Visualize

Read Data

# 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)

## 
## ── Column specification ────────────────────────────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   X1 = col_datetime(format = "")
## )
## ℹ Use `spec()` for the full column specifications.

names(d_ac) <- names(read_csv(csv_ac))

## 
## ── Column specification ────────────────────────────────────────────────────────────────────────────────
## cols(
##   .default = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

# Show data
datatable(d_ac)

Plot data statically with ggplot2

Simple line plot + geom_line()

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)

# 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

Trend line + geom_smooth()

p_coast +
  geom_smooth(method = 'lm')

## `geom_smooth()` using formula 'y ~ x'

Distribution of values + geom_histogram()

Note that geom_histogram() plots the frequency of values for a single variable.

d_coast %>%
  # Setup aesthetics
  ggplot(aes(x = total_fisheries_revenue_coastwide)) + 
  # Add geometry
  geom_histogram(bins = 10)

Series line plot aes(color = region)

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

Update labels + labs()

p_rgn <- p_rgn + 
  labs(
    title = 'Fisheries Revenue',
    x = 'Year',
    y = 'Millions $ (year 2015)',
    color = 'Region'
  )
p_rgn

Multiple plots with facet_wrap()

p_rgn + 
  facet_wrap(vars(region))

Bar plot + geom_col()

Note that geom_col() plots both an x and y variable (e.g. revenue by region), while geom_bar() plots only an x variable (e.g. the number of revenue values for each region).

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'
  )

Contrast with geom_bar(). Also note that color only changes the outline color, while fill changes the color of the entire bar.

d_rgn %>%
  ggplot(aes(x=region, color=region)) + geom_bar()

Variation of series with + geom_boxplot()

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'
  )

Variation of series with + geom_violin()

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

Change theme with theme()

p_rgn_violin + 
  theme_classic()

Plot interactively with plotly or dygraphs

Make ggplot interactive with plotly::ggplotly()

plotly::ggplotly(p_rgn)

Create interactive time series with dygraphs::dygraph()

This package is written more specifically for time series data. It requires wide format data.

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)

d_rgn_wide %>%
  dygraph() %>%
  dyRangeSelector()

Other packages of potential interest

• trelliscopejs