Clase 09.03.23

#install.packages("collapsibleTree")
library(collapsibleTree)
df1=data.frame(
  gen=gl(3,5,15,c('g1','g2','g3')),
  rto=rnorm(n = 15,mean = 3,sd = 0.3))

collapsibleTreeSummary(df1,hierarchy = c('gen','rto'),collapsed = FALSE)

#FACTORIAL COMPLETO COMPLETAMENTE AL AZAR INCOMPLETO DESCARADO
df1 = data.frame(
  gen = gl(3, 6, 18, c('g1','g2','g3')),
  amb = gl(2, 3, 18, c('norte','sur')),
  rto = rnorm(n = 18, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('amb','gen','rto'),
                       collapsed = FALSE)

# DESCARADO FACTORIAL INCOMPLETO - COMPLETAMENTE AL AZAR
df1 = data.frame(
  gen = gl(6, 3, 18, paste0('g',1:6)),
  amb = gl(2, 3, 18, c('norte','sur')),
  rto = rnorm(n = 18, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('amb','gen','rto'),
                       collapsed = FALSE)

# SOLAPADO FACTORIAL INCOMPLETO - COMPLETAMENTE AL AZAR
df1 = data.frame(
  fert = gl(3, 6, 18, c('química','orgánica','mixta')),
  dosis = gl(2, 3, 18, c('d0','d10')),
  rto = rnorm(n = 18, mean = 3, sd = 0.3))
collapsibleTreeSummary(df1,
                       hierarchy = c('fert','dosis','rto'),
                       collapsed = FALSE)

# SOLAPADO FACTORIAL INCOMPLETO - COMPLETAMENTE AL AZAR
df1 = data.frame(
  escuela = gl(3, 6, 18, c('publica','privada','mixta')),
  profesor = gl(2, 3, 18, c('planta','ocasional')),
  rto = rnorm(n = 18, mean = 3, sd = 0.3))
collapsibleTreeSummary(df1,
                       hierarchy = c('escuela','profesor','rto'),
                       collapsed = FALSE)

# SOLAPADO FACTORIAL INCOMPLETO - COMPLETAMENTE AL AZAR
df1 = data.frame(
  platino = gl(3, 6, 18, c('pt1','pt2','sin')),
  uv = gl(2, 3, 18, c('con','sin')),
  vel_reac = rnorm(n = 18, mean = 3, sd = 0.3))
collapsibleTreeSummary(df1,
                       hierarchy = c('platino','uv','vel_reac'),
                       collapsed = FALSE)

# DESCARADO FACTORIAL INCOMPLETO - COMPLETAMENTE AL AZAR
df1 = data.frame(
  fert = gl(3, 6, 18, c('gallinaza','pollinaza','triple15')),
  dosis = gl(2, 3, 18, c('d0','d10')),
  rto = rnorm(n = 18, mean = 3, sd = 0.3))
collapsibleTreeSummary(df1,
                       hierarchy = c('fert','dosis','rto'),
                       collapsed = FALSE)

# SOLAPADO FACTORIAL INCOMPLETO - COMPLETAMENTE AL AZAR
set.seed(123)
df1 = data.frame(
  fert = gl(3, 6, 18, c('química','orgánica','mixta')),
  dosis = gl(2, 3, 18, c('d0','d10')),
  rto = rnorm(n = 18, mean = 3, sd = 0.3))
collapsibleTreeSummary(df1,
                       hierarchy = c('fert','dosis'),
                       collapsed = FALSE)

df1$rto=interaction(df1$fert,df1$dosis)
collapsibleTreeSummary(df1,hierarchy = c('rto','fert'))

xy = expand.grid(x=1:6, y=1:3)
xy$trt_ale = sample(df1$rto)
library(ggplot2)
ggplot(xy)+
  aes(x,y, label = trt_ale)+
  geom_tile(color='white')+
  geom_text(color='white')

#FACTORIAL SIMPLE EN ARREGLO DE BLOQUES
df1 = data.frame(
  #FACTOR GENOTIPO
  gen = gl(3, 6, 18, c('g1','g2','g3')),
  #BLOQUEO - PROCEDENCIA
  proce = gl(2, 3, 18, c('norte','sur')),
  #RESPUESTA
  rto = rnorm(n = 18, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('proce','gen','rto'),
                       collapsed = FALSE)

#FACTORIAL SIMPLE, ARREGLO DE BLOQUES COMPLETOS GENERALIZADOS AL AZAR
df1 = data.frame(
  #FACTOR GENOTIPO
  gen = gl(3, 2, 6, c('g1','g2','g3')),
  #BLOQUEO - PROCEDENCIA
  proce = gl(2, 1, 6, c('norte','sur')),
  #RESPUESTA
  rto = rnorm(n = 6, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('proce','gen','rto'),
                       collapsed = FALSE)

#CUADRADO LATINO, ARREGLO DE BLOQUES COMPLETOS GENERALIZADOS AL AZAR
df1 = data.frame(
  #FACTOR VARIEDAD
  variedad = gl(3, 12, 24, c('v1','v2','v3')),
  #BLOQUEO - PROCEDENCIA
  proce = gl(2, 6, 24, c('vendedor 1','vendedor 2')),
  #BLOQUEO 2 - POSICIÓN INVERNADERO (microclima)
  pos_inver = gl(2, 3, 24, c('anterior','posterior')),
  #RESPUESTA
  rto = rnorm(n = 24, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('pos_inver','proce','variedad','rto'),
                       collapsed = FALSE)

#CUADRADO LDE JODEN, NO HAY UN MISMO NÚMERO DE VARIABLES
df1 = data.frame(
  #FACTOR VARIEDAD
  variedad = gl(3, 12, 36, c('v1','v2','v3')),
  #BLOQUEO - PROCEDENCIA
  proce = gl(2, 6, 36, c('vendedor 1','vendedor 2')),
  #BLOQUEO 2 - POSICIÓN INVERNADERO (microclima)
  pos_inver = gl(3, 2, 36, c('anterior','central','posterior')),
  #RESPUESTA
  rto = rnorm(n = 36, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('pos_inver','proce','variedad','rto'),
                       collapsed = FALSE)

#CUADRADO LDE JODEN, NO HAY UN MISMO NÚMERO DE VARIABLES
df1 = data.frame(
  #FACTOR VARIEDAD
  temp = gl(3, 12, 36, c('15C','20C','25C')),
  #BLOQUEO - PROCEDENCIA
  pres = gl(2, 6, 36, c('1 atm','2 atm')),
  #BLOQUEO 2 - POSICIÓN INVERNADERO (microclima)
  cra = gl(3, 2, 36, c('anterior','central','posterior')),
  #RESPUESTA
  rto = rnorm(n = 36, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('temp','pres','cra'),
                       collapsed = FALSE)

# PARCELAS DIVIDIDAS
df1 = data.frame(
  # FACTOR 1 - variedad
  var = gl(3, 12, 36, c('v1','v2','v3')),
  # FACTOR 2 - densidad de siembra
  dens = gl(2, 6, 36, c('d1','d2')),
  # RESPUESTA
  rto = rnorm(n = 36, mean = 3, sd = 0.3)
)
collapsibleTreeSummary(df1,
                       hierarchy = c('var','dens','rto'),
                       collapsed = FALSE)