How to plot lines of residuals from linear regression in ggplot?-CodePudding

Data

Here is my data:

work <- structure(list(Mins_Sleep = c(300L, 540L, 540L, 480L, 480L, 480L, 
480L, 420L, 300L, 240L, 480L, 300L, 420L, 360L, 390L, 405L, 420L, 
360L, 420L, 350L, 420L, 450L, 445L, 480L, 300L, 400L, 310L, 390L, 
350L, 450L, 390L, 390L, 510L, 452L, 310L, 360L, 500L, 360L, 420L, 
420L, 382L, 430L, 393L, 240L, 400L, 480L, 450L, 450L, 359L, 420L, 
361L, 360L, 480L, 570L, 340L, 450L, 420L, 180L, 510L, 420L, 425L, 
407L, 360L, 510L, 360L, 368L, 410L, 360L, 510L, 436L, 291L, 420L, 
240L, 300L, 420L, 420L, 424L, 520L, 240L, 390L, 480L, 300L, 480L, 
390L, 300L, 360L, 420L, 360L, 480L, 330L, 375L, 390L, 458L, 517L, 
435L, 482L, 300L, 589L, 440L, 391L, 420L, 545L, 574L, 529L, 420L, 
579L, 317L, 532L, 506L, 346L, 559L, 402L, 444L, 450L, 405L, 480L, 
438L, 477L, 480L, 300L, 420L, 240L, 390L, 420L, 420L, 300L, 360L, 
360L, 360L, 585L, 300L, 420L, 420L, 304L, 420L, 420L, 300L, 467L, 
230L, 460L, 400L, 513L, 498L, 240L, 360L, 320L, 420L, 224L, 396L, 
360L, 300L, 360L, 360L, 480L, 412L, 822L, 300L, 300L, 416L, 360L, 
420L, 430L, 370L, 360L, 420L, 420L, 420L, 173L, 360L, 319L, 420L, 
360L, 415L, 540L, 370L, 504L, 480L, 525L, 300L, 480L, 360L, 360L, 
360L, 420L, 420L, 570L, 420L, 360L, 420L, 420L, 450L, 360L, 450L, 
480L, 420L, 360L, 420L, 420L, 420L, 360L, 420L, 420L, 420L, 480L, 
300L, 360L, 420L, 485L, 420L, 462L, 331L, 483L, 494L, 360L, 514L, 
349L, 408L, 480L, 480L, 480L, 447L, 363L, 442L, 441L, 407L, 522L, 
300L, 300L, 405L, 405L, 471L, 90L, 449L, 420L, 450L, 447L, 499L, 
401L, 429L, 430L, 381L, 407L, 440L, 388L, 414L, 500L, 468L, 287L, 
420L, 423L, 420L, 355L, 477L, 495L, 455L, 300L, 427L, 471L, 361L, 
300L, 360L, 480L, 367L, 357L, 443L, 438L, 390L, 240L, 393L, 350L, 
427L, 480L, 400L, 706L, 359L, 257L, 481L, 60L, 400L, 465L, 434L, 
460L, 435L, 519L, 365L), Time_Wake = c(500L, 715L, 600L, 600L, 
700L, 600L, 700L, 500L, 500L, 500L, 500L, 700L, 645L, 700L, 630L, 
645L, 700L, 600L, 700L, 550L, 700L, 730L, 725L, 800L, 600L, 640L, 
600L, 730L, 650L, 830L, 630L, 630L, 830L, 722L, 641L, 800L, 720L, 
700L, 700L, 700L, 622L, 710L, 632L, 400L, 640L, 700L, 730L, 830L, 
659L, 800L, 701L, 700L, 900L, 930L, 650L, 930L, 700L, 300L, 830L, 
800L, 705L, 647L, 800L, 830L, 830L, 838L, 650L, 500L, 830L, 800L, 
321L, 700L, 400L, 400L, 700L, 600L, 604L, 700L, 730L, 700L, 700L, 
500L, 700L, 630L, 500L, 600L, 700L, 600L, 830L, 600L, 500L, 600L, 
738L, 758L, 645L, 702L, 500L, 849L, 656L, 831L, 700L, 805L, 834L, 
849L, 407L, 739L, 717L, 852L, 826L, 446L, 919L, 842L, 754L, 900L, 
845L, 900L, 848L, 757L, 927L, 500L, 700L, 430L, 430L, 600L, 700L, 
300L, 600L, 600L, 400L, 945L, 500L, 700L, 700L, 504L, 700L, 700L, 
400L, 747L, 200L, 740L, 441L, 833L, 815L, 400L, 600L, 600L, 700L, 
344L, 636L, 600L, 300L, 600L, 600L, 700L, 822L, 360L, 600L, 945L, 
656L, 400L, 700L, 744L, 710L, 600L, 700L, 700L, 700L, 253L, 600L, 
819L, 700L, 600L, 655L, 835L, 848L, 654L, 630L, 745L, 300L, 730L, 
700L, 700L, 700L, 700L, 200L, 700L, 500L, 500L, 700L, 700L, 730L, 
700L, 830L, 825L, 700L, 600L, 700L, 700L, 700L, 700L, 700L, 700L, 
700L, 700L, 300L, 500L, 700L, 705L, 700L, 723L, 531L, 841L, 845L, 
744L, 742L, 830L, 648L, 630L, 645L, 634L, 727L, 603L, 648L, 721L, 
647L, 842L, 750L, 650L, 645L, 645L, 751L, 130L, 729L, 830L, 730L, 
727L, 709L, 641L, 709L, 710L, 621L, 747L, 720L, 628L, 654L, 633L, 
548L, 428L, 700L, 733L, 700L, 556L, 757L, 815L, 735L, 500L, 707L, 
751L, 601L, 500L, 600L, 800L, 607L, 557L, 723L, 718L, 630L, 400L, 
633L, 550L, 607L, 621L, 640L, 636L, 559L, 417L, 701L, 100L, 640L, 
629L, 614L, 545L, 615L, 550L, 755L)), class = "data.frame", row.names = c(NA, 
-285L))

Problem

I consistently see this kind of plot, but I never have found how to make it in R. When I google "scatterplot with residuals" or anything to that effect, I have problems finding this:

The best I know how to do so far is to make a standalone regression plot in ggplot:

library(tidyverse)

ggplot(work,
       aes(x=Time_Wake,
           y=Mins_Sleep)) 
  geom_point(alpha=.4) 
  geom_smooth(method = "lm",
              se=F,
              color = "purple") 
  labs(title="An Attempt at Regression Plotting",
       x="Onset Wake Time",
       y="Minutes of Sleep")

However, I still don't know how to actually make the lines that represent the distance between raw values and the line of fit. How do I make this in R? I feel like this has to be something easy and I'm just not finding it.

CodePudding user response：

I would fit the lm to the data, then use broom::augment() to obtain the fitted values. You can then supply those to geom_segment.

library(ggplot2)
library(magrittr) # for pipes or load tidyverse
library(broom)

work %>% 
  lm(Mins_Sleep ~ Time_Wake, data = .) %>% 
  augment() %>% 
  ggplot(aes(Time_Wake, Mins_Sleep))   
  geom_point()   
  geom_smooth(method = "lm", se = FALSE, color = "purple")   
  geom_segment(aes(xend = Time_Wake, yend = .fitted))  
  labs(title = "An Attempt at Regression Plotting",
       x = "Onset Wake Time",
       y = "Minutes of Sleep")

Result:

This is what augment generates:

work %>% 
  lm(Mins_Sleep ~ Time_Wake, data = .) %>% 
  augment()

Result:

A tibble: 285 × 8
   Mins_Sleep Time_Wake .fitted .resid    .hat .sigma .cooksd .std.resid
        <int>     <int>   <dbl>  <dbl>   <dbl>  <dbl>   <dbl>      <dbl>
 1        300       500    353.  -52.5 0.00774   68.5 0.00232     -0.771
 2        540       715    429.  111.  0.00408   68.2 0.00543      1.63 
 3        540       600    388.  152.  0.00407   67.9 0.0101       2.23 
 4        480       600    388.   92.0 0.00407   68.3 0.00371      1.35 
 5        480       700    423.   56.5 0.00382   68.5 0.00131      0.828
 6        480       600    388.   92.0 0.00407   68.3 0.00371      1.35 
 7        480       700    423.   56.5 0.00382   68.5 0.00131      0.828
 8        420       500    353.   67.5 0.00774   68.4 0.00382      0.990
 9        300       500    353.  -52.5 0.00774   68.5 0.00232     -0.771
10        240       500    353. -113.  0.00774   68.2 0.0106      -1.65 
# … with 275 more rows