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> A <- matrix( c(1,2,3,4,5,1,2,3,4,5,1,2,3,4,5), nrow=5, ncol=3 ) #ãè¡åAãä½ã > A [,1] [,2] [,3] [1,] 1 1 1 [2,] 2 2 2 [3,] 3 3 3 [4,] 4 4 4 [5,] 5 5 5 > A1 <- t ( A ) #ãã転置è¡åA1ãä½ã > A1 [,1] [,2] [,3] [,4] [,5] [1,] 1 2 3 4 5 [2,] 1 2 3 4 5 [3,] 1 2 3 4 5 > A2 <- data.frame( A1 ) #ãããããªãã¯å½¢å¼ A1 ããã¼ã¿ãã¬ã¼ã A2 ã«ãã > colnames( A2 ) <- c( "å¤æ°ã", "å¤æ°ã", "å¤æ°ã", "å¤æ°ã", "å¤æ°ã") > rownames( A2 ) <- c( "å¤æ°ï¼¡", "å¤æ°ï¼¢
(This article was first published on Ecology in silico, and kindly contributed to R-bloggers) Violin plots are useful for comparing distributions. When data are grouped by a factor with two levels (e.g. males and females), you can split the violins in half to see the difference between groups. Consider a 2 x 2 factorial experiment: treatments A and B are crossed with groups 1 and 2, with N=1000. 1
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