The result is 15. The myMean function takes an input vector assignment2, which is the dataset you provided. Inside the function, it uses the sum function to calculate the sum of all the values in assignment2 and then divides that sum by the length of assignment2 to compute the mean (average) of the dataset. The result will be printed to the console.

Comments

Post a Comment

Popular posts from this blog

Module # 2 Google Table, Tableau and Geographic map

Image
  For this week's dataset, I looked into the 5000 fastest private growing companies in the United States over 4 years. Using Tableau I created a visual to show me where the most successful companies were based. On the map, the darker states correspond with both the rank and amount of workers in each of the companies. Darker shades led to more workers and higher rank, while lighter shades represented the opposite.  Here we can see South Dakota as one of the lighter-colored states in the United States, with a rank of 4,937 and only 64 workers we can see why it is so lightly shaded.  Compared to that of New York's darker-shaded state, showing a much more significant ranking of 794,043 and 77k plus workers.  Ways to improve this map would be a legend on the side of the United States map, here 2-4 different shades could be added. Showing the general difference between the state's rank and workers. Gestalt Laws would also be useful here, as the bigger ranked states could b...
Read more

Module # 7 assignment (Visual Analytics)

Image
  In this example, I've created two scatter plots, one for mpg vs. hp and another for mpg vs. wt, using mtcars and arranged them in a grid using the grid.arrange function.  My opinion on Few's recommendations.  Simplicity:   Few emphasize simplicity to avoid overwhelming the audience. The scatter plots are relatively simple, displaying two variables at a time (mpg vs. hp, mpg vs. wt). This simplicity aids in easy interpretation without unnecessary complexity. Clarity: Is a key principle in Few's recommendations. The use of grid arrangement helps in comparing the two scatter plots side by side, making it easier for viewers to identify patterns and differences between the variables. Accuracy: This is crucial, and Few often advocate for accurate representation of data. The scatter plots accurately represent the relationship between variables, allowing viewers to make informed observations about the distribution and correlation. Color Usage: Few often advise agai...
Read more

Module # 3 assignment

First, let's create the data sets Set#1 and Set#2: set1 <- c(10, 2, 3, 2, 4, 2, 5) set2 <- c(20, 12, 13, 12, 14, 12, 15)   Now, let's calculate the measures of central tendency for each set: # Mean for Set#1 and Set#2 mean_set1 <- mean(set1) mean_set2 <- mean(set2)   # Median for Set#1 and Set#2 median_set1 <- median(set1) median_set2 <- median(set2)   # Mode for Set#1 and Set#2 mode_set1 <- as.numeric(names(sort(table(set1), decreasing = TRUE)[1])) mode_set2 <- as.numeric(names(sort(table(set2), decreasing = TRUE)[1]))   Now, let's calculate the measures of variation for each set:   # Range for Set#1 and Set#2 range_set1 <- range(set1) range_set2 <- range(set2)   # Interquartile Range (IQR) for Set#1 and Set#2 iqr_set1 <- IQR(set1) iqr_set2 <- IQR(set2)   # Variance for Set#1 and Set#2 variance_set1 <- var(set1) variance_set2 <- var(set2)   # S...
Read more