Removing (influential) cases from fitted svyglm model - r

I am running a logistic regression model using complex survey data using the survey package in R. After fitting the model, I performed regression diagnostics using the car package. I noticed outlying and influential observations that I would like to remove and then refit the model to check for their effects on the regression coefficients but my current approach is not giving me want I expect.
My dataset has about 10,000 observations. Here is sample data and code I have tried using:
library(car); library(survey)
dat <- structure(list(id = c(1009918, 1012826, 1029625, 1000926, 1027525,
1000115, 1000201, 1000202, 1000214, 1000219, 1000313, 1000324,
1000510, 1000521, 1000624, 1000708, 1000811, 1000817, 1000818,
1000906, 1000922, 1001002, 1001005, 1001401, 1001411, 1001413,
1001420, 1001424, 1001501, 1001510, 1001518, 1001526, 1001621,
1001807, 1001922, 1001926, 1002106, 1002217, 1002406, 1002416,
1002618, 1002709, 1003004, 1003017, 1003103, 1003108, 1003304,
1003319, 1003723, 1003804, 1003811, 1003819, 1004014, 1008902,
1008913, 1009011, 1009022, 1009123, 1009212, 1009215), strata = c(1,
2, 6, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), cluster = c(785,
938, 2337, 28, 2122, 3, 6, 6, 6, 6, 10, 10, 16, 16, 19, 22, 24,
24, 24, 28, 28, 33, 33, 45, 45, 45, 45, 45, 50, 50, 50, 50, 53,
60, 63, 63, 69, 74, 96, 96, 100, 102, 111, 111, 115, 115, 122,
122, 178, 193, 193, 193, 210, 755, 755, 759, 759, 762, 765, 765
), weights = c(621.921704979739, 5440.9107594311, 8450.49341643626,
2457.37241774248, 7174.79930450487, 930.492019594546, 443.253676607562,
443.253676607562, 886.507353215123, 443.253676607562, 1552.30979801343,
517.436599337811, 403.146111343943, 806.292222687886, 439.775494378883,
839.561001668328, 1210.77101540146, 403.590338467152, 403.590338467152,
457.23211170669, 914.464223413381, 584.557580338056, 584.557580338056,
233.135312658304, 233.135312658304, 233.135312658304, 466.270625316608,
233.135312658304, 287.94933168791, 287.94933168791, 287.94933168791,
287.94933168791, 2354.32022397843, 213.628591090648, 300.596873749779,
300.596873749779, 1121.27419052962, 528.482361549292, 1936.60489456861,
1291.06992971241, 282.360930726457, 3526.73915258957, 337.531162185852,
337.531162185852, 2183.63202546241, 2729.54003182802, 1035.32340123929,
1552.98510185893, 1400.62601417017, 717.92144006312, 358.96072003156,
1435.84288012624, 275.058410167952, 557.874242565598, 278.937121282799,
1687.48015279064, 1012.48809167438, 424.663883556537, 227.805527040477,
227.805527040477), age = c(20, 19, 93, 24, 18, 23, 22, 23, 24,
19, 18, 24, 20, 19, 18, 17, 19, 23, 19, 19, 21, 22, 21, 20, 23,
24, 24, 19, 21, 22, 20, 23, 21, 23, 20, 22, 23, 15, 20, 23, 24,
18, 24, 24, 15, 21, 24, 16, 22, 20, 20, 18, 21, 20, 21, 21, 24,
22, 24, 18), gender = structure(c(1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), levels = c("Male", "Female"), class = "factor"),
educ = structure(c(4L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 4L, 2L, 3L, 2L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L,
4L, 4L, 4L, 3L, 3L), levels = c("No formal education", "Primary",
"Secondary", "Tertiary"), class = "factor"), employ = structure(c(4L,
3L, 4L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 4L, 3L, 2L, 2L, 2L, 4L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L,
4L, 4L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 4L, 3L, 2L, 4L, 4L,
2L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 4L, 3L, 4L), levels = c("Unemployed",
"Employed", "Self-employed", "Other"), class = "factor"),
know = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), levels = c("No/Don't know", "Yes"), class = "factor"),
status = structure(c(2L, 4L, 2L, 1L, 5L, 3L, 2L, 2L, 2L,
4L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 5L, 4L, 2L, 5L, 5L, 4L, 3L,
2L, 3L, 2L, 2L, 2L, 2L, 4L, 3L, 3L, 4L, 3L, 2L, 2L, 3L, 1L,
2L, 1L, 1L, 2L, 2L, 2L), levels = c("1", "2", "3", "4", "5"
), class = "factor"), smoker = structure(c(2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("no",
"yes"), class = "factor")), row.names = c(81L, 4174L, 6722L,
1255L, 2712L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L,
52L, 53L, 54L, 55L), class = "data.frame")
## Create survey design object
dat_svy <- survey::svydesign(ids = ~cluster, strat = ~strata, weights = ~weights, data = dat, nest = TRUE)
options(survey.lonely.psu = "adjust")
## fit logistic regression model
mod <- survey::svyglm(formula = smoker ~ age + educ + gender + employ + educ + know + status, design = dat_svy, family = "quasibinomial")
I have tried the following:
update(mod, subset = !(rownames(dat_svy) %in% c(2, 5, 9, 13, 21))) # returns an error
update(mod, subset = -c(2, 5, 9, 13, 21)) # only removes one (first specified) observation

Related

R ggpattern custom legend

I'm not able to customise the legends I have with ggplot/ggpattern.
The "SPEED" legend should not display any pattern (just the 3 colors)
The "TYPE" legend should have custom texts instead of 0 and 1
thanks!
code:
remotes::install_github("coolbutuseless/ggpattern")
library(ggplot2)
library(ggpattern)
list <- c("1" = "circle", "0" = "none")
ggplot(tt) +
aes(x = SOUND, y = R, fill = SPEED, pattern = TYPE) +
geom_boxplot_pattern(pattern_fill = "white",
pattern_density = 0.6,
pattern_spacing = 0.012,
outlier.size = 1) +
scale_pattern_manual(values = list) +
scale_fill_brewer(palette = "Set1") +
labs(y = "R", title = "R") +
ylim(0,100) +
theme_minimal()
data sample:
structure(list(ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L,
35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L,
36L, 36L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L,
37L, 38L, 38L, 38L, 38L, 38L, 38L), .Label = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47"), class = "factor"),
TYPE = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1"), class = "factor"), SPEED = structure(c(1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L,
2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L,
2L, 3L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L,
3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("1",
"2", "3"), class = "factor"), SOUND = structure(c(1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 4L, 4L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 4L, 4L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 4L, 4L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 4L,
4L, 1L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 4L, 4L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 4L, 4L, 4L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 4L, 4L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
4L, 4L, 4L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("1", "2",
"3", "4"), class = "factor"), R = c(64, 77, 32, 75, 80, 38,
79, 57, 17, 54, 54, 50, 52, 58, 34, 30, 46, 0, 63, 51, 1,
18, 55, 18, 76, 77, 26, 60, 17, 0, 68, 62, 0, 1, 64, 43,
71, 37, 38, 53, 59, 20, 67, 55, 37, 43, 61, 58, 81, 73, 62,
54, 36, 51, 57, 27, 55, 49, 56, 75, 92, 62, 92, 92, 62, 87,
92, 62, 89, 92, 92, 92, 60, 36, 73, 14, 62, 76, 55, 45, 78,
68, 18, 92, 85, 38, 92, 86, 43, 87, 66, 40, 81, 71, 81, 36,
92, 22, 92, 82, 42)), row.names = c(NA, -101L), class = c("tbl_df",
"tbl", "data.frame"))
the editor asks to add some more text since my post is mostly code but I don';t have much else to add. most code in fact it's just the sample data that I don't know how else to produce (I tried a couple of R default sets but they didn't work)

You can change legend key labels within the relevant scale_*_*() function by passing a vector with the labels to the labels argument.
To change the legend without changing the plot you most often need override.aes in guide_legend(). Here is an example using it within scale_pattern_manual() and overriding the pattern aesthetic by setting it to "none".
ggplot(tt) +
aes(x = SOUND, y = R, fill = SPEED, pattern = TYPE) +
geom_boxplot_pattern(pattern_fill = "white",
pattern_density = 0.6,
pattern_spacing = 0.012,
outlier.size = 1) +
scale_pattern_manual(values = list,
labels = c("Text1", "Text2")) +
scale_fill_brewer(palette = "Set1",
guide = guide_legend(override.aes = list(pattern = "none"))) +
labs(y = "R", title = "R") +
ylim(0,100) +
theme_minimal()
Created on 2021-07-02 by the reprex package (v2.0.0)

Error in `row.names<-.data.frame`(`*tmp*`, value = value) : duplicate 'row.names' are not allowed

I using PCA function of R to study the principal components analysis.
This is to make the question reproducible:
> dput(DATA_FINAL[1:50,])
structure(list(DataCRMSanoflore.Year_Sales = c(2, 1, 2, 1, 2,
1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,
1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1,
1, 1, 1), DataCRMSanoflore.Month_Sales = c(9, 9, 2, 5, 9, 4,
7, 9, 3, 9, 7, 12, 3, 11, 3, 12, 3, 3, 6, 3, 4, 7, 5, 3, 5, 8,
8, 1, 9, 5, 4, 1, 10, 9, 5, 4, 9, 3, 2, 12, 9, 4, 4, 3, 6, 8,
6, 4, 4, 12), DataCRMSanoflore.Date_Sales = c(13, 3, 10, 22,
23, 26, 13, 1, 12, 2, 25, 11, 10, 26, 9, 4, 10, 18, 9, 9, 1,
7, 30, 9, 14, 24, 4, 2, 10, 17, 2, 28, 22, 17, 4, 14, 22, 30,
2, 5, 29, 13, 2, 10, 25, 5, 10, 23, 1, 6), DataCRMSanoflore.HOURS_INSCR = c(17,
14, 18, 17, 16, 11, 22, 14, 23, 17, 9, 21, 18, 16, 19, 12, 11,
17, 16, 21, 20, 11, 16, 18, 14, 19, 22, 17, 14, 10, 22, 15, 13,
19, 13, 21, 16, 19, 23, 19, 11, 21, 11, 22, 20, 13, 11, 15, 17,
15), DataCRMSanoflore.Year_Creation_Sales = c(2, 1, 2, 1, 2,
1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,
2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1,
1, 1, 1), DataCRMSanoflore.Month_Creation_Sales = c(9, 9, 2,
10, 10, 9, 7, 9, 12, 9, 7, 12, 3, 11, 4, 2, 6, 3, 6, 10, 4, 7,
6, 3, 5, 8, 3, 1, 9, 7, 4, 11, 11, 9, 5, 4, 9, 3, 2, 12, 10,
4, 4, 3, 10, 8, 6, 4, 4, 12), DataCRMSanoflore.Day_Creation_Sales = c(13,
11, 15, 2, 31, 26, 23, 1, 5, 2, 25, 16, 10, 27, 13, 7, 3, 18,
9, 8, 27, 7, 8, 18, 18, 24, 6, 2, 26, 4, 4, 24, 16, 17, 12, 15,
22, 30, 10, 5, 1, 14, 2, 10, 5, 5, 10, 27, 25, 6), DataCRMSanoflore.Year_Validation_Sales = c(2,
1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,
2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1,
1, 1, 1, 1, 1, 1, 1), DataCRMSanoflore.Month_Validation_Sales = c(9,
9, 2, 10, 11, 10, 7, 9, 12, 9, 7, 12, 3, 12, 4, 2, 6, 3, 6, 10,
4, 7, 6, 3, 5, 8, 3, 1, 10, 7, 4, 11, 11, 9, 5, 4, 9, 4, 2, 12,
10, 4, 4, 3, 10, 8, 6, 4, 4, 12), DataCRMSanoflore.Day_Validation_Sales = c(15,
14, 16, 3, 3, 1, 27, 2, 6, 5, 27, 21, 19, 1, 27, 8, 5, 21, 10,
9, 30, 9, 9, 21, 26, 27, 7, 4, 1, 6, 15, 25, 17, 18, 13, 20,
29, 1, 11, 7, 2, 16, 3, 20, 6, 6, 13, 29, 29, 8), DataCRMSanoflore.AGE_CUSTUMER = c(33,
37, 24, 34, 32, 46, 52, 60, 44, 55, 37, 29, 34, 30, 30, 31, 37,
57, 48, 44, 42, 28, 34, 43, 45, 33, 37, 53, 43, 35, 55, 62, 60,
57, 33, 51, 32, 51, 35, 54, 42, 47, 59, 33, 45, 35, 36, 54, 28,
42), DataCRMSanoflore.MEAN_PURCHASE = c(0, 71.75, 50.7142857142857,
18.6666666666667, 0, 0, 54.7, 22, 0.666666666666667, 38, 6.5,
0, 83.3333333333333, 0.333333333333333, 44.3333333333333, 25.7777777777778,
24.1818181818182, 23.3846153846154, 35.5294117647059, 21.6363636363636,
1.125, 40.6428571428571, 0, 46.8461538461538, 6, 8.66666666666667,
18.4, 16.9285714285714, 15.0666666666667, 110.25, 0, 8.85714285714286,
0, 36.5, 21.5, 18.5714285714286, 28.125, 8.38888888888889, 101.333333333333,
0, 2, 0, 20.9166666666667, 69.1428571428571, 16.6666666666667,
1.5, 87.1666666666667, 0, 48.25, 13.3333333333333), DataCRMSanoflore.NUMBER_GIFTS = c(1,
1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 4, 3, 4, 2, 2,
1, 2, 1, 1, 1, 2, 4, 1, 1, 1, 1, 3, 1, 3, 2, 4, 1, 1, 1, 1, 2,
2, 1, 1, 1, 1, 2, 3), DataCRMSanoflore.Year_Sales = c(2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), DataCRMSanoflore.Month_Sales = c(9L, 9L, 2L, 5L, 9L, 4L, 7L,
9L, 3L, 9L, 7L, 12L, 3L, 11L, 3L, 12L, 3L, 3L, 6L, 3L, 4L, 7L,
5L, 3L, 5L, 8L, 8L, 1L, 9L, 5L, 4L, 1L, 10L, 9L, 5L, 4L, 9L,
3L, 2L, 12L, 9L, 4L, 4L, 3L, 6L, 8L, 6L, 4L, 4L, 12L), DataCRMSanoflore.Date_Sales = c(13L,
3L, 10L, 22L, 23L, 26L, 13L, 1L, 12L, 2L, 25L, 11L, 10L, 26L,
9L, 4L, 10L, 18L, 9L, 9L, 1L, 7L, 30L, 9L, 14L, 24L, 4L, 2L,
10L, 17L, 2L, 28L, 22L, 17L, 4L, 14L, 22L, 30L, 2L, 5L, 29L,
13L, 2L, 10L, 25L, 5L, 10L, 23L, 1L, 6L), DataCRMSanoflore.Year_Creation_Sales = c(2L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), DataCRMSanoflore.Month_Creation_Sales = c(9L, 9L, 2L, 10L,
10L, 9L, 7L, 9L, 12L, 9L, 7L, 12L, 3L, 11L, 4L, 2L, 6L, 3L, 6L,
10L, 4L, 7L, 6L, 3L, 5L, 8L, 3L, 1L, 9L, 7L, 4L, 11L, 11L, 9L,
5L, 4L, 9L, 3L, 2L, 12L, 10L, 4L, 4L, 3L, 10L, 8L, 6L, 4L, 4L,
12L), DataCRMSanoflore.Day_Creation_Sales = c(13L, 11L, 15L,
2L, 31L, 26L, 23L, 1L, 5L, 2L, 25L, 16L, 10L, 27L, 13L, 7L, 3L,
18L, 9L, 8L, 27L, 7L, 8L, 18L, 18L, 24L, 6L, 2L, 26L, 4L, 4L,
24L, 16L, 17L, 12L, 15L, 22L, 30L, 10L, 5L, 1L, 14L, 2L, 10L,
5L, 5L, 10L, 27L, 25L, 6L), DataCRMSanoflore.Year_Validation_Sales = c(2L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), DataCRMSanoflore.Month_Validation_Sales = c(9L, 9L, 2L,
10L, 11L, 10L, 7L, 9L, 12L, 9L, 7L, 12L, 3L, 12L, 4L, 2L, 6L,
3L, 6L, 10L, 4L, 7L, 6L, 3L, 5L, 8L, 3L, 1L, 10L, 7L, 4L, 11L,
11L, 9L, 5L, 4L, 9L, 4L, 2L, 12L, 10L, 4L, 4L, 3L, 10L, 8L, 6L,
4L, 4L, 12L), DataCRMSanoflore.Day_Validation_Sales = c(15L,
14L, 16L, 3L, 3L, 1L, 27L, 2L, 6L, 5L, 27L, 21L, 19L, 1L, 27L,
8L, 5L, 21L, 10L, 9L, 30L, 9L, 9L, 21L, 26L, 27L, 7L, 4L, 1L,
6L, 15L, 25L, 17L, 18L, 13L, 20L, 29L, 1L, 11L, 7L, 2L, 16L,
3L, 20L, 6L, 6L, 13L, 29L, 29L, 8L), TYPE_PEAU = c(3L, 4L, 5L,
1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 4L, 3L, 1L, 3L, 1L, 3L, 3L,
3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 5L, 1L, 5L, 2L, 1L, 5L,
5L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L),
SENSIBILITE = c(4L, 4L, 4L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 4L, 4L, 1L, 3L, 1L, 3L, 3L, 4L, 1L, 1L, 1L, 2L, 1L, 1L,
4L, 1L, 2L, 3L, 1L, 4L, 4L, 1L, 3L, 4L, 4L, 1L, 4L, 1L, 1L,
1L, 1L, 4L, 1L, 1L, 1L, 1L, 4L, 1L), IMPERFECTIONS = c(3L,
4L, 3L, 1L, 2L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 3L, 3L, 1L, 2L,
1L, 3L, 2L, 3L, 1L, 1L, 1L, 4L, 1L, 1L, 3L, 1L, 3L, 2L, 1L,
4L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 3L, 1L), BRILLANCE = c(4L, 2L, 2L, 1L, 4L, 1L, 1L,
1L, 4L, 1L, 1L, 1L, 4L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 1L, 1L,
1L, 4L, 1L, 1L, 4L, 1L, 4L, 4L, 1L, 2L, 3L, 1L, 4L, 4L, 4L,
1L, 4L, 1L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 4L, 1L), GRAIN_PEAU = c(4L,
4L, 4L, 1L, 4L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 2L, 1L, 2L,
1L, 2L, 4L, 4L, 1L, 1L, 1L, 4L, 1L, 1L, 3L, 1L, 2L, 2L, 1L,
3L, 2L, 1L, 2L, 4L, 4L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L), RIDES_VISAGE = c(2L, 2L, 2L, 1L, 4L, 1L,
1L, 1L, 4L, 1L, 1L, 1L, 4L, 4L, 1L, 2L, 1L, 4L, 2L, 4L, 1L,
1L, 1L, 2L, 1L, 1L, 4L, 1L, 4L, 4L, 1L, 4L, 4L, 1L, 2L, 4L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 4L, 1L),
ALLERGIES = c(2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L), MAINS = c(3L, 4L, 4L,
1L, 4L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 3L,
3L, 3L, 1L, 1L, 1L, 4L, 1L, 1L, 4L, 1L, 3L, 2L, 1L, 4L, 4L,
1L, 3L, 4L, 4L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
3L, 1L), PEAU_CORPS = c(2L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 3L,
1L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 3L, 1L), INTERET_ALIM_NATURELLE = c(2L,
4L, 4L, 1L, 2L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
1L, 4L, 2L, 2L, 1L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
3L, 4L, 1L, 4L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L), INTERET_ORIGINE_GEO = c(2L, 4L, 2L, 1L,
2L, 1L, 1L, 1L, 5L, 1L, 1L, 1L, 2L, 5L, 1L, 2L, 1L, 2L, 5L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 5L, 2L, 1L, 4L, 2L, 1L,
2L, 5L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L), INTERET_VACANCES = c(3L, 4L, 2L, 1L, 3L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 3L, 2L, 1L, 2L, 1L, 3L, 4L, 3L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 4L, 3L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L), INTERET_ENVIRONNEMENT = c(3L,
5L, 5L, 1L, 5L, 1L, 1L, 1L, 5L, 1L, 1L, 1L, 3L, 3L, 1L, 3L,
1L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L,
5L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
1L, 1L, 3L, 1L), INTERET_COMPOSITION = c(2L, 2L, 2L, 1L,
4L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 3L, 4L, 1L,
4L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 4L,
1L)), .Names = c("DataCRMSanoflore.Year_Sales", "DataCRMSanoflore.Month_Sales",
"DataCRMSanoflore.Date_Sales", "DataCRMSanoflore.HOURS_INSCR",
"DataCRMSanoflore.Year_Creation_Sales", "DataCRMSanoflore.Month_Creation_Sales",
"DataCRMSanoflore.Day_Creation_Sales", "DataCRMSanoflore.Year_Validation_Sales",
"DataCRMSanoflore.Month_Validation_Sales", "DataCRMSanoflore.Day_Validation_Sales",
"DataCRMSanoflore.AGE_CUSTUMER", "DataCRMSanoflore.MEAN_PURCHASE",
"DataCRMSanoflore.NUMBER_GIFTS", "DataCRMSanoflore.Year_Sales",
"DataCRMSanoflore.Month_Sales", "DataCRMSanoflore.Date_Sales",
"DataCRMSanoflore.Year_Creation_Sales", "DataCRMSanoflore.Month_Creation_Sales",
"DataCRMSanoflore.Day_Creation_Sales", "DataCRMSanoflore.Year_Validation_Sales",
"DataCRMSanoflore.Month_Validation_Sales", "DataCRMSanoflore.Day_Validation_Sales",
"TYPE_PEAU", "SENSIBILITE", "IMPERFECTIONS", "BRILLANCE", "GRAIN_PEAU",
"RIDES_VISAGE", "ALLERGIES", "MAINS", "PEAU_CORPS", "INTERET_ALIM_NATURELLE",
"INTERET_ORIGINE_GEO", "INTERET_VACANCES", "INTERET_ENVIRONNEMENT",
"INTERET_COMPOSITION"), row.names = c(NA, 50L), class = "data.frame")
The first step is to write this code to create a PCA object as this:
library(FactoMineR)
library("factoextra")
res.pca <- PCA(as.data.frame(DATA_FINAL), graph = FALSE)
Then, to plot variables, I used the fviz_pca_var function like this:
fviz_pca_var(res.pca, col.var = "black")
I get this error:
Error in row.names<-.data.frame(*tmp*, value = value) :
duplicate 'row.names' are not allowed In addition: Warning messages:
1: In data.row.names(row.names, rowsi, i) : some row.names
duplicated: 14,15,16,17,18,19,20,21,22 --> row.names NOT used 2:
non-unique values when setting 'row.names':
‘DataCRMSanoflore.Date_Sales’, ‘DataCRMSanoflore.Day_Creation_Sales’,
‘DataCRMSanoflore.Day_Validation_Sales’,
‘DataCRMSanoflore.Month_Creation_Sales’,
‘DataCRMSanoflore.Month_Sales’,
‘DataCRMSanoflore.Month_Validation_Sales’,
‘DataCRMSanoflore.Year_Creation_Sales’, ‘DataCRMSanoflore.Year_Sales’,
‘DataCRMSanoflore.Year_Validation_Sales’
How resolve this issue please?

You have duplicate columns in your input data so simply by removing these columns you should be all set.
df <- DATA_FINAL[, -c(1:3, 5:10)]
and then run PCA
library(FactoMineR)
library(factoextra)
res.pca <- PCA(df, graph = F)
fviz_pca_var(res.pca, col.var = "black")

How to make create two y-axis labels with a grid of facets with a single x-axis label

I have been struggling with ggplot to display these plots how I would like. My data have 2 factors, quarter and species. Station will be on the x-axis, value on the y-axis, and the constituent will be used with the facet_wrap. I want quarter differentiated with shapes, and species with colors.
The issue is I'm trying to replicate a figure done in SigmaPlot. It is 4x4 grid of plots, with the first two rows of the first column are empty, to allow for the placement of the legend. My original plan was to have two separate facets made using facet-wrap, and combine those, however, this doesn't maintain the 4x4 arrangement, it transforms it into a 1x2, which ruins alignment of plots and shrinks the larger faceted grid.
My next thought was to create each plot individually, then arrange them in a grid using cowplot. This presents the plots how I'd like them arranged, but I can't figure out how to have two y-axis labels, due to different units. One label would be centered on the two leftmost plots, and one centered on the left of the next column of 4 plots.
I'm trying to use this code (just copy the example data below, and run):
library(ggplot)
library(gridExtra)
test.data1 <- test.data[1:95, ]
test.data2 <- test.data[96:111, ]
testplot1 <- ggplot(test.data1, aes(Station, value)) +
geom_point(aes(shape = factor(quarter), fill = Species)) +
scale_shape_manual(values = c(21, 22)) +
labs(x = "Station", y = "Unit a", shape = "Sampling Quarter", fill = "Species") +
theme(legend.position = "none", legend.title = element_blank()) +
guides(fill = guide_legend(override.aes = list(shape = 21), nrow = 2, byrow = TRUE), shape = guide_legend(nrow = 2, byrow = TRUE)) +
facet_wrap( ~ constituent, ncol = 3, scales = "free_y")
testplot2 <- ggplot(test.data2, aes(Station, value)) +
geom_point(aes(shape = factor(quarter), fill = Species))
scale_shape_manual(values = c(21, 22)) +
labs(x = "Station", y = "Unit b", shape = "Sampling Quarter", fill = "Species") +
theme(legend.position = "top", legend.title = element_blank()) +
guides(fill = guide_legend(override.aes = list(shape = 21), nrow = 2, byrow = TRUE), shape = guide_legend(nrow = 2, byrow = TRUE)) +
facet_wrap( ~ constituent, ncol = 1, scales = "free_y")
grid.arrange(testplot2, testplot1, ncol = 2)
Which generates this:
But I want it to be arranged like this, where the XX and YY plots from above are normalized in size with the other plots (this was done using individual plots, and using plot_grid):
Example data from a larger set:
test.data <- structure(list(Station = structure(c(1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("StA", "StB"), class = "factor"),
CollectionDate = structure(c(3L, 2L, 3L, 1L, 3L, 1L, 3L,
1L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L,
3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 3L,
1L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L,
3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L,
1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 1L, 3L, 2L, 3L,
1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L,
3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L), .Label = c("10/1/2017",
"10/16/2017", "4/1/2017"), class = "factor"), Species = structure(c(1L,
2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L,
1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L,
3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L,
2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L,
2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L,
1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L,
3L, 1L, 2L, 2L, 3L), .Label = c("SpA", "SpB", "SpC"), class = "factor"),
quarter = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("2017 Q2",
"2017 Q4"), class = "factor"), constituent = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L
), .Label = c("A", "B", "C", "D", "E", "F", "G", "H", "I",
"J", "K", "L", "XX", "YY"), class = "factor"), value = c(16,
35, 46, 23, 40, 19, 9, 50, 0.2, 1, 0.5698, 0.322, 1, 0.45,
0.322, 0.5, 16, 9, 6, 19, 14, 13, 16, 9, 0, 0.004, 0, 0.004,
1, 0.32, 1, 0.678, 0, 0.39, 0.23, 0, 0, 1.1, 0.5, 0.5, 9,
4.9, 7, 4.768, 9, 8.65, 4.768, 6.54, 195, 195, 46, 46, 124,
124, 218, 218, 2, 1, 1, 1, 1, 2, 1, 1, 0.1, 0.4, 0.22, 0.4,
0.22, 0.4, 0.22, 0.1, 0.99, 0.99, 1.2, 0.45, 0.765, 0.99,
0.99, 0.99, 0.99, 1.2, 4.3, 0.98, 0.99, 1.2, 1.2, 34, 34,
65, 98, 150, 34, 65, 65, 2, 0, 4, 1.3, 5, 3.3, 1.56, 1, 9,
0.36, 4, 4, 11, 2, 2.22, 11)), class = "data.frame", row.names = c(NA,
-111L))

Tick mark grouping by year in ggplot2

I have a time series in which stem densities were measured both early and late in the growing season. I would like to present trends in stem densities over time, but including tick mark labels such as 2005 Early, 2005 Late, 2006 Early, 2006 Late, and so on is too cluttered for presentation. Is it possible to group the tick marks from each year, such that each mark indicates only either "Early" or "Late", with the year indicated below each pair?
Here is what I have come up with so far, but I know beforehand that it won't give me exactly what I have in mind, but worse yet, it doesn't work. It results in a plot without any x-axis tick labels whatsoever:
dt<-ggplot(nzsum, aes(x = Date, y = Average.Stem.Density, colour = Species)) + geom_line(aes(group = Species)) +
geom_point() + geom_errorbar(aes(ymin=Average.Stem.Density-se, ymax=Average.Stem.Density+se), width = 0.25) + facet_grid(Heat~Treatment)
dt<-dt+ scale_x_discrete(breaks = 1:18, labels = c("2005 Early", "Late", "2006 Early", "Late", "2007 Early","Late","2008 Early", "Late",
"2009 Early","Late", "2010 Early","Late","2011 Early","Late","2012 Early","Late", "2013 Early","Late"))
dt + theme(axis.text.x = element_text(angle=90, vjust=0.5, size=10)) + ylab('Stem Density')
To possibly further complicate things, I have missing data for some of the measurement dates, but would like to have either the gaps remain in the plot, or to have a vertical line separating the periods of consistent measurements.
Any suggestions are appreciated!
output of dput, as requested:
> dput(nzsum)
structure(list(Date = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L), .Label = c("2005 Early",
"2005 Late", "2006 Early", "2006 Late", "2007 Early", "2007 Late",
"2008 Early", "2008 Late", "2009 Early", "2009 Late", "2010 Early",
"2010 Late", "2011 Early", "2011 Late", "2012 Early", "2012 Late",
"2013 Early", "2013 Late"), class = "factor"), Treatment = structure(c(1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("Ambient Precipitation",
"Increased Precipitation Variability"), class = "factor"), Heat = structure(c(1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L), .Label = c("Ambient Temperature",
"Increased Temperature"), class = "factor"), Species = structure(c(1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("A. gerardii",
"S. nutans"), class = "factor"), N = c(6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6), Average.Stem.Density = c(59.75,
52.4166666666667, 59.4166666666667, 38.8333333333333, 47.1666666666667,
44.6666666666667, 34.6666666666667, 47.1666666666667, 45.5833333333333,
51, 46.8333333333333, 40.8333333333333, 36.0833333333333, 43.5,
30.9166666666667, 53.1666666666667, 59.9166666666667, 47.8333333333333,
54.8333333333333, 40.1666666666667, 45.0833333333333, 38.5833333333333,
38.6666666666667, 38.3333333333333, 41.5, 57.3333333333333, 41.4166666666667,
50.25, 32.8333333333333, 41.5833333333333, 29.5, 41.25, 57.75,
34, 49.4166666666667, 45.1666666666667, 36.5833333333333, 22.6666666666667,
33.0833333333333, 30.1666666666667, 37.8333333333333, 43.9166666666667,
34.3333333333333, 44.4166666666667, 30.6666666666667, 31.1666666666667,
28.0833333333333, 41.0833333333333, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 52.8333333333333, 43.5, 39,
38.4166666666667, 40.5833333333333, 28.8333333333333, 33.3333333333333,
26.6666666666667, 34.5833333333333, 53.3333333333333, 24.6666666666667,
38.8333333333333, 26.9166666666667, 35.3333333333333, 22.1666666666667,
16.3333333333333, 51.0833333333333, 58.25, 34.8333333333333,
46.5833333333333, 47.5, 40, 35.4166666666667, 29.0833333333333,
24.5, 62, 20.1666666666667, 40.6666666666667, 22.9166666666667,
50, 20.25, 23.25, 32, 45.1666666666667, 30, 36.5, 31.6666666666667,
36.9166666666667, 21.6666666666667, 18.4166666666667, NA, NA,
NA, NA, NA, NA, NA, NA, 27.9166666666667, 29.75, 24.8333333333333,
20.0833333333333, 19, 21.8333333333333, 15.5833333333333, 8.16666666666667,
25, 42.25, 20.5833333333333, 29.25, 19.8333333333333, 23.1666666666667,
19.4166666666667, 13.8333333333333, 46.6666666666667, 35, 36.4166666666667,
29.4166666666667, 36, 22.6666666666667, 29.0833333333333, 18.5833333333333,
33.3333333333333, 31.25, 25.8333333333333, 28.3333333333333,
23.1666666666667, 14.0833333333333, 18.4166666666667, 15.3333333333333
), sd = c(32.2020962050609, 15.1407287363147, 25.7884017858158,
14.1833235409289, 13.7501515143167, 22.0333081189972, 11.0574258607809,
23.6107320231006, 25.6542718989775, 13.2815661727072, 15.4099534933324,
11.2590704175197, 8.15730756234351, 15.52417469626, 11.8423674434915,
20.9300422041301, 36.3612660212302, 14.7738507731284, 16.7022952514517,
14.9788740119766, 7.61194237147567, 13.6872812006865, 12.9794709702155,
16.7022952514517, 20.7797978815964, 13.47095641247, 10.4566565720916,
18.5654248537436, 7.85281265959316, 14.756072196444, 9.46572765295939,
20.3168649156311, 37.9983552275622, 15.3068612066615, 18.5240834231189,
16.9813623324711, 8.59893404246519, 9.99833319442129, 9.61985793380893,
16.0831174424198, 24.0409373084052, 17.8841177212259, 10.5340717040785,
13.9191115616862, 8.09732466106347, 12.5445871461227, 9.43618920256831,
21.0081333456037, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 32.7744819435284, 25.4872517153184, 6.09918027279076,
26.1962147392837, 13.5033946349304, 13.6479546697176, 10.1176413588675,
19.7855165883195, 13.9298839430437, 28.6856526275186, 6.8239773348588,
23.1984194863932, 12.654709268358, 18.4842275106824, 4.79235502302017,
13.1288486420808, 25.7864628568299, 31.8680874857592, 6.17791766428355,
26.5582692709195, 19.2041662146525, 19.0446842977247, 18.5941298980798,
18.0205900754295, 11.6790410565251, 28.8946361804401, 4.34357763446985,
23.2350310235787, 8.88491230495083, 25.7740179250345, 9.23985930628816,
11.5617905187735, 20.2410474037289, 22.2994768249541, 9.18694726228468,
17.0117606378646, 17.229819112999, 20.4093524313406, 17.1571170849495,
11.9558214551183, NA, NA, NA, NA, NA, NA, NA, NA, 15.0180447018467,
17.2880016196205, 11.8939760663399, 10.3605823517149, 9.92975326984513,
18.1457065628943, 9.86618805145466, 7.92254167970524, 17.5499287747842,
14.7300712829232, 8.01508993503296, 15.5298100439123, 12.3193614553136,
15.25013661141, 12.7766062264854, 10.7780641428165, 23.0079696337305,
13.9319776054945, 10.9060380829459, 15.5609018590397, 21.0214176496258,
14.0558410159857, 23.8189350447636, 15.6314320094695, 16.2777967387072,
16.3240619944914, 6.17791766428355, 14.4798710859823, 14.770466027403,
12.076492316342, 13.8759384067048, 12.6912043032435), se = c(13.146450725069,
6.18117662297757, 10.5280709428545, 5.79031758868007, 5.6134758493389,
8.99506037284415, 4.51417520459472, 9.63904098503119, 10.4733126458527,
5.42217668469038, 6.29108716978058, 4.59649625016466, 3.33020686711468,
6.33771778061052, 4.8346262638503, 8.54465394917261, 14.844424692269,
6.03139932169789, 6.81868348322786, 6.11509970846302, 3.10756246026439,
5.58780915127844, 5.29884683471581, 6.81868348322786, 8.48331696134635,
5.49949492630409, 4.26891216952412, 7.57930295827613, 3.20589734361189,
6.02414124815959, 3.86436713231718, 8.29432536939161, 15.5127635620908,
6.2489999199872, 7.56242538989826, 6.93261214197874, 3.51050012264793,
4.08180243411059, 3.92729055598273, 6.5659052011974, 9.81467155730531,
7.30116048614496, 4.30051676481379, 5.68245349983419, 3.30571895021004,
5.12130625697954, 3.85230811044207, 8.57653452417959, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 13.3801262242842,
10.4051269414009, 2.48997991959775, 10.6945598839368, 5.51273777516923,
5.57175416224051, 4.13050978828414, 8.07740332312584, 5.68685130610761,
11.7108686460247, 2.78587708112026, 9.47071509678358, 5.16626342512437,
7.54615428178118, 1.95647074544389, 5.35983001388829, 10.5272793784107,
13.010092236414, 2.52212432507026, 10.8423680275318, 7.84006802691575,
7.77495980697007, 7.5910217435541, 7.35687509144413, 4.76794854558366,
11.79618582424, 1.77325814376939, 9.48566169425084, 3.62725025941752,
10.5221987562803, 3.77215676591876, 4.72008121399057, 8.26337299977033,
9.10372329202606, 3.75055551440939, 6.94502219818867, 7.03404419788345,
8.33208323956927, 7.00436371921898, 4.88094367014321, NA, NA,
NA, NA, NA, NA, NA, NA, 6.1310910756388, 7.05779710674655, 4.85569539590137,
4.22969003329769, 4.05380479714223, 7.40795368356051, 4.02785440540136,
3.23436409686836, 7.16472842006823, 6.01352641966426, 3.27214676389132,
6.34001840165574, 5.02935825373819, 6.22584220094849, 5.21602764989263,
4.4001262608147, 9.39296427001497, 5.68770604022395, 4.45237140309646,
6.35271158202893, 8.58195781858662, 5.73827306580802, 9.72403951269453,
6.38150539536802, 6.64538269109546, 6.66427040267725, 2.52212432507026,
5.9113826169894, 6.03001750504185, 4.93020734294659, 5.66482813311911,
5.18116246072679), ci = c(33.7940274369502, 15.889220349886,
27.0632679299923, 14.8844852160264, 14.4298990530937, 23.1225388048852,
11.604056783811, 24.7779436690751, 26.922507246369, 13.9381488952758,
16.1717544050426, 11.8156697682437, 8.56056928151632, 16.2916222062267,
12.4278024559438, 21.9647322335412, 38.1588084744178, 15.5042055398258,
17.5279839049389, 15.7193642336799, 7.98824361346093, 14.3639207052787,
13.6211194231394, 17.5279839049389, 21.8070604867824, 14.1369017627314,
10.9735880809055, 19.4832185113292, 8.24102147840344, 15.4855480678265,
9.93367195656454, 21.3212421334155, 39.8768282332316, 16.0635656852119,
19.4398333406273, 17.8208468456823, 9.02402784927781, 10.4926071937808,
10.0954217664752, 16.878196644708, 25.2294164279454, 18.7682305247498,
11.0548302794798, 14.6072117485218, 8.49762108712868, 13.1647368389222,
9.90267325397684, 22.0466838605638, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 34.3947094306661, 26.7472303130552,
6.40069715241714, 27.4912413777728, 14.1709435894762, 14.3226500420865,
10.6178134336812, 20.7636262615357, 14.6185166694449, 30.1037462209939,
7.16132502104324, 24.3452481982785, 13.280302918737, 19.3980071256559,
5.02926816019191, 13.7778816757996, 27.0612331488113, 33.4435067828789,
6.48332697724211, 27.8711943068573, 20.1535364601427, 19.9861704525997,
19.5133426079005, 18.9114494771115, 12.2564019245259, 30.3230610095818,
4.55830517426775, 24.3836696502324, 9.32414363016577, 27.0481729938491,
9.69663766364338, 12.1333550314124, 21.2416765342972, 23.4018657311414,
9.64110987888638, 17.8527479107548, 18.0815862461422, 21.4183018286465,
18.0052901468143, 12.5468651392342, NA, NA, NA, NA, NA, NA, NA,
NA, 15.760471351669, 18.1426450422092, 12.4819623840869, 10.872764369967,
10.4206369767492, 19.0427511781958, 10.3539293711124, 8.31419759724407,
18.4175207338946, 15.458261782508, 8.41132103479517, 16.2975361408962,
12.928376971967, 16.0040368732962, 13.4082259309908, 11.3108846405566,
24.1453833352814, 14.6207138334386, 11.4451850543063, 16.3301649998,
22.0606248826558, 14.7507005108872, 24.9964393403423, 16.4041818533482,
17.0825000365819, 17.1310524448908, 6.48332697724211, 15.1956927787292,
15.5006534670296, 12.6735014416993, 14.5619043009976, 13.318602109025
)), .Names = c("Date", "Treatment", "Heat", "Species", "N", "Average.Stem.Density",
"sd", "se", "ci"), row.names = c(NA, -144L), class = "data.frame")

You can customize your tick marks and labels as much as you like. For example:
# Set tick marks on y axis
# a tick mark is shown on every 5
p + scale_y_continuous(breaks=seq(0,40,5))
# Tick marks can be spaced randomly
p + scale_y_continuous(breaks=c(5,7.5, 20, 25))
# Change x and y axis labels, and limits
sp + scale_x_continuous(name="Speed of cars", limits=c(0, 30)) +
scale_y_continuous(name="Stopping distance", limits=c(0, 150))
You can find full details and lots of examples here.
To address your 2nd question I suggest gap.plot from plotrix.
twogrp<-c(rnorm(5)+4,rnorm(5)+20,rnorm(5)+5,rnorm(5)+22)
gpcol<-c(2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5)
gap.plot(twogrp,gap=c(8,16),xlab="Index",ylab="Group values",
main="Gap on Y axis",col=gpcol)
gap.plot(twogrp,rnorm(20),gap=c(8,16),gap.axis="x",xlab="X values",
xtics=c(4,7,17,20),ylab="Y values",main="Gap on X axis with added lines")
gap.plot(c(seq(3.5,7.5,by=0.5),seq(16.5,22.5,by=0.5)),
rnorm(22),gap=c(8,16),gap.axis="x",type="l",add=TRUE,col=2,)
gap.plot(twogrp,gap=c(8,16,25,35),
xlab="X values",ylab="Y values",xlim=c(1,30),ylim=c(0,42),
main="Test two gap plot with the lot",xtics=seq(0,30,by=5),
ytics=c(4,6,18,20,22,38,40,42),
lty=c(rep(1,10),rep(2,10)),
pch=c(rep(2,10),rep(3,10)),
col=c(rep(2,10),rep(3,10)),
type="b")
gap.plot(21:30,rnorm(10)+40,gap=c(8,16,25,35),add=TRUE,
lty=rep(3,10),col=rep(4,10),type="l")
You could also combine axis.break with manual subsetting and re-scaling of the data. By re-scaling I mean that after omitting any range of data you don't want, subtract the size of the range from the observations with greater values, then add the axis break zigzag and change the tick mark labels to reflect where the data was before rescaling.

R scatterplot3d: a custom axis step and ticks

Greeting to all.
I am striving with a scatterplot3d plot -- a graphical representation of a data.frame of three variables where one of them is a response variable, where I have a wrong representation of the axis steps. Here is the code ("temp" is a data.frame):
library(scatterplot3d)
temp[,1] <- as.numeric(levels(temp[,1]))[temp[,1]]
for (m in temp[,2]) m <- as.factor(as.numeric(m))
for (m in temp[,3]) m <- as.factor(as.numeric(m))
colnames(temp) = c("Values", "Factors", "AntiFactors") # "Values" is that responce variable
xtickmarks<-c("AntiFactor1","AntiFactor1", "AntiFactor3")
ytickmarks<-c("Factor1","Factor2")
plot3d <- scatterplot3d(temp[,3], temp[,2], temp[,1], color = "blue",
pch = 19, type = "h", box = T, xaxt = "n",
x.ticklabs=xtickmarks, y.ticklabs=ytickmarks,
zlab = "Time, min.")
dput(temp)
structure(list(Values = c(395, 310, 235, 290, 240, 490, 270,
225, 430, 385, 170, 55, 295, 320, 270, 130, 300, 285, 130, 200,
225, 90, 205, 340, 3, 8, 1, 0, 0, 0, 3, 2, 5, 2, 3, 5, 2, 3,
200, 5, 5, 10, 5, 5, 5, 10, 10, 130, 5, 200, 80, 10, 360, 10,
5, 8, 30, 8, 10, 10, 10, 5, 240, 120, 3, 10, 25, 5, 5, 10, 190,
30, 115, 1, 1, 1, 2, 3, 5, 2, 5, 3, 3, 3, 2, 3, 2, 3, 0, 0, 195,
150, 2, 2, 0, 2, 1, 1, 2, 1, 2, 1, 1, 1, 3, 2, 2, 1, 2, 2, 1,
1, 2, 3, 2, 2, 1, 3, 1, 1), Factors = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("Factor1", "Factor2"), class = "factor"),
AntiFactors = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("AntiFactor1", "AntiFactor2",
"AntiFactor3"), class = "factor")), .Names = c("Values",
"Factors", "AntiFactors"), row.names = c(NA, -120L), class = "data.frame")
Here is the picture of that plot I got:
The trouble is what I got twice more ticks at the x and y axis than it is needed. It is intended to have just one set of those Factor1..2 and AntiFactor1..3 ticks at each of those x, y axis. If I run that scatterplot3d without using x.ticklabs option, it gives "0, 0.5, 1, 1.5, 2.0, ...3.0" ticks etc at the axis. What is the way to set my step in x, y axis to be just a strong integer "1", so that all my discrete ticks to be displayed in their right place?

It seems that scatterplot3d coerces your discrete explanatory variables 'Factor' and 'AntiFactor' from factor to numeric. See e.g.:
levels(df$Factors)
# [1] "Factor1" "Factor2"
unique(as.numeric(df$Factors))
# [1] 1 2
levels(df$AntiFactors)
# [1] "AntiFactor1" "AntiFactor2" "AntiFactor3"
unique(as.numeric(df$AntiFactors))
# [1] 1 2 3
The labels you have created are recycled to get a label at each (default) tick mark. Also note your typo in 'xtickmarks' - I assume the second 'AntiFactor1' should be 'AntiFactor2'.
You may consider alternative ways to visualize your data, e.g. something like this:
library(ggplot2)
ggplot(data = temp, aes(x = AntiFactors, y = Values, fill = Factors)) +
geom_boxplot()

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