Data analysis discussion

Student NameComponent
Selecting Data Set
Data Set/Tidying
Requirements
Use of dplyr
verbs/functions.
Data Set/Tidying
Requirements
Use of stringr
functions and/ or
lubridate
functions
Possible
Points
15
25
25
Excellent
(points possible)
You select a data
set and scenario
that is unique (no
other student in
the class has
selected the data
set) and you sign
up for the data
set using the
Sign-up sheet on
Canvas by
Sunday April 21.
(15)
Use at least five
verbs/functions
from the R dpylr
package.
(25)
Use at least five
functions from
the R stringr
and/or lubridate
package to
modify/clean
character
variables or
modify/clean
date variables
Good
(points possible)
Unsatisfactory
(points possible)
You do not select
a data scenario
and sign up using
the Sign-Up sheet
in Canvas.
Use three to four
verbs/functions
from the R dplyr
package.
(0)
Use no
verbs/functions
from the R dplyr
package.
OR
OR
Use more R base
functions to tidy
the data than
dplyr
verbs/functions.
Use all base R
functions to tidy
the data.
(15-24)
Use three to four
functions from
the R stringr
and/or lubridate
package to
modify/clean
character
variables.
(0)
Use no functions
from the R stringr
and lubridate
package.
OR
Use more R base
functions to
modify/clean
character and date
OR
Use all base R
functions to
modify/clean
character and
date variables.
Student’
s score
Variables than
stringr or lubridate
functions.
(25)
(0)
(15-24)
Data Set/Tidying
Requirements
Creating final
graph.
Discussion Post
Requirements
Specify the week
you selected.
Discussion Post
Requirements
Describe selected
data set and
scenario.
Discussion Post
Requirements
Code description.
15
5
15
25
Final graph is
constructed using
ggplot().
Final graph is not
constructed using
ggplot().
(15)
You state what
specific week you
selected. For
example, “I
selected the Tidy
Tuesday data set
from the week
2022-02-01.”
(0)
You do not state
what specific
week from the
Tidy Tuesday
website that you
selected.
(5)
Fully and clearly
describe the data
set and the
scenario you
selected.
(0)
No description of
the data set and
scenario
provided.
Somewhat
describes the
data set and
scenario, but the
reader is still left
questioning what
the data and
scenario
represents.
(15)
(1-14)
(0)
Concisely and
accurately
describe how the
tidyverse code
you have posted
creates a tidy
data set. You
must tie the code
you developed
back to the exact
definition of tidy
The code
description is
accurate, that is,
you accurately
describe what
your code does,
but the code does
not relate to the
definition of a
tidy data set as
The code
description is not
accurate, that is,
what you are
claiming that
your code does, it
does not do.
AND
Discussion Post
Requirements
Describe key
takeaway in final
graph.
Discussion Post
Requirements
Reply post-reason
you selected peer
post.
10
5
data (as defined
in R for Data
Science).
defined in R for
Data Science.
The description
does not relate
back to the
definition of a
tidy data set as
defined in R for
Data Science.
(25)
Final graph
should have a
clear, engaging
title with any and
all axes clearly
labeled. One to
two sentences
that describes the
takeaway
message you
want your peer to
have rather than
a dry, technical
description of
what the final
graph is
visualizing.
(1-24)
Final graph does
not have an
engaging title, it
is more
descriptive in
nature.
(0)
Final graph does
not have a title
and no axes are
labeled.
(10)
You clearly state
the reason that
you replied to the
peer post you
selected.
AND
OR
Axes are not
clearly labeled.
OR
There is no
written
description of the
key takeaway
message you
want to convey to
a peer that reads
your post.
(1-9)
There is no
written
description of the
key takeaway
message you
want to convey to
a peer that reads
your post.
(0)
You do not state
a reason why you
replied to one of
your peer’s post.
OR
The reason for
replying to a
particular peer is
not clearly stated
or it is a reason
that is not related
to the class
content.
(0)
(5)
Discussion Post
Requirements
Reply post –
something you
learned about
ggplot().
10
Your description
of what you
learned about
ggplot() by
studying your
peer’s code
reflects an indepth
understanding of
ggplot(). You
have stated
something that
you have learned
that has
substance and
depth to it.
Your description
of what you
learned about
ggplot() by
studying your
peer’s code does
not reflect an indepth
understanding of
the course
material. It is
clear from the
response that you
do not have a
solid
understanding of
ggplot().
Your description
is weak meaning
that the response
is disorganized
and illustrates
that you do not
have an
understanding of
ggplot().
OR
You state
something that is
incorrect about
basic
functionality of
ggplot().
OR
You state
something that is
minorly incorrect
about ggplot().
(10)
Total score:
Grading Notes:
(1-9)
(0)
Overview:
For the final project you are going to participate in #Tidy Tuesday! Tidy Tuesday is a data repository
where R users go use the tidy tools we have covered this semester to wrangle data and create
interesting visualizations.
Selecting your Data Set and Scenario
Once you have made your final selection of your data set and scenario from the Tidy Tuesday website,
provide your name, week of Tidy Tuesday data you selected (YYYY-MM-DD format), and a short
description of the data in the Google spreadsheet entitled ‘Sign-Up Sheet for #Tidy Tuesday Data’ in the
Final Project module in Canvas.
You need to select and sign up for the data set that you will use for the project by Sunday April 21. No
two students should select the same data set. Check the Sign-Up Sheet before you make your final
selection and ensure another student has not selected your week.
When you select your data in the Sign-up sheet this means that you have imported the data into R from
the Tidy Tuesday github website. And, you have spent some time exploring the data and you are
confident that the data set meets the requirements of the assignment (that is, you can apply dplyr
functions, data set has character variables that can have stringr functions applied to them and/or you
can apply lubridate functinos, etc….).
Data Set/Tidying Requirements:
Select a previous or current week from the Tidy Tuesday github page (explained in the Monday
15Apr2025 Lecture). Using the data you select, tidy the data and create a useful graph using ggplot2.
When tidying the data the following requirements need to be met (you will be graded on these
requirements-see rubric for more details):
1. Use at least five verbs/functions from the dplyr package.
2. Use at least five functions from the stringr package and/or lubridate (so you will need to select a data
scenario/data set that has character values and/or date values that need to be modified in some
manner).
3. The final visualization must be constructed using the ggplot2 package.
You can use any R programming skills necessary to tidy the data beyond the requirements in 1-3 above.
You can use a combination of base R code and any other skills in the tidyverse packages that you have
learned in the class. MIS 5559 Data Wrangling Final Project Description and Instructions
Discussion Post Requirements (Initial Post):
Once you have created a tidy data set and create a visualization, create a report and post it to the ‘Data
Wrangling Final Project (#Tidy Tuesday)’ Discussion Post that is in ‘Module 12 Data Wrangling Final
Project (#Tidy Tuesday)’ module in Canvas. Your report (initial post) needs to include the following:
1. A short description of the data scenario you selected. Provide enough information so that a peer that
is reading your post will understand the context and the scenario that is related to your selected data
set.
2. The code that you used to tidy the data set and create the visualization.
3. A concise description of the code used to tidy the data (you do not have to explain the ggplot() code).
There is no required number of sentences but keep the description short and ‘to the point.’ A peer will
be reading your post and replying so you need to describe what tidyverse techniques clearly and
concisely you employed. This description needs to directly tie back to the definition of tidy data. This
section needs to describe how you used tidyverse concepts to make the data tidy.
The end goal is to create a visualization so you may be tidying the data to make the data frame
appropriate for ggplot(). You can also describe how you prepared the data to be in an appropriate form
for ggplot().
4. Describe the key takeaway that your graph conveys to the peer that reads your post.
An important note about the code you submit for this assignment:
The professor understands when you start looking at individuals who have participated in #TidyTuesday,
many of them provide their code using a link to a github website. You may certainly look at how people
have participated in #TidyTuesday and examine their code on their github websites. You learn by looking
at what other people have done. You can look at other people’s participation in #TidyTuesday to get
ideas.
You cannot and should not directly copy any code (any tidyverse (including ggplot2) or base R code)
directly from someone’s github website or other website. Copying somebody else’s code directly will
result in a grade of 0.
Topics
X-Men Mutant Moneyball
Date: 2024-03-19
https://github.com/EliCash82/mutantmoneyball
https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-03-19/readme.md

Mutant Moneyball: A Data Driven Ultimate X-Men