I am using the following code to make my graph: .
#Labels
label1 <- data.frame( x = 2, y = 2, Type = "FYS", label = "N=15")
label2 <- data.frame( x = 2, y = 2, Type = "SNR", label = "N=24")
# make graph
ggplot(data = Q, mapping = aes(y = Rating, x = weeks, group= StudentFactor, colour=StudentFactor))+
geom_point()+
geom_line()+
facet_grid(Type ~.)+
geom_smooth(method = 'lm', formula = y ~ poly(x), colour= "black", aes(group=1), se= FALSE)+
theme(legend.position="none") +
labs (x= "Date", y="Students' Average Engagement over Time")+
geom_text(data = label1, aes(x = x, y = y, label = label), inherit.aes = FALSE)+
geom_text(data = label2, aes(x = x, y = y, label = label), inherit.aes = FALSE)
However, the dates at the bottom are out of order. Instead of using x= weeks, I could use x=timePeriod which would make the points be in order, but the labels to be wrong.
I have tried adding the following code to order the levels of weeks,
df$weeks <- factor(df$weeks, order=TRUE, levels=weeks)
but I keep getting an error saying object of type 'closure' is not subsettable.
I have attached my data below:
> dput (Q)
structure(list(StudentFactor = structure(c(1L, 3L, 4L, 8L, 11L,
13L, 14L, 15L, 18L, 19L, 21L, 22L, 24L, 30L, 31L, 32L, 36L, 38L,
27L, 34L, 35L, 1L, 3L, 4L, 8L, 11L, 13L, 14L, 18L, 19L, 21L,
22L, 24L, 2L, 5L, 6L, 7L, 9L, 10L, 12L, 16L, 17L, 20L, 23L, 25L,
26L, 28L, 29L, 30L, 31L, 32L, 33L, 36L, 37L, 38L, 40L, 41L, 34L,
39L, 1L, 3L, 4L, 8L, 11L, 13L, 14L, 15L, 18L, 19L, 21L, 24L,
2L, 5L, 6L, 7L, 9L, 10L, 12L, 16L, 17L, 20L, 23L, 25L, 28L, 30L,
31L, 33L, 36L, 37L, 38L, 40L, 41L, 34L, 35L, 39L, 1L, 3L, 4L,
8L, 11L, 14L, 15L, 18L, 21L, 22L, 24L, 2L, 6L, 7L, 9L, 10L, 12L,
16L, 17L, 20L, 23L, 31L, 33L, 36L, 37L, 40L, 27L, 34L, 1L, 3L,
4L, 8L, 11L, 13L, 14L, 15L, 18L, 19L, 21L, 22L, 2L, 5L, 6L, 7L,
9L, 10L, 12L, 16L, 17L, 20L, 23L, 28L, 30L, 31L, 32L, 33L, 36L,
38L, 41L, 27L, 34L, 35L, 1L, 3L, 4L, 11L, 14L, 15L, 18L, 19L,
21L, 22L, 24L, 2L, 5L, 6L, 9L, 10L, 12L, 16L, 20L, 23L, 29L,
30L, 31L, 32L, 33L, 36L, 38L, 41L, 27L, 34L, 35L, 1L, 3L, 11L,
13L, 14L, 15L, 18L, 19L, 21L, 22L, 24L, 2L, 6L, 7L, 9L, 10L,
12L, 16L, 17L, 20L, 23L, 28L, 29L, 30L, 31L, 36L, 37L, 38L, 40L,
41L, 27L, 34L, 35L, 39L, 1L, 3L, 4L, 11L, 13L, 14L, 15L, 18L,
19L, 21L, 22L, 24L, 2L, 7L, 10L, 12L, 16L, 17L, 20L, 28L, 29L,
30L, 31L, 32L, 33L, 36L, 37L, 38L, 40L, 41L, 27L, 34L, 35L, 1L,
11L, 13L, 14L, 18L, 19L, 21L, 22L, 24L, 2L, 6L, 7L, 10L, 12L,
16L, 28L, 30L, 31L, 33L, 36L, 34L, 1L, 4L, 14L, 15L, 18L, 19L,
21L, 22L, 24L, 2L, 7L, 9L, 10L, 12L, 16L, 17L, 20L, 23L, 29L,
30L, 31L, 32L, 33L, 36L, 37L, 40L, 41L, 27L, 34L, 39L, 1L, 3L,
4L, 11L, 13L, 14L, 15L, 18L, 22L, 24L, 2L, 6L, 7L, 9L, 10L, 12L,
16L, 17L, 20L, 23L, 30L, 31L, 36L, 37L, 38L, 41L, 27L), .Label = c("789331",
"796882", "805933", "826523", "827911", "830271", "831487", "832929",
"834598", "836364", "838607", "839802", "841903", "843618", "852125",
"855524", "873527", "876406", "879972", "885409", "885650", "888712",
"894218", "903303", "928026", "932196", "952797", "955389", "956952",
"957206", "957759", "959200", "962490", "965873", "967416", "968728",
"969005", "971179", "975424", "976863", "981621"), class = "factor"),
Type = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 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, 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, 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, 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, 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, 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, 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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), .Label = c("FYS", "SNR"), class = "factor"),
weeks = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 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, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 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("Apr5",
"Feb1", "Feb15", "Feb8", "Jan11", "Jan25", "Mar1", "Mar15",
"Mar22", "Mar29", "Mar8"), class = "factor"), timePeriod = structure(c(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, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L), class = "factor", .Label = c("Rt1", "Rt2", "Rt3", "Rt4",
"Rt5", "Rt6", "Rt7", "Rt8", "Rt9", "Rt10", "Rt11")), Rating = c(3.6,
4.8, 4.4, 3.8, 5, 3.2, 4.4, 3.2, 3.6, 3.8, 4, 4.4, 3.6, 4,
3.8, 3, 3.6, 4.4, 3.6, 3.4, 4.2, 3.8, 4, 4.2, 3.8, 5, 4.2,
4.4, 4, 3.8, 4.4, 4, 3.8, 4.4, 4.2, 4.6, 4.4, 5, 4, 3.4,
5, 3.8, 4.8, 4.4, 4.6, 3.2, 5, 4.2, 4.4, 4.4, 3.4, 3.8, 3.8,
3.6, 4.8, 4.4, 4.8, 4.75, 4, 4, 4, 4.2, 3.8, 5, 4.2, 4.6,
3.8, 4.2, 3.8, 4, 4.6, 4, 3.6, 4.8, 4.2, 3.8, 4, 2, 4.6,
3.8, 4.6, 4.4, 4.8, 4.6, 4, 4.4, 4.2, 3.6, 4.6, 4.4, 5, 4.6,
5, 4.2, 3.4, 4.2, 3.6, 4.4, 4, 5, 4.4, 4, 4, 4, 4.2, 4, 4,
5, 4.6, 4, 4, 1.8, 4.6, 4.2, 4.8, 4.6, 4.4, 4.2, 3.4, 4.4,
3.8, 4, 5, 3.4, 3.2, 4.6, 3.6, 5, 3.6, 4.4, 3.8, 4, 4, 4.2,
4.4, 2.8, 3.4, 5, 4.4, 4.2, 3.6, 4.2, 4.2, 4, 4.4, 5, 4,
4, 3.8, 3.2, 4.2, 3.4, 4.4, 5, 4.4, 4, 4.2, 2.4, 3.2, 4.6,
4.4, 4.4, 3.6, 2.4, 4.2, 4, 4.4, 3.4, 3.6, 3.4, 4.4, 4, 3.2,
2.2, 4.4, 4.4, 5, 3.2, 4.4, 4, 3, 4.6, 3, 4.25, 4.2, 3.6,
3.8, 4.4, 3, 3.2, 4.2, 4, 4.4, 3.6, 2.8, 4, 4.4, 4.6, 3.8,
2.8, 4.8, 4.2, 4, 3.6, 3, 4.8, 4.2, 4.2, 5, 4.4, 4.4, 4,
3.2, 1, 4.4, 4.2, 3.6, 3.8, 4, 1.4, 4.6, 2.8, 3.2, 3.2, 4.6,
4.4, 3.4, 4.2, 4, 3.8, 4, 4.2, 3.8, 3.6, 1.4, 4.6, 3.6, 4.2,
4, 4.4, 4.4, 4.6, 4.2, 4.2, 3.2, 4, 3.6, 3, 4.6, 4.8, 3.6,
4.2, 4.2, 2.2, 5, 3.2, 3.8, 4.2, 3.6, 3, 4, 3.8, 4.2, 3.8,
2.2, 5, 4.8, 3.4, 2.8, 5, 4.4, 4, 3, 1, 3, 1.6, 3.6, 4.2,
4, 3.4, 3.2, 4, 4, 4, 3.6, 2, 4.4, 4, 3.4, 1.8, 4.2, 3.8,
3.8, 4, 4.2, 3.8, 4.2, 4.2, 3.2, 1.6, 4.6, 4, 5, 4, 3.4,
3.6, 4, 3.2, 4.2, 3.6, 4.6, 4.4, 4.6, 4.2, 4.6, 4.6, 4.2,
5, 4.6, 4.2, 4, 4, 4.6, 4.4, 3.6, 5, 4.4, 4.6, 1.6, 4.6,
5, 5, 4)), class = "data.frame", row.names = c(NA, -333L), .Names = c("StudentFactor",
"Type", "weeks", "timePeriod", "Rating"))
I just changed the format of the week column. Does it work for you?
newdate <- as.Date(Q[, 3], "%b%d")
newdate <- strftime(newdate,"%m %d")
QQ <- cbind(Q, newdate)
ggplot(data = QQ, mapping = aes(y = Rating, x = factor(newdate), group= StudentFactor, colour=StudentFactor))+
geom_point()+
geom_line()+
facet_grid(Type ~.)+
geom_smooth(method = 'lm', formula = y ~ poly(x), colour= "black", aes(group=1), se= FALSE)+
theme(legend.position="none") +
labs (x= "Date", y="Students' Average Engagement over Time")+
geom_text(data = label1, aes(x = x, y = y, label = label), inherit.aes = FALSE)+
geom_text(data = label2, aes(x = x, y = y, label = label), inherit.aes = FALSE)
Related
I managed to create a faceted boxplot with my 2 quantitative variables;
I know how to run a kruskal-wallis followed by a Wilcoxon test and show the significant differences with letters in the boxplot but only in a simple boxplot, with one variable and without facet. How can I do ?
(If possible, I would like to put the siginificant differences with letters, I wish I would be able to post the pictures of what I already done but apparently I'm not allowed)
Also, I have another question; Which test does the function stat_function_mean execute ? I tried to use this function, but I don't know how to use it... Here is my code without the test, only the facetted boxplot with my two variables :
Code for my facet boxplot with 2 measured variables ( FF and FM)
dat.m2 <- melt(pheno,id.vars=c("fusion","Genotype","Hormone"),
measure.vars=c('FF','MF'))
dat.m2$fusion<-factor(dat.m2$fusion, levels=c("Control", "CK 20 mg/L", "CK 100 mg/L", "CK 500 mg/L", "GA 20 mg/L", "GA 100 mg/L", "GA 500 mg/L"))
levels(dat.m2$fusion)
ggplot(dat.m2) +
geom_boxplot(aes(x=fusion, y=value, colour=variable))+
facet_wrap(~Genotype)+
xlab(" ")+
ylab("Days after sowing")
Code to add significant differences on the graph, with letters, but with only 1 measured variable (FF), without facet
mymat <-tri.to.squ(pp$p.value)
mymat
myletters <- multcompLetters(mymat,compare="<=",threshold=0.05,Letters=letters)
myletters
myletters_df <- data.frame(fusion=names(myletters$Letters),letter = myletters$Letters )
myletters_df
ggplot(pheno, aes(x=fusion, y=FF, colour=fusion))+
geom_boxplot()+
geom_text(data = myletters_df, aes(label = letter, y = 30 ), colour="black", size=5)+
ylab("Days after sowing")+
xlab("")+
labs(title="Days to female flower production")+
theme(plot.title = element_text(hjust = 0.5))+
> dput(pheno)
structure(list(Genotype = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("F1045",
"FF", "M1585", "M1610"), class = "factor"), X = structure(c(1L,
105L, 116L, 127L, 138L, 149L, 160L, 171L, 182L, 2L, 13L, 24L,
35L, 46L, 57L, 68L, 79L, 90L, 101L, 106L, 107L, 108L, 109L, 110L,
111L, 112L, 113L, 114L, 115L, 117L, 118L, 119L, 120L, 121L, 122L,
123L, 124L, 125L, 126L, 128L, 129L, 130L, 131L, 132L, 133L, 134L,
135L, 136L, 137L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L,
147L, 148L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L,
159L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L,
172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 183L,
184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 47L, 48L,
49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
77L, 78L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 91L,
92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 102L, 103L, 104L
), .Label = c("H1", "H10", "H100", "H101", "H102", "H103", "H104",
"H105", "H106", "H107", "H108", "H109", "H11", "H110", "H111",
"H112", "H113", "H114", "H115", "H116", "H117", "H118", "H119",
"H12", "H120", "H121", "H122", "H123", "H124", "H125", "H126",
"H127", "H128", "H129", "H13", "H130", "H131", "H132", "H133",
"H134", "H135", "H136", "H137", "H138", "H139", "H14", "H140",
"H141", "H142", "H143", "H144", "H145", "H146", "H147", "H148",
"H149", "H15", "H150", "H151", "H152", "H153", "H154", "H155",
"H156", "H157", "H158", "H159", "H16", "H160", "H161", "H162",
"H163", "H164", "H165", "H166", "H167", "H168", "H169", "H17",
"H170", "H171", "H172", "H173", "H174", "H175", "H176", "H177",
"H178", "H179", "H18", "H180", "H181", "H182", "H183", "H184",
"H185", "H186", "H187", "H188", "H189", "H19", "H190", "H191",
"H192", "H2", "H20", "H21", "H22", "H23", "H24", "H25", "H26",
"H27", "H28", "H29", "H3", "H30", "H31", "H32", "H33", "H34",
"H35", "H36", "H37", "H38", "H39", "H4", "H40", "H41", "H42",
"H43", "H44", "H45", "H46", "H47", "H48", "H49", "H5", "H50",
"H51", "H52", "H53", "H54", "H55", "H56", "H57", "H58", "H59",
"H6", "H60", "H61", "H62", "H63", "H64", "H65", "H66", "H67",
"H68", "H69", "H7", "H70", "H71", "H72", "H73", "H74", "H75",
"H76", "H77", "H78", "H79", "H8", "H80", "H81", "H82", "H83",
"H84", "H85", "H86", "H87", "H88", "H89", "H9", "H90", "H91",
"H92", "H93", "H94", "H95", "H96", "H97", "H98", "H99"), class = "factor"),
Hormone = 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, 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, 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, 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, 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("CK", "Control", "GA"), class = "factor"),
Hormone.quantity = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
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, 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("100", "20", "500", "Control"
), class = "factor"), fusion = structure(c(4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 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, 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("CK 100 mg/L",
"CK 20 mg/L", "CK 500 mg/L", "Control", "GA 100 mg/L", "GA 20 mg/L",
"GA 500 mg/L"), class = "factor"), Sowing.date = 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, 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, 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, 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), .Label = "25-mrt", class = "factor"),
BT = structure(c(6L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 6L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 6L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 8L, 4L, 6L, 6L, 6L, 4L, 3L, 4L, 4L, 3L,
4L, 3L, 3L, 3L, 3L, 6L, 6L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 6L, 6L, 8L, 6L, 4L, 4L,
4L, 8L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 6L, 3L, 5L, 4L, 5L, 5L,
4L, 3L), .Label = c("16-apr", "17-apr", "18-apr", "19-apr",
"21-mei", "23-apr", "26-apr", "30-apr"), class = "factor"),
ff = structure(c(14L, 20L, 4L, 10L, 20L, 3L, 1L, 14L, 9L,
11L, 20L, 11L, 9L, 9L, 9L, 11L, 12L, 12L, 6L, 12L, 12L, 16L,
12L, 12L, 17L, 17L, 12L, 16L, 17L, 18L, 12L, 6L, 20L, 20L,
15L, 15L, 15L, 20L, 20L, 11L, 11L, 11L, 9L, 9L, 9L, 9L, 20L,
20L, 20L, 4L, 1L, 4L, 4L, 4L, 8L, 20L, 4L, 20L, 12L, 4L,
14L, 14L, 11L, 11L, 15L, 15L, 11L, 11L, 9L, 15L, 9L, 9L,
11L, 11L, 14L, 1L, 5L, 4L, 4L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 15L, 15L, 14L, 13L, 15L, 15L, 11L, 9L, 9L,
11L, 9L, 11L, 1L, 20L, 1L, 20L, 20L, 20L, 20L, 1L, 1L, 4L,
20L, 20L, 20L, 15L, 15L, 14L, 15L, 1L, 15L, 15L, 20L, 11L,
11L, 11L, 11L, 15L, 10L, 10L, 16L, 10L, 12L, 10L, 17L, 8L,
16L, 12L, 8L, 4L, 4L, 8L, 20L, 10L, 1L, 20L, NA, 12L, 10L,
20L, 20L, 20L, 1L, 20L, 1L, 20L, 12L, 16L, 12L, 2L, 8L, 4L,
10L, 4L, 4L, 4L, 10L, 8L, 4L, 8L, 20L, 20L, 20L, NA, 20L,
1L, 20L, 1L, 8L, 20L, 1L, 1L, 7L, 17L, 19L, 19L, 12L, 10L,
12L, 19L, 10L, 10L, 10L, 17L), .Label = c("10-mei", "13-jun",
"14-apr", "14-mei", "17-mei", "18-jun", "21-jun", "21-mei",
"23-apr", "24-mei", "26-apr", "28-mei", "3-apr", "3-mei",
"30-apr", "31-mei", "4-jun", "5-jul", "7-jun", "7-mei"), class = "factor"),
FH = c(3.5, 6, 9, 16, 5.5, 12, 11.5, 4, 4.5, 6, 8, 5, 4.5,
3.5, 4, 5, 20, 42, 14, 40, 27, 42, 27, 26, 16, 18, 35, 17,
20, 28, 15, 20, 33, 32, 14.5, 14.5, 14.5, 35, 32, 12.5, 13.5,
12, 14.5, 12, 15, 14.5, 18, 18, 18.5, 35, 23, 25, 30, 37,
53, 27.5, 37, 25.5, 35, 47, 8.5, 20.5, 13, 14.5, 13.5, 18.5,
10.5, 10, 14.3, 18.5, 15.3, 11.7, 16, 15, 13.5, 26, 36, 30,
43, 23.5, 23.5, 31.5, 29, 30.5, 30, 29, 30, 24.5, 19, 23,
21.5, 26.5, 18.5, 20, 15, 12.3, 17, 12, 15, 13, 43614, 25,
27, 22.5, 35, 23.5, 30, 42, 42, 55, 32.5, 26, 26, 9.5, 4.5,
5.5, 5, 15.5, 10, 4.5, 8.5, 6, 5, 5.5, 5, 4.5, 30, 20, 16,
16, 20, 22, 30, 22, 25, 11, 13.5, 11, 11, 14, 6, NA, 5.5,
7, NA, 12, 14, 7, 9.5, 6.5, 9, 8.5, 12.5, 8, 27, 33, 35,
32, 17, 14, 22, 11, 17, 12, 25, 22, 15, 10, 5, 3, 4, NA,
5, 8, 4.5, 6, 7, 5, 5.5, 7, 42, 23, 23, 21, 14, 21, 17, 22,
19, 18, 17, 17), SRDT = structure(c(2L, 7L, 14L, NA, 7L,
8L, 7L, NA, NA, NA, 3L, NA, 18L, 15L, 17L, 17L, 18L, 18L,
NA, 18L, 15L, 17L, 15L, 20L, 2L, NA, 11L, 17L, 18L, 2L, 2L,
2L, 14L, 12L, 17L, 15L, 12L, 9L, 9L, 6L, 6L, 15L, 15L, 15L,
15L, NA, 17L, 15L, 10L, 11L, 11L, 10L, 11L, 17L, 5L, 21L,
6L, NA, 20L, 5L, 12L, 7L, NA, 17L, 17L, 15L, 15L, 10L, 10L,
6L, 10L, 10L, 21L, NA, 15L, 15L, 5L, 15L, 15L, 11L, 10L,
21L, 1L, 21L, 21L, 21L, 1L, 5L, 18L, 2L, 9L, 9L, NA, 12L,
10L, NA, 16L, 6L, 6L, 15L, 6L, 10L, 10L, 10L, 1L, 10L, 1L,
21L, 21L, 1L, 21L, 5L, 18L, 2L, 17L, 20L, 9L, 14L, 5L, 9L,
9L, 11L, NA, 18L, 10L, 18L, 20L, 4L, 9L, 7L, 2L, 2L, 7L,
5L, 17L, 17L, 11L, 10L, 12L, 2L, 14L, 19L, 19L, 19L, NA,
NA, 2L, 11L, 17L, 14L, 17L, 9L, 10L, 10L, 2L, 7L, 17L, 14L,
2L, 11L, 20L, 2L, 15L, 15L, 11L, 5L, NA, 10L, NA, 2L, 8L,
NA, NA, 14L, 5L, 15L, 15L, NA, 22L, NA, 9L, 9L, 19L, 9L,
9L, 22L, 20L, 13L, 7L, 20L, 15L, 20L), .Label = c("10-mei",
"11-jun", "13-jun", "13-mei", "14-mei", "17-mei", "18-jun",
"2-jul", "21-jun", "21-mei", "24-mei", "25-jun", "26-jun",
"28-jun", "28-mei", "3-mei", "31-mei", "4-jun", "5-jul",
"7-jun", "7-mei", "9-jul"), class = "factor"), MH = c(26,
50, 58, NA, 46, 58, 61, NA, NA, NA, 40, NA, 68, 48, 47, 42,
26, 50, NA, 48, 27, 42, 27, 48, 25, NA, 25, 17, 20, 18, 32,
19, 75, 75, 65, 70, 73, 73, 71, 65, 70, 60, 80, 70, 70, NA,
54, 45, 45, 45, 45, 40, 49, 53, 45, 27.5, 44, NA, NA, 47,
47, 62, NA, 75, 60, 75, 70, 65, 80, 67, 80, 75, 52, NA, 67,
68, 26, 55, 60, 60, 60, 31.5, 39, 30.5, 30, 29, 39, 39, 86,
74, 80, 76, NA, 69, 80, NA, 44, 70, 70, 65, 43, 60, 57, 57,
45, 60, 39, 35, 32.5, 27, 32.5, 43, 70, 75, 60, 66, 58, 48,
41, NA, 44, 42, NA, 44, 39, 40, 48, 53, 50, 50, 45, 45, 50,
13, 25, 11, 21, 20.5, 46, 44, 54, 25, 20, 25, NA, NA, 28,
33, 36, 40, 21, 36, 23.5, 21, 44, 60, 37, 37, 55, 24, 45,
45, 35, 30, 25, 12, 27, 10, NA, 53, 35, NA, NA, 43, 11, 13,
7, NA, 22, NA, 42, 46, NA, 41, 43, 40, 26, 45, 35, 29, 17,
22), SEEDT = structure(c(2L, 4L, 9L, NA, 4L, 5L, 4L, NA,
NA, NA, 4L, NA, 12L, 11L, 11L, 11L, 4L, 3L, NA, 4L, 15L,
4L, 8L, 5L, 7L, NA, 2L, 2L, 8L, 13L, 8L, NA, 13L, 8L, 15L,
15L, 8L, 7L, 7L, 10L, 10L, 11L, 6L, 10L, 10L, NA, 3L, 11L,
12L, 12L, 12L, 12L, 4L, 4L, 12L, 12L, 12L, NA, 9L, 12L, NA,
4L, NA, 2L, 15L, 2L, 15L, 14L, 10L, 12L, 12L, 11L, 11L, NA,
2L, 12L, 8L, 3L, 15L, 11L, 11L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 2L, 2L, 7L, 7L, NA, 8L, 10L, NA, 10L, 10L, 10L,
15L, 10L, 12L, 12L, 10L, 11L, 11L, 10L, 10L, 10L, 11L, 10L,
11L, 12L, 2L, 12L, 4L, 7L, 9L, 10L, 7L, 7L, 10L, NA, 12L,
10L, 15L, 2L, 4L, 8L, 8L, 4L, 4L, 13L, 12L, NA, NA, 4L, 7L,
NA, 7L, 13L, 13L, 13L, NA, NA, NA, 2L, 2L, NA, NA, NA, 8L,
NA, NA, 4L, 4L, 2L, NA, 4L, 2L, 7L, 7L, 7L, 2L, 2L, 15L,
1L, 15L, NA, 2L, 5L, NA, NA, 5L, 13L, NA, NA, NA, NA, NA,
16L, 16L, 13L, 16L, 7L, 1L, 7L, 16L, 7L, 7L, 7L, NA), .Label = c("11-jul",
"11-jun", "13-jun", "18-jun", "2-jul", "20-mei", "21-jun",
"25-jun", "28-jun", "28-mei", "31-mei", "4-jun", "5-jul",
"6-apr", "7-jun", "9-jul"), class = "factor"), FERMK = c(7L,
8L, 8L, 7L, 8L, 8L, 8L, 4L, NA, NA, 5L, 7L, 7L, 6L, 7L, 6L,
4L, 6L, NA, 4L, 3L, 4L, 4L, 4L, 2L, NA, 2L, 2L, 2L, 1L, 2L,
2L, 8L, 6L, 6L, 6L, 7L, 7L, 7L, 6L, 6L, 7L, 7L, 6L, 4L, 6L,
6L, 5L, 6L, 5L, 5L, 6L, 5L, 4L, 2L, 5L, NA, NA, 4L, 2L, 5L,
5L, NA, 7L, 7L, 8L, 6L, 6L, 7L, NA, 7L, 7L, 6L, 5L, 5L, 5L,
4L, 4L, 6L, 7L, 6L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 8L, 7L, 7L,
7L, 7L, 7L, 7L, NA, 7L, 7L, 7L, 7L, 5L, 5L, 4L, 5L, 6L, 4L,
6L, 2L, 2L, 2L, 5L, 4L, 7L, 6L, 8L, 7L, 6L, 6L, 8L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L, 4L, 4L, 4L, 4L, 2L, 2L, NA,
3L, 2L, NA, 3L, 6L, 5L, 5L, 6L, NA, 6L, 4L, 6L, 5L, 5L, 5L,
5L, 4L, 5L, 4L, 4L, 6L, 5L, 6L, 5L, 7L, 7L, 7L, 3L, 2L, 3L,
3L, 4L, NA, 5L, 5L, NA, 5L, 5L, 3L, 2L, 3L, NA, 4L, NA, 5L,
4L, 5L, 5L, 6L, 4L, 4L, 3L, 3L, 4L, 5L, NA), PLRMK = c(1L,
2L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L, 2L, 0L, 0L, 0L, 0L,
1L, 1L, NA, 1L, 1L, 2L, 1L, 1L, 4L, NA, 5L, 5L, 4L, 5L, 3L,
4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, NA,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 4L, 5L, NA, NA, 5L, 6L, 1L,
1L, NA, 1L, 1L, 0L, 1L, 1L, 1L, NA, 2L, 1L, 2L, NA, 2L, NA,
4L, 3L, 2L, 2L, 1L, 4L, 5L, 5L, 4L, 5L, 7L, 6L, 1L, 1L, 1L,
1L, NA, 1L, 2L, NA, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 4L, 5L, 2L,
4L, 7L, 5L, 8L, 5L, 2L, 0L, 1L, 1L, 1L, 7L, 1L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, NA, 2L, 3L, 1L, 1L, 2L, 1L, 2L, 6L, 6L, NA,
4L, 4L, NA, 2L, 2L, 1L, 1L, 1L, NA, 1L, 1L, 3L, 1L, 1L, 1L,
1L, NA, NA, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 5L, 5L, 4L,
1L, 4L, NA, 2L, 1L, NA, NA, 2L, 2L, 0L, 0L, NA, 1L, NA, 4L,
2L, 1L, 2L, 1L, 2L, 4L, 1L, 2L, 4L, 3L, NA), FF = c(39L,
43L, 50L, 60L, 43L, 20L, 46L, 39L, 29L, 32L, 43L, 32L, 29L,
29L, 29L, 32L, 64L, 64L, 85L, 64L, 64L, 67L, 64L, 64L, 71L,
71L, 64L, 67L, 71L, 102L, 64L, 85L, 43L, 43L, 36L, 36L, 36L,
43L, 43L, 32L, 32L, 32L, 29L, 29L, 29L, 29L, 43L, 43L, 43L,
50L, 46L, 50L, 50L, 50L, 57L, 43L, 50L, 43L, 64L, 50L, 39L,
39L, 32L, 32L, 36L, 36L, 32L, 32L, 29L, 36L, 29L, 29L, 32L,
32L, 39L, 46L, 53L, 50L, 50L, 43L, 43L, 43L, 43L, 43L, 43L,
43L, 43L, 43L, 36L, 36L, 39L, 9L, 36L, 36L, 32L, 29L, 29L,
32L, 29L, 32L, 46L, 43L, 46L, 43L, 43L, 43L, 43L, 46L, 46L,
50L, 43L, 43L, 43L, 36L, 36L, 39L, 36L, 46L, 36L, 36L, 43L,
32L, 32L, 32L, 32L, 36L, 60L, 60L, 67L, 60L, 64L, 60L, 71L,
57L, 67L, 64L, 57L, 50L, 50L, 57L, 43L, 60L, 46L, 43L, NA,
64L, 60L, 43L, 43L, 43L, 46L, 43L, 46L, 43L, 64L, 67L, 64L,
80L, 57L, 50L, 60L, 50L, 50L, 50L, 60L, 57L, 50L, 57L, 43L,
43L, 43L, NA, 43L, 46L, 43L, 46L, 57L, 43L, 46L, 46L, 88L,
71L, 74L, 74L, 64L, 60L, 64L, 74L, 60L, 60L, 60L, 71L), MF = c(78L,
85L, 95L, NA, 85L, 99L, 85L, NA, NA, NA, 80L, NA, 71L, 64L,
67L, 67L, 71L, 71L, NA, 71L, 64L, 67L, 64L, 74L, 78L, NA,
60L, 67L, 71L, 78L, 78L, 78L, 95L, 92L, 67L, 64L, 92L, 88L,
88L, 53L, 53L, 64L, 64L, 64L, 64L, NA, 67L, 64L, 57L, 60L,
60L, 57L, 60L, 67L, 50L, 43L, 53L, NA, 74L, 50L, 92L, 85L,
NA, 67L, 67L, 64L, 64L, 57L, 57L, 53L, 57L, 57L, 43L, NA,
64L, 64L, 50L, 64L, 64L, 60L, 57L, 43L, 46L, 43L, 43L, 43L,
46L, 50L, 71L, 78L, 88L, 88L, NA, 92L, 57L, NA, 39L, 53L,
53L, 64L, 53L, 57L, 57L, 57L, 46L, 57L, 46L, 43L, 43L, 46L,
43L, 50L, 71L, 78L, 67L, 74L, 88L, 95L, 50L, 88L, 88L, 60L,
NA, 71L, 57L, 71L, 74L, 49L, 88L, 85L, 78L, 78L, 85L, 50L,
67L, 67L, 60L, 57L, 92L, 78L, 95L, 102L, 102L, 102L, NA,
NA, 78L, 60L, 67L, 95L, 67L, 88L, 57L, 57L, 78L, 85L, 67L,
95L, 78L, 60L, 74L, 78L, 64L, 64L, 60L, 50L, NA, 57L, NA,
78L, 99L, NA, NA, 95L, 50L, 64L, 64L, NA, 106L, NA, 88L,
88L, 102L, 88L, 88L, 106L, 74L, 93L, 85L, 74L, 64L, 74L),
speed = c(0.08974359, 0.139534884, 0.18, 0.266666667, 0.127906977,
0.6, 0.25, 0.102564103, 0.155172414, 0.1875, 0.186046512,
0.15625, 0.155172414, 0.120689655, 0.137931034, 0.15625,
0.3125, 0.65625, 0.164705882, 0.625, 0.421875, 0.626865672,
0.421875, 0.40625, 0.225352113, 0.253521127, 0.546875, 0.253731343,
0.281690141, 0.274509804, 0.234375, 0.235294118, 0.76744186,
0.744186047, 0.402777778, 0.402777778, 0.402777778, 0.813953488,
0.744186047, 0.390625, 0.421875, 0.375, 0.5, 0.413793103,
0.517241379, 0.5, 0.418604651, 0.418604651, 0.430232558,
0.7, 0.5, 0.5, 0.6, 0.74, 0.929824561, 0.639534884, 0.74,
0.593023256, 0.546875, 0.94, 0.217948718, 0.525641026, 0.40625,
0.453125, 0.375, 0.513888889, 0.328125, 0.3125, 0.493103448,
0.513888889, 0.527586207, 0.403448276, 0.5, 0.46875, 0.346153846,
0.565217391, 0.679245283, 0.6, 0.86, 0.546511628, 0.546511628,
0.73255814, 0.674418605, 0.709302326, 0.697674419, 0.674418605,
0.697674419, 0.569767442, 0.527777778, 0.638888889, 0.551282051,
2.944444444, 0.513888889, 0.555555556, 0.46875, 0.424137931,
0.586206897, 0.375, 0.517241379, 0.40625, 948.1304348, 0.581395349,
0.586956522, 0.523255814, 0.813953488, 0.546511628, 0.697674419,
0.913043478, 0.913043478, 1.1, 0.755813953, 0.604651163,
0.604651163, 0.263888889, 0.125, 0.141025641, 0.138888889,
0.336956522, 0.277777778, 0.125, 0.197674419, 0.1875, 0.15625,
0.171875, 0.15625, 0.125, 0.5, 0.333333333, 0.23880597, 0.266666667,
0.3125, 0.366666667, 0.422535211, 0.385964912, 0.373134328,
0.171875, 0.236842105, 0.22, 0.22, 0.245614035, 0.139534884,
NA, 0.119565217, 0.162790698, NA, 0.1875, 0.233333333, 0.162790698,
0.220930233, 0.151162791, 0.195652174, 0.197674419, 0.27173913,
0.186046512, 0.421875, 0.492537313, 0.546875, 0.4, 0.298245614,
0.28, 0.366666667, 0.22, 0.34, 0.24, 0.416666667, 0.385964912,
0.3, 0.175438596, 0.11627907, 0.069767442, 0.093023256, NA,
0.11627907, 0.173913043, 0.104651163, 0.130434783, 0.122807018,
0.11627907, 0.119565217, 0.152173913, 0.477272727, 0.323943662,
0.310810811, 0.283783784, 0.21875, 0.35, 0.265625, 0.297297297,
0.316666667, 0.3, 0.283333333, 0.23943662), ratiofm = c(7,
4, 8, 7, 8, 8, 8, NA, NA, NA, 5, 3.5, NA, NA, NA, NA, 4,
6, NA, 4, 3, 2, 4, 4, 0.5, NA, 0.4, 0.4, 0.5, 0.2, 0.666666667,
0.5, 8, 6, 6, 6, 7, 7, 7, 3, 3, 3.5, 3.5, 6, 4, NA, 3, 2.5,
3, 5, 2.5, 3, 5, 4, 0.5, 1, NA, NA, 0.8, 0.333333333, 5,
5, NA, 7, 7, NA, 6, 6, 7, NA, 3.5, 7, 3, NA, 2.5, NA, 1,
1.333333333, 3, 3.5, 6, 1, 0.4, 0.4, 0.5, 0.4, 0.285714286,
0.333333333, 8, 7, 7, 7, NA, 7, 3.5, NA, 7, 7, 7, 7, 1.666666667,
1.666666667, 4, 1.25, 1.2, 2, 1.5, 0.285714286, 0.4, 0.25,
1, 2, NA, 6, 8, 7, 0.857142857, 6, NA, 7, 7, NA, NA, NA,
NA, NA, 3.5, 1.666666667, 5, 4, 2, 4, 2, 0.333333333, 0.333333333,
NA, 0.75, 0.5, NA, 1.5, 3, 5, 5, 6, NA, 6, 4, 2, 5, 5, 5,
5, NA, NA, 4, 4, 3, 2.5, 3, 2.5, 2.333333333, 3.5, 3.5, 0.6,
0.4, 0.75, 3, 1, NA, 2.5, 5, NA, NA, 2.5, 1.5, NA, NA, NA,
4, NA, 1.25, 2, 5, 2.5, 6, 2, 1, 3, 1.5, 1, 1.666666667,
NA)), class = "data.frame", row.names = c(NA, -192L))
It would be more clear with pictures of my graphs, but apparently I'm not allowed yet to include pictures in my posts, sorry
Thanks in advance for your help
you can try
library(tidyverse)
df %>%
as_tibble() %>%
ggplot(aes(x=fusion, y=FF)) +
geom_boxplot(aes(colour=fusion))+
ggsignif::geom_signif(comparisons = combn(levels(df$fusion), 2, simplify = F), step_increase = 0.3) +
ggpubr::stat_compare_means() +
facet_wrap(~Genotype)+
xlab(" ")+
ylab("Days after sowing")
I cannot figure out how to use a loop to plot one histogram for each unique combination of levels from TWO factors.
Here is my data: https://www.dropbox.com/sh/exsjhu23fnpwf4r/AABvitLBN1nRMpXcyYMVIOIDa?dl=0
# perhaps need to have factors
df$freq <- as.factor(df$freq)
df$time <- as.factor(df$time)
I learned how to use a loop to plot histograms for ONE factor levels:
# space for plots
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21), nrow=3, ncol=7, byrow=T))
layout.show(a)
# loop
for (i in 1:length(unique(df$freq))) {
value <- subset(df, freq == unique (df$freq)[i])
hist(value$thr, main=paste0("freq: ", unique(df$freq)[i]))
}
I tried variations of this loop for TWO factors but that unfortunately does not work:
for (i in 1:length(unique(df[c("freq", "time")]))) {
value <- subset(df, freq == unique (df$freq)[i] & time == unique(df$time)[i])
hist(value$thr, main=paste0("freq: ", unique(df$freq)[i]))
}
I would also like to learn how to label each histogram based on the levels of TWO factors (not just one)...
It's more convenient to use by here.
For the titles we prefer characters to factors.
df1[c("freq", "time")] <- lapply(df1[c("freq", "time")], as.character)
Then open windows,
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(1:21, 3, 7))
layout.show(a)
and plot.
by(df1, df1[c("freq", "time")], function(x)
hist(x$thr, main=paste("freq:", paste(x[1, c(1, 3)], collapse=","))))
Result
Edit
To get the specific order we probably have to do some more stuff.
df1[c("freq", "time")] <- lapply(df1[c("freq", "time")], as.character)
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(1:21, 3, 7, byrow=TRUE)) # with byrow
layout.show(a)
l <- split(df1, df1[c("freq", "time")])
m <- t(sapply(l, function(x) x[1, c(1, 3)])) # matrix of first rows of each subset
m[, 2] <- sub("m", "", m[, 2]) # use the values...
m <- apply(m, 1:2, as.numeric) # ... make numeric
Now we obtain the histograms within a lapply over the list ordered by m.
lapply(l[order(m[, 2], m[, 1])], function(x)
hist(x$thr, main=paste("freq:", paste(x[1, c(1, 3)], collapse=","))))
New Result
Data
df1 <- structure(list(freq = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L), .Label = c("4",
"8", "12.5", "16", "20", "25", "31.5"), class = "factor"), thr = c(60L,
25L, 20L, 15L, 15L, 30L, 35L, 60L, 25L, 10L, 15L, 15L, 30L, 35L,
55L, 30L, 15L, 15L, 10L, 25L, 40L, 50L, 25L, 15L, 10L, 15L, 20L,
40L, 50L, 30L, 10L, 15L, 15L, 20L, 25L, 50L, 25L, 10L, 10L, 10L,
20L, 25L, 45L, 20L, 10L, 10L, 10L, 20L, 25L, 45L, 15L, 10L, 10L,
10L, 20L, 30L, 60L, 30L, 10L, 10L, 10L, 15L, 30L, 50L, 25L, 10L,
10L, 10L, 20L, 30L, 45L, 25L, 15L, 10L, 15L, 30L, 35L, 50L, 25L,
15L, 10L, 15L, 25L, 35L, 60L, 25L, 10L, 10L, 15L, 20L, 30L, 60L,
25L, 5L, 5L, 10L, 20L, 30L, 45L, 20L, 5L, 10L, 10L, 20L, 30L,
45L, 20L, 10L, 10L, 10L, 20L, 30L, 60L, 30L, 15L, 10L, 15L, 25L,
30L, 55L, 25L, 10L, 10L, 10L, 20L, 30L, 55L, 35L, 10L, 10L, 10L,
20L, 30L, 60L, 35L, 15L, 10L, 10L, 15L, 25L, 50L, 30L, 10L, 10L,
10L, 20L, 25L, 55L, 25L, 10L, 10L, 15L, 25L, 25L, 65L, 30L, 10L,
10L, 15L, 20L, 30L, 60L, 30L, 15L, 15L, 15L, 15L, 30L, 55L, 35L,
15L, 15L, 15L, 25L, 35L, 55L, 35L, 15L, 15L, 15L, 25L, 35L, 60L,
35L, 15L, 15L, 15L, 25L, 35L, 60L, 30L, 10L, 10L, 15L, 25L, 35L,
55L, 30L, 15L, 10L, 10L, 25L, 30L, 50L, 25L, 10L, 10L, 10L, 20L,
30L, 55L, 30L, 10L, 10L, 15L, 20L, 30L, 55L, 30L, 10L, 15L, 20L,
25L, 35L, 55L, 25L, 15L, 15L, 15L, 25L, 40L, 50L, 20L, 10L, 10L,
20L, 30L, 40L, 45L, 25L, 10L, 10L, 10L, 20L, 30L, 50L, 25L, 10L,
10L, 10L, 20L, 25L, 55L, 20L, 10L, 10L, 15L, 25L, 35L, 50L, 20L,
10L, 10L, 15L, 25L, 30L, 45L, 20L, 15L, 10L, 10L, 20L, 30L, 50L,
20L, 15L, 15L, 15L, 20L, 30L, 60L, 35L, 15L, 10L, 15L, 25L, 30L,
60L, 35L, 15L, 15L, 15L, 30L, 35L, 55L, 25L, 10L, 15L, 15L, 25L,
35L, 50L, 30L, 10L, 15L, 15L, 25L, 35L, 55L, 25L, 20L, 15L, 15L,
25L, 30L, 55L, 25L, 15L, 15L, 15L, 30L, 35L), time = 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, 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, 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,
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, 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, 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, 3L, 3L, 3L,
3L), .Label = c("3m", "6m", "9m"), class = "factor")), row.names = c(NA,
-322L), class = "data.frame")
I am new to R and trying to figure out a way to plot means for individual samples as well as group means with ggplot.
I am following this articles on R-bloggers (last paragraph):
https://www.r-bloggers.com/plotting-individual-observations-and-group-means-with-ggplot2/
This is my code:
gd <- meanplot1 %>%
group_by(treatment, value) %>%
summarise(measurement = mean(measurement))
ggplot(meanplot1, aes(x=value, y=measurement, color=treatment)) +
geom_line(aes(group=sample), alpha=0.3) +
geom_line(data=gd, size=3, alpha=0.9) +
theme_bw()
Whilst the sample means are being shown, the group means aren´t. I get the error
geom_path: Each group consists of only one observation. Do you need
to adjust the group aesthetic?
Upon adding group=1, I get a weirdly mixed category mean, but not what I am looking for..
I scrolled through a lot of articles already, but couldnt find an answer - I would be so happy if somebody could help me out here!! :)
My data (meanplot1) is formatted like this:
treatment sample value measurement
1 control, control 1, initial, 20,
2 control, control 1, 26, NA,
3 control, control 1, 26', 28,
12 control, control 2, initial, 22,
13 control control 2, 26, NA,
14 control control 2, 26', 36,
15 control control 2, 28, 45,
67 stressed, stress 1, initial, 37,
68 stressed, stress 1, 26, NA,
69 stressed, stress 1, 26', 17,
78 stressed, stress 2, initial, 36,
79 stressed, stress 2, 26, NA,
80 stressed, stress 2, 26', 25,
I am hoping to see 6 lines, one mean for stress 1, stress 2, control 1 and control 2, and one mean for all treatment=control, and one for all treatment=stressed
output dput(gd):
structure(list(treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("control", "stressed"), class = "factor"), value = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L), .Label = c("26", "26'", "28", "28'",
"30", "30'", "32", "32'", "34", "34'", "initial"), class = "factor"),
measurement = c(NA, 32.3333333333333, 39.5, 30.3333333333333,
31.8333333333333, 31.8333333333333, NA, 36, 34.6666666666667,
36, 24.6666666666667, NA, 25.3333333333333, 33.3333333333333,
32, 50.1666666666667, 39.1666666666667, NA, 33.5, 24.3333333333333,
27.3333333333333, 36)), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -22L), vars = list(treatment), drop = TRUE, .Names = c("treatment",
"value", "measurement"))
output dput(meanplot1):
structure(list(treatment = 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, 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, 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, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("control",
"stressed"), class = "factor"), sample = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), .Label = c("control 1",
"control 2", "control 3", "control 4", "control 5", "control 6",
"stress 1", "stress 2", "stress 3", "stress 4", "stress 5", "stress 6"
), class = "factor"), value = structure(c(11L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("26", "26'",
"28", "28'", "30", "30'", "32", "32'", "34", "34'", "initial"
), class = "factor"), measurement = c(20L, NA, 28L, 18L, 17L,
19L, 34L, NA, 23L, 29L, 27L, 22L, NA, 36L, 45L, 31L, 40L, 44L,
NA, 49L, 40L, 39L, 32L, NA, 35L, 57L, 30L, 37L, 29L, NA, 44L,
37L, 46L, 20L, NA, 39L, 27L, 30L, 40L, 25L, NA, 29L, 50L, 30L,
26L, NA, 28L, 45L, 47L, 27L, 35L, NA, 24L, 22L, 35L, 28L, NA,
28L, 45L, 27L, 28L, 24L, NA, 47L, 30L, 39L, 37L, NA, 17L, 29L,
29L, 31L, 29L, NA, 37L, 21L, 27L, 36L, NA, 25L, 41L, 51L, 66L,
50L, NA, 33L, 25L, 22L, 36L, NA, 33L, 45L, 26L, 72L, 59L, NA,
33L, 26L, 25L, 33L, NA, 21L, 33L, 25L, 29L, 21L, NA, 26L, 20L,
16L, 22L, NA, 30L, 27L, 28L, 57L, 41L, NA, 28L, 23L, 17L, 52L,
NA, 26L, 25L, 33L, 46L, 35L, NA, 44L, 31L, 57L)), .Names = c("treatment",
"sample", "value", "measurement"), class = "data.frame", row.names = c(NA,
-132L))
I suppose you are aiming to plot the treatment means.
By default, since you are using a categorical x-axis, the grouping is set to the interaction between x and color. You only want to group by treatment, however. So we'll add the correct grouping to the call.
ggplot(meanplot1, aes(x = value, y = measurement, color=treatment)) +
geom_line(aes(group=sample), alpha=0.3) +
geom_line(aes(group = treatment), gd, size=3, alpha=0.9) +
theme_bw()
Also note that
ggplot(meanplot1, aes(x=value, y=measurement, color=treatment)) +
geom_line(aes(group=sample), alpha=0.3) +
stat_summary(aes(group = treatment), fun.y = mean, geom = 'line', size=3, alpha=0.9) +
theme_bw()
Gives the same plot, without the interruption.
I am using systemfit to run system of equations using SUR method. I need to read long (multiple line) formula.My simple reproducible dataset can be accessed using following codes.
dat<-structure(list(Time = structure(c(9L, 7L, 15L, 1L, 17L, 13L,
11L, 3L, 23L, 21L, 19L, 5L, 10L, 8L, 16L, 2L, 18L, 14L, 12L,
4L, 24L, 22L, 20L, 6L), .Label = c("Apr-00", "Apr-01", "Aug-00",
"Aug-01", "Dec-00", "Dec-01", "Feb-00", "Feb-01", "Jan-00", "Jan-01",
"Jul-00", "Jul-01", "Jun-00", "Jun-01", "Mar-00", "Mar-01", "May-00",
"May-01", "Nov-00", "Nov-01", "Oct-00", "Oct-01", "Sep-00", "Sep-01"
), class = "factor"), 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), .Label = c("A", "B"), class = "factor"), y1 = c(25L,
14L, 45L, 15L, 24L, 17L, 18L, 19L, 14L, 15L, 25L, 14L, 45L, 15L,
24L, 17L, 18L, 19L, 14L, 15L, 25L, 14L, 45L, 15L), y2 = c(4L,
3L, 4L, 5L, 1L, 4L, 5L, 3L, 6L, 4L, 2L, 5L, 4L, 3L, 4L, 5L, 1L,
4L, 5L, 3L, 6L, 4L, 2L, 5L), x1 = c(3L, 4L, 1L, 8L, 6L, 7L, 9L,
7L, 3L, 1L, 2L, 5L, 6L, 3L, 4L, 1L, 8L, 6L, 7L, 9L, 7L, 3L, 1L,
2L), x2 = c(4L, 3L, 4L, 5L, 1L, 4L, 5L, 3L, 6L, 4L, 2L, 5L, 4L,
3L, 4L, 5L, 1L, 4L, 5L, 3L, 6L, 4L, 2L, 5L), x3 = c(3L, 4L, 2L,
8L, 6L, 7L, 9L, 7L, 3L, 1L, 2L, 5L, 6L, 3L, 4L, 2L, 8L, 6L, 7L,
9L, 7L, 3L, 1L, 2L), x4 = c(4L, 3L, 4L, 5L, 1L, 4L, 5L, 3L, 6L,
4L, 2L, 5L, 4L, 3L, 4L, 5L, 1L, 4L, 5L, 3L, 6L, 4L, 2L, 5L),
x5 = c(3L, 4L, 3L, 8L, 6L, 7L, 9L, 7L, 3L, 1L, 2L, 5L, 6L,
3L, 4L, 3L, 8L, 6L, 7L, 9L, 7L, 3L, 1L, 2L)), .Names = c("Time",
"ID", "y1", "y2", "x1", "x2", "x3", "x4", "x5"), class = "data.frame", row.names = c(NA,
-24L))
My example formula is like this,
model1<- y1 ~ x1 + x2 + x3
+ x4 +x5
eqSystem <- list(model1)
library(systemfit)
fit_prod_SUR <- systemfit(eqSystem, method= "SUR", data=dat)
print(fit_prod_SUR)
I have to include several very long formulas into eqSystem. But my problem is since my formula (e.g. model1) are very long it has got multiple lines. When I run the eqSystem with systemfit, it reads only the variables in the very first line of each formula. I tried with following code, but it does not work.
model1<- (get(paste("y1 ~ x1 + x2 + x3",
" + x4 + x5", sep="")))
But it does not take as a formula. Please could anyone help me to how to read all variables (in multiple lines) of formula in R.
I'm relatively new to R, and I wondered if anyone could help me with a barchart I'm trying to create with the lattice package. I've managed to create the plot below (can't post because I'm a new user). Each panel represents the abundance of a separate species, while the bars represent the stacked abundances of larval stages of each species at specific depths. The problem is that I'd like to present the depths in a more intuitive way, with 0 m at the top of each panel, and 90 m at the bottom - this means 'flipping' the axis along with the bars. I created this plot using the following code:
# create a new column for Species and Depth as factors
stn8_9$Depth_mF<-as.factor(stn8_9$Depth_m)
stn8_9$SpeciesF<-as.factor(stn8_9$Species)
# log root transform data
stn8_9$logAbundance_per_m3<-(stn8_9$Abundance_per_m3)^(1/4)
# now create chart
barchart(Depth_mF~logAbundance_per_m3 | SpeciesF,
data=stn8_9[stn8_9$SpeciesF!="CYP" & stn8_9$Stn==9,],
horiz=TRUE, ylab="depth (m)",xlab="Abundance (#/m3)",
main="Station 9", origin=0,
col=c("red","orange","yellow","green","blue","purple"),
stack=TRUE, groups=stn8_9$Stage,
key=
list(title="Stage", cex.title=1,text=list(c("1","2","3","4","5","6")),
space="right", rectangles=list(size=2,border="white",
col=c("red","orange","yellow","green","blue","purple"))))
The dataset is provided at the bottom (hope it's in the right format)
I understand that barchart turns my 'depth' values into factors, and I have tried using reorder() and relevel(), and have managed to get the axis labels to flip, but the bars remain in the same place (not sure why). I'd like the '0 m' bar at the top, and the '90 m' bar at the bottom - can anyone please help?
Dataset:
dput(stn8_9)
structure(list(Stn = c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L), Depth_m = c(90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 60L, 60L, 60L, 60L, 60L, 60L, 60L,
60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L,
60L, 60L, 60L, 60L, 60L, 60L, 40L, 40L, 40L, 40L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 40L, 40L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L,
70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L,
70L, 70L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L,
40L, 40L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), Species = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L), .Label = c("BB",
"BC", "CH", "CYP", "SB", "VS"), class = "factor"), Stage = c(2L,
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L,
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L,
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L,
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L,
5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L,
5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L,
5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L,
6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L,
6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L,
7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L,
2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L,
2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L,
3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L,
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L,
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L,
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L,
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L,
5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L), Abundance_per_m3 = c(0,
0, 0, 1.267024758, 1.267024758, 0, 0, 0, 0, 0, 5.068099033, 0,
0, 0, 0, 25.34049517, 0, 0, 3.801074275, 0, 0, 2.534049517, 7.60214855,
12.67024758, 6.335123791, 0, 0, 0, 0, 3.044963144, 4.059950858,
2.029975429, 1.014987715, 4.059950858, 5.074938573, 7.104914002,
3.044963144, 0, 2.029975429, 0, 0, 30.44963144, 0, 0, 4.059950858,
2.029975429, 0, 11.16486486, 4.059950858, 11.16486486, 2.029975429,
1.014987715, 0, 0, 0, 0, 0, 9.899386594, 4.949693297, 15.83901855,
16.82895721, 10.88932525, 3.959754638, 6.929570616, 0, 0, 0,
24.74846649, 0, 0, 5.939631957, 0, 0.989938659, 1.979877319,
0.989938659, 1.979877319, 0.989938659, 1.979877319, 0, 0, 0,
0, 0, 17.89544764, 1.988383071, 9.941915354, 5.965149212, 7.953532283,
0, 15.90706457, 3.976766141, 1.988383071, 0, 23.86059685, 1.988383071,
9.941915354, 1.988383071, 0, 0, 0, 9.941915354, 61.63987519,
51.69795984, 9.941915354, 0, 0, 0, 0, 0, 28.83473086, 48.05788476,
33.64051933, 14.41736543, 0, 4.805788476, 38.44630781, 4.805788476,
0, 0, 28.83473086, 19.2231539, 28.83473086, 43.25209628, 33.64051933,
0, 72.08682714, 163.3968082, 692.0335406, 321.9878279, 86.50419257,
0, 0, 0, 0, 0, 19.85102993, 9.925514965, 9.925514965, 29.7765449,
0, 9.925514965, 39.70205986, 19.85102993, 0, 0, 29.7765449, 9.925514965,
29.7765449, 9.925514965, 19.85102993, 0, 29.7765449, 178.6592694,
744.4136224, 416.8716286, 49.62757483, 0, 0, 0, 0, 0, 11.25305392,
3.215158262, 12.86063305, 16.07579131, 8.037895656, 19.29094957,
6.430316525, 4.822737393, 0, 0, 51.4425322, 1.607579131, 3.215158262,
1.607579131, 1.607579131, 1.607579131, 1.607579131, 8.037895656,
6.430316525, 1.607579131, 1.607579131, 0, 0, 0, 0, 0, 30.07822022,
12.03128809, 15.03911011, 15.03911011, 9.023466065, 13.5351991,
6.015644043, 0, 1.503911011, 0, 27.07039819, 0, 6.015644043,
3.007822022, 0, 1.503911011, 9.023466065, 25.56648718, 16.54302112,
15.03911011, 6.015644043, 0, 0, 0, 4.939940207, 0, 39.51952166,
14.81982062, 4.939940207, 4.939940207, 4.939940207, 0, 4.939940207,
4.939940207, 0, 0, 29.63964124, 4.939940207, 4.939940207, 14.81982062,
0, 0, 29.63964124, 197.5976083, 568.0931238, 261.816831, 54.33934228,
0, 0, 0, 10.62671701, 0, 21.25343402, 10.62671701, 21.25343402,
21.25343402, 31.88015104, 0, 0, 0, 0, 0, 10.62671701, 31.88015104,
0, 31.88015104, 21.25343402, 0, 138.1473212, 244.4144913, 371.9350954,
212.5343402, 31.88015104, 0, 0, 0, 69.50153499, 19.85758143,
39.71516285, 29.78637214, 79.4303257, 49.64395357, 79.4303257,
9.928790713, 39.71516285, 89.35911642, 9.928790713, 0, 29.78637214,
79.4303257, 19.85758143, 178.7182328, 148.9318607, 99.28790713,
317.7213028, 615.5850242, 1211.312467, 327.6500935, 69.50153499
), Depth_mF = structure(c(7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 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, 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, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 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), .Label = c("5", "10", "20", "40", "60",
"70", "90"), class = "factor"), SpeciesF = structure(c(1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L,
6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L,
6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L), .Label = c("BB",
"BC", "CH", "CYP", "SB", "VS"), class = "factor"), logAbundance_per_m3 = c(0,
0, 0, 1.06095331789381, 1.06095331789381, 0, 0, 0, 0, 0, 1.50041457128417,
0, 0, 0, 0, 2.24364310060701, 0, 0, 1.39629309072760, 0, 0, 1.26169323444953,
1.6604816781224, 1.88667144022303, 1.58649525090772, 0, 0, 0,
0, 1.32097777276068, 1.41948299515758, 1.19363816214162, 1.00372605177853,
1.41948299515758, 1.50092052805914, 1.63263727001607, 1.32097777276068,
0, 1.19363816214162, 0, 0, 2.34906757441938, 0, 0, 1.41948299515758,
1.19363816214162, 0, 1.82794602415898, 1.41948299515758, 1.82794602415898,
1.19363816214162, 1.00372605177853, 0, 0, 0, 0, 0, 1.77378946506859,
1.49157320259098, 1.99495023677811, 2.02541630374003, 1.81656207258872,
1.41064284060004, 1.62246964847031, 0, 0, 0, 2.23042217070931,
0, 0, 1.56113292684677, 0, 0.99747511829459, 1.18620450786389,
0.99747511829459, 1.18620450786389, 0.99747511829459, 1.18620450786389,
0, 0, 0, 0, 0, 2.05676958572452, 1.18747647396250, 1.77569149802404,
1.562806928309, 1.67934533442396, 0, 1.99708942036914, 1.41215547164577,
1.18747647396250, 0, 2.21014275343154, 1.18747647396250, 1.77569149802404,
1.18747647396250, 0, 0, 0, 1.77569149802404, 2.80198262342921,
2.68144165217603, 1.77569149802404, 0, 0, 0, 0, 0, 2.31728246613553,
2.63294121550744, 2.40832821053419, 1.94859451890365, 0, 1.48061165227674,
2.49008206159717, 1.48061165227674, 0, 0, 2.31728246613553, 2.09390107914848,
2.31728246613553, 2.56449460796253, 2.40832821053419, 0, 2.91382843883587,
3.57528685520031, 5.12898921614741, 4.23603815839925, 3.04971523426329,
0, 0, 0, 0, 0, 2.11079356271994, 1.77495874023182, 1.77495874023182,
2.33597707218005, 0, 1.77495874023182, 2.5101707230885, 2.11079356271994,
0, 0, 2.33597707218005, 1.77495874023182, 2.33597707218005, 1.77495874023182,
2.11079356271994, 0, 2.33597707218005, 3.65600169507374, 5.2234035271001,
4.51856552762811, 2.65418238899045, 0, 0, 0, 0, 0, 1.83154502725781,
1.33906170112536, 1.89371921865949, 2.00236428275782, 1.68378094745549,
2.09574482014368, 1.59242170246783, 1.48191537399859, 0, 0, 2.67812340235484,
1.12601218427985, 1.33906170112536, 1.12601218427985, 1.12601218427985,
1.12601218427985, 1.12601218427985, 1.68378094745549, 1.59242170246783,
1.12601218427985, 1.12601218427985, 0, 0, 0, 0, 0, 2.34187135068818,
1.86242173581786, 1.96927122377906, 1.96927122377906, 1.73317871692943,
1.91807754972377, 1.56610376120967, 0, 1.10740258963914, 0, 2.28099146904783,
0, 1.56610376120967, 1.31693103877130, 0, 1.10740258963914, 1.73317871692943,
2.24862878114404, 2.01675761776174, 1.96927122377906, 1.56610376120967,
0, 0, 0, 1.49083789392989, 0, 2.50728048152353, 1.96205300969286,
1.49083789392989, 1.49083789392989, 1.49083789392989, 0, 1.49083789392989,
1.49083789392989, 0, 0, 2.33328739913925, 1.49083789392989, 1.49083789392989,
1.96205300969286, 0, 0, 2.33328739913925, 3.74925881222597, 4.88207990404496,
4.02253091444491, 2.71505476657560, 0, 0, 0, 1.80550950406603,
0, 2.14712474844036, 1.80550950406603, 2.14712474844036, 2.14712474844036,
2.37618413862639, 0, 0, 0, 0, 0, 1.80550950406603, 2.37618413862639,
0, 2.37618413862639, 2.14712474844036, 0, 3.42835366591009, 3.95395514204289,
4.39153946529161, 3.8181877309365, 2.37618413862639, 0, 0, 0,
2.88734446548044, 2.11096769918679, 2.51037780725583, 2.33616978566337,
2.98535914973356, 2.65440135387150, 2.98535914973356, 1.77510517087316,
2.51037780725583, 3.07457234475636, 1.77510517087316, 0, 2.33616978566337,
2.98535914973356, 2.11096769918679, 3.65630330777027, 3.49338864171756,
3.15663297601737, 4.22193539810782, 4.98106273377854, 5.899484260135,
4.25453963683082, 2.88734446548044)), .Names = c("Stn", "Depth_m",
"Species", "Stage", "Abundance_per_m3", "Depth_mF", "SpeciesF",
"logAbundance_per_m3"), row.names = c(NA, -286L), class = "data.frame")
I hope I'm reading you correctly, as I saw only meters from 5 up to 90, none with 0. But if you simply make that variable an ordered factor with the values ordered "in reverse" you'll get what I think you describe:
d$Depth_mF <- factor(d$Depth_m,
levels = rev(sort(unique(d$Depth_m))),
ordered = TRUE)