# Load data set “brca” from the package “dslabs”. Check the help file for the # description. The…

# Load data set “brca” from the package “dslabs”. Check the help file for the # description. The…
 # Load data set “brca” from the package “dslabs”. Check the help file for the # description. The data are provided as a list, we need it as data frame temp <- dslabs::brca="" slabs::brca="" df=""><- cbind(as.data.frame(temp$x),="" outcome="tempsy)" #="" we="" are="" interested="" what="" variables="" might="" be="" the="" best="" indicators="" for="" the="" "outcome"="" #="" malignant="" ("m")="" or="" benign="" ("b").="" there="" are="" 30="" features="" (variables)="" and="" we="" #="" want="" to="" select="" three="" variables="" that="" have="" the="" largest="" difference="" between="" means="" #="" for="" groups="" m="" and="" b.=""> 1.     Use “dplyr” functionality to find these variables – no loops. 2.      Create data visualisation to show the difference in distributions for these # variables in two groups (M/B) – use ggplot2 package, plot them all on one graph. 3.      Briefly discuss the result – can you predict outcome based on these # distributions. # Hints: # 1.     All variables have different measurements, to be able to compare # them you need to “standardise” the data – check function scale() 2.     . To find the difference you can use subtraction or function diff(). Remember # that difference can be positive or negative – you need three largest # differences by absolute values. 3.     . To plot three selected variables together you might need to transform # the data set from wide table into long table.. 4.     Use your common sense and data understanding for results reporting and # interpretation. E.g. if the graph does not make sense, then you have to # change it. # —- your code and comments here —-#   May 05 2022 06:09 PM

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