Highlight several specific points using ggplot 2 - r

Question

My other Questions was marked as an duplicate (I used a common example, not my real data), therefore I opened a new one.
So again, I hope this time it becomes clear, what my problem is.
I have following data frame called "sample" (it´s extracted from my real dataframe):
county testscr str
1 Alameda 690.80 17.88991
2 Butte 661.20 21.52466
3 Butte 643.60 18.69723
4 Butte 647.70 17.35714
5 Butte 640.85 18.67133
6 Fresno 605.55 21.40625
7 San Joaquin 606.75 19.50000
8 Kern 609.00 20.89412
9 Fresno 612.50 19.94737
10 Sacramento 612.65 20.80556
11 Merced 615.75 21.23809
12 Fresno 616.30 21.00000
13 Tulare 616.30 20.60000
14 Tulare 616.30 20.00822
15 Tulare 616.45 18.02778
16 Tulare 617.35 20.25196
17 Kern 618.05 16.97787
18 Kern 618.30 16.50980
19 Los Angeles 619.80 22.70402
20 Kern 620.30 19.91111
I have plotted the variable testscr against str and added a linear Regression line to the plot using ggplot
ggplot(data=sample,aes(x=str,y=testscr))+
geom_point()+
geom_smooth(method="lm")
Now I want to highlight/color all points, which have "Butte", "Los Angeles" and "Fresno" as County value. All three of them should have different Colors and the rest of the points should be black.
dput(sample)
structure(list(county = structure(c(1L, 2L, 2L, 2L, 2L, 6L, 29L,
11L, 6L, 25L, 19L, 6L, 42L, 42L, 42L, 42L, 11L, 11L, 15L, 11L,
9L, 42L, 11L, 42L, 19L, 42L, 20L, 11L, 42L, 42L, 28L, 20L, 15L,
20L, 27L, 15L, 19L, 6L, 31L, 11L, 44L, 19L, 11L, 11L, 24L, 15L,
33L, 11L, 11L, 33L, 15L, 16L, 20L, 32L, 15L, 15L, 15L, 25L, 20L,
44L, 42L, 25L, 22L, 12L, 12L, 11L, 15L, 12L, 28L, 37L, 11L, 15L,
12L, 19L, 32L, 27L, 4L, 8L, 36L, 36L, 44L, 6L, 19L, 19L, 6L,
27L, 24L, 15L, 11L, 42L, 25L, 13L, 33L, 2L, 31L, 42L, 15L, 9L,
9L, 15L, 11L, 11L, 39L, 18L, 27L, 26L, 15L, 2L, 11L, 44L, 6L,
15L, 16L, 22L, 42L, 33L, 9L, 28L, 35L, 42L, 40L, 42L, 6L, 20L,
42L, 24L, 37L, 15L, 40L, 31L, 36L, 11L, 38L, 43L, 31L, 5L, 19L,
29L, 6L, 25L, 38L, 19L, 44L, 8L, 8L, 28L, 13L, 8L, 44L, 40L,
25L, 29L, 36L, 38L, 6L, 22L, 22L, 12L, 42L, 28L, 35L, 19L, 39L,
28L, 15L, 11L, 39L, 28L, 27L, 22L, 37L, 35L, 40L, 43L, 36L, 8L,
4L, 43L, 23L, 37L, 37L, 38L, 35L, 8L, 42L, 7L, 37L, 14L, 9L,
14L, 22L, 37L, 32L, 8L, 39L, 35L, 11L, 28L, 34L, 24L, 11L, 33L,
9L, 29L, 40L, 8L, 35L, 15L, 21L, 42L, 11L, 25L, 26L, 28L, 39L,
6L, 4L, 36L, 29L, 33L, 12L, 38L, 29L, 23L, 26L, 5L, 27L, 35L,
21L, 31L, 12L, 35L, 3L, 17L, 28L, 33L, 39L, 21L, 8L, 37L, 31L,
40L, 22L, 27L, 15L, 8L, 27L, 30L, 33L, 5L, 15L, 10L, 32L, 16L,
36L, 37L, 21L, 42L, 42L, 43L, 15L, 19L, 31L, 33L, 37L, 11L, 31L,
43L, 23L, 38L, 14L, 35L, 42L, 15L, 33L, 15L, 37L, 11L, 35L, 23L,
36L, 37L, 16L, 8L, 5L, 37L, 40L, 37L, 37L, 23L, 34L, 8L, 27L,
23L, 5L, 22L, 7L, 31L, 32L, 27L, 37L, 33L, 32L, 28L, 22L, 32L,
34L, 7L, 37L, 21L, 12L, 28L, 14L, 44L, 43L, 36L, 37L, 28L, 37L,
8L, 11L, 42L, 33L, 11L, 12L, 28L, 28L, 42L, 28L, 22L, 15L, 15L,
17L, 33L, 40L, 8L, 28L, 35L, 11L, 33L, 22L, 5L, 5L, 23L, 5L,
8L, 15L, 23L, 23L, 37L, 31L, 21L, 16L, 30L, 14L, 6L, 37L, 37L,
31L, 5L, 23L, 28L, 5L, 21L, 37L, 8L, 41L, 21L, 23L, 44L, 41L,
35L, 21L, 8L, 37L, 28L, 17L, 33L, 15L, 37L, 20L, 37L, 33L, 37L,
37L, 38L, 17L, 32L, 37L, 17L, 34L, 31L, 35L, 34L, 34L, 4L, 32L,
17L, 33L, 34L, 33L, 32L, 28L, 31L, 17L, 17L, 4L, 28L, 31L, 4L,
4L, 31L, 32L, 31L, 33L, 31L, 33L, 44L, 45L, 45L), .Label = c("Alameda",
"Butte", "Calaveras", "Contra Costa", "El Dorado", "Fresno",
"Glenn", "Humboldt", "Imperial", "Inyo", "Kern", "Kings", "Lake",
"Lassen", "Los Angeles", "Madera", "Marin", "Mendocino", "Merced",
"Monterey", "Nevada", "Orange", "Placer", "Riverside", "Sacramento",
"San Benito", "San Bernardino", "San Diego", "San Joaquin", "San Luis Obispo",
"San Mateo", "Santa Barbara", "Santa Clara", "Santa Cruz", "Shasta",
"Siskiyou", "Sonoma", "Stanislaus", "Sutter", "Tehama", "Trinity",
"Tulare", "Tuolumne", "Ventura", "Yuba"), class = "factor"),
testscr = c(690.8, 661.2, 643.6, 647.7, 640.85, 605.55, 606.75,
609, 612.5, 612.65, 615.75, 616.3, 616.3, 616.3, 616.45,
617.35, 618.05, 618.3, 619.8, 620.3, 620.5, 621.4, 621.75,
622.05, 622.6, 623.1, 623.2, 623.45, 623.6, 624.15, 624.55,
624.95, 625.3, 625.85, 626.1, 626.8, 626.9, 627.1, 627.25,
627.3, 628.25, 628.4, 628.55, 628.65, 628.75, 629.8, 630.35,
630.4, 630.55, 630.55, 631.05, 631.4, 631.85, 631.9, 631.95,
632, 632.2, 632.25, 632.45, 632.85, 632.95, 633.05, 633.15,
633.65, 633.9, 634, 634.05, 634.1, 634.1, 634.15, 634.2,
634.4, 634.55, 634.7, 634.9, 634.95, 635.05, 635.2, 635.45,
635.6, 635.6, 635.75, 635.95, 636.1, 636.5, 636.6, 636.7,
636.9, 636.95, 637, 637.1, 637.35, 637.65, 637.95, 637.95,
638, 638.2, 638.3, 638.3, 638.35, 638.55, 638.7, 639.25,
639.3, 639.35, 639.5, 639.75, 639.8, 639.85, 639.9, 640.1,
640.15, 640.5, 640.75, 640.9, 641.1, 641.45, 641.45, 641.55,
641.8, 642.2, 642.2, 642.4, 642.75, 643.05, 643.2, 643.25,
643.4, 643.4, 643.5, 643.5, 643.7, 643.7, 644.2, 644.2, 644.4,
644.45, 644.45, 644.5, 644.55, 644.7, 644.95, 645.1, 645.25,
645.55, 645.55, 645.6, 645.75, 645.75, 646, 646.2, 646.35,
646.4, 646.5, 646.55, 646.7, 646.9, 646.95, 647.05, 647.25,
647.3, 647.6, 647.6, 648, 648.2, 648.25, 648.35, 648.7, 648.95,
649.15, 649.3, 649.5, 649.7, 649.85, 650.45, 650.55, 650.6,
650.65, 650.9, 650.9, 651.15, 651.2, 651.35, 651.4, 651.45,
651.8, 651.85, 651.9, 652, 652.1, 652.1, 652.3, 652.3, 652.35,
652.4, 652.4, 652.5, 652.85, 653.1, 653.4, 653.5, 653.55,
653.55, 653.7, 653.8, 653.85, 653.95, 654.1, 654.2, 654.2,
654.3, 654.6, 654.85, 654.85, 654.9, 655.05, 655.05, 655.05,
655.2, 655.3, 655.35, 655.35, 655.4, 655.55, 655.7, 655.8,
655.85, 656.4, 656.5, 656.55, 656.65, 656.7, 656.8, 656.8,
657, 657, 657.15, 657.4, 657.5, 657.55, 657.65, 657.75, 657.8,
657.9, 658, 658.35, 658.6, 658.8, 659.05, 659.15, 659.35,
659.4, 659.4, 659.8, 659.9, 660.05, 660.1, 660.2, 660.3,
660.75, 660.95, 661.35, 661.45, 661.6, 661.6, 661.85, 661.85,
661.85, 661.9, 661.9, 661.95, 662.4, 662.4, 662.45, 662.5,
662.55, 662.55, 662.65, 662.7, 662.75, 662.9, 663.35, 663.45,
663.5, 663.85, 663.85, 663.9, 664, 664, 664.15, 664.15, 664.3,
664.4, 664.45, 664.7, 664.75, 664.95, 664.95, 665.1, 665.2,
665.35, 665.65, 665.9, 665.95, 666, 666.05, 666.1, 666.15,
666.15, 666.45, 666.55, 666.6, 666.65, 666.65, 666.7, 666.85,
666.85, 667.15, 667.2, 667.45, 667.45, 667.6, 668, 668.1,
668.4, 668.6, 668.65, 668.8, 668.9, 668.95, 669.1, 669.3,
669.3, 669.35, 669.35, 669.8, 669.85, 669.95, 670, 670.7,
671.25, 671.3, 671.6, 671.6, 671.65, 671.7, 671.75, 671.9,
671.9, 671.95, 672.05, 672.05, 672.3, 672.35, 672.45, 672.55,
672.7, 673.05, 673.25, 673.3, 673.55, 673.55, 673.9, 674.25,
675.4, 675.7, 676.15, 676.55, 676.6, 676.85, 676.95, 677.25,
677.95, 678.05, 678.4, 678.8, 679.4, 679.5, 679.65, 679.75,
679.8, 680.05, 680.45, 681.3, 681.3, 681.6, 681.9, 682.15,
682.45, 682.55, 682.65, 683.35, 683.4, 684.3, 684.35, 684.8,
684.95, 686.05, 686.7, 687.55, 689.1, 691.05, 691.35, 691.9,
693.95, 694.25, 694.8, 695.2, 695.3, 696.55, 698.2, 698.25,
698.45, 699.1, 700.3, 704.3, 706.75, 645, 672.2, 655.75),
str = c(17.88991, 21.52466, 18.69723, 17.35714, 18.67133,
21.40625, 19.5, 20.89412, 19.94737, 20.80556, 21.23809, 21,
20.6, 20.00822, 18.02778, 20.25196, 16.97787, 16.5098, 22.70402,
19.91111, 18.33333, 22.61905, 19.44828, 25.05263, 20.67544,
18.68235, 22.84553, 19.26667, 19.25, 20.54545, 20.60697,
21.07268, 21.53581, 19.904, 21.19407, 21.86535, 18.32965,
16.22857, 19.17857, 20.27737, 22.98614, 20.44444, 19.82085,
23.20522, 19.26697, 23.30189, 21.18829, 20.8718, 19.01749,
21.91938, 20.10124, 21.47651, 20.06579, 20.3751, 22.44648,
22.89524, 20.49797, 20, 22.25658, 21.56436, 19.47737, 17.67002,
21.94756, 21.78339, 19.14, 18.1105, 20.68242, 22.62361, 21.7865,
18.58293, 21.54545, 21.15289, 16.63333, 21.14438, 19.78182,
18.98373, 17.66767, 17.75499, 15.27273, 14, 20.59613, 16.31169,
21.12796, 17.48801, 17.88679, 19.30676, 20.89231, 21.28684,
20.1956, 24.95, 18.13043, 20, 18.72951, 18.25, 18.99257,
19.88764, 19.37895, 20.46259, 22.29157, 20.70474, 19.06005,
20.23247, 19.69012, 20.36254, 19.75422, 19.37977, 22.92351,
19.3734, 19.15516, 21.3, 18.30357, 21.07926, 18.79121, 19.62662,
19.59016, 20.87187, 21.115, 20.08452, 19.91049, 17.81285,
18.13333, 19.22221, 18.66072, 19.6, 19.28384, 22.81818, 18.80922,
21.37363, 20.02041, 21.49862, 15.42857, 22.4, 20.12709, 19.03798,
17.34216, 17.01863, 20.8, 21.15385, 18.45833, 19.14082, 19.40766,
19.56896, 21.5012, 17.52941, 16.43017, 19.79654, 17.18613,
17.61589, 20.12537, 22.16667, 19.96154, 19.03945, 15.22436,
21.14475, 19.6439, 21.04869, 20.17544, 21.3913, 20.00833,
20.29137, 17.66667, 18.22055, 20.271, 20.19895, 21.38424,
20.97368, 20, 17.15328, 22.34977, 22.17007, 18.18182, 18.95714,
19.74533, 16.42623, 16.6254, 16.38177, 20.07416, 17.99544,
19.3913, 16.42857, 16.72949, 24.41345, 18.26415, 18.95504,
21.03896, 20.74074, 18.1, 19.84615, 21.6, 22.44242, 23.01438,
17.74892, 18.28664, 19.26544, 22.66667, 19.29412, 17.36364,
19.82143, 20.43378, 21.03721, 19.92462, 19.00986, 23.82222,
19.36909, 19.82857, 15.25885, 17.16129, 21.81333, 19.07471,
25.78512, 18.21261, 18.16606, 16.97297, 21.50087, 20.6, 16.99029,
20.77954, 15.51247, 19.88506, 21.39882, 20.49751, 19.36376,
17.65957, 21.01796, 19.05565, 22.53846, 21.10787, 20.05135,
14.20176, 18.47687, 18.63542, 20.94595, 21.08548, 18.69288,
20.86808, 19.82558, 19.75, 19.5, 18.3908, 18.78676, 19.77018,
19.33333, 21.46392, 23.08492, 21.06299, 18.68687, 20.77024,
19.30556, 20.1328, 20.66964, 22.28155, 20.60027, 20.82734,
19.22492, 17.65477, 17, 16.49773, 19.78261, 22.30216, 17.73077,
20.44836, 20.37169, 20.16479, 21.61538, 20.56143, 19.95551,
21.18387, 18.81042, 20.57838, 18.32461, 18.82063, 20.81633,
20, 19.68182, 19.39018, 20.92732, 19.94437, 20.79109, 19.20354,
19.02439, 17.62058, 20.23715, 19.29374, 18.82998, 20.33949,
19.229, 17.8913, 19.51881, 19.08451, 19.93548, 18.87326,
20.14178, 23.55637, 21.46479, 19.19101, 20.1308, 25.8, 18.77774,
19.10982, 19.70109, 18.61594, 20.99721, 20, 20.98325, 21.64262,
20.02967, 19.8114, 18, 19.35811, 20.17912, 21.11986, 23.38974,
22.18182, 19.94283, 17.78826, 14.70588, 19.04077, 20.89195,
19.83851, 19.52191, 20.68622, 18.18182, 18.89224, 24.88889,
18.58064, 18.04, 17.73399, 21.45455, 19.92343, 20.33942,
22.54608, 21.10344, 18.19743, 20.10768, 19.15984, 19.54545,
20.88889, 18.3915, 19.1799, 19.39771, 21.67827, 19.28889,
20.34927, 20.96416, 19.46039, 19.28572, 20.91979, 20.90021,
20.59575, 19.375, 19.95122, 18.84973, 18.11787, 19.18341,
22, 21.58416, 20.38889, 16.2931, 18.27778, 19.37472, 18.90909,
16.40693, 15.5914, 18.70694, 18.32985, 17.90235, 18.91157,
20.32497, 20.02457, 24, 17.60784, 19.34853, 19.67846, 18.72861,
15.88235, 20.05491, 17.98825, 16.96629, 19.23937, 19.19586,
19.59906, 20.54348, 18.58848, 15.60419, 15.29304, 17.65537,
17.57976, 22.33333, 18.75, 18.10241, 20.25641, 18.80207,
18.7723, 20.40521, 18.65079, 20.70707, 22, 17.69978, 21.48329,
16.70103, 19.57567, 17.25806, 17.37526, 17.34931, 16.26229,
17.70045, 20.12881, 18.26539, 14.54214, 19.15261, 17.36574,
15.13898, 17.84266, 15.40704, 18.86534, 16.47413, 17.86263,
21.88586, 20.2, 19.0364)), class = "data.frame", row.names = c(NA,
-420L))

Answer 1

First order of business is to not use $ in aes calls.
Second, create a variable in the data the hold the 3 factor levels you want, and all other levels collapsed into an "other" level, which you'll use to assign color. The easiest way to do that is with forcats::fct_other, where you specify the levels to keep.
You can assign specific colors by name; for a quick example, I didn't, and just put the "other" color last, knowing that fct_other puts this as the last level.
library(ggplot2)
library(dplyr)
hilite_counties <- as_tibble(sample) %>%
mutate(county2 = forcats::fct_other(county, keep = c("Butte", "Los Angeles", "Fresno")))
ggplot(hilite_counties, aes(x = str, y = testscr)) +
geom_point(aes(color = county2)) +
geom_smooth(method = lm) +
scale_color_manual(values = c("red", "blue", "orange", "black"))
Edit: Taking a second pass to make the color palette more flexible. Like I said, you can assign names to colors to make sure you match the county to the color. I'll put black as the last color because "Other" is the last level, but I could assign them in any order and keep the colors and counties matched by name.
Instead of manually naming colors, I'll add another county to the highlighted group, pull a palette from Color Brewer with the length of the county2 levels minus 1, and tack on "black" as the last color, then assign names. Again, I could do this out of order as well.
hilite_counties <- as_tibble(sample) %>%
mutate(county2 = forcats::fct_other(county, keep = c("Butte", "Los Angeles", "Fresno", "Sacramento")))
county_lvls <- levels(hilite_counties$county2)
pal <- c(RColorBrewer::brewer.pal(n = length(county_lvls) - 1, name = "Dark2"), "black")
names(pal) <- county_lvls
pal
#> Butte Fresno Los Angeles Sacramento Other
#> "#1B9E77" "#D95F02" "#7570B3" "#E7298A" "black"
ggplot(hilite_counties, aes(x = str, y = testscr)) +
geom_point(aes(color = county2)) +
geom_smooth(method = lm) +
scale_color_manual(values = pal)
One note: by default, geom_smooth will make lines for each group i.e. color. I'm guessing that's not what you wanted, but you can avoid that by moving the color assignment to a separate aes that only applies to geom_point.

Answer 2

After doing :
p = ggplot(data=sample,aes(x=str, y=testscr))+
geom_point()+
geom_smooth(method="lm")
You could use dplyr library to show in red points of interest :
p + geom_point(data=filter(sample,county %in% c('Butte','Los Angeles','Fresno')),aes(x=str,y=testscr),colour='red')
Or you can add a column indicating if you want to highlight specific points :
sample$code = ifelse(sample$county %in% c('Butte','Los Angeles','Fresno'), TRUE, FALSE)
ggplot(data=sample,aes(x=str,y=testscr))+
geom_point(aes(colour=code),sample)+
geom_smooth(method="lm") +
scale_colour_manual(name = 'County', values = c("black", "red"), labels = c('Others', 'B, LA, F'))
[edit]
Or with one color by city :
city = c('Butte','Los Angeles','Fresno')
sample %>% mutate_if(is.factor, as.character) -> sample
sample$code = ifelse(sample$county %in% city, sample$county, 'others')
ggplot(data=sample,aes(x=str,y=testscr))+
geom_point(aes(colour=code),sample)+
geom_smooth(method="lm") +
scale_colour_manual(name = 'County', values = c("blue", "red","green","black"))

Answer 3

Another option would be to create two separate layers, one for the special counties and another for the rest. You can do that by subsetting the default dataset in the specification of each layer.
special_county <- c("Butte", "Los Angeles", "Fresno")
ggplot(data=sample, aes(x=str,y=testscr))+
geom_smooth(method="lm") +
geom_point(data = function(x) subset(x, !county %in% special_county)) +
geom_point(data = function(x) subset(x, county %in% special_county),
aes(color = county))
For completeness sake, you can also get the result you want by using scale_color_manual to specify the color for each of the 45 counties, but I guess that wouldn't be very elegant.