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<!DOCTYPE html>
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<title>Visualize</title>
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<h1 class="title toc-ignore">Visualize</h1>
<h4 class="author">Diana LaScala-Gruenewald</h4>
<h4 class="date">7/1/2021</h4>
</div>
<div id="read-data" class="section level2">
<h2>Read Data</h2>
<pre class="r"><code># load libraries
library(here)
library(readr)
library(DT)
# define variables
url_ac <- 'https://oceanview.pfeg.noaa.gov/erddap/tabledap/cciea_AC.csv'
csv_ac <- here('data/cciea_AC_url_download.csv')
# Download data
if (!file.exists(csv_ac)) # No {}?
download.file(url_ac, csv_ac)
# Read data
d_ac <- read_csv(csv_ac, col_names=F, skip=2)</code></pre>
<pre><code>##
## ── Column specification ────────────────────────────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## X1 = col_datetime(format = "")
## )
## ℹ Use `spec()` for the full column specifications.</code></pre>
<pre class="r"><code>names(d_ac) <- names(read_csv(csv_ac))</code></pre>
<pre><code>##
## ── Column specification ────────────────────────────────────────────────────────────────────────────────
## cols(
## .default = col_character()
## )
## ℹ Use `spec()` for the full column specifications.</code></pre>
<pre class="r"><code># Show data
datatable(d_ac)</code></pre>
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class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>time<\/th>\n <th>consumption_fish<\/th>\n <th>consumption_per_cap<\/th>\n <th>cps_landings_ca<\/th>\n <th>cps_landings_coastwide<\/th>\n <th>cps_landings_or<\/th>\n <th>cps_landings_wa<\/th>\n <th>cps_revenue_ca<\/th>\n <th>cps_revenue_coastwide<\/th>\n <th>cps_revenue_or<\/th>\n <th>cps_revenue_wa<\/th>\n <th>crab_landings_ca<\/th>\n <th>crab_landings_coastwide<\/th>\n <th>crab_landings_or<\/th>\n <th>crab_landings_wa<\/th>\n <th>crab_revenue_ca<\/th>\n <th>crab_revenue_coastwide<\/th>\n <th>crab_revenue_or<\/th>\n <th>crab_revenue_wa<\/th>\n <th>fertilizer_applications<\/th>\n <th>finfish_production<\/th>\n <th>groundfish_landings_ca<\/th>\n <th>groundfish_landings_coastwide<\/th>\n <th>groundfish_landings_or<\/th>\n <th>groundfish_landings_wa<\/th>\n <th>groundfish_revenue_ca<\/th>\n <th>groundfish_revenue_coastwide<\/th>\n <th>groundfish_revenue_or<\/th>\n <th>groundfish_revenue_wa<\/th>\n <th>habitat_modification<\/th>\n <th>hms_landings_ca<\/th>\n <th>hms_landings_coastwide<\/th>\n <th>hms_landings_or<\/th>\n <th>hms_landings_wa<\/th>\n <th>hms_revenue_ca<\/th>\n <th>hms_revenue_coastwide<\/th>\n <th>hms_revenue_or<\/th>\n <th>hms_revenue_wa<\/th>\n <th>oil_gas_production<\/th>\n <th>other_species_landings_ca<\/th>\n <th>other_species_landings_coastwide<\/th>\n <th>other_species_landings_or<\/th>\n <th>other_species_landings_wa<\/th>\n <th>other_species_revenue_ca<\/th>\n <th>other_species_revenue_coastwide<\/th>\n <th>other_species_revenue_or<\/th>\n <th>other_species_revenue_wa<\/th>\n <th>pacific_hake_landings_ca<\/th>\n <th>pacific_hake_landings_coastwide<\/th>\n <th>pacific_hake_landings_or<\/th>\n <th>pacific_hake_landings_wa<\/th>\n <th>pacific_hake_revenue_ca<\/th>\n <th>pacific_hake_revenue_coastwide<\/th>\n <th>pacific_hake_revenue_or<\/th>\n <th>pacific_hake_revenue_wa<\/th>\n <th>recreational_landings_ca<\/th>\n <th>recreational_landings_coastwide<\/th>\n <th>recreational_landings_or<\/th>\n <th>recreational_landings_wa<\/th>\n <th>salmon_com_landings_ca<\/th>\n <th>salmon_com_landings_coastwide<\/th>\n <th>salmon_com_landings_or<\/th>\n <th>salmon_com_landings_wa<\/th>\n <th>salmon_rec_landings_ca<\/th>\n <th>salmon_rec_landings_coastwide<\/th>\n <th>salmon_rec_landings_or<\/th>\n <th>salmon_rec_landings_wa<\/th>\n <th>salmon_revenue_ca<\/th>\n <th>salmon_revenue_coastwide<\/th>\n <th>salmon_revenue_or<\/th>\n <th>salmon_revenue_wa<\/th>\n <th>shellfish_production<\/th>\n <th>shipping_distance_traveled<\/th>\n <th>shrimp_landings_ca<\/th>\n <th>shrimp_landings_coastwide<\/th>\n <th>shrimp_landings_or<\/th>\n <th>shrimp_landings_wa<\/th>\n <th>shrimp_revenue_ca<\/th>\n <th>shrimp_revenue_coastwide<\/th>\n <th>shrimp_revenue_or<\/th>\n <th>shrimp_revenue_wa<\/th>\n <th>squid_landings_ca<\/th>\n <th>squid_landings_coastwide<\/th>\n <th>squid_landings_or<\/th>\n <th>squid_revenue_ca<\/th>\n <th>squid_revenue_coastwide<\/th>\n <th>squid_revenue_or<\/th>\n <th>total_fisheries_landings_ca<\/th>\n <th>total_fisheries_landings_coastwide<\/th>\n <th>total_fisheries_landings_or<\/th>\n <th>total_fisheries_landings_wa<\/th>\n <th>total_fisheries_revenue_ca<\/th>\n <th>total_fisheries_revenue_coastwide<\/th>\n <th>total_fisheries_revenue_or<\/th>\n <th>total_fisheries_revenue_wa<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
</div>
<div id="plot-data-statically-with-ggplot2" class="section level2">
<h2>Plot data statically with <code>ggplot2</code></h2>
<div id="simple-line-plot-geom_line" class="section level3">
<h3>Simple line plot <code>+ geom_line()</code></h3>
<pre class="r"><code>library(dplyr)
library(ggplot2)
# Subset data
d_coast <- d_ac %>%
# Select columns
select(time, total_fisheries_revenue_coastwide) %>%
# Filter rows
filter(!is.na(total_fisheries_revenue_coastwide))
datatable(d_coast)</code></pre>
<div id="htmlwidget-9359cc5d0423a053513f" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-9359cc5d0423a053513f">{"x":{"filter":"none","data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40"],["1981-01-01T00:00:00Z","1982-01-01T00:00:00Z","1983-01-01T00:00:00Z","1984-01-01T00:00:00Z","1985-01-01T00:00:00Z","1986-01-01T00:00:00Z","1987-01-01T00:00:00Z","1988-01-01T00:00:00Z","1989-01-01T00:00:00Z","1990-01-01T00:00:00Z","1991-01-01T00:00:00Z","1992-01-01T00:00:00Z","1993-01-01T00:00:00Z","1994-01-01T00:00:00Z","1995-01-01T00:00:00Z","1996-01-01T00:00:00Z","1997-01-01T00:00:00Z","1998-01-01T00:00:00Z","1999-01-01T00:00:00Z","2000-01-01T00:00:00Z","2001-01-01T00:00:00Z","2002-01-01T00:00:00Z","2003-01-01T00:00:00Z","2004-01-01T00:00:00Z","2005-01-01T00:00:00Z","2006-01-01T00:00:00Z","2007-01-01T00:00:00Z","2008-01-01T00:00:00Z","2009-01-01T00:00:00Z","2010-01-01T00:00:00Z","2011-01-01T00:00:00Z","2012-01-01T00:00:00Z","2013-01-01T00:00:00Z","2014-01-01T00:00:00Z","2015-01-01T00:00:00Z","2016-01-01T00:00:00Z","2017-01-01T00:00:00Z","2018-01-01T00:00:00Z","2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"],[745.8688,721.13403,578.6155,574.1629,586.1377,645.53094,844.0349,884.9662,693.7063,600.5629,571.39594,508.84323,478.95215,500.8642,573.317,555.9465,527.6679,367.63782,438.4124,506.79126,387.0088,387.4853,432.76245,489.54605,517.85223,480.68527,467.4202,512.49585,453.80682,584.7683,749.9219,711.2088,746.77356,727.9395,602.1406,619.4114,695.57367,650.6496,546.5579,431.23926]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>time<\/th>\n <th>total_fisheries_revenue_coastwide<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":2},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<pre class="r"><code># Create ggplot object
p_coast <- d_coast %>%
# Setup aesthetics
ggplot(aes(x = time, y = total_fisheries_revenue_coastwide)) +
# Add geometry
geom_line()
# Show plot
p_coast</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-3-1.png" width="672" /></p>
</div>
<div id="trend-line-geom_smooth" class="section level3">
<h3>Trend line <code>+ geom_smooth()</code></h3>
<pre class="r"><code>p_coast +
geom_smooth(method = 'lm')</code></pre>
<pre><code>## `geom_smooth()` using formula 'y ~ x'</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-4-1.png" width="672" /></p>
</div>
<div id="distribution-of-values-geom_histogram" class="section level3">
<h3>Distribution of values <code>+ geom_histogram()</code></h3>
<p>Note that <code>geom_histogram()</code> plots the frequency of values for a single variable.</p>
<pre class="r"><code>d_coast %>%
# Setup aesthetics
ggplot(aes(x = total_fisheries_revenue_coastwide)) +
# Add geometry
geom_histogram(bins = 10)</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-5-1.png" width="672" /></p>
</div>
<div id="series-line-plot-aescolor-region" class="section level3">
<h3>Series line plot <code>aes(color = region)</code></h3>
<pre class="r"><code>library(stringr)
library(tidyr)
# Create data aggregated by region
d_rgn <- d_ac %>%
# Select columns
select(
time,
starts_with('total_fisheries_revenue')) %>%
# Exclude coastwide data
select(-total_fisheries_revenue_coastwide) %>%
# Pivot longer
pivot_longer(-time) %>%
# Create region column
mutate(
region = name %>%
str_replace('total_fisheries_revenue_', '') %>%
str_to_upper()) %>%
# Filter missing values
filter(!is.na(value)) %>%
# Select columns
select(
time,
region,
value
)
# Create plot object
p_rgn <- d_rgn %>%
# Setup aesthetics
ggplot(aes(
x = time,
y = value,
group = region,
color = region)) +
# Add geometry
geom_line()
# Show
p_rgn</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-6-1.png" width="672" /></p>
</div>
<div id="update-labels-labs" class="section level3">
<h3>Update labels <code>+ labs()</code></h3>
<pre class="r"><code>p_rgn <- p_rgn +
labs(
title = 'Fisheries Revenue',
x = 'Year',
y = 'Millions $ (year 2015)',
color = 'Region'
)
p_rgn</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-7-1.png" width="672" /></p>
</div>
<div id="multiple-plots-with-facet_wrap" class="section level3">
<h3>Multiple plots with <code>facet_wrap()</code></h3>
<pre class="r"><code>p_rgn +
facet_wrap(vars(region))</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-8-1.png" width="672" /></p>
</div>
</div>
<div id="bar-plot-geom_col" class="section level2">
<h2>Bar plot <code>+ geom_col()</code></h2>
<p>Note that <code>geom_col()</code> plots both an x and y variable (e.g. revenue by region), while <code>geom_bar()</code> plots only an x variable (e.g. the number of revenue values for each region).</p>
<pre class="r"><code>library(glue)
library(lubridate)
# Get most recent year in the data
yr_max <- year(max(d_rgn$time))
# Plot revenue by region for most recent year
d_rgn %>%
# Filter by most recent time
filter(year(time) == yr_max) %>%
# Setup aesthetics
ggplot(aes(x = region, y = value, fill = region)) +
# Add geometry
geom_col() +
# Add labels
labs(
title = glue('Fisheries Revenue for {yr_max}'),
x = 'Region',
y = 'Millions $ (year 2015)',
fill = 'Region'
)</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-9-1.png" width="672" /></p>
<p>Contrast with <code>geom_bar()</code>. Also note that <code>color</code> only changes the outline color, while <code>fill</code> changes the color of the entire bar.</p>
<pre class="r"><code>d_rgn %>%
ggplot(aes(x=region, color=region)) + geom_bar()</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-10-1.png" width="672" /></p>
<div id="variation-of-series-with-geom_boxplot" class="section level3">
<h3>Variation of series with <code>+ geom_boxplot()</code></h3>
<pre class="r"><code>d_rgn %>%
ggplot(aes(x = region, y = value, fill = region)) +
geom_boxplot() +
labs(
title = 'Fisheries Revenue Variability',
x = 'Region',
y = 'Millions $ (year 2015)') +
# Do not include a legend since it's redundant with the x axis
theme (
legend.position = 'none'
)</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-11-1.png" width="672" /></p>
</div>
<div id="variation-of-series-with-geom_violin" class="section level3">
<h3>Variation of series with <code>+ geom_violin()</code></h3>
<pre class="r"><code>p_rgn_violin <- d_rgn %>%
ggplot(aes(x = region, y = value, fill = region)) +
geom_violin() +
labs(
title = 'Fisheries Revenue Variability, Violin Plot',
x = 'Region',
y = 'Millions $ (year 2015)') +
theme(
legend.position = 'none'
)
p_rgn_violin</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-12-1.png" width="672" /></p>
</div>
<div id="change-theme-with-theme" class="section level3">
<h3>Change theme with <code>theme()</code></h3>
<pre class="r"><code>p_rgn_violin +
theme_classic()</code></pre>
<p><img src="visualize_files/figure-html/unnamed-chunk-13-1.png" width="672" /></p>
</div>
</div>
<div id="plot-interactively-with-plotly-or-dygraphs" class="section level2">
<h2>Plot interactively with <code>plotly</code> or <code>dygraphs</code></h2>
<div id="make-ggplot-interactive-with-plotlyggplotly" class="section level3">
<h3>Make ggplot interactive with <code>plotly::ggplotly()</code></h3>
<pre class="r"><code>plotly::ggplotly(p_rgn)</code></pre>
<div id="htmlwidget-37af535e2d595db14de3" style="width:672px;height:480px;" class="plotly html-widget"></div>
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/>region: WA","time: 1995-01-01<br />value: 156.26985<br />region: WA<br />region: WA","time: 1996-01-01<br />value: 138.34158<br />region: WA<br />region: WA","time: 1997-01-01<br />value: 155.19836<br />region: WA<br />region: WA","time: 1998-01-01<br />value: 130.88240<br />region: WA<br />region: WA","time: 1999-01-01<br />value: 130.77141<br />region: WA<br />region: WA","time: 2000-01-01<br />value: 167.14781<br />region: WA<br />region: WA","time: 2001-01-01<br />value: 136.33293<br />region: WA<br />region: WA","time: 2002-01-01<br />value: 143.63700<br />region: WA<br />region: WA","time: 2003-01-01<br />value: 165.50175<br />region: WA<br />region: WA","time: 2004-01-01<br />value: 175.72230<br />region: WA<br />region: WA","time: 2005-01-01<br />value: 181.53741<br />region: WA<br />region: WA","time: 2006-01-01<br />value: 179.25426<br />region: WA<br />region: WA","time: 2007-01-01<br />value: 179.71341<br />region: WA<br />region: WA","time: 2008-01-01<br />value: 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</div>
<div id="create-interactive-time-series-with-dygraphsdygraph" class="section level3">
<h3>Create interactive time series with <code>dygraphs::dygraph()</code></h3>
<p>This package is written more specifically for time series data. It requires wide format data.</p>
<pre class="r"><code>library(dygraphs)
d_rgn_wide <- d_rgn %>%
mutate(
Year = year(time)) %>%
select(Year, region, value) %>%
pivot_wider(
names_from = region,
values_from = value)
datatable(d_rgn_wide)</code></pre>
<div id="htmlwidget-b29d5c734eeac4b23e30" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-b29d5c734eeac4b23e30">{"x":{"filter":"none","data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40"],[1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020],[361.73218,353.93332,346.22916,320.9361,271.73944,282.94305,342.73645,374.8401,291.321,266.4745,285.06732,246.80489,223.86409,223.52966,277.20386,286.7257,254.5092,152.40706,210.75441,200.51323,148.25436,137.71295,157.57072,172.44366,174.99821,161.4941,157.39717,138.24156,164.09813,201.86139,240.65817,273.75418,263.59125,252.96075,184.50212,162.85358,229.5135,190.87851,141.35948,144.76035],[162.50978,146.05115,94.752014,85.664566,104.41137,153.69577,213.80852,215.3039,160.77565,139.26753,127.64022,128.87564,106.50497,110.3072,139.84328,130.87918,117.96034,84.34836,96.8866,139.13022,102.42149,106.135345,109.68997,141.38007,161.31662,139.93692,130.30962,150.49887,109.421555,149.16336,194.01657,179.71385,210.26495,214.74638,165.28351,188.05083,186.8704,201.88347,184.57875,169.77666],[221.62683,221.14958,137.63432,167.56224,209.98692,208.89212,287.48996,294.8222,241.60962,194.8209,158.68837,133.16266,148.58307,167.02731,156.26985,138.34158,155.19836,130.8824,130.77141,167.14781,136.33293,143.637,165.50175,175.7223,181.53741,179.25426,179.71341,223.75543,180.28712,233.74358,315.24713,257.74075,272.91736,260.23242,252.35497,268.50696,279.1898,257.88757,220.6197,116.70225]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Year<\/th>\n <th>CA<\/th>\n <th>OR<\/th>\n <th>WA<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<pre class="r"><code>d_rgn_wide %>%
dygraph() %>%
dyRangeSelector()</code></pre>
<div id="htmlwidget-9aef22b1e0c340fc07e0" style="width:672px;height:480px;" class="dygraphs html-widget"></div>
<script type="application/json" data-for="htmlwidget-9aef22b1e0c340fc07e0">{"x":{"attrs":{"labels":["Year","CA","OR","WA"],"legend":"auto","retainDateWindow":false,"axes":{"x":{"pixelsPerLabel":60}},"showRangeSelector":true,"rangeSelectorHeight":40,"rangeSelectorPlotFillColor":" #A7B1C4","rangeSelectorPlotStrokeColor":"#808FAB","interactionModel":"Dygraph.Interaction.defaultModel"},"annotations":[],"shadings":[],"events":[],"format":"numeric","data":[[1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020],[361.73218,353.93332,346.22916,320.9361,271.73944,282.94305,342.73645,374.8401,291.321,266.4745,285.06732,246.80489,223.86409,223.52966,277.20386,286.7257,254.5092,152.40706,210.75441,200.51323,148.25436,137.71295,157.57072,172.44366,174.99821,161.4941,157.39717,138.24156,164.09813,201.86139,240.65817,273.75418,263.59125,252.96075,184.50212,162.85358,229.5135,190.87851,141.35948,144.76035],[162.50978,146.05115,94.752014,85.664566,104.41137,153.69577,213.80852,215.3039,160.77565,139.26753,127.64022,128.87564,106.50497,110.3072,139.84328,130.87918,117.96034,84.34836,96.8866,139.13022,102.42149,106.135345,109.68997,141.38007,161.31662,139.93692,130.30962,150.49887,109.421555,149.16336,194.01657,179.71385,210.26495,214.74638,165.28351,188.05083,186.8704,201.88347,184.57875,169.77666],[221.62683,221.14958,137.63432,167.56224,209.98692,208.89212,287.48996,294.8222,241.60962,194.8209,158.68837,133.16266,148.58307,167.02731,156.26985,138.34158,155.19836,130.8824,130.77141,167.14781,136.33293,143.637,165.50175,175.7223,181.53741,179.25426,179.71341,223.75543,180.28712,233.74358,315.24713,257.74075,272.91736,260.23242,252.35497,268.50696,279.1898,257.88757,220.6197,116.70225]]},"evals":["attrs.interactionModel"],"jsHooks":[]}</script>
</div>
</div>
<div id="other-packages-of-potential-interest" class="section level2">
<h2>Other packages of potential interest</h2>
<ul>
<li><a href="https://cran.r-project.org/web/packages/trelliscopejs/vignettes/trelliscopejs.html">trelliscopejs</a></li>
</ul>
</div>
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