---
title: "Report on animal initial conditions and weekly weights"
output: word_document
params:
  data: ../../DATA/body_weights_measurement.xlsx
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

```{r message = FALSE, warning = FALSE}
# Load required packages
library(readxl)
library(tidyverse)
library(ggbeeswarm)
```

```{r}
# Create some functions that are used in later code

## Function to read in mouse weights. This takes a filepath to an Excel sheet
## that follows the template of the animal weight collection template. It 
## identifies all the sheets in that file and reads in all the ones that 
## measure weekly weights. It returns one large dataframe with all of the 
## measured weights. 
read_mouse_weights <- function(filepath) {
  
  # getting info about all excel sheets
  mouse_weights_sheets <- readxl::excel_sheets(filepath)[-1] # First sheet is initial data, not mouse weights
  
  mouse_weights <- purrr::map(mouse_weights_sheets, 
                              ~ readxl::read_excel(filepath, sheet = .x, 
                                                   col_types = c("text",   # who_collected
                                                                 "text",   # date_collected
                                                                 "text",   # notch_id
                                                                 "numeric", # weight
                                                                 "text",   # unit
                                                                 "text",   # existing_cage_number
                                                                 "text",   # new_cage_number
                                                                 "text",   # group
                                                                 "text"    # notes
                                                                 ))) %>% 
    dplyr::bind_rows() %>% 
    mutate(date_collected = lubridate::mdy(date_collected))

  return(mouse_weights)
}

# Function to get the next cage number based on the 
# existing cage number and notch ID. If the mouse does not
# switch cages again, the output is a vector of length 0. 
# This takes the dataframe and existing identifiers (notch id and
# existing cage number) as inputs. It returns the next cage
# that the mouse was moved to. If the mouse has not moved
# from the existing case, the output has length 0.
get_next_cage <- function(existing_cage_number, notch_id, 
                          df = our_mouse_weights){
  next_cage <- df %>% 
    filter(.data$existing_cage_number == {{existing_cage_number}} &
             .data$notch_id == {{notch_id}} & 
             !is.na(.data$new_cage_number)) %>% 
    pull(new_cage_number)
  
  return(next_cage)
}

# Function to get the full list of cages for each individual 
# mouse, over the course of all data collected to date. This 
# inputs the starting identifiers of the mouse (starting cage ID 
# and notch ID). It then works through any cage changes to create
# a list for that mouse of all cages it was put in over the 
# course of the experiment. 
get_mouse_cages <- function(mouse_starting_cage, mouse_notch_id, 
                            df = our_mouse_weights){
  mouse_cage_list <- mouse_starting_cage
  i <- 1
  
  while(TRUE){
    next_cage <- get_next_cage(existing_cage_number =
                               mouse_cage_list[i],
                               notch_id = mouse_notch_id, 
                               df = df)
    if(length(next_cage) == 0) {
      break
      }
    i <- i + 1
    mouse_cage_list[i] <- next_cage
    }
  
  return(mouse_cage_list)
}
```


```{r}
# Read in and process the data

# Read in the mouse weights from the Excel template. This creates one large
# dataframe with the weights from all the timepoints. 
our_mouse_weights <- read_mouse_weights(filepath = params$data)

# Add a unique mouse ID for the first time point. This will become each mouse's
# unique ID across all measured timepoints.
our_mouse_weights <- our_mouse_weights %>% 
  mutate(mouse_id = 1:n(), 
         mouse_id = ifelse(date_collected ==
                                    first(date_collected), 
                                  mouse_id, 
                                  NA))

# Create a dataframe that lists all mice at the first time point, 
# as well as a list of all the cages they have been in over the
# experiment
mice_cage_lists <- our_mouse_weights %>% 
  filter(date_collected == first(date_collected)) %>% 
  select(notch_id, existing_cage_number, mouse_id) %>% 
  mutate(cage_list = map2(.x = existing_cage_number, 
                          .y = notch_id, 
                          .f = ~ get_mouse_cages(.x, .y, df = our_mouse_weights)))

# Add a column with the latest cage to the weight dataframe
our_mouse_weights$latest_cage <- NA

# Loop through all the individual mice, based on mice with a 
# measurement at the first time point. Add the unique ID for 
# each mouse, which will apply throughout the experiment. Also 
# add the most recent cage ID, so the mouse can be identified
# by lab members based on it's current location
for(i in 1:nrow(mice_cage_lists)){
  this_notch_id <- mice_cage_lists[i, ]$notch_id
  this_cage_list <- mice_cage_lists[i, ]$cage_list[[1]]
  this_unique_id <- mice_cage_lists[i, ]$mouse_id
  latest_cage <- this_cage_list[length(this_cage_list)]
  
  our_mouse_weights$mouse_id[our_mouse_weights$notch_id == this_notch_id & 
                       our_mouse_weights$existing_cage_number %in% 
                       this_cage_list] <- this_unique_id
  
  our_mouse_weights$latest_cage[our_mouse_weights$notch_id == this_notch_id & 
                       our_mouse_weights$existing_cage_number %in% 
                       this_cage_list] <- latest_cage
}

# Add a label for each mouse based on its notch_id and latest cage
our_mouse_weights <- our_mouse_weights %>% 
  mutate(mouse_label = paste("Cage:", latest_cage, 
                             "Notch:", notch_id))

# Read in the data from the original file with the initial animal 
# characteristics
mouse_initial <- readxl::read_excel(params$data, 
                                      sheet = 1, 
                                      col_types = c("text", # notch_id
                                                    "text", # starting_cage_number
                                                    "text", # dob
                                                    "text", # species
                                                    "text", # sex
                                                    "text" # group
                                                    )) %>%
  mutate(dob = lubridate::mdy(dob), 
         sex = forcats::as_factor(sex))

# Figure out the starting cage for each mouse, so they can be incorporated
# with the initial data so we can get the mouse ID that was added for the 
# starting time point
mouse_ids <- our_mouse_weights %>% 
  filter(date_collected == first(date_collected)) %>% 
  select(notch_id, existing_cage_number, mouse_id) %>% 
  rename(starting_cage_number = existing_cage_number)

# Merge in the mouse IDs with the dataframe of initial mouse characteristics
mouse_initial <- mouse_initial %>% 
  left_join(mouse_ids, by = c("notch_id", "starting_cage_number"))

# Join the initial data with the weekly weights data into one large dataset
our_mouse_weights <- our_mouse_weights %>% 
  left_join(mouse_initial, by = c("mouse_id", "notch_id", "group"))
```

# Summary of experimental animals

This section gives some information on the experimental animals both 
at the start of the experiment and over the course of the experiment. 

```{r}
# Create a table that summarizes the animals at the start of the experiment
mouse_initial %>% 
  summarize(Species = paste(unique(species), collapse = ", "), 
            `Total animals` = n(), 
            `Sex distribution` = paste0("male: ", sum(sex == "m"), 
                                      ", female: ", sum(sex == "f")),
            `Experimental groups` = paste(unique(group), collapse = ", "),
            `N. of starting cages` =
              length(unique(starting_cage_number))) %>% 
  mutate_all(as.character) %>% 
  pivot_longer(everything()) %>% 
  mutate(name = paste0(name, ":")) %>% 
  knitr::kable(col.names = c("", ""), 
               caption = "Summary of experimental animals at the start of the experiment", 
               align = c("r", "l"))
```

\newpage

# Animal weights

This section provides summaries of mouse weights over the course of the 
experiment. 

```{r}
# Create a plot of mouse weights over time
our_mouse_weights %>% 
  ggplot(aes(x = date_collected, y = weight, 
             group = mouse_id, color = sex)) + 
  geom_line() + 
  facet_wrap(~ group) + 
  ggtitle("Animal weights over time by experiment group") +
  labs(x = "Date collected", 
       y = "Weight (g)")
```

\newpage

```{r}
# Plot animal weight boxplots for the latest time point 
our_mouse_weights %>% 
  filter(date_collected == last(date_collected)) %>% 
  ggplot(aes(x = group, y = weight)) + 
  geom_beeswarm(aes(color = sex)) + 
  geom_boxplot(fill = NA, color = "dodgerblue") + 
  ggtitle("Animal weights at last collection by experimental group") + 
  labs(x = "Experimental group", 
       y = "Weight (g)")
```

\newpage

```{r}
# Plot animal weights by animal handler
our_mouse_weights %>% 
  ggplot(aes(x = date_collected, y = weight, color = who_collected)) + 
  geom_point() + 
  ggtitle("Animal weights by animal handler") + 
  labs(x = "Date collected", 
       y = "Weight (g)",
       color = "Person who\nhandled the\nanimal")
```

\newpage

```{r}
# Create table of animal weight changes since previous time point
our_mouse_weights %>% 
  select(date_collected, weight, group, mouse_label, sex) %>% 
  group_by(mouse_label) %>% 
  mutate(weight_change = (weight - lag(weight)) / lag(weight)) %>% 
  ungroup() %>% 
  filter(date_collected == last(date_collected)) %>% 
  mutate(formatted_weight_change = paste0(formatC(weight_change * 100, 
                                                  digits = 1, format = "f"), "%")) %>% 
  arrange(weight_change) %>% 
  select(mouse_label, group, sex, weight, formatted_weight_change) %>% 
  knitr::kable(col.names = c("Mouse", "Experimental group", "Sex", 
                             "Weight (g)", "Weight change since last measure"), 
               caption = "Individual data on weight changes in mice between current measurement and previous measurement.")
```

```{r}
# Write out processed data into a CSV file
write_csv(our_mouse_weights, "mouse_weights_output.csv")
```