src_mowerplus <- function(backup_id, data_loc = "~/Data", overwrite = TRUE) {
require(XML, quietly = TRUE, warn.conflicts = FALSE) # to read plist (property list) files
require(tidyverse, quietly = TRUE, warn.conflicts = FALSE) # for printing and access to sqlite dbs
# root of mobile backup dir for `backup_id`
mb <- path.expand(file.path("~/Library/Application Support/MobileSync/Backup", backup_id))
stopifnot(dir.exists(mb))
data_loc <- path.expand(data_loc)
stopifnot(dir.exists(data_loc))
tf <- tempfile(fileext = ".sqlite")
on.exit(unlink(tf), add=TRUE)
# path to the extracted sqlite file
out_db <- file.path(data_loc, "mowtrack.sqlite")
file.copy(file.path(mb, "Manifest.db"), tf, overwrite = TRUE)
manifest_db <- src_sqlite(tf)
fils <- tbl(manifest_db, "Files")
filter(fils, relativePath == "Library/Application Support/MowTracking.sqlite") %>%
pull(fileID) -> mowtrackdb_loc
file.copy(
file.path(mb, sprintf("%s/%s", substr(mowtrackdb_loc, 1, 2), mowtrackdb_loc)),
file.path(data_loc, "mowtrack.sqlite"),
overwrite = overwrite
)
src_sqlite(out_db)
}
from_coredata_ts <- function(x, tz = NULL) {
.POSIXct(ifelse(
test = floor(log10(x)) >= 10, # If you're still using R in 2317 then good on ya and edit this
yes = as.POSIXct(x/10e8, origin = "2001-01-01"), # nanoseconds coredata
no = as.POSIXct(x, origin = "2001-01-01") # seconds coredata
), tz = tz)
}library(hrbrthemes)
mow_db <- src_mowerplus("28500cd31b9580aaf5815c695ebd3ea5f7455628")
mow_db
## src: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## tbls: Z_METADATA, Z_MODELCACHE, Z_PRIMARYKEY, ZACTIVITY, ZDEALER,
## ZMOWALERT, ZMOWER, ZMOWLOCATION, ZSMARTCONNECTOR, ZUSERSee what’s in the ZMOWER table after 2 mows
glimpse(tbl(mow_db, "ZMOWER"))
## Observations: ??
## Variables: 23
## Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## $ Z_PK <int> 1
## $ Z_ENT <int> 7
## $ Z_OPT <int> 11
## $ ZDECKSIZEINCHES <int> 48
## $ ZDISMISSEDFULLSERVICETASK <int> 0
## $ ZDISMISSEDPERIODICTASK <int> 0
## $ ZSMARTCONNECTOR <int> NA
## $ ZUSER <int> 1
## $ ZBATTERYCHARGE <dbl> NA
## $ ZENGINEHOURS <dbl> 3.474705
## $ ZFULLSERVICEPERFORMED <dbl> NA
## $ ZHMCLASTSEEN <dbl> NA
## $ ZHMCOFFSET <dbl> 0
## $ ZPERIODICSERVICEPERFORMED <dbl> NA
## $ ZSCLASTCONNECTED <dbl> NA
## $ ZGENERICTYPE <chr> NA
## $ ZHMCIDENTIFIER <chr> NA
## $ ZMODEL <chr> "E140"
## $ ZSCPIN <chr> NA
## $ ZSCPERIPHERALID <chr> NA
## $ ZSERIALNUMBER <chr> "1GXE140EKKK116940"
## $ ZSERIES <chr> "E100"
## $ ZSCDATADICTIONARY <blob> <NA>This is the one from last time which has all the mowing metadata/summary data
glimpse(tbl(mow_db, "ZACTIVITY"))
## Observations: ??
## Variables: 20
## Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## $ Z_PK <int> 1, 2
## $ Z_ENT <int> 3, 3
## $ Z_OPT <int> 124, 93
## $ ZMONTH <int> 6, 6
## $ ZYEAR <int> 2019, 2019
## $ ZMOWER <int> 1, 1
## $ ZUSER <int> 1, 1
## $ ZISCOMPLETE <int> 1, 1
## $ ZISMISSEDMOW <int> 0, 0
## $ ZLASTLOCATION <int> 7016, 12548
## $ ZCREATEDAT <dbl> 581100260, 581778616
## $ ZENGINEHOURS <dbl> NA, NA
## $ ZAREACOVERED <dbl> 3.761875, 2.286811
## $ ZAVERAGESPEED <dbl> 3.727754, 2.894269
## $ ZDISTANCEMOWED <dbl> 7.758894, 4.716564
## $ ZMOWINGTIME <dbl> 6960.000, 5548.939
## $ ZNOTES <chr> "First mow!", NA
## $ ZINTERVALNAME <chr> NA, NA
## $ ZTYPE <chr> NA, NA
## $ ZUUID <blob> blob[238 B], blob[238 B]
tbl(mow_db, "ZACTIVITY")%>%
collect() -> activity
activity %>%
select(
mow_date = ZCREATEDAT,
area_covered = ZAREACOVERED,
avg_speed = ZAVERAGESPEED,
distance = ZDISTANCEMOWED,
duration = ZMOWINGTIME
) %>%
arrange(mow_date) %>%
mutate(
duration = duration / 60 / 60, # hours
mow_date = format(from_coredata_ts(mow_date), "%b %d"), # factors make better bars
mow_date = factor(mow_date, levels = unique(mow_date)) # when there are just 2-of-em
) %>%
gather(measure, value, -mow_date) %>%
ggplot(aes(mow_date, value)) +
geom_col(aes(fill = measure), width = 0.5, show.legend = FALSE) +
scale_y_comma() +
scale_fill_ipsum() +
facet_wrap(~measure, scales = "free") +
theme_ipsum_rc(grid="Y")
This has all of the captured mowing track data
zloc <- tbl(mow_db, "ZMOWLOCATION")
glimpse(zloc)
## Observations: ??
## Variables: 16
## Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## $ Z_PK <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1…
## $ Z_ENT <int> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,…
## $ Z_OPT <int> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ ZISPAUSEDPOINT <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ ZORDER <int> 1, 2, 0, 11, 20, 58, 38, 43, 30, 25, 21, 10,…
## $ ZSESSION <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ ZSESSION2 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ ZALTITUDE <dbl> 42.64804, 42.70590, 40.99661, 39.54770, 38.2…
## $ ZCOURSE <dbl> 358.242188, 332.226562, 18.281250, 260.85937…
## $ ZHORIZONTALACCURACY <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
## $ ZLATITUDE <dbl> 43.25913, 43.25914, 43.25913, 43.25915, 43.2…
## $ ZLONGITUDE <dbl> -70.80069, -70.80069, -70.80069, -70.80067, …
## $ ZSPEED <dbl> 0.0000000, 0.4250179, 0.5592341, 0.3802792, …
## $ ZTIMESTAMP <dbl> 581100271, 581100272, 581100270, 581100281, …
## $ ZVERTICALACCURACY <dbl> 6, 6, 8, 6, 4, 4, 4, 3, 4, 4, 4, 6, 4, 4, 4,…
## $ ZKLVDATA <blob> <NA>, <NA>, <NA>, <NA>, <NA>, <NA>, <NA>, <…Try to figure out which is the “key” for unique moes
distinct(zloc, Z_ENT)
## # Source: lazy query [?? x 1]
## # Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## Z_ENT
## <int>
## 1 8
distinct(zloc, Z_OPT)
## # Source: lazy query [?? x 1]
## # Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## Z_OPT
## <int>
## 1 1
## 2 2
distinct(zloc, ZSESSION)
## # Source: lazy query [?? x 1]
## # Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## ZSESSION
## <int>
## 1 1
## 2 2
count(zloc, Z_OPT)
## # Source: lazy query [?? x 2]
## # Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## Z_OPT n
## <int> <int>
## 1 1 12358
## 2 2 209
count(zloc, ZSESSION)
## # Source: lazy query [?? x 2]
## # Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
## ZSESSION n
## <int> <int>
## 1 1 7018
## 2 2 5549Def looks like ZSESSION is it:
group_by(zloc, ZSESSION) %>%
summarise(min_ts = min(ZTIMESTAMP), max_ts = max(ZTIMESTAMP)) %>%
ungroup() %>%
collect() %>%
mutate_all(from_coredata_ts)
## # A tibble: 2 x 3
## ZSESSION min_ts max_ts
## <dttm> <dttm> <dttm>
## 1 2000-12-31 19:00:01 2019-06-01 12:44:29 2019-06-01 14:41:26
## 2 2000-12-31 19:00:02 2019-06-09 09:10:19 2019-06-09 10:42:47Speed check
zloc %>%
select(
id = ZSESSION,
zorder = ZORDER,
lat = ZLATITUDE,
lng = ZLONGITUDE,
speed = ZSPEED,
ts = ZTIMESTAMP
) %>%
collect() %>%
mutate(
id = factor(id),
ts = from_coredata_ts(ts)
) -> sessions
ggplot(sessions, aes(id, speed)) +
ggbeeswarm::geom_quasirandom(
aes(fill = id), show.legend = FALSE,
shape = 21, size = 2, color = "white", stroke = 0.75
) +
scale_fill_ipsum() +
labs(x = "Mowing Session", y = "MPH", title = "Mowing Speed Comparison (mph)") +
theme_ipsum_rc(grid="Y")
Track check
arrange(sessions, ts) %>%
ggplot(aes(lng, lat)) +
geom_path(
aes(color = id, group = id), show.legend = FALSE,
size = 1, alpha = 1/2
) +
scale_color_ipsum() +
coord_quickmap() +
facet_wrap(~id) +
labs(title = "Mowing Path Comparison") +
theme_ipsum_rc(grid="Y") +
worldtilegrid::theme_enhance_wtg() # sourcehut|gitlab|gitugh hrbrmstr/worldtilegrid