Data Visualization: ggplot
part 2
Learning Goals
At the end of this exercise, you will be able to:
1. Produce boxplots using ggplot.
##Resources
- ggplot2
cheatsheet
- ggplot
themes
- Rebecca
Barter ggplot Tutorial
Load the libraries
library(tidyverse)
library(skimr)
library(janitor)
library(palmerpenguins)Review
Now that you have been introduced to ggplot, let’s
practice a few more plot types. Remember that plots are built in layers:
plot= data + geom_ + aesthetics. We have to specify
each of these in order for a plot to be produced. If you get stuck, it
is often helpful to stop and make a quick sketch of what you want or
expect to see on a piece of scratch paper.
Let’s review using the penguins data. First, get an idea
of the structure: Are the data tidy? Are there NA’s? Are the variables
discrete, categorical, or continuous?
penguins## # A tibble: 344 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>In a previous lab, we asked how many penguins were measured on each island.
penguins %>%
count(island)## # A tibble: 3 × 2
## island n
## <fct> <int>
## 1 Biscoe 168
## 2 Dream 124
## 3 Torgersen 52Make this output more visual by adding a plot…
penguins %>%
count(island) %>%
ggplot(aes(x=island, y=n))+
geom_col()
penguins %>%
count(island) %>%
ggplot(aes(x=island, y=n, fill=island)) +
geom_col() +
labs(title="Penguin Counts by Island", x="Island", y="Count") +
theme_minimal()
Practice
- What if we wanted a plot that showed the number of measured penguins for each species?
penguins %>%
count(species) %>%
ggplot(aes(x=species, y=n))+
geom_col()
- How about average bill length by sex?
penguins %>%
filter(sex!="NA") %>%
group_by(sex) %>%
summarise(ave_bill_length=mean(bill_length_mm))## # A tibble: 2 × 2
## sex ave_bill_length
## <fct> <dbl>
## 1 female 42.1
## 2 male 45.9penguins %>%
filter(sex!="NA") %>%
group_by(sex) %>%
summarise(ave_bill_length=mean(bill_length_mm)) %>%
ggplot(aes(x=sex, y=ave_bill_length)) +
geom_col()
Box Plots
For the next series of examples, we will use the
homerange data. Database of vertebrate home range
sizes.
Reference: Tamburello N, Cote IM, Dulvy NK (2015) Energy and the
scaling of animal space use. The American Naturalist 186(2):196-211. http://dx.doi.org/10.1086/682070.
Data: http://datadryad.org/resource/doi:10.5061/dryad.q5j65/1
homerange <- read_csv("data/Tamburelloetal_HomeRangeDatabase.csv")Boxplots help us visualize a range of values. So, on the x-axis we
typically have something categorical and the y-axis is the range. In the
case below, we are plotting log10.mass by taxonomic class
in the homerange data. geom_boxplot() is the
geom type for a standard box plot. The center line in each box
represents the median, not the mean.
Let’s look at the variable log10.mass grouped by
taxonomic class.
homerange %>%
group_by(class) %>%
summarize(min_log10.mass=min(log10.mass),
max_log10.mass=max(log10.mass),
median_log10.mass=median(log10.mass))## # A tibble: 4 × 4
## class min_log10.mass max_log10.mass median_log10.mass
## <chr> <dbl> <dbl> <dbl>
## 1 actinopterygii -0.658 3.55 2.08
## 2 aves 0.712 4.95 1.82
## 3 mammalia 0.620 6.60 3.33
## 4 reptilia 0.539 4.03 2.51homerange %>%
ggplot(aes(x = class, y = log10.mass)) +
geom_boxplot()
Practice
- There are more herbivores than carnivores in the homerange data, but
how do their masses compare? Make a summary and boxplot that compares
their masses. Use
log10.mass.
names(homerange)## [1] "taxon" "common.name"
## [3] "class" "order"
## [5] "family" "genus"
## [7] "species" "primarymethod"
## [9] "N" "mean.mass.g"
## [11] "log10.mass" "alternative.mass.reference"
## [13] "mean.hra.m2" "log10.hra"
## [15] "hra.reference" "realm"
## [17] "thermoregulation" "locomotion"
## [19] "trophic.guild" "dimension"
## [21] "preymass" "log10.preymass"
## [23] "PPMR" "prey.size.reference"homerange %>%
group_by(trophic.guild) %>%
summarize(mean_log10.mass=mean(log10.mass))## # A tibble: 2 × 2
## trophic.guild mean_log10.mass
## <chr> <dbl>
## 1 carnivore 2.24
## 2 herbivore 3.13homerange %>%
ggplot(aes(x=trophic.guild, y=log10.mass))+
geom_boxplot()
- Have a closer look at carnivorous mammals. Summarize the range of log10.mass by family.
homerange %>%
filter(class=="mammalia" & trophic.guild=="carnivore") %>%
group_by(family) %>%
summarize(mean_mass=mean(log10.mass))## # A tibble: 18 × 2
## family mean_mass
## <chr> <dbl>
## 1 canidae 3.73
## 2 chrysochloridae 2.00
## 3 cricetidae 1.39
## 4 dasyuridae 2.32
## 5 didelphidae 1.38
## 6 erinaceidae 2.69
## 7 eupleridae 3.98
## 8 felidae 4.16
## 9 herpestidae 3.16
## 10 hyanidae 4
## 11 macroscelididae 2.27
## 12 mustelidae 3.08
## 13 peramelidae 2.74
## 14 soricidae 0.882
## 15 tachyglossidae 2.41
## 16 talpidae 1.90
## 17 ursidae 4.99
## 18 viverridae 3.49- Now use a boxplot to visualize the range of log10.mass by family of mammalian carnivore.
homerange %>%
filter(class=="mammalia" & trophic.guild=="carnivore") %>%
select(family, trophic.guild, log10.mass) %>%
ggplot(aes(x=family, y=log10.mass))+
geom_boxplot()+
coord_flip()
glimpse(homerange)## Rows: 569
## Columns: 24
## $ taxon <chr> "lake fishes", "river fishes", "river fishe…
## $ common.name <chr> "american eel", "blacktail redhorse", "cent…
## $ class <chr> "actinopterygii", "actinopterygii", "actino…
## $ order <chr> "anguilliformes", "cypriniformes", "cyprini…
## $ family <chr> "anguillidae", "catostomidae", "cyprinidae"…
## $ genus <chr> "anguilla", "moxostoma", "campostoma", "cli…
## $ species <chr> "rostrata", "poecilura", "anomalum", "fundu…
## $ primarymethod <chr> "telemetry", "mark-recapture", "mark-recapt…
## $ N <chr> "16", NA, "20", "26", "17", "5", "2", "2", …
## $ mean.mass.g <dbl> 887.00, 562.00, 34.00, 4.00, 4.00, 3525.00,…
## $ log10.mass <dbl> 2.9479236, 2.7497363, 1.5314789, 0.6020600,…
## $ alternative.mass.reference <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ mean.hra.m2 <dbl> 282750.00, 282.10, 116.11, 125.50, 87.10, 3…
## $ log10.hra <dbl> 5.4514026, 2.4504031, 2.0648696, 2.0986437,…
## $ hra.reference <chr> "Minns, C. K. 1995. Allometry of home range…
## $ realm <chr> "aquatic", "aquatic", "aquatic", "aquatic",…
## $ thermoregulation <chr> "ectotherm", "ectotherm", "ectotherm", "ect…
## $ locomotion <chr> "swimming", "swimming", "swimming", "swimmi…
## $ trophic.guild <chr> "carnivore", "carnivore", "carnivore", "car…
## $ dimension <dbl> 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3…
## $ preymass <dbl> NA, NA, NA, NA, NA, NA, 1.39, NA, NA, NA, N…
## $ log10.preymass <dbl> NA, NA, NA, NA, NA, NA, 0.1430148, NA, NA, …
## $ PPMR <dbl> NA, NA, NA, NA, NA, NA, 530, NA, NA, NA, NA…
## $ prey.size.reference <chr> NA, NA, NA, NA, NA, NA, "Brose U, et al. 20…Wrap-up
Please review the learning goals and be sure to use the code here as a reference when completing the homework.
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