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+# Install datawizard package (for calculating mode with 'distribution_mode()')
+
+if (!require(datawizard)) {
+  install.packages("datawizard")
+  library(datawizard)
+}
+
+
+# Define the Salaries vector
+Salaries <- c(12567, 15400, 11345, 13130, 12567, 12812, 14908)
+
+
+# Calculate the mode
+modeS <- distribution_mode(Salaries)
+modeS
+
+# Calculate the median
+meadianS <- median(Salaries)
+meadianS
+
+# Calculate the mean
+meanS <- mean(Salaries)
+meanS
+
+
+
+
+# The first loss function ---------------------
+## For each value in vector "Salaries", check whether it is:
+#+ 1. different from the mode
+#+ 2. different from the median
+#+ 3. different from the mean
+#+ ...And sum up the results (the "True" results)
+
+# How many values are different from the mode? 
+# (how many people make something other than the mode?)
+sum(Salaries != modeS)
+# How many people make something other than the median?
+sum(Salaries != meadianS)
+# Etc
+sum(Salaries != meanS)
+
+
+
+
+# Second loss function ----------------------------
+  sum(             abs                 (Salaries - modeS))
+# ^ summarize the  ^ absolute value of  ^ each salary minus the modal salary
+sum(abs(Salaries - meadianS))
+sum(abs(Salaries - meanS))
+
+
+
+# Third loss function ----------------------------
+sum((Salaries - modeS)^2)
+sum((Salaries - meadianS)^2)
+sum((Salaries - meanS)^2)
+
+#..........................................Exercise 2
+#Create a vector of numbers
+potato <- runif(n=20, min=1, max=20)
+
+#Calculate the mean, median and mod of the vector of numbers you created
+
+potato_mean <- mean(potato)
+potato_mean
+potato_median <- median(potato)
+potato_median
+potato_mode=distribution_mode(potato)
+potato_mode
+# print results
+sprintf("Mean %s Median %s Mode %s", potato_mean, potato_median, potato_mode)
+
+#Calculate the error for each loss function for each of the
+#measures you calculated (the mean, median and mod)
+
+# 1st loss function
+sum(potato != potato_mode)
+
+# 2nd loss function
+
+sum(abs(potato - potato_median))
+
+# 3rd loss function
+
+sum((potato - potato_mean)^2)