setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Env_muestra/data")
datos<-read.table("granofin.csv", header=T, sep=',')
datos$curva <- factor(datos$curva, levels = c("1", "2", "3"),
labels = c("P3", "P1", "P2"))
datos$gen<-as.factor(datos$gen)
datos$curva<-as.factor(datos$curva)
datos$id<-as.factor(datos$id)
datos$muestra<-as.factor(datos$muestra)
datos$dia<-as.factor(datos$dia)
library(ggplot2)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ tibble 3.1.6 ✓ purrr 0.3.4
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.1 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::arrange() masks plyr::arrange()
## x purrr::compact() masks plyr::compact()
## x dplyr::count() masks plyr::count()
## x dplyr::failwith() masks plyr::failwith()
## x dplyr::filter() masks stats::filter()
## x dplyr::id() masks plyr::id()
## x dplyr::lag() masks stats::lag()
## x dplyr::mutate() masks plyr::mutate()
## x dplyr::rename() masks plyr::rename()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()
library(ggpubr)
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
##
## mutate
library(rstatix)
##
## Attaching package: 'rstatix'
## The following objects are masked from 'package:plyr':
##
## desc, mutate
## The following object is masked from 'package:stats':
##
## filter
##Summary statistics
summ<-datos %>%
group_by(curva, gen, dia) %>%
get_summary_stats(cd.grano.c, type = "mean_sd")
summ %>% as_tibble() %>% print(n=Inf)
## # A tibble: 36 × 7
## curva gen dia variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 P3 CCN51 0 cd.grano.c 3 8.59 0.933
## 2 P3 CCN51 2 cd.grano.c 3 8.40 0.262
## 3 P3 CCN51 5 cd.grano.c 3 7.49 0.181
## 4 P3 CCN51 6 cd.grano.c 3 7.67 0.413
## 5 P3 ICS95 0 cd.grano.c 3 11.8 0.664
## 6 P3 ICS95 2 cd.grano.c 3 11.4 0.767
## 7 P3 ICS95 5 cd.grano.c 3 10.9 0.435
## 8 P3 ICS95 6 cd.grano.c 3 10.2 0.703
## 9 P3 TCS01 0 cd.grano.c 3 8.91 2.42
## 10 P3 TCS01 2 cd.grano.c 3 7.85 0.757
## 11 P3 TCS01 5 cd.grano.c 3 7.78 0.921
## 12 P3 TCS01 6 cd.grano.c 3 8.26 1.09
## 13 P1 CCN51 0 cd.grano.c 3 8.38 0.997
## 14 P1 CCN51 2 cd.grano.c 3 9.20 0.745
## 15 P1 CCN51 5 cd.grano.c 3 7.98 0.967
## 16 P1 CCN51 6 cd.grano.c 3 7.92 0.397
## 17 P1 ICS95 0 cd.grano.c 3 9.02 1.13
## 18 P1 ICS95 2 cd.grano.c 3 9.43 0.558
## 19 P1 ICS95 5 cd.grano.c 3 9.63 1.36
## 20 P1 ICS95 6 cd.grano.c 3 9.63 0.824
## 21 P1 TCS01 0 cd.grano.c 3 6.78 0.148
## 22 P1 TCS01 2 cd.grano.c 3 7.41 1.63
## 23 P1 TCS01 5 cd.grano.c 3 6.56 0.397
## 24 P1 TCS01 6 cd.grano.c 3 6.67 0.432
## 25 P2 CCN51 0 cd.grano.c 3 6.88 0.779
## 26 P2 CCN51 2 cd.grano.c 3 5.97 1.10
## 27 P2 CCN51 5 cd.grano.c 3 6.42 0.185
## 28 P2 CCN51 6 cd.grano.c 3 7.15 0.314
## 29 P2 ICS95 0 cd.grano.c 3 11.9 0.555
## 30 P2 ICS95 2 cd.grano.c 3 11.6 0.313
## 31 P2 ICS95 5 cd.grano.c 3 11.7 0.381
## 32 P2 ICS95 6 cd.grano.c 3 10.8 0.495
## 33 P2 TCS01 0 cd.grano.c 3 8.88 0.554
## 34 P2 TCS01 2 cd.grano.c 3 6.69 0.393
## 35 P2 TCS01 5 cd.grano.c 3 8.85 0.394
## 36 P2 TCS01 6 cd.grano.c 3 8.21 0.16
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "cd.grano.c",
color = "dia", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, dia) %>%
identify_outliers(cd.grano.c)
## [1] curva gen dia muestra id cd.grano
## [7] curva.1 protocolo gen.1 muestra.1 dia.1 Testa
## [13] Grano cd.grano.c cd.grano.a cd.grano.d X is.outlier
## [19] is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm<-datos %>%
group_by(curva, gen, dia) %>%
shapiro_test(cd.grano.c)
norm %>% as_tibble() %>% print(n=Inf)
## # A tibble: 36 × 6
## curva gen dia variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 P3 CCN51 0 cd.grano.c 0.812 0.143
## 2 P3 CCN51 2 cd.grano.c 0.841 0.216
## 3 P3 CCN51 5 cd.grano.c 0.803 0.122
## 4 P3 CCN51 6 cd.grano.c 0.927 0.477
## 5 P3 ICS95 0 cd.grano.c 0.994 0.847
## 6 P3 ICS95 2 cd.grano.c 0.992 0.827
## 7 P3 ICS95 5 cd.grano.c 0.755 0.0110
## 8 P3 ICS95 6 cd.grano.c 0.969 0.663
## 9 P3 TCS01 0 cd.grano.c 0.979 0.720
## 10 P3 TCS01 2 cd.grano.c 0.960 0.617
## 11 P3 TCS01 5 cd.grano.c 0.824 0.172
## 12 P3 TCS01 6 cd.grano.c 0.913 0.429
## 13 P1 CCN51 0 cd.grano.c 0.852 0.246
## 14 P1 CCN51 2 cd.grano.c 0.986 0.770
## 15 P1 CCN51 5 cd.grano.c 0.908 0.413
## 16 P1 CCN51 6 cd.grano.c 0.854 0.251
## 17 P1 ICS95 0 cd.grano.c 0.952 0.576
## 18 P1 ICS95 2 cd.grano.c 0.974 0.689
## 19 P1 ICS95 5 cd.grano.c 0.973 0.685
## 20 P1 ICS95 6 cd.grano.c 0.828 0.182
## 21 P1 TCS01 0 cd.grano.c 0.963 0.630
## 22 P1 TCS01 2 cd.grano.c 0.952 0.579
## 23 P1 TCS01 5 cd.grano.c 0.886 0.343
## 24 P1 TCS01 6 cd.grano.c 0.880 0.324
## 25 P2 CCN51 0 cd.grano.c 0.757 0.0159
## 26 P2 CCN51 2 cd.grano.c 1.00 0.972
## 27 P2 CCN51 5 cd.grano.c 0.917 0.442
## 28 P2 CCN51 6 cd.grano.c 0.976 0.702
## 29 P2 ICS95 0 cd.grano.c 0.972 0.682
## 30 P2 ICS95 2 cd.grano.c 0.975 0.696
## 31 P2 ICS95 5 cd.grano.c 0.971 0.674
## 32 P2 ICS95 6 cd.grano.c 0.809 0.135
## 33 P2 TCS01 0 cd.grano.c 0.880 0.324
## 34 P2 TCS01 2 cd.grano.c 1.00 0.986
## 35 P2 TCS01 5 cd.grano.c 0.994 0.854
## 36 P2 TCS01 6 cd.grano.c 0.888 0.349
##Create QQ plot for each cell of design:
ggqqplot(datos, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(dia~ curva*gen, labeller = "label_both")
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev<-datos %>%
group_by(dia) %>%
levene_test(cd.grano.c ~ curva*gen)
lev %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## dia df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 8 18 0.892 0.543
## 2 2 8 18 0.843 0.578
## 3 5 8 18 0.779 0.627
## 4 6 8 18 0.441 0.881
##Computation
res.aov <- anova_test(
data = datos, dv = cd.grano.c, wid = id,
within = dia, between = c(curva, gen)
)
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2.00 18.00 6.595 7.00e-03 * 0.228
## 2 gen 2.00 18.00 95.539 2.60e-10 * 0.811
## 3 dia 2.19 39.47 2.446 9.50e-02 0.075
## 4 curva:gen 4.00 18.00 11.174 9.83e-05 * 0.501
## 5 curva:dia 4.39 39.47 3.557 1.20e-02 * 0.191
## 6 gen:dia 4.39 39.47 1.331 2.74e-01 0.081
## 7 curva:gen:dia 8.77 39.47 1.271 2.84e-01 0.144
##Splitting dataframe by temperature ramp
## Protocol 3 (P3)
datos.curve1<-filter(datos, curva=="P3")
##Check assumptions
##Outliers
datos.curve1 %>%
group_by(gen, dia) %>%
identify_outliers(cd.grano.c)
## [1] gen dia curva muestra id cd.grano
## [7] curva.1 protocolo gen.1 muestra.1 dia.1 Testa
## [13] Grano cd.grano.c cd.grano.a cd.grano.d X is.outlier
## [19] is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm1<-datos.curve1 %>%
group_by(gen, dia) %>%
shapiro_test(cd.grano.c)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen dia variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.812 0.143
## 2 CCN51 2 cd.grano.c 0.841 0.216
## 3 CCN51 5 cd.grano.c 0.803 0.122
## 4 CCN51 6 cd.grano.c 0.927 0.477
## 5 ICS95 0 cd.grano.c 0.994 0.847
## 6 ICS95 2 cd.grano.c 0.992 0.827
## 7 ICS95 5 cd.grano.c 0.755 0.0110
## 8 ICS95 6 cd.grano.c 0.969 0.663
## 9 TCS01 0 cd.grano.c 0.979 0.720
## 10 TCS01 2 cd.grano.c 0.960 0.617
## 11 TCS01 5 cd.grano.c 0.824 0.172
## 12 TCS01 6 cd.grano.c 0.913 0.429
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(dia~ curva*gen, labeller = "label_both")
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev1<-datos.curve1 %>%
group_by(dia) %>%
levene_test(cd.grano.c ~ gen)
lev1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## dia df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 1.15 0.378
## 2 2 2 6 0.686 0.539
## 3 5 2 6 0.528 0.615
## 4 6 2 6 0.429 0.670
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = cd.grano.c, wid = id,
within = dia, between = gen
)
get_anova_table(res.aov1)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2 6 27.867 0.000918 * 0.757
## 2 dia 3 18 2.554 0.088000 0.221
## 3 gen:dia 6 18 0.495 0.804000 0.099
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.ccn1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 3.253 0.102 0.532
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.ics1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 4.272 0.062 0.529
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.tcs1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 0.42 0.745 0.126
## Protocol 1 (P1)
datos.curve2<-filter(datos, curva=="P1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, dia) %>%
identify_outliers(cd.grano.c)
## [1] gen dia curva muestra id cd.grano
## [7] curva.1 protocolo gen.1 muestra.1 dia.1 Testa
## [13] Grano cd.grano.c cd.grano.a cd.grano.d X is.outlier
## [19] is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm2<-datos.curve2 %>%
group_by(gen, dia) %>%
shapiro_test(cd.grano.c)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen dia variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.852 0.246
## 2 CCN51 2 cd.grano.c 0.986 0.770
## 3 CCN51 5 cd.grano.c 0.908 0.413
## 4 CCN51 6 cd.grano.c 0.854 0.251
## 5 ICS95 0 cd.grano.c 0.952 0.576
## 6 ICS95 2 cd.grano.c 0.974 0.689
## 7 ICS95 5 cd.grano.c 0.973 0.685
## 8 ICS95 6 cd.grano.c 0.828 0.182
## 9 TCS01 0 cd.grano.c 0.963 0.630
## 10 TCS01 2 cd.grano.c 0.952 0.579
## 11 TCS01 5 cd.grano.c 0.886 0.343
## 12 TCS01 6 cd.grano.c 0.880 0.324
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(dia~ curva*gen, labeller = "label_both")
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev2<-datos.curve2 %>%
group_by(dia) %>%
levene_test(cd.grano.c ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## dia df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.778 0.501
## 2 2 2 6 0.789 0.496
## 3 5 2 6 0.681 0.542
## 4 6 2 6 0.229 0.802
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = cd.grano.c, wid = id,
within = dia, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 12.534 0.007 * 0.672
## 2 dia 1.44 8.66 1.529 0.262 0.115
## 3 gen:dia 2.89 8.66 0.778 0.532 0.117
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 1.688 0.268 0.37
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 0.699 0.586 0.083
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 0.615 0.63 0.175
## Protocol 2 (P2)
datos.curve3<-filter(datos, curva=="P2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, dia) %>%
identify_outliers(cd.grano.c)
## [1] gen dia curva muestra id cd.grano
## [7] curva.1 protocolo gen.1 muestra.1 dia.1 Testa
## [13] Grano cd.grano.c cd.grano.a cd.grano.d X is.outlier
## [19] is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm2<-datos.curve3 %>%
group_by(gen, dia) %>%
shapiro_test(cd.grano.c)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen dia variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.757 0.0159
## 2 CCN51 2 cd.grano.c 1.00 0.972
## 3 CCN51 5 cd.grano.c 0.917 0.442
## 4 CCN51 6 cd.grano.c 0.976 0.702
## 5 ICS95 0 cd.grano.c 0.972 0.682
## 6 ICS95 2 cd.grano.c 0.975 0.696
## 7 ICS95 5 cd.grano.c 0.971 0.674
## 8 ICS95 6 cd.grano.c 0.809 0.135
## 9 TCS01 0 cd.grano.c 0.880 0.324
## 10 TCS01 2 cd.grano.c 1.00 0.986
## 11 TCS01 5 cd.grano.c 0.994 0.854
## 12 TCS01 6 cd.grano.c 0.888 0.349
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(dia~ curva*gen, labeller = "label_both")
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev2<-datos.curve3 %>%
group_by(dia) %>%
levene_test(cd.grano.c ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## dia df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.0309 0.970
## 2 2 2 6 1.52 0.292
## 3 5 2 6 0.431 0.668
## 4 6 2 6 0.332 0.730
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = cd.grano.c, wid = id,
within = dia, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2 6 169.964 5.22e-06 * 0.956
## 2 dia 3 18 9.486 5.59e-04 * 0.492
## 3 gen:dia 6 18 5.420 2.00e-03 * 0.525
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 2.034 0.211 0.385
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 2.897 0.124 0.57
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.grano.c, wid = id,
within = dia
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dia 3 6 64.657 5.84e-05 * 0.881
## Gráficas por réplica y genotipo
datos$dia<-as.numeric(as.character(datos$dia))
##Gráfica por réplica compuesta
pht<- ggplot(datos, aes(x = dia)) +
facet_grid(curva~gen*muestra) +
geom_line(aes(y=cd.grano.c)) +
geom_point(aes(y=cd.grano.c)) +
scale_y_continuous(name = expression("Cd (mg*Kg"^"-1)")) + # Etiqueta de la variable continua
scale_x_continuous(name = "dÃa", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
size = 0.25)) +
theme(text = element_text(size = 12))
pht
## Gráfica por genotipo
datos2<-summarySE (datos, measurevar = "cd.grano.c", groupvars = c("curva", "gen","dia"))
write.csv(datos2, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Env_muestra/data/datos_mean_grano.csv")
pht2<- ggplot(datos2, aes(x = dia)) +
facet_grid(curva~gen) +
geom_errorbar(aes(ymin=cd.grano.c-ci, ymax=cd.grano.c+ci), width=.1) +
geom_line(aes(y=cd.grano.c)) +
geom_point(aes(y=cd.grano.c)) +
scale_y_continuous(name = expression("Cd (mg*Kg"^"-1)")) + # Etiqueta de la variable continua
scale_x_continuous(name = "dÃa", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
size = 0.25)) +
theme(text = element_text(size = 15))
pht2
