Sow Diet and Its Effects on Litter Outcomes

library(tidyverse)
library(conflicted)
library(readr)
library(tidyr)
library(dplyr)
library(patchwork)

1. Introduction

A test was conducted to evaluate how different dietary treatments influenced litter weight gain and the reproductive performance of sows at the following farrowing cycle (Henman et al., 2023). I chose this data-set to focus on as the relationship between the several average weight data-sets and the treatment data-set was extremely interesting. Taking a look between the relationship of these variables will provide insight to what treatment is best for a heavier litter.

Cutest chubby piggy. Source: LeszczuNico on r/pigs

I wish to investigate the correlation between what diet the mother is fed, and whether the feed affects the weight of the piglets overtime. I am also interested what feed makes the litter the heaviest and how it compares to a litter with a normal diet.

Therefore I would like to know:

1. Which treatment creates the most significant weight gain within the litter.
2. How each treatment affects each piglets weight overtime.

To address these questions, I will analyse the following variables:

• Treatment = Categorical (Nominal)

• Average weight kg = Numerical

• Average weight D3 kg = Numerical

• Average weight D14 = Numerical

• Average wean weight = Numerical

The treatment variable will help me to compare the treatments that each sow receives and therefore connect that with how much weight a piglet has gained.

The average weight variable is the initial weight that the piglets are born in.

The average weight D3 and D14 variable will allow for me to view how the litter weight has increased overtime.

The average wean weight is the last variable provided, and will show the ‘end result’ of the piglets weight.

2. Exploratory Analysis

Import Data

sows_data <- read_csv('/Users/sabrinagarcia/Desktop/envx/assignment/Describing Data Report/sows.csv')
sows_data$Treatment <- factor(sows_data$Treatment)
str(sows_data)

spc_tbl_ [144 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Parity                : num [1:144] 3 3 3 4 3 3 3 3 3 4 ...
 $ Treatment             : Factor w/ 3 levels "A","B","C": 1 1 1 1 1 1 1 1 1 1 ...
 $ Total_Born            : num [1:144] 9 7 14 12 8 14 12 9 11 13 ...
 $ Born_Alive            : num [1:144] 7 7 12 12 8 13 11 9 10 10 ...
 $ Average_weight_kg     : num [1:144] 1.4 1.671 1.436 0.953 1.562 ...
 $ Average_weight_D3_kg  : num [1:144] 1.7 2.23 1.31 1.8 1.9 ...
 $ Average_weight_D14_kg : num [1:144] 5.26 4.76 4.03 5.47 3.35 ...
 $ Average_wean_weight_kg: num [1:144] 9.14 7.51 7.62 8.43 4.92 ...
 - attr(*, "spec")=
  .. cols(
  ..   Parity = col_double(),
  ..   Treatment = col_character(),
  ..   Total_Born = col_double(),
  ..   Born_Alive = col_double(),
  ..   Average_weight_kg = col_double(),
  ..   Average_weight_D3_kg = col_double(),
  ..   Average_weight_D14_kg = col_double(),
  ..   Average_wean_weight_kg = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

I will first analyse and compare the average weights of the litter at each recorded checkpoint using a histogram and the summary function.

Average Litter weight Upon Birth

ggplot(sows_data, aes(x = Average_weight_kg)) + 
  geom_histogram (bins = 25 , fill = "rosybrown2" , color = "rosybrown" ) +
  labs(
    title = "Average Litter Weight Upon Birth",
    x = "Weight (Kg)",
    y = "Litter Count"
  )

Figure 1. Average Litter Weight Upon Birth

summary(sows_data$Average_weight_kg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.9533  1.4138  1.5375  1.5662  1.6917  2.2750 

Visually, piglets within this litter range from being 0.5kg-2.4kgs seen in @avg-weight-birth

Average Litter weight Day 3

ggplot(sows_data, aes(x = Average_weight_D3_kg)) + 
  geom_histogram (bins = 25 , fill = "rosybrown2" , color = "rosybrown" ) +
  labs(
    title = "Average Litter Weight, 3 Days After Birth",
    x = "Weight (Kg)",
    y = "Litter Count"
  )

Figure 2. Average Litter Weight, 3 Days After Birth

summary(sows_data$Average_weight_D3_kg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.111   1.838   2.000   2.002   2.214   2.944 

Average Litter weight Day 14

ggplot(sows_data, aes(x = Average_weight_D14_kg)) + 
  geom_histogram (bins = 25 , fill = "rosybrown2" , color = "rosybrown" ) +
  labs(
    title = "Average Litter Weight, 14 Days After Birth",
    x = "Weight (Kg)",
    y = "Litter Count"
  )

Figure 3. Average Litter Weight, 14 Days After Birth

summary(sows_data$Average_weight_D14_kg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.900   4.418   4.941   4.888   5.367   6.425 

Average Litter weight when Weaned

ggplot(sows_data, aes(x = Average_wean_weight_kg)) + 
  geom_histogram (bins = 25 , fill = "rosybrown2" , color = "rosybrown" ) +
  labs(
    title = "Average Litter weight, when Weaned",
    x = "Weight (Kg)",
    y = "Litter Count"
  )

Figure 4. Average Litter weight, when Weaned

summary(sows_data$Average_wean_weight_kg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  4.917   7.378   7.893   7.995   8.647  11.475 

Relationship between Variables

p1 <- ggplot(sows_data, aes(x = Treatment, y = Average_weight_kg)) +
  geom_boxplot( fill = "rosybrown1" , color = "rosybrown3") +
  labs(
    title = "Avg. Birthweight of Piglets",
    x = "Treatment Types",
    y = "Weight (Kg)"
  )

#| label: box-avg-weight-d3
p2 <- ggplot(sows_data, aes(x = Treatment, y = Average_weight_D3_kg)) +
  geom_boxplot( fill = "rosybrown2" , color = "rosybrown") +
  labs(
    title = "Avg. Weight of Piglets, D3",
    x = "Treatment",
    y = "Weight (Kg)"
  )

#| label: box-avg-weight-d14
p3 <- ggplot(sows_data, aes(x = Treatment, y = Average_weight_D14_kg)) +
  geom_boxplot( fill = "rosybrown3" , color = "rosybrown4") +
  labs(
    title = "Avg. Weight of Piglets, D14",
    x = "Treatment",
    y = "Weight (Kg)"
  ) 

#| label: box-avg-weight-weaned
p4 <- ggplot(sows_data, aes(x = Treatment, y = Average_wean_weight_kg)) +
  geom_boxplot( fill = "rosybrown" , color = "rosybrown4") +
  labs(
    title = "Avg. Weight of Piglets, Weaned",
    x = "Treatment Types",
    y = "Weight (Kg)"
  ) 
(p1 + p2) / (p3 + p4)

All Average Birthweight of Piglets

sows_long <- sows_data |>
  select(Treatment,
         Average_weight_kg,
         Average_weight_D3_kg,
         Average_weight_D14_kg,
         Average_wean_weight_kg) |>
  pivot_longer(
    cols      = -Treatment,
    names_to  = "Timepoint",
    values_to = "Weight_kg"
  ) |>
  mutate(
    Day = case_when(
      Timepoint == "Average_weight_kg"      ~ 0,
      Timepoint == "Average_weight_D3_kg"   ~ 3,
      Timepoint == "Average_weight_D14_kg"  ~ 14,
      Timepoint == "Average_wean_weight_kg" ~ 28
    ),
    Treatment = factor(Treatment, levels = c("A", "B", "C"))
  )
p <- ggplot(sows_long, aes(x = Day, y = Weight_kg, colour = Treatment)) +

  geom_jitter(alpha = 0.40, size = 2, width = 1) +

  stat_summary(fun = mean, geom = "line",  linewidth = 1.1, aes(group = Treatment)) +
  stat_summary(fun = mean, geom = "point", size = 3.5, shape = 18) +

  scale_x_continuous(
    breaks = c(0, 3, 14, 28),
    labels = c("Day 0\n(Birth)", "Day 3", "Day 14", "Day 28\n(Weaning)")
  ) +

  scale_colour_manual(
    values = c("A" = "rosybrown1", "B" = "rosybrown", "C" = "rosybrown4"),
    name = "Treatment"
  ) +
  
  labs(
    title    = "Piglet Growth from Birth to Weaning by Diet",
    subtitle = "Points = individual litter means  |  Diamond + line = treatment mean",
    x        = "Days",
    y        = "Average Weight (kg)"
  ) +

  theme_bw(base_size = 13) +
  theme(
    plot.title       = element_text(face = "bold", size = 14),
    plot.subtitle    = element_text(colour = "grey40", size = 10),
    legend.position = "right",
    panel.grid.minor = element_blank()
  )

p 

Figure 8: Piglet Growth from Birth to Weaning by Diet

4. Discussion of Findings

Key Insights

Limitations

The study only recorded until piglets were at the weaning stage, around 21 days — and does not go beyond that. It would’ve been useful to see the long term effects of the diet, and see if there is any increase or decrease in weight after being weaned.

2nd cutest piggy. Source: Tiger Productions on YouTube

5. References and Reflection

Anthropic. (2023). Claude. Claude.ai. https://claude.ai

Henman, D., Lean, I. J., Block, E., & Golder, H. M. (2023). Data on the effects of the anionic protein meal BioChlorⓇ on sows before and after farrowing. Data in Brief, 48, 109168. https://doi.org/10.1016/j.dib.2023.109168

Throughout this task, I did find several small aspects of this tasks slightly difficult and confusing — such as having my code not work based off of one several small errors. Furthermore it was a bit challenging to keep within the word limit as in the scatterplot section, I could not specifically point out a box plot without increasing my word count

I used Claude for troubleshooting errors in my code (specifically the YAML), and to help create the scatterplot, as I wanted to visualise the data in a way that went beyond the functions covered in the practicals. I relied on Claude as a trusted source as it has strong reasoning when providing responses.

Henman et al., 2023 was the source of my dataset, and allowed for me to understand the dataset further, as I initially thought that the piglets were being fed the treatment directly.

I also acknowledge that I did have to ask for help on ED Discussion for assistance with the code for the histographs, as I had made the numerical data factors.