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Hello all, I'm trying to graph a scatterplot of a large (5,000 x,y coordinates) of data with the caveat that many of the data points overlap with each other (share the same x AND y coordinates). In using the usual "plot" command, > plot(education, xlab="etc", ylab="etc") it seems that the overlap of points is not shown in the graph. Namely, there are

Hello all, I seem to be having a problem importing a data set from Excel into R. I'm using the "read.table" command to import the data with the following line of code: > newborn<-read.table("newborn edit.csv", header=T, sep=",") where "newborn edit.csv" is the name of the file. Unfortunately, I'm getting back the following error message:

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Hello all, I have to run a 2 Sample Confidence Interval on some data; the command for such intervals is "confint(...)", but in the help documentation it says that you need a "fitted model object" in order to run this command. What does that mean? The data is very small, it's: x=c(8,12,10,14,2,0,0) y=c(-6,0,1,2,-3,-4,2) and I want to be able to run a Confidence

Hello all, I was wondering if anyone knew how to construct a multiple line graph on R, where there are 2 (or more) sets of data points plotted against some x axis of data, and you can draw a line on the graph connecting each set of data points. For example: A B C D 0.6566 2.1185 1.2320 5 0.647 2.0865 1.2325 10 0.6532

I have 2 questions about the Biplot function: Both of the questions refer to the following type of graph, which is a biplot of a principal component analysis: biplot(prcomp(dataset)) 1. Does anyone know how to change the appearance of data points on the biplot? As it is currently, for this type of graph, the label for each data point shows up on the graph - does anyone know how to get rid

Hello all, I was wondering if anyone knew how to get R to only spit out a certain portion of PRcomp data; namely, when I enter the following code, I get: > Summary(prcomp(USArrests)) Importance of components: PC1 PC2 PC3 PC4 Standard Deviation 83.732 14.212 6.489 2.483 Proportion of Variance 0.966 0.0278 0.0058 0.00085 Cumulative Proportion 0.966

Hello all, I'm currently using R to do PCA Analysis, and was wondering if anyone knew the specific R Code that could limit the output of the PCA Analysis so that you only get the Principal Component features as your output and none of the extraneous words or numbers that you don't want. If that was unclear, let me use linear regression as an example: "lm(y~x)" is the normal