Area Under Disease Gradient (AUDG) Calculation

Disease gradient information provides the spatial distribution of the epidemics at distance from the focus. If you are interested in learning how to calculate the area under the disease gradient curve to make comparison of several scenarios or treatments, this would be a good resource.

I am using Steven Worthington’s ipak function to install and load several packages in R.

ipak <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}

# usage
packages <- c("tidyverse", "readxl", "agricolae", "ggpubr", "ggpubr", "magrittr", "reshape", "knitr")
ipak(packages)
## Loading required package: tidyverse
## Warning: package 'tidyverse' was built under R version 4.3.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   4.0.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: readxl
## Warning: package 'readxl' was built under R version 4.3.3
## Loading required package: agricolae
## Warning: package 'agricolae' was built under R version 4.3.3
## Loading required package: ggpubr
## Warning: package 'ggpubr' was built under R version 4.3.3
## Loading required package: magrittr
## 
## Attaching package: 'magrittr'
## 
## The following object is masked from 'package:purrr':
## 
##     set_names
## 
## The following object is masked from 'package:tidyr':
## 
##     extract
## 
## Loading required package: reshape
## 
## Attaching package: 'reshape'
## 
## The following object is masked from 'package:lubridate':
## 
##     stamp
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## The following object is masked from 'package:dplyr':
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##     rename
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## The following objects are masked from 'package:tidyr':
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##     expand, smiths
## 
## Loading required package: knitr
## tidyverse    readxl agricolae    ggpubr    ggpubr  magrittr   reshape     knitr 
##      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE

data <- read_excel("field observation.xlsx") %>% 
        as.data.frame()
head(data, 10)
##    Distance Treatments     R1      R2    R3    R4
## 1        10         T4  0.375  0.0125  1.75  1.75
## 2        15         T4  0.750  0.0175  1.25  2.50
## 3        20         T4  2.500  1.2500  2.50  3.75
## 4        25         T4  2.750  2.5000  3.75  6.25
## 5        30         T4  2.500  5.0000  3.50 12.50
## 6        35         T4  3.500  6.2500  4.25 15.00
## 7        40         T4  3.000 12.5000  7.50 17.50
## 8        45         T4  5.500 17.5000  6.25 27.50
## 9        50         T4 10.000 27.5000 12.50 30.00
## 10       55         T4 10.000 30.0000 14.00 18.00
audpc_data <- data %>% 
  melt(id=c("Distance", "Treatments")) %>%
  group_by(Treatments, variable) %>% 
  summarize(AUDPC = audpc(value, Distance))
## `summarise()` has grouped output by 'Treatments'. You can override using the
## `.groups` argument.

audpc_data %>% 
  spread(variable, AUDPC) %>% 
  arrange(Treatments) %>%       
  kable() 

plot_graph <- ggplot(audpc_data, aes(Treatments, AUDPC))+
  geom_boxplot(aes(fill=factor(Treatments))) +
  ggtitle("AUDPC")+
  xlab("Treatment Applied")+
  ylab("Area Under Disease Gradient")+
  theme_bw(base_size = 14)+
  theme(aspect.ratio=1.25,plot.title = element_text(hjust = 0.5), 
        panel.grid=element_blank())+
  scale_fill_manual(name="Treatments", 
                    breaks = c("T1", "T2", 
                               "T3", "T4", "T5",
                               "T6", "T7"),
                    labels=c("T1=Inoculated control", "T2=CP culling at 3 days", "T3=CP cull 'ring' x 3","T4=CP cull at 1 day","T5=FP of CP at 3 days","T6=CP cull + FP of non-CP","T7=FP of entire plot at 3 days"),
                    values=c("#999999","#E69F00","#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00"))


plot_graph

ggsave(filename = "Boxplot.png", width = , height = , units = "in",
       dpi = 600, limitsize = TRUE)
## Saving 7 x 5 in image