Espectroscopía NIR Nothofagus
Jorge González Campos
2025-08-21
1.1. Carga de directorio & paquetes
1.2. Carga de datos
# 4 agosto
data1 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/1-59.txt", header=FALSE, comment.char="#")
# 5 agosto
data2 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/60-110.txt", header=FALSE, comment.char="#")
# 6 agosto
data3 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/1-10.txt", header=FALSE, comment.char="#")
# 21 agosto
data4 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/a10-a54.txt", header=FALSE, comment.char="#")
# 26 agosto
data5 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/Signatures-3123-20250825-145939.txt", header=FALSE, comment.char="#")
# 26 agosto
data6 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/Signatures-3123-20250826-111132.txt", header=FALSE, comment.char="#")
# 27 agosto
data7 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/Signatures-3123-20250827-150441.txt", header=FALSE, comment.char="#")
# 28 agosto
data8 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/Signatures-3123-20250828-155056.txt", header=FALSE, comment.char="#")
#02 septiembre
data9 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/Signatures-3123-20250902-162037.txt", header=FALSE, comment.char="#")
#03 septiembre
data10 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/Signatures-3123-20250903-120930.txt", header=FALSE, comment.char="#")
#08 septiembre
data11 <- read.delim("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/Signatures-3123-20250908-155156.txt", header=FALSE, comment.char="#")
# Quitar la primera fila a cada data frame
data1 <- data1[-1, ]
data2 <- data2[-1, ]
data3 <- data3[-1, ]
data4 <- data4[-1, ]
data5 <- data5[-1, ]
data6 <- data6[-1, ]
data7 <- data7[-c(1:2), ]
data8 <- data8[-1, ]
data9 <- data9[-1, ]
data10 <- data10[-1, ]
data11 <- data11[-1, ]
# Revisa resultados
#data1
#data2
#data3
#data4
#data5
#data6
#data7
#data8
#data9
#data10
#data11 1.2.1. Homogenizar las variables categoricas
1.2.2. Generar funciones de limpieza
library(dplyr)
library(dplyr)
# Función de limpieza
limpiar_datos <- function(df) {
df %>%
select(-c(1:9, 11:16, 117:123)) %>%
rename(sample = V10)
}
# Función para forzar numéricos
forzar_numericas <- function(df) {
df[, 2:101] <- lapply(df[, 2:101], function(x) as.numeric(as.character(x)))
df
}
aplicar_limpieza <- function(df) {
df %>%
limpiar_datos() %>%
forzar_numericas()
}1.2.3. Aplicar funciones de limpieza
1.2.4. Revisar resultados
1.2.5. Verificamos si hay data set negativos
## ⚠️ Bases de datos que contienen valores negativos en las columnas 2:101:
## [1] "data4x"1.2.6. Conteo de muestras por base por ID
## Warning: package 'purrr' was built under R version 4.3.3##
## Attaching package: 'purrr'## The following object is masked from 'package:maps':
##
## map## --- Conteo de Muestras (sample) por Base de Datos ---## Base sample n
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## 903 data11x m7-003 51.2.7. Conteo de muestras por base por ID / Nota: correciones en los ID
## Muestras únicas en data7x después de la limpieza:## [1] "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33"
## [16] "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
## [31] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59"## Muestras únicas en data8x después de la limpieza:## [1] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120" "121" "122"
## [13] "123" "124" "125" "126" "127" "128" "129" "130" "131" "132" "133" "134"
## [25] "135" "136" "137" "138" "139" "140" "141" "142" "143" "144" "145" "146"
## [37] "147" "148" "149" "150" "151" "152" "153" "154" "155" "156" "157" "158"
## [49] "159" "160" "161" "162" "163" "164" "165" "166" "167" "168" "169" "170"
## [61] "171" "172" "173" "174"## Muestras únicas en data9x después de la limpieza:## [1] "175" "176" "177" "178" "179" "180" "181" "182" "183" "184" "185" "186"
## [13] "187" "188" "189" "190" "191" "192" "193" "194" "195" "196" "197" "198"
## [25] "199" "200" "201" "202" "203" "204" "205" "206" "207" "208" "209" "210"
## [37] "211" "212" "213" "214" "215" "216" "217" "218" "219" "220" "221" "222"
## [49] "223" "224" "225" "226" "227" "228" "232" "233" "234" "235" "236" "237"
## [61] "238" "239" "240" "241" "242" "243" "244" "245" "246" "247" "248" "249"
## [73] "250" "251" "252" "253" "254" "255" "256" "257" "258" "259" "260" "261"
## [85] "262"## Muestras únicas en data10x después de la limpieza:## [1] "263" "264" "265" "266" "267" "268" "269" "270" "271" "272" "273" "274"
## [13] "275" "276" "277" "278" "279" "280" "281" "282"## Muestras únicas en data11x después de la limpieza:## [1] "m1" "m2" "m3" "m4" "m5" "m6" "m7" "6" "7" "9" "10" "11"
## [13] "12" "13" "14" "15" "16" "17" "18" "245" "246" "247" "248" "249"
## [25] "250" "251" "252" "253" "254" "255" "256" "257" "258" "259" "260" "261"
## [37] "262"1.2.8. Revisar resultados
📢📢📢📢📢📢📢📢 Revisión 📢📢📢📢📢📢📢📢
## Muestras únicas en data1x ordenadas## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59## Muestras únicas en data2x ordenadas## [1] 1 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77
## [20] 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
## [39] 97 98 99 100 101 102 103 104 105 106 107 108 109 110## Muestras únicas en data3x ordenadas## [1] 1 2 3 4 5 6 7 8 9 10## Muestras únicas en data4x ordenadas## [1] "a10" "a11" "a12" "a13" "a14" "a15" "a16" "a17" "a18" "a19" "a20" "a21"
## [13] "a22" "a23" "a24" "a25" "a26" "a27" "a28" "a29" "a3" "a30" "a31" "a32"
## [25] "a34" "a35" "a36" "a37" "a38" "a39" "a40" "a41" "a42" "a43" "a44" "a45"
## [37] "a46" "a47" "a48" "a49" "a50" "a51" "a52" "a53" "a54"## Muestras únicas en data5x ordenadas## [1] "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20" "b21"
## [13] "b22" "b23" "b24" "b25" "b8" "b9"## Muestras únicas en data6x ordenadas## [1] "b26" "b27" "b28" "b29" "b30" "b31" "b32" "b33" "b34" "b35" "b36" "b37"
## [13] "b38" "b39" "b40" "b41" "b42" "b43" "b44" "b45" "b46" "b47" "b48"## Muestras únicas en data7x ordenadas## [1] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
## [26] 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59## Muestras únicas en data8x ordenadas## [1] 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129
## [20] 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148
## [39] 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
## [58] 168 169 170 171 172 173 174## Muestras únicas en data9x ordenadas## [1] 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193
## [20] 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212
## [39] 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 232 233 234
## [58] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253
## [77] 254 255 256 257 258 259 260 261 262## Muestras únicas en data10x ordenadas## [1] 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281
## [20] 282## Muestras únicas en data11x ordenadas## [1] "10" "11" "12" "13" "14" "15" "16" "17" "18" "245" "246" "247"
## [13] "248" "249" "250" "251" "252" "253" "254" "255" "256" "257" "258" "259"
## [25] "260" "261" "262" "6" "7" "9" "m1" "m2" "m3" "m4" "m5" "m6"
## [37] "m7"## <environment: R_GlobalEnv>## ✅ El proceso de promediado dinámico (cada 5 espectros) se ha aplicado a:## data1x_prom, data2x_prom, data3x_prom, data5x_prom, data6x_prom, data7x_prom, data8x_prom, data9x_prom, data10x_prom, data11x_prom## Ahora están listos para la unión final. 🚀1.2.9. Revisar resultados
1.4 Visualizar espectros
## ## Generando 10 gráficos interactivos...1.6 Base de datos espectral
library(readxl)
otros_id <- read_excel("C:/Users/jmgon/OneDrive - Instituto Forestal/INFOR/3. Propuestas De Proyectos/NIR - CTPF/NutriSpec/Trabajo exploratorio Flavia/NOMENCLATURA.xlsx",
sheet = "Nothofagus", col_types = c("text",
"text", "text", "text", "numeric",
"numeric", "text"))
otros_idlibrary(dplyr)
# Opción A: tres bases explícitas
base_unida <- bind_rows(
list(base1 = data1x_prom, base2 = data2x_prom, base3 = data3x_prom, base5 = data5x_prom),
.id = "base"
)
#base5 = data5x_prom,
#base6 = data6x_prom,
#base7 = data7x_prom,
#base8 = data8x_prom,
#base9 = data8x_prom,
#base10 = data10x_prom,
#base11 = data11x_prom
# (Opcional) Si tienes muchas bases con nombres tipo data5x_prom, data6x_prom, ...
# bases <- mget(ls(pattern = "^data\\d+x_prom$"))
# base_unida <- bind_rows(bases, .id = "base")base_final_con_metadatos <- base_unida %>%
inner_join(otros_id, by = "sample")
base_final_con_metadatosBloque de revisión
library(stringr)
# Filtra el sample de interés (ej. "1")
df_x <- filter(base_final_con_metadatos, sample == "1")
# Columnas espectrales
spectral_cols <- grep("^V\\d+$", names(df_x), value = TRUE)
# LARGO + réplica por base (por si quieres ver todas las curvas de cada base)
df_long <- df_x %>%
group_by(base) %>%
mutate(replica = row_number()) %>%
ungroup() %>%
select(base, replica, all_of(spectral_cols)) %>%
pivot_longer(-c(base, replica), names_to = "band", values_to = "value") %>%
mutate(idx = as.integer(str_remove(band, "^V")))
# eje X
df_long <- if (exists("wavelength")) {
mutate(df_long, wl = wavelength[idx])
} else {
mutate(df_long, wl = idx)
}
ggplot(df_long, aes(x = wl, y = value,
group = interaction(base, replica),
color = base)) +
geom_line(alpha = 0.6) +
labs(x = "Longitud de onda", y = "Intensidad/Reflectancia", color = "Base") +
theme_minimal()