setwd("~/Google Drive/Agrosavia/Colaboraciones/Fabricio/data")
library(Plasticity)
library(agricolae)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
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## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
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## filter, lag
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## intersect, setdiff, setequal, union
library(ggplot2)
library(psych)
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## Attaching package: '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'
## The following object is masked from 'package:psych':
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## headtail
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## mapvalues
library(forcats)
library(Hmisc)
## Loading required package: survival
## Loading required package: Formula
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## Attaching package: 'Hmisc'
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## describe
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## src, summarize
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## is.discrete, summarize
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## format.pval, units
library("PerformanceAnalytics")
## Loading required package: xts
## Loading required package: zoo
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## Attaching package: 'zoo'
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## as.Date, as.Date.numeric
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## Attaching package: 'xts'
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## first, last
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## Attaching package: 'PerformanceAnalytics'
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## kurtosis, skewness
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## legend
library(onewaytests)
## Registered S3 methods overwritten by 'car':
## method from
## hist.boot FSA
## confint.boot FSA
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## Attaching package: 'onewaytests'
## The following object is masked from 'package:Hmisc':
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## describe
## The following object is masked from 'package:psych':
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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.low <- filter(clone, clone$env == "100" | clone$env=="300")
clone.low.fin <- droplevels(clone.low)
clone.low.fin$Clon<-as.factor(clone.low.fin$Clon)
clone.low.fin$env<-as.factor(clone.low.fin$env)
## Calculo del RDPI para todos los caracteres luz baja (100-300)
A.low.rdpi<-rdpi(clone.low.fin, Clon, A, env)
##
## Attaching package: 'sciplot'
## The following object is masked from 'package:FSA':
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## se
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.672 0.03535 1.946 0.011 *
## Residuals 300 5.450 0.01817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.268 0.0647 0.0162 a
## 2 TCS 02 0.359 0.0743 0.0186 a
## 3 TCS 03 0.388 0.208 0.0521 a
## 4 TCS 04 0.267 0.135 0.0339 a
## 5 TCS 05 0.226 0.0948 0.0237 a
## 6 TCS 08 0.278 0.0651 0.0163 a
## 7 TCS 10 0.243 0.173 0.0433 a
## 8 TCS 11 0.298 0.158 0.0396 a
## 9 TCS 12 0.327 0.220 0.0550 a
## 10 TCS 20 0.369 0.198 0.0495 a
## 11 TCS 40 0.336 0.129 0.0323 a
## 12 TCS 41 0.307 0.0739 0.0185 a
## 13 TCS 42 0.268 0.0545 0.0136 a
## 14 TCS 43 0.329 0.0990 0.0247 a
## 15 TCS 44 0.306 0.116 0.0289 a
## 16 TCS 45 0.271 0.0548 0.0137 a
## 17 TCS 46 0.303 0.0709 0.0177 a
## 18 TCS 47 0.253 0.0960 0.0240 a
## 19 TCS 48 0.325 0.108 0.0270 a
## 20 TCS 49 0.386 0.242 0.0605 a
E.low.rdpi<-rdpi(clone.low.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.230 0.11738 4.327 1.52e-08 ***
## Residuals 300 8.138 0.02713
## ---
## 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 0.165 0.142 0.0356 abc
## 2 TCS 02 0.121 0.0954 0.0238 bc
## 3 TCS 03 0.364 0.346 0.0864 a
## 4 TCS 04 0.202 0.145 0.0363 abc
## 5 TCS 05 0.132 0.133 0.0332 bc
## 6 TCS 08 0.137 0.103 0.0258 bc
## 7 TCS 10 0.138 0.106 0.0266 bc
## 8 TCS 11 0.165 0.154 0.0386 abc
## 9 TCS 12 0.207 0.176 0.0439 abc
## 10 TCS 20 0.250 0.222 0.0554 ab
## 11 TCS 40 0.124 0.115 0.0289 bc
## 12 TCS 41 0.0325 0.0162 0.00404 c
## 13 TCS 42 0.0683 0.0511 0.0128 bc
## 14 TCS 43 0.212 0.176 0.0440 abc
## 15 TCS 44 0.190 0.167 0.0417 abc
## 16 TCS 45 0.0463 0.0330 0.00824 bc
## 17 TCS 46 0.146 0.101 0.0254 bc
## 18 TCS 47 0.128 0.122 0.0306 bc
## 19 TCS 48 0.139 0.121 0.0302 bc
## 20 TCS 49 0.357 0.336 0.0841 a
WUE.low.rdpi<-rdpi(clone.low.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.976 0.05138 3.21 1.12e-05 ***
## Residuals 300 4.802 0.01601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.248 0.146 0.0366 abc
## 2 TCS 02 0.363 0.0808 0.0202 a
## 3 TCS 03 0.335 0.200 0.0501 ab
## 4 TCS 04 0.222 0.105 0.0262 abc
## 5 TCS 05 0.235 0.130 0.0325 abc
## 6 TCS 08 0.259 0.108 0.0269 abc
## 7 TCS 10 0.150 0.100 0.0251 c
## 8 TCS 11 0.269 0.0719 0.0180 abc
## 9 TCS 12 0.200 0.114 0.0284 bc
## 10 TCS 20 0.241 0.165 0.0412 abc
## 11 TCS 40 0.344 0.110 0.0275 ab
## 12 TCS 41 0.327 0.0524 0.0131 ab
## 13 TCS 42 0.261 0.0848 0.0212 abc
## 14 TCS 43 0.366 0.221 0.0553 a
## 15 TCS 44 0.285 0.137 0.0343 abc
## 16 TCS 45 0.245 0.0918 0.0230 abc
## 17 TCS 46 0.285 0.143 0.0356 abc
## 18 TCS 47 0.249 0.116 0.0291 abc
## 19 TCS 48 0.314 0.0874 0.0219 ab
## 20 TCS 49 0.222 0.132 0.0331 abc
gsw.low.rdpi<-rdpi(clone.low.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.490 0.13105 5.802 2.32e-12 ***
## Residuals 300 6.776 0.02259
## ---
## 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 0.0977 0.0766 0.0192 d
## 2 TCS 02 0.0976 0.0753 0.0188 d
## 3 TCS 03 0.319 0.286 0.0714 ab
## 4 TCS 04 0.180 0.127 0.0317 bcd
## 5 TCS 05 0.204 0.159 0.0398 bcd
## 6 TCS 08 0.0963 0.0697 0.0174 d
## 7 TCS 10 0.201 0.140 0.0351 bcd
## 8 TCS 11 0.0829 0.0562 0.0140 d
## 9 TCS 12 0.301 0.253 0.0632 abc
## 10 TCS 20 0.219 0.181 0.0452 abcd
## 11 TCS 40 0.168 0.135 0.0337 bcd
## 12 TCS 41 0.0905 0.0560 0.0140 d
## 13 TCS 42 0.150 0.118 0.0295 bcd
## 14 TCS 43 0.0294 0.0197 0.00493 d
## 15 TCS 44 0.153 0.120 0.0300 bcd
## 16 TCS 45 0.154 0.132 0.0330 bcd
## 17 TCS 46 0.129 0.0897 0.0224 cd
## 18 TCS 47 0.160 0.130 0.0325 bcd
## 19 TCS 48 0.0928 0.0810 0.0202 d
## 20 TCS 49 0.402 0.306 0.0766 a
cica.low.rdpi<-rdpi(clone.low.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.1618 0.008518 3.029 3.17e-05 ***
## Residuals 300 0.8435 0.002812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.106 0.0368 0.00919 abcd
## 2 TCS 02 0.108 0.0272 0.00681 abcd
## 3 TCS 03 0.164 0.0651 0.0163 a
## 4 TCS 04 0.104 0.0334 0.00835 abcd
## 5 TCS 05 0.146 0.0971 0.0243 abc
## 6 TCS 08 0.121 0.0141 0.00352 abcd
## 7 TCS 10 0.100 0.0633 0.0158 abcd
## 8 TCS 11 0.0809 0.0455 0.0114 cd
## 9 TCS 12 0.111 0.0420 0.0105 abcd
## 10 TCS 20 0.0780 0.0710 0.0177 d
## 11 TCS 40 0.125 0.0303 0.00759 abcd
## 12 TCS 41 0.135 0.0612 0.0153 abcd
## 13 TCS 42 0.141 0.0803 0.0201 abcd
## 14 TCS 43 0.152 0.0307 0.00768 ab
## 15 TCS 44 0.0980 0.0271 0.00677 abcd
## 16 TCS 45 0.104 0.0614 0.0154 abcd
## 17 TCS 46 0.130 0.0442 0.0111 abcd
## 18 TCS 47 0.120 0.0577 0.0144 abcd
## 19 TCS 48 0.103 0.00849 0.00212 abcd
## 20 TCS 49 0.0957 0.0660 0.0165 bcd
low.rdpi<-data.frame(A.low.rdpi$sp, A.low.rdpi$rdpi, E.low.rdpi$rdpi, WUE.low.rdpi$rdpi, gsw.low.rdpi$rdpi,
cica.low.rdpi$rdpi)
colnames(low.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
low.rdpi$treatment <- "low"
## analysis of variance
anova_A <- aov(low.rdpi$Ardpi ~ low.rdpi$gen, data = low.rdpi)
anova_E <- aov(low.rdpi$Erdpi ~ low.rdpi$gen, data = low.rdpi)
anova_WUE <- aov(low.rdpi$WUErdpi ~ low.rdpi$gen, data = low.rdpi)
anova_gsw <- aov(low.rdpi$gswrdpi ~ low.rdpi$gen, data = low.rdpi)
anova_cica <- aov(low.rdpi$cicardpi ~ low.rdpi$gen, data = low.rdpi)
## Mean contrast for A
A_emm <- emmeans(anova_A, "gen")
pwpp(A_emm)
multcomp::cld(A_emm, alpha = 0.10, Letters = LETTERS)
## gen emmean SE df lower.CL upper.CL .group
## TCS 05 0.226 0.0337 300 0.159 0.292 A
## TCS 10 0.243 0.0337 300 0.177 0.309 AB
## TCS 47 0.253 0.0337 300 0.187 0.319 AB
## TCS 04 0.267 0.0337 300 0.201 0.334 AB
## TCS 42 0.268 0.0337 300 0.201 0.334 AB
## TCS 01 0.268 0.0337 300 0.202 0.334 AB
## TCS 45 0.271 0.0337 300 0.204 0.337 AB
## TCS 08 0.278 0.0337 300 0.212 0.344 AB
## TCS 11 0.298 0.0337 300 0.232 0.365 AB
## TCS 46 0.303 0.0337 300 0.237 0.370 AB
## TCS 44 0.306 0.0337 300 0.240 0.373 AB
## TCS 41 0.307 0.0337 300 0.241 0.374 AB
## TCS 48 0.325 0.0337 300 0.259 0.392 AB
## TCS 12 0.327 0.0337 300 0.261 0.394 AB
## TCS 43 0.329 0.0337 300 0.263 0.395 AB
## TCS 40 0.336 0.0337 300 0.269 0.402 AB
## TCS 02 0.359 0.0337 300 0.292 0.425 AB
## TCS 20 0.369 0.0337 300 0.303 0.435 AB
## TCS 49 0.386 0.0337 300 0.320 0.452 B
## TCS 03 0.388 0.0337 300 0.322 0.455 B
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 20 estimates
## significance level used: alpha = 0.1
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
## 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(low.rdpi, gen) %>%
summarise(w=mean(Ardpi)) %>%
arrange(desc(w))
dt_E <- group_by(low.rdpi, gen) %>%
summarise(w=mean(Erdpi)) %>%
arrange(desc(w))
dt_WUE <- group_by(low.rdpi, gen) %>%
summarise(w=mean(WUErdpi)) %>%
arrange(desc(w))
dt_gsw <- group_by(low.rdpi, gen) %>%
summarise(w=mean(gswrdpi)) %>%
arrange(desc(w))
dt_cica <- group_by(low.rdpi, gen) %>%
summarise(w=mean(cicardpi)) %>%
arrange(desc(w))
## extracting the compact letter display and adding to the Tk table
dt_A$cldA <- cld_A$`low.rdpi$gen`$Letters
print(dt_A)
## # A tibble: 20 × 3
## gen w cldA
## <fct> <dbl> <chr>
## 1 TCS 03 0.388 a
## 2 TCS 49 0.386 a
## 3 TCS 20 0.369 a
## 4 TCS 02 0.359 a
## 5 TCS 40 0.336 a
## 6 TCS 43 0.329 a
## 7 TCS 12 0.327 a
## 8 TCS 48 0.325 a
## 9 TCS 41 0.307 a
## 10 TCS 44 0.306 a
## 11 TCS 46 0.303 a
## 12 TCS 11 0.298 a
## 13 TCS 08 0.278 a
## 14 TCS 45 0.271 a
## 15 TCS 01 0.268 a
## 16 TCS 42 0.268 a
## 17 TCS 04 0.267 a
## 18 TCS 47 0.253 a
## 19 TCS 10 0.243 a
## 20 TCS 05 0.226 a
dt_E$cldE <- cld_E$`low.rdpi$gen`$Letters
print(dt_E)
## # A tibble: 20 × 3
## gen w cldE
## <fct> <dbl> <chr>
## 1 TCS 03 0.364 a
## 2 TCS 49 0.357 a
## 3 TCS 20 0.250 ab
## 4 TCS 43 0.212 abc
## 5 TCS 12 0.207 abc
## 6 TCS 04 0.202 abc
## 7 TCS 44 0.190 abc
## 8 TCS 11 0.165 abc
## 9 TCS 01 0.165 abc
## 10 TCS 46 0.146 bc
## 11 TCS 48 0.139 bc
## 12 TCS 10 0.138 bc
## 13 TCS 08 0.137 bc
## 14 TCS 05 0.132 bc
## 15 TCS 47 0.128 bc
## 16 TCS 40 0.124 bc
## 17 TCS 02 0.121 bc
## 18 TCS 42 0.0683 bc
## 19 TCS 45 0.0463 bc
## 20 TCS 41 0.0325 c
dt_WUE$cldWUE <- cld_WUE$`low.rdpi$gen`$Letters
print(dt_WUE)
## # A tibble: 20 × 3
## gen w cldWUE
## <fct> <dbl> <chr>
## 1 TCS 43 0.366 a
## 2 TCS 02 0.363 a
## 3 TCS 40 0.344 ab
## 4 TCS 03 0.335 ab
## 5 TCS 41 0.327 ab
## 6 TCS 48 0.314 ab
## 7 TCS 46 0.285 abc
## 8 TCS 44 0.285 abc
## 9 TCS 11 0.269 abc
## 10 TCS 42 0.261 abc
## 11 TCS 08 0.259 abc
## 12 TCS 47 0.249 abc
## 13 TCS 01 0.248 abc
## 14 TCS 45 0.245 abc
## 15 TCS 20 0.241 abc
## 16 TCS 05 0.235 abc
## 17 TCS 04 0.222 abc
## 18 TCS 49 0.222 abc
## 19 TCS 12 0.200 bc
## 20 TCS 10 0.150 c
dt_gsw$cldgsw <- cld_gsw$`low.rdpi$gen`$Letters
print(dt_gsw)
## # A tibble: 20 × 3
## gen w cldgsw
## <fct> <dbl> <chr>
## 1 TCS 49 0.402 a
## 2 TCS 03 0.319 ab
## 3 TCS 12 0.301 abc
## 4 TCS 20 0.219 abcd
## 5 TCS 05 0.204 bcd
## 6 TCS 10 0.201 bcd
## 7 TCS 04 0.180 bcd
## 8 TCS 40 0.168 bcd
## 9 TCS 47 0.160 bcd
## 10 TCS 45 0.154 bcd
## 11 TCS 44 0.153 bcd
## 12 TCS 42 0.150 bcd
## 13 TCS 46 0.129 cd
## 14 TCS 01 0.0977 d
## 15 TCS 02 0.0976 d
## 16 TCS 08 0.0963 d
## 17 TCS 48 0.0928 d
## 18 TCS 41 0.0905 d
## 19 TCS 11 0.0829 d
## 20 TCS 43 0.0294 d
dt_cica$cldcica <- cld_cica$`low.rdpi$gen`$Letters
print(dt_cica)
## # A tibble: 20 × 3
## gen w cldcica
## <fct> <dbl> <chr>
## 1 TCS 03 0.164 a
## 2 TCS 43 0.152 ab
## 3 TCS 05 0.146 abc
## 4 TCS 42 0.141 abcd
## 5 TCS 41 0.135 abcd
## 6 TCS 46 0.130 abcd
## 7 TCS 40 0.125 abcd
## 8 TCS 08 0.121 abcd
## 9 TCS 47 0.120 abcd
## 10 TCS 12 0.111 abcd
## 11 TCS 02 0.108 abcd
## 12 TCS 01 0.106 abcd
## 13 TCS 45 0.104 abcd
## 14 TCS 04 0.104 abcd
## 15 TCS 48 0.103 abcd
## 16 TCS 10 0.100 abcd
## 17 TCS 44 0.0980 abcd
## 18 TCS 49 0.0957 bcd
## 19 TCS 11 0.0809 cd
## 20 TCS 20 0.0780 d
### group low.rdpi by gen and return some averages and estandar errors
low2 <- low.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 low
dt_AF <- read.table("letterA.csv", header=T, sep=",")
low2<-merge(dt_AF, low2, by="gen")
low2<-merge(dt_A, low2, by="gen")
low2<-merge(dt_E, low2, by="gen")
low2<-merge(dt_WUE, low2, by="gen")
low2<-merge(dt_gsw, low2, by="gen")
## Warning in merge.data.frame(dt_gsw, low2, by = "gen"): column names 'w.x', 'w.y'
## are duplicated in the result
low2<-merge(dt_cica, low2, by="gen")
## saving the data frames
write.csv(low2, "~/Google Drive/Agrosavia/Colaboraciones/Fabricio/data/low.csv")
lows <- read.table("low.csv", header=T, sep=",")
attach(lows)
##Reordering gen by mean
new_order3 <- with(lows, reorder(gen, Ardpi_m, mean, na.rm=T))
new_order4 <- with(lows, reorder(gen, Erdpi_m, mean, na.rm=T))
new_order5 <- with(lows, reorder(gen, WUErdpi_m, mean, na.rm=T))
new_order6 <- with(lows, reorder(gen, gswrdpi_m, mean, na.rm=T))
new_order7 <- with(lows, reorder(gen, cicardpi_m, mean, na.rm=T))
##Barplots
Aplot=lows %>%
arrange(Ardpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=Ardpi_m, fill=cld_FA)) +
scale_fill_brewer(palette = "Paired") +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
geom_errorbar(aes(ymin=Ardpi_m-se_A, ymax=Ardpi_m+se_A), width=0.2)+
geom_text(aes(label = cld_FA, y = Ardpi_m), nudge_x =0, nudge_y = 0.07, 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=lows %>%
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=lows %>%
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=lows %>%
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 = 0.09, 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=lows %>%
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(lows)