salcedo_ryan_final_take_home
Ryan Salcedo
2026-05-04
Set up
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("maps")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("datasets")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)## Warning: package 'datasets' is a base package, and should not be updatedinstall.packages("treemapify")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("ggraph")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)library(ggraph)## Loading required package: ggplot2library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.2 ✔ tidyr 1.3.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(ggplot2)
library(maps)##
## Attaching package: 'maps'
##
## The following object is masked from 'package:purrr':
##
## maplibrary(datasets)
library(treemapify)Plot 1
iris_long=iris %>%
mutate(id=row_number()) %>%
pivot_longer(cols=1:4, names_to="measurement",
values_to="value")
ggplot(iris_long, aes(x=measurement, y=value, group=id,color=Species))+
geom_line(alpha=0.4)+
labs(
title="Parallel Coordinates Plot of Flower Measurements Across Species",
x="Measurement",
y="Value"
)+
theme_minimal()+
theme(axis.text.x=element_text(angle = 45, hjust = 1))
This plot type works well with comparing the different flower
measurements because we can see how each species varies within each
measurement. We see how the species vary very differenlty yet when
comparing sepal width the species start to blend together and the
difference is not so obvious.
Plot 2
head(USArrests)## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7arrests=USArrests %>%
rownames_to_column("state") %>%
mutate(state=tolower(state))
map=map_data("state")
map_df=map %>%
left_join(arrests, by=c("region"="state"))
ggplot(map_df,aes(long,lat,group=group, fill=Murder))+
geom_polygon(color="white", linewidth=1)+
scale_fill_viridis_c()+
labs(
title="Murder Arrests by State",
x="latitude",
y="longitude",
fill="Muder Rate"
)+
theme_minimal()
This plot works nicely to show geographic variation because of the
shading each state has. It works well because the data is sorted by
location not, which gives us the opportunity to place points on a map.
As well as having the colorblind friendly pallet means very minimal
people will confused or lost when interpreting the graph. Here we can
see that Murder rate increases the further south you go in the United
States.
Plot 3
head(USArrests)## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7df=USArrests %>%
scale() %>%
as.data.frame() %>%
mutate(state=rownames(USArrests))
dist.mat=dist(df[,1:4],method="euclidean")
dist.df=as.data.frame(as.matrix(dist.mat))
dist.df$state1=rownames(dist.df)
dist.long=dist.df %>%
pivot_longer(cols=-state1,names_to="state2",values_to = "distance") %>%
filter(state1 !=state2) %>%
mutate(similarity = 1/(1 + distance)) %>%
group_by(state1) %>%
slice_max(similarity, n=2) %>%
ungroup()
edge=dist.long
node=tibble(
state=df$state,
angle=seq(0,2*pi, length.out=nrow(df)),
x=cos(angle),
y=sin(angle)
)
edge2=edge %>%
left_join(node,by=c("state1"="state")) %>%
rename(x1=x, y1=y) %>%
left_join(node, by=c("state2"="state")) %>%
rename(x2=x,y2=y)
ggplot()+
geom_segment(data=edge2,aes(x=x1,y=y1,xend=x2,yend=y2),size=0.5,alpha=.7)+
geom_point(data=node, aes(x=x,y=y),size=1.5,alpha=0.5)+
geom_text(data=node,aes(x=x,y=y,label=state),size=3, angle=25)+
labs(
title = "Network Diagram Showing Similarities of Arrests based on Distance",
x="",
y=""
)+
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
This plot when done correctly can help show connections/similarities
between variables. In this case the state names blend in with each other
making it slightly confusing to read however still possible. The lines
indicate similiarity in distance across all types of arrests.
Plot 4
head(ToothGrowth)## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5ggplot(ToothGrowth, aes(x=supp, y=len, fill=supp)) +
geom_violin(trim=FALSE, alpha=0.5)+
geom_boxplot(width=0.15, outlier.shape = NA, alpha=0.7)+
geom_jitter(width = 0.1, alpha=0.7)+
labs(
title="comparing Tooth Lenght by Supplement Type",
x="supplement type",
y="length(cm)")+
scale_fill_viridis_d(option="D")+
theme_minimal()
A rain cloud plot is very helpful when comparing two different groups.
The colorblind friendly colors allow for minimal confusion when reading
the graph. Rain cloud plots provide your standard box plot,violin, and
regular points all at once. This helps support your figures when all
figure types match and work together.
Student Portfolio
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1#how do car characteristics vary among gear type
fig1=ggplot(mtcars, aes(x=factor(gear), y=mpg, fill=factor(gear)))+
geom_boxplot(alpha=0.7)+
geom_violin(trim=FALSE, alpha=0.5)+
geom_jitter(width = 0.1, alpha=0.7)+
labs(
title="Gear Type compared to MPG",
x= "Gear Type",
y= "mpg",
)+
theme_minimal()
fig1
fig2=ggplot(mtcars, aes(x=factor(gear), y=hp, color=factor(gear)))+
geom_point(size=2,alpha=0.7)+
labs(
title="Gear Type Compared to Horse Power",
x="Gear type",
y= "Horse Power"
)+
theme_minimal()
fig2
I wanted to look at how gear type can effect car performance. To look at this, I created two figures each looking at a different aspect. For figure 1. I created a rain cloud plot looking at the MPG that each gear type gets. We can see that cars with 4 gears typically have a higher MPG than the others, but only marginally when compared to cars with 5 gears. This plot type worked well with what we were looking at because we are comparing numerical values among three different groups.
The next graph I created was a scatter plot, but this time I looked at horse power or “hp”. The figure showed that cars with 5 gears had much more horse power when compared with the other two types of cars. This graph gives us a clear indicator of cars that have higher horse power than others. When combining the data from both graphs it is a reasonable assumption to make that cars with higher gears can on average put oput more horsepower but in return have a lower mpg than cars with 4 gears.