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lettuce_report.Rmd
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lettuce_report.Rmd
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---
title: "This is a reproducible document"
author: "Dr. Brendan Palmer"
date: "11th March 2019"
output:
word_document:
fig_height: 4
fig_width: 6
---
# This is the beginning of the project
Our initial reports might be restricted to lab meetings etc. We can use `R Markdown` to show the code we are using, so that the meetings are not just a demonstration of the results, but also an examination of the `code` used to obtain them.
## Data overview
```{r packages and setup, include = FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
# Load your packages here
library(tidyverse)
library(knitr)
```
The plot below is call from the ggplot object entitled `report_plot` created in the script `03_final_analysis.R`.
```{r Plots from script, echo = FALSE}
source("analysis/scripts/03_final_analysis.R")
# The location of the Rmd file dictates whether the path to other files is intact
report_plot
```
**Fig. 1.** Flavonoid content of three lettuce varieties under three experimental conditions.
Or we can also recreate the code within the R Markdown document as seen below.
```{r Plot from code, echo = TRUE}
source("analysis/scripts/01_data_clean.R")
lettuce_variety <- c(cos = "Cos Dixter",
red = "Red Oakleaf",
sky = "Skyphos")
data %>%
filter(week_no == "3") %>%
ggplot(aes(x = variety, y = flavonoids)) +
geom_boxplot(outlier.shape = NA) + # Hides the outlier points
geom_beeswarm() +
scale_x_discrete(breaks = c("cos", "red", "sky"),
labels = c("Cos Dixter", "Red Oakleaf", "Skyphos")) +
ylim(0,5) +
labs(x = "",
y = "Flavonoids (ppm)",
title = "Lettuce variety vs flavonoid content") +
theme(panel.background = element_blank(), #Remove grey background
axis.title = element_text(face = "bold", size = 12),
axis.text = element_text(face = "bold", size = 10),
axis.line = element_line(colour = "black", size = 1),
plot.title = element_text(hjust = 0.0))
```
## Statistical tests
This table summarises the statistical tests we conducted in the script `03_analysis.R`.
**Table 1.** Pairwise comparisons from a two way Analysis of Variance of flavonoid content by lettuce type and filter.
```{r AOV table, echo = FALSE}
kable(fla_aov, digits = 3)
```
The data suggests that we do not have enough evidence to conclude that filter type alters the mean flavonoid content of the lettuce varieties tested.
The data does support the alternative hypothesis that the mean flavonoid content differs between the lettuce varieties used in this experiment.
The flavonoid content of Cos Dixter was observed to be higher overall than Red Oak leaf (`r fla_aov$Estimate[fla_aov$Comparison == "cos-red"]` ppm, 95% CI: `r round(fla_aov[[1, 4]], digits = 3)` - `r round(fla_aov[[1, 5]], digits = 3)`).
## Conclusion
The information presented here can be traced all the way back to the raw CSV data file. If an error is detected, it can be corrected. If Reviewer #2 requests changes to any element of the work, it can be done in a straightforward manner.
### Reviewer # 2 comments
I don't like B/W images. Give me some colour!
### Response
No problem
```{r Make the reviewer happy, echo = FALSE}
source("analysis/scripts/01_data_clean.R")
lettuce_variety <- c(cos = "Cos Dixter",
red = "Red Oakleaf",
sky = "Skyphos")
data %>%
filter(week_no == "3") %>%
ggplot(aes(x = variety, y = flavonoids, colour = variety)) +
geom_boxplot(outlier.shape = NA) +
geom_beeswarm() +
scale_color_manual(values = c("red", "orange", "purple")) +
scale_x_discrete(breaks = c("cos", "red", "sky"),
labels = c("Cos Dixter", "Red Oakleaf", "Skyphos")) +
ylim(0,5) +
labs(x = "",
y = "Flavonoids (ppm)",
title = "How do you like them for apples") +
theme(panel.background = element_blank(),
axis.title = element_text(face = "bold", size = 12),
axis.text = element_text(face = "bold", size = 10),
axis.line = element_line(colour = "black", size = 1),
plot.title = element_text(hjust = 0.0),
legend.position = "")
```