L Little 2020-07-10
Purpose: Throughout this course, you’ll complete a large number of exercises and challenges. Exercises are meant to introduce content with easy-to-solve problems, while challenges are meant to make you think more deeply about and apply the content. The challenges will start out highly-scaffolded, and become progressively open-ended.
In this challenge, you will go through the process of exploring, documenting, and sharing an analysis of a dataset. We will use these skills again and again in each challenge.
Unlike exercises, challenges will be graded. The following rubrics define how you will be graded, both on an individual and team basis.
| Category | Unsatisfactory | Satisfactory |
|---|---|---|
| Effort | Some task q’s left unattempted | All task q’s attempted |
| Observed | Did not document observations | Documented observations based on analysis |
| Supported | Some observations not supported by analysis | All observations supported by analysis (table, graph, etc.) |
| Code Styled | Violations of the style guide hinder readability | Code sufficiently close to the style guide |
| Category | Unsatisfactory | Satisfactory |
|---|---|---|
| Documented | No team contributions to Wiki | Team contributed to Wiki |
| Referenced | No team references in Wiki | At least one reference in Wiki to member report(s) |
| Relevant | References unrelated to assertion, or difficult to find related analysis based on reference text | Reference text clearly points to relevant analysis |
All the deliverables stated in the rubrics above are due on the day of the class discussion of that exercise. See the Syllabus for more information.
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
In this first stage, you will explore the diamonds dataset and
document your observations.
q1 Create a plot of price vs carat of the diamonds dataset
below. Document your observations from the visual.
Hint: We learned how to do this in e-vis00-basics!
## TASK: Plot `price` vs `carat` below
## Your code here!
ggplot(data= diamonds) + geom_smooth(mapping = aes(y = price, x = carat))## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Observations: This plot shows that up to ~2.5 carats, higher carat correlates with higher price. Beyond 2.5 carats the correlation generally still applies, but there is a ‘valley’ in between 2.5 and 3.5 carats. I will further examine a histogram to see if if data is sparse beyond 2.5 carats as I suspect.
ggplot(data = diamonds, aes(carat)) + geom_histogram(binwidth = 0.02)
ggplot(data = diamonds, aes(carat)) + geom_histogram(binwidth = 0.3)
Observations These two histograms provide very different information. - smaller binning shows that some sizes are much more common than others - larger binning show that smaller diamonds are much more common than larger diamonds. This makes sense as diamonds are often used in rings and very large rings are undesirable
q2 Create a visualization showing variables carat, price, and
cut simultaneously. Experiment with which variable you assign to which
aesthetic (x, y, etc.) to find an effective visual.
## TASK: Plot `price`, `carat`, and `cut` below
## Your code here!
ggplot(data=diamonds) +
geom_smooth(mapping = aes(x = carat, y = price, color = cut))## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Observations:
- Seems like the strange price dip is because there are not high quality diamonds that exist at large carats.
- Generally, at a given carat, higher quality diamonds are more expensive.
Further plots
I was also interested in seeing what the average carat was for each size of diamond.
ggplot(data=filter(diamonds)) + geom_boxplot(mapping = aes(x = cut, y = carat),
outlier.shape = NA)
Generally, the higher the quality, the smaller the average diamond of that quality.
I was also curious how many of each diamond type there were.
ggplot(data = diamonds, aes(cut)) +
geom_bar() +
scale_fill_brewer(palette= "Dark2")
It seems that there are many more high quality diamonds than low quality diamonds.