Intro

Customize your plot using different sizes, shapes and colors in the points of your figures.

Data

Load or install the package seamonas

devtools::install_github("MiriamLL/seamonas")

library(seamonas)

Load data

The package contains data from a random generated density data frame.

density_df<-density_df

The data frame contains latitude, longitude and density estimations.

To check the data lets use ggplot.

First call the library.

library(ggplot2)

Then check the data contained on the data frame.

ggplot()+
  geom_point(data=density_df,
          aes(x = longitude,
              y= latitude))

Each of the dots contains density data, to check the values you can use the function range for the minimal and maximal values.

range(density_df$densities)

[1] 0.00000 4.94831

Classify

Based on the range of the densities, define how many classes you want to use and where the cuts will be made.

class0<-0
class1<-1
class2<-2.5
class3<-5

Using the function mutate from the package tidyverse add a new column with a classification.
Note that using alphanumerical order is important for the order in the legend.

library(tidyverse)

density_df<-density_df %>% 
    mutate(density_class = case_when(
      densities ==  class0 ~ as.character("group0"),
      densities >=  class0 & densities <=  class1 ~ as.character("group1"),
      densities >=  class1 & densities <=  class2 ~ as.character("group2"),
      densities >=  class2 & densities <=  class3 ~ as.character("group3"),
      densities >=  class3 ~ as.character("group4"),
      TRUE~"U"))

Check number of observations to make sure your classification was working and that you have at least one value per group.

density_df %>%
  group_by(density_class)%>%
  tally()

# A tibble: 4 × 2
  density_class     n
  <chr>         <int>
1 group0           25
2 group1            4
3 group2            5
4 group3           16

Define

To define the sizes, numbers can be used.

density_sizes<-c("group0"=0.5, "group1"=1,"group2"=2, "group3"=3, "group4"=5)

To define the text on the labels, characters can be used.

density_labels<-c('0','> 0-1','> 1-2.5','> 2.5-5','> 5')

class(density_labels)

[1] "character"

To define shapes, R uses numbers. Here, each group is assigned a different shape.

density_shapes<-c("group0"=3, "group1"=21,"group2"=21, "group3"=21, "group4"=21)

For reference on the shape types:

Base plot

To visualize your data, create a base plot with defined shape, size and labels.
To define the shape and size, the argument was added inside the aes.
To assign the values and labels, fill the arguments scale_shape_manual and scale_size_manual.

ggplot()+
  geom_point(data=density_df,
          aes(x = longitude,
              y= latitude,
              shape = density_class,
              size= density_class),
              fill= "#d00000")+
  
  scale_shape_manual(values = density_shapes,labels=density_labels)+
  scale_size_manual(values =  density_sizes,labels=density_labels)

Add map

Since we need reference map, the package sf contains the function geom_sf that allows plotting shapefiles, and the package GermanNorthSea contains shapefiles from the North Sea readily available for use.

Call (or install) the packages

library(sf)
library(GermanNorthSea)

Some parameters need to be adjusted first, such as which CRS to use, and the limits on the map.
For more details go to: Mapping in R and GermanNorthSea

Here the parameters define that we are going to use CRS 4326, color the land in yellow and the water in blue, and define the coordinates to plot.

my_CRS<-4326
Europa<-sf::st_transform(German_land, my_CRS)
EEZ<-sf::st_transform(German_EEZ, my_CRS)
color_land='#f7bf54'
color_water='#3668b4'
xval<-c(3,9)
yval<-c(53,56)

Create a base map.

base_plot<-ggplot2::ggplot()+
    # maps
    ggplot2::geom_sf(data = EEZ, colour = 'black', fill = color_water)+
    ggplot2::geom_sf(data = Europa, colour = 'black', fill = color_land)+ 
    ggplot2::coord_sf(xlim = xval, ylim = yval)+

    NULL
base_plot

Add dots

Now add the density data on top of the base map.

density_wmap<-base_plot+
  geom_point(data=density_df,
          aes(x = longitude,
              y= latitude,
              shape = density_class,
              size= density_class),
              fill= "#d00000")+
  
  scale_shape_manual(values = density_shapes,labels=density_labels)+
  scale_size_manual(values =  density_sizes,labels=density_labels)
density_wmap

Now you have a base map with density information on different shape and size.

Custom points in a map