You can remove the NaNs from the data first, then plot the filtered data. To do that, you can first find the NaNs using np.isnan(data), then perform the bitwise inversion of that Boolean array using the ~: bitwise inversion operator. Use that to index the data array, and you filter out the NaNs. filtered_data = … Read more
It’s easier to group them like this when data is in a long format vice wide. Starting with your vectors: DF2 <- data.frame( x = c(c(A1, A2, A3), c(B1, B2, B3)), y = rep(c(“A”, “B”), each = 15), z = rep(rep(1:3, each=5), 2), stringsAsFactors = FALSE ) str(DF2) # ‘data.frame’: 30 obs. of 3 variables: … Read more
The following commands will create the ordering you need by rebuilding the Treatment and Species factors, with explicit manual ordering of the levels: mydata$Treatment = factor(mydata$Treatment,c(“L”,”M”,”H”)) mydata$Species = factor(mydata$Species,c(“G”,”R”,”B”)) edit 1 : oops I had set it to HML instead of LMH. fixing. edit 2 : what factor(X,Y) does: If you run factor(X,Y) on an … Read more
Here is a solution, which is based on creating fake data: Firstly, a new row is added to the data frame. It contains a data point for the non-existing combination of factor levels (Mar and A). The value of Height has to be outside the range of the real Height data. Data2 <- rbind(Data, data.frame(Month … Read more
This works using ggplot2 version 0.9.1 (and R 2.15.0) library(ggplot2) DF <- data.frame(x=c(“A”,”B”), min=c(1,2), low=c(2,3), mid=c(3,4), top=c(4,5), max=c(5,6)) ggplot(DF, aes(x=x, ymin = min, lower = low, middle = mid, upper = top, ymax = max)) + geom_boxplot(stat = “identity”) See the “Using precomputed statistics” example here
I find this hybrid boxplot very, very lovely so I wanted to recreate it too. I wrote a geom_boxjitter that inherits from geom_boxplot and only adds minor changes: It draws the geom_rect only on the left half. It jitters the points with default width of the right half, default height 0.4*resolution and can also take … Read more
Have you tried this: df2$variable <- factor(df2$variable, levels = c(‘vph.shr’,’vnu.shr’),ordered = TRUE) I just picked an ordering there, since my system is configured slightly differently than yours I suspect, so my ‘default ordering’ may differ. But you can just switch the position of levels when specifying them. A few other options, depend on your tastes: … Read more
Is this anything like what you’re after? With stat_summary, as requested: # function for number of observations give.n <- function(x){ return(c(y = median(x)*1.05, label = length(x))) # experiment with the multiplier to find the perfect position } # function for mean labels mean.n <- function(x){ return(c(y = median(x)*0.97, label = round(mean(x),2))) # experiment with the … Read more
You can use either egg, cowplot or patchwork packages to combine those two plots. See also this answer for more complex examples. library(dplyr) library(ggplot2) plt1 <- my_df %>% select(value) %>% ggplot(aes(x=””, y = value)) + geom_boxplot(fill = “lightblue”, color = “black”) + coord_flip() + theme_classic() + xlab(“”) + theme(axis.text.y=element_blank(), axis.ticks.y=element_blank()) plt2 <- my_df %>% select(id, … Read more
geom_boxplot with stat_summary can do it: # define the summary function f <- function(x) { r <- quantile(x, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)) names(r) <- c(“ymin”, “lower”, “middle”, “upper”, “ymax”) r } # sample data d <- data.frame(x=gl(2,50), y=rnorm(100)) # do it ggplot(d, aes(x, y)) + stat_summary(fun.data = f, geom=”boxplot”) # example with … Read more