I want to plot multiple lines together in a single plot using geom_abline, but each line has unique-CodePudding

So I have a bunch of studies and I want to plot the regression lines obtained from all of them.

I have a set of slope and intercept values, and x limits for each line, but I need to set the x limits for each line separately as they're all different...

I've racked my brain for days but can't figure out how I can achieve this using ggplot, if I can give a list of xlim values to pass to the slope and intercept values?

Here's a subset of my dataframe...

empirical_studies <- tibble(slope = c(-1.52, -1.42, -1.56, -1.57, -1.57, -1.68, -1.67, -1.6, -1.73, -1.69, -1.79), intercept = c(12.07, 11.29, 12.21, 12.26, 12.14, 12.5, 12.58, 12.28, 12.72, 12.53, 12.29), xmin = c(9.00, 6.00, 6.00, 6.00, 6.00, 9.00, 7.00, 7.00, 7.00, 7.00, 18.00), xmax = c(26.00  59.00, 53.00, 53.00, 53.00, 63.00, 67.00, 64.00, 64.00, 64.00, 50.00))

Would really appreciate any help!

I didn't really try much to be honest, I know I can't pass in a set of xlim values... I tried writing a loop (below) but this doesn't seem to work either:

plot = ggplot(empirical_studies, aes(x=empirical_studies$diameter_stem_average, y=empirical_studies$density))

for (i in 1:(length(empirical_studies)-1)){ 
    plot=plot geom_abline(slope = empirical_studies$slope[i], intercept = empirical_studies$intercept[i])  
      xlim(empirical_studies$min_dq[i], empirical_studies$max_dq[i])
    i <- i   1
}
print(plot)

CodePudding user response：

If you want to plot separate line segments you can use geom_line() with a start and end point.

Since you already have the x values of the min and max, you can simply use the slope and intercept to calculate the y value for those points and then plot it. For these manipulations you need to uniquely identify each curve so I just applied a number for each, but you could use more informative naming depending on your data.

I show two options below depending on if you want to overplot everything or if you want separate plots for each. For the faceted option, you can specify sacles = "free" to let each facet rescale the axes individually. Otherwise it picks the widest range that captures all the data.

library(tidyverse)

empirical_studies <- tibble(slope = c(-1.52, -1.42, -1.56, -1.57, -1.57, -1.68, -1.67, -1.6, -1.73, -1.69, -1.79), 
                            intercept = c(12.07, 11.29, 12.21, 12.26, 12.14, 12.5, 12.58, 12.28, 12.72, 12.53, 12.29), 
                            xmin = c(9.00, 6.00, 6.00, 6.00, 6.00, 9.00, 7.00, 7.00, 7.00, 7.00, 18.00), 
                            xmax = c(26.00, 59.00, 53.00, 53.00, 53.00, 63.00, 67.00, 64.00, 64.00, 64.00, 50.00))

# generate list of start and end points for each line
d <- empirical_studies %>% 
  mutate(study_id = row_number()) %>% 
  mutate(across(starts_with("x"), ~ slope * .   intercept, .names = "y{str_sub(.col, 2, 4)}")) %>%
  pivot_longer(starts_with(c("x", "y"))) %>% 
  separate(name, into = c("var", "position"), sep = 1) %>% 
  pivot_wider(names_from = var)

d
#> # A tibble: 22 × 6
#>    slope intercept study_id position     x      y
#>    <dbl>     <dbl>    <int> <chr>    <dbl>  <dbl>
#>  1 -1.52      12.1        1 min          9  -1.61
#>  2 -1.52      12.1        1 max         26 -27.4 
#>  3 -1.42      11.3        2 min          6   2.77
#>  4 -1.42      11.3        2 max         59 -72.5 
#>  5 -1.56      12.2        3 min          6   2.85
#>  6 -1.56      12.2        3 max         53 -70.5 
#>  7 -1.57      12.3        4 min          6   2.84
#>  8 -1.57      12.3        4 max         53 -71.0 
#>  9 -1.57      12.1        5 min          6   2.72
#> 10 -1.57      12.1        5 max         53 -71.1 
#> # … with 12 more rows

# plotted together
d %>% 
  ggplot(aes(x, y))  
  geom_line(aes(color = factor(study_id)))


# plotted separately
d %>% 
  ggplot(aes(x, y))  
  geom_line()  
  facet_wrap(~study_id) # can add scales = "free" to scale axes separately

^{Created on 2022-11-09 with reprex v2.0.2}