frequency-distribution

I have five columns with numbers. I want to plot the frequency distribution of five columns in one graph with different colors in R. Can some one help me out how i can do this with an example. I am very new to R. Using the sample data from @eddi, you can also consider the "lattice" package: set.seed(1) d <- data.frame(a = rnorm(100), b = rnorm(100, 1), c = rnorm(100, 2), d = rnorm(100, 3), e = rnorm(100, 4)) library(lattice) densityplot(~ a + b + c + d + e, data = d) This will yield: If you have many columns, you can also create your plot by first creating a formula : myFormula <- as.formula

Please see the Edits in the answer below this question. I have written a script to plot the frequency spectrum of a sinusoidal signal with c++. Here are the steps Applying Hanning window Apply FFT using fftw3 library I have three graphs: Signal, Signal when is multiplied to Hanning function, and the frequency spectrum. The frequency spectrum looks wrong. It should have a peak at 50 Hz. Any suggestion would be appreciated. Here is the code: #include <stdlib.h> #include <stdio.h> #include <time.h> #include <fftw3.h> #include <iostream> #include <cmath> #include <fstream> using namespace std; int

I need to calculate the frequency of individuals by age and marital status so normally I'd use: table(age, marital_status) However each individual has a different weight after the sampling of the data. How do I incorporate this into my frequency table? You can use function svytable from package survey , or wtd.table from rgrs . EDIT : rgrs is now called questionr : df <- data.frame(var = c("A", "A", "B", "B"), wt = c(30, 10, 20, 40)) library(questionr) wtd.table(x = df$var, weights = df$wt) # A B # 40 60 That's also possible with dplyr : library(dplyr) count(x = df, var, wt = wt) # # A tibble:

How can I, in R calculate the overall variance and the variance for each group from a dataset that looks like this (for example): Group Count Value A 3 5 A 2 8 B 1 11 B 3 15 I know to calculate the variance as a whole, ignoring the groups I would do: var(rep(x$Value, x$Count)), but how do I automatically calculate the variance for each group accounting for the frequency? E.g., the variance for group A, group B, etc.,.. I would like my output to have the following headers: Group, Total Count, Group Variance I have also reviewed this link; R computing mean, median, variance from file with

I have a data set that is as follows: library(data.table) test <- data.table(structure(list(Issue.Date = structure(c(16041, 16056, 16042,15990, 15996, 16001, 15995, 15981, 15986, 15996, 15996, 16002,16015, 16020, 16025, 16032, 16023, 16084, 16077, 16102, 16104,16107, 16112, 16113, 16115, 16121, 16125, 16128, 16104, 16132,16133, 16135, 16139, 16146, 16151), class = "Date"), Complaint = structure(c(1L,4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,5L, 3L, 1L, 3L, 1L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L,3L, 3L), .Label = c("A", "B", "C", "D", "E"), class = "factor"), yr = c

My question today refers to a data frame I'm working on in R. The header of the data frame looks like the following: String(unique), Integer N[0-23] Those 24 Integer values represent the frequency of the String associated with each hour of the day. Logically, the int values in each row sum up to the number how often the string appears in the data in general. Thing is, I don't need the real frequency of the string at a certain hour but the percentage this frequency represents in relation to the sum of the integer values in all rows. My lecturer hinted that table() might be the right R tool for