Your colleague's email: 

>I hope you are doing well. I am writing to request assistance with my statistical analysis for an aquaculture clinical field trial in the Bay of Fundy, Canada. Please find a description of the study below.  
>
>The objective of our research was to compare the performance of different vaccines on the growth and survival of Atlantic salmon under standard production conditions. 
On day 0 of the study, we applied 4 different vaccines to a total of 100 randomly selected fish. 
Salmons were individually tagged in February 2005 and followed through to harvest in August 2007. 
All fish were always kept in the same cage. 
We recorded (1) presence of jaw deformity, and (2) weight throughout the trial. 
As jaw deformities are formed at an early stage of growth and are not healed, detection of a jaw deformity at any sampling event implied that the fish was labelled as having a jaw deformity throughout the entire growth period. 
>
>The outcome of interest is the weight, as weights increase from means of 60 g at day 0, to 5,700 g at harvest. However, it is also important to know whether there are differences in propensity to presenting jaw deformity among vaccines. 
>
>I need help with the following:  
>
>- Describing the experiment design for materials and methods. 
>- Analyzing the data and determining: 
  - If there are differences between vaccines, especially at harvest, 
  - If there is a "better vaccine" for final weight, 
  - If there is difference in jaw deformity between vaccine treatments. 

REMEMBER:

1. Read the data and its description and answer the researcher's questions. 
2. Mention one way they could do to improve the power of their experiment to detect differences in final weight between treatments. 


You may want to use the following packages: 

```{r message=FALSE, warning=FALSE}
library(tidyverse) # data wrangling 
library(glmmTMB) # generalized linear mixed models (+ repeated measures)
library(lme4) # linear mixed models 
library(DHARMa) # model diagnostics 
```

Data 
```{r}
url <- "https://raw.githubusercontent.com/stat870/fall2025/refs/heads/main/data/fish_vaccines.csv"
fish <- read.csv(url)
```

```{r}
fish %>% 
  ggplot(aes(day, wt))+
  geom_point(aes(fill = factor(vaccine), 
                 group = factor(vaccine)),
             position = position_dodge(width = 60),
             shape=21, size =3.5, alpha =.7)+
  labs(y = expression(Weight~(g~fish^{-1})), x = "Time (days)")+
  theme_classic()+
  labs(fill="Vaccine treatment")+
  theme(legend.position = "bottom")+
  scico::scale_fill_scico_d()
```

