Last updated: 2023-02-20

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Rmd 7e65dea ajpelu 2023-02-20 add quadrats

Consultas Base de Datos

Hemos ejecutado la siguientes consultas en la base de datos :

  • Altura máxima promedio Quadrat: CONSULTAS_ALTURA/ALTURA_MAX_PROMEDIO_QUADRAT
SELECT ALT_MAX_PROMEDIO_INT.ZONA,
       ALT_MAX_PROMEDIO_INT.PARCELA,
       ALT_MAX_PROMEDIO_INT.QUADRAT,
       ALT_MAX_PROMEDIO_INT.FECHA,
       Sum(ALT_MAX_PROMEDIO_INT.ALT_REL) AS ALT_PROM,
       ALT_MAX_PROMEDIO_INT.FACTOR_CORREC_INFOCA,
       [ALT_PROM]*[FACTOR_CORREC_INFOCA]/2 AS ALT_CORR
FROM ALT_MAX_PROMEDIO_INT
GROUP BY ALT_MAX_PROMEDIO_INT.ZONA,
         ALT_MAX_PROMEDIO_INT.PARCELA,
         ALT_MAX_PROMEDIO_INT.QUADRAT,
         ALT_MAX_PROMEDIO_INT.FECHA,
         ALT_MAX_PROMEDIO_INT.FACTOR_CORREC_INFOCA;

El resultado lo hemos guardado en el archivo ALTURA_MAX_PROMEDIO_QUADRAT.xlsx que está en la ruta data/raw/ALTURA_MAX_PROMEDIO_QUADRAT.xlsx

  • Altura moda promedio Quadrat: CONSULTAS_ALTURA/ALTURA_MODA_PROMEDIO_QUADRAT
SELECT ALT_MODA_PROMEDIO_INT.ZONA,
       ALT_MODA_PROMEDIO_INT.PARCELA,
       ALT_MODA_PROMEDIO_INT.QUADRAT,
       ALT_MODA_PROMEDIO_INT.FECHA,
       Avg(ALT_MODA_PROMEDIO_INT.ALT_REL) AS ALTURA_PROMEDIO,
       ALT_MODA_PROMEDIO_INT.FACTOR_CORREC_INFOCA,
       [ALTURA_PROMEDIO]*[FACTOR_CORREC_INFOCA]/2 AS ALT_CORR
FROM ALT_MODA_PROMEDIO_INT
GROUP BY ALT_MODA_PROMEDIO_INT.ZONA,
         ALT_MODA_PROMEDIO_INT.PARCELA,
         ALT_MODA_PROMEDIO_INT.QUADRAT,
         ALT_MODA_PROMEDIO_INT.FECHA,
         ALT_MODA_PROMEDIO_INT.FACTOR_CORREC_INFOCA;

El resultado lo hemos guardado en el archivo ALTURA_MODA_PROMEDIO_QUADRAT.xlsx que está en la ruta data/raw/ALTURA_MODA_PROMEDIO_QUADRAT.xlsx

  • Suelo desnudo Quadrat: CONSULTAS_DESNUDO/DESNUDO_QUADRAT
SELECT GEO_ZONA.Nombre_zona AS ZONA,
       GEO_PARCELA.NOMBRE AS PARCELA,
       GEO_QUADRAT.NOMBRE AS QUADRAT,
       GEO_QUADRAT.RANGO_INFOCA,
       DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
       GEO_QUADRAT.FACTOR_CORREC_INFOCA,
       TAB_VISITA_EVAL_VEG_QUAD.COB_DESNUDO AS DESNUDO
FROM DICC_FECHA_MUESTREOS_VEGE_OVEJA
INNER JOIN (GEO_ZONA
            INNER JOIN ((GEO_PARCELA
                         INNER JOIN GEO_QUADRAT ON GEO_PARCELA.OBJECTID_1 = GEO_QUADRAT.COD_PARCELA)
                        INNER JOIN TAB_VISITA_EVAL_VEG_QUAD ON GEO_QUADRAT.OBJECTID = TAB_VISITA_EVAL_VEG_QUAD.COD_QUADRAT) ON GEO_ZONA.OBJECTID = GEO_PARCELA.COD_ZONA) ON DICC_FECHA_MUESTREOS_VEGE_OVEJA.ID_FECHA_MUESTREOS_VEG = TAB_VISITA_EVAL_VEG_QUAD.COD_FECHA_MUESTREO
WHERE (((DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS)<>#10/9/2019#));

El resultado lo hemos guardado en el archivo DESNUDO_QUADRAT.xlsx que está en la ruta data/raw/DESNUDO_QUADRAT.xlsx

  • Diversidad Quadrat: CONSULTAS_DIVERSIDAD/DIVERSIDAD_QUADRAT
SELECT DIVERSIDAD_QUADRAT_INT.ZONA,
       DIVERSIDAD_QUADRAT_INT.PARCELA,
       DIVERSIDAD_QUADRAT_INT.QUADRAT,
       DIVERSIDAD_QUADRAT_INT.RANGO_INFOCA,
       DIVERSIDAD_QUADRAT_INT.FACTOR_CORREC_INFOCA,
       DIVERSIDAD_QUADRAT_INT.FECHA_MUESTREOS,
       Sum(DIVERSIDAD_QUADRAT_INT.PI_LNPI) AS I_SHANNON
FROM DIVERSIDAD_QUADRAT_INT
GROUP BY DIVERSIDAD_QUADRAT_INT.ZONA,
         DIVERSIDAD_QUADRAT_INT.PARCELA,
         DIVERSIDAD_QUADRAT_INT.QUADRAT,
         DIVERSIDAD_QUADRAT_INT.RANGO_INFOCA,
         DIVERSIDAD_QUADRAT_INT.FACTOR_CORREC_INFOCA,
         DIVERSIDAD_QUADRAT_INT.FECHA_MUESTREOS;

El resultado lo hemos guardado en el archivo DIVERSIDAD_QUADRAT.xlsx que está en la ruta data/raw/DIVERSIDAD_QUADRAT.xlsx

  • Tasa de Fitovolumen Total Quadrat: CONSULTAS_FITOVOLUMEN/FITOVOL_TOTAL_QUADRAT
SELECT GEO_ZONA.Nombre_zona AS ZONA,
       GEO_PARCELA.NOMBRE AS PARCELA,
       GEO_QUADRAT.NOMBRE AS QUADRAT,
       GEO_QUADRAT.RANGO_INFOCA,
       GEO_QUADRAT.FACTOR_CORREC_INFOCA,
       DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
       Sum([EVAL_VEG_QUADRAT_SP]![COBERTURA]*[EVAL_VEG_QUADRAT_SP]![ALTURA_MOD]) AS FITOVOL
FROM DICC_FECHA_MUESTREOS_VEGE_OVEJA
INNER JOIN ((GEO_ZONA
             INNER JOIN ((GEO_PARCELA
                          INNER JOIN GEO_QUADRAT ON GEO_PARCELA.OBJECTID_1 = GEO_QUADRAT.COD_PARCELA)
                         INNER JOIN TAB_VISITA_EVAL_VEG_QUAD ON GEO_QUADRAT.OBJECTID = TAB_VISITA_EVAL_VEG_QUAD.COD_QUADRAT) ON GEO_ZONA.OBJECTID = GEO_PARCELA.COD_ZONA)
            INNER JOIN (DICC_ESPECIES
                        INNER JOIN EVAL_VEG_QUADRAT_SP ON DICC_ESPECIES.ID_ESPECIE = EVAL_VEG_QUADRAT_SP.COD_ESPECIE) ON TAB_VISITA_EVAL_VEG_QUAD.ID_VISITA_QUADRAT = EVAL_VEG_QUADRAT_SP.Cod_visita_quadrat) ON DICC_FECHA_MUESTREOS_VEGE_OVEJA.ID_FECHA_MUESTREOS_VEG = TAB_VISITA_EVAL_VEG_QUAD.COD_FECHA_MUESTREO
GROUP BY GEO_ZONA.Nombre_zona,
         GEO_PARCELA.NOMBRE,
         GEO_QUADRAT.NOMBRE,
         GEO_QUADRAT.RANGO_INFOCA,
         GEO_QUADRAT.FACTOR_CORREC_INFOCA,
         DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS
HAVING (((DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS)<>#10/9/2019#));

El resultado lo hemos guardado en el archivo FITOVOL_TOTAL_QUADRAT.xlsx que está en la ruta data/raw/FITOVOL_TOTAL_QUADRAT.xlsx

  • Suelo mantillo promedio Quadrat: CONSULTAS_MANTILLO/MANTILLO_QUADRAT
SELECT GEO_ZONA.Nombre_zona AS ZONA,
       GEO_PARCELA.NOMBRE AS PARCELA,
       GEO_QUADRAT.NOMBRE AS QUADRAT,
       GEO_QUADRAT.RANGO_INFOCA,
       DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
       GEO_QUADRAT.FACTOR_CORREC_INFOCA,
       TAB_VISITA_EVAL_VEG_QUAD.COB_MANTILLO AS MANTILLO
FROM DICC_FECHA_MUESTREOS_VEGE_OVEJA
INNER JOIN (GEO_ZONA
            INNER JOIN ((GEO_PARCELA
                         INNER JOIN GEO_QUADRAT ON GEO_PARCELA.OBJECTID_1 = GEO_QUADRAT.COD_PARCELA)
                        INNER JOIN TAB_VISITA_EVAL_VEG_QUAD ON GEO_QUADRAT.OBJECTID = TAB_VISITA_EVAL_VEG_QUAD.COD_QUADRAT) ON GEO_ZONA.OBJECTID = GEO_PARCELA.COD_ZONA) ON DICC_FECHA_MUESTREOS_VEGE_OVEJA.ID_FECHA_MUESTREOS_VEG = TAB_VISITA_EVAL_VEG_QUAD.COD_FECHA_MUESTREO
WHERE (((DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS)<>#10/9/2019#));

El resultado lo hemos guardado en el archivo MANTILLO_QUADRAT.xlsx que está en la ruta data/raw/MANTILLO_QUADRAT.xlsx

  • Tasa de Recubrimiento Total Quadrat: CONSULTAS_RECUBRIMIENTO_TOTAL/REC_TOTAL_QUADRAT
SELECT GEO_ZONA.Nombre_zona AS ZONA,
       GEO_PARCELA.NOMBRE AS PARCELA,
       GEO_QUADRAT.NOMBRE AS QUADRAT,
       GEO_QUADRAT.RANGO_INFOCA,
       DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
       TAB_VISITA_EVAL_VEG_QUAD.COB_TOTAL,
       GEO_QUADRAT.FACTOR_CORREC_INFOCA
FROM GEO_ZONA
INNER JOIN ((GEO_PARCELA
             INNER JOIN GEO_QUADRAT ON GEO_PARCELA.OBJECTID_1 = GEO_QUADRAT.COD_PARCELA)
            INNER JOIN (DICC_FECHA_MUESTREOS_VEGE_OVEJA
                        INNER JOIN TAB_VISITA_EVAL_VEG_QUAD ON DICC_FECHA_MUESTREOS_VEGE_OVEJA.ID_FECHA_MUESTREOS_VEG = TAB_VISITA_EVAL_VEG_QUAD.COD_FECHA_MUESTREO) ON GEO_QUADRAT.OBJECTID = TAB_VISITA_EVAL_VEG_QUAD.COD_QUADRAT) ON GEO_ZONA.OBJECTID = GEO_PARCELA.COD_ZONA
WHERE (((DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS)<>#10/9/2019#));

El resultado lo hemos guardado en el archivo REC_TOTAL_QUADRAT.xlsx que está en la ruta data/raw/REC_TOTAL_QUADRAT.xlsx

  • Tasa de Recubrimiento Vegetal Quadrat: CONSULTAS_RECUBRIMIENTO_VEGETAL/REC_VEG_QUADRAT
SELECT GEO_ZONA.Nombre_zona AS ZONA,
       GEO_PARCELA.NOMBRE AS PARCELA,
       GEO_QUADRAT.NOMBRE AS QUADRAT,
       GEO_QUADRAT.RANGO_INFOCA,
       GEO_QUADRAT.FACTOR_CORREC_INFOCA,
       DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
       Sum(EVAL_VEG_QUADRAT_SP.COBERTURA) AS REC_VEG,
       EVAL_VEG_QUADRAT_SP.MUERTA
FROM DICC_FECHA_MUESTREOS_VEGE_OVEJA
INNER JOIN ((GEO_ZONA
             INNER JOIN ((GEO_PARCELA
                          INNER JOIN GEO_QUADRAT ON GEO_PARCELA.OBJECTID_1 = GEO_QUADRAT.COD_PARCELA)
                         INNER JOIN TAB_VISITA_EVAL_VEG_QUAD ON GEO_QUADRAT.OBJECTID = TAB_VISITA_EVAL_VEG_QUAD.COD_QUADRAT) ON GEO_ZONA.OBJECTID = GEO_PARCELA.COD_ZONA)
            INNER JOIN EVAL_VEG_QUADRAT_SP ON TAB_VISITA_EVAL_VEG_QUAD.ID_VISITA_QUADRAT = EVAL_VEG_QUADRAT_SP.Cod_visita_quadrat) ON DICC_FECHA_MUESTREOS_VEGE_OVEJA.ID_FECHA_MUESTREOS_VEG = TAB_VISITA_EVAL_VEG_QUAD.COD_FECHA_MUESTREO
GROUP BY GEO_ZONA.Nombre_zona,
         GEO_PARCELA.NOMBRE,
         GEO_QUADRAT.NOMBRE,
         GEO_QUADRAT.RANGO_INFOCA,
         GEO_QUADRAT.FACTOR_CORREC_INFOCA,
         DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
         EVAL_VEG_QUADRAT_SP.MUERTA
HAVING (((DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS)<>#10/9/2019#)
        AND ((EVAL_VEG_QUADRAT_SP.MUERTA)=FALSE));

El resultado lo hemos guardado en el archivo REC_VEG_QUADRAT.xlsx que está en la ruta data/raw/REC_VEG_QUADRAT.xlsx

  • Riqueza Quadrat: CONSULTAS_RIQUEZA/RIQUEZA_QUADRAT
SELECT GEO_ZONA.Nombre_zona,
       GEO_PARCELA.NOMBRE,
       GEO_QUADRAT.NOMBRE,
       GEO_QUADRAT.RANGO_INFOCA,
       GEO_QUADRAT.FACTOR_CORREC_INFOCA,
       DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
       Count(GEO_QUADRAT.NOMBRE) AS RIQUEZA,
       [RIQUEZA]*[FACTOR_CORREC_INFOCA] AS RIQUEZA_COR
FROM DICC_FECHA_MUESTREOS_VEGE_OVEJA
INNER JOIN ((GEO_ZONA
             INNER JOIN ((GEO_PARCELA
                          INNER JOIN GEO_QUADRAT ON GEO_PARCELA.OBJECTID_1 = GEO_QUADRAT.COD_PARCELA)
                         INNER JOIN TAB_VISITA_EVAL_VEG_QUAD ON GEO_QUADRAT.OBJECTID = TAB_VISITA_EVAL_VEG_QUAD.COD_QUADRAT) ON GEO_ZONA.OBJECTID = GEO_PARCELA.COD_ZONA)
            INNER JOIN (DICC_ESPECIES
                        INNER JOIN EVAL_VEG_QUADRAT_SP ON DICC_ESPECIES.ID_ESPECIE = EVAL_VEG_QUADRAT_SP.COD_ESPECIE) ON TAB_VISITA_EVAL_VEG_QUAD.ID_VISITA_QUADRAT = EVAL_VEG_QUADRAT_SP.Cod_visita_quadrat) ON DICC_FECHA_MUESTREOS_VEGE_OVEJA.ID_FECHA_MUESTREOS_VEG = TAB_VISITA_EVAL_VEG_QUAD.COD_FECHA_MUESTREO
GROUP BY GEO_ZONA.Nombre_zona,
         GEO_PARCELA.NOMBRE,
         GEO_QUADRAT.NOMBRE,
         GEO_QUADRAT.RANGO_INFOCA,
         GEO_QUADRAT.FACTOR_CORREC_INFOCA,
         DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS;

El resultado lo hemos guardado en el archivo RIQUEZA_QUADRAT.xlsx que está en la ruta data/raw/RIQUEZA_QUADRAT.xlsx

  • Severidad Quemas Quadrat: CONSULTAS_SEVERIDAD_QUEMA/SEV_QUEMA_QUAD_TOT
SELECT GEO_ZONA.Nombre_zona AS ZONA,
       GEO_PARCELA.NOMBRE AS PARCELA,
       GEO_QUADRAT.NOMBRE AS QUADRAT,
       DICC_FECHA_MUESTREOS_VEGE_OVEJA.FECHA_MUESTREOS,
       GEO_QUADRAT.RANGO_INFOCA,
       GEO_QUADRAT.FACTOR_CORREC_INFOCA,
       TAB_VISITA_EVAL_QUEMA.PORC_SOFLAMADO,
       TAB_VISITA_EVAL_QUEMA.PORC_QUEMADO,
       DICC_RANGO_QUEMA.RANGO AS RANGO_QUEMA
FROM DICC_FECHA_MUESTREOS_VEGE_OVEJA
INNER JOIN (GEO_ZONA
            INNER JOIN ((GEO_PARCELA
                         INNER JOIN GEO_QUADRAT ON GEO_PARCELA.OBJECTID_1 = GEO_QUADRAT.COD_PARCELA)
                        INNER JOIN (DICC_RANGO_QUEMA
                                    INNER JOIN TAB_VISITA_EVAL_QUEMA ON DICC_RANGO_QUEMA.ID_RANGO_QUEMA = TAB_VISITA_EVAL_QUEMA.COD_RANGO_QUEMA) ON GEO_QUADRAT.OBJECTID = TAB_VISITA_EVAL_QUEMA.COD_QUADRAT) ON GEO_ZONA.OBJECTID = GEO_PARCELA.COD_ZONA) ON DICC_FECHA_MUESTREOS_VEGE_OVEJA.ID_FECHA_MUESTREOS_VEG = TAB_VISITA_EVAL_QUEMA.COD_FECHA_MUESTREO;

El resultado lo hemos guardado en el archivo SEV_QUEMA_QUAD_TOT.xlsx que está en la ruta data/raw/SEV_QUEMA_QUAD_TOT.xlsx

  • Tasa de Consumo Quadrat: CONSULTAS_TASA_DE_CONSUMO/TASA_CONSUMO_PROMEDIO_QUADRAT
SELECT TASA_CONSUMO_SP_QUADRAT.Nombre_zona AS ZONA,
       TASA_CONSUMO_SP_QUADRAT.GEO_PARCELA.NOMBRE AS PARCELA,
       TASA_CONSUMO_SP_QUADRAT.GEO_QUADRAT.NOMBRE AS QUADRAT,
       TASA_CONSUMO_SP_QUADRAT.FACTOR_CORREC_INFOCA,
       TASA_CONSUMO_SP_QUADRAT.FECHA_MUESTREOS,
       Sum([TASA_CONSUMO_SP_QUADRAT]![TASA_CONSUMO]*[TASA_CONSUMO_SP_QUADRAT]![COBERTURA]/[REC_VEG_QUADRAT]![REC_VEG]) AS TASA_CONSUMO
FROM TASA_CONSUMO_SP_QUADRAT
INNER JOIN REC_VEG_QUADRAT ON (TASA_CONSUMO_SP_QUADRAT.FECHA_MUESTREOS = REC_VEG_QUADRAT.FECHA_MUESTREOS)
AND (TASA_CONSUMO_SP_QUADRAT.GEO_QUADRAT.NOMBRE = REC_VEG_QUADRAT.QUADRAT)
GROUP BY TASA_CONSUMO_SP_QUADRAT.Nombre_zona,
         TASA_CONSUMO_SP_QUADRAT.GEO_PARCELA.NOMBRE,
         TASA_CONSUMO_SP_QUADRAT.GEO_QUADRAT.NOMBRE,
         TASA_CONSUMO_SP_QUADRAT.FACTOR_CORREC_INFOCA,
         TASA_CONSUMO_SP_QUADRAT.FECHA_MUESTREOS;

El resultado lo hemos guardado en el archivo TASA_CONSUMO_PROMEDIO_QUADRAT.xlsx que está en la ruta data/raw/TASA_CONSUMO_PROMEDIO_QUADRAT.xlsx

Estandarización a DwC (ExtendMeasurementOrFact, emof)

library(tidyverse) # Easily Install and Load the 'Tidyverse'
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   0.3.5 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.4.1 
✔ readr   2.1.3      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(here) # A Simpler Way to Find Your Files
here() starts at /Users/ajpelu/SERPAM Dropbox/14_GBIF/01_O2P/dwc_o2p
library(janitor) # Simple Tools for Examining and Cleaning Dirty Data

Attaching package: 'janitor'

The following objects are masked from 'package:stats':

    chisq.test, fisher.test
dicc_variables <- read.csv(here::here("data/raw/dic_var.csv"), sep = ";", 
                           encoding = 'UTF-8') |> 
  rename(measurementType = name_var,
         measurementUnit = units,
         measurementMethod = methods,
         measurementRemarks = url_controlled)
files <- list.files(path = here::here("data/raw/quadrats/"),
                    pattern = "*.xlsx", 
                    full.names = FALSE)

Genera una función para formatear todos los excels y dejarlos con un formato parecido a DwC emof

files |> 
  purrr::map(function(filename){
    aux <- readxl::read_excel(here::here("data/raw/quadrats/", basename(filename))) |> 
      janitor::clean_names() |> 
      rename_with(
        ~ case_when(
          . == "fecha" ~ "fecha_muestreos",
          . == "nombre_zona" ~ "zona",
          . == "geo_quadrat_nombre" ~ "quadrat",
          . == "geo_parcela_nombre" ~ "parcela",
          TRUE ~ .))
    
    aux_event <- aux |> 
      dplyr::select(-contains("correc"), 
                    -contains("cor"),
                    -contains("muerta"),
                    -contains("rango_infoca")) |> 
      mutate(
        treatment_name = case_when(
          zona == "Quemado con pastoreo" ~ "QOP",
          zona == "Quemado sin pastoreo" ~ "QONP",
          zona == "Quemado primavera" ~ "QPP")) |> 
      mutate(date = gsub("-","",fecha_muestreos)) |> 
      unite("eventID", c(quadrat, date), remove = FALSE) |> 
      rename(measurementDeterminedDate = date) |> 
      dplyr::select(-zona, -parcela, -fecha_muestreos) |> 
      pivot_longer(-c(eventID, quadrat, measurementDeterminedDate, treatment_name)) |> 
      dplyr::select(-quadrat, -treatment_name) |> 
      rename(measurementValue = value)
    
    
    assign(x = tolower(str_remove(filename, ".xlsx")),
           value = aux_event, 
           envir = .GlobalEnv)
  })
[[1]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name     measurementValue
   <chr>          <chr>                     <chr>               <dbl>
 1 P04Q1_20181121 20181121                  alt_prom             41.2
 2 P04Q1_20190604 20190604                  alt_prom             10.8
 3 P04Q1_20191218 20191218                  alt_prom             23.9
 4 P04Q1_20200519 20200519                  alt_prom             19.5
 5 P04Q1_20201117 20201117                  alt_prom             19.8
 6 P04Q1_20210519 20210519                  alt_prom             28.6
 7 P04Q2_20181121 20181121                  alt_prom             67.4
 8 P04Q2_20190604 20190604                  alt_prom             24.7
 9 P04Q2_20191218 20191218                  alt_prom             89.9
10 P04Q2_20200519 20200519                  alt_prom             40.0
# … with 534 more rows

[[2]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name            measurementValue
   <chr>          <chr>                     <chr>                      <dbl>
 1 P04Q1_20181121 20181121                  altura_promedio            3.88 
 2 P04Q1_20190604 20190604                  altura_promedio            0.452
 3 P04Q1_20191218 20191218                  altura_promedio            0.723
 4 P04Q1_20200519 20200519                  altura_promedio            0.607
 5 P04Q1_20201117 20201117                  altura_promedio            1.05 
 6 P04Q1_20210519 20210519                  altura_promedio            1.78 
 7 P04Q2_20181121 20181121                  altura_promedio            6.91 
 8 P04Q2_20190604 20190604                  altura_promedio            0.947
 9 P04Q2_20191218 20191218                  altura_promedio            0.681
10 P04Q2_20200519 20200519                  altura_promedio            0.793
# … with 534 more rows

[[3]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name    measurementValue
   <chr>          <chr>                     <chr>              <dbl>
 1 P04Q1_20181121 20181121                  desnudo               60
 2 P04Q1_20190604 20190604                  desnudo               70
 3 P04Q1_20191218 20191218                  desnudo               12
 4 P04Q1_20200519 20200519                  desnudo               83
 5 P04Q1_20201117 20201117                  desnudo               82
 6 P04Q1_20210519 20210519                  desnudo               65
 7 P04Q2_20181121 20181121                  desnudo                7
 8 P04Q2_20190604 20190604                  desnudo               15
 9 P04Q2_20191218 20191218                  desnudo               60
10 P04Q2_20200519 20200519                  desnudo               38
# … with 534 more rows

[[4]]
# A tibble: 539 × 4
   eventID         measurementDeterminedDate name      measurementValue
   <chr>           <chr>                     <chr>                <dbl>
 1 NP07Q1_20181121 20181121                  i_shannon            -1.57
 2 NP07Q1_20190604 20190604                  i_shannon            -2.07
 3 NP07Q1_20191218 20191218                  i_shannon            -1.99
 4 NP07Q1_20200519 20200519                  i_shannon            -2.21
 5 NP07Q1_20201117 20201117                  i_shannon            -2.09
 6 NP07Q1_20210519 20210519                  i_shannon            -1.95
 7 NP07Q2_20181121 20181121                  i_shannon            -1.20
 8 NP07Q2_20190604 20190604                  i_shannon            -1.88
 9 NP07Q2_20191218 20191218                  i_shannon            -1.44
10 NP07Q2_20200519 20200519                  i_shannon            -2.15
# … with 529 more rows

[[5]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name    measurementValue
   <chr>          <chr>                     <chr>              <dbl>
 1 P04Q1_20181121 20181121                  fitovol             871.
 2 P04Q1_20190604 20190604                  fitovol             173.
 3 P04Q1_20191218 20191218                  fitovol             118.
 4 P04Q1_20200519 20200519                  fitovol             241.
 5 P04Q1_20201117 20201117                  fitovol             204.
 6 P04Q1_20210519 20210519                  fitovol             546.
 7 P04Q2_20181121 20181121                  fitovol            4361.
 8 P04Q2_20190604 20190604                  fitovol             238.
 9 P04Q2_20191218 20191218                  fitovol             640.
10 P04Q2_20200519 20200519                  fitovol             442.
# … with 534 more rows

[[6]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name     measurementValue
   <chr>          <chr>                     <chr>               <dbl>
 1 P04Q1_20181121 20181121                  mantillo                5
 2 P04Q1_20190604 20190604                  mantillo                8
 3 P04Q1_20191218 20191218                  mantillo               76
 4 P04Q1_20200519 20200519                  mantillo                1
 5 P04Q1_20201117 20201117                  mantillo                2
 6 P04Q1_20210519 20210519                  mantillo                2
 7 P04Q2_20181121 20181121                  mantillo               18
 8 P04Q2_20190604 20190604                  mantillo               60
 9 P04Q2_20191218 20191218                  mantillo               25
10 P04Q2_20200519 20200519                  mantillo               40
# … with 534 more rows

[[7]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name      measurementValue
   <chr>          <chr>                     <chr>                <dbl>
 1 P04Q1_20181121 20181121                  cob_total               35
 2 P04Q1_20190604 20190604                  cob_total               22
 3 P04Q1_20191218 20191218                  cob_total               12
 4 P04Q1_20200519 20200519                  cob_total               16
 5 P04Q1_20201117 20201117                  cob_total               16
 6 P04Q1_20210519 20210519                  cob_total               33
 7 P04Q2_20181121 20181121                  cob_total               75
 8 P04Q2_20190604 20190604                  cob_total               25
 9 P04Q2_20191218 20191218                  cob_total               15
10 P04Q2_20200519 20200519                  cob_total               22
# … with 534 more rows

[[8]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name    measurementValue
   <chr>          <chr>                     <chr>              <dbl>
 1 P04Q1_20181121 20181121                  rec_veg             29.5
 2 P04Q1_20190604 20190604                  rec_veg             27.4
 3 P04Q1_20191218 20191218                  rec_veg              9.4
 4 P04Q1_20200519 20200519                  rec_veg             22.3
 5 P04Q1_20201117 20201117                  rec_veg             17.6
 6 P04Q1_20210519 20210519                  rec_veg             31.7
 7 P04Q2_20181121 20181121                  rec_veg             81.4
 8 P04Q2_20190604 20190604                  rec_veg             25.1
 9 P04Q2_20191218 20191218                  rec_veg             15.5
10 P04Q2_20200519 20200519                  rec_veg             18.5
# … with 534 more rows

[[9]]
# A tibble: 640 × 4
   eventID        measurementDeterminedDate name    measurementValue
   <chr>          <chr>                     <chr>              <dbl>
 1 P04Q1_20181121 20181121                  riqueza                8
 2 P04Q1_20190604 20190604                  riqueza               14
 3 P04Q1_20191009 20191009                  riqueza               11
 4 P04Q1_20191218 20191218                  riqueza               10
 5 P04Q1_20200519 20200519                  riqueza               20
 6 P04Q1_20201117 20201117                  riqueza               10
 7 P04Q1_20210519 20210519                  riqueza               10
 8 P04Q2_20181121 20181121                  riqueza               11
 9 P04Q2_20190604 20190604                  riqueza               10
10 P04Q2_20191009 20191009                  riqueza                8
# … with 630 more rows

[[10]]
# A tibble: 288 × 4
   eventID        measurementDeterminedDate name           measurementValue
   <chr>          <chr>                     <chr>                     <dbl>
 1 P04Q1_20190305 20190305                  rango_quema                   2
 2 P04Q1_20190305 20190305                  porc_soflamado               75
 3 P04Q1_20190305 20190305                  porc_quemado                 25
 4 P04Q2_20190305 20190305                  rango_quema                   4
 5 P04Q2_20190305 20190305                  porc_soflamado               30
 6 P04Q2_20190305 20190305                  porc_quemado                 70
 7 P04Q3_20190305 20190305                  rango_quema                   1
 8 P04Q3_20190305 20190305                  porc_soflamado               40
 9 P04Q3_20190305 20190305                  porc_quemado                  5
10 P04Q4_20190305 20190305                  rango_quema                   5
# … with 278 more rows

[[11]]
# A tibble: 544 × 4
   eventID        measurementDeterminedDate name         measurementValue
   <chr>          <chr>                     <chr>                   <dbl>
 1 P04Q1_20181121 20181121                  tasa_consumo            0    
 2 P04Q1_20190604 20190604                  tasa_consumo            0    
 3 P04Q1_20191218 20191218                  tasa_consumo            1.5  
 4 P04Q1_20200519 20200519                  tasa_consumo            1.64 
 5 P04Q1_20201117 20201117                  tasa_consumo            0.972
 6 P04Q1_20210519 20210519                  tasa_consumo            0.896
 7 P04Q2_20181121 20181121                  tasa_consumo            0    
 8 P04Q2_20190604 20190604                  tasa_consumo            0    
 9 P04Q2_20191218 20191218                  tasa_consumo            1.77 
10 P04Q2_20200519 20200519                  tasa_consumo            0.973
# … with 534 more rows

Genera emof para cada variable

Variables relacionadas con la estructura

Altura máxima Quadrat

event_altura_max <- 
  altura_max_promedio_quadrat |> 
  mutate(measurementValue = round(measurementValue, 2)) |> 
  mutate(name = case_when(name == "alt_prom" ~ "maxheight")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Altura moda Quadrat

event_altura_moda <- 
  altura_moda_promedio_quadrat |> 
  mutate(measurementValue = round(measurementValue, 2)) |> 
  mutate(name = case_when(name == "altura_promedio" ~ "modeheight")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Fitovolumen Total Quadrat

event_fitovol <- 
  fitovol_total_quadrat |> 
  mutate(name = case_when(name == "fitovol" ~ "fitovolumen")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Variables relacionadas con la cobertura

Suelo desnudo Quadrat

event_desnudo <- 
  desnudo_quadrat |> 
  mutate(name = case_when(name == "desnudo" ~ "bare_soil_percent")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Mantillo Quadrat

event_mantillo <- 
  mantillo_quadrat |> 
  mutate(name = case_when(name == "mantillo" ~ "litter_soil_percent")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Rec Total Quadrat

event_rec_total <- 
  rec_total_quadrat |> 
  mutate(name = case_when(name == "cob_total" ~ "tcover_percent")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Rec Veg Quadrat

event_rec_veg <- 
  rec_veg_quadrat |> 
  mutate(name = case_when(name == "rec_veg" ~ "vcover_percent")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Variables relacionadas con la biodiversidad

Diversidad Quadrat

event_diversidad <- 
  diversidad_quadrat |> 
  mutate(measurementValue = round(abs(measurementValue),3)) |> 
  mutate(name = case_when(name == "i_shannon" ~ "diversity")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Riqueza Quadrat

event_riqueza <- 
  riqueza_quadrat |> 
  mutate(name = case_when(name == "riqueza" ~ "richness")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Otras variables

Severidad de Quema Quadrat

event_severidad <- 
  sev_quema_quad_tot |> 
  mutate(name = 
           case_when(name == "rango_quema" ~ "burning",
                     name == "porc_soflamado" ~ "blowing_level_percent",
                     name == "porc_quemado" ~ "burning_level_percent")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Tasa Consumo Quadrat

event_tasa_consumo <- 
  tasa_consumo_promedio_quadrat |>
  mutate(measurementValue = round(measurementValue, 2)) |> 
  mutate(name = case_when(name == "tasa_consumo" ~ "tc")) |> 
  inner_join(dicc_variables,
    by = c("name" = "raw_key")) |>
  unite("measurementID", c(eventID, id), remove = FALSE) |> 
  dplyr::select(-code, -name, -id)

Genera un dataframe con todos los datos

o <- ls(pattern = "event_*") 

emof_quadrats <- bind_rows(mget(o)) |> 
  relocate(measurementType, measurementValue, measurementUnit, .after = eventID)

La tabla se exporta en csv en el siguiente enlace data/dwc_db/emof_quadrats.csv.

# Export table
write_csv(emof_quadrats, 
          here::here("data/dwc_db/emof_quadrats.csv"))

Aspecto de la tabla:


sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.3.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] janitor_2.1.0   here_1.0.1      forcats_0.5.2   stringr_1.4.1  
 [5] dplyr_1.0.10    purrr_0.3.5     readr_2.1.3     tidyr_1.2.1    
 [9] tibble_3.1.8    ggplot2_3.4.0   tidyverse_1.3.2 workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] httr_1.4.4          sass_0.4.4          bit64_4.0.5        
 [4] vroom_1.6.0         jsonlite_1.8.4      modelr_0.1.9       
 [7] bslib_0.4.2         assertthat_0.2.1    getPass_0.2-2      
[10] googlesheets4_1.0.1 cellranger_1.1.0    yaml_2.3.7         
[13] pillar_1.8.1        backports_1.4.1     glue_1.6.2         
[16] digest_0.6.31       promises_1.2.0.1    rvest_1.0.3        
[19] snakecase_0.11.0    colorspace_2.0-3    htmltools_0.5.4    
[22] httpuv_1.6.8        pkgconfig_2.0.3     broom_1.0.1        
[25] haven_2.5.1         scales_1.2.1        processx_3.7.0     
[28] whisker_0.4         later_1.3.0         tzdb_0.3.0         
[31] git2r_0.30.1        googledrive_2.0.0   generics_0.1.3     
[34] DT_0.26             ellipsis_0.3.2      cachem_1.0.6       
[37] withr_2.5.0         cli_3.6.0           magrittr_2.0.3     
[40] crayon_1.5.2        readxl_1.4.1        evaluate_0.18      
[43] ps_1.7.1            fs_1.5.2            fansi_1.0.3        
[46] xml2_1.3.3          tools_4.2.1         hms_1.1.2          
[49] gargle_1.2.1        lifecycle_1.0.3     munsell_0.5.0      
[52] reprex_2.0.2        callr_3.7.3         compiler_4.2.1     
[55] jquerylib_0.1.4     rlang_1.0.6         grid_4.2.1         
[58] rstudioapi_0.14     htmlwidgets_1.5.4   crosstalk_1.2.0    
[61] rmarkdown_2.18      gtable_0.3.1        DBI_1.1.3          
[64] R6_2.5.1            lubridate_1.8.0     knitr_1.41         
[67] fastmap_1.1.0       bit_4.0.4           utf8_1.2.2         
[70] rprojroot_2.0.3     stringi_1.7.8       parallel_4.2.1     
[73] Rcpp_1.0.9          vctrs_0.5.1         dbplyr_2.2.1       
[76] tidyselect_1.2.0    xfun_0.35