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working code, I'm having trouble completing the assessment, I have done most of the code but some are not working especially task 2 and 4. would appreciate some help

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################################################################################
# MATH 2032 - Continuous Assessment Test 3
# SOLUTIONS
################################################################################
# This is a graded test worth 15% of your final mark.
# Complete tasks listed below. Type your code in each section.
# Provide your discussion where required as comments.
# Meaningful discussion is equally important as a correct code
# and will be marked accordingly.
# Save this file with your code and submit it through LearnOnline
# Task 1 - (15 points) ---------------------------------------------------------
# This is a repeat of the question 4 (part 1) from Test 2. Produce the same
# calculations and appropriate data visualisation BUT using different tools:
# 1. use apply() family functions from base package, no loops
# 2. use dplyr or purrr package, no loops
# 3. use ggplot2 for data visualisation
# 4. no discussion required on the results
# Hint: Don't forget to load libraries you use!
################################################################################
# Some researchers propose an alternative measure of tailedness. It is
# calculated as a standard deviation divided by mean absolute deviations.
# Please google "calculate mean absolute deviations in R".
# 1. Make a code that calculates kurtosis and new measure proposed above for
# each column of the data set below. You will get values of kurtosis and
# corresponding values of new measure. Plot the graph to study a relationship
# between these two variables.
################################################################################
mydata <- datasets::volcano
# ---- your code and comments here ---- #
# Task 2 - (35 points) ---------------------------------------------------------
# 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
?dslabs::brca
df <- cbind(as.data.frame(temp$x), outcome = temp$y)
# 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 ---- #
# Task 3 - (25 points) ---------------------------------------------------------
# Load data set "ToothGrowth" from the package "datasets".
# Check the help file for the description.
df <- datasets::ToothGrowth
?datasets::ToothGrowth
# We are interested in the relationship between tooth length and supplement
# type/dosage. The variable "dose" - does in milligrams per day - is a numerical
# variable, however it has a very limited variability.
print(unique(df$dose))
# There are just three values for "dose" - that is, we can treat them as three
# groups. You need to do appropriate data conversion for analysis/plotting.
# 1. Get statistical and graphical summaries for two groups of supplements (OJ
# and VC). Provide a discussion what supplement is better for tooth growth.
# 2. Get statistical and graphical summaries for groups of supplements and dosages.
# Provide a discussion what combinations of supplement and dosages are better
# for tooth growth.
# ---- your code and comments here ---- #
# Task 4 - (25 points) ---------------------------------------------------------
# Load data set "trump_tweets" from the package "dslabs".
# Check the help file for the description.
df <- dslabs::trump_tweets
?dslabs::trump_tweets
# Former US presendent Donald Trump is remembered as a prolific tweeter user.
# The data set includes all tweets from Donald Trump's twitter account
# from 2009 to 2017. We want to analyse how "productive" was Donald Trump.
# You have to find out:
# 1. How many tweets in average per week were created by Donald Trump? How many
# retweets in average per week were created in response to Donald Trump's
# tweets, that is, how popular were his tweets? Provide a brief summary of
# your findings.
# 2. Make a historical plot of tweets per week created by Donald Trump over
# eight years? Provide brief comments on his "performance".
# 3. Make a graph showing a relationship between the number of tweets and the
# number of retweets per week. Provide brief comments on a possible relationship.
# Hint: There is a variable "created_at" that you can use to group data in weeks.
# Package "lubridate" has a set of functions to deal with date/time related
# variables. E.g. day(), week(), month(), year(), etc. You can find them useful.
# However you are free to use any other functions or packages.
# Beware: there are several years of data, so the week with the same number might
# appear in the data several times.
# ---- your code and comments here ---- #
# THE END - DON'T FORGET TO SAVE YOUR R-SCRIPT ---------------------------------

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