If this is the first time you use git on your machine, you should start with chapter 7
Follow the step-by-step instructions in Chapter 12 of Happy git with R, create your own public repo under your account. Note: don’t do the last step. DO NOT delete the repo!
Describe your experience: make a note of each step. In case you run into a problem, describe it and the error message.
please write your experience below
Will’s note: xxx
Will’s note: xxx
Create a new file or add any types of file into your local repository and practice the “add-commit-pull-push” cycle. Then check your remote on GitHub to make sure it succeeds.
paste the URL of your public repo here
paste it here:
Download the RMarkdown file with these homework instructions to use as a template for your work. Make sure to replace “Your Name” in the YAML with your name.
The R package mclust
contains a data set called
banknote
, consisting of (physical) measurements on 200
Swiss bank notes, 100 of which are genuine, while the other half is
counterfeit. Load this data set (you might have to install the package)
using the code below.
# install.packages("mclust")
library(mclust) # if this throws an error of the form 'there is no package called mcmclust', uncomment the line above, run it once, then comment the line out again and run the code chunk again.
data(banknote)
Use one of our object inspecting functions and interpret the result in the data that you see.
Use the package ggplot2
to draw a barchart of
Status
. Map Status
to the fill color of the
barchart.
Use the package ggplot2
to draw a histogram of one
of the variables in the dataset that shows a distinction between genuine
and counterfeit banknotes. Use fill color to show this difference.
Choose the binwidth such that there are no gaps in the middle range of
the histogram.
Use the package ggplot2
to draw a scatterplot of two
(continuous) measurements, color by Status
. Find a pair of
measurements that allow you to separate perfectly between genuine and
counterfeit banknotes.
For each of the three figures above, write a three sentence summary, describing the
main message of the plot: what is your main finding, i.e. what do you want viewers to learn from the plot?
additional message: point out anomalies or outliers, if there are any.
For the submission: submit your solution in an R Markdown file. Also submit the corresponding html file with it.