2 - Building a track table

Simon Garnier

library(readr)
library(dplyr)
library(lubridate)
library(trackdf)

raw <- read_csv(system.file("extdata/video/01.csv", package = "trackdf")) %>%
  filter(., !ignore)
raw

## # A tibble: 21,982 x 8
##       id     x     y  size frame track ignore track_fixed
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>        <dbl>
##  1     1  629.  882.  1154     1     1 FALSE            1
##  2     2 1056.  657.  1064     1     2 FALSE            2
##  3     3  508.  375.  1624     1     3 FALSE            3
##  4     4 1278.  374.  1443     1     4 FALSE            4
##  5     5 1379.  343.  1431     1     5 FALSE            5
##  6     6 1137.  175.  1321     1     6 FALSE            6
##  7     7  737.  116.  1419     1     7 FALSE            7
##  8     8  922.  103.  1237     1     8 FALSE            8
##  9     1  629.  882.  1148     2     1 FALSE            1
## 10     2 1056.  656.  1068     2     2 FALSE            2
## # … with 21,972 more rows

vid_df <- track(x = raw$x, y = raw$y, t = raw$frame, id = raw$track, 
                origin = "2019-03-24 12:55:23", period = period("1 second") / 30, 
                tz = "America/New_York", table = "df")
head(vid_df)

## Track table [6 observations]
## Number of tracks:  6 
## Dimensions:  2D 
## Geographic:  FALSE 
## Table class:  data frame
##   id                   t         x        y
## 1  1 2019-03-24 12:55:23  629.3839 882.4783
## 2  2 2019-03-24 12:55:23 1056.1692 656.5207
## 3  3 2019-03-24 12:55:23  508.0092 375.2451
## 4  4 2019-03-24 12:55:23 1277.6466 373.7491
## 5  5 2019-03-24 12:55:23 1379.2844 343.0853
## 6  6 2019-03-24 12:55:23 1137.1378 174.5110

vid_tbl <- track(x = raw$x, y = raw$y, t = raw$frame, id = raw$track, 
                 origin = "2019-03-24 12:55:23", period = period("1 second") / 30, 
                 tz = "America/New_York", table = "tbl")
print(vid_tbl)

## Track table [21982 observations]
## Number of tracks:  83 
## Dimensions:  2D 
## Geographic:  FALSE 
## Table class:  tibble
## # A tibble: 21,982 x 4
##    id    t                       x     y
##    <chr> <dttm>              <dbl> <dbl>
##  1 1     2019-03-24 12:55:23  629.  882.
##  2 2     2019-03-24 12:55:23 1056.  657.
##  3 3     2019-03-24 12:55:23  508.  375.
##  4 4     2019-03-24 12:55:23 1278.  374.
##  5 5     2019-03-24 12:55:23 1379.  343.
##  6 6     2019-03-24 12:55:23 1137.  175.
##  7 7     2019-03-24 12:55:23  737.  116.
##  8 8     2019-03-24 12:55:23  922.  103.
##  9 1     2019-03-24 12:55:23  629.  882.
## 10 2     2019-03-24 12:55:23 1056.  656.
## # … with 21,972 more rows

vid_dt <- track(x = raw$x, y = raw$y, t = raw$frame, id = raw$track, 
                origin = "2019-03-24 12:55:23", period = period("1 second") / 30, 
                tz = "America/New_York", table = "dt")
vid_dt

## Track table [21982 observations]
## Number of tracks:  83 
## Dimensions:  2D 
## Geographic:  FALSE 
## Table class:  data table
##        id                   t         x        y
##     1:  1 2019-03-24 12:55:23  629.3839 882.4783
##     2:  2 2019-03-24 12:55:23 1056.1692 656.5207
##     3:  3 2019-03-24 12:55:23  508.0092 375.2451
##     4:  4 2019-03-24 12:55:23 1277.6466 373.7491
##     5:  5 2019-03-24 12:55:23 1379.2844 343.0853
##    ---                                          
## 21978: 82 2019-03-24 12:56:56  580.7614 587.2513
## 21979: 34 2019-03-24 12:56:56  493.5477 529.5454
## 21980: 47 2019-03-24 12:56:56  498.8001 432.5990
## 21981: 58 2019-03-24 12:56:56  562.6123 266.9754
## 21982: 67 2019-03-24 12:56:56 1046.3904 146.4723

raw <- read_csv(system.file("extdata/gps/01.csv", package = "trackdf")) %>%
  na.omit()
raw

## # A tibble: 3,595 x 4
##    date       time       lon   lat
##    <date>     <time>   <dbl> <dbl>
##  1 2015-09-10 07:00:00  15.8 -22.4
##  2 2015-09-10 07:00:01  15.8 -22.4
##  3 2015-09-10 07:00:04  15.8 -22.4
##  4 2015-09-10 07:00:05  15.8 -22.4
##  5 2015-09-10 07:00:08  15.8 -22.4
##  6 2015-09-10 07:00:09  15.8 -22.4
##  7 2015-09-10 07:00:09  15.8 -22.4
##  8 2015-09-10 07:00:10  15.8 -22.4
##  9 2015-09-10 07:00:11  15.8 -22.4
## 10 2015-09-10 07:00:12  15.8 -22.4
## # … with 3,585 more rows

gps_df <- track(x = raw$lon, y = raw$lat, t = paste(raw$date, raw$time), id = 1,  
                proj = "+proj=longlat", tz = "Africa/Windhoek", table = "df")
head(gps_df)

## Track table [6 observations]
## Number of tracks:  1 
## Dimensions:  2D 
## Geographic:  TRUE 
## Projection:  +proj=longlat +ellps=WGS84 
## Table class:  data frame
##   id                   t        x         y
## 1  1 2015-09-10 07:00:00 15.76468 -22.37957
## 2  1 2015-09-10 07:00:01 15.76468 -22.37957
## 3  1 2015-09-10 07:00:04 15.76468 -22.37958
## 4  1 2015-09-10 07:00:05 15.76468 -22.37958
## 5  1 2015-09-10 07:00:08 15.76467 -22.37959
## 6  1 2015-09-10 07:00:09 15.76467 -22.37959

gps_tbl <- track(x = raw$lon, y = raw$lat, t = paste(raw$date, raw$time), id = 1,  
                 proj = "+proj=longlat", tz = "Africa/Windhoek", table = "tbl")
gps_tbl

## Track table [3595 observations]
## Number of tracks:  1 
## Dimensions:  2D 
## Geographic:  TRUE 
## Projection:  +proj=longlat +ellps=WGS84 
## Table class:  tibble
## # A tibble: 3,595 x 4
##    id    t                       x     y
##    <chr> <dttm>              <dbl> <dbl>
##  1 1     2015-09-10 07:00:00  15.8 -22.4
##  2 1     2015-09-10 07:00:01  15.8 -22.4
##  3 1     2015-09-10 07:00:04  15.8 -22.4
##  4 1     2015-09-10 07:00:05  15.8 -22.4
##  5 1     2015-09-10 07:00:08  15.8 -22.4
##  6 1     2015-09-10 07:00:09  15.8 -22.4
##  7 1     2015-09-10 07:00:09  15.8 -22.4
##  8 1     2015-09-10 07:00:10  15.8 -22.4
##  9 1     2015-09-10 07:00:11  15.8 -22.4
## 10 1     2015-09-10 07:00:12  15.8 -22.4
## # … with 3,585 more rows

gps_dt <- track(x = raw$lon, y = raw$lat, t = paste(raw$date, raw$time), id = 1,  
                proj = "+proj=longlat", tz = "Africa/Windhoek", table = "dt")
gps_dt

## Track table [3595 observations]
## Number of tracks:  1 
## Dimensions:  2D 
## Geographic:  TRUE 
## Projection:  +proj=longlat +ellps=WGS84 
## Table class:  data table
##       id                   t        x         y
##    1:  1 2015-09-10 07:00:00 15.76468 -22.37957
##    2:  1 2015-09-10 07:00:01 15.76468 -22.37957
##    3:  1 2015-09-10 07:00:04 15.76468 -22.37958
##    4:  1 2015-09-10 07:00:05 15.76468 -22.37958
##    5:  1 2015-09-10 07:00:08 15.76467 -22.37959
##   ---                                          
## 3591:  1 2015-09-10 07:59:55 15.76231 -22.37752
## 3592:  1 2015-09-10 07:59:56 15.76230 -22.37752
## 3593:  1 2015-09-10 07:59:57 15.76229 -22.37752
## 3594:  1 2015-09-10 07:59:58 15.76229 -22.37751
## 3595:  1 2015-09-10 07:59:59 15.76228 -22.37751