---
author: "Simon Garnier"
title: "4 - Detecting errors"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{4 - Detecting errors}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

Missing observations and recording errors are fairly common in tracking data. 
They can be caused by hardware failures, object occultation, faulty data writing,
etc. `trackdf` provides a few functions to help detect this missing or erroneous 
data so that you can fix them or omit them altogether from your analysis. 

But first, let's load some "flawed" data provided with `trackdf`: 

```{r}
library(trackdf)
raw <- read.csv(system.file("extdata/gps/01.csv", package = "trackdf"))
tt <- track(x = raw$lon, y = raw$lat, t = paste(raw$date, raw$time), id = 1,  
            proj = "+proj=longlat", tz = "Africa/Windhoek")
print(tt, max = 10 * ncol(tt))
```

---

## 4.1 - Missing observations

These are observations that have not been recorded at all. If the data is 
recorded at regular intervals, then these missing observations can be easily 
detected using the `missing_data` function as follows: 

```{r}
missing <- missing_data(tt)
missing
```

The output is a track table with each row corresponding to a time stamp at which
at least one coordinate is missing. 

Note that you can specify the beginning (`begin`) and end (`end`) of the 
observation window in which you want to detect missing data, as well as the time
difference (`step`) between successive observations. 

---

## 4.2 - Duplicated observations

These are observations that are repeated multiple times throughout the data set
(e.g., two observations with identical time stamps for a given individual). 
These duplicated observations can be detected using the `duplicated_data` 
function as follows:

```{r}
dups <- duplicated_data(tt, type = "t")
dups
```

The output is a track table with each row corresponding to an observation that 
was partially or completely duplicated, depending on the `type` argument. This 
argument is a character string or a vector of character strings indicating the 
type of duplications to look for. The strings can be any combination of "t" (for 
time duplications) and "x", "y", "z" (for coordinate duplications). For instance, 
the string "txy" will return data with duplicated time stamps and duplicated x 
and y coordinates. 

---

## 4.3 - Inconsistent observations

These are observations whose coordinates are too different from the surrounding 
(timewise) observations, for instance, because of sporadic errors in GPS 
recordings. These inconsistent observations can be detected using the 
`duplicated_data` function as follows:

```{r}
inconsistent <- inconsistent_data(tt, s = 15)
inconsistent
```

The output is a track table with each row corresponding to an inconsistent 
observation. 

Note that the detection of inconsistencies requires specifying a threshold (`s`)
for distinguishing between consistent and inconsistent observations. Higher 
threshold values will result in a lower number of detected inconsistencies, and
reciprocally for lower threshold values.