Visualize

3.1 Read Data

# libraries
library(here)
library(readr)
library(DT)

# variables
# this access the data from the webpage and not downloaded data. Here also allows for searching for the file in the parent directories if not in the working directory
url_ac <- "https://oceanview.pfeg.noaa.gov/erddap/tabledap/cciea_AC.csv"
csv_ac <- here("data/cciea_AC.csv")

# read data
d_ac <- read_csv(url_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(url_ac))

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

# show data
datatable(d_ac)

3.2 Plot statically with ggplot2

3.2.1 Simple line plot + geom_line()

library(dplyr)
library(ggplot2)

# subset data
d_coast <- d_ac %>%
  # select columns that you want to see
  select(time, total_fisheries_revenue_coastwide) %>%
  # filter rows to exclude missing data
  filter(!is.na(total_fisheries_revenue_coastwide))
datatable(d_coast)

# 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

### 3.2.2 Trend line + geom_smooth()

# add a smooth layer based on a linear model (method = "lm")
p_coast +
  geom_smooth(method=NULL)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# other methods can be used as well: lm, glm, gam, loess. NULL chooses model based on largest group across all panels

3.2.3 Distribution of values + geom_historgram()

d_coast %>%
  #setup aestetics
  ggplot(aes(x=total_fisheries_revenue_coastwide)) +
  # add geometry (can include a specified bin width for the histogram)
  geom_histogram(binwidth = 50)

3.2.4 Series line plot aes(color=region)

library(stringr)
library(tidyr)

d_rgn <- d_ac %>%
  # select columns
  select(
    time,
    starts_with("total_fisheries_revenue")) %>%
  # exclude column
  select(-total_fisheries_revenue_coastwide) %>%
  # pivot longer
  pivot_longer(-time) %>%
  # mutate region by stripping other
  mutate(
    region = name %>%
      str_replace("total_fisheries_revenue_","") %>%
      str_to_upper()) %>%
  # filter for not NA
  filter(!is.na(value)) %>%
  #select columns
  select(time, region, value)

# create plot object
  p_rgn <- ggplot(
    d_rgn,
    #aestetics
    aes(
      x=time,
      y=value,
      group=region,
      color=region)) +
    #geometry
    geom_line()

# show plot
p_rgn

3.2.5 Update labels =labs()

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

3.2.6 Multiple plots with facet_wrap()

p_rgn +
  facet_wrap(vars(region))

3.2.7 Bar plot + geom_col()

library(glue)
library(lubridate)

yr_max <- year(max(d_rgn$time))

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"  )

3.2.8 Variation of series with + geom_boxplot()

d_rgn %>%
  # setup aestetics
  ggplot(aes(x=region,y=value,fill=region))+
  # add geometry
  geom_boxplot()+
  # add labels
  labs(
    title="Fisheries Revenue Variability",
    x="Region",
    y="Millions $ (year 2015)") +
  # drop legend since redundant with x axis
  theme( legend.position = "none")

3.2.9 Variation of series with + geom_violin()

p_rgn_violin <- d_rgn %>%
  # setup aestetics
  ggplot(aes(x=region,y=value,fill=region)) +
  # add geometry
  geom_violin()+
  # add labels
  labs(
    title="Fisheries Revenue Variability",
    x="Region",
    y="Millions $ (year 2015)"  ) +
  # drop legend since redundant with x axis
  theme(legend.position = "none")
p_rgn_violin

3.2.10 Change Theme theme()

p_rgn_violin +
  theme_minimal()

3.3 Plot interactively with plotly or dygraphs

3.3.1 Make ggplot interactive with plotly::ggplotly()

plotly::ggplotly(p_rgn)

3.3.2 Create interactive time series with dygraphs::dygraphy()

library(dygraphs)

#dygraphs requires data to be in wide format
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()