setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Env_muestra/data")
datos<-read.table("granofinmod.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$diam2<-as.factor(datos$diam2)
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, diam2) %>%
get_summary_stats(cd.grano.c, type = "mean_sd")
summ %>% as_tibble() %>% print(n=Inf)
## # A tibble: 36 × 7
## curva gen diam2 variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 P3 CCN51 0 cd.grano.c 3 8.76 0.807
## 2 P3 CCN51 2 cd.grano.c 3 8.23 0.315
## 3 P3 CCN51 5 cd.grano.c 3 7.78 0.312
## 4 P3 CCN51 6 cd.grano.c 3 7.39 0.137
## 5 P3 ICS95 0 cd.grano.c 3 11.8 0.664
## 6 P3 ICS95 2 cd.grano.c 3 11.5 0.69
## 7 P3 ICS95 5 cd.grano.c 3 10.9 0.43
## 8 P3 ICS95 6 cd.grano.c 3 10.2 0.624
## 9 P3 TCS01 0 cd.grano.c 3 9.67 1.76
## 10 P3 TCS01 2 cd.grano.c 3 8.30 0.355
## 11 P3 TCS01 5 cd.grano.c 3 7.78 0.919
## 12 P3 TCS01 6 cd.grano.c 3 7.05 0.342
## 13 P1 CCN51 0 cd.grano.c 3 9.53 0.448
## 14 P1 CCN51 2 cd.grano.c 3 8.37 0.615
## 15 P1 CCN51 5 cd.grano.c 3 7.88 0.551
## 16 P1 CCN51 6 cd.grano.c 3 7.69 0.489
## 17 P1 ICS95 0 cd.grano.c 3 10.0 0.77
## 18 P1 ICS95 2 cd.grano.c 3 9.63 0.824
## 19 P1 ICS95 5 cd.grano.c 3 9.30 0.831
## 20 P1 ICS95 6 cd.grano.c 3 8.75 1.11
## 21 P1 TCS01 0 cd.grano.c 3 7.71 1.30
## 22 P1 TCS01 2 cd.grano.c 3 6.75 0.419
## 23 P1 TCS01 5 cd.grano.c 3 6.55 0.337
## 24 P1 TCS01 6 cd.grano.c 3 6.41 0.396
## 25 P2 CCN51 0 cd.grano.c 3 7.27 0.486
## 26 P2 CCN51 2 cd.grano.c 3 6.98 0.512
## 27 P2 CCN51 5 cd.grano.c 3 6.47 0.139
## 28 P2 CCN51 6 cd.grano.c 3 5.70 0.729
## 29 P2 ICS95 0 cd.grano.c 3 12.2 0.301
## 30 P2 ICS95 2 cd.grano.c 3 11.5 0.228
## 31 P2 ICS95 5 cd.grano.c 3 11.4 0.156
## 32 P2 ICS95 6 cd.grano.c 3 10.8 0.495
## 33 P2 TCS01 0 cd.grano.c 3 8.98 0.476
## 34 P2 TCS01 2 cd.grano.c 3 8.75 0.442
## 35 P2 TCS01 5 cd.grano.c 3 8.21 0.16
## 36 P2 TCS01 6 cd.grano.c 3 6.69 0.393
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "cd.grano.c",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(cd.grano.c)
## [1] curva gen diam2 muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d is.outlier 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, diam2) %>%
shapiro_test(cd.grano.c)
norm %>% as_tibble() %>% print(n=Inf)
## # A tibble: 36 × 6
## curva gen diam2 variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 P3 CCN51 0 cd.grano.c 0.934 0.504
## 2 P3 CCN51 2 cd.grano.c 0.913 0.428
## 3 P3 CCN51 5 cd.grano.c 0.781 0.0705
## 4 P3 CCN51 6 cd.grano.c 0.904 0.400
## 5 P3 ICS95 0 cd.grano.c 0.994 0.847
## 6 P3 ICS95 2 cd.grano.c 0.971 0.672
## 7 P3 ICS95 5 cd.grano.c 0.774 0.0533
## 8 P3 ICS95 6 cd.grano.c 0.919 0.449
## 9 P3 TCS01 0 cd.grano.c 0.991 0.820
## 10 P3 TCS01 2 cd.grano.c 0.756 0.0134
## 11 P3 TCS01 5 cd.grano.c 0.824 0.173
## 12 P3 TCS01 6 cd.grano.c 0.994 0.857
## 13 P1 CCN51 0 cd.grano.c 0.999 0.940
## 14 P1 CCN51 2 cd.grano.c 0.857 0.258
## 15 P1 CCN51 5 cd.grano.c 0.893 0.363
## 16 P1 CCN51 6 cd.grano.c 0.993 0.836
## 17 P1 ICS95 0 cd.grano.c 0.978 0.718
## 18 P1 ICS95 2 cd.grano.c 0.828 0.182
## 19 P1 ICS95 5 cd.grano.c 0.844 0.226
## 20 P1 ICS95 6 cd.grano.c 0.785 0.0783
## 21 P1 TCS01 0 cd.grano.c 0.785 0.0788
## 22 P1 TCS01 2 cd.grano.c 0.754 0.00911
## 23 P1 TCS01 5 cd.grano.c 0.968 0.658
## 24 P1 TCS01 6 cd.grano.c 1.00 0.979
## 25 P2 CCN51 0 cd.grano.c 0.989 0.799
## 26 P2 CCN51 2 cd.grano.c 0.972 0.678
## 27 P2 CCN51 5 cd.grano.c 0.946 0.551
## 28 P2 CCN51 6 cd.grano.c 0.912 0.424
## 29 P2 ICS95 0 cd.grano.c 0.950 0.567
## 30 P2 ICS95 2 cd.grano.c 0.986 0.770
## 31 P2 ICS95 5 cd.grano.c 0.973 0.683
## 32 P2 ICS95 6 cd.grano.c 0.809 0.135
## 33 P2 TCS01 0 cd.grano.c 0.813 0.147
## 34 P2 TCS01 2 cd.grano.c 0.932 0.496
## 35 P2 TCS01 5 cd.grano.c 0.888 0.349
## 36 P2 TCS01 6 cd.grano.c 1.00 0.986
##Create QQ plot for each cell of design:
ggqqplot(datos, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(diam2~ 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(diam2) %>%
levene_test(cd.grano.c ~ curva*gen)
lev %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 8 18 0.659 0.720
## 2 2 8 18 0.245 0.976
## 3 5 8 18 0.476 0.857
## 4 6 8 18 0.338 0.940
##Computation
res.aov <- anova_test(
data = datos, dv = cd.grano.c, wid = id,
within = diam2, 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 * 0.326
## 2 gen 2.00 18.00 95.539 2.60e-10 * 0.875
## 3 diam2 1.68 30.21 71.579 1.76e-11 * 0.576
## 4 curva:gen 4.00 18.00 11.174 9.83e-05 * 0.621
## 5 curva:diam2 3.36 30.21 1.686 1.87e-01 0.060
## 6 gen:diam2 3.36 30.21 1.176 3.38e-01 0.043
## 7 curva:gen:diam2 6.71 30.21 1.286 2.91e-01 0.089
##Splitting dataframe by temperature ramp
## Protocol 3 (P3)
datos.curve1<-filter(datos, curva=="P3")
##Check assumptions
##Outliers
datos.curve1 %>%
group_by(gen, diam2) %>%
identify_outliers(cd.grano.c)
## [1] gen diam2 curva muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d is.outlier 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, diam2) %>%
shapiro_test(cd.grano.c)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.934 0.504
## 2 CCN51 2 cd.grano.c 0.913 0.428
## 3 CCN51 5 cd.grano.c 0.781 0.0705
## 4 CCN51 6 cd.grano.c 0.904 0.400
## 5 ICS95 0 cd.grano.c 0.994 0.847
## 6 ICS95 2 cd.grano.c 0.971 0.672
## 7 ICS95 5 cd.grano.c 0.774 0.0533
## 8 ICS95 6 cd.grano.c 0.919 0.449
## 9 TCS01 0 cd.grano.c 0.991 0.820
## 10 TCS01 2 cd.grano.c 0.756 0.0134
## 11 TCS01 5 cd.grano.c 0.824 0.173
## 12 TCS01 6 cd.grano.c 0.994 0.857
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(diam2~ 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(diam2) %>%
levene_test(cd.grano.c ~ gen)
lev1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.897 0.456
## 2 2 2 6 0.499 0.630
## 3 5 2 6 0.376 0.701
## 4 6 2 6 0.826 0.482
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = cd.grano.c, wid = id,
within = diam2, 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 9.18e-04 * 0.844
## 2 diam2 3 18 18.299 1.06e-05 * 0.560
## 3 gen:diam2 6 18 1.017 4.45e-01 0.124
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 6.034 0.03 * 0.645
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.ics1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 6.312 0.028 * 0.59
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 7.238 0.02 * 0.57
## Protocol 1 (P1)
datos.curve2<-filter(datos, curva=="P1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(cd.grano.c)
## [1] gen diam2 curva muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d is.outlier 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, diam2) %>%
shapiro_test(cd.grano.c)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.999 0.940
## 2 CCN51 2 cd.grano.c 0.857 0.258
## 3 CCN51 5 cd.grano.c 0.893 0.363
## 4 CCN51 6 cd.grano.c 0.993 0.836
## 5 ICS95 0 cd.grano.c 0.978 0.718
## 6 ICS95 2 cd.grano.c 0.828 0.182
## 7 ICS95 5 cd.grano.c 0.844 0.226
## 8 ICS95 6 cd.grano.c 0.785 0.0783
## 9 TCS01 0 cd.grano.c 0.785 0.0788
## 10 TCS01 2 cd.grano.c 0.754 0.00911
## 11 TCS01 5 cd.grano.c 0.968 0.658
## 12 TCS01 6 cd.grano.c 1.00 0.979
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(diam2~ 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(diam2) %>%
levene_test(cd.grano.c ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.271 0.772
## 2 2 2 6 0.151 0.863
## 3 5 2 6 0.249 0.787
## 4 6 2 6 0.319 0.739
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = cd.grano.c, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.0 12.534 0.007 * 0.756
## 2 diam2 1.13 6.8 19.749 0.003 * 0.458
## 3 gen:diam2 2.27 6.8 0.861 0.477 0.069
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 19.176 0.002 * 0.733
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 18.459 0.002 * 0.288
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 2.592 0.148 0.421
## Protocol 2 (P2)
datos.curve3<-filter(datos, curva=="P2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(cd.grano.c)
## [1] gen diam2 curva muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d is.outlier 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, diam2) %>%
shapiro_test(cd.grano.c)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.989 0.799
## 2 CCN51 2 cd.grano.c 0.972 0.678
## 3 CCN51 5 cd.grano.c 0.946 0.551
## 4 CCN51 6 cd.grano.c 0.912 0.424
## 5 ICS95 0 cd.grano.c 0.950 0.567
## 6 ICS95 2 cd.grano.c 0.986 0.770
## 7 ICS95 5 cd.grano.c 0.973 0.683
## 8 ICS95 6 cd.grano.c 0.809 0.135
## 9 TCS01 0 cd.grano.c 0.813 0.147
## 10 TCS01 2 cd.grano.c 0.932 0.496
## 11 TCS01 5 cd.grano.c 0.888 0.349
## 12 TCS01 6 cd.grano.c 1.00 0.986
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(diam2~ 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(diam2) %>%
levene_test(cd.grano.c ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.122 0.887
## 2 2 2 6 0.363 0.710
## 3 5 2 6 0.0112 0.989
## 4 6 2 6 0.187 0.834
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = cd.grano.c, wid = id,
within = diam2, 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 * 0.973
## 2 diam2 3 18 60.864 1.27e-09 * 0.787
## 3 gen:diam2 6 18 2.952 3.50e-02 * 0.263
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 14.126 0.004 * 0.668
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 8.365 0.015 * 0.777
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.grano.c, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 90.215 2.21e-05 * 0.888
## Gráficas por réplica y genotipo
datos$diam2<-as.numeric(as.character(datos$diam2))
##Gráfica por réplica compuesta
pht<- ggplot(datos, aes(x = diam2)) +
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","diam2"))
write.csv(datos2, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Env_muestra/data/datos_mean_grano.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
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
