2  Location & Time

The values calculated here are derivatives from data in the individual photos. Specifically, the distance and time between images is calculated here.

The distance and time lapse between photos will be used later to do things like group the images.

## Standard Packages
library(devtools)      ## Load github packages
library(tidyverse)     ## Many useful functions
library(ggplot2)       ## Graphics functions
library(dplyr)         ## Data crunching functions
library(lubridate)     ## Time functions
library(gt)            ## Tables

## Specialized Packages
library(geosphere)     ## Geographic calculations

## Packages from github/kimbridges
## install_github("kimbridges/milestones")
library(milestones)    ## Milestones and Bar-timelines

## Initialize defaults
column <- lolli_styles()

## Get Baseinfo (originates in photos chapter)
baseinfo <- read.table("baseinfo.txt")
source   <- baseinfo$source
folder   <- baseinfo$folder
thumbs_folder <- baseinfo$thumbs_folder
files_folder  <- baseinfo$files_folder

2.1 Calculate Distances & Times

Here is where the main calculations are done. There aren’t any surprises here. The distances are calculated from the latitude and longitudes. They are “straight line” distances, not driving distances.

## Get the stored table_info
file_location <- paste0(files_folder,"/photo_info.txt")
data <- read.table(file = file_location)

## Get the distances between photos
data$dist_meters[1] <- 0.0

for(i in 2:nrow(data)){
    ## Isolate the origin & destination.
    origin <- c(data$lon[i-1],data$lat[i-1])
    destination <- c(data$lon[i],data$lat[i])
    ## Calculate the distance.
    distance <- distm(origin, 
                      destination, 
                      fun=distHaversine)
    ## Save the distance.
    data$dist_meters[i] <- round(distance,digits = 1)
    ## Convert meter to miles.
    ## dist_miles <- distance * 0.000621371
    ## data$dist_miles[[i]] <- round(dist_miles,digits = 0)
    } ## end for loop

## Get the time between photos
data$timespan[i] <- 0.0

for(i in 2:nrow(data)){
    ## Isolate the previous & current times.
    previous_time <- (paste0(data$time[i-1],":00"))
    current_time  <- (paste0(data$time[i],":00"))
    
    span <- parse_date_time(previous_time, "%T") %--%
            parse_date_time(current_time, "%T")
    
    ## Save the interval.
    data$timespan[i] <- round(span/60,digits = 1)
    } ## end for loop

table3 <- data |>
  select(number,time,dist_meters,timespan)

gt(table3)

number	time	dist_meters	timespan
1	9:40	0.0	NA
2	11:57	88120.6	137
3	12:19	8472.4	22
4	12:40	991.8	21
5	12:55	60.6	15
6	15:15	447.5	140
7	15:18	18.6	3
8	15:46	18.8	28
9	16:10	28.7	24
10	16:12	74.3	2
11	16:26	301.2	14
12	17:32	851.8	66
13	17:45	0.2	13
14	18:25	1.2	40
15	19:36	0.4	71
16	19:49	20.6	13

## Save the updated data.
file_location <- paste0(files_folder,"/photo_info.txt")
write.table(data, file = file_location)

2.2 Show Photo Times

A milestone chart shows when during the day each of the photos was taken. This might give some insight into whether parts of the day were missed, if there is excessive clumping of the photos and other things that might need attention.

## Get the information.
file_location <- paste0(files_folder,"/photo_info.txt")
data <- read.table(file = file_location)

## Get the date.
photodate <- data$date[1]

## Make the data table for milestones.
data$datetime = paste(data$date,data$time) 

## Build the full date + time variable.
data$newdate <- parse_date_time(data$datetime, 
                                "ymd HM", tz="")

## Configure a data subset with proper variable names.
data <- data |>
  rename(event = number) |>
  select(event, newdate) |>
  rename(date = newdate)

## Make a confirmation table.
gt(data) |>
   tab_source_note(source_note=source)

event	date
1	2024-05-03 09:40:00
2	2024-05-03 11:57:00
3	2024-05-03 12:19:00
4	2024-05-03 12:40:00
5	2024-05-03 12:55:00
6	2024-05-03 15:15:00
7	2024-05-03 15:18:00
8	2024-05-03 15:46:00
9	2024-05-03 16:10:00
10	2024-05-03 16:12:00
11	2024-05-03 16:26:00
12	2024-05-03 17:32:00
13	2024-05-03 17:45:00
14	2024-05-03 18:25:00
15	2024-05-03 19:36:00
16	2024-05-03 19:49:00
Europe 2024

## Add source info to the milestones chart.
column$source_info  <- source
column$title <- photodate

## Make a milestones chart.
milestones(datatable = data, styles = column)