1  Extract Model from geom_smooth model

Author

Chrissy h Roberts

Example code to extract x and y values from a geom_smooth.

The practical example used her is to take discrete values of a value n at monthly intervals across a year. The geom smooth is used to draw a smoothed line across the chart. Then the values of n are extracted from the model before being floored so that we can estimate a new value for each month.

This would be helpful where you have a situation in which there’s a month missing in the middle, or where there’s an outlier or two. You could achieve the same thing using a regression, but this is a useful approach that ultimately leans back on to polynomial regressions.

library(tidyverse)
library(knitr)

Make a dummy data set with 11 observations and increasing number

df<-tibble(
           month=1:11,
           n = c(100,140,200,260,360,470,560,630,770,990,1100)
           )
kable(df)
month n
1 100
2 140
3 200
4 260
5 360
6 470
7 560
8 630
9 770
10 990
11 1100

Make a ggplot object with a geom_smooth()

p <- ggplot(df, aes(month, n))+geom_smooth(method = "lm") +geom_point()
p

Use ggplot_build to find the code behind the curve - the first element of data in the result is the geom_smooth curve

pp<- ggplot_build(p)$data[[1]] %>% 
  rename (
    month = x,
    n = y
  )
kable(pp)
month n ymin ymax se flipped_aes PANEL group colour fill linewidth linetype weight alpha
1.000000 5.00000 -80.699424 90.69942 37.88394 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
1.126582 17.71577 -66.439648 101.87118 37.20140 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
1.253165 30.43153 -52.191714 113.05477 36.52410 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
1.379747 43.14730 -37.956292 124.25088 35.85232 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
1.506329 55.86306 -23.734100 135.46022 35.18640 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
1.632911 68.57883 -9.525902 146.68355 34.52666 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
1.759494 81.29459 4.667484 157.92170 33.87347 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
1.886076 94.01036 18.845183 169.17553 33.22721 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.012658 106.72612 33.006263 180.44598 32.58830 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.139241 119.44189 47.149728 191.73405 31.95718 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.265823 132.15765 61.274512 203.04079 31.33431 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.392405 144.87342 75.379479 214.36736 30.72021 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.518987 157.58918 89.463417 225.71495 30.11540 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.645570 170.30495 103.525033 237.08486 29.52046 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.772152 183.02071 117.562952 248.47848 28.93599 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
2.898734 195.73648 131.575706 259.89725 28.36265 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.025317 208.45224 145.561741 271.34275 27.80112 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.151899 221.16801 159.519403 282.81662 27.25213 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.278481 233.88377 173.446945 294.32060 26.71646 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.405063 246.59954 187.342517 305.85656 26.19492 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.531646 259.31530 201.204173 317.42644 25.68837 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.658228 272.03107 215.029868 329.03227 25.19772 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.784810 284.74684 228.817460 340.67621 24.72391 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
3.911392 297.46260 242.564717 352.36048 24.26794 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.037975 310.17837 256.269325 364.08741 23.83081 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.164557 322.89413 269.928894 375.85937 23.41360 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.291139 335.60990 283.540975 387.67882 23.01738 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.417721 348.32566 297.103075 399.54825 22.64325 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.544304 361.04143 310.612678 411.47018 22.29233 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.670886 373.75719 324.067267 423.44712 21.96573 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.797468 386.47296 337.464355 435.48156 21.66454 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
4.924051 399.18872 350.801513 447.57593 21.38985 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.050633 411.90449 364.076404 459.73257 21.14269 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.177215 424.62025 377.286822 471.95368 20.92402 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.303797 437.33602 390.430726 484.24131 20.73476 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.430380 450.05178 403.506283 496.59728 20.57571 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.556962 462.76755 416.511896 509.02320 20.44759 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.683544 475.48331 429.446245 521.52038 20.35096 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.810127 488.19908 442.308311 534.08985 20.28629 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
5.936709 500.91484 455.097402 546.73229 20.25387 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.063291 513.63061 467.813167 559.44805 20.25387 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.189873 526.34638 480.455607 572.23714 20.28629 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.316456 539.06214 493.025071 585.09921 20.35096 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.443038 551.77791 505.522253 598.03356 20.44759 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.569620 564.49367 517.948170 611.03917 20.57571 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.696203 577.20944 530.304144 624.11473 20.73476 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.822785 589.92520 542.591770 637.25863 20.92402 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
6.949367 602.64097 554.812883 650.46905 21.14269 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.075949 615.35673 566.969522 663.74394 21.38985 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.202532 628.07250 579.063895 677.08110 21.66454 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.329114 640.78826 591.098338 690.47819 21.96573 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.455696 653.50403 603.075278 703.93278 22.29233 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.582279 666.21979 614.997206 717.44238 22.64325 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.708861 678.93556 626.866636 731.00448 23.01738 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.835443 691.65132 638.686086 744.61656 23.41360 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
7.962025 704.36709 650.458047 758.27613 23.83081 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.088608 717.08285 662.184970 771.98074 24.26794 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.215190 729.79862 673.869244 785.72799 24.72391 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.341772 742.51438 685.513182 799.51559 25.19772 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.468354 755.23015 697.119018 813.34128 25.68837 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.594937 767.94591 708.688892 827.20294 26.19492 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.721519 780.66168 720.224851 841.09851 26.71646 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.848101 793.37745 731.728840 855.02605 27.25213 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
8.974683 806.09321 743.202708 868.98371 27.80112 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.101266 818.80898 754.648204 882.96975 28.36265 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.227848 831.52474 766.066979 896.98250 28.93599 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.354430 844.24051 777.460591 911.02042 29.52046 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.481013 856.95627 788.830505 925.08204 30.11540 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.607595 869.67204 800.178098 939.16598 30.72021 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.734177 882.38780 811.504661 953.27094 31.33431 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.860760 895.10357 822.811408 967.39573 31.95718 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
9.987342 907.81933 834.099474 981.53919 32.58830 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
10.113924 920.53510 845.369924 995.70027 33.22721 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
10.240506 933.25086 856.623755 1009.87797 33.87347 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
10.367089 945.96663 867.861900 1024.07136 34.52666 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
10.493671 958.68239 879.085233 1038.27955 35.18640 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
10.620253 971.39816 890.294571 1052.50175 35.85232 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
10.746835 984.11392 901.490680 1066.73717 36.52410 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
10.873418 996.82969 912.674276 1080.98510 37.20140 FALSE 1 -1 #3366FF grey60 1 1 1 0.4
11.000000 1009.54545 923.846030 1095.24488 37.88394 FALSE 1 -1 #3366FF grey60 1 1 1 0.4

Plot the data from what we just extracted, to prove the principle.

ggplot() + 
  geom_smooth(aes(month, n), df,color="blue",lwd=2)+
  geom_line(aes(month, n), pp,color="red",lwd=1)

To find a start of month amount, find the floor of each integer on x axis

pp$month<-floor(pp$month)

Remove replicates and select the values of x and y

pp <- pp %>% mutate(dup = duplicated(month)) %>% 
             filter(dup==FALSE) %>% 
             select(month,n)

df<-bind_cols(df,pp[,2])
kable(df)
month n …3
1 100 5.0000
2 140 106.7261
3 200 208.4522
4 260 310.1784
5 360 411.9045
6 470 513.6306
7 560 615.3567
8 630 717.0829
9 770 818.8090
10 990 920.5351
11 1100 1009.5455