setwd("~/Google Drive/Agrosavia/Colaboraciones/Fabricio/data")
library(Plasticity)
library(agricolae)
library(Rmisc)
## Loading required package: lattice
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library(dplyr)
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## summarize
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library(ggplot2)
library(psych)
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## %+%, alpha
library(FSA)
## ## FSA v0.9.1. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.
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## Attaching package: 'FSA'
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library(forcats)
library(Hmisc)
## Loading required package: survival
## Loading required package: Formula
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## describe
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## format.pval, units
library("PerformanceAnalytics")
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## as.Date, as.Date.numeric
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## first, last
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## kurtosis, skewness
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library(onewaytests)
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## describe
library(emmeans)
library(ggthemes)
library(multcompView)
clone <- read.table("res_fin.csv", header=T, sep=",")
clone$Repetición<-as.factor(clone$Repetición)
clone$Clon<-as.factor(clone$Clon)
clone$env<-as.factor(clone$env)
# Generando bases de datos para los rangos ambientales a evaluar (100-300, 500-2000)
clone.high <- filter(clone, clone$env == "500" | clone$env=="2000")
clone.high.fin <- droplevels(clone.high)
clone.high.fin$env<-as.factor(clone.high.fin$env)
clone.high.fin$Clon<-as.factor(clone.high.fin$Clon)
# Calculo del RDPI para todos los caracteres luz alta (500-2000)
A.high.rdpi<-rdpi(clone.high.fin, Clon, A, env)
##
## Attaching package: 'sciplot'
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## se
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 4.734 0.2491 11.32 <2e-16 ***
## Residuals 300 6.600 0.0220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.258 0.0997 0.0249 cdef
## 2 TCS 02 0.143 0.0887 0.0222 ef
## 3 TCS 03 0.415 0.242 0.0605 abcd
## 4 TCS 04 0.250 0.143 0.0359 cdef
## 5 TCS 05 0.322 0.116 0.0291 bcde
## 6 TCS 08 0.213 0.0610 0.0152 ef
## 7 TCS 10 0.581 0.197 0.0493 a
## 8 TCS 11 0.209 0.119 0.0296 ef
## 9 TCS 12 0.457 0.231 0.0579 ab
## 10 TCS 20 0.234 0.185 0.0463 def
## 11 TCS 40 0.130 0.0860 0.0215 f
## 12 TCS 41 0.273 0.0758 0.0190 bcdef
## 13 TCS 42 0.133 0.0916 0.0229 f
## 14 TCS 43 0.246 0.0637 0.0159 def
## 15 TCS 44 0.153 0.102 0.0255 ef
## 16 TCS 45 0.122 0.0895 0.0224 f
## 17 TCS 46 0.223 0.178 0.0446 ef
## 18 TCS 47 0.304 0.127 0.0318 bcdef
## 19 TCS 48 0.162 0.127 0.0317 ef
## 20 TCS 49 0.435 0.277 0.0694 abc
E.high.rdpi<-rdpi(clone.high.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 4.087 0.21512 7.674 <2e-16 ***
## Residuals 300 8.410 0.02803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.445 0.124 0.0309 abcde
## 2 TCS 02 0.279 0.133 0.0332 ef
## 3 TCS 03 0.523 0.294 0.0734 ab
## 4 TCS 04 0.376 0.183 0.0456 bcdef
## 5 TCS 05 0.439 0.119 0.0298 bcde
## 6 TCS 08 0.383 0.162 0.0406 bcdef
## 7 TCS 10 0.652 0.0949 0.0237 a
## 8 TCS 11 0.368 0.163 0.0408 bcdef
## 9 TCS 12 0.502 0.220 0.0551 abcd
## 10 TCS 20 0.287 0.161 0.0402 ef
## 11 TCS 40 0.174 0.121 0.0303 f
## 12 TCS 41 0.436 0.120 0.0300 bcde
## 13 TCS 42 0.304 0.137 0.0343 cdef
## 14 TCS 43 0.381 0.192 0.0481 bcdef
## 15 TCS 44 0.235 0.164 0.0411 ef
## 16 TCS 45 0.290 0.0485 0.0121 def
## 17 TCS 46 0.322 0.188 0.0469 bcdef
## 18 TCS 47 0.312 0.163 0.0409 bcdef
## 19 TCS 48 0.257 0.151 0.0377 ef
## 20 TCS 49 0.510 0.242 0.0605 abc
WUE.high.rdpi<-rdpi(clone.high.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.488 0.02567 2.293 0.00188 **
## Residuals 300 3.358 0.01119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.225 0.130 0.0325 ab
## 2 TCS 02 0.162 0.0631 0.0158 abc
## 3 TCS 03 0.242 0.177 0.0442 a
## 4 TCS 04 0.167 0.0945 0.0236 abc
## 5 TCS 05 0.154 0.104 0.0261 abc
## 6 TCS 08 0.188 0.126 0.0316 abc
## 7 TCS 10 0.132 0.0896 0.0224 abc
## 8 TCS 11 0.201 0.0805 0.0201 abc
## 9 TCS 12 0.167 0.0885 0.0221 abc
## 10 TCS 20 0.0794 0.0533 0.0133 c
## 11 TCS 40 0.160 0.122 0.0304 abc
## 12 TCS 41 0.190 0.0686 0.0171 abc
## 13 TCS 42 0.195 0.0559 0.0140 abc
## 14 TCS 43 0.223 0.167 0.0417 ab
## 15 TCS 44 0.107 0.0792 0.0198 bc
## 16 TCS 45 0.182 0.0991 0.0248 abc
## 17 TCS 46 0.143 0.111 0.0278 abc
## 18 TCS 47 0.190 0.126 0.0314 abc
## 19 TCS 48 0.159 0.0729 0.0182 abc
## 20 TCS 49 0.129 0.104 0.0261 abc
gsw.high.rdpi<-rdpi(clone.high.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 5.191 0.27320 8.764 <2e-16 ***
## Residuals 300 9.352 0.03117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.441 0.0921 0.0230 abcde
## 2 TCS 02 0.278 0.150 0.0376 defg
## 3 TCS 03 0.516 0.255 0.0637 abc
## 4 TCS 04 0.396 0.185 0.0461 bcdef
## 5 TCS 05 0.411 0.180 0.0451 bcdef
## 6 TCS 08 0.377 0.150 0.0374 bcdef
## 7 TCS 10 0.646 0.214 0.0536 a
## 8 TCS 11 0.430 0.0878 0.0219 abcdef
## 9 TCS 12 0.561 0.247 0.0616 ab
## 10 TCS 20 0.231 0.160 0.0401 efg
## 11 TCS 40 0.144 0.119 0.0296 g
## 12 TCS 41 0.471 0.138 0.0344 abcd
## 13 TCS 42 0.271 0.168 0.0421 defg
## 14 TCS 43 0.373 0.0828 0.0207 bcdef
## 15 TCS 44 0.208 0.146 0.0364 fg
## 16 TCS 45 0.292 0.187 0.0468 defg
## 17 TCS 46 0.302 0.217 0.0542 cdefg
## 18 TCS 47 0.410 0.182 0.0456 bcdef
## 19 TCS 48 0.235 0.131 0.0328 efg
## 20 TCS 49 0.525 0.277 0.0692 ab
cica.high.rdpi<-rdpi(clone.high.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.1964 0.010336 3.78 3.93e-07 ***
## Residuals 300 0.8203 0.002734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0880 0.0275 0.00688 abcde
## 2 TCS 02 0.0617 0.0348 0.00870 bcde
## 3 TCS 03 0.129 0.0800 0.0200 a
## 4 TCS 04 0.0760 0.0458 0.0115 abcde
## 5 TCS 05 0.100 0.0724 0.0181 abcd
## 6 TCS 08 0.0794 0.0473 0.0118 abcde
## 7 TCS 10 0.0721 0.0563 0.0141 abcde
## 8 TCS 11 0.101 0.0546 0.0137 abcd
## 9 TCS 12 0.0925 0.0520 0.0130 abcde
## 10 TCS 20 0.0878 0.0707 0.0177 abcde
## 11 TCS 40 0.0339 0.0211 0.00528 e
## 12 TCS 41 0.121 0.0648 0.0162 ab
## 13 TCS 42 0.105 0.0607 0.0152 abc
## 14 TCS 43 0.0853 0.0371 0.00927 abcde
## 15 TCS 44 0.0450 0.0276 0.00690 cde
## 16 TCS 45 0.0849 0.0653 0.0163 abcde
## 17 TCS 46 0.0597 0.0456 0.0114 bcde
## 18 TCS 47 0.0924 0.0653 0.0163 abcde
## 19 TCS 48 0.0354 0.0200 0.00500 de
## 20 TCS 49 0.0744 0.0375 0.00936 abcde
high.rdpi<-data.frame(A.high.rdpi$sp, A.high.rdpi$rdpi, E.high.rdpi$rdpi, WUE.high.rdpi$rdpi, gsw.high.rdpi$rdpi,
cica.high.rdpi$rdpi)
colnames(high.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
high.rdpi$treatment <- "high"
# analysis of variance
anova_A <- aov(high.rdpi$Ardpi ~ high.rdpi$gen, data = high.rdpi)
anova_E <- aov(high.rdpi$Erdpi ~ high.rdpi$gen, data = high.rdpi)
anova_WUE <- aov(high.rdpi$WUErdpi ~ high.rdpi$gen, data = high.rdpi)
anova_gsw <- aov(high.rdpi$gswrdpi ~ high.rdpi$gen, data = high.rdpi)
anova_cica <- aov(high.rdpi$cicardpi ~ high.rdpi$gen, data = high.rdpi)
# Tukey's test
tukey_A <- TukeyHSD(anova_A)
tukey_E <- TukeyHSD(anova_E)
tukey_WUE <- TukeyHSD(anova_WUE)
tukey_gsw <- TukeyHSD(anova_gsw)
tukey_cica <- TukeyHSD(anova_cica)
# compact letter display
cld_A <- multcompLetters4(anova_A, tukey_A)
cld_E <- multcompLetters4(anova_E, tukey_E)
cld_WUE <- multcompLetters4(anova_WUE, tukey_WUE)
cld_gsw <- multcompLetters4(anova_gsw, tukey_gsw)
cld_cica <- multcompLetters4(anova_cica, tukey_cica)
# table with factors and 3rd quantile
dt_A <- group_by(high.rdpi, gen) %>%
summarise(w=mean(Ardpi)) %>%
arrange(desc(w))
dt_E <- group_by(high.rdpi, gen) %>%
summarise(w=mean(Erdpi)) %>%
arrange(desc(w))
dt_WUE <- group_by(high.rdpi, gen) %>%
summarise(w=mean(WUErdpi)) %>%
arrange(desc(w))
dt_gsw <- group_by(high.rdpi, gen) %>%
summarise(w=mean(gswrdpi)) %>%
arrange(desc(w))
dt_cica <- group_by(high.rdpi, gen) %>%
summarise(w=mean(cicardpi)) %>%
arrange(desc(w))
# extracting the compact letter display and adding to the Tk table
dt_A$cldA <- cld_A$`high.rdpi$gen`$Letters
print(dt_A)
## # A tibble: 20 × 3
## gen w cldA
## <fct> <dbl> <chr>
## 1 TCS 10 0.581 a
## 2 TCS 12 0.457 ab
## 3 TCS 49 0.435 abc
## 4 TCS 03 0.415 abcd
## 5 TCS 05 0.322 bcde
## 6 TCS 47 0.304 bcdef
## 7 TCS 41 0.273 bcdef
## 8 TCS 01 0.258 cdef
## 9 TCS 04 0.250 cdef
## 10 TCS 43 0.246 def
## 11 TCS 20 0.234 def
## 12 TCS 46 0.223 ef
## 13 TCS 08 0.213 ef
## 14 TCS 11 0.209 ef
## 15 TCS 48 0.162 ef
## 16 TCS 44 0.153 ef
## 17 TCS 02 0.143 ef
## 18 TCS 42 0.133 f
## 19 TCS 40 0.130 f
## 20 TCS 45 0.122 f
dt_E$cldE <- cld_E$`high.rdpi$gen`$Letters
print(dt_E)
## # A tibble: 20 × 3
## gen w cldE
## <fct> <dbl> <chr>
## 1 TCS 10 0.652 a
## 2 TCS 03 0.523 ab
## 3 TCS 49 0.510 abc
## 4 TCS 12 0.502 abcd
## 5 TCS 01 0.445 abcde
## 6 TCS 05 0.439 bcde
## 7 TCS 41 0.436 bcde
## 8 TCS 08 0.383 bcdef
## 9 TCS 43 0.381 bcdef
## 10 TCS 04 0.376 bcdef
## 11 TCS 11 0.368 bcdef
## 12 TCS 46 0.322 bcdef
## 13 TCS 47 0.312 bcdef
## 14 TCS 42 0.304 cdef
## 15 TCS 45 0.290 def
## 16 TCS 20 0.287 ef
## 17 TCS 02 0.279 ef
## 18 TCS 48 0.257 ef
## 19 TCS 44 0.235 ef
## 20 TCS 40 0.174 f
dt_WUE$cldWUE <- cld_WUE$`high.rdpi$gen`$Letters
print(dt_WUE)
## # A tibble: 20 × 3
## gen w cldWUE
## <fct> <dbl> <chr>
## 1 TCS 03 0.242 a
## 2 TCS 01 0.225 ab
## 3 TCS 43 0.223 ab
## 4 TCS 11 0.201 abc
## 5 TCS 42 0.195 abc
## 6 TCS 41 0.190 abc
## 7 TCS 47 0.190 abc
## 8 TCS 08 0.188 abc
## 9 TCS 45 0.182 abc
## 10 TCS 12 0.167 abc
## 11 TCS 04 0.167 abc
## 12 TCS 02 0.162 abc
## 13 TCS 40 0.160 abc
## 14 TCS 48 0.159 abc
## 15 TCS 05 0.154 abc
## 16 TCS 46 0.143 abc
## 17 TCS 10 0.132 abc
## 18 TCS 49 0.129 abc
## 19 TCS 44 0.107 bc
## 20 TCS 20 0.0794 c
dt_gsw$cldgsw <- cld_gsw$`high.rdpi$gen`$Letters
print(dt_gsw)
## # A tibble: 20 × 3
## gen w cldgsw
## <fct> <dbl> <chr>
## 1 TCS 10 0.646 a
## 2 TCS 12 0.561 ab
## 3 TCS 49 0.525 ab
## 4 TCS 03 0.516 abc
## 5 TCS 41 0.471 abcd
## 6 TCS 01 0.441 abcde
## 7 TCS 11 0.430 abcdef
## 8 TCS 05 0.411 bcdef
## 9 TCS 47 0.410 bcdef
## 10 TCS 04 0.396 bcdef
## 11 TCS 08 0.377 bcdef
## 12 TCS 43 0.373 bcdef
## 13 TCS 46 0.302 cdefg
## 14 TCS 45 0.292 defg
## 15 TCS 02 0.278 defg
## 16 TCS 42 0.271 defg
## 17 TCS 48 0.235 efg
## 18 TCS 20 0.231 efg
## 19 TCS 44 0.208 fg
## 20 TCS 40 0.144 g
dt_cica$cldcica <- cld_cica$`high.rdpi$gen`$Letters
print(dt_cica)
## # A tibble: 20 × 3
## gen w cldcica
## <fct> <dbl> <chr>
## 1 TCS 03 0.129 a
## 2 TCS 41 0.121 ab
## 3 TCS 42 0.105 abc
## 4 TCS 11 0.101 abcd
## 5 TCS 05 0.100 abcd
## 6 TCS 12 0.0925 abcde
## 7 TCS 47 0.0924 abcde
## 8 TCS 01 0.0880 abcde
## 9 TCS 20 0.0878 abcde
## 10 TCS 43 0.0853 abcde
## 11 TCS 45 0.0849 abcde
## 12 TCS 08 0.0794 abcde
## 13 TCS 04 0.0760 abcde
## 14 TCS 49 0.0744 abcde
## 15 TCS 10 0.0721 abcde
## 16 TCS 02 0.0617 bcde
## 17 TCS 46 0.0597 bcde
## 18 TCS 44 0.0450 cde
## 19 TCS 48 0.0354 de
## 20 TCS 40 0.0339 e
### group high.rdpi by gen and return some averages
high2 <- high.rdpi %>%
select(gen, Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi) %>%
group_by(gen) %>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi),
n=n(),
sd_A=sd(Ardpi),
sd_E=sd(Erdpi),
sd_WUE=sd(WUErdpi),
sd_gsw=sd(gswrdpi),
sd_cica=sd(cicardpi)
) %>%
mutate(se_A=sd_A/sqrt(n)) %>%
mutate(se_E=sd_E/sqrt(n)) %>%
mutate(se_WUE=sd_WUE/sqrt(n)) %>%
mutate(se_gsw=sd_gsw/sqrt(n)) %>%
mutate(se_cica=sd_cica/sqrt(n))
## Adding Tukey letters to high
high2<-merge(dt_A, high2, by="gen")
high2<-merge(dt_E, high2, by="gen")
high2<-merge(dt_WUE, high2, by="gen")
high2<-merge(dt_gsw, high2, by="gen")
## Warning in merge.data.frame(dt_gsw, high2, by = "gen"): column names 'w.x',
## 'w.y' are duplicated in the result
high2<-merge(dt_cica, high2, by="gen")
## saving the data frames
write.csv(high2, "~/Google Drive/Agrosavia/Colaboraciones/Fabricio/data/high.csv")
highs <- read.table("high.csv", header=T, sep=",")
attach(highs)
##Reordering gen by mean
new_order8 <- with(highs, reorder(gen, Ardpi_m, mean, na.rm=T))
new_order9 <- with(highs, reorder(gen, Erdpi_m, mean, na.rm=T))
new_order10 <- with(highs, reorder(gen, WUErdpi_m, mean, na.rm=T))
new_order11 <- with(highs, reorder(gen, gswrdpi_m, mean, na.rm=T))
new_order12 <- with(highs, reorder(gen, cicardpi_m, mean, na.rm=T))
##Barplots
Aplot=highs %>%
arrange(Ardpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=Ardpi_m, fill=cldA)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=Ardpi_m-se_A, ymax=Ardpi_m+se_A), width=0.2)+
geom_text(aes(label = cldA, y = Ardpi_m), nudge_x =0, nudge_y = 0.13, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[A])) +
theme_classic()
Aplot + theme(axis.text = element_text(size = 12))
ggsave("Aplot.tiff", plot= Aplot, width = 4, height = 3, dpi = 1000)
Eplot=highs %>%
arrange(Erdpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=Erdpi_m, fill=cldE)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=Erdpi_m-se_E, ymax=Erdpi_m+se_E), width=0.2)+
geom_text(aes(label = cldE, y = Erdpi_m), nudge_x =0, nudge_y = 0.1, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[E])) +
theme_classic()
Eplot + theme(axis.text = element_text(size = 12))
ggsave("Eplot.tiff", plot= Eplot, width = 4, height = 3, dpi = 1000)
WUEplot=highs %>%
arrange(WUErdpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=WUErdpi_m, fill=cldWUE)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=WUErdpi_m-se_WUE, ymax=WUErdpi_m+se_WUE), width=0.2)+
geom_text(aes(label = cldWUE, y = WUErdpi_m), nudge_x =0, nudge_y = 0.09, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[WUE])) +
theme_classic()
WUEplot + theme(axis.text = element_text(size = 12))
ggsave("WUEplot.tiff", plot= WUEplot, width = 4, height = 3, dpi = 1000)
gswplot=highs %>%
arrange(gswrdpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=gswrdpi_m, fill = cldgsw)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=gswrdpi_m-se_gsw, ymax=gswrdpi_m+se_gsw), width=0.2)+
geom_text(aes(label = cldgsw, y = gswrdpi_m), nudge_x =0, nudge_y = 0.13, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[gSw])) +
theme_classic()
gswplot + theme(axis.text = element_text(size = 12))
ggsave("gswplot.tiff", plot= gswplot, width = 4, height = 3, dpi = 1000)
cicaplot=highs %>%
arrange(cicardpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=cicardpi_m, fill = cldcica )) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=cicardpi_m-se_cica, ymax=cicardpi_m+se_cica), width=0.2)+
geom_text(aes(label = cldcica, y = cicardpi_m), nudge_x =0, nudge_y = 0.07, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[Ci/Ca])) +
theme_classic()
cicaplot + theme(axis.text = element_text(size = 12))
ggsave("cicaplot.tiff", plot= cicaplot, width = 4, height = 3, dpi = 1000)
detach(highs)