density-plot

My goal is to compare the distribution of various socioeconomic factor such as income over multiple years to see how the population has evolved in particular region in say, over 5 years. The primary data for this comes from the Public Use Microdata Sample . I am using R + ggplot2 as my preferred tool. When comparing two years worth of data (2005 and 2010) I have two data frames hh2005 and hh2010 with the household data for the two years. The income data for the two years are stored in the variable hincp in both data frames. Using ggplot2 I am going about creating the density plot for

I would like to plot the density function of a Poisson distribution. I am not sure why I get a jaggy line (in blue). On the sample plot, the normal density curve (in red) looks smooth. It is because the reason the Poisson density function doesn't accept decimal values? How to eliminate the zigzag in the Poisson density plot? Thanks very much for any help. library(ggplot2) ggplot(data.frame(X = seq(5, 30)), aes(x = X)) + stat_function(fun=dpois, geom="line", size=2, color="blue3", args = list(lambda = 15)) + stat_function(fun=dnorm, geom="line", size=2, color="red4", args = list(mean=20, sd=2))

I would like to get a plot that combines the density of observations and the cdf. The usual problem with that is that the scales of the two are way off. How can this be remedied, i.e., two scales be used or, alternatively, one of the data series be rescaled (preferably within ggplot, as I would like to separate computation and display of data). Here's the code so far: >dput(tmp) yields structure(list(drivenkm = c(8, 11, 21, 4, 594, 179, 19, 7, 10, 36)), .Names = "drivenkm", class = c("data.table", "data.frame" ), row.names = c(NA, -10L), .internal.selfref = <pointer: 0x223cb78>) then I do p =

This question already has answers here : Closed 2 years ago . Find the probability density of a new data point using “density” function in R (3 answers) Density Value for each Return (3 answers) I was wondering if there is a R base way to obtain the exact kernel density at any point desired? As an example, how can I get the exact kernel density at the 3 following points -2, 0, +2 on X-Axis in a plot like below? set.seed(2937107) plot( density(rnorm(1e4)) ) Use linear interpolation to find it. d <- density(rnorm(10000)) approx(d$x, d$y, xout = c(-2, 0, 2)) The precision of interpolation can be

How can I get the area under overlapping density curves? How can I solve the problem with R? (There is a solution for python here: Calculate overlap area of two functions ) set.seed(1234) df <- data.frame( sex=factor(rep(c("F", "M"), each=200)), weight=round(c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5))) ) (Source: http://www.sthda.com/english/wiki/ggplot2-density-plot-quick-start-guide-r-software-and-data-visualization ) ggplot(df, aes(x=weight, color=sex, fill=sex)) + geom_density(aes(y=..density..), alpha=0.5) "The points used in the plot are returned by ggplot_build(), so you can

I'm trying to plot the median values of some data on a density distribution using the ggplot2 R library. I would like to print the median values as text on top of the density plot. You'll see what I mean with an example (using the "diamonds" default dataframe): I'm printing three itmes: the density plot itself, a vertical line showing the median price of each cut, and a text label with that value. But, as you can see, the median prices overlap on the "y" axis (this aesthetic is mandatory in the geom_text() function). Is there any way to dynamically assign a "y" value to each median price, so