# https://forestgeo.github.io/fgeo.tool/#installation
library(fgeo.tool)
# Install with `install.packages("pkg")`
library(fs)
library(tidyverse)
#> -- Attaching packages --------------------------------------------- tidyverse 1.2.1 --
#> v ggplot2 2.2.1 v purrr 0.2.4
#> v tibble 1.4.2 v dplyr 0.7.4
#> v tidyr 0.8.0 v stringr 1.3.1
#> v readr 1.1.1 v forcats 0.3.0
#> -- Conflicts ------------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()Change your input and output directory before knitting this file, and re-run every day you come back from the field.
# My input directory
params$input_dir
#> [1] "C:/Users/LeporeM/Documents/R/R-3.5.0/library/fgeo.tool/extdata/first_census"
dir(params$input_dir, "xlsx")
#> [1] "census.xlsx"
# My output directory (temporary)
params$output_dir
#> [1] "C:\\Users\\LeporeM\\AppData\\Local\\Temp\\1\\RtmpYr5gZl"xl_sheets_to_csv(params$input_dir, params$output_dir, first_census = TRUE)
#> Warning: `.x` has no dataframe new_secondary_stems
#> Warning: `.x` has no dataframe recruits
# Output is one .csv per workbook
dir(params$output_dir, "csv")
#> [1] "census.csv"# Now input multiple .csv and output a single dataframe
combo <- csv_to_df(params$output_dir)
#> Parsed with column specification:
#> cols(
#> submission_id = col_character(),
#> quadrat = col_character(),
#> tag = col_character(),
#> stem_tag = col_integer(),
#> species = col_character(),
#> species_code = col_character(),
#> dbh = col_double(),
#> status = col_character(),
#> codes = col_character(),
#> notes = col_character(),
#> pom = col_double(),
#> sheet = col_character(),
#> section_id = col_integer(),
#> unique_stem = col_character(),
#> date = col_character()
#> )
combo
#> # A tibble: 49 x 15
#> submission_id quadrat tag stem_tag species species_code dbh status
#> <chr> <chr> <chr> <int> <chr> <chr> <dbl> <chr>
#> 1 c01b37d5-2c30~ 0816 007 1 Americ~ TILIAM 51.2 LI
#> 2 efd8e991-2e34~ 0916 004 1 Americ~ TILIAM 31.6 LI
#> 3 efd8e991-2e34~ 0916 007 1 Ironwo~ OSTRVI 10.2 LI
#> 4 efd8e991-2e34~ 0916 008 1 Ironwo~ OSTRVI 2.7 LI
#> 5 efd8e991-2e34~ 0916 010 1 Hackbe~ CELTOC 25.9 LI
#> 6 efd8e991-2e34~ 0916 012 1 Bur Oak QUERMA 35.2 LI
#> 7 7997596a-c94e~ 1017 001 1 Americ~ TILIAM 65.3 LI
#> 8 572ddae1-d02c~ 0816 002 1 Hackbe~ CELTOC 45.1 LI
#> 9 c01b37d5-2c30~ 0816 007 2 TILIAM <NA> 42.4 LI
#> 10 c01b37d5-2c30~ 0816 007 3 TILIAM <NA> 39.8 LI
#> # ... with 39 more rows, and 7 more variables: codes <chr>, notes <chr>,
#> # pom <dbl>, sheet <chr>, section_id <int>, unique_stem <chr>,
#> # date <chr># Read data of tree positions
where_dir <- example_path("first_census/position.csv")
where <- read_csv(where_dir)
#> Parsed with column specification:
#> cols(
#> PtID = col_character(),
#> East = col_double(),
#> North = col_double()
#> )
# Compare
where
#> # A tibble: 23 x 3
#> PtID East North
#> <chr> <dbl> <dbl>
#> 1 0816006 150. 312.
#> 2 0816007 153. 301.
#> 3 0816008 143. 303.
#> 4 0816009 147. 302.
#> 5 0916001 170. 317.
#> 6 0916002 170. 314.
#> 7 0916003 168. 310.
#> 8 0916003 168. 310.
#> 9 0916004 165. 317.
#> 10 0916005 165. 312.
#> # ... with 13 more rows
select(combo, quadrat, tag)
#> # A tibble: 49 x 2
#> quadrat tag
#> <chr> <chr>
#> 1 0816 007
#> 2 0916 004
#> 3 0916 007
#> 4 0916 008
#> 5 0916 010
#> 6 0916 012
#> 7 1017 001
#> 8 0816 002
#> 9 0816 007
#> 10 0816 007
#> # ... with 39 more rows# Create a variable that we can later use to merge the two datasets
combo <- mutate(combo, PtID = paste0(quadrat, tag))
combo2 <- left_join(combo, where)
#> Joining, by = "PtID"
# Reorganize columns for easier visualization
combo2 <- select(combo2, PtID, East, North, date, everything())
combo2
#> # A tibble: 50 x 18
#> PtID East North date submission_id quadrat tag stem_tag species
#> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 08160~ 153. 301. 2018~ c01b37d5-2c30~ 0816 007 1 America~
#> 2 09160~ 165. 317. 2018~ efd8e991-2e34~ 0916 004 1 America~
#> 3 09160~ 163. 309. 2018~ efd8e991-2e34~ 0916 007 1 Ironwood
#> 4 09160~ 162. 305. 2018~ efd8e991-2e34~ 0916 008 1 Ironwood
#> 5 09160~ 165. 303. 2018~ efd8e991-2e34~ 0916 010 1 Hackber~
#> 6 09160~ 179. 304. 2018~ efd8e991-2e34~ 0916 012 1 Bur Oak
#> 7 10170~ 198. 325. 2018~ 7997596a-c94e~ 1017 001 1 America~
#> 8 08160~ NA NA 2018~ 572ddae1-d02c~ 0816 002 1 Hackber~
#> 9 08160~ 153. 301. 2018~ c01b37d5-2c30~ 0816 007 2 TILIAM
#> 10 08160~ 153. 301. 2018~ c01b37d5-2c30~ 0816 007 3 TILIAM
#> # ... with 40 more rows, and 9 more variables: species_code <chr>,
#> # dbh <dbl>, status <chr>, codes <chr>, notes <chr>, pom <dbl>,
#> # sheet <chr>, section_id <int>, unique_stem <chr># Helpers to avoid duplication
set_brk <- function(max, by) seq(0, max, by)
set_lim <- function(max) c(0, max)
xmax <- 560
ymax <- 360
ggplot(combo2, aes(East, North, color = date)) +
geom_point() +
coord_equal() +
scale_x_continuous(minor_breaks = set_brk(xmax, 20), limits = set_lim(xmax)) +
scale_y_continuous(minor_breaks = set_brk(ymax, 20), limits = set_lim(ymax)) +
theme_bw()
#> Warning: Removed 8 rows containing missing values (geom_point).
ggplot2 removes missing values from East and North but there may be missing values in date.
filter(combo2, is.na(date))
#> # A tibble: 2 x 18
#> PtID East North date submission_id quadrat tag stem_tag species
#> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 NA001 NA NA <NA> a4e952e6-dcad-4~ <NA> 001 1 Bur Oak
#> 2 08180~ 155. 353. <NA> a4e952e6-dcad-4~ 0818 002 1 Bur Oak
#> # ... with 9 more variables: species_code <chr>, dbh <dbl>, status <chr>,
#> # codes <chr>, notes <chr>, pom <dbl>, sheet <chr>, section_id <int>,
#> # unique_stem <chr>