Hi community
Im try to help a friend to analyses this data. Is about dry tolerance in a crop.
In the data, the white space is when the plant dead.
For understand the data, Im make somes plots.
library(readxl)
library(tidyverse)
library(hrbrthemes)
library(lubridate)
ACUTIF_R2<- read_excel("path")
ACUTIF_R2$GENOTIPO <- as.factor(ACUTIF_R2$GENOTIPO)
ACUTIF_R2$FECHA <- as.Date(ACUTIF_R2$FECHA, format="%d-%b-%Y")
# General plot
ggplot(ACUTIF_R2, aes(x=FECHA,y=PESO, fill=GENOTIPO, color=GENOTIPO))+
geom_line(mapping =aes(x=FECHA,y=PESO, fill=GENOTIPO))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1,size = 12))+
scale_x_date(date_breaks = "1 week", date_minor_breaks = "1 week",
date_labels = "%d-%b-%Y")
# The same for others `GENOTIPO`
ggplot(subset(ACUTIF_R2,GENOTIPO=='G1'), aes(x=FECHA,y=PESO,fill=GENOTIPO)) +
geom_area(fill="#69b3a2", alpha=0.5) +
geom_line(color="#69b3a2") +
scale_y_continuous(limits=c(4200, 4700))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1,size = 12))+
scale_x_date(date_breaks = "1 day", date_minor_breaks = "1 day",
date_labels = "%d-%b-%Y")
# facet_wrap `GENOTIPO`
ggplot(ACUTIF_R2, aes(x=FECHA,y=PESO,fill=GENOTIPO, color=GENOTIPO)) +
geom_line() +
scale_color_manual(values=c('#6E0069','#A56F00','#05236F','#AFD600','#DE1500'))+
scale_y_continuous(limits=c(4200, 4700))+
theme_ipsum()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1,size = 12))+
scale_x_date(date_breaks = "1 week", date_minor_breaks = "1 week",
date_labels = "%d-%b-%Y")+
facet_wrap(~GENOTIPO, scales = 'free')
# lm
modelo <- lm(PESO~GENOTIPO + TRATAMIENTO + FECHA, data=ACUTIF_R2)
summary(modelo)
# Call:
# lm(formula = PESO ~ GENOTIPO + TRATAMIENTO + FECHA, data = ACUTIF_R2) # is good this?
#
# Residuals:
# Min 1Q Median 3Q Max
# -284.784 -16.701 5.163 23.944 115.421
#
# Coefficients: (4 not defined because of singularities)
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -1.837e+04 2.041e+03 -9.000 < 2e-16 ***
# GENOTIPOG2 7.736e+01 1.268e+01 6.103 1.93e-09 ***
# GENOTIPOG3 -3.769e+00 8.332e+00 -0.452 0.651173
# GENOTIPOG4 2.082e+01 8.557e+00 2.434 0.015257 *
# GENOTIPOG5 -3.613e+01 8.373e+00 -4.315 1.88e-05 ***
# TRATAMIENTOG1P2 2.240e+01 8.332e+00 2.689 0.007380 **
# TRATAMIENTOG1P3 -4.356e+01 8.332e+00 -5.228 2.41e-07 ***
# TRATAMIENTOG2P1 -5.148e+00 1.735e+01 -0.297 0.766747
# TRATAMIENTOG2P2 4.900e+01 1.551e+01 3.159 0.001670 **
# TRATAMIENTOG2P3 NA NA NA NA
# TRATAMIENTOG3P1 1.119e+02 8.837e+00 12.664 < 2e-16 ***
# TRATAMIENTOG3P2 -2.317e+01 8.373e+00 -2.767 0.005842 **
# TRATAMIENTOG3P3 NA NA NA NA
# TRATAMIENTOG4P1 2.943e+01 8.557e+00 3.439 0.000627 ***
# TRATAMIENTOG4P2 -1.925e+01 8.557e+00 -2.249 0.024875 *
# TRATAMIENTOG4P3 NA NA NA NA
# TRATAMIENTOG5P1 9.925e+01 1.118e+01 8.875 < 2e-16 ***
# TRATAMIENTOG5P2 9.160e+01 1.100e+01 8.330 6.06e-16 ***
# TRATAMIENTOG5P3 NA NA NA NA
# FECHA 1.185e+00 1.056e-01 11.222 < 2e-16 ***
# ---
# Signif. codes: 0 โ***โ 0.001 โ**โ 0.01 โ*โ 0.05 โ.โ 0.1 โ โ 1
#
# Residual standard error: 42.49 on 570 degrees of freedom
# (194 observations deleted due to missingness)
# Multiple R-squared: 0.5396, Adjusted R-squared: 0.5275
# F-statistic: 44.54 on 15 and 570 DF, p-value: < 2.2e-16
But dont now which statistics test use, like lm, mixed model or another.
You could suggest how make a good analyses of this data. Some links?