boxplot

I am trying to get a grouped boxplot working using Seaborn as per the example I can get the above example working, however the line: tips = sns.load_dataset("tips") is not explained at all. I have located the tips.csv file, but I can't seem to find adequate documentation on what load_dataset specifically does. I tried to create my own csv and load this, but to no avail. I also renamed the tips file and it still worked... My question is thus: Where is load_dataset actually looking for files? Can I actually use this for my own boxplots? EDIT: I managed to get my own boxplots working using my own

There is probably a very easy solution to my problem but I couldn't find a satisfying answer online. Using the following command I was able to create the following boxplot graph and overlay it with the individual data points: ggplot(data = MYdata, aes(x = Age, y = Richness)) + geom_boxplot(aes(group=Age)) + geom_point(aes(color = Age)) There are several things I would like to add/change: 1. Change the line color and/or fill of each boxplot (depending on "Age") using 6 different colors from left to right: c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00") I tried ggplot(data =

I have the following problem: I would like to visualize a discrete and a continuous variable on a boxplot in which the latter has a few extreme high values. This makes the boxplot meaningless (the points and even the "body" of the chart is too small), that is why I would like to show this on a log10 scale. I am aware that I could leave out the extreme values from the visualization, but I am not intended to. Let's see a simple example with diamonds data: m <- ggplot(diamonds, aes(y = price, x = color)) The problem is not serious here, but I hope you could imagine why I would like to see the

I'm using the code below to enrich a box plot with a summary table for categorical variable created on the x-axis. # Libs require(ggplot2); require(gridExtra); require(grid); require(ggthemes) # Data data(mtcars) # Function to summarise the data fun_dta_sum <- function(var_sum, group, data) { sum_dta <- data.frame( aggregate(var_sum ~ group, FUN = min, data = data), aggregate(var_sum ~ group, FUN = max, data = data), aggregate(var_sum ~ group, FUN = mean, data = data)) sum_dta <- sum_dta[,c(1,2,4,6)] colnames(sum_dta) <- c("Group (x axis)", "min", "max", "mean") rownames(sum_dta) <- NULL sum

I'm trying to make a similar plot to Fig. 2d-f in an article published on Nature this year. It's basically a half boxplot with points on the other half. Can anyone give me some hints? Thank you very much! These are my data and code which produced full boxes with points inside require(magrittr) require(tidyverse) dat <- structure(list(p1 = c(0.0854261831077604, 0.408418657218253, 0.577793646477315, 0.578028229977424, 0.48933166218204, 0.53117814324334, 0.526653494462464, 0.00687616283435221, 0.444300425796509, 0.00287319455358522, 0.949821402532831, 0.96832469523368, 0.953281969982759, 0

Some time ago I asked a question about drawing boxplot Link1 . I have got some data with 3 different groups (or labels) Please down load here . I can use the following R codes to get the boxplot library(reshape2) library(ggplot2) morphData <- read.table(".\\TestData3.csv", sep=",", header=TRUE); morphData.reshaped <- melt(morphData, id.var = "Label") ggplot(data = morphData.reshaped, aes(x=variable, y=value)) + + geom_boxplot(aes(fill=Label)) Here I just wondering how to put the significant level above the boxplot. To make myself clear I put a screenshot cut from a paper here: I don't quite