# INBO CODING CLUB
26 November, 2019

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, but do not fill in your code in this section*)

Your snippets:

### Challenge 1

Difference between `geom_bar` and `geom_histogram`. The first is for discrete variables, the second for continous variables, so yo uneed to group x- values in bins!

In our case, if you put binwidth = 1, you get the same plot:

ggplot(data = surveys, 
       mapping = aes(x = year)) +
           geom_bar()
ggplot(data = surveys, 
       mapping = aes(x = year)) +
  geom_histogram(binwidth = 1)

Patrik's snippet:

## CHALLENGE 1


# Plot the hindfoot_length as function of weight using points

ggplot(data = survey, aes(x = hindfoot_length, y=weight)) + geom_point()
  

# Plot the weight as function of species using boxplot

ggplot(data = survey, aes(x = species_id, y=weight)) + geom_boxplot()

# Replace the box plot with a violin plot

ggplot(data = survey, aes(x = species_id, y=weight)) + geom_violin()

# How many surveys per gender? Show it as histogram

ggplot(data = survey, aes(x = sex)) + geom_bar()

# How many surveys per year? Show it as histogram

ggplot(data = survey, aes(x = year)) + geom_bar()

### Challenge 2

BELANGRIJK!

Definieer in aesthetics (`aes()`) de variabelen die je wil visualiseren. Andere (vaste) properties, definieer je daarbuiten (bv. `alpha` met vaste waarde).

Sander's snippet:

ggplot(data = survey, mapping = aes(x = log(weight),
                                    y = hindfoot_length,
                                    color = sex)) +
  geom_point(alpha = 0.5) +
  ylab("hindfoot length") + 
  labs(title = "hindfoot length vs weight") +
  scale_colour_manual(values = c("F"="red", "M" = "yellow" ), labels = c("M"="Males", "F"="Females"))

Emma's snippet:

ggplot(data = survey, mapping = aes(x = year, fill = sex)) +
  geom_bar(position = "dodge", alpha = 0.5)+
  labs(title = "Number of surveys per year", y = "number of surveys") +
  coord_flip()

Patrik's Snippet:

ggplot(data = survey, mapping = aes(x = log(weight),
                                    y = hindfoot_length, color = sex, alpha = 0.5)) +
  geom_point() +
  ylab("hindfoot length") +
  ggtitle ("hindfoot length vs weight") +
  scale_color_manual(values=c("F" = "red", "M"= "yellow"))

### Challenge 3

Plot Emma:

library(plotly)

p <- inat_bxl %>%
  mutate(year = as.factor(year)) %>% 
  ggplot(aes(x = year, fill = species, width = 1)) +
  geom_bar(stat = "count", position = "dodge")

ggplotly(p)

## Plot Peter

inat_bxl_bin <- inat_bxl %>% group_by(year, species) %>% count()
ggplot(data = inat_bxl_bin, mapping = aes(
  x = year,
  y = factor(species, levels = rev(levels(factor(species))))
)) +
  geom_point(aes(size = n)) +
  xlab("Year") +
  ylab("Species")

## Plot Patrik

ggplot(data = inat_bxl, mapping = aes(x=year, fill = species)) +
  geom_bar(position = "fill") +
  facet_wrap(facets = vars(phylum))

## Plot Tanja

plot6 <- ggplot(data = inat_bxl) +
  geom_bar(aes(x = species, fill = as.factor(year)), stat = "count", position = "stack") +
  scale_fill_brewer(palette = "Spectral") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, face = "italic", hjust = 1),
        axis.line.x = element_blank(),
        axis.ticks.x = element_blank()) +
  labs(fill = "Year")
plot6

inat_bxl_stat <- inat_bxl %>%
  group_by(year, species) %>%
  count()

plot7 <- ggplot(data = inat_bxl_stat, aes(x = year, y = n)) +
  geom_line(colour = "red", size = 1) +
  facet_wrap(~ species) +
  ylab("count") +
  ggtitle("Evolution of observations by year")
plot7

## Plot Marijke en Wim

ggplot(inat_bxl, aes( x = year)) + geom_bar(fill = "black") + 
  facet_wrap(facet = vars(species)) + 
  theme_bw()

## Plot Andy

ggplot(data = inat_bxl, mapping = aes(x = year))+
  geom_bar()+
  facet_wrap(~species, scales = "free_y")+
  xlab("Jaar") +
  ylab("Aantal")+
  ggtitle("Aantallen per jaar en per soort")
  
`


  

Which plot is your favourite?

Emma
Marijke/Wim *
Peter *
Tanja (plot 6) *