---
title: "tican vignettes"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{tican-vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(tican)
```

## Using tic_analyse to analyse time-intensity curve data

Using the tic_analyse requires a dataframe with a column indicating time values
and at least one column with intensity values. Passing only the dataframe and name of these
two columns will generate an intensity-time plot and return a dataframe with peak 
intensity, time to peak and area under the curve (AUC). This analysis is performed by fitting
a LOESS curve to the raw data, using the loess() function. AUC is calculated using the
trapezium method for integration.

Generated plots display the raw data as black dots, loess curve in red, with blue
dashed lines showing calculated peak intensity and time to peak.

```{r}

# Simulating example data
set.seed(123)
example_data <- data.frame(time = seq(0, 82, by = 0.25))
random_vals <- sample(1:10, nrow(example_data), replace = TRUE)
example_data$regionA_intensity <- log(example_data$time + 1) * 50 -
  example_data$time * 2 + random_vals
example_data$regionB_intensity <- log(example_data$time + 7, base = 10) *
  80 - example_data$time * 1.5 + random_vals

# Showing dataframe structure

head(example_data,5)

```

```{r}

# Analysing using defaults

result <- tic_analyse(example_data,"time","regionA_intensity")

print(result)

```

### Time to peak proportion and adjusting area under the curve analysis

Time to peak proportion (for example time to 90 percent of peak) can be calculated
using the peakproportion argument. This will be added to the plot as a dashed green
line.

For area under the curve analysis a maximum time can be specified, for example AUC up
to 10 seconds.

```{r}
result <- tic_analyse(example_data,"time","regionA_intensity",
                           peakproportion = 0.9, #to calculate time to 90 percent peak
                           AUCmax = 30)

print(result)
```

### Adjusting the LOESS curve

The loess.span argument can be adjusted to a number between 0 and 1, with larger
values representing a greater degree of smoothing. In addition, any argument which can
be passed into the loess() function can be passed into tic_analyse().

```{r}

result <- tic_analyse(example_data,"time","regionA_intensity",
                           loess.span = 0.5, # altering from default of 0.1
                           degree = 1) # adding a loess() argument

print(result)

```

### Analysing multiple regions

For loops can be used to analyse multiple regions and output a single result dataframe.

```{r}
results <- data.frame() #making empty dataframe to hold results

for(region in c("regionA_intensity","regionB_intensity")){
  resulttemp <- tic_analyse(example_data,"time",region) #storing results
  resulttemp$Region <- region # adding column for region
  results <- rbind(results, resulttemp) # combining results for different regions
}

print(results)

```

### Analysing multiple dataframes

For loops can be used to analyse multiple dataframes and output a single result dataframe.

```{r}

example_data2 <- example_data #creating a second dataframe

results <- data.frame() #making empty dataframe to hold results

for(df in c("example_data","example_data2")){
  resulttemp <- tic_analyse(get(df), # get() to get the dataframe object
                                 "time","regionA_intensity") 
  
  resulttemp$data <- df # adding column for which dataframe results are from
  results <- rbind(results, resulttemp) # combining results for different dataframes
}

print(results)

```
