I want to plot the data by using function(). My data consists of 4 vectors, say a, b, c and d.
I have to plot them by choice of vector at that time.
For example
i want to plot vector a and c then graph must have 2 lines....
if i want 2 plot all 4 vectors then there must be 4 lines in graph.
Till I have tried switch() but i think thats not suitable related to my work.
Is it even possible to write such code in an anonymous function ?
If yes, what is the right way, and if not, is there any workaround ?
Related
I have a r code question that has kept me from completing several tasks for the last year, but I am relatively new to r. I am trying to loop over a list to create two variables with a specified correlation structure. I have been able to "cobble" this together with a "for" loop. To further complicate matters, I need to be able to put the correlation number into a data frame two times.
For my ultimate usage, I am concerned about speed, efficiency, and long-term effectiveness of my code.
library(mvtnorm)
n=100
d = NULL
col = c(0, .3, .5)
for (j in 1:length(col)){
X.corr = matrix(c(1, col[j], col[j], 1), nrow=2, ncol=2)
x=rmvnorm(n, mean=c(0,0), sigma=X.corr)
x1=x[,1]
x2=x[,2]
}
d = rbind(d, c(j))
Let me describe my code, so my logic is clear. This is part of a larger simulation. I am trying to draw 2 correlated variables from the mvtnorm function with 3 different correlation levels per pass using 100 observations [toy data to get the coding correct]. d is a empty data frame. The 3 correlation levels will occur in the following way pass 1 uses correlation 0 then create the variables, and yes other code will occur; pass 2 uses correlation .3 to create 2 new variables, and then other code will occur; pass 3 uses correlation .5 to create 2 new variables, and then other code will occur. Within my larger code, the for-loop gets the job done. The last line puts the number of the correlation into the data frame. I realize as presented here it will only put 1 number into this data frame, but when it is incorporated into my larger code it works as desired by putting 3 different numbers in a single column (1=0, 2=.3, and 3=.5). To reiterate, the for-loop gets the job done, but I believe there is a better way--perhaps something in the apply family. I do not know how to construct this and still access which correlation is being used. Would someone help me develop this little piece of code? Thank you.
I am learning graphical analysis using R. Here is the code, which I can not understand.
barplotVS <- barplot(table(mtcarsData$vs), xlab="Type of engine")
text(barplotVS,table(mtcarsData$vs)/2,table(mtcarsData$vs),cex=1.25)
The output is like below. I can not understand the function of text(), I googled the text() function, which shows that the parameter of text(x,y) is numeric vectors of coordinates where the text labels should be written. Can anyone tell me what is barplotVS,table(mtcarsData$vs)/2,table(mtcarsData$vs),cex=1.25 in my code.
barplotVS <- barplot(table(mtcarsData$vs), xlab="Type of engine")
print(barplotVS)
outputs:
[,1]
[1,] 0.7
[2,] 1.9
These are the positions where the center of the bars in the barplot are on the x axis.
print(table(mtcarsData$vs))
outputs:
0 1
18 14
the numbers below are the occurrences of each value that is present in mtcarsData$vs and the numbers above are the actual value that is counted.
When you run the function:
text(barplotVS,table(mtcarsData$vs)/2,table(mtcarsData$vs),cex=1.25)
the first value will be the x positions where to put the labels (i.e. 0.7 and 1.9), the second parameter will be the y positions set in this case to total counts divided by two (i.e. 9 and 7) meaning to put the labels halfway in the bars, the third will be the labels (i.e. 18 and 14) and finally cex is a value that allows to change the size of the font.
Anyway R has in general a good documentation that you can call by using the ? operator (as suggested in the comments). In order to understand try to run the code and check what each variable contains with print or str functions. If you use a IDE (e.g. RStudio) have the content of the variables in a graphical panel so you don't event need to print.
I am trying to create some lines on a graph based on a third coordinate (x,y, temp). I would like to get a vector of indexes so I can split them into x and y vectors for each duplicate temperature. To make this more clear, I will include my actual data set:
DataFrame
I am trying to make multiple lines that have the same temp value. For example, I would like to have the following coordinates on the same line [0,14] [0,22] [0,26] [0,28]. They all have the temp value of 5.8. Once I find the duplicates, I will record the indexes in a vector which will allow me to retrieve the x and y coordinates. One other aspect is that I will not always know how many entries are going to be in the data.frame.
My question is how can I find the duplicates and store their indices in a vector? Once I have the indices for the duplicate temps, I can be sure to grab their x y coordinates and use that to create lines.
If you can answer my question or have any advice on how I can do this better, all help is appreciated
Consider the following:
df <- data.frame(temp = sample.int(n=3, size=5, replace=T))
df
temp
1 3
2 3
3 1
4 3
5 1
duplicated(df$temp)
[1] FALSE TRUE FALSE TRUE TRUE
which(duplicated(df$temp))
[1] 2 4 5
You've stated in the comments that you're looking to make an isopleth graph. The procedure you have described will not generate anything resembling an isopleth graph. Since it looks like your data is arranged in a regular grid, you should do something like the solutions presented in this question and answer, which use functions specifically designed for extracting contours from a grid of values. Another option is the contourLines function in the gDevices package. If you want higher-resolution, less jagged contours, you might look into using either the interp.surface or Krig functions from the fields package to interpolate your data to the resolution you require.
My data is (sort of) like this:
thing value
a 100
a 101
. .
a 99
b 201
b 202
. .
b 199
I want to compare the median of the values by thing. Normally I would make a boxplot with something like boxplot(value ~ thing, data = table), but there is a catch.
The problem
The number of values of each thing is about 100,000. Consequently I have had to process my table in sections. I process all values for thing a, then all values for thing b, etc.
Is there a way to make a boxplot and then add more plots to it, like using plot and dots? I want to use the base plotting system if possible (to make the plots consistent).
However, if that isn't possible, I guess ggplot2 might have to be the go.
I should add, what I'm hoping to achieve is something like the image here:
http://www.statmethods.net/graphs/images/boxplot1.jpg
I want to differentiate data vectors to find those that are similar. For example:
A=[4,5,6,7,8];
B=[4,5,6,6,8];
C=[4,5,6,7,7];
D=[1,2,3,9,9];
E=[1,2,3,9,8];
In the previous example I want to distinguish that A,B,C vectors are similar (not the same) to each other and D,E are similiar to each other. The result should be something like: A,B,C are similar and D,E are similar, but the group A,B,C is not similar to the group of D,E. Matlab can do this?
I was thinking using some classification algorithm or Kmeans,ROC,etc.. but I'm not sure which one will be the best one.
Any suggestion? Thanks in advance
One of my new favourite methods for this sort of thing is agglomerate clustering.
First, concatenate all your vectors into a matrix, where each row is a separate vector. This makes such methods much easier to use:
F = [A; B; C; D; E];
Then the linkages can be found:
Z = linkage(F, 'ward', 'euclidean');
This can be plotted using:
dendrogram(Z);
This shows a tree, where each leaf at the bottom is one of the original vectors. Lengths of the branches show similarities and dissimilarities.
As you can see, 1, 2 and 3 are shown to be very close, as are 4 and 5. This even gives a measure of closeness, and shows that vectors 1 and 3 are deemed to be closer than vectors 2 and 3 (in the sense that, percentagewise, 7 is closer to 8 than 6 is to 7).
If all the vectors you are comparing are of the same length, a suitable norm on pairwise differences may well be enough. The norm to choose will depend on your particular criteria of closeness, of course, but with the examples you show, simply summing the absolute values of the components of the pairwise differences gives:
A B C D E
A 0 1 1 12 11
B 0 2 13 12
C 0 13 12
D 0 1
E 0
which doesn't need a particularly well-tuned threshold to work.
You can use pdist(), this function gives you the pairwise distances.
Various distance (opposite of similarity) metrics are already implemented, 'euclidean' seems appropriate for your situation, although you may want to try out the effect of different metrics.
Here it goes the solution I propose based on your results:
Z = [A;B;C;D;E];
Y = pdist(Z);
matrix = SQUAREFORM(Y);
matrix_round = round(matrix);
Now that we have the vector we can set the threshold based on the maximun value and decide with which theshold is the most appropriate.
It would be nice to create some cluster plot showing the differences between them.
Best regards