Code-along 🦞 → Tables, Figures, & Lobsters (OH MY!)
Replication data from study: Moland et al., 2013
EDS 241
2025-01-29
say("MPAs - Are they worth the swim?", "fish")##
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## < MPAs - Are they worth the swim? >
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## \
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## ><((((Âş> ><((((Âş> ><((((Âş> ><((((Âş> ><((((Âş>
## Kiyoko Gotanda
## Data source & applied study:
Open source data was utilized for this class exercise from the study (Moland et al., 2013):Moland, Even, et al. (2013). Lobster and cod benefit from small-scale northern marine protected areas: inference from an empirical before–after control-impact study. Proceedings of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rspb.2012.2679
Figure: Homarus Gammarus
Coding Goals
1. Make a pretty table
2. Make a pretty figure
3. Explore Norwegian lobster data
4. Have fun!
Load packages
library(tidyverse); library(janitor); library(here) #BASICS
library(gt); library(gtExtras) # TABLES
library(praise); library(cowsay); library(beepr) # FUN
library(ggridges); library(bbplot); library(ggthemes) # THEMES
library(RColorBrewer); library(viridis) # COLOR
### Use code below to install packages: `bbplot` & `gtExtras` ###
# install.packages('devtools')
# devtools::install_github('bbc/bbplot')
# remotes::install_github("jthomasmock/gtExtras")say("Let's get started!", "buffalo")##
## --------------------
## < Let's get started! >
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## Read in the 🦞 data → → → moland13_lobsters.csv
lob_data <- read_csv(here("data", "moland13_lobsters.csv")) %>%
mutate(year = factor(year)) %>%
mutate(region= factor(region))Variable descriptions:
year(5-levels): Years measured from 2006 to 2010region(3-levels):bol= Bolærne ,kve= Kvernskjær ,flo= Flødevigentreat(2-levels):mpa= treatment ,con= controlcpue: “Catch per unit effort”
Make pretty tables with the {gt} package
Create a table with total CPUE for each year and region
tbl_2way <- lob_data %>%
group_by(year, region) %>%
summarize(
total_cpue = sum(cpue, na.rm = TRUE),
.groups = "drop") %>% # <<< Same as `ungroup()`
pivot_wider(
names_from = region,
values_from = total_cpue) %>%
arrange(year) %>%
adorn_totals("row")Customize table using gt
tbl_2way %>%
gt(rowname_col = "year") %>%
tab_header(
title = "European Lobster Catch by Region and Year",
subtitle = "Total Catch Per Unit Effort (CPUE) by year and region") %>%
cols_label(
bol = "Bolærne",
flo = "Kvernskjær",
kve = "Flødevigen") %>%
tab_source_note(
"Source: Moland et al., 2013")| European Lobster Catch by Region and Year | |||
| Total Catch Per Unit Effort (CPUE) by year and region | |||
| Bolærne | Kvernskjær | Flødevigen | |
|---|---|---|---|
| 2006 | 127 | 122 | 177 |
| 2007 | 269 | 93 | 276 |
| 2008 | 249 | 151 | 367 |
| 2009 | 484 | 168 | 466 |
| 2010 | 463 | 175 | 449 |
| Total | 1592 | 709 | 1735 |
| Source: Moland et al., 2013 | |||
beep(sound=2); praise()## [1] "You are great!"say("Create more tables we must", "yoda")##
## ----------------------------
## < Create more tables we must >
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## Create a table with Total CPUE grouped by year,
region, & treatment
Wrange data for 3-way table
tbl_3way <- lob_data %>%
group_by(year, region, treat) %>%
summarize(total_cpue = sum(cpue, na.rm = TRUE),
.groups = "drop") %>%
pivot_wider(names_from = c(region, treat), values_from = total_cpue) %>%
arrange(year)Customize table using {gt}
fancy_table <- tbl_3way %>%
gt(rowname_col = "year") %>%
tab_header(
title = "European Lobster Catch by Year, Region and Treatment",
subtitle = "Total Catch Per Unit Effort (CPUE)"
) %>%
tab_spanner(
label = "Bolærne",
columns = c("bol_con", "bol_mpa")
) %>%
tab_spanner(
label = "Flødevigen",
columns = c("flo_con", "flo_mpa")
) %>%
tab_spanner(
label = "Kvernskjær",
columns = c("kve_con", "kve_mpa")
) %>%
cols_label(
bol_con = "Control",
bol_mpa = "MPA",
flo_con = "Control",
flo_mpa = "MPA",
kve_con = "Control",
kve_mpa = "MPA")
fancy_table| European Lobster Catch by Year, Region and Treatment | ||||||
| Total Catch Per Unit Effort (CPUE) | ||||||
|
Bolærne
|
Flødevigen
|
Kvernskjær
|
||||
|---|---|---|---|---|---|---|
| Control | MPA | Control | MPA | Control | MPA | |
| 2006 | 52 | 75 | 54 | 68 | 125 | 52 |
| 2007 | 98 | 171 | 33 | 60 | 114 | 162 |
| 2008 | 78 | 171 | 55 | 96 | 178 | 189 |
| 2009 | 187 | 297 | 51 | 117 | 244 | 222 |
| 2010 | 148 | 315 | 64 | 111 | 198 | 251 |
Color with tables? Of course!
Pick a pallete & add color
colorful_table <- fancy_table %>%
data_color(
columns = c("bol_con", "flo_con", "kve_con"),
colors = scales::col_numeric(
palette = viridis::mako(
n=2,
begin=.5,
end=1,
direction=-1),
domain = NULL)) %>%
data_color(
columns = c("bol_mpa", "flo_mpa", "kve_mpa"),
colors = scales::col_numeric(
palette = viridis::rocket(
n=2,
begin=.5,
end=1,
direction=-1),
domain = NULL))
colorful_table| European Lobster Catch by Year, Region and Treatment | ||||||
| Total Catch Per Unit Effort (CPUE) | ||||||
|
Bolærne
|
Flødevigen
|
Kvernskjær
|
||||
|---|---|---|---|---|---|---|
| Control | MPA | Control | MPA | Control | MPA | |
| 2006 | 52 | 75 | 54 | 68 | 125 | 52 |
| 2007 | 98 | 171 | 33 | 60 | 114 | 162 |
| 2008 | 78 | 171 | 55 | 96 | 178 | 189 |
| 2009 | 187 | 297 | 51 | 117 | 244 | 222 |
| 2010 | 148 | 315 | 64 | 111 | 198 | 251 |
Plots in tables? Why not!
Add inline plots to a gt table using the
{gtExtras} package
table_w_plots <- lob_data %>%
group_by(year) %>%
summarize(
total_cpue = sum(cpue, na.rm = TRUE),
dist_cpue = list(cpue),
.groups = "drop") %>%
arrange(year) %>%
gt() %>%
tab_header(
title = "European Lobster Catch Totals and Distribution (2006-2010)",
subtitle = "Total Catch Per Unit Effort (CPUE)") %>%
cols_label(
year = "Year",
total_cpue = "Total CPUE",
dist_cpue = "Density CPUE") %>%
gtExtras::gt_plt_dist( # Add in-line plots!
dist_cpue,
type = "density",
line_color = "blue",
fill_color = "red")
table_w_plots | European Lobster Catch Totals and Distribution (2006-2010) | ||
| Total Catch Per Unit Effort (CPUE) | ||
| Year | Total CPUE | Density CPUE |
|---|---|---|
| 2006 | 426 | |
| 2007 | 638 | |
| 2008 | 767 | |
| 2009 | 1118 | |
| 2010 | 1087 | |
praise("${EXCLAMATION}!- You've ${created} something ${adjective}!"); beep(1)## [1] "HURRAH!- You've built something kryptonian!"say("Time for plotting", "squirrel2")##
## -------------------
## < Time for plotting >
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## ML \\\\\\___/_/___/Make pretty plots using {ggthemes} &
{bbplot}
lob_size <- read_csv(here("data", "moland13_lobstersize.csv")) %>%
mutate(region = factor(region, labels = c("Kve", "Flo", "Bol"))) %>%
mutate(treat = factor(treat, labels = c("Control Sites", "MPA Sites")))
lob_size %>%
ggplot( aes(x = size, y = region , fill = region)) +
geom_density_ridges(alpha=.9) +
labs(title = "European Lobster Size: Total Length",
subtitle = "Data from Moland et al., 2013",
caption = "Total Length (mm)" ) +
facet_wrap(~treat) +
scale_fill_wsj() +
theme_wsj() +
theme(
legend.position = "none",
plot.title = element_text(size = 16),
plot.subtitle = element_text(size = 12),
plot.caption = element_text(size = 12),
strip.text = element_text(size = 12, face = "bold", family = "mono"))
Save plot figure
ggsave(here("figures", "lbstr-ridge-plot.png"), dpi=300, height=5, width=7, units="in")## Picking joint bandwidth of 0.711## Picking joint bandwidth of 0.86Change plot theme to follow the BBC viz conventions
(bbplot::bbc_style())
lob_size %>%
group_by(year,treat) %>%
summarize(
mean_size = mean(size, na.rm = TRUE),
.groups = "drop") %>%
ggplot( aes(x = year, y = mean_size , color = treat)) +
geom_line(size=1) +
labs(title = "European Lobster Size (2006-2010)",
subtitle = "Total Length (mm)" ) +
bbc_style()
say("I guess this is the END :) ", "stegosaurus"); beep(3) ##
## ----------------------------
## < I guess this is the END :) >
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## ~-.__| /_ - ~ ^| /- _ `..-' f: f:
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## |_____| |_____| ~ - . _ _ _ _ _>R Package Documentation & Examples
- {
gt} package (Iannone et al., 2024):
- {
janitor} package (adorn_; Firke, 2023):
- {
ggthemes} & {bbplot} package (Arnold, 2024):
# More themes!
?theme_economist()
?theme_fivethirtyeight()
?theme_wsj()
?theme_solarized()
?bbc_style()- Colors: {
viridis} & {RColorBrewer} packages
- {
cowsay} package (Chamberlain & Dobbyn, 2024):
names(animals) # Check out all the animals!## [1] "cow" "cow_borg" "cow_greedy" "cow_sleepy" "cow_dead"
## [6] "cow_tired" "cow_wired" "cow_young" "chicken" "chuck"
## [11] "clippy" "poop" "bigcat" "ant" "pumpkin"
## [16] "ghost" "spider" "rabbit" "pig" "snowman"
## [21] "frog" "hypnotoad" "shortcat" "longcat" "fish"
## [26] "signbunny" "facecat" "behindcat" "stretchycat" "anxiouscat"
## [31] "longtailcat" "cat" "trilobite" "shark" "buffalo"
## [36] "grumpycat" "smallcat" "yoda" "mushroom" "endlesshorse"
## [41] "bat" "bat2" "turkey" "monkey" "daemon"
## [46] "egret" "duckling" "duck" "owl" "squirrel"
## [51] "squirrel2" "goldfish" "alligator" "stegosaurus" "whale"
## [56] "wolf"- {
praise} package (Csardi & Sorhus, 2015)
praise_parts## $adjective
## [1] "ace" "amazing" "astonishing" "astounding"
## [5] "awe-inspiring" "awesome" "badass" "beautiful"
## [9] "bedazzling" "bee's knees" "best" "breathtaking"
## [13] "brilliant" "cat's meow" "cat's pajamas" "classy"
## [17] "cool" "dandy" "dazzling" "delightful"
## [21] "divine" "doozie" "epic" "excellent"
## [25] "exceptional" "exquisite" "extraordinary" "fabulous"
## [29] "fantastic" "fantabulous" "fine" "finest"
## [33] "first-class" "first-rate" "flawless" "funkadelic"
## [37] "geometric" "glorious" "gnarly" "good"
## [41] "grand" "great" "groovy" "groundbreaking"
## [45] "hunky-dory" "impeccable" "impressive" "incredible"
## [49] "kickass" "kryptonian" "laudable" "legendary"
## [53] "lovely" "luminous" "magnificent" "majestic"
## [57] "marvelous" "mathematical" "mind-blowing" "neat"
## [61] "outstanding" "peachy" "perfect" "phenomenal"
## [65] "pioneering" "polished" "posh" "praiseworthy"
## [69] "premium" "priceless" "prime" "primo"
## [73] "rad" "remarkable" "riveting" "sensational"
## [77] "shining" "slick" "smashing" "solid"
## [81] "spectacular" "splendid" "stellar" "striking"
## [85] "stunning" "stupendous" "stylish" "sublime"
## [89] "super" "super-duper" "super-excellent" "superb"
## [93] "superior" "supreme" "swell" "terrific"
## [97] "tiptop" "top-notch" "transcendent" "tremendous"
## [101] "ultimate" "unreal" "well-made" "wicked"
## [105] "wonderful" "wondrous" "world-class"
##
## $adverb
## [1] "beautifully" "bravely" "brightly" "calmly"
## [5] "carefully" "cautiously" "cheerfully" "clearly"
## [9] "correctly" "courageously" "daringly" "deliberately"
## [13] "doubtfully" "eagerly" "easily" "elegantly"
## [17] "enormously" "enthusiastically" "faithfully" "fast"
## [21] "fondly" "fortunately" "frankly" "frantically"
## [25] "generously" "gently" "gladly" "gracefully"
## [29] "happily" "healthily" "honestly" "joyously"
## [33] "justly" "kindly" "neatly" "openly"
## [37] "patiently" "perfectly" "politely" "powerfully"
## [41] "quickly" "quietly" "rapidly" "really"
## [45] "regularly" "repeatedly" "rightfully" "seriously"
## [49] "sharply" "smoothly" "speedily" "successfully"
## [53] "swiftly" "tenderly" "thoughtfully" "truthfully"
## [57] "warmly" "well" "wisely"
##
## $adverb_manner
## [1] "beautifully" "bravely" "brightly" "calmly"
## [5] "carefully" "cautiously" "cheerfully" "clearly"
## [9] "correctly" "courageously" "daringly" "deliberately"
## [13] "doubtfully" "eagerly" "easily" "elegantly"
## [17] "enormously" "enthusiastically" "faithfully" "fast"
## [21] "fondly" "fortunately" "frankly" "frantically"
## [25] "generously" "gently" "gladly" "gracefully"
## [29] "happily" "healthily" "honestly" "joyously"
## [33] "justly" "kindly" "neatly" "openly"
## [37] "patiently" "perfectly" "politely" "powerfully"
## [41] "quickly" "quietly" "rapidly" "really"
## [45] "regularly" "repeatedly" "rightfully" "seriously"
## [49] "sharply" "smoothly" "speedily" "successfully"
## [53] "swiftly" "tenderly" "thoughtfully" "truthfully"
## [57] "warmly" "well" "wisely"
##
## $created
## [1] "assembled" "brewed" "built" "created" "composed"
## [6] "constructed" "designed" "devised" "forged" "formed"
## [11] "initiated" "invented" "made" "organized" "planned"
## [16] "prepared" "set up"
##
## $creating
## [1] "assembling" "brewing" "building" "creating" "composing"
## [6] "constructing" "designing" "devising" "forging" "forming"
## [11] "initiating" "inventing" "making" "organizing" "planning"
## [16] "preparin" "setting up"
##
## $exclamation
## [1] "ah" "aha" "ahh" "ahhh" "aw" "aww" "awww"
## [8] "aye" "gee" "ha" "hah" "hmm" "ho-ho" "huh"
## [15] "heh" "hooray" "hurrah" "hurray" "huzzah" "mhm" "mm"
## [22] "mmh" "mmhm" "mmm" "oh" "ole" "uh-hu" "wee"
## [29] "whee" "whoa" "wow" "wowie" "yahoo" "yay" "yeah"
## [36] "yee-haw" "yikes" "yippie" "yow" "yowza"
##
## $rpackage
## [1] "code" "library (or package?)" "package"
## [4] "program" "project" "software"
## [7] "R package"- {
beepr} package (Rasmus BĂĄĂĄth, 2024)
# Try: `beep(999)` to pick a sound at random (there are 11 sounds)!
beep(999)say("That's all folks!", "egret")##
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