Ggplot ggstance::position_dodgev not working for lines - r

I am trying to make a line plot but a lot of the lines overlap and I cannot fix that. I found a function that works with the dots but it is not working with the lines.
Here is my data:
structure(list(id = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("14",
"15", "18", "19", "20", "21", "22"), class = "factor"), session = c(1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10), item = c("2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2"
), score = c(NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, NA, 4L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, NA, 1L, 1L, 2L, 3L, 1L, 2L, 1L,
2L, NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 2L,
2L, 2L, 2L, NA, NA, NA, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, NA,
NA, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, NA)), row.names = c(NA, -70L
), class = c("tbl_df", "tbl", "data.frame"))
And here is my code for the plot:
ggplot(data = iwi, aes(x = session, y = score, group = id)) +
geom_point(size = 2,aes(colour = id),
position=ggstance::position_dodgev(height=0.3)) +
geom_line(size = 1.2,aes(group = id, colour = id),
position=ggstance::position_dodgev(height=0.3)) +
scale_y_continuous(name = "score",
limits = c(0,4),
breaks = c(0,1,2,3,4))
It looks like this:
You can see how it is messing up with the lines, so instead of joining point 1 to point 2 and so on, it joins point 2 to 1 and then point 3. Here is how the plot looks without using position_dodgev():
As you can see, everyone with a score of 2 gets into a single line which is useless. How can I fix this? position_dodgev() works amazing for the points, only doesn't work with the lines.

Related

ggplot: geom_text does not center-align above geom_col()

Please find my data p below. I had to include 100 samples to reproduce the error.
Question: why is geom_text not printing consistently center-aligned above the geom_col - e.g. 21 and 28 in All in the right SSA-facet? I tried adjusting position.dodge2 and vjust, but that did not work.
This thread addressed the issue but did not solve my problem.
My script
ggplot(p %>%
mutate(nystudie=as.character(study),
best.resp =as.factor(response)) %>%
group_by(nystudie,best.resp) %>%
summarise(N=n(),Val=unique(treatment)) %>%
bind_rows(p %>% filter(response %in% 1:4, treatment!="Control") %>% droplevels() %>%
mutate(nystudie=as.character(study),
best.resp =as.factor(response)) %>%
group_by(best.resp,treatment) %>% summarise(N=n()) %>%
mutate(nystudie="All") %>%
rename(Val=treatment)),
aes(nystudie, N, color = best.resp, fill= best.resp)) +
geom_col(position = position_dodge2(preserve = "single", padding = 0.1)) +
facet_wrap(~Val,ncol = 2, scales="free") +
scale_fill_grey(name="") +
scale_color_grey(name="") +
scale_y_continuous(breaks = seq(0,120,20)) +
geom_text(aes(label=N),position = position_dodge2(.5), vjust=0, fontface=2, cex=4.5, show.legend = F) +
theme(strip.background = element_blank(),
strip.text = element_text(color = "black", size = 15),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
plot.margin = unit(c(1,3,1,1), "lines"))
Data
p <- structure(list(study = structure(c(8L, 12L, 12L, 12L, 4L, 4L,
1L, 11L, 11L, 13L, 1L, 13L, 14L, 9L, 9L, 10L, 12L, 11L, 4L, 11L,
11L, 12L, 8L, 11L, 13L, 11L, 6L, 15L, 6L, 4L, 7L, 13L, 11L, 4L,
1L, 6L, 1L, 11L, 16L, 1L, 10L, 15L, 1L, 11L, 1L, 6L, 1L, 11L,
12L, 11L, 13L, 16L, 1L, 8L, 11L, 10L, 4L, 4L, 12L, 10L, 6L, 15L,
12L, 14L, 12L, 1L, 1L, 16L, 12L, 12L, 8L, 7L, 1L, 1L, 13L, 13L,
14L, 9L, 14L, 2L, 11L, 4L, 1L, 16L, 15L, 11L, 9L, 4L, 13L, 12L,
6L, 16L, 4L, 1L, 15L, 6L, 4L, 1L, 9L, 2L), .Label = c("1", "2",
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14",
"15", "22"), class = "factor"), response = c("1", "3", "4", "4",
"3", "3", "3", "4", "4", "4", "4", "4", "3", "4", "4", "4", "3",
"4", "4", "4", "4", "3", "1", "4", "4", "4", "3", "4", "3", "3",
"4", "4", "4", "3", "4", "4", "4", "4", "4", "3", "4", "4", "3",
"4", "4", "3", "3", "4", "3", "4", "4", "4", "4", "3", "3", "4",
"4", "3", "3", "4", "3", "4", "4", "4", "3", "3", "4", "4", "4",
"4", "2", "4", "4", "4", "4", "4", "3", "4", "3", "3", "4", "4",
"4", "4", "4", "4", "3", "3", "4", "4", "3", "4", "4", "4", "4",
"3", "3", "4", "2", "3"), treatment = structure(c(2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L), .Label = c("SSTR", "SSA"), class = "factor")), row.names = c(NA,
-100L), class = "data.frame")

When adding labels you have to take care to use the same positioning as for geom_col. To align the labels with the bars use position_dodge2(preserve = "single", width = .9, padding = 0.1):
library(ggplot2)
library(dplyr)
d1 <- p %>%
mutate(
nystudie = as.character(study),
best.resp = as.factor(response)
) %>%
group_by(nystudie, best.resp) %>%
summarise(N = n(), Val = unique(treatment))
#> `summarise()` regrouping output by 'nystudie' (override with `.groups` argument)
d2 <- p %>%
filter(response %in% 1:4, treatment != "Control") %>%
droplevels() %>%
mutate(
nystudie = as.character(study),
best.resp = as.factor(response)
) %>%
group_by(best.resp, treatment) %>%
summarise(N = n()) %>%
mutate(nystudie = "All") %>%
rename(Val = treatment)
#> `summarise()` regrouping output by 'best.resp' (override with `.groups` argument)
d <- bind_rows(d1, d2)
ggplot(d, aes(nystudie, N, color = best.resp, fill = best.resp)) +
geom_col(position = position_dodge2(preserve = "single", padding = 0.1)) +
facet_wrap(~Val, ncol = 2, scales = "free") +
scale_fill_grey(name = "") +
scale_color_grey(name = "") +
scale_y_continuous(breaks = seq(0, 120, 20)) +
geom_text(aes(label = N), position = position_dodge2(preserve = "single", width = .9, padding = 0.1), vjust = 0, fontface = 2, cex = 4.5, show.legend = F) +
theme(
strip.background = element_blank(),
strip.text = element_text(color = "black", size = 15),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
plot.margin = unit(c(1, 3, 1, 1), "lines")
)

Why do the outliers appear at whisker when I add them via geom_jitter with ggplot?

I have the following boxplots:
As you can see, at the fourth boxplot, the outliers appear already at 4 on the y-axis. The whisker goes until 6.
I couldn't really figure out where the problem in my code is. The intention to show the outlier with geom_jitter is because the plot does not give enough information on the actual distribution.
Here is my code and below an excerpt from the data to try
my_xlab <- paste(levels(df$income_class),"\n(N=",table(df$income_class),")",sep="")
df %>%
group_by(income_class) %>%
mutate(outlier = nr_trips > median(nr_trips) + IQR(nr_trips) * 1.5) %>%
ggplot(aes(x=income_class, y=nr_trips)) +
geom_boxplot(varwidth = TRUE, outlier.shape = NA)+
scale_x_discrete(labels=my_xlab)+
stat_summary(fun.data = "mean_cl_normal",
aes(shape="mean"),
size=4,
colour = "red",
geom="point") +
scale_shape_manual("", values=c("mean"="x"))+
theme_minimal() +
geom_jitter(data = function(x) dplyr::filter_(x, ~ outlier), width = 0.25, height = 0, alpha = .1, aes(color = "outlier")) +
scale_color_manual(name = "", values = c("outlier" = "black"))+
labs(x = "Income class", y="Number of AToD trips")+
scale_y_continuous(breaks=c(0,2,4,6,8, 10, 12, 14), limits = c(0, 12))
The data:
structure(list(income_class = c("1", "1", "1", "3", "3", "3",
"3", "2", "3", "2", "4", "1", "2", "3", "2", "3", "2", "2", "3",
"1", "2", "2", "2", "2", "2", "2", "4", "2", "3", "2", "2", "3",
"3", "3", "4", "4", "2", "3", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "1", "3", "3", "2", "2",
"3", "2", "2", "2", "4", "4", "4", "3", "3", "2", "3", "3", "1",
"2", "2", "2", "3", "4", "3", "3", "2", "2", "2", "3", "3", "2",
"3", "3", "1", "1", "4", "4", "2", "2", "4", "4", "2", "2", "2",
"2", "2", "2", "2", "1", "2", "2", "4", "4", "4", "1", "1", "3",
"3", "3", "3", "4", "4", "4", "4", "4", "4", "4", "4", "4", "3",
"4", "4", "4", "4", "4", "4", "4", "2", "3", "3", "3", "3", "2",
"2", "2", "2", "3", "4", "3", "3", "2", "3", "3", "2", "3", "3",
"2", "3", "2", "2", "2", "2", "2", "2", "2", "2", "1", "1", "2",
"4", "4", "3", "3", "3", "3", "1", "2", "4", "4", "2", "2", "2",
"2", "4", "2", "2", "3", "2", "4", "3", "3", "3", "3", "2", "2",
"3", "3", "3", "3", "3", "4", "4", "4", "4", "3", "2", "2", "2",
"3", "2", "2", "2", "4", "4", "4", "3", "1", "3", "3", "2", "2",
"2", "3", "3", "2", "2", "1", "1", "1", "2", "2", "2", "2", "1",
"4", "4", "2", "3", "3", "1", "2", "4", "4", "2", "2", "2", "3",
"3", "2", "2", "3", "3", "3", "1", "4", "4", "4", "1", "1", "3",
"4", "4", "1", "3", "1", "2", "2", "2", "4", "1", "2", "1", "2",
"3", "2", "2", "3", "3", "2", "2", "1", "3", "3", "4", "4", "2",
"2", "2", "3", "2", "3", "2", "4", "4", "2", "2", "2", "1", "1",
"2", "1", "1", "2", "2", "2", "3", "3", "3", "3", "3", "3", "3",
"2", "4", "4", "3", "3", "3", "2", "2", "2", "2", "2", "2", "3",
"4", "2", "2", "2", "3", "3", "4", "4", "1", "2", "3", "4", "1",
"4", "4", "4", "4", "4", "3", "3", "3", "2", "2", "2", "3", "3",
"2", "2", "2", "2", "3", "3", "2", "2", "3", "4", "4", "4", "2",
"2", "3", "3", "2", "2", "2", "3", "3", "4", "4", "4", "1", "2",
"2", "2", "2", "1", "1", "2", "4", "3", "1", "1", "1", "4", "2",
"2", "1", "3", "3", "3", "3", "4", "4", "4", "4", "4", "4", "3",
"3", "3", "3", "3", "3", "2", "4", "3", "4", "4", "2", "3", "3",
"3", "1", "2", "2", "2", "4", "4", "4", "4", "4", "4", "2", "4",
"3", "2", "2", "2", "4", "3", "3", "3", "3", "4", "4", "1", "2",
"1", "4", "4", "4", "1", "1", "4", "3", "3", "4", "4", "1", "1",
"4", "4", "4", "3", "3", "2", "4", "4", "4", "2", "2", "2", "4",
"4", "4", "4", "2", "2", "3", "1", "1", "1", "3", "3", "4", "1",
"2", "3", "2", "4", "2", "2", "4", "3", "3", "3", "3", "3", "4",
"2", "2", "2", "3", "2", "2", "2", "4", "4", "3", "2", "2", "3",
"3", "3", "3", "2", "2", "2", "2", "2", "2", "2", "3", "3", "4",
"4", "4", "4", "4", "4", "4", "2", "4", "4", "2", "4", "4", "1",
"3", "3", "3", "3", "4", "4", "2", "2", "3", "2", "2", "2", "2",
"2", "2", "2", "1", "3", "3", "3", "1", "4", "2", "2", "3", "3",
"3", "4", "4", "3", "3", "2", "2", "1", "1", "2", "4", "4", "4",
"1", "2", "2", "2", "2", "4", "4", "4", "4", "4", "4", "1", "1",
"2", "1", "4", "4", "1", "4", "4", "3", "2", "2", "4", "2", "4",
"4", "4", "4", "2", "2", "4", "4", "4", "1", "1", "1", "2", "3",
"4", "2", "4", "4", "2", "2", "4", "4", "4", "4", "4", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "1", "4", "2", "2",
"2", "3", "3", "3", "3", "3", "2", "2", "2", "2", "4", "4", "3",
"3", "1", "3", "3", "2", "2", "3", "3", "3", "3", "3", "3", "3",
"4", "4", "4", "4", "2", "2", "2", "2", "2", "3", "2", "2", "2",
"2", "2", "4", "4", "1", "4", "4", "3", "3", "3", "3", "2", "2",
"2", "4", "4", "1", "3", "3", "3", "3", "3", "4", "1", "3", "3",
"1", "1", "4", "3", "3", "4", "4", "4", "1", "2", "2", "2", "3",
"3", "4", "3", "3", "2", "2", "2", "2", "2", "4", "4", "2", "3",
"3", "2", "3", "3", "1", "2", "3", "3", "3", "4", "4", "4", "4",
"2", "3", "3", "3", "3", "2", "2", "3", "3", "3", "3", "2", "2",
"3", "4", "4", "3", "3", "2", "2", "3", "3", "3", "3", "3", "3",
"3", "3", "3", "3", "3", "2", "2", "3", "3", "3", "2", "2", "2",
"2", "1", "1", "2", "4", "4", "2", "2", "3", "3", "3", "1", "3",
"3", "2", "2", "2", "2", "2", "3", "4", "3", "3", "3", "4", "3",
"1", "2", "2", "2", "2", "2", "4", "1", "2", "1", "2", "3", "3",
"2", "1", "3", "2", "4", "2", "2", "2", "2", "2", "1", "3", "3",
"3", "2", "1", "1", "3", "1", "1", "2", "2", "2", "2", "2", "4",
"4", "1", "2", "3", "2", "2", "2", "2", "3", "3", "2", "3", "3",
"4", "4", "3", "3", "3", "4", "3", "3", "3", "2", "4", "4", "1",
"2", "2", "2", "2", "2", "1", "3", "3", "3", "2", "2", "2", "4",
"4", "3", "4", "3", "2", "2", "3", "2", "2", "4", "4", "4", "4",
"4", "4", "4", "3", "3", "3", "2", "2", "4", "4", "2", "3", "2",
"2", "2", "2", "3", "3", "4", "4", "4", "2", "2", "3", "2", "3",
"3", "3", "3", "2", "4", "3", "3", "3", "3", "3", "3", "4", "2",
"2", "2", "4", "4", "2", "3", "3", "2", "4", "2", "4", "2", "3",
"3", "2", "3", "3", "4", "2", "1", "1", "2", "2", "4", "4", "1",
"1", "2", "1", "3", "3", "3", "2", "2", "2", "4", "4", "2", "3",
"3", "2", "2", "3", "3", "2", "2", "4", "2", "2", "3", "4", "3",
"3", "3", "2", "4", "4", "4", "4", "2", "2", "2", "3", "2", "2",
"3", "3", "4", "4", "4", "4", "2", "2", "2", "2", "3", "2", "2",
"4", "4", "4", "3", "4", "4", "3", "2", "2", "2", "3", "4", "3",
"3", "3", "3", "1", "2", "4", "2", "4", "4", "4", "3", "3", "2",
"2", "4", "4", "4", "3", "3", "2", "2", "3", "2", "2", "3", "3",
"1", "2", "1", "1", "4", "2", "2", "4", "2", "3", "2", "2", "2",
"3", "3", "3", "3", "4", "4", "4", "2", "1", "2", "2", "2", "3",
"2", "2", "2", "2", "4", "4", "4", "4", "3", "3", "2", "2", "2",
"2", "2", "2", "4", "2", "2", "4", "2", "2", "2", "2", "3", "2",
"2", "3", "2", "2", "2", "2", "2", "2", "2", "2", "3", "1", "1",
"1", "3", "2", "4", "4", "4", "4", "2", "2", "4", "4", "2", "2",
"2", "2", "1", "4", "4", "2", "3", "2", "3", "4", "2", "2", "1",
"1", "2", "2", "3", "3", "4", "3", "3", "1", "3", "3", "4", "4",
"2", "3", "3", "1", "4", "2", "2", "3", "2", "1", "3", "3", "1",
"2", "2", "4", "2", "2", "2", "2", "2", "4", "4", "4", "2", "4",
"2", "3", "3", "2", "3", "3", "3", "1", "4", "3", "3", "3", "3",
"1", "3", "3", "4", "4", "2", "2", "2", "2", "2", "2", "3", "4",
"3", "3", "1", "3", "2", "2", "2", "2", "3", "3", "3", "1", "2",
"2", "2", "2", "2", "2", "3", "3", "4", "4", "1", "4", "2", "2",
"2", "2", "2", "2", "2", "1", "3", "2", "2", "4", "4", "4", "4",
"3", "3", "1", "1", "1", "4", "4", "4", "2", "3", "3", "3", "1",
"3", "3", "3", "3", "3", "2", "3", "3", "3", "3", "4", "4", "4",
"2", "2", "2", "1", "2", "2", "2", "4", "4", "2", "2", "2", "2",
"3", "3", "3", "3", "3", "2", "4", "4", "4", "4", "1", "1", "2",
"2", "4", "4", "4", "3", "3", "3", "3", "4", "4", "4", "2", "1",
"4", "4", "4", "4", "1", "4", "4", "4", "4", "4", "2", "3", "3",
"3", "3", "3", "4", "4", "4", "1", "1", "4", "2", "3", "3", "2",
"3", "3", "1", "4", "4", "4", "4", "1", "4", "4", "4", "4", "2",
"2", "3", "3", "4", "4", "1", "3", "3", "2", "1", "4", "1", "2",
"4", "4", "4", "4", "2", "4", "2", "3", "2", "2", "2", "4", "4",
"1", "1", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3",
"3", "3", "4", "1", "1", "3", "2", "2", "4", "4", "4", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "4", "4", "4", "4", "2",
"2", "2", "2", "4", "4", "2", "2", "2", "4", "4", "4", "3", "3",
"3", "3", "3", "3", "3", "3", "3", "2", "2", "2", "2", "2", "3",
"3", "2", "2", "2", "3", "3", "4", "3", "3", "3", "3", "2", "2",
"2", "3", "3", "4", "3", "2", "2", "4", "2", "3", "3", "3", "3",
"1", "4", "2", "2", "2", "2", "2", "4", "4", "1", "4", "3", "2",
"4", "4", "4", "4", "4", "4", "3", "3", "4", "4", "4", "4", "2",
"1", "2", "4", "4", "2", "2", "2", "2", "1", "1", "4", "3", "1",
"3", "3", "1", "2", "2", "2", "2", "3", "2", "2", "3", "3", "2",
"3", "3", "2", "4", "2", "2", "2", "2", "3", "2", "2", "1", "1",
"1", "2", "2", "2", "4", "2", "2", "3", "3", "2", "2", "4", "1",
"2", "2", "2", "2", "1", "1", "2", "2", "2", "1", "2", "3", "3",
"2", "2", "2", "3", "4", "3", "3", "1", "2", "2", "1", "1", "4",
"2", "2", "1", "1", "2", "3", "2", "2", "2", "1", "4", "4", "4",
"3", "1", "1", "3", "3", "4", "2", "2", "2", "2", "3", "3", "4",
"4", "3", "2", "2", "2", "2", "4", "4", "4", "3", "3", "3", "3",
"3", "3", "3", "2", "3", "3", "2", "2", "3", "3", "3", "3", "3",
"3", "3", "3", "4", "2", "4", "4", "2", "2", "2", "3", "3", "3",
"3", "3", "4", "2", "4", "4", "3", "3", "3", "4", "3", "2", "2",
"3", "3", "3", "2", "2", "2", "2", "3", "3", "2", "2", "1", "1",
"4", "3", "2", "2", "2", "4", "4", "2", "2", "2", "2", "4", "4",
"4", "2", "4", "3", "1", "3", "3", "2", "3", "1", "2", "4", "3",
"2", "2", "3", "3", "3", "2", "3", "3", "1", "2", "3", "2", "2",
"2", "2", "2", "4", "4", "4", "2", "4", "4", "3", "3", "3", "3",
"3", "2", "2", "3", "3", "3", "4", "3", "3", "3", "3", "2", "2",
"3", "3", "3", "3", "3", "3", "2", "4", "3", "3", "3", "4", "4",
"4", "2", "2", "2", "1", "2", "2", "3", "3", "2", "4", "3", "3",
"3", "1", "3", "2", "2", "3", "3", "3", "4", "3", "3", "1", "4",
"4", "4", "4", "2", "2", "2", "4", "2", "2", "3", "3", "3", "3",
"3", "3", "4", "4", "3", "3", "3", "2", "4", "2", "2", "1", "3",
"4", "4", "2", "2", "1", "2", "3", "3", "3", "2", "2", "2", "4",
"4", "3", "3", "4", "4", "2", "1", "3", "3", "3", "2", "3", "3",
"3", "1", "2", "2", "4", "2", "2", "2", "2", "2", "2", "2", "4",
"2", "2", "3", "3", "3", "3", "3", "2", "2", "3", "3", "3", "2",
"2", "3", "3", "3", "1", "4", "2", "3", "3", "3", "4", "2", "4",
"3", "3", "3", "3", "3", "2", "2", "2", "4", "3", "3", "3", "3",
"1", "4", "2", "2", "2", "3", "3", "3", "3", "3", "3", "3", "2",
"2", "2", "2", "2", "4", "1", "3", "2", "4", "4", "2", "2", "2",
"2", "2", "4", "2", "2", "2", "2", "2", "1", "2", "2", "3", "3",
"3", "3", "3", "3", "3", "2", "4", "4", "3", "3", "4", "4", "3",
"2", "3", "3", "2", "3", "2", "3", "2", "3", "3", "3", "3", "3",
"3", "3", "3", "2", "3", "2", "3", "3", "2", "2", "2", "1", "1",
"3", "2", "4", "4", "1", "3", "3", "2", "2", "3", "2", "2", "2",
"2", "3", "4", "4", "2", "4", "4", "3", "3", "3", "2", "2", "4",
"3", "3", "3", "3", "1", "1", "2", "1", "3", "3", "4", "4", "4",
"4", "3", "3", "3", "4", "4", "4", "2", "2", "3", "3", "2", "3",
"4", "4", "2", "2", "2", "4", "4", "4", "2", "1", "4", "3", "3",
"3", "2", "2", "2", "2", "4", "4", "3", "3", "3", "3", "2", "3",
"2", "2", "2", "1", "4", "4", "3", "3", "3", "3", "3", "2", "3",
"3", "4", "4", "4", "4", "1", "1", "3", "2", "1", "2", "2", "2",
"2", "2", "2", "4", "3", "2", "2", "2", "3", "2", "2", "2", "3",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "1", "2", "2",
"3", "3", "2", "1", "3", "2", "2", "2", "2", "2", "2", "2", "3",
"2", "2", "3", "3", "3", "2", "1", "2", "3", "3", "3", "3", "2",
"2", "2", "2", "4", "4", "4", "2", "3", "3", "4", "3", "3", "2",
"3", "3", "4", "4", "1", "1", "2", "2", "3", "3", "2", "4", "3",
"3", "3", "2", "2", "2", "2", "1", "3", "3", "2", "2", "2", "2",
"4", "4", "2", "2", "2", "2", "4", "4", "4", "1", "2", "4", "3",
"3", "3", "1", "1", "3", "2", "2", "2", "3", "3", "2", "2", "2",
"2", "3", "3", "3", "3", "3", "2", "2", "2", "4", "2", "2", "3",
"3", "3", "3", "4", "2", "2", "3", "4", "4", "4", "4", "2", "2",
"2", "3", "2", "3", "3", "2", "3", "3", "3", "2", "4", "2", "2",
"2", "2", "2", "2", "3", "4", "4", "3", "1", "2", "1", "2", "3",
"2", "2", "2", "1", "2", "2", "2", "2", "2", "2", "4", "4", "1",
"3", "2", "3", "2", "2", "2", "1", "2", "3", "3", "4", "1", "2",
"1", "2", "2", "2", "3", "2", "2", "2", "3", "1", "3", "2", "2",
"4", "4", "1", "3", "2", "3", "1", "1", "1", "2", "2", "3", "3",
"4", "1", "2", "2", "1", "4", "4", "4", "2", "1", "3", "3", "1",
"3", "2", "2", "1", "2", "2", "2", "2", "2", "2", "2", "2", "3",
"3", "3", "1", "3", "2", "2", "4", "3", "1", "2", "3", "3", "3",
"2", "3", "1", "4", "4", "2", "3", "3", "2", "4", "3", "2", "2",
"3", "2", "3", "3", "2", "2", "3", "3", "3", "4", "2", "2", "1",
"2", "1", "2", "4", "1", "1", "2", "2", "2", "2", "2", "4", "1",
"3", "2", "2", "3", "3", "3", "2", "2", "3", "2", "2", "4", "1",
"1", "3", "3", "2", "2", "2", "2", "3", "2", "1", "1", "1", "2",
"2", "2", "2", "4", "4", "2", "3", "3", "4", "4", "2", "2", "3",
"2", "2", "2", "2", "4", "1", "2", "1", "1", "3", "2", "2", "1",
"2", "2", "2", "1", "2", "2", "1", "2", "2", "2", "2", "2", "1",
"2", "2", "2", "1", "1", "2", "2", "1", "1", "1", "2", "1", "1",
"1", "2", "1", "2", "3", "3", "4", "4", "4", "4", "3", "4", "1",
"3", "3", "3", "2", "2", "2", "2", "2", "3", "3", "2", "3", "3",
"3", "3", "2", "2", "4", "1", "2", "3", "3", "3", "2", "4", "4",
"4", "4", "3", "3", "2", "2", "4", "1", "1", "2", "2", "3", "1",
"3", "3", "2", "2", "2", "3", "3", "3", "3", "3", "3", "3", "3",
"2", "2", "4", "4", "3", "3", "3", "4", "4", "4", "4", "3", "4",
"3", "3", "4", "4", "3", "3", "3", "3", "3", "4", "4", "1", "1",
"1", "4", "2", "4", "4", "4", "2", "2", "2", "3", "3", "3", "2",
"2", "2", "4", "4", "4", "2", "2", "2", "2", "2", "1", "2", "4",
"2", "3", "3", "3", "3", "4", "4", "3", "3", "2", "2", "4", "4",
"4", "4", "4", "1", "1", "1", "1", "2", "2", "2", "2", "3", "3",
"3", "2", "2", "3", "3", "4", "4", "2", "2", "2", "2", "2", "2",
"4", "4", "2", "2", "1", "1", "1", "1", "2", "2", "2", "3", "4",
"4", "4", "4", "4", "4", "4", "2", "2", "4", "4", "4", "3", "1",
"1", "2", "2", "2", "2", "1", "4", "4", "2", "2", "2", "3", "3",
"3", "3", "2", "3", "3", "4", "4", "4", "3", "3", "3", "3", "3",
"3", "3", "3", "3", "2", "2", "2", "3", "3", "3", "4", "4", "4",
"4", "3", "2", "4", "3", "2", "2", "3", "1", "3", "3", "1", "4",
"4", "2", "3", "3", "2", "2", "1", "2", "4", "4", "4", "4", "3",
"4", "2", "2", "2", "4", "4", "4", "4", "4", "4", "2", "2", "2",
"2", "2", "3", "3", "3", "2", "3", "3", "2", "3", "3", "2", "3",
"1", "2", "2", "4", "1", "2", "2", "2", "4", "4", "4", "3", "3",
"3", "3", "3", "4", "4", "4", "4", "4", "3", "3", "3", "3", "1",
"1", "3", "3", "3", "3", "2", "4", "4", "3", "2", "1", "1", "4",
"4", "4", "1", "3", "2", "4", "4", "4", "4", "4", "4", "3", "4",
"3", "3", "3", "3", "3", "2", "2", "2", "2", "2", "2", "3", "4",
"4", "4", "4", "4", "4", "3", "3", "1", "4", "4", "2", "2", "3",
"3", "4", "4", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3",
"3", "3", "3", "3", "2", "2", "4", "3", "3", "3", "3", "4", "3",
"4", "4", "4", "4", "4", "3", "3", "3", "2", "1", "4", "4", "4",
"4", "3", "3", "3", "3", "3", "3", "2", "2", "1", "1", "3", "3",
"3", "3", "4", "1", "1", "2", "4", "4", "1", "2", "2", "2", "2",
"2", "3", "3", "3", "3", "2", "2", "2", "2", "1", "1", "2", "3",
"1", "1", "4", "1", "4", "2", "2", "2", "2", "2", "2", "2", "3",
"2", "4", "4", "4", "4", "1", "4", "2", "2", "2", "4", "3", "3",
"4", "3", "3", "4", "4", "4", "3", "2", "2", "2", "4", "4", "4",
"1", "2", "2", "1", "1", "3", "3", "2", "2", "2", "2", "4", "4",
"1", "2", "4", "4", "2", "2", "3", "3", "2", "2", "2", "2", "1",
"2", "2", "2", "2", "2", "2", "1", "4", "2", "2", "2", "2", "2",
"2", "2", "3", "3", "2", "2", "2", "2", "2", "2", "1", "2", "2",
"2", "3", "2", "3", "1", "4", "4", "3", "3", "3", "3", "3", "3",
"3", "2", "2", "2", "3", "3", "2", "3", "3", "3", "1", "1", "1",
"1", "2", "4", "2", "2", "1", "2", "4", "3", "3", "4", "4", "4",
"4", "4", "4", "2", "2", "1", "1", "2", "3", "2", "1", "1", "3",
"3", "4", "4", "4"), nr_trips = c(0L, 0L, 1L, 1L, 0L, 0L, 2L,
2L, 0L, 3L, 0L, 1L, 0L, 1L, 1L, 1L, 2L, 2L, 0L, 2L, 5L, 4L, 3L,
0L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 3L,
2L, 2L, 3L, 0L, 2L, 4L, 6L, 1L, 2L, 2L, 2L, 2L, 0L, 0L, 5L, 2L,
6L, 1L, 1L, 1L, 0L, 2L, 7L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 2L,
0L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 0L, 1L, 4L, 3L, 3L, 0L, 2L,
2L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 6L,
3L, 3L, 1L, 2L, 2L, 2L, 0L, 1L, 4L, 2L, 2L, 0L, 1L, 2L, 4L, 2L,
2L, 3L, 0L, 2L, 1L, 4L, 1L, 4L, 6L, 2L, 5L, 2L, 4L, 1L, 7L, 3L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 4L, 2L, 2L, 6L, 2L, 1L, 3L, 2L, 2L,
2L, 5L, 3L, 1L, 2L, 0L, 0L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 2L, 3L, 4L, 1L, 4L, 1L, 2L, 3L, 1L, 4L, 4L,
2L, 4L, 3L, 2L, 2L, 1L, 0L, 0L, 1L, 2L, 3L, 3L, 1L, 0L, 1L, 5L,
1L, 1L, 0L, 0L, 1L, 4L, 2L, 1L, 1L, 1L, 2L, 2L, 0L, 0L, 1L, 1L,
4L, 1L, 1L, 0L, 2L, 1L, 1L, 0L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L,
5L, 3L, 1L, 3L, 0L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 4L, 1L, 0L, 2L,
0L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 2L,
2L, 3L, 1L, 8L, 4L, 1L, 0L, 0L, 2L, 3L, 2L, 4L, 2L, 2L, 2L, 1L,
5L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 1L,
1L, 1L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 0L, 1L, 6L, 1L, 0L, 2L, 2L,
2L, 4L, 2L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 4L, 0L, 1L, 3L, 2L, 1L,
1L, 2L, 7L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 4L, 2L, 1L, 4L, 1L, 3L, 4L, 4L, 0L, 3L, 0L, 2L, 3L,
1L, 4L, 3L, 3L, 1L, 0L, 1L, 3L, 1L, 3L, 1L, 0L, 0L, 0L, 2L, 2L,
6L, 3L, 1L, 2L, 1L, 3L, 0L, 2L, 4L, 1L, 2L, 1L, 1L, 3L, 3L, 6L,
3L, 2L, 2L, 3L, 4L, 1L, 1L, 1L, 4L, 2L, 3L, 2L, 2L, 3L, 6L, 4L,
2L, 4L, 4L, 2L, 1L, 4L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 0L,
4L, 0L, 3L, 2L, 1L, 4L, 0L, 3L, 2L, 1L, 1L, 4L, 2L, 1L, 2L, 2L,
4L, 3L, 1L, 0L, 3L, 2L, 1L, 4L, 0L, 4L, 4L, 5L, 4L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 0L, 3L, 4L, 1L, 1L, 0L, 3L, 3L, 3L, 3L, 6L,
0L, 0L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 1L, 3L, 3L, 0L, 2L,
4L, 0L, 3L, 1L, 3L, 2L, 1L, 1L, 0L, 0L, 2L, 0L, 2L, 2L, 0L, 4L,
1L, 1L, 3L, 2L, 2L, 4L, 2L, 2L, 1L, 2L, 5L, 2L, 4L, 1L, 2L, 4L,
2L, 4L, 2L, 1L, 1L, 2L, 1L, 0L, 2L, 4L, 2L, 2L, 1L, 2L, 3L, 1L,
3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 4L, 1L, 2L, 2L, 1L, 1L, 4L, 2L,
2L, 2L, 4L, 3L, 2L, 2L, 1L, 4L, 2L, 2L, 4L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 0L, 2L, 1L, 2L, 4L, 3L, 4L, 1L, 2L, 2L, 1L, 0L, 4L,
1L, 2L, 4L, 3L, 1L, 3L, 5L, 0L, 0L, 4L, 1L, 2L, 2L, 1L, 3L, 4L,
3L, 1L, 0L, 3L, 2L, 5L, 2L, 2L, 1L, 2L, 2L, 1L, 5L, 1L, 3L, 0L,
0L, 1L, 2L, 4L, 1L, 0L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 4L,
1L, 1L, 3L, 3L, 8L, 1L, 3L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 3L, 1L,
1L, 0L, 1L, 2L, 1L, 1L, 4L, 1L, 0L, 3L, 2L, 2L, 7L, 1L, 3L, 3L,
2L, 4L, 3L, 5L, 1L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 5L, 1L, 3L, 2L,
2L, 1L, 0L, 1L, 2L, 5L, 1L, 2L, 2L, 0L, 2L, 1L, 2L, 2L, 2L, 0L,
2L, 2L, 1L, 4L, 2L, 1L, 4L, 1L, 4L, 2L, 1L, 5L, 1L, 4L, 1L, 2L,
3L, 3L, 1L, 0L, 2L, 1L, 2L, 1L, 1L, 3L, 2L, 0L, 5L, 3L, 2L, 6L,
2L, 0L, 5L, 3L, 0L, 1L, 2L, 5L, 2L, 2L, 0L, 2L, 2L, 2L, 1L, 0L,
2L, 3L, 2L, 2L, 1L, 2L, 4L, 2L, 1L, 1L, 4L, 4L, 3L, 3L, 2L, 2L,
2L, 1L, 3L, 5L, 4L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 6L, 3L, 2L, 2L, 3L, 1L, 1L, 3L, 4L, 2L, 3L, 1L, 1L, 3L,
1L, 3L, 5L, 3L, 2L, 2L, 4L, 2L, 1L, 6L, 5L, 2L, 1L, 0L, 1L, 0L,
2L, 1L, 0L, 5L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 4L, 3L,
1L, 0L, 1L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 4L, 1L, 2L, 4L, 4L,
3L, 2L, 0L, 2L, 1L, 1L, 2L, 3L, 0L, 1L, 2L, 0L, 1L, 0L, 0L, 0L,
1L, 0L, 3L, 0L, 0L, 2L, 2L, 4L, 3L, 0L, 0L, 1L, 2L, 2L, 0L, 0L,
0L, 2L, 1L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 4L, 1L, 1L, 1L, 0L,
3L, 1L, 2L, 2L, 0L, 0L, 0L, 5L, 0L, 3L, 3L, 1L, 0L, 1L, 0L, 1L,
2L, 0L, 0L, 2L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 2L, 1L, 2L, 2L, 1L,
3L, 1L, 4L, 2L, 2L, 0L, 2L, 2L, 3L, 1L, 0L, 2L, 2L, 0L, 2L, 0L,
2L, 0L, 1L, 2L, 0L, 1L, 4L, 2L, 1L, 2L, 2L, 3L, 0L, 2L, 5L, 2L,
2L, 1L, 0L, 1L, 0L, 1L, 2L, 2L, 0L, 1L, 1L, 0L, 0L, 1L, 6L, 1L,
2L, 3L, 4L, 2L, 4L, 0L, 2L, 2L, 0L, 0L, 0L, 3L, 3L, 0L, 2L, 1L,
2L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 4L, 1L, 2L, 0L, 4L, 3L, 2L, 2L,
5L, 1L, 4L, 2L, 2L, 3L, 2L, 2L, 0L, 0L, 3L, 0L, 2L, 2L, 0L, 2L,
1L, 2L, 0L, 1L, 1L, 3L, 2L, 2L, 0L, 0L, 1L, 0L, 1L, 4L, 3L, 0L,
0L, 1L, 0L, 3L, 0L, 3L, 0L, 4L, 1L, 0L, 2L, 1L, 2L, 0L, 1L, 0L,
0L, 0L, 2L, 2L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 2L, 2L, 0L, 2L, 5L,
3L, 5L, 3L, 5L, 0L, 0L, 2L, 1L, 1L, 1L, 0L, 1L, 1L, 4L, 0L, 2L,
1L, 1L, 2L, 0L, 2L, 2L, 2L, 2L, 5L, 3L, 1L, 1L, 2L, 0L, 2L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 2L, 1L, 1L, 1L, 4L, 2L, 0L, 0L, 0L, 3L,
3L, 0L, 2L, 1L, 1L, 2L, 1L, 0L, 0L, 2L, 0L, 2L, 0L, 1L, 1L, 2L,
3L, 2L, 3L, 1L, 1L, 3L, 0L, 0L, 2L, 3L, 2L, 0L, 0L, 1L, 1L, 0L,
0L, 2L, 1L, 1L, 0L, 0L, 2L, 2L, 0L, 5L, 2L, 1L, 0L, 2L, 2L, 1L,
0L, 2L, 0L, 1L, 2L, 0L, 1L, 1L, 2L, 3L, 2L, 2L, 0L, 1L, 3L, 0L,
3L, 1L, 2L, 3L, 0L, 2L, 4L, 2L, 0L, 0L, 0L, 1L, 0L, 1L, 2L, 2L,
3L, 1L, 2L, 3L, 2L, 1L, 3L, 1L, 0L, 2L, 3L, 2L, 2L, 1L, 0L, 1L,
0L, 2L, 3L, 0L, 2L, 1L, 2L, 0L, 1L, 0L, 0L, 2L, 1L, 1L, 3L, 2L,
2L, 0L, 1L, 1L, 0L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 2L, 2L, 1L, 4L, 1L, 2L, 1L, 1L, 3L, 4L, 3L, 1L, 2L, 2L, 3L,
1L, 1L, 2L, 1L, 4L, 0L, 1L, 2L, 1L, 2L, 0L, 4L, 1L, 1L, 4L, 1L,
1L, 1L, 0L, 2L, 0L, 2L, 4L, 2L, 3L, 3L, 1L, 2L, 1L, 1L, 6L, 1L,
2L, 3L, 0L, 2L, 1L, 0L, 1L, 3L, 2L, 1L, 1L, 0L, 1L, 0L, 0L, 0L,
3L, 1L, 1L, 2L, 1L, 0L, 2L, 1L, 4L, 2L, 3L, 3L, 1L, 1L, 3L, 2L,
3L, 1L, 1L, 4L, 2L, 1L, 2L, 1L, 2L, 3L, 0L, 2L, 3L, 1L, 2L, 1L,
2L, 0L, 2L, 1L, 1L, 3L, 4L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 1L, 2L,
0L, 2L, 4L, 1L, 2L, 1L, 4L, 2L, 1L, 0L, 0L, 1L, 3L, 0L, 2L, 1L,
0L, 3L, 0L, 3L, 2L, 0L, 3L, 5L, 2L, 0L, 2L, 1L, 5L, 0L, 1L, 1L,
7L, 2L, 0L, 0L, 0L, 2L, 2L, 3L, 2L, 2L, 5L, 1L, 2L, 2L, 2L, 2L,
1L, 3L, 2L, 0L, 2L, 4L, 4L, 0L, 0L, 0L, 0L, 5L, 2L, 6L, 3L, 2L,
1L, 6L, 2L, 2L, 2L, 2L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 2L, 2L,
1L, 1L, 2L, 0L, 2L, 2L, 0L, 0L, 1L, 4L, 2L, 2L, 2L, 4L, 3L, 2L,
1L, 2L, 1L, 4L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 3L, 2L, 0L, 1L, 0L, 2L, 3L,
2L, 0L, 1L, 2L, 1L, 3L, 0L, 4L, 1L, 1L, 1L, 2L, 5L, 5L, 2L, 1L,
3L, 2L, 0L, 1L, 1L, 2L, 3L, 3L, 2L, 1L, 3L, 0L, 1L, 1L, 1L, 2L,
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3L, 0L, 1L, 5L, 3L, 0L, 2L, 2L, 2L, 3L, 0L, 1L, 1L, 1L, 2L, 0L,
2L, 2L, 5L, 1L, 0L, 1L, 2L, 4L, 0L, 1L, 0L, 4L, 2L, 1L, 0L, 2L,
2L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 2L, 1L, 0L, 2L, 0L, 3L, 1L,
2L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 0L, 4L, 2L, 1L,
2L, 2L, 2L, 1L, 0L, 1L, 0L, 1L, 3L, 2L, 0L, 1L, 0L, 1L, 1L, 1L,
0L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 2L, 1L, 1L, 1L,
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0L, 2L, 2L, 2L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 2L, 1L, 3L, 0L, 0L,
2L, 0L, 2L, 2L, 3L, 3L, 2L, 1L, 1L, 2L, 0L, 4L, 4L, 0L, 3L, 1L,
1L, 0L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 4L, 2L, 3L, 2L, 0L, 4L, 2L,
3L, 2L, 1L, 4L, 0L, 6L, 2L, 2L, 1L, 2L, 1L, 4L, 2L, 4L, 0L, 0L,
0L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 0L, 0L, 2L, 2L, 0L, 0L, 4L, 0L,
2L, 5L, 1L, 2L, 3L, 5L, 2L, 0L, 1L, 3L, 0L, 2L, 0L, 0L, 2L, 1L,
2L, 5L, 2L, 0L, 3L, 1L, 0L, 3L, 5L, 6L, 0L, 3L, 0L, 2L, 1L, 2L,
0L, 2L, 0L, 3L, 0L, 0L, 1L, 1L, 2L, 0L, 1L, 4L, 1L, 1L, 1L, 2L,
0L, 0L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 4L, 1L, 1L, 0L, 3L,
1L, 2L, 3L, 3L, 2L, 2L, 1L, 3L, 0L, 2L, 0L, 1L, 2L, 2L, 2L, 2L,
1L, 0L, 5L, 1L, 2L, 1L, 0L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 6L, 2L,
0L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 4L, 2L, 0L,
1L, 4L, 2L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 0L, 1L,
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2L, 1L, 2L, 2L, 2L, 4L, 0L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 0L, 1L, 2L, 2L, 2L, 2L, 6L, 1L, 1L, 3L, 4L, 1L,
5L, 2L, 0L, 1L, 1L, 0L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 3L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 2L, 1L, 2L, 2L, 4L, 2L, 1L,
0L, 0L, 1L, 4L, 1L, 3L, 0L, 0L, 1L, 2L, 1L, 3L, 0L, 1L, 2L, 3L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 0L, 1L, 1L, 1L, 1L, 2L, 2L,
3L, 0L, 2L, 0L, 2L, 1L, 2L, 2L, 4L, 6L, 0L, 4L, 0L, 0L, 1L, 3L,
0L, 2L, 3L, 4L, 2L, 0L, 2L, 2L, 2L, 1L, 0L, 1L, 0L, 2L, 0L, 0L,
1L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 4L, 2L, 0L, 0L, 2L, 1L, 0L,
1L, 2L, 3L, 0L, 2L, 0L, 1L, 3L, 2L, 0L, 1L, 2L, 1L, 1L, 2L, 2L,
3L, 2L, 2L, 2L, 3L, 0L, 5L, 1L, 3L, 2L, 1L, 4L, 2L, 1L, 2L, 1L,
1L, 3L, 1L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 0L, 2L, 3L, 1L, 0L,
2L, 1L, 4L, 0L, 7L, 0L, 1L, 0L, 1L, 0L, 2L, 0L, 5L, 2L, 2L, 0L,
2L, 0L, 2L, 1L, 1L, 1L, 0L, 3L, 1L, 3L, 2L, 2L, 1L, 2L, 1L, 2L,
3L, 0L, 1L, 2L, 0L, 0L, 2L, 0L, 3L, 1L, 2L, 3L, 3L, 0L, 1L, 1L,
1L, 3L, 2L, 0L, 2L, 2L, 0L, 3L, 3L, 0L, 3L, 1L, 2L, 1L, 2L, 2L,
1L, 7L, 2L, 1L, 2L, 2L, 2L, 1L, 0L, 2L, 1L, 0L, 2L, 0L, 1L, 2L,
4L, 3L, 1L, 1L, 2L, 9L, 2L, 0L, 0L, 3L, 2L, 1L, 0L, 0L, 0L, 0L,
1L, 2L, 2L, 0L, 1L, 0L, 4L, 2L, 2L, 2L, 2L, 3L, 1L, 0L, 2L, 0L,
1L, 2L, 0L, 0L, 2L, 2L, 1L, 1L, 3L, 0L, 1L, 2L, 0L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 0L, 1L, 2L, 0L, 3L, 1L, 2L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 4L, 1L, 3L, 1L, 0L, 1L, 0L, 2L, 2L, 2L, 0L, 2L, 0L,
0L, 2L, 3L, 4L, 2L, 1L, 2L, 4L, 1L, 3L, 2L, 2L, 1L, 2L, 2L, 3L,
1L, 2L, 4L, 1L, 4L, 2L, 1L, 2L, 1L, 1L, 0L, 0L, 0L, 2L, 2L, 1L,
2L, 1L, 2L, 0L, 2L, 1L, 1L, 4L, 2L, 0L, 0L, 2L, 0L, 3L, 0L, 0L,
1L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 4L, 0L, 2L, 1L, 3L, 5L,
3L, 4L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 7L, 1L, 1L, 2L, 1L, 2L, 1L,
0L, 0L, 2L, 1L, 0L, 0L, 2L, 4L, 0L, 1L, 2L, 1L, 2L, 3L, 3L, 0L,
1L, 4L, 5L, 3L, 4L, 2L, 0L, 1L, 1L, 2L, 1L, 1L, 0L, 3L, 0L, 0L,
1L, 1L, 1L, 2L, 0L, 1L, 2L, 2L, 1L, 1L, 0L, 3L, 1L, 0L, 4L, 2L,
3L, 1L, 1L, 1L, 0L, 1L, 2L, 2L, 1L, 4L, 1L, 3L, 1L, 1L, 1L, 3L,
2L, 2L, 0L, 1L, 4L, 2L, 0L, 1L, 3L, 2L, 0L, 1L, 1L, 1L, 3L, 2L,
3L, 2L, 1L, 2L, 1L, 6L, 3L, 1L, 2L, 4L, 2L, 0L, 2L, 2L, 0L, 1L,
2L, 6L, 1L, 1L, 2L, 0L, 2L, 2L, 4L, 1L, 2L, 2L, 0L, 3L, 0L, 2L,
1L, 0L, 0L, 2L, 0L, 2L, 3L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 2L, 2L,
0L, 5L, 2L, 1L, 2L, 1L, 3L, 2L, 0L, 0L, 2L, 1L, 3L, 2L, 1L, 2L,
2L, 1L, 1L, 5L, 2L, 1L, 1L, 1L, 1L, 0L, 2L, 4L, 1L, 3L, 0L, 2L,
1L, 3L, 1L, 3L, 1L, 2L, 0L, 5L, 0L, 1L, 1L, 2L, 3L, 1L, 2L, 0L,
2L, 1L, 1L, 1L, 1L, 4L, 1L, 0L, 2L, 2L, 2L, 2L, 6L, 0L, 1L, 0L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 0L, 3L, 3L, 0L, 3L, 4L, 2L,
2L, 4L, 2L, 2L, 4L, 0L, 2L, 2L, 1L, 1L, 0L, 1L, 4L, 1L, 0L, 3L,
2L, 1L, 2L, 2L, 3L, 2L, 4L, 2L, 2L, 2L, 1L, 2L, 1L, 3L, 4L, 1L,
2L, 2L, 0L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 5L, 1L, 4L,
1L, 3L, 4L, 1L, 2L, 2L, 0L, 4L, 2L, 0L, 1L, 1L, 3L, 2L, 0L, 2L,
3L, 3L, 2L, 1L, 4L, 0L, 2L, 2L, 1L, 2L, 0L, 6L, 1L, 3L, 2L, 1L,
3L, 4L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 2L, 0L, 2L, 0L, 2L,
2L, 3L, 2L, 2L, 4L, 2L, 3L, 1L, 2L, 4L, 4L, 2L, 2L, 3L, 1L, 0L,
2L, 2L, 1L, 2L, 0L, 1L, 1L, 1L, 1L, 4L, 1L, 3L, 2L, 0L, 1L, 2L,
1L, 2L, 2L, 1L, 3L, 1L, 0L, 3L, 0L, 2L, 2L, 0L, 0L, 1L, 1L, 5L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 1L, 2L, 1L, 2L, 1L, 2L, 4L, 4L,
0L, 0L, 1L, 1L, 0L, 4L, 1L, 2L, 2L, 2L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 3L, 2L, 4L, 3L, 2L, 4L, 4L, 3L, 0L, 1L, 2L, 2L, 2L, 0L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 0L, 0L, 2L, 3L, 3L, 3L, 2L, 2L, 0L, 2L,
2L, 3L, 3L, 2L, 2L, 3L, 2L, 0L, 0L, 3L, 1L, 2L, 3L, 2L, 1L, 4L,
0L, 1L, 3L, 1L, 2L, 3L, 4L, 2L, 1L, 2L, 3L, 2L, 2L, 0L, 1L, 1L,
3L, 6L, 2L, 1L, 2L, 1L, 2L, 0L, 1L, 1L, 1L, 1L, 5L, 3L, 2L, 1L,
2L, 3L, 4L, 2L, 1L, 3L, 1L, 3L, 0L, 3L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 0L, 3L, 2L, 3L, 0L, 7L, 3L, 3L, 1L, 4L, 2L, 2L, 2L, 1L, 2L,
1L, 1L, 5L, 3L, 3L, 3L, 3L, 4L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 2L,
3L, 0L, 2L, 4L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 5L, 2L, 1L, 2L,
2L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 3L, 2L, 0L, 3L, 1L, 5L, 1L, 3L,
2L, 1L, 2L, 4L, 1L, 1L, 2L, 2L, 3L, 0L, 1L, 1L, 2L, 1L, 2L, 0L,
1L, 0L, 1L, 0L, 0L, 4L, 1L, 1L, 1L, 1L, 4L, 1L, 0L, 3L, 1L, 2L,
1L, 2L, 3L, 0L, 5L, 2L, 2L, 2L, 1L, 2L, 1L, 4L, 1L, 1L, 3L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 5L, 2L,
2L, 2L, 2L, 2L, 0L, 1L, 3L, 2L, 3L, 2L, 1L, 4L, 1L, 2L, 1L, 2L,
4L, 1L, 1L, 1L, 2L, 0L, 1L, 0L, 0L, 1L, 2L, 1L, 0L, 4L, 1L, 3L,
2L, 1L, 0L, 1L, 2L, 2L, 0L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L,
2L)), row.names = c(NA, 3000L), class = "data.frame")

The reason you are getting this behavior is because the whisker extends beyond median + 1.5 * IQR. From help("geom_boxplot"):
The lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles). This differs slightly from the method used by the boxplot() function...
The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge (where IQR is the inter-quartile range, or distance between the first and third quartiles).
A slight change to your mutate call will fix this issue:
df %>%
group_by(income_class) %>%
mutate(outlier = nr_trips > quantile(nr_trips,0.75) + IQR(nr_trips) * 1.5) %>%
ggplot(aes(x=income_class, y=nr_trips)) +
geom_boxplot(varwidth = TRUE, outlier.shape = NA)+
scale_x_discrete(labels=my_xlab)+
stat_summary(fun.data = "mean_cl_normal",
aes(shape="mean"),
size=4,
colour = "red",
geom="point") +
scale_shape_manual("", values=c("mean"="x"))+
theme_minimal() +
geom_jitter(data = function(x) dplyr::filter_(x, ~ outlier), width = 0.25, height = 0, alpha = .1, aes(color = "outlier")) +
scale_color_manual(name = "", values = c("outlier" = "black"))+
labs(x = "Income class", y="Number of AToD trips")+
scale_y_continuous(breaks=c(0,2,4,6,8, 10, 12, 14), limits = c(0, 12))

mlogit: multinominal probit model with intercept returns error

I am experiencing a problem fitting a multinominal probit with the package mlogit.
I am interested in a model with a single alternative specific covariate with a generic coefficient (more details on notation here). In brief, 2001 respondents are faced with 6 choices. Each respondent possesses a utility score associated with each option. The distribution of choices comes from real data but, for the purpose of this exercise, the utility scores (in a 0 to 100 scale) have been randomly generated to avoid multicollinearity. Since the choice scenario was real, a model with intercept is estimated.
Full data is available here, and the code to transform the data to long format is available here. Below I provide a head of the original data which I subsequently converted into a long format.
#preparing sample data
library(mlogit)
data_head<-head(original_data) ##from https://pastebin.com/28XpX7A4
DT_head_long <- mlogit.data(data_head, shape="wide", varying=1:6, choice="choice_with_6_alternative")
DT_head_long<-structure(list(choice_with_6_alternative = c(FALSE, FALSE, FALSE,
TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE), alt = c(1, 2, 3, 4,
5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1,
2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6), random_utility = c(18, 25,
12, 13, 2, 37, 3, 21, 38, 3, 6, 51, 8, 33, 32, 9, 6, 29, 43,
6, 5, 25, 15, 17, 24, 11, 12, 29, 33, 13, 16, 22, 12, 5, 21,
3), chid = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L)), reshapeLong = list(varying = structure(list(
random_utility = c("random_utility.1", "random_utility.2",
"random_utility.3", "random_utility.4", "random_utility.5",
"random_utility.6")), v.names = "random_utility", times = c(1,
2, 3, 4, 5, 6)), v.names = "random_utility", idvar = "chid",
timevar = "alt"), row.names = c("1.1", "1.2", "1.3", "1.4",
"1.5", "1.6", "2.1", "2.2", "2.3", "2.4", "2.5", "2.6", "3.1",
"3.2", "3.3", "3.4", "3.5", "3.6", "4.1", "4.2", "4.3", "4.4",
"4.5", "4.6", "5.1", "5.2", "5.3", "5.4", "5.5", "5.6", "6.1",
"6.2", "6.3", "6.4", "6.5", "6.6"), class = c("mlogit.data",
"data.frame"), index = structure(list(chid = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L,
6L, 6L, 6L), .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"),
alt = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("1",
"2", "3", "4", "5", "6"), class = "factor")), class = "data.frame", row.names = c("1.1",
"1.2", "1.3", "1.4", "1.5", "1.6", "2.1", "2.2", "2.3", "2.4",
"2.5", "2.6", "3.1", "3.2", "3.3", "3.4", "3.5", "3.6", "4.1",
"4.2", "4.3", "4.4", "4.5", "4.6", "5.1", "5.2", "5.3", "5.4",
"5.5", "5.6", "6.1", "6.2", "6.3", "6.4", "6.5", "6.6")), choice = "choice_with_6_alternative")
#fitting the model with probit=TRUE
probit <- mlogit(choice_with_6_alternative ~ random_utility,
data=DT_head_long,
probit=TRUE)
//The model returns the error:
Error in if (is.null(initial.value) || lnl <= initial.value) break :
missing value where TRUE/FALSE needed
#fitting the model with probit=FALSE
//model without intercept: runs well
probit <- mlogit(choice_with_6_alternative ~ random_utility|0,
data=DT_head_long,
probit=FALSE)
//model with intercept: runs well
logit <- mlogit(choice_with_6_alternative ~ random_utility,
data=DT_head_long,
probit=FALSE)
#my questions:
1- To what the error is related?
2- Is the model non-identified?

Imbricated axis in ggplot

I want to plot under two X,Y axis
The first one would have X axis Timecode and Y as the cumul of each line as line =1 across Timecode
The biggest axis is Y as Sample_Hour and X as Type
df=structure(list(Sample_ID = c(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, 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, 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
), Sample_Hour = structure(c(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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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("17:03:13", "17:05:44", "17:08:44", "17:11:39",
"17:14:39", "17:18:06", "17:21:13", "17:24:02", "17:27:05", "17:32:43",
"17:35:59", "17:38:40", "17:41:26", "17:44:43", "17:48:14", "17:51:14",
"17:55:00", "17:57:16", "18:00:12", "18:03:16", "18:06:03", "18:09:59",
"18:12:49", "18:15:37", "18:18:01", "18:21:31", "18:26:09", "18:27:58",
"18:29:52", "18:33:21", "18:36:40", "18:38:50", "18:41:25", "18:44:35",
"18:48:45", "18:52:08", "18:54:19", "18:59:52", "19:02:28", "19:06:04",
"19:11:18", "19:15:03", "19:18:06", "19:21:37"), class = "factor"),
Direction = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Timecode = structure(c(1545667346,
1545667367, 1545667368, 1545667369, 1545667370, 1545667379,
1545667382, 1545667383, 1545667387, 1545667390, 1545667391,
1545667482, 1545667482, 1545667483, 1545667483, 1545667483,
1545667483, 1545667483, 1545667483, 1545667484, 1545667484,
1545667484, 1545667484, 1545667484, 1545667484, 1545667484,
1545667484, 1545667484, 1545667484, 1545667484, 1545667484,
1545667484, 1545667484, 1545667485, 1545667485, 1545667485,
1545667485, 1545667485, 1545667485, 1545667485, 1545667485,
1545667485, 1545667485, 1545667485, 1545667485, 1545667485,
1545667485, 1545667486, 1545667486, 1545667486, 1545667486,
1545667486, 1545667486, 1545667486, 1545667486, 1545667486,
1545667486, 1545667486, 1545667487, 1545667487, 1545667487,
1545667487, 1545667487, 1545667487, 1545667487, 1545667487,
1545667487, 1545667487, 1545667487, 1545667487, 1545667487,
1545667487, 1545667488, 1545667488, 1545667488, 1545667488,
1545667488, 1545667488, 1545667488, 1545667488, 1545667488,
1545667489, 1545667489, 1545667489, 1545667489, 1545667489,
1545667489, 1545667489, 1545667490, 1545667490, 1545667490,
1545667490, 1545667491, 1545667491, 1545667491, 1545667491,
1545667491, 1545667491, 1545667491, 1545667492, 1545667492,
1545667493, 1545667493, 1545667493, 1545667494, 1545667494,
1545667494, 1545667494, 1545667495, 1545667495, 1545667496,
1545667497, 1545667497, 1545667498, 1545667498, 1545667498,
1545667498, 1545667499, 1545667499, 1545667500, 1545667500,
1545667501, 1545667501, 1545667501, 1545667502, 1545667503,
1545667503, 1545667503, 1545667504, 1545667504, 1545667504,
1545667504, 1545667505, 1545667505, 1545667505, 1545667505,
1545667505, 1545667506, 1545667506, 1545667506, 1545667507,
1545667507, 1545667507, 1545667508, 1545667508, 1545667509,
1545667509, 1545667509, 1545667509, 1545667510, 1545667510,
1545667510, 1545667510, 1545667510, 1545667511, 1545667511,
1545667511, 1545667512, 1545667512, 1545667512, 1545667513,
1545667513, 1545667514, 1545667514, 1545667514, 1545667515,
1545667515, 1545667516, 1545667516, 1545667516, 1545667516,
1545667517, 1545667517, 1545667517, 1545667518, 1545667518,
1545667518, 1545667519, 1545667520, 1545667520, 1545667521,
1545667521, 1545667522, 1545667522, 1545667522, 1545667522,
1545667522, 1545667523, 1545667523, 1545667523, 1545667523,
1545667523, 1545667524, 1545667524, 1545667524, 1545667524,
1545667524, 1545667525, 1545667525, 1545667526), class = c("POSIXct",
"POSIXt"), tzone = ""), Type = c("14", "1", "6", "14", "4",
"6", "14", "6", "4", "1", "6", "5", "6", "1", "8", "5", "4",
"7", "2", "2", "6", "5", "16", "8", "3", "5", "1", "1", "8",
"7", "9", "6", "11", "7", "5", "4", "9", "6", "5", "2", "1",
"12", "4", "4", "14", "14", "12", "7", "1", "6", "3", "4",
"5", "2", "12", "4", "5", "2", "9", "1", "14", "7", "1",
"4", "12", "5", "7", "14", "3", "5", "2", "9", "14", "4",
"6", "1", "2", "5", "4", "3", "9", "3", "1", "9", "4", "4",
"3", "1", "4", "4", "7", "1", "3", "4", "7", "1", "6", "4",
"4", "14", "14", "4", "4", "4", "14", "4", "4", "4", "4",
"6", "4", "1", "4", "7", "6", "7", "14", "7", "6", "7", "4",
"6", "4", "7", "6", "4", "7", "4", "7", "7", "1", "7", "6",
"14", "7", "4", "4", "6", "6", "7", "4", "4", "6", "7", "4",
"7", "4", "7", "4", "6", "7", "4", "4", "1", "6", "4", "4",
"6", "4", "4", "6", "4", "14", "6", "6", "6", "7", "4", "6",
"7", "4", "7", "4", "6", "14", "6", "1", "6", "7", "6", "6",
"4", "6", "6", "7", "7", "6", "7", "7", "6", "4", "1", "7",
"7", "14", "6", "7", "7", "6", "7"), Duration = c(0, 0, 0,
23, 0, 11, 13, 4, 17, 23, 8, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2,
1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 2, 1, 1, 0, 1,
1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 0, 0, 2,
1, 2, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 2, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 3, 1, 2, 1, 1, 1, 3,
0, 0, 4, 0, 2, 0, 0, 2, 1, 0, 0, 1, 4, 1, 6, 1, 7, 3, 0,
4, 1, 1, 1, 3, 2, 1, 1, 1, 2, 2, 0, 1, 0, 7, 0, 3, 7, 1,
2, 0, 1, 0, 1, 2, 0, 1, 2, 1, 1, 1, 0, 0, 3, 1, 1, 0, 6,
1, 1, 0, 1, 1, 0, 1, 1, 9, 1, 0, 1, 5, 3, 1, 1, 0, 1, 1,
1, 4, 1, 8, 1, 3, 1, 1, 4, 1, 0, 2, 0, 0, 1, 0, 1, 2, 5,
1, 0, 6, 1, 0, 1, 1, 1)), .Names = c("Sample_ID", "Sample_Hour",
"Direction", "Timecode", "Type", "Duration"), row.names = c(NA,
-200L), class = c("tbl_df", "tbl", "data.frame"))

dplyr-approach
first, create a table with an increasing 1 per group (=cumsum of observations/persons)
result <- df %>%
group_by( Sample_ID, Type ) %>%
mutate( nb = 1:n() )
then create a facetted lins/point plot.
The free_x in the facet_grid keeps the result readable. You have to set the axis-labels/breaks manually
ggplot( data = result, aes( x = Timecode, y = nb ) ) +
geom_line( aes( group = 1 ), size = 1 ) +
geom_point() +
facet_grid( Type ~ Sample_ID, scales = "free_x" ) +
scale_x_datetime( date_breaks = "10 secs", labels = date_format( "%H:%M:%S", tz = "" ) ) +
theme(axis.text.x = element_text( angle = 90, vjust = 0.5, hjust = 1 ) )

Visualization of Groups of Poisson random samples using ggridges

I have two sets of data, all in one data frame. The first set is related to data collected in Location 1 and the second set is collected in Location 2. Each location has different count data (column value) for 5 months.
# DataSet
-----------------
rp_data <- structure(list(Month = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1",
"2", "3", "4", "5"), class = "factor"), location = c("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", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2"), value = c(0L, 1L, 1L, 1L,
2L, 1L, 0L, 0L, 1L, 1L, 3L, 2L, 1L, 4L, 1L, 3L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 0L, 2L, 4L, 3L, 5L, 5L, 0L, 4L, 3L, 3L, 4L, 2L, 5L,
2L, 3L, 10L, 6L, 5L, 6L, 4L, 6L, 4L, 5L, 6L, 5L, 3L, 7L, 1L,
1L, 1L, 1L, 0L, 0L, 2L, 1L, 2L, 0L, 2L, 3L, 4L, 1L, 2L, 1L, 2L,
0L, 2L, 2L, 4L, 4L, 5L, 1L, 4L, 5L, 4L, 5L, 1L, 4L, 3L, 7L, 7L,
4L, 2L, 5L, 4L, 1L, 5L, 3L, 7L, 3L, 4L, 8L, 5L, 7L, 1L, 1L, 6L,
3L)), .Names = c("Month", "location", "value"), row.names = c(NA,
-100L), class = "data.frame")
I used this example below, as illustrated on the ggridges examples webpage, to display the various count values across different months.
# Plot 1 , filtering data related to location = 1
#---------------
ggplot(rp_data[rp_data$location == '1',], aes(x = value, y = Month, group = Month)) +
geom_density_ridges2(aes(fill = Month), stat = "binline", binwidth = 1, scale = 0.95) +
geom_text(stat = "bin",
aes(y = group + 0.95*(..count../max(..count..)),
label = ifelse(..count..>0, ..count.., "")),
vjust = 1.4, size = 3, color = "white", binwidth = 1) +
scale_x_continuous(breaks = c(0:12), limits = c(-.5, 13), expand = c(0, 0),
name = "random value") +
scale_y_discrete(expand = c(0.01, 0), name = "Month",
labels = c("5.0", "4.0", "3.0", "2.0", "1.0")) +
scale_fill_cyclical(values = c("#0000B0", "#7070D0")) +
labs(title = "Poisson random samples location 1 different Month",
subtitle = "sample size n=10") +
guides(y = "none") +
theme_ridges(grid = FALSE) +
theme(axis.title.x = element_text(hjust = 0.5),
axis.title.y = element_text(hjust = 0.5))
# Plot 2 , filtering data related to location = 2
#---------------
ggplot(rp_data[rp_data$location == '2',], aes(x = value, y = Month, group = Month)) +
geom_density_ridges2(aes(fill = Month), stat = "binline", binwidth = 1, scale = 0.95) +
geom_text(stat = "bin",
aes(y = group + 0.95*(..count../max(..count..)),
label = ifelse(..count..>0, ..count.., "")),
vjust = 1.4, size = 3, color = "white", binwidth = 1) +
scale_x_continuous(breaks = c(0:12), limits = c(-.5, 13), expand = c(0, 0),
name = "random value") +
scale_y_discrete(expand = c(0.01, 0), name = "Month",
labels = c("5.0", "4.0", "3.0", "2.0", "1.0")) +
scale_fill_cyclical(values = c("#0000B0", "#7070D0")) +
labs(title = "Poisson random samples location 2 different Month",
subtitle = "sample size n=10") +
guides(y = "none") +
theme_ridges(grid = FALSE) +
theme(axis.title.x = element_text(hjust = 0.5),
axis.title.y = element_text(hjust = 0.5))
Result for plot 1:
My question is how can I combine these two plots, sort of like an overlay plot as shown in this example:
I don't want to plot them in two separate plots.

You need to create a grouping variable that contains both Month and location. You can do that by using paste0(Month, location). For now, I'm leaving out the text labels, though they may be possible with a little more thought as well. (But I think they'd make the figure too busy.)
ggplot(rp_data,
aes(x = value, y = Month,
group = paste0(Month, location),
fill = paste0(Month, location))) +
geom_density_ridges2(stat = "binline", binwidth = 1,
scale = 0.95, alpha = 0.7) +
scale_x_continuous(breaks = c(0:12), limits = c(-.5, 13),
expand = c(0, 0), name = "random value") +
scale_y_discrete(expand = c(0.01, 0), name = "Month",
labels = c("5.0", "4.0", "3.0", "2.0", "1.0")) +
scale_fill_cyclical(values = c("#0000B0", "#B00000",
"#7070D0", "#FC5E5E")) +
labs(title = "Poisson random samples location 1 different Month",
subtitle = "sample size n=10") +
guides(y = "none") +
theme_ridges(grid = FALSE, center = TRUE)
Edit: Now with text labels.
ggplot(rp_data, aes(x = value, y = Month, group = paste0(Month, location), fill = paste0(Month, location))) +
geom_density_ridges2(stat = "binline", binwidth = 1, scale = 0.95, alpha = 0.7) +
geom_text(stat = "bin",
aes(y = ceiling(group/2) + 0.95*(..count../max(..count..)),
label = ifelse(..count..>0, ..count.., ""), color = location),
vjust = 1.4, size = 3, binwidth = 1, fontface = "bold") +
scale_x_continuous(breaks = c(0:12), limits = c(-.5, 13), expand = c(0, 0),
name = "random value") +
scale_y_discrete(expand = c(0.01, 0), name = "Month",
labels = c("5.0", "4.0", "3.0", "2.0", "1.0")) +
scale_fill_cyclical(values = c("#0000B0", "#B00000", "#7070D0", "#FC5E5E")) +
scale_color_cyclical(values = c("white", "black")) +
labs(title = "Poisson random samples location 1 different Month",
subtitle = "sample size n=10") +
guides(y = "none") +
theme_ridges(grid = FALSE, center = TRUE)
Again, not sure it's a good idea, but there you go.

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