REPITIENDO CLASE 2
Inty limani chindoy luna
25/10/2021
library(readxl)
spectro <- read_excel("C:/Users/intyc/Desktop/D. Experimental/spectro.xlsx",
sheet = "Hoja1")
head(spectro)## # A tibble: 6 x 19
## tratam rep bloque Chla Chlb ChlT N RES NDVI2 GNDVI DDI TCARI2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 1553. 513. 2067. 0.631 0.843 0.127 0.204 -0.188 1.08
## 2 1 1 1 1753. 562. 2315. 0.631 0.744 0.230 0.302 -0.0920 1.16
## 3 1 1 1 1130. 349. 1479. 0.631 0.817 0.128 0.189 -0.156 1.08
## 4 1 1 1 836. 275. 1112. 0.631 0.858 0.0850 0.143 -0.187 1.05
## 5 1 1 1 982. 296. 1278. 0.631 0.854 0.101 0.162 -0.200 1.06
## 6 1 1 1 848. 252. 1101. 0.631 0.803 0.0836 0.129 -0.0988 1.06
## # ... with 7 more variables: TCARI6 <dbl>, TCARI9 <dbl>, NDRE1 <dbl>,
## # NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse) ## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v stringr 1.4.0
## v tidyr 1.1.4 v forcats 0.5.1
## v readr 2.0.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extract16 %>% sqrt## [1] 4sqrt(16)## [1] 416 %>% sqrt %>% + 18## [1] 22sqrt(16) + 18## [1] 22mean((spectro$Chla))## [1] 3038.431spectro %>%summarise(mean(Chla))## # A tibble: 1 x 1
## `mean(Chla)`
## <dbl>
## 1 3038.spectro %>%
group_by(tratam) %>%
summarise(mean(Chla))## # A tibble: 4 x 2
## tratam `mean(Chla)`
## <dbl> <dbl>
## 1 1 1467.
## 2 2 3393.
## 3 3 3476.
## 4 4 3819.spectro %>%
filter(tratam == 1) %>%
group_by(bloque) %>%
summarise(mean(Chla))## # A tibble: 4 x 2
## bloque `mean(Chla)`
## <dbl> <dbl>
## 1 1 1233.
## 2 2 1171.
## 3 3 1665.
## 4 4 1798.tablaresumen <- spectro %>%
group_by(tratam) %>%
summarise(media_cloroA = mean(Chla), var.Chla = var(Chla), n_obs = n())
tablaresumen## # A tibble: 4 x 4
## tratam media_cloroA var.Chla n_obs
## <dbl> <dbl> <dbl> <int>
## 1 1 1467. 216893. 48
## 2 2 3393. 280015. 48
## 3 3 3476. 176357. 48
## 4 4 3819. 498603. 48dos funciones con pipes
spectro %>% summarise(media_cloroA = mean(Chla), media_cloroB = mean(Chlb))## # A tibble: 1 x 2
## media_cloroA media_cloroB
## <dbl> <dbl>
## 1 3038. 1079.plot(x = c(1,2), y = c(4,5))
## variable filtrada con un rango usando & o and
tablaresumen_2 <- tablaresumen %>%
filter(media_cloroA > 2000 & media_cloroA < 3400)
tablaresumen_2## # A tibble: 1 x 4
## tratam media_cloroA var.Chla n_obs
## <dbl> <dbl> <dbl> <int>
## 1 2 3393. 280015. 48filtra una variable con rango establecido con ori( en mi teclado no funciono la linea vertical)
tablaresumen_3 <- tablaresumen %>%
filter(media_cloroA > 2000 | media_cloroA < 3400)
tablaresumen_3## # A tibble: 4 x 4
## tratam media_cloroA var.Chla n_obs
## <dbl> <dbl> <dbl> <int>
## 1 1 1467. 216893. 48
## 2 2 3393. 280015. 48
## 3 3 3476. 176357. 48
## 4 4 3819. 498603. 48signo de exclamacion ! sirve para negar, negamos todos los NA, osea datos faltantes
spectro %>%
filter(!is.na(spectro)) %>%
group_by(tratam, bloque) %>%
summarise(med.Chla = mean(Chla,na.rm=TRUE), var.Chla=var(Chla,na.rm=TRUE),n_obs = n()) %>%
filter(n_obs > 10)## `summarise()` has grouped output by 'tratam'. You can override using the `.groups` argument.## # A tibble: 16 x 5
## # Groups: tratam [4]
## tratam bloque med.Chla var.Chla n_obs
## <dbl> <dbl> <dbl> <dbl> <int>
## 1 1 1 1233. 190590. 12
## 2 1 2 1171. 148222. 12
## 3 1 3 1665. 107720. 12
## 4 1 4 1798. 162357. 12
## 5 2 1 3723. 502636. 12
## 6 2 2 3205. 114358. 12
## 7 2 3 3282. 221487. 12
## 8 2 4 3361. 186032. 12
## 9 3 1 3230. 159673. 12
## 10 3 2 3603. 277044. 12
## 11 3 3 3390. 94065. 12
## 12 3 4 3680. 85838. 12
## 13 4 1 3521. 156090. 12
## 14 4 2 4155. 849173. 12
## 15 4 3 3564. 626807. 12
## 16 4 4 4035. 156323. 12filtrado tradicional
filter(spectro, bloque == 1)## # A tibble: 48 x 19
## tratam rep bloque Chla Chlb ChlT N RES NDVI2 GNDVI DDI TCARI2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 1553. 513. 2067. 0.631 0.843 0.127 0.204 -0.188 1.08
## 2 1 1 1 1753. 562. 2315. 0.631 0.744 0.230 0.302 -0.0920 1.16
## 3 1 1 1 1130. 349. 1479. 0.631 0.817 0.128 0.189 -0.156 1.08
## 4 1 1 1 836. 275. 1112. 0.631 0.858 0.0850 0.143 -0.187 1.05
## 5 1 1 1 982. 296. 1278. 0.631 0.854 0.101 0.162 -0.200 1.06
## 6 1 1 1 848. 252. 1101. 0.631 0.803 0.0836 0.129 -0.0988 1.06
## 7 1 1 1 488. 128. 617. 0.631 0.692 0.104 0.143 -0.0189 1.08
## 8 1 1 1 904. 273. 1178. 0.631 0.718 0.134 0.179 -0.0430 1.10
## 9 1 1 1 1221. 403. 1624. 0.631 0.711 0.155 0.198 -0.0425 1.12
## 10 1 1 1 1679. 582. 2261. 0.631 0.727 0.217 0.280 -0.0725 1.16
## # ... with 38 more rows, and 7 more variables: TCARI6 <dbl>, TCARI9 <dbl>,
## # NDRE1 <dbl>, NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>filtado con pipes
spectro %>% filter(bloque == 1)## # A tibble: 48 x 19
## tratam rep bloque Chla Chlb ChlT N RES NDVI2 GNDVI DDI TCARI2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 1553. 513. 2067. 0.631 0.843 0.127 0.204 -0.188 1.08
## 2 1 1 1 1753. 562. 2315. 0.631 0.744 0.230 0.302 -0.0920 1.16
## 3 1 1 1 1130. 349. 1479. 0.631 0.817 0.128 0.189 -0.156 1.08
## 4 1 1 1 836. 275. 1112. 0.631 0.858 0.0850 0.143 -0.187 1.05
## 5 1 1 1 982. 296. 1278. 0.631 0.854 0.101 0.162 -0.200 1.06
## 6 1 1 1 848. 252. 1101. 0.631 0.803 0.0836 0.129 -0.0988 1.06
## 7 1 1 1 488. 128. 617. 0.631 0.692 0.104 0.143 -0.0189 1.08
## 8 1 1 1 904. 273. 1178. 0.631 0.718 0.134 0.179 -0.0430 1.10
## 9 1 1 1 1221. 403. 1624. 0.631 0.711 0.155 0.198 -0.0425 1.12
## 10 1 1 1 1679. 582. 2261. 0.631 0.727 0.217 0.280 -0.0725 1.16
## # ... with 38 more rows, and 7 more variables: TCARI6 <dbl>, TCARI9 <dbl>,
## # NDRE1 <dbl>, NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>#filtrado con resticciones, ponemos la restriccion por cualquier criterio, eliminamos tratos, 2 y 4
spectro %>% filter(tratam %in% seq(1, 3))## # A tibble: 144 x 19
## tratam rep bloque Chla Chlb ChlT N RES NDVI2 GNDVI DDI TCARI2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 1553. 513. 2067. 0.631 0.843 0.127 0.204 -0.188 1.08
## 2 1 1 1 1753. 562. 2315. 0.631 0.744 0.230 0.302 -0.0920 1.16
## 3 1 1 1 1130. 349. 1479. 0.631 0.817 0.128 0.189 -0.156 1.08
## 4 1 1 1 836. 275. 1112. 0.631 0.858 0.0850 0.143 -0.187 1.05
## 5 1 1 1 982. 296. 1278. 0.631 0.854 0.101 0.162 -0.200 1.06
## 6 1 1 1 848. 252. 1101. 0.631 0.803 0.0836 0.129 -0.0988 1.06
## 7 1 1 1 488. 128. 617. 0.631 0.692 0.104 0.143 -0.0189 1.08
## 8 1 1 1 904. 273. 1178. 0.631 0.718 0.134 0.179 -0.0430 1.10
## 9 1 1 1 1221. 403. 1624. 0.631 0.711 0.155 0.198 -0.0425 1.12
## 10 1 1 1 1679. 582. 2261. 0.631 0.727 0.217 0.280 -0.0725 1.16
## # ... with 134 more rows, and 7 more variables: TCARI6 <dbl>, TCARI9 <dbl>,
## # NDRE1 <dbl>, NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>#filtrado entre columnas trato 3
spectro %>% filter(between(tratam, 3, 4))## # A tibble: 96 x 19
## tratam rep bloque Chla Chlb ChlT N RES NDVI2 GNDVI DDI TCARI2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 1 2 3132. 1210. 4343. 2.06 0.478 0.529 0.598 0.212 1.57
## 2 3 1 2 3236. 1090. 4325. 2.06 0.492 0.508 0.588 0.193 1.53
## 3 3 1 2 3600. 1343. 4943. 2.06 0.502 0.510 0.592 0.182 1.53
## 4 3 1 2 3986. 1480. 5465. 2.06 0.483 0.519 0.599 0.190 1.56
## 5 3 1 2 3817. 1417. 5234. 2.06 0.502 0.528 0.613 0.197 1.54
## 6 3 1 2 3178. 1121. 4299. 2.06 0.490 0.482 0.550 0.190 1.51
## 7 3 1 2 4469. 1545. 6014. 2.06 0.451 0.555 0.645 0.248 1.64
## 8 3 1 2 3538. 1252. 4791. 2.06 0.526 0.519 0.608 0.176 1.52
## 9 3 1 2 3156. 1004. 4160. 2.06 0.525 0.470 0.547 0.159 1.47
## 10 3 1 2 4306. 1645. 5951. 2.06 0.443 0.565 0.639 0.256 1.66
## # ... with 86 more rows, and 7 more variables: TCARI6 <dbl>, TCARI9 <dbl>,
## # NDRE1 <dbl>, NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>spectro %>% filter(bloque %in% c(1,2,4))## # A tibble: 144 x 19
## tratam rep bloque Chla Chlb ChlT N RES NDVI2 GNDVI DDI TCARI2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 1553. 513. 2067. 0.631 0.843 0.127 0.204 -0.188 1.08
## 2 1 1 1 1753. 562. 2315. 0.631 0.744 0.230 0.302 -0.0920 1.16
## 3 1 1 1 1130. 349. 1479. 0.631 0.817 0.128 0.189 -0.156 1.08
## 4 1 1 1 836. 275. 1112. 0.631 0.858 0.0850 0.143 -0.187 1.05
## 5 1 1 1 982. 296. 1278. 0.631 0.854 0.101 0.162 -0.200 1.06
## 6 1 1 1 848. 252. 1101. 0.631 0.803 0.0836 0.129 -0.0988 1.06
## 7 1 1 1 488. 128. 617. 0.631 0.692 0.104 0.143 -0.0189 1.08
## 8 1 1 1 904. 273. 1178. 0.631 0.718 0.134 0.179 -0.0430 1.10
## 9 1 1 1 1221. 403. 1624. 0.631 0.711 0.155 0.198 -0.0425 1.12
## 10 1 1 1 1679. 582. 2261. 0.631 0.727 0.217 0.280 -0.0725 1.16
## # ... with 134 more rows, and 7 more variables: TCARI6 <dbl>, TCARI9 <dbl>,
## # NDRE1 <dbl>, NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>selecciono las que quiero
spectro %>% select(tratam, bloque, Chla)## # A tibble: 192 x 3
## tratam bloque Chla
## <dbl> <dbl> <dbl>
## 1 1 1 1553.
## 2 1 1 1753.
## 3 1 1 1130.
## 4 1 1 836.
## 5 1 1 982.
## 6 1 1 848.
## 7 1 1 488.
## 8 1 1 904.
## 9 1 1 1221.
## 10 1 1 1679.
## # ... with 182 more rowselimino columnas menos las que le pongo -
spectro %>% select(-bloque, -rep)## # A tibble: 192 x 17
## tratam Chla Chlb ChlT N RES NDVI2 GNDVI DDI TCARI2 TCARI6
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1553. 513. 2067. 0.631 0.843 0.127 0.204 -0.188 1.08 1.29
## 2 1 1753. 562. 2315. 0.631 0.744 0.230 0.302 -0.0920 1.16 1.60
## 3 1 1130. 349. 1479. 0.631 0.817 0.128 0.189 -0.156 1.08 1.29
## 4 1 836. 275. 1112. 0.631 0.858 0.0850 0.143 -0.187 1.05 1.19
## 5 1 982. 296. 1278. 0.631 0.854 0.101 0.162 -0.200 1.06 1.23
## 6 1 848. 252. 1101. 0.631 0.803 0.0836 0.129 -0.0988 1.06 1.18
## 7 1 488. 128. 617. 0.631 0.692 0.104 0.143 -0.0189 1.08 1.23
## 8 1 904. 273. 1178. 0.631 0.718 0.134 0.179 -0.0430 1.10 1.31
## 9 1 1221. 403. 1624. 0.631 0.711 0.155 0.198 -0.0425 1.12 1.37
## 10 1 1679. 582. 2261. 0.631 0.727 0.217 0.280 -0.0725 1.16 1.56
## # ... with 182 more rows, and 6 more variables: TCARI9 <dbl>, NDRE1 <dbl>,
## # NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>rotulo de columnas para nombrar mejor los datos
spectro %>% select(tratam, ClorofilaA = Chla, ClorofilaB = Chlb)## # A tibble: 192 x 3
## tratam ClorofilaA ClorofilaB
## <dbl> <dbl> <dbl>
## 1 1 1553. 513.
## 2 1 1753. 562.
## 3 1 1130. 349.
## 4 1 836. 275.
## 5 1 982. 296.
## 6 1 848. 252.
## 7 1 488. 128.
## 8 1 904. 273.
## 9 1 1221. 403.
## 10 1 1679. 582.
## # ... with 182 more rowsfuncion everething organiza , recordar que primero se pone el argumento luego la funcion
spectro %>% select(tratam, bloque, DDI, everything())## # A tibble: 192 x 19
## tratam bloque DDI rep Chla Chlb ChlT N RES NDVI2 GNDVI TCARI2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 -0.188 1 1553. 513. 2067. 0.631 0.843 0.127 0.204 1.08
## 2 1 1 -0.0920 1 1753. 562. 2315. 0.631 0.744 0.230 0.302 1.16
## 3 1 1 -0.156 1 1130. 349. 1479. 0.631 0.817 0.128 0.189 1.08
## 4 1 1 -0.187 1 836. 275. 1112. 0.631 0.858 0.0850 0.143 1.05
## 5 1 1 -0.200 1 982. 296. 1278. 0.631 0.854 0.101 0.162 1.06
## 6 1 1 -0.0988 1 848. 252. 1101. 0.631 0.803 0.0836 0.129 1.06
## 7 1 1 -0.0189 1 488. 128. 617. 0.631 0.692 0.104 0.143 1.08
## 8 1 1 -0.0430 1 904. 273. 1178. 0.631 0.718 0.134 0.179 1.10
## 9 1 1 -0.0425 1 1221. 403. 1624. 0.631 0.711 0.155 0.198 1.12
## 10 1 1 -0.0725 1 1679. 582. 2261. 0.631 0.727 0.217 0.280 1.16
## # ... with 182 more rows, and 7 more variables: TCARI6 <dbl>, TCARI9 <dbl>,
## # NDRE1 <dbl>, NDRE2 <dbl>, mSR <dbl>, DSR2 <dbl>, BND <dbl>variables que comiencen por c
spectro %>% select(starts_with("C"))## # A tibble: 192 x 3
## Chla Chlb ChlT
## <dbl> <dbl> <dbl>
## 1 1553. 513. 2067.
## 2 1753. 562. 2315.
## 3 1130. 349. 1479.
## 4 836. 275. 1112.
## 5 982. 296. 1278.
## 6 848. 252. 1101.
## 7 488. 128. 617.
## 8 904. 273. 1178.
## 9 1221. 403. 1624.
## 10 1679. 582. 2261.
## # ... with 182 more rowsspectro %>% select(tratam, bloque, contains("Ch"))## # A tibble: 192 x 5
## tratam bloque Chla Chlb ChlT
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1553. 513. 2067.
## 2 1 1 1753. 562. 2315.
## 3 1 1 1130. 349. 1479.
## 4 1 1 836. 275. 1112.
## 5 1 1 982. 296. 1278.
## 6 1 1 848. 252. 1101.
## 7 1 1 488. 128. 617.
## 8 1 1 904. 273. 1178.
## 9 1 1 1221. 403. 1624.
## 10 1 1 1679. 582. 2261.
## # ... with 182 more rowsspectro %>% select(tratam, bloque, contains("Ch"))## # A tibble: 192 x 5
## tratam bloque Chla Chlb ChlT
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1553. 513. 2067.
## 2 1 1 1753. 562. 2315.
## 3 1 1 1130. 349. 1479.
## 4 1 1 836. 275. 1112.
## 5 1 1 982. 296. 1278.
## 6 1 1 848. 252. 1101.
## 7 1 1 488. 128. 617.
## 8 1 1 904. 273. 1178.
## 9 1 1 1221. 403. 1624.
## 10 1 1 1679. 582. 2261.
## # ... with 182 more rowsspectro %>% select(N)## # A tibble: 192 x 1
## N
## <dbl>
## 1 0.631
## 2 0.631
## 3 0.631
## 4 0.631
## 5 0.631
## 6 0.631
## 7 0.631
## 8 0.631
## 9 0.631
## 10 0.631
## # ... with 182 more rowsjalar datos
GNDVI <- spectro %>% pull(GNDVI)luego selescciono el dato y aparece
GNDVI[122]## [1] 0.6174512