# INBO CODING CLUB

30 March 2023

Welcome!

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## 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
--- | ---
Damiano Oldoni |
Pieter Huybrechts |**
Dirk Maes | ***
Adriaan Seynaeve |
Rhea Maesele | ***
Nele Mullens | *
Ward Langeraert | ***
Joppe Massant |
Margot Vanhellemont | ***
Marijke Thoonen | *

## Challenge 1

### Pieter's solution

### 1.1

ggplot(data = ias_be) +
  aes(x = first_observed) +
  geom_bar(fill = "blue",
           color = "red",
           ) +
  labs(x = "year of introduction",
       y = "number of taxa")


### 1.3

ias_be %>%
  filter(first_observed > 1979) %>%
  ggplot() +
  aes(x = first_observed) +
  geom_bar(
    fill = "blue",
    color = "red",
  ) +
  labs(
    x = "year of introduction",
    y = "number of taxa"
  )

### 1.5

ias_be %>%
  filter(first_observed > 1979) %>%
  ggplot() +
  aes(x = first_observed) +
  geom_histogram(
    fill = "blue",
    color = "red",
    binwidth = 5
  ) +
  labs(
    x = "year of introduction",
    y = "number of taxa"
  )

### Solution Dirk

p <- ggplot(ias_be,
            aes(x = first_observed)) +
  geom_bar(colour = "red",
           fill = "blue") +
  xlab("year of introduction") +
  ylab("number of taxa") +
  xlim(1980, 2020) +
  scale_x_binned(n.breaks = 15) +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 0.5,
                                   vjust = 0.5),
        axis.text.y = element_text(angle = 0,
                                   hjust = 0),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 15,
                                  face = "bold"),
        legend.text = element_text(size = 10),
        legend.position = "none",
        legend.title = element_text(size = 15),
        panel.background = element_rect(fill = "white",
                                        colour = "grey",
                                        size = 1,
                                        linetype = "solid"), 
        panel.grid.major = element_line(size = 0.5,
                                        linetype = "solid",
                                        colour = "grey"))
p

### Solution Ward

# geom_bar() makes the height of the bar proportional to the number of cases 
# in each group
# geom_col() makes the height of the bar represent values in the data
ias_be %>%
  filter(first_observed >= 1980) %>%
  ggplot() +
    geom_bar(aes(x = cut(first_observed, length(unique(first_observed)) / 5)),
             fill = "cornflowerblue", colour = "firebrick") +
    labs(x = "year of introduction", y = "number of taxa")

### Nele's solution

# 1.1
ggplot(data = ias_be, 
         mapping = aes(x = first_observed)) + geom_bar()

# 1.2
ggplot(data = ias_be, 
       mapping = aes(x = first_observed)) + geom_bar() + labs(x="year of introduction", y= "number of taxa")

# 1.3
filtered_ias_be <- dplyr::filter(ias_be, first_observed > 1979)
#ggplot(data = filtered_ias_be, 
#       mapping = aes(x = first_observed)) + geom_bar()

ggplot(data = filtered_ias_be, 
       mapping = aes(x = first_observed)) + geom_histogram(binwidth = 5) 

# 1.4
ggplot(data = filtered_ias_be, 
       mapping = aes(x = first_observed)) + geom_histogram(fill = "blue", colour = "red") 

# 1.5
ggplot(data = filtered_ias_be, 
       mapping = aes(x = first_observed)) + geom_histogram(binwidth = 5, fill = "blue", colour = "red") 

### Solution Joppe

value_counts <- count(ias_be, first_observed)
value_counts <- value_counts %>% filter(first_observed >= 1980)

ggplot(data = value_counts,
       mapping = aes(
         x = first_observed,
         y = n)) + 
  scale_x_binned(n.breaks=8) + 
  geom_col(fill = "blue", color = "red") + 
  labs(x="Year of introduction",
       y="Number of taxa")

## Challenge 2

### Solution Ward

# 1
ias_regions %>%
  ggplot() +
    geom_bar(aes(x = first_observed, fill = locationId), colour = "black") +
    labs(x = "year of introduction", y = "number of taxa", fill = "location")

# 2
ias_regions %>%
  ggplot() +
    geom_bar(aes(x = first_observed, fill = locationId), position = "dodge") +
    labs(x = "year of introduction", y = "number of taxa", fill = "location")

# 3
ias_regions %>%
  ggplot() +
    geom_bar(aes(x = first_observed, fill = locationId), position = "dodge") +
    facet_wrap(~kingdom, scales = "free_y") +
    labs(x = "year of introduction", y = "number of taxa", fill = "location")

# 4
ias_regions %>%
  mutate(
    kingdom = factor(kingdom,
                     levels = c("Animalia", "Plantae", "Fungi", "Chromista"),
                     ordered = TRUE)
  ) %>%
  ggplot() +
    geom_bar(aes(x = first_observed, fill = locationId), position = "dodge") +
    facet_wrap(~kingdom, scales = "free_y") +
    labs(x = "year of introduction", y = "number of taxa", fill = "location")

# alternative solution
ias_regions %>%
  group_by(kingdom) %>%
  mutate(n = n()) %>%
  ungroup() %>%
  mutate(kingdom = reorder(kingdom, n, decreasing = TRUE)) %>%
  ggplot() +
    geom_bar(aes(x = first_observed, fill = locationId), position = "dodge") +
    facet_wrap(~kingdom, scales = "free_y") +
    labs(x = "year of introduction", y = "number of taxa", fill = "location")

# 5
ias_regions %>%
  group_by(kingdom) %>%
  mutate(n = n()) %>%
  ungroup() %>%
  mutate(kingdom = reorder(kingdom, n, decreasing = TRUE)) %>%
  ggplot() +
    geom_bar(aes(x = first_observed, fill = locationId), position = "dodge") +
    facet_grid(kingdom~locationId, scales = "free_y") +
    labs(x = "year of introduction", y = "number of taxa", fill = "location")

### Solution Dirk

ias_regions <- ias_regions %>% 
  mutate(region = case_when(locationId == "ISO_3166:BE-VLG" ~ "Flanders",
                            locationId == "ISO_3166:BE-BRU" ~ "Brussels",
                            locationId == "ISO_3166:BE-WAL" ~ "Wallonia"))
head(ias_regions)

ias_regions$kingdom <- factor(ias_regions$kingdom,
                              levels = c("Animalia",
                                         "Plantae",
                                         "Fungi",
                                         "Chromista"))

p <- ggplot(ias_regions,
            aes(x = first_observed)) +
  geom_bar(aes(fill = region),
           position = "dodge") +
  xlab("year of introduction") +
  ylab("number of taxa") +
  facet_wrap(~kingdom,
             scales = "free",
             ncol = 2)
p

## part2

p <- ggplot(ias_regions,
            aes(x = first_observed)) +
  geom_bar(aes(fill = region),
           position = "dodge") +
  xlab("year of introduction") +
  ylab("number of taxa") +
  facet_grid(kingdom~region,
             scales = "free_y")
p

### Solution Marijke

ia`_regions$kingdom <- as_factor(ias_regions$kingdom)

ias_regions %>% ggplot() +
  geom_bar(aes(x = first_observed, fill = locationId)) +
  xlab("year of introduction") +
  ylab("number of taxa") +
  facet_grid(rows = vars(kingdom), cols = vars(locationId), scales = "free_y")
  

### Pieter's solution

ggplot(ias_regions) +
  aes(x = first_observed, fill = locationId) +
  geom_histogram(bins = 30L) +
  scale_fill_hue(direction = -1) +
  theme_dark() +
  facet_wrap(vars(kingdom))

ggplot(ias_regions) +
  aes(x = first_observed, fill = locationId) +
  geom_histogram(bins = 5L) +
  scale_fill_viridis_d(option = "inferno", direction = -1) +
  theme_dark() +
  facet_wrap(scales = "free_y",
             ~factor(kingdom, levels = c("Animalia",
                        "Plantae",
                        "Fungi",
                        "Chromista"))) +
  labs(
    x = "year of introduction",
    y = "number of taxa"
  )

### Nele's solution

# 2.1 - 2.2

ggplot(data = ias_regions, 
       mapping = aes(x = first_observed)) + geom_histogram() + facet_wrap(vars(locationId))

# 2.3

ggplot(
  data = ias_regions,
  mapping = aes(x = first_observed, fill = locationId)
) +
  geom_bar() +
  facet_wrap(vars(kingdom), scales = "free_y")
  
# 2.5

ggplot(data = ias_regions, 
       mapping = aes(x = first_observed, fill = locationId)) + geom_bar() + facet_wrap(vars(kingdom, locationId))

### Joppe's solution

### 2.2
ggplot(data = ias_regions,
         mapping = aes(x = first_observed, fill = locationId)) + 
  geom_bar(position="dodge")

### 2.3
ggplot(data = ias_regions,
       mapping = aes(x = first_observed, fill = locationId)) + 
  geom_bar() +
  facet_wrap(vars(locationId))

### 2.3
ggplot(data = ias_regions,
       mapping = aes(x = first_observed, fill = locationId)) + 
  geom_bar(position = 'dodge') +
  facet_wrap(vars(kingdom))

### 2.4
ggplot(data = ias_regions,
       mapping = aes(x = first_observed, fill = locationId)) + 
  geom_bar(position = 'dodge') +
  facet_grid(rows = vars(kingdom), cols = vars(locationId))
 

### Margot's solution

gg`lot(data = ias_regions,
       mapping = aes(x = first_observed, 
       fill = factor(locationId,levels = c("ISO_3166:BE-VLG", "ISO_3166:BE-WAL", "ISO_3166:BE-BRU"), 
                                                       labels = c("FLANDERS", "WALLONIA", "BRUSSELS")))) +
  geom_bar() +
  labs(x = "year of introduction",
       y = "number of taxa") + 
  facet_grid(factor(kingdom, levels = c('Animalia', 'Plantae', 'Fungi', 'Chromista')) ~ 
               factor(locationId, levels = c("ISO_3166:BE-VLG", "ISO_3166:BE-WAL", "ISO_3166:BE-BRU"), 
                      labels = c("FLANDERS", "WALLONIA", "BRUSSELS")), 
             scales = 'free_y') +
  scale_fill_discrete("location", labels = c("FLANDERS", "WALLONIA", "BRUSSELS"))

## Challenge 3

### Solution Ward

# part 1
library(INBOtheme)

ias_path %>%
  ggplot() +
    geom_bar(aes(x = pathway, fill = kingdom), position = "dodge") +
    labs(y = "number of taxa") +
    coord_flip()

# part 2
bin <- 10

ias_path %>%
  mutate(bins_first_observed_label = floor(.data$first_observed / bin) * bin
  ) %>%
  mutate(bins_first_observed_label = paste(
    as.character(.data$bins_first_observed_label),
    "-",
    as.character(.data$bins_first_observed_label + bin - 1)
  )) %>%
  group_by(pathway, bins_first_observed_label) %>%
  mutate(n = n()) %>%
  ggplot(aes(x = bins_first_observed_label, y = n, colour = pathway)) +
    geom_point(shape = 1, size = 3) +
    geom_line(aes(group = pathway)) +
    labs(x = "time period", y = "number of taxa")

### Pieter's solution

ggplot(ias_path) +
  aes(x = pathway, fill = kingdom) +
  geom_bar(position = "dodge") +
  scale_fill_hue(direction = 1) +
  labs(y = "Number of introduced taxa") +
  coord_flip() +
  theme(
    legend.position = "top",
    axis.title.y = element_text(size = 19L,
                                face = "bold"),
    axis.title.x = element_text(size = 19L,
                                face = "bold")
  ) 

## Bonus challenge

### Solution Ward

ggplot(ToothGrowth, aes(x = factor(dose), y = len, color = supp)) +
  geom_boxplot() +
  geom_point(aes(group = supp), 
             position = position_jitterdodge(jitter.width = 0.5)) +
  labs(x = "dose", y = "length")