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Module #4

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 This week we were asked to create a boxplot and histogram of faux local hospital data. As well as discuss the outcome of our results regarding patients' BPs & MD’s Ratings. From the first graph (boxplot), we can observe that patients with a higher final decision had visited the hospital more frequently than the lower final decision. We can also observe that those with higher blood pressure visited the hospital more frequently than those with lower blood pressure.  The second graph (histogram), displays that there were more "bad" first assessments with the patients with one N/A.   The second graph (histogram2), displays that there were more "high" blood pressure assesments by doctor #2.   library(ggplot2) #Data  p1<- c(0.6,103,'bad','low','low') p2<- c(0.3,87,'bad','low','high') p3<- c(0.4,32,'bad','high','low') p4<- c(0.4,42,'bad','high','high') p5...
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Module #6

 In this module, we did more math with matrices!  We were asked to examine two matrices, add them, minus them, and then use the diag() function. We were also asked to replicate/recreate a matrix with certain criteria.  #Start with two matrices A = matrix(c(2, 0, 1, 3), ncol = 2) B = matrix(c(5, 2,4, -1), ncol = 2) #Call/View matrices A      [,1] [,2] [1,]    2    1 [2,]    0    3 B      [,1] [,2] [1,]    5    4 [2,]    2   -1 #It appears that both matrices are the same size, therefore can be combined, added, subtracted.. etc..  # Find A + B C <- A + B #Call / View matrix C      [,1] [,2] [1,]    7    5 [2,]    2    2 # Find A - B D <- A - B #Call / View matrix D      [,1] [,2] [1,]   -3   -3 [2,]   -2    4 #Use the diag() function to build a mat...
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