axis-labels

I have two lists, when I plot with the following code, the x axis only shows up to 12 (max is 15). May I know how can I show all of the values in x list to the x axis? Thanks in advance. x = [4,5,6,7,8,9,10,11,12,13,14,15,0,1,2,3] y = [10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160] fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(np.arange(len(x)), y, 'o') ax1.set_xticklabels(x) plt.show() If I set minor=True in the set_xticklabels function, it shows me all x=2,4,6,8,..,16... but I want ALL values. P.S. My x axis is not sorted, should display as it shows. Add this: ax1.set

I am trying to produce a graph and I am having some issues annotating it. My graph has a log scale on the x-axis, showing time. What I want to be able to do is keep the existing (but not predictable) numeric tick labels at 100 units, 1000 units, 10000 units, etc but also add custom tick labels to the x-axis that make it clear where more "human readable" time intervals occur---for instance I want to be able to label 'one week', 'one month', '6 months', etc. I can use matplotlib.pyplot.annotate() to mark the points but it doesn't really do what I want. I don't really want text and arrows on top

Hi I'm trying to create plots which incorporate parallel conversion scales for two sets of units on the y-axis; using the two different styles of: offset ('parasitic') y-axes and overlaid/shared y-axes to replicate the style of the left-hand y-axes in the attached example images. I'd like to find the simplest generic way of producing both of the above example plots, which also allows me to generate the y-axis conversion scales by defining the relationship between the two sets of units as a function (in this example: mmHg = kPa * 7.5). If it's possible to add the third right-hand y axes (vapour

I have a ggplot map, for example: library(ggmap) ggmap(get_map()) I'd like the axis labels to be automatically labeled as N-S / W-E: in the above case, for example, instead of lon -95.4 it should show 95.4°E. I have tried to mess with the scales package and using scale_x_continuous and scale_y_continuous labels and breaks options, but I have not managed to make it work. It would be awesome to have a scale_y_latitude and scale_x_longitude . EDIT: Thanks to @Jaap 's answer I got to the following: scale_x_longitude <- function(xmin=-180, xmax=180, step=1, ...) { ewbrks <- seq(xmin,xmax,step)

I posted my original question yesterday which got solved perfectly here Original post I made a few addition to my code library(lubridate) library(ggplot2) library(grid) ### Set up dummy data. dayVec <- seq(ymd('2016-01-01'), ymd('2016-01-10'), by = '1 day') dayCount <- length(dayVec) dayValVec1 <- c(0,-0.22,0.15,0.3,0.4,0.10,0.17,0.22,0.50,0.89) dayValVec2 <- c(0,0.2,-0.17,0.6,0.16,0.41,0.55,0.80,0.90,1.00) dayValVec3 <- dayValVec2 dayDF <- data.frame(Date = rep(dayVec, 3), DataType = factor(c(rep('A', dayCount), rep('B', dayCount), rep('C', dayCount))), Value = c(dayValVec1, dayValVec2,

In Excel, with data organized like so: Bare NP Singular-Marked NP Plural-Marked NP BrP Speakers AmE Speakers BrP Speakers AmE Speakers BrP Speakers AmE Speakers Plural Interpretation 0.005747126 0.006896552 0.194117647 0.124567474 0.872093023 0.985815603 Plural & Singular Interpretation 0.649425287 0.910344828 0.029411765 0.051903114 0.127906977 0.014184397 Singular Interpretation 0.344827586 0.082758621 0.776470588 0.823529412 0 0 I am able to produce a stacked barchart that looks like this: Is there an easy way to reproduce the way that the x-axis is partitioned into the separate conditions

I'm looking to reduce the display radius of a pie chart built in ggplot2 relative to the rest of the plot (as the defaults keep cutting off my category labels). Here's some dummy data and code that should show you what I'm experiencing: library(ggplot2) library(scales) library(grid) Region <- c("North America", "Central America", "South America", "Carribbean", "Western Africa", "Northern Africa", "Southern Afica", "Eastern Africa") Conti <- c(rep("Americas",4), rep("Africa",4)) Freq <- c(runif(8, 1, 100)) Pct <- c(Freq/sum(Freq)) Pos <- c(cumsum(360*Pct)-(360*Pct/2)) Pos <- c(ifelse(Pos<=180