Assignment 6

Question 1

df = read.csv("Data/MBT_ebird.csv")

# Calculate the total number of species seen each month of each year, in each location 
# I'm assuming the number of unique species? 
df.mo = df %>% group_by(location, year, month) %>% 
  summarise(number_of_species = length(unique(common_name)), .groups="drop")


# Plot the number of species seen each month, with the colour of points indicating year, faceting by location 
df.mo %>% 
  ggplot(aes(month, number_of_species, colour=year)) + 
  geom_hline(yintercept=0, colour="grey20") + 
  geom_jitter(size=1.6, alpha=0.8, width=0.1) + 
  theme_dark() + 
  facet_wrap(~location) + 
  scale_x_continuous(breaks=seq(1,12,1))+ 
  scale_colour_viridis() + 
  theme(panel.grid.major.y=element_blank(),
        panel.grid.minor.y=element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.background = element_rect(fill="grey50") ,
        text=element_text(size=15, face="bold", family="mono"),
        axis.text.x = element_text(angle=315, hjust=0.5)) + 
  labs(x="Month", y="Number of Unique Species Seen", title = "Unique Species by Location")

## If save as svg, can edit with Inkscape if you want to!!! 
## theme_set(theme_bw())

Question 2

df_samples = read.csv("Results/tidy_tibble.csv")
df_mass = read.csv("Results/residual_mass_group.csv")

# Plot comparison of mass by treatment including indiv obs, the mean and SE.  Use point colour or shape to indicate sex. 
p1 = df_samples %>% 
  filter(ID == "mass") %>% 
  mutate(group=factor(group, levels=c("Control", "Treatment"))) %>% 
  ggplot(aes(group, value)) + 
  geom_point(aes(fill=sex), position=position_nudge(x=0.2),size=3.5,shape=21,colour="black", alpha=0.7) + 
  stat_summary(geom="errorbar", size=2, width=0.2, colour="grey60") + 
  stat_summary(fun = mean, geom = "point", size=3, shape=21, fill="grey60", colour="black", stroke=1.5) + 
  theme_bw() + 
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.x=element_blank(), 
        # panel.background = element_rect(fill="black"),
        #axis.text.x = element_text(angle=90, hjust=0.95,vjust=0.2),
        legend.position = c(0.15,0.1), 
        legend.box.background = element_rect(color="black"),
        text=element_text(size=14, face="bold")) + 
  labs(x="Group", y="Mass (kg?)", subtitle = "Mass by Group")
# p1



# Scatter plot of age and mass, indicate treatment with shape or colour, fit separate reg lines (no CI) to each treatment

# missing some 
p2 = df_samples %>% 
  filter(ID %in% c("mass", "age")) %>% 
  pivot_wider(id_cols=c(sample_name,group), names_from=ID, values_from=value) %>% 
  ggplot(aes(age, mass, fill=group, colour=group)) + 
  geom_point(shape=21, size=3, colour="black") + 
  geom_smooth(method="lm", se=F) + 
  scale_colour_manual(values=c("blue", "red2"), aesthetics=c("colour", "fill")) + 
  theme_bw() + 
  labs(x="Age (years)", y="Mass (kg)", subtitle = "Mass vs Age, by group" ) + 
  theme(text=element_text(size=14, face="bold"), 
        legend.position = c(0.2,0.1), 
        legend.box.background = element_rect(color="black"))
# p2

# Combine plots, tag each panel with a number or letter, include a title 
p1 + p2 + plot_annotation(title = "Mass by Group and Age", 
                          theme = theme(plot.title = element_text(size=25, face="bold", family="serif", colour="grey30")), 
                          tag_levels = "A")

## No summary function supplied, defaulting to `mean_se()`

## `geom_smooth()` using formula 'y ~ x'