# INBO CODING CLUB

March 23rd 2025

Welcome!

## Share your code snippet

If you want to share your code snippet, copy paste your snippet within a section of three backticks (```):

As an **example**:

library(tidyverse)

(*you can copy paste this example and add your code further down*)

## Yellow sticky notes

No yellow sticky notes online. Put your name + " | " and add a "*" each time you solve a challenge (see below).

## Participants

Name | Challenges
--- | ---
Raïsa Carmen | ***
Jorre |
Pieter Huybrechts |
Falk Mielke | ** +
Dirk Maes | ***
Kaat Thienpont |
Margot Vermeylen |*
Isaac Vermeulen

## general

Install `colorblindr`:

install.packages("remotes")
install.packages("colorspace", repos = "http://R-Forge.R-project.org")
remotes::install_github("clauswilke/colorblindr")

([source](https://github.com/clauswilke/colorblindr))

## Challenge 1

### Raïsa's solution (dummy example)

Copy paste this section to show your solutions.

# dummy code
print("This is how to insert code.")

### Jorre

occs_benelux_animals |>
  ggplot(aes(x=year,y=count,group=country,color=country)) +
  geom_point() +
  ggtitle('GBIF occurrence records in the Benelux') +
  xlab('year') +
  ylab('number of occurrences') +
  scale_y_continuous(trans='log10') +
  geom_smooth()

occs_benelux_animals |>
  ggplot(aes(x=year,y=count,group=country,fill=country)) +
  geom_bar(stat='identity',position = 'dodge') +
  ggtitle('GBIF occurrence records in the Benelux') +
  xlab('year') +
  ylab('number of occurrences') +
  scale_y_continuous(trans='log10')

Put titles in separate function for reuse:

titles <- function(){
  list(
    ggtitle('GBIF occurrence records in the Benelux'),
    xlab('year'),
    ylab('number of occurrences'),
    scale_y_continuous(trans='log10')
    )
  }

occs_benelux_animals |>
  ggplot(aes(x=year,y=count,group=country,color=country)) +
  geom_point() +
  geom_smooth() +
  titles()

occs_benelux_animals |>
  ggplot(aes(x=year,y=count,group=country,fill=country)) +
  geom_bar(stat='identity',position = "dodge") +
  titles()

### Falks attepmt

occs_benelux_animals %>%
  mutate_at(factor, .vars = vars(kingdom, country)) %>%
  ggplot(aes(x = year, y = count, color = country)) +
  geom_point(shape = 4) +
  geom_smooth(formula = 'y ~ x', method = "loess", se = TRUE) +
  # geom_bar(aes(fill = country), stat = "identity", position = "dodge") +
  labs(title = "GBIF occurrence records in the Benelux") +
  xlab("year") +
  ylab("number of occurrences") +
  scale_y_log10() +
  theme_minimal()

## Hanna

p1 <- ggplot(occs_benelux_animals, aes(x = year, y = count)) +
  geom_point(aes(color = country))+#add title
  labs(title = "GBIF occurrences records in the Benelux", x="year",y="number of occurrences")+
  scale_y_log10()+#color loess by country+show confidence interval
  geom_smooth(method = "loess", se = TRUE, aes(color = country))


p2 <- ggplot(occs_benelux_animals, aes(x = year, y = count,fill=country)) +
  geom_bar(stat="identity",position=position_dodge())+
  labs(title = "GBIF occurrences records in the Benelux", x="year",y="number of occurrences")+
  scale_y_log10()

## Pieter

# Make a scatter plot (=points) with the number of occurrences (y) per year (x).
# Distinguish the countries by color.

# Add a title (e.g. "GBIF occurrence records in the Benelux") and labels for the
# axes (e.g. "year" and "number of occurrences").

# To better represent the data, use a logarithmic scale for the y-axis.

# Add a smoother to the plot. Use "loess" method and color the smoother by
# country. Show the confidence interval (standard error).

occs_benelux_animals %>%
  ggplot(mapping = aes(
    x = year,
    y = count,
    colour = country
  )) +
  ggtitle("GBIF occurrence records in the Benelux") +
  labs(
    x = "year",
    y = "number of occurrences"
  ) +
  scale_y_log10() +
  geom_smooth(
    method = "loess",
    se = TRUE,
    level = 0.95
  ) +
  geom_point()

# This kind of data could be also represented by a bar plot. Try to do it
# (without smoother). The grammar of graphics makes this kind of change quite
# easy, isn't? What do you think about? Which one is more informative: the
# scatter plot of the bar plot? And why?

occs_benelux_animals %>%
  ggplot(mapping = aes(
    x = year,
    y = count,
    fill = country
  )) +
  geom_col()

occs_benelux_animals %>%
  ggplot(mapping = aes(
    x = year,
    y = count,
    fill = country
  )) +
  geom_bar(stat = "identity")

## Dirk's attempt

p <- ggplot(occs_benelux_animals,
            aes(x = year,
                y = count,
                colour = country))+
  geom_point(size = 2) +
  ggtitle("GBIF occurrence records in the Benelux") +
  xlab("year") +
  ylab("number of occurrences") +
  scale_y_log10() +
  geom_smooth(method = loess,
              se = TRUE)
p

# Bar plot
p <- ggplot(occs_benelux_animals,
            aes(x = year,
                y = count,
                fill = country))+
  geom_bar(stat = "identity",
           position = "dodge") +
  ggtitle("GBIF occurrence records in the Benelux") +
  xlab("year") +
  ylab("number of occurrences") +
  facet_wrap(~ country,
             scales = "free")
p

## Challenge 2

### Falks suggestion

country_colors <- c(
  "BE" = "#111111", # "black",
  "NL" = "#87CEEB", # "skyblue",
  "LU" = "#CD5C5C", # "indian red"
  "default" = "#FFFFFF"
)


g <- occs_benelux_animals %>%
  mutate_at(factor, .vars = vars(kingdom, country)) %>%
  ggplot(aes(x = year, y = count, group = country, color = country)) +
  geom_point(shape = 4) +
  geom_smooth(formula = 'y ~ x', method = "loess", se = TRUE) +
  # geom_bar(aes(fill = country), stat = "identity") +
  labs(title = "GBIF occurrence records in the Benelux") +
  xlab("year") +
  ylab("number of occurrences") +
  scale_y_log10() +
  # scale_color_viridis_d() +
  # scale_color_manual(values = country_colors) +
  # scale_colour_paletteer_d("nbapalettes::supersonics_holiday") +
  scale_colour_paletteer_d("rockthemes::facelift") +
  theme_minimal()

colorblindr::cvd_grid(g)

### Lawrence

p <- ggplot(data = occs_benelux_animals, mapping = aes(x = year, y = count, colour = country)) + 
  geom_point() +
  labs(x = "Year", y = "Number of occurrences", title = "GBIF occurrence records in the Benelux") + 
  scale_y_log10() + 
  geom_smooth(formula = 'y ~ x', method = "loess", se = TRUE) +
  scale_colour_manual(values = c("black", "skyblue", "indianred"), limits = c("BE", "NL", "LU"))
p

p <- ggplot(data = occs_benelux_animals, mapping = aes(x = year, y = count, colour = country)) + 
  geom_point() +
  labs(x = "Year", y = "Number of occurrences", title = "GBIF occurrence records in the Benelux") + 
  scale_y_log10() + 
  geom_smooth(formula = 'y ~ x', method = "loess", se = TRUE) +
  scale_colour_manual(values = c("#000000", "#87ceeb", "#cd5c5c"), limits = c("BE", "NL", "LU"))
p

p <- ggplot(data = occs_benelux_animals, mapping = aes(x = year, y = count, colour = country)) + 
  geom_point() +
  labs(x = "Year", y = "Number of occurrences", title = "GBIF occurrence records in the Benelux") + 
  scale_y_log10() + 
  geom_smooth(formula = 'y ~ x', method = "loess", se = TRUE) +
  scale_colour_viridis_d()
p

p <- ggplot(data = occs_benelux_animals, mapping = aes(x = year, y = count, colour = country)) + 
  geom_point() +
  labs(x = "Year", y = "Number of occurrences", title = "GBIF occurrence records in the Benelux") + 
  scale_y_log10() + 
  geom_smooth(formula = 'y ~ x', method = "loess", se = TRUE) +
  paletteer::scale_colour_paletteer_d("beyonce::X39") 
p

cvd_grid(p)

## Isaac

occs_benelux_animals |>
  ggplot(aes(x = year, y = count, group = country, color = country)) +
  geom_point() +
  geom_smooth() +
  ggtitle('GBIF occurrence records in the Benelux') +
  xlab('Year') +
  ylab('Number of occurrences') +
  scale_y_continuous(trans = 'log10') +
  scale_color_manual(values = c("black", "skyblue", "indianred"),
                     limits = c("BE", "NL", "LU"), 
                     name = "Country", 
                     labels = c("BE", "NL", "LU"))


occs_benelux_animals |>
  ggplot(aes(x = year, y = count, group = country, color = country)) +
  geom_point() +
  geom_smooth() +
  ggtitle('GBIF occurrence records in the Benelux') +
  xlab('Year') +
  ylab('Number of occurrences') +
  scale_y_continuous(trans = 'log10') +
  scale_color_manual(values = c(#00000, ##0000ff, #cd5c5c),
                     limits = c("BE", "NL", "LU"), 
                     name = "Country", 
                     labels = c("BE", "NL", "LU"))
                     
                     
 occs_benelux_animals |>
 ggplot(aes(x = year, y = count, group = country, color = country)) +
 geom_point() +
 geom_smooth() +
 ggtitle('GBIF occurrence records in the Benelux') +
 xlab('Year') +
 ylab('Number of occurrences') +
 scale_y_continuous(trans = 'log10') +
 scale_color_viridis_d(option = "plasma",  
                       name = "Country", 
                       labels = c("BE", "NL", "LU"))
                     

## Pieter

# Set colors manually. Use "black" for Belgium, "skyblue" for The Netherlands
# and "indian red" for Luxembourg.

occs_benelux_animals %>%
  ggplot(mapping = aes(
    x = year,
    y = count,
    colour = country
  )) +
  scale_y_log10() +
  scale_colour_manual(
    values = c(BE = "black",
               NL = "skyblue",
               LU = "sienna3")
  ) +
  geom_point()

# Create same plot using the hexcodes.

col_to_hex <- function(colour) rgb(t(col2rgb(colour)), maxColorValue = 256)

occs_benelux_animals %>%
  ggplot(mapping = aes(
    x = year,
    y = count,
    colour = country
  )) +
  scale_y_log10() +
  scale_colour_manual(
    values = c(BE = col_to_hex("grey10"),
               NL = col_to_hex("steelblue"),
               LU = col_to_hex("indianred2"))
  ) +
  geom_point()

# Let's set the color automatically by using the famous viridis palette.
occs_benelux_animals %>%
  ggplot(mapping = aes(
    x = year,
    y = count,
    colour = country
  )) +
  scale_colour_viridis_d(option = "cividis") +
  theme_grey() +
  scale_y_log10() +
  geom_point()

# Use the R Graph Gallery's color palette finder to explore palettes
# interactively. Pick a palette and use it in your plot

if (system.file(package = "wesanderson") == "") install.packages("wesanderson")

gbh_plot <-
  occs_benelux_animals %>%
  ggplot(mapping = aes(
    x = year,
    y = count,
    colour = country
  )) +
  scale_colour_paletteer_d("wesanderson::GrandBudapest1") +
  scale_y_log10() +
  geom_point()

gbh_plot

# Use the {colorblindr} package to simulate how the plots you’ve just created
# may look to people with colour blindness.

cvd_grid(gbh_plot)

### Margot

## 1
a <- ggplot(data = occs_benelux_animals,
       mapping = aes(x = year,
                     y = count, colour = country)) +
  geom_point() +
  labs(x = "year", y = "number of occurrences", title = "GBIF occurrence records in the Benelux") +
  scale_y_log10() +
  stat_smooth(method = "loess", se = T, level = 0.95) +
  scale_color_manual(
    values = c("BE" = "black", "LU" = "indianred", "NL" = "skyblue"))

## 2
ggplot(data = occs_benelux_animals,
       mapping = aes(x = year,
                     y = count, colour = country)) +
  geom_point() +
  labs(x = "year", y = "number of occurrences", title = "GBIF occurrence records in the Benelux") +
  scale_y_log10() +
  stat_smooth(method = "loess", se = T, level = 0.95) +
  scale_color_manual(
    values = c("BE" = "#000000", "LU" = "#cd5c5c", "NL" = "#87ceeb"))

## 3
b <- ggplot(data = occs_benelux_animals,
       mapping = aes(x = year,
                     y = count, colour = country)) +
  geom_point() +
  labs(x = "year", y = "number of occurrences", title = "GBIF occurrence records in the Benelux") +
  scale_y_log10() +
  stat_smooth(method = "loess", se = T, level = 0.95) +
  scale_colour_viridis_d(
  name = waiver(),
  alpha = 1,
  begin = 0,
  end = 1,
  direction = 1,
  option = "D",
  aesthetics = "colour"
)

## 4
c <- ggplot(data = occs_benelux_animals,
       mapping = aes(x = year,
                     y = count, colour = country)) +
  geom_point() +
  labs(x = "year", y = "number of occurrences", title = "GBIF occurrence records in the Benelux") +
  scale_y_log10() +
  stat_smooth(method = "loess", se = T, level = 0.95) +
  scale_color_paletteer_d("rcartocolor::BluYl")

## 5
cvd_grid(a)
cvd_grid(b)
cvd_grid(c)

### Jorre

g <- occs_benelux_animals |>
  ggplot(aes(x=year,y=count,color=country,fill=country)) +
  geom_point() +
  geom_smooth() +
  ggtitle('GBIF occurrence records in the Benelux') +
  xlab('year') +
  ylab('number of occurrences') +
  scale_y_continuous(trans='log10')

# Set colors manually. Use "black" for Belgium, "skyblue" for The Netherlands
# and "indian red" for Luxembourg. Hint: look at the cheatsheet.

cols <- c('BE'="black",'NL'="skyblue",'LU'="indian red")
g + scale_color_manual(values=cols) + scale_fill_manual(values=cols)

# A popular way of defining colours is by hex codes. Hex codes are an hash, #,
# followed by a combination of six characters - (digits 0 - 9, or letters A -
# F). There are many tools to pick colors and get their hex codes. For example,
# colorhexa: enter the color names used above to get their correspondent
# hexcodes. Create same plot using the hexcodes.

cols2 <- c('BE'="#000000",'NL'="#87ceeb",'LU'="#cd5c5c")
g + scale_color_manual(values=cols2) + scale_fill_manual(values=cols2)


# You just created a 3-color palette: a vector with colors. Thousands of
# predefined palettes exist. Let's set the color automatically by using the
# famous viridis palette. The viridis palette is so famous that ggplot has its
# own set of functions for dealing with it.

g + scale_color_viridis_d(option='H') + scale_fill_viridis_d(option='H')


# Use the R Graph Gallery's color palette finder to explore palettes
# interactively. Pick a palette and use it in your plot. Tip: you could need to
# use the {paletteer} package.

g2 <- g +
  paletteer::scale_colour_paletteer_d("lisa::OskarSchlemmer") +
  paletteer::scale_fill_paletteer_d("lisa::OskarSchlemmer")
g2


# Accessibility is important. Use the {colorblindr} package to simulate how the
# plots you’ve just created may look to people with colour blindness. Tip: check
# section "Accessible Choices" from Nicola Rennie's blogpost.

cvd_grid(g2)

## Challenge 3A

### Jorre

g <- occs_benelux |>
  ggplot(aes(x=year,y=count,color=country,fill=country)) +
  geom_point() +
  geom_smooth() +
  ggtitle('GBIF occurrence records in the Benelux') +
  xlab('year') +
  ylab('number of occurrences') +
  scale_y_continuous(trans='log10') 

g + facet_wrap(~kingdom,ncol=1)
g + facet_wrap(~kingdom,ncol=2)

### Hanna

p4 <- ggplot(occs_benelux_animals, aes(x = year, y = count)) +
  geom_point() +
  facet_wrap(~country)+
  labs(title = "GBIF occurrences records in the Benelux", x="year",y="number of occurrences")+
  scale_y_log10()+#color loess by country+show confidence interval
  geom_smooth(method = "loess", se = TRUE)

p5 <- ggplot(occs_benelux_animals, aes(x = year, y = count)) +
  geom_point() +
  facet_wrap(~country, nrow = 2,ncol=2)+
  labs(title = "GBIF occurrences records in the Benelux", x="year",y="number of occurrences")+
  scale_y_log10()+#color loess by country+show confidence interval
  geom_smooth(method = "loess", se = TRUE)

### Lawrence

p <- ggplot(data = occs_benelux, mapping = aes(x = year, y = count, colour = country)) + 
  geom_point() +
  labs(x = "Year", y = "Number of occurrences", title = "GBIF occurrence         records in the Benelux") + 
  scale_y_log10() + 
  geom_smooth(formula = 'y ~ x', method = "loess", se = TRUE) +
  scale_colour_manual(values = c("black", "skyblue", "indianred"), limits =     c("BE", "NL", "LU"))

p + facet_grid(~kingdom)
p + facet_wrap(~kingdom)

### Pieter 3A

    # Show scatter plots for each kingdom separately on one row.
occs_benelux %>%
  ggplot(mapping = aes(x = year,
                       y = count,
                       colour = country)
  ) +
  facet_grid(kingdom ~.) +
  geom_point()
    # Show scatter plots for each kingdom separately: 2 rows and 2 columns.

occs_benelux %>%
  ggplot(mapping = aes(x = year,
                       y = count,
                       colour = country)
  ) +
  scale_y_log10() +
  facet_wrap(vars(kingdom), ncol = 2, nrow = 2) +
  geom_point()

## Challenge 3B

### Jorre

n_species_per_order_iucn <- species_be %>%
  group_by(phylum, order, iucnRedListCategory) %>%
  summarise(n_species = n_distinct(species)) %>%
  arrange(desc(n_species))
n_species_per_order_iucn |>
  ggplot(aes(x=order,y=n_species,fill=iucnRedListCategory)) +
  geom_bar(stat='identity') + 
  facet_grid(phylum~iucnRedListCategory,scales='free_x') +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

### Falk
(a bit of a different attempt)

inspiration: <https://www.data-to-viz.com/graph/treemap.html>

data_tree <- treemap::treemap(
  n_species_per_order_iucn,
  index=c("phylum", "order"),
  vSize="n_species",
  type="categorical",
  vColor = "iucnRedListCategory",
  algorithm = "pivotSize",
  # sortID = "id_tree",
  # mirror.y = TRUE,
  # mirror.x = TRUE,
  border.lwds = 0.5,
  # aspRatio = 5/3
)

### Raïsa

n_species_per_order_iucn %>%
  group_by(phylum, iucnRedListCategory) %>%
  summarize(n = n())
#there are only onbservations in each cell for phylum == "Tracheophyta" and for iucnRedListCategory == "NE"
#We can split up the graph:

n_species_per_order_iucn %>%
  dplyr::filter(iucnRedListCategory == "NE") %>%
  ggplot() +
  geom_col(aes(x = order, y = n_species), position = "dodge") +
  labs(title = "Number of species per order for the IUCN red list category `NE`",
       x = "order",
       y = "number of species"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_wrap(vars(phylum), scales = "free")


n_species_per_order_iucn %>%
  dplyr::filter(phylum == "Tracheophyta") %>%
  ggplot() +
  geom_col(aes(x = order, y = n_species), position = "dodge") +
  labs(title = "Number of species for Tracheophyta in each IUCN red list category",
       x = "order",
       y = "number of species"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(vars(iucnRedListCategory), scales = "free")

## Pieter 3B

n_species_per_order_iucn <- species_be %>%
  group_by(phylum, order, iucnRedListCategory) %>%
  summarise(n_species = n_distinct(species)) %>%
  arrange(desc(n_species))
n_species_per_order_iucn

n_species_per_order_iucn %>%
  ggplot(mapping = aes(x = order,
                       y = n_species,
                       fill = iucnRedListCategory)) +
  facet_wrap(phylum ~ iucnRedListCategory,
             scales = "free",
             drop = TRUE) +
  scale_x_discrete(guide = guide_axis(n.dodge=2)) +
  geom_col() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1))

## Bonus Challenge

### Falks Favorite Plotting Library

Is ggplot the best choice?

Well, now you ask...

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib_venn import venn3

# load the data
species_be = pd.read_csv("data/20250325/20250325_species_in_BE.tsv", sep = "\t")
print(species_be.sample(3).T)

# list of the relevant boolean columns
region_columns = ["inFlanders", "inWallonia", "inBrussels"]

# we do not need to group, but here you go:
"""
n_species_region = species_be \
    .groupby(region_columns) \
    .count() \
    .loc[:, "species"] \
    .sort_values(ascending = False) \
    .reset_index()

print(n_species_region)
"""

# sets come natively in python
region_sets = {}
for col in region_columns:
    region_sets[col] = set(species_be.loc[species_be[col].values, "species"])
print(region_sets)

# there is a special library for venn plots, which integrates with matplotlib:
# `matplotlib_venn` (https://github.com/konstantint/matplotlib-venn)
venn3(
    [region_sets[recol] for recol in region_columns],
    set_labels = [recol[2:] for recol in region_columns]
)
plt.show()

(<https://drive.google.com/file/d/1yFer1iIxPCYcXRrPoGZlF9DHDUhmYHCs/view?usp=sharing>)

;)

Thumbs up from Jorre ;-)

### Raïsa's solution

library(VennDiagram)
venn.diagram(
x = list(
Flanders = species_be %>%
dplyr::filter(inFlanders & !is.na(species)) %>%
dplyr::pull(species) %>%
unique(),
Wallonia = species_be %>%
dplyr::filter(inWallonia & !is.na(species)) %>%
dplyr::pull(species) %>%
unique(),
Brussels = species_be %>%
dplyr::filter(inBrussels & !is.na(species)) %>%
dplyr::pull(species) %>%
unique()),
filename = 'src/20250325/venn.png',
output = TRUE ,
imagetype = "png" ,
height = 480 ,
width = 480 ,
resolution = 300,
compression = "lzw",
lwd = 1,
col = c("#440154ff", '#21908dff', '#fde725ff'),
fill = c(alpha("#440154ff", 0.3), alpha('#21908dff', 0.3),
alpha('#fde725ff', 0.3)),
cex = 0.5,
fontfamily = "sans",
cat.cex = 0.3,
cat.default.pos = "outer",
cat.pos = c(-27, 27, 135),
cat.dist = c(0.055, 0.055, 0.085),
cat.fontfamily = "sans",
cat.col = c("#440154ff", '#21908dff', '#fde725ff'),
rotation = 1)