Negative valued factors in stacked barplot - r

I am trying to figure out a way of introducing negative values of factors in a stacked barplot in ggplot2. The data is level of support for basic income among Finnish MPs. It is at the bottom of the post.
I can get a plot that is like the one I want (minus the negatively valued factors) with the following code:
library(forcats)
library(ggplot2)
support.plot <- ggplot(mpsupport.df, aes(fct_infreq(Party))) +
geom_bar (aes(fill=Support)) +
coord_flip() +
theme(legend.position = "bottom")+
ylab("Party") +
xlab("Number of MPs")
This gives the following:
What I would like is for the graph to be centred on the green-turquoise border, so that support for basic income was to the right, while opposition was to the left. Does this make sense?
Data:
> dput(mpsupport.df)
structure(list(Party = structure(c(1L, 2L, 2L, 2L, 2L, 2L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 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, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 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, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 5L, 6L, 7L, 7L, 7L), .Label = c("National Coalition",
"Centre Party", "Social Democratic Party", "Left Alliance", "Christian Democrats",
"True Finns", "Swedish People's Party", "Greens"), class = "factor"),
Support = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L), .Label = c("fully.agree", "partially.agree",
"partially.disagree", "fully.disagree"), class = "factor")), .Names = c("Party",
"Support"), row.names = c("1", "2", "2.1", "2.2", "2.3", "2.4",
"4", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "4.7", "6", "8",
"8.1", "8.2", "8.3", "8.4", "8.5", "8.6", "8.7", "8.8", "8.9",
"8.10", "8.11", "8.12", "8.13", "8.14", "9", "9.1", "9.2", "9.3",
"9.4", "9.5", "9.6", "9.7", "10", "10.1", "10.2", "10.3", "10.4",
"10.5", "10.6", "10.7", "10.8", "10.9", "10.10", "10.11", "10.12",
"10.13", "10.14", "10.15", "10.16", "10.17", "10.18", "10.19",
"10.20", "10.21", "10.22", "10.23", "10.24", "10.25", "10.26",
"10.27", "10.28", "10.29", "10.30", "10.31", "10.32", "10.33",
"11", "11.1", "11.2", "11.3", "12", "12.1", "12.2", "12.3", "13",
"14", "14.1", "14.2", "14.3", "14.4", "14.5", "14.6", "14.7",
"14.8", "14.9", "14.10", "14.11", "14.12", "14.13", "14.14",
"14.15", "14.16", "14.17", "14.18", "14.19", "14.20", "15", "15.1",
"17", "17.1", "17.2", "17.3", "17.4", "17.5", "17.6", "17.7",
"17.8", "17.9", "17.10", "17.11", "17.12", "17.13", "17.14",
"17.15", "17.16", "17.17", "17.18", "17.19", "18", "18.1", "18.2",
"18.3", "18.4", "18.5", "18.6", "18.7", "19", "19.1", "19.2",
"19.3", "19.4", "19.5", "19.6", "19.7", "19.8", "19.9", "19.10",
"19.11", "19.12", "19.13", "19.14", "19.15", "19.16", "19.17",
"19.18", "19.19", "19.20", "19.21", "19.22", "19.23", "21", "21.1",
"22", "22.1", "22.2", "22.3", "22.4", "22.5", "22.6", "22.7",
"22.8", "22.9", "22.10", "22.11", "22.12", "23", "23.1", "23.2",
"23.3", "25", "25.1", "25.2", "25.3", "25.4", "25.5", "25.6",
"27", "27.1", "27.2", "27.3", "27.4", "27.5", "29", "30", "31",
"31.1", "31.2"), class = "data.frame")


Try something along these lines:
library(ggplot)
library(forcats)
mpsupport.df$dummy = ifelse(mpsupport.df$Support %in% c("fully.agree", "partially.agree"), 1, -1)
agg = aggregate(dummy ~ Support + Party, data = mpsupport.df, FUN = sum)
ggplot(data = agg)+
geom_bar (aes(y = dummy, x= fct_infreq(Party), fill = factor(Support, levels = c("fully.agree", "partially.agree", "fully.disagree" ,"partially.disagree"))), stat= "identity") +
coord_flip()+
theme(legend.position = "bottom", legend.title = element_blank())

Related

Calculating kappa statistics using irr R library

This was the question where it was shown how to use the concept.
I have naive question about this function which was give here which is this where it assigns predicted levels the cluster.
pred2labels = function(pred,actual){
pred = as.character(pred)
actual = as.character(actual)
tab = as.matrix(table(pred,actual))
assignment = colnames(tab)[max.col(tab)]
names(assignment) = rownames(tab)
assignment[pred]
}
I tried to do the same my question is do i need to generate a predicted labels for my data of clusters?
here is my data frame
dput(bb)
structure(list(FAB = structure(c(4L, 2L, 5L, 3L, 4L, 5L, 4L,
4L, 5L, 3L, 4L, 2L, 4L, 3L, 2L, 3L, 5L, 5L, 4L, 3L, 2L, 5L, 3L,
5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 2L, 3L, 5L, 3L, 5L, 3L,
2L, 1L, 6L, 6L, 5L, 5L, 5L, 6L, 6L, 3L, 7L, 3L, 5L, 6L, 2L, 5L,
2L, 3L, 3L, 2L, 6L, 2L, 2L, 2L, 2L, 1L, 6L, 2L, 5L, 2L, 2L, 9L,
5L, 1L, 5L, 2L, 5L, 5L, 6L, 2L, 3L, 6L, 5L, 2L, 1L, 8L, 3L, 5L,
3L, 6L, 1L, 2L, 2L, 5L, 3L, 5L, 6L, 5L, 5L, 3L, 5L, 3L, 2L, 3L,
3L, 2L, 6L, 1L, 2L, 3L, 6L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L,
2L, 5L, 2L, 3L, 2L, 3L, 5L, 1L, 3L, 1L, 6L, 5L, 5L, 3L, 5L, 3L,
2L, 1L, 2L, 5L, 7L, 8L, 6L, 2L, 8L, 3L, 3L, 1L, 2L, 2L, 2L, 1L,
3L, 6L, 5L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 5L, 2L, 9L, 2L, 1L, 1L,
2L, 6L, 6L), .Label = c("M0", "M1", "M2", "M3", "M4", "M5", "M6",
"M7", "nc"), class = "factor"), RISK_CYTO = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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, 4L, 4L, 4L, 4L, 2L,
4L, 2L, 4L, 2L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 2L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L), .Label = c("Good", "Intermediate",
"N.D.", "Poor"), class = "factor"), Class = c(1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 4L, 1L, 1L, 4L, 1L, 1L, 4L, 1L, 2L, 4L, 1L, 4L,
1L, 4L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 4L, 5L,
3L, 2L, 2L, 4L, 3L, 6L, 2L, 2L, 6L, 2L, 2L, 2L, 3L, 6L, 5L, 2L,
2L, 3L, 6L, 2L, 4L, 5L, 6L, 2L, 3L, 3L, 4L, 5L, 3L, 5L, 4L, 2L,
4L, 3L, 4L, 2L, 3L, 4L, 4L, 5L, 2L, 2L, 5L, 5L, 2L, 4L, 4L, 6L,
6L, 4L, 2L, 3L, 5L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 4L, 2L, 3L, 2L,
4L, 3L, 5L, 4L, 6L, 2L, 5L, 4L, 3L, 4L, 5L, 4L, 2L, 4L, 6L, 4L,
1L, 4L, 5L, 6L, 1L, 4L, 4L, 5L, 4L, 2L, 3L, 5L, 3L, 5L, 2L, 2L,
4L, 2L, 1L, 4L, 3L, 5L, 5L, 6L, 2L, 2L, 3L, 6L, 1L, 5L, 5L, 5L,
5L, 3L, 3L, 6L, 5L, 4L, 6L, 3L, 5L, 5L, 5L, 5L, 5L, 2L, 1L, 5L,
6L, 5L, 5L, 6L, 2L, 2L)), row.names = c(NA, -170L), class = "data.frame")
My steps were this
library(irr)
clus_arrange = bb %>% dplyr::select(Class,FAB)
names(clus_arrange)[1] = "clus"
clus_arrange$predicted_label = pred2labels(clus_arrange$clus,clus_arrange$FAB)
kappam.light(cluster_r)
My output is this
Light's Kappa for m Raters
Subjects = 170
Raters = 2
Kappa = 0.266
z = 6.62
p-value = 3.58e-11
My question is the approach right way of doing it what i had followed from that answer?
UPDATED ANSWER BASED ON THIS TUTORIAL
table <- table(clus_arrange$FAB, clus_arrange$clus)
table
kappam.fleiss(table, detail=TRUE)
My question is which one is methodically and logically correct

Using nest_by or nest, group_by or looping to perform several chi square test to several variables in R (likert scales)

Let's say I have these categorical variables in my data set. All variables are related to the people's concerns of the COVID-19 and were assessed two times (with different participants..).
And my main goal is to check if time (will be "constant") is associated with the prevalence of each item (economy, social cohesion, and so on) (will vary). Therefore, I'll need to perform several Chi-square tests.
I've followed some instructions using nest_by or xtabs , but I'm not getting the right results.
I would like to keep the tidyverse environment in this analysis.
The main goal is to have several chi-squared tests, such as this one:
ds_plot_likert %>%
pivot_longer(cols = -c(time),
names_to = "item", values_to = "response") %>%
group_by(item, time, response) %>%
summarise(N = n()) %>%
mutate(pct = N / sum(N)) %>%
filter(item == "Children's academic achievement") %>% #need to change all the time...
xtabs(formula = pct ~ time + response, data = .) %>%
chisq.test()
But for all variables in my dataset (and preferably using tidyverse).
Thank you!
The following code gives you the possibility to reproduce.
ds <- structure(list(time = c("First", "First", "First", "First", "First",
"First", "First", "First", "First", "First", "First", "First",
".Second", "First", "First", "First", "First", ".Second", "First",
"First", "First", "First", "First", "First", "First", "First",
".Second", "First", "First", ".Second", "First", "First", "First",
"First", "First", "First", "First", "First", "First", "First",
".Second", "First", "First", "First", ".Second", ".Second", "First",
"First", "First", ".Second", ".Second", "First", "First", "First",
"First", ".Second", ".Second", "First", "First", "First", "First",
"First", ".Second", "First", "First", "First", "First", ".Second",
"First", "First", "First", "First", "First", "First", "First",
".Second", "First", ".Second", "First", "First", "First", "First",
"First", "First", "First", "First", "First", "First", "First",
".Second", "First", "First", "First", "First", "First", "First",
"First", ".Second", ".Second", "First"), Economy = structure(c(4L,
3L, 3L, 4L, 3L, 4L, 4L, 1L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 4L, 4L,
3L, 3L, NA, 2L, 3L, 4L, 3L, 3L, 4L, 4L, 2L, 3L, 4L, 4L, 3L, 2L,
4L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 3L, 3L, 3L, 4L, 3L, 3L,
4L, 2L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 4L, 3L, 2L,
3L, 3L, 3L, 4L, NA, 2L, 4L, 3L, 4L, 2L, 3L, 3L, 2L, NA, 3L, 2L,
3L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 4L, 3L, 3L, 2L, 3L,
3L, 3L, 4L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `My personal finance` = structure(c(3L,
2L, 4L, 2L, 4L, 4L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 4L, 3L, 4L, 4L,
2L, 3L, NA, 3L, 3L, 4L, 3L, 3L, 4L, 3L, 4L, 3L, 4L, 4L, 3L, 4L,
3L, 4L, 2L, 2L, 3L, 2L, 2L, 4L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 2L,
3L, 2L, 4L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 4L, 4L, 2L,
3L, 3L, 4L, 3L, NA, 2L, 3L, 3L, 4L, 2L, 3L, 2L, 3L, NA, 3L, 2L,
2L, 2L, 3L, 4L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 4L, 3L, 2L, 2L, 3L,
3L, 3L, 3L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `My own health` = structure(c(3L,
2L, 4L, 3L, 4L, 4L, 3L, 4L, 2L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 4L,
3L, 2L, NA, 3L, 4L, 3L, 4L, 3L, 2L, 1L, 2L, 3L, 3L, 4L, 2L, 4L,
2L, 4L, 4L, 3L, 2L, 2L, 4L, 4L, 2L, 4L, 3L, 3L, 2L, 3L, 3L, 2L,
2L, 3L, 3L, 1L, 3L, 4L, 4L, 3L, 3L, 2L, 2L, 4L, 3L, 3L, 4L, 2L,
3L, 3L, 4L, 4L, NA, 2L, 2L, 3L, 4L, 1L, 3L, 4L, 3L, NA, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 2L, 4L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 4L,
2L, 3L, 3L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `My friends and family health` = structure(c(4L,
3L, 4L, 4L, 4L, 4L, 3L, 4L, 3L, 4L, 3L, NA, 3L, 4L, 3L, 3L, 4L,
4L, 3L, NA, 3L, 4L, 3L, 4L, 3L, 3L, 2L, 2L, 3L, 4L, 4L, 2L, 4L,
1L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L,
4L, 3L, 3L, 4L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 4L, 3L, 4L, 4L, 2L,
3L, 3L, 4L, 4L, NA, 2L, 4L, 3L, 4L, 2L, 3L, 4L, 3L, NA, 3L, 4L,
3L, 3L, 4L, 3L, 4L, 4L, 3L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 4L,
4L, 3L, 4L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `Social cohesion` = structure(c(3L,
3L, 2L, 4L, 4L, 2L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 4L,
3L, 3L, NA, 3L, 3L, 4L, 3L, 3L, 2L, 1L, 2L, 2L, 4L, 4L, 3L, 3L,
1L, 3L, NA, 2L, 3L, 2L, 4L, 4L, 2L, 3L, 3L, 4L, 4L, 3L, 3L, 3L,
2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 2L,
3L, 3L, 3L, 4L, NA, 2L, 3L, 3L, 2L, 1L, 1L, 3L, 2L, NA, 3L, NA,
3L, 3L, 4L, 2L, 4L, 3L, 1L, 4L, 2L, 4L, 3L, 2L, 4L, 2L, 3L, 4L,
4L, 2L, 2L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `Food and pharmaceutical drugs` = structure(c(2L,
3L, 4L, 4L, 3L, 2L, 4L, 1L, 2L, 4L, 3L, NA, 2L, 3L, 3L, 2L, 4L,
3L, 3L, NA, 3L, 4L, 3L, 3L, 3L, 1L, 1L, 2L, 3L, 4L, 2L, 2L, 4L,
4L, 4L, 1L, 4L, 2L, 2L, 3L, 4L, 2L, 4L, 3L, 2L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 4L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 3L, 3L, 4L, 2L,
3L, 3L, 3L, 4L, NA, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, NA, 1L, 1L,
2L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 4L, 2L, 3L, 3L,
2L, 1L, 2L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `Price of grocery products` = structure(c(2L,
2L, 3L, 4L, 3L, 4L, 3L, 2L, 2L, 4L, 3L, 3L, 2L, 4L, 3L, 3L, 4L,
4L, 3L, NA, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 4L, 2L, 4L, 3L,
4L, 4L, 4L, 4L, 2L, 2L, 3L, 4L, 2L, 4L, 3L, 3L, 3L, 3L, 4L, 2L,
4L, 2L, 3L, 4L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 4L, 3L, 4L, 4L, 2L,
3L, 3L, 4L, 4L, NA, 2L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, NA, 1L, 1L,
2L, 2L, 3L, 1L, 3L, 4L, 2L, 3L, 2L, 2L, 2L, 2L, 4L, 3L, 3L, 3L,
4L, 1L, 2L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `Stock prices` = structure(c(2L,
2L, 2L, 4L, 3L, 2L, 1L, 1L, 3L, 4L, 2L, 2L, 2L, 4L, 3L, 3L, 4L,
3L, 2L, NA, 1L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L,
4L, 4L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 4L, 3L, 4L, 3L, 3L, 2L, 2L,
2L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, NA, 2L, 4L, 2L,
3L, 3L, 4L, 4L, NA, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, NA, 3L, 1L,
3L, 3L, 4L, 1L, 3L, 4L, 1L, 3L, 1L, 4L, 2L, 2L, 2L, NA, 3L, 2L,
4L, 1L, 2L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor"), `Children's academic achievement` = structure(c(4L,
3L, 4L, 1L, NA, NA, 1L, 1L, 1L, 1L, 3L, 3L, 2L, 4L, 3L, 4L, NA,
4L, 3L, NA, 1L, 1L, 3L, 3L, 3L, 1L, 4L, 1L, 3L, 4L, 3L, 2L, 4L,
1L, 4L, 1L, 1L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 1L,
1L, 1L, 4L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 3L, 4L, 2L,
2L, 1L, 1L, 4L, NA, 2L, 2L, 2L, 4L, 1L, 2L, 1L, 2L, NA, 2L, 1L,
NA, 3L, 2L, 2L, 1L, 4L, 2L, 3L, 1L, 4L, 1L, 1L, 1L, 3L, 1L, 1L,
1L, 1L, 2L), .Label = c("Not at all", "A little", "Moderately",
"Very much"), class = "factor")), class = "data.frame", row.names = c(NA,
-100L))

You can store the result in a list for each item -
library(dplyr)
library(tidyr)
ds %>%
pivot_longer(cols = -c(time),
names_to = "item", values_to = "response") %>%
group_by(item, time, response) %>%
summarise(N = n()) %>%
mutate(pct = N / sum(N)) %>%
group_by(item) %>%
summarise(test = list(xtabs(formula = pct ~ time + response,
data = cur_data()) %>% chisq.test())) -> result
result$test
#[[1]]
# Pearson's Chi-squared test
#data: .
#X-squared = 0.0099329, df = 3, p-value = 0.9997
#[[2]]
#
# Pearson's Chi-squared test
#data: .
#X-squared = 0.026631, df = 3, p-value = 0.9989
#...
#...

error bars should not be very long in barplots in r

I am plotting grouped barplots with error bars, but my error bars are very long as in this image
[![https://i.stack.imgur.com/VUByO.png][1]][1].
I would like shorter error bars as in this image
[![https://i.stack.imgur.com/JhaUJ.png][2]][2]
The code used
per$Leaf_Location <- factor(per$Leaf_Location, levels = unique(per$Leaf_Location))
per$Time <- factor(per$Time, levels = unique(per$Time))
ggplot(per, aes(x=Leaf_Location, y=Damage, fill=as.factor(Time))) +
stat_summary(fun.y=mean,
geom="bar",position=position_dodge(),colour="black",width=.7,size=.7) +
stat_summary(fun.ymin=min,fun.ymax=max,geom="errorbar",
color="black",position=position_dodge(.7), width=.2) +
stat_summary(geom = 'text', fun.y = max, position = position_dodge(.7),
label = c("a","b","c","d","d","a","b","c","d","d","a","b","c","d","d"), vjust = -0.5) +
scale_fill_manual("Legend", values = c("grey36","grey46","grey56","grey76","grey86","grey96")) +
xlab("Leaf Location") +
ylab("Damage ") +
theme_bw()
data:
per =
structure(list(Site = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 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("Defathers",
"Kariithi", "Kimbimbi"), class = "factor"), Field = structure(c(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, 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, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 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, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 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, 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,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L
), .Label = c("F1", "F2", "F3", "F4", "F5"), class = "factor"),
Leaf_Location = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("Lower", "Intermediate",
"Upper"), class = "factor"), Time = structure(c(1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L), .Label = c("20_days",
"40_days", "60_days", "80_days", "100_days"), class = "factor"),
Damage = c(25.25, 26.07, 24.43, 20.73, 17.8, 6.9, 45.05,
33.47, 24.43, 51.67, 41.72, 34.17, 81.67, 73.33, 55.83, 34.28,
26.08, 13.28, 26.27, 14.1, 6.93, 37.55, 29.33, 23.62, 49.17,
38.45, 31.38, 70.83, 60.83, 44.2, 31.03, 25.2, 14.97, 14.38,
6.5, 4.33, 52.2, 39.17, 30.97, 75, 62.5, 38.33, 87.5, 62.5,
57.5, 45.02, 31.02, 26.07, 46.72, 34.32, 21.5, 50.83, 34.23,
25.25, 45.83, 33.47, 27.7, 67.67, 57.5, 52.67, 30.98, 23.62,
9.1, 18.17, 18.57, 10.15, 46.67, 34.27, 23.62, 54.17, 40.05,
29.37, 70.83, 59.17, 47.53, 8.67, 5.63, 0.87, 9.87, 3.03,
0, 17.75, 6.88, 0, 62.5, 37.5, 27.7, 70.83, 57.5, 50.83,
6.5, 2.17, 1.3, 6.93, 3.03, 0.53, 14.82, 5.2, 0, 37.5, 28.52,
13, 75, 37.5, 37.5, 15.3, 9.53, 5.63, 9.43, 3.03, 0.43, 16.4,
6.07, 0, 57.5, 34.23, 21.98, 78.33, 62.5, 37.5, 12.08, 6.5,
1.3, 10.73, 3.03, 0, 15.2, 3.9, 0.43, 62.5, 37.5, 21.98,
64.17, 55.83, 41.73, 8.73, 3.57, 0, 8.57, 2.17, 0, 16.5,
7.7, 0.43, 42.58, 36.68, 13, 65.83, 47.5, 37.5, 8.03, 5.07,
0.43, 10.68, 7.27, 3.5, 48.38, 38.42, 24.83, 45.03, 38.4,
30.8, 73.33, 63.33, 50.83, 3.37, 2.17, 0.9, 9, 6.02, 5.2,
21.07, 12.37, 6.02, 45.02, 32.65, 21.67, 68.78, 56.68, 50,
0, 0, 0, 7.8, 4.33, 4.33, 25.17, 20.65, 13.15, 48.37, 39.23,
27.17, 75.83, 62.5, 49, 11.78, 12.72, 3.8, 20.18, 14.87,
8.95, 46.7, 39.32, 33.03, 49.18, 40.05, 24.43, 69.17, 60,
48.33, 0, 0, 0, 15.25, 9.82, 7.75, 45.9, 38.47, 35.52, 50.88,
37.61, 33.47, 79.17, 71.67, 58.33)), .Names = c("Site", "Field",
"Leaf_Location", "Time", "Damage"), row.names = c(NA, -225L), class = "data.frame")

Here's a simplified reproducible example to explain
first, some dummy data:
per = data.frame(x=rep(c('a','b'), each=100), y=c(2+rnorm(100), 3+rnorm(100,0,2)))
Now you are plotting the error bars, using fun.ymin=min, fun.ymax=max, which will cause them to extend the full range of the data, as in the following graph:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
geom_point(position = position_jitter(0.1)) +
stat_summary(fun.ymin=min, fun.ymax=max, geom="errorbar", width=0.4) +
theme_bw()
Whereas, it is more conventional to use error bars that extend either +/- one standard deviation, as in the following:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
stat_summary(
fun.ymin=function(y) {mean(y) - sd(y)},
fun.ymax=function(y) {mean(y) + sd(y)},
geom="errorbar", width=0.2) +
theme_bw()
Or one standard error, like this:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
stat_summary(
fun.ymin=function(y) {mean(y) - sqrt(var(y)/length(y))},
fun.ymax=function(y) {mean(y) + sqrt(var(y)/length(y))},
geom="errorbar", width=0.2) +
theme_bw()
EDIT - example data were added to question, after this answer was originally posted
We can applying exactly the same approach as above to your example data:
ggplot(per, aes(x=Leaf_Location, y=Damage, fill=as.factor(Time))) +
stat_summary(fun.y=mean, geom="bar",position=position_dodge(),colour="black",width=.7,size=.7) +
stat_summary(
fun.ymin=function(y) {mean(y) - sqrt(var(y)/length(y))},
fun.ymax=function(y) {mean(y) + sqrt(var(y)/length(y))},
geom="errorbar",
position=position_dodge(.7), width=.2)

Changing labels R ggplot in two variable facet wrapped plot

I'm trying to create a facet wrapped ggplot boxplot with dataframe dataw and I'm trying to modify the labels of each subplot.
dataw <- structure(list(base = 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, 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, 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, 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, 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, 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), .Label = c("A", "C", "G", "T"), class = "factor"), pos = 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, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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, 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, 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, 6L, 6L, 6L, 6L, 6L, 6L), values = c(13, 22, 16, 21, 52, 1,
1.709, 2.121, 2.061, 2.233, 3.388, 1, 5, 6, 6, 2, 1, 0.856, 1.116,
1.207, 1.175, 0.95, 76, 45, 5, 1, 1, 15, 8.558, 5.44, 1.147,
0.857, 0.831, 10, 7, 40, 4, 10, 5, 1.547, 1.174, 4.777, 1.071,
1.356, 7, 0, 1, 6, 1, 8, 1.322, 0.728, 0.83, 1.178, 0.831, 4,
2, 0, 1, 3, 0, 1.098, 0.96, 0.63, 0.888, 1.013, 13, 22, 16, 21,
52, 1, 1.709, 2.121, 2.061, 2.233, 3.388, 3, 6, 7, 2, 9, 11,
0.952, 1.474, 1.45, 0.967, 1.306, 13, 22, 16, 21, 52, 1, 1.709,
2.121, 2.061, 2.233, 3.388, 3, 8, 15, 0, 5, 2, 1.014, 1.583,
2.289, 0.773, 1.135, 10, 3, 8, 1, 4, 2, 1.504, 1.03, 1.244, 0.884,
1.047, 4, 1, 0, 2, 5, 1, 1.066, 0.862, 0.689, 0.963, 1.125, 2,
0, 0, 2, 0, 1, 0.919, 0.723, 0.479, 0.922, 0.721, 7, 8, 0, 8,
7, 0, 1.299, 1.236, 0.779, 1.298, 1.224, 13, 22, 16, 21, 52,
1, 1.709, 2.121, 2.061, 2.233, 3.388, 45, 38, 41, 13, 34, 1,
2.817, 2.264, 2.398, 1.374, 3.848, 3, 0, 1, 1, 2, 14, 0.973,
0.641, 0.846, 0.866, 0.909, 13, 22, 16, 21, 52, 1, 1.709, 2.121,
2.061, 2.233, 3.388, 7, 0, 0, 1, 2, 1, 1.37, 0.436, 0.706, 0.685,
0.902, 0, 5, 5, 0, 7, 1, 0.597, 1.113, 1.079, 0.71, 1.222, 3,
1, 4, 0, 23, 8, 0.992, 0.84, 1.07, 0.762, 2.399, 17, 7, 18, 6,
10, 1, 2.4, 1.315, 1.948, 1.135, 1.306, 21, 8, 50, 4, 6, 12,
2.412, 1.254, 3.857, 1.075, 1.168, 13, 22, 16, 21, 52, 1, 1.709,
2.121, 2.061, 2.233, 3.388), type = structure(c(2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L), .Label = c("ipdRatio", "score"), class = "factor"),
labels = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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, 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, 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, 6L, 6L, 6L, 6L, 6L, 6L
), .Label = c("D<U+2192>", "G<U+2192>", "A<U+2192>", "K<U+2192>",
"C<U+2192>", "T<U+2192>"), class = "factor")), .Names = c("base",
"pos", "values", "type", "labels"), row.names = c("1", "2", "3",
"4", "5", "3942", "3943", "3944", "3945", "3946", "3947", "11",
"21", "31", "41", "51", "63", "64", "65", "66", "67", "68", "12",
"22", "32", "42", "52", "2953", "2954", "2955", "2956", "2957",
"2958", "13", "23", "33", "43", "53", "2461", "2462", "2463",
"2464", "2465", "2466", "14", "24", "34", "44", "54", "7493",
"7494", "7495", "7496", "7497", "7498", "111", "214", "311",
"411", "511", "4874", "4875", "4876", "4877", "4878", "4879",
"121", "221", "321", "421", "521", "9356", "9357", "9358", "9359",
"9360", "9361", "131", "231", "331", "431", "531", "9221", "9222",
"9223", "9224", "9225", "9226", "15", "25", "35", "45", "55",
"93561", "93571", "93581", "93591", "93601", "93611", "112",
"215", "312", "412", "512", "1579", "1580", "1581", "1582", "1583",
"1584", "122", "222", "322", "422", "522", "1782", "1783", "1784",
"1785", "1786", "1787", "132", "232", "332", "432", "532", "3398",
"3399", "3400", "3401", "3402", "3403", "16", "26", "36", "46",
"56", "2257", "2258", "2259", "2260", "2261", "2262", "113",
"216", "313", "413", "513", "1027", "1028", "1029", "1030", "1031",
"1032", "123", "223", "323", "423", "523", "8654", "8655", "8656",
"8657", "8658", "8659", "133", "233", "333", "433", "539", "702",
"703", "704", "705", "706", "707", "17", "27", "37", "47", "57",
"8123", "8124", "8125", "8126", "8127", "8128", "114", "217",
"314", "414", "514", "93562", "93572", "93582", "93592", "93602",
"93612", "124", "224", "324", "424", "524", "3700", "3701", "3702",
"3703", "3704", "3705", "134", "234", "334", "434", "5310", "8233",
"8234", "8235", "8236", "8237", "8238", "18", "28", "38", "48",
"58", "1542", "1543", "1544", "1545", "1546", "1547", "115",
"218", "315", "415", "515", "533", "534", "535", "536", "537",
"538", "125", "225", "325", "425", "525", "208", "209", "210",
"211", "212", "213", "135", "235", "335", "435", "5311", "93563",
"93573", "93583", "93593", "93603", "93613"), class = "data.frame")
These are the first few rows of dataw
head(dataw)
base pos values type labels
1 A 1 13 score D<U+2192>
2 A 1 22 score D<U+2192>
3 A 1 16 score D<U+2192>
4 A 1 21 score D<U+2192>
5 A 1 52 score D<U+2192>
3942 A 1 1 score D<U+2192>
I'm plotting it like so.
prettify <- theme(panel.background = element_rect(fill = NA,color="gray"),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(size=.1, color="black",linetype="dotted"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_line(size=.1, color="black"),
legend.position="bottom")
ggplot(dataw,aes(x = base, y = values, color = type, group = base)) +
geom_boxplot() +
facet_wrap(type ~ pos, scales="free_y", nrow = 2) +
theme_gray() %+replace% prettify
Currently the sublabels are the type value followed by a comma and the pos value. However I would like to get rid of the type value, and label it so that the labels of each subplot are in the format: "Position [pos value], [labels value]"
What would be the best way to go about this? Thank you.

Try replacing the entire ggplot statement with
ggplot(data=transform(dataw, plt_labels = paste("Position ", pos, ", ", labels, sep="")),aes(x = base, y = values, color = type, group = base)) +
geom_boxplot() +
facet_grid(type ~ plt_labels, scales="free_y") +
theme_gray() %+replace% prettify
which should give

How to get the median line in bwplot thicker?

The following code is a minimal (for some value of minimal....) example that uses lattice to produce boxplots. But the median line on those boxplot is a) coloured and b) very thin. How to get them to be black and tick?
a71<-structure(list(n = structure(c(1L, 2L, 2L, 4L, 4L, 1L, 1L, 4L,
2L, 1L, 1L, 2L, 2L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 2L, 3L, 2L, 2L,
2L, 4L, 3L, 3L, 4L, 2L, 4L, 2L, 1L, 3L, 2L, 3L, 4L, 1L, 4L, 1L,
3L, 3L, 2L, 1L, 1L, 3L, 3L, 1L, 2L, 4L, 3L, 2L, 3L, 1L, 4L, 1L,
4L, 2L, 3L, 4L, 4L, 4L, 1L, 3L, 3L, 3L, 4L, 2L, 2L, 2L, 4L, 4L,
4L, 1L, 4L, 3L, 2L, 2L, 4L, 4L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L,
3L, 1L, 3L, 3L, 4L, 1L, 3L, 2L, 1L, 3L, 1L, 2L), .Label = c("100",
"200", "400", "800"), class = "factor"), g = structure(c(3L,
3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 1L,
3L, 1L, 2L, 3L, 2L, 2L, 2L, 3L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 3L,
1L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 1L,
1L, 1L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 3L,
3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 1L,
3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L,
3L, 1L, 3L), .Label = c("0", "0.5", "1"), class = "factor"),
cr = structure(c(1L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L,
3L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 3L, 1L,
3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 3L, 1L,
1L, 1L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 1L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 2L, 2L
), .Label = c("-0.4", "0", "0.4"), class = "factor"), bias = c(0.0162558992812201,
0.138354243932496, 0.0205686041691062, 0.269714433604472,
0.381044037439145, 0.0869422119950729, 0.331379037601084,
0.686894150152472, 0.0140922903231885, 0.225078933454863,
0.554444988164574, 0.076032683077827, 0.335284040888653,
0.0630810396519646, 0.358402154233125, 0.260940142571834,
0.141353291599136, 0.0220267076189838, 0.242149484071382,
0.278319984858078, 0.193105829691662, 0.0259815643559331,
0.318504899459259, 0.00277002060524357, 0.212681621053374,
0.418358846098857, 0.358916156777489, 0.438248724241505,
0.194398889511096, 0.2266870834128, 0.144338808446284, 0.149227951210927,
0.268111328952192, 0.123265441389974, 0.0376832357983068,
0.0353605481767078, 0.021227873083535, 0.0385614926552725,
0.130640111978654, 0.161865326447675, 0.174151298764213,
0.292085797406362, 0.198391364913347, 0.0779507859721407,
0.0045571464157577, 0.114734038438965, 0.0469613758623325,
0.64238405800387, 0.74508519247034, 0.0251182457091362, 0.217835062247358,
0.131159910126724, 0.130034859007596, 0.222418419987533,
0.0861715693619894, 0.185660520258661, 0.0940670543815277,
0.105680179626893, 0.215966730684923, 0.109008340760604,
0.0474735195202623, 0.192326789813641, 0.022147195644035,
0.277372858009381, 0.237574293593955, 0.123383946121193,
0.46406480500022, 0.123698482002945, 0.671442441453945, 0.0406004813894845,
0.260472754754191, 0.0151116521560003, 0.0422855023583402,
0.0405517218780402, 0.0441583998205882, 0.0958995639409343,
0.37588506579263, 0.098494760958735, 0.0928763466294421,
0.111205748449328, 0.413083543393392, 0.0138839674143682,
0.22407421093074, 0.72309883706409, 0.423231501875638, 0.141932050342199,
0.133808548118004, 0.331500621801688, 0.127652280721512,
0.132083126730013, 0.261864564503826, 0.208243130464985,
0.18657049493156, 0.333701537602998, 0.404884075502013, 0.470789398932934,
0.115008599462104, 0.177984001517338, 0.331717679106776,
0.0862418839846533), group = structure(c(3L, 2L, 2L, 3L,
1L, 3L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 1L,
3L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L,
3L, 2L, 3L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 2L,
3L, 3L, 3L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 2L, 1L, 3L,
3L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 3L,
1L, 1L, 3L, 2L, 1L, 3L), .Label = c("1", "2", "3"), class = "factor")), .Names = c("n",
"g", "cr", "bias", "group"), row.names = c(8721L, 6970L, 6686L,
9624L, 352L, 10545L, 7505L, 4216L, 6170L, 3309L, 10429L, 4302L,
5602L, 5680L, 1530L, 9234L, 5007L, 8004L, 721L, 10038L, 502L,
4891L, 2946L, 8502L, 622L, 1972L, 2403L, 3383L, 5880L, 1038L,
4756L, 9506L, 2169L, 1023L, 8506L, 6239L, 7768L, 3221L, 9536L,
5981L, 1507L, 4883L, 414L, 3117L, 3993L, 1923L, 9143L, 2673L,
4430L, 9520L, 9363L, 10602L, 95L, 1141L, 9660L, 4285L, 10704L,
154L, 531L, 6440L, 4876L, 7052L, 4397L, 3375L, 5075L, 1295L,
2620L, 334L, 9510L, 4690L, 4288L, 3576L, 2248L, 7693L, 8820L,
8135L, 4026L, 1906L, 10164L, 8616L, 423L, 5290L, 418L, 6486L,
4485L, 7042L, 955L, 2215L, 9031L, 8049L, 2323L, 1627L, 4212L,
8689L, 439L, 2590L, 8649L, 5447L, 1957L, 10570L), class = "data.frame")
library(lattice)
cl<-c('red','green','blue')
mypanel<-function(...){
panel.bwplot(...,pch="|",col="black",cex=4,fill=cl)
}
o1<-bwplot(a71$bias~a71$group|a71$cr*a71$g,type=c("l","g"),ylim=c(0,1),panel=mypanel)
plot(o1)

By changing some of the parameters of box.rectangle (a lattice-specific graphical parameter), you can manipulate the lines (including the median line) surrounding each of the box plots. This will change all the lines around the boxes, however, not just the median line.
myPars <- list(box.rectangle = list(lwd = 2, col = "black"))
lwd changes the line width (thickness). colchanges the color of the lines. Then pass this list to the par.settings argument in bwplot.
o1 <- bwplot(a71$bias ~ a71$group | a71$cr * a71$g,
type = c("l", "g"), ylim = c(0, 1), panel = mypanel,
par.settings = myPars)
plot(o1)
To see all of the parameters associated with box.rectangle, use
trellis.par.get("box.rectangle")

OP is happy with all lines thicker by #BenBarnes, but for completeness, it is possible to just thicken the median line. Using the fact that box.width defaults to 1/2:
bwplot(a71$bias~a71$group|a71$cr*a71$g,type=c("l","g"),ylim=c(0,1),
panel=function(x,y,...){
panel.bwplot(x,y,...,pch="|",col="black",cex=4,fill=cl)
medy <- by(y,list(x),median)
xx <- sort(unique(as.numeric(x)))
panel.segments(xx-.25,medy,xx+.25,medy,lwd=2)
}
)

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