H3", "H3", "H3", "H3", "H3", "H3"), Vol_mL = c(63, 62, 
60, 59, 58, 56, 54, 52.5, 50, 48.5, 46.5, 82, 77, 73, 68, 65, 
51, 56, 52, 47.5, 42.5, 37.5, 69, 67, 65, 63, 61, 60, 58, 56, 
54, 51.5, 49), Sqrt_Time.x = c(0, 5.477225575, 7.745966692, 9.486832981, 
10.95445115, 12.24744871, 13.41640786, 14.49137675, 15.49193338, 
16.43167673, 17.32050808, 0, 5.477225575, 7.745966692, 9.486832981, 
10.95445115, 12.24744871, 13.41640786, 14.49137675, 15.49193338, 
16.43167673, 17.32050808, 0, 5.477225575, 7.745966692, 9.486832981, 
10.95445115, 12.24744871, 13.41640786, 14.49137675, 15.49193338, 
16.43167673, 17.32050808), Cal_Vol_cm = c(0, 0.124339799, 0.373019398, 
0.497359197, 0.621698996, 0.870378595, 1.119058194, 1.305567893, 
1.616417391, 1.80292709, 2.051606688, 0, 0.621698996, 1.119058194, 
1.74075719, 2.113776588, 3.854533778, 3.232834782, 3.730193979, 
4.289723076, 4.911422072, 5.533121068, 0, 0.248679599, 0.497359197, 
0.746038796, 0.994718394, 1.119058194, 1.367737792, 1.616417391, 
1.865096989, 2.175946488, 2.486795986)), row.names = c(NA, 33L
), class = "data.frame")

Plotting code

# For the "stat_smooth_func", use the Laurae package.
# devtools::install_github("Laurae2/Laurae")

library(gridExtra)
library(purrr)
library(tidyverse)
library(Laurae)

plot_5 <-
    Infil_Data2 %>% 
    split(.$Site_ID) %>% 
    map2(names(.),
         ~ggplot(.x, aes(Sqrt_Time.x, Cal_Vol_cm)) + 
         geom_point() +
         labs(title = paste(.y)) +
         theme(plot.title = element_text(hjust = 0.5)) + 
         stat_smooth(mapping = aes(x = Sqrt_Time.x, y = Cal_Vol_cm),
                     method = "lm", se = FALSE, 
                     formula = y ~ poly(x, 2, raw = TRUE),
                     color = "red") +
         theme(plot.margin = unit(c(1, 5, 1, 1), "cm")) +
         stat_smooth_func(geom="text", method = "lm", hjust=0, parse=TRUE))


pdf("allplots5.pdf", onefile = TRUE)
walk(plot_5, print)
dev.off()