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[![Travis-CI Build
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Status](https://travis-ci.org/hrbrmstr/deere.svg?branch=master)](https://travis-ci.org/hrbrmstr/deere)
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[![Coverage
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Status](https://codecov.io/gh/hrbrmstr/deere/branch/master/graph/badge.svg)](https://codecov.io/gh/hrbrmstr/deere)
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[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/deere)](https://cran.r-project.org/package=deere)
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# deere
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Catchall Functions for All Things ‘John Deere’
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## Description
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Initially a convenience package to access ‘John Deere’ ‘MowerPlus’
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databases from ‘iOS’ backups but perpaps will be something more
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all-encompassing.
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Ref:
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- <https://rud.is/b/2019/06/02/trawling-through-ios-backups-for-treasure-a-k-a-how-to-fish-for-target-files-in-ios-backups-with-r/>
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- <https://rud.is/b/2019/06/09/wrapping-up-exploration-of-john-deeres-mowerplus-database/>
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## What’s Inside The Tin
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The following functions are implemented:
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- `from_coredata_ts`: Convert timestampes from Apple “CoreData” format
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to something usable
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- `list_ios_backups`: List iOS backups available on this system
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- `platform_ios_backup_dir`: List iOS backups available on this system
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- `src_mowerplus`: Find and sync a copy of the latest MowerPlus
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database file from an iOS backup
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## Installation
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``` r
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devtools::install_git("https://git.sr.ht/~hrbrmstr/deere.git")
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# or
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devtools::install_git("https://git.rud.is/hrbrmstr/deere.git")
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# or
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devtools::install_gitlab("hrbrmstr/deere")
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# or
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devtools::install_bitbucket("hrbrmstr/deere")
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# or
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devtools::install_github("hrbrmstr/deere")
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```
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## Usage
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``` r
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library(deere)
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library(hrbrthemes)
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library(tidyverse)
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# current version
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packageVersion("deere")
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## [1] '0.2.0'
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```
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### Sample from the latest mow
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``` r
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list_ios_backups()
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## # A tibble: 2 x 3
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## path modification_time size
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## <chr> <dttm> <fs::bytes>
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## 1 28500cd31b9580aaf5815c695ebd3ea5f7455628 2019-06-19 14:31:41 8.19K
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## 2 28500cd31b9580aaf5815c695ebd3ea5f7455628-20190601 2019-06-01 17:23:05 8.22K
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mow_db <- src_mowerplus()
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mow_db
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## src: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
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## tbls: Z_METADATA, Z_MODELCACHE, Z_PRIMARYKEY, ZACTIVITY, ZDEALER, ZMOWALERT, ZMOWER, ZMOWLOCATION, ZSMARTCONNECTOR,
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## ZUSER
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glimpse(tbl(mow_db, "ZMOWER"))
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## Observations: ??
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## Variables: 23
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## Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
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## $ Z_PK <int> 1
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## $ Z_ENT <int> 7
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## $ Z_OPT <int> 15
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## $ ZDECKSIZEINCHES <int> 48
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## $ ZDISMISSEDFULLSERVICETASK <int> 0
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## $ ZDISMISSEDPERIODICTASK <int> 0
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## $ ZSMARTCONNECTOR <int> NA
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## $ ZUSER <int> 1
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## $ ZBATTERYCHARGE <dbl> NA
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## $ ZENGINEHOURS <dbl> 4.854714
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## $ ZFULLSERVICEPERFORMED <dbl> NA
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## $ ZHMCLASTSEEN <dbl> NA
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## $ ZHMCOFFSET <dbl> 0
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## $ ZPERIODICSERVICEPERFORMED <dbl> NA
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## $ ZSCLASTCONNECTED <dbl> NA
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## $ ZGENERICTYPE <chr> NA
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## $ ZHMCIDENTIFIER <chr> NA
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## $ ZMODEL <chr> "E140"
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## $ ZSCPIN <chr> NA
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## $ ZSCPERIPHERALID <chr> NA
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## $ ZSERIALNUMBER <chr> "1GXE140EKKK116940"
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## $ ZSERIES <chr> "E100"
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## $ ZSCDATADICTIONARY <blob> <NA>
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glimpse(tbl(mow_db, "ZACTIVITY"))
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## Observations: ??
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## Variables: 20
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## Database: sqlite 3.22.0 [/Users/hrbrmstr/Data/mowtrack.sqlite]
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## $ Z_PK <int> 1, 2, 3, 4
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## $ Z_ENT <int> 3, 3, 3, 3
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## $ Z_OPT <int> 124, 93, 52, 36
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## $ ZMONTH <int> 6, 6, 6, 6
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## $ ZYEAR <int> 2019, 2019, 2019, 2019
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## $ ZMOWER <int> 1, 1, 1, 1
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## $ ZUSER <int> 1, 1, 1, 1
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## $ ZISCOMPLETE <int> 1, 1, 1, 1
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## $ ZISMISSEDMOW <int> 0, 0, 0, 0
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## $ ZLASTLOCATION <int> 7016, 12548, 15500, 17514
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## $ ZCREATEDAT <dbl> 581100260, 581778616, 582506930, 582659215
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## $ ZENGINEHOURS <dbl> NA, NA, NA, NA
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## $ ZAREACOVERED <dbl> 3.761875, 2.286811, 1.292296, 1.078715
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## $ ZAVERAGESPEED <dbl> 3.727754, 2.894269, 3.011241, 3.650042
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## $ ZDISTANCEMOWED <dbl> 7.758894, 4.716564, 2.665370, 2.224857
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## $ ZMOWINGTIME <dbl> 6960.000, 5548.939, 2933.049, 2034.981
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## $ ZNOTES <chr> "First mow!", NA, NA, NA
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## $ ZINTERVALNAME <chr> NA, NA, NA, NA
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## $ ZTYPE <chr> NA, NA, NA, NA
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## $ ZUUID <blob> blob[238 B], blob[238 B], blob[238 B], blob[238 B]
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tbl(mow_db, "ZACTIVITY")%>%
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collect() -> activity
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activity %>%
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select(
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mow_date = ZCREATEDAT,
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area_covered = ZAREACOVERED,
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avg_speed = ZAVERAGESPEED,
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distance = ZDISTANCEMOWED,
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duration = ZMOWINGTIME
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) %>%
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arrange(mow_date) %>%
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mutate(
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duration = duration / 60 / 60, # hours
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mow_date = format(from_coredata_ts(mow_date), "%b %d"), # factors make better bars
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mow_date = factor(mow_date, levels = unique(mow_date)) # when there are just 2-of-em
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) %>%
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gather(measure, value, -mow_date) %>%
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ggplot(aes(mow_date, value)) +
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geom_col(aes(fill = measure), width = 0.5, show.legend = FALSE) +
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scale_y_comma() +
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scale_fill_ipsum() +
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facet_wrap(~measure, scales = "free") +
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theme_ipsum_rc(grid="Y")
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```
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<img src="README_files/figure-gfm/mow-1.png" width="672" />
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``` r
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zloc <- tbl(mow_db, "ZMOWLOCATION")
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zloc %>%
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select(
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id = ZSESSION,
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zorder = ZORDER,
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lat = ZLATITUDE,
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lng = ZLONGITUDE,
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speed = ZSPEED,
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ts = ZTIMESTAMP
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) %>%
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collect() %>%
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mutate(
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id = factor(id),
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ts = from_coredata_ts(ts)
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) -> sessions
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ggplot(sessions, aes(id, speed)) +
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ggbeeswarm::geom_quasirandom(
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aes(fill = id), show.legend = FALSE,
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shape = 21, size = 2, color = "white", stroke = 0.75
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) +
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scale_fill_ipsum() +
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labs(x = "Mowing Session", y = "MPH", title = "Mowing Speed Comparison (mph)") +
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theme_ipsum_rc(grid="Y")
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```
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<img src="README_files/figure-gfm/mow-2.png" width="672" />
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``` r
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arrange(sessions, ts) %>%
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ggplot(aes(lng, lat)) +
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geom_path(
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aes(color = id, group = id), show.legend = FALSE,
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size = 1, alpha = 1/2
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) +
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scale_color_ipsum() +
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coord_quickmap() +
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facet_wrap(~id) +
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labs(title = "Mowing Path Comparison") +
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theme_ipsum_rc(grid="Y") +
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ggthemes::theme_map()
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```
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<img src="README_files/figure-gfm/mow-3.png" width="672" />
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## deere Metrics
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| Lang | \# Files | (%) | LoC | (%) | Blank lines | (%) | \# Lines | (%) |
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| :--- | -------: | ---: | --: | ---: | ----------: | ---: | -------: | ---: |
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| Rmd | 1 | 0.12 | 75 | 0.53 | 33 | 0.56 | 43 | 0.38 |
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| R | 7 | 0.88 | 67 | 0.47 | 26 | 0.44 | 69 | 0.62 |
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## Code of Conduct
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Please note that this project is released with a [Contributor Code of
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Conduct](CONDUCT.md). By participating in this project you agree to
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abide by its terms.
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