getwd()
## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20231212_Tomato_shoot_growth_IAA_la_248_salt_soil"
setwd( "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20231212_Tomato_shoot_growth_IAA_la_248_salt_soil")
list.files(pattern = ".csv")
## [1] "20231109_FW_IAA_tomato_shoot_spray_soil_salt.csv"
## [2] "20231109_FW_IAA_tomato_shoot_spray_soil_salt.csv.xlsx"
## [3] "all-data-Crop-IAA-20231121.csv"
## [4] "all-data-Crop-IAA-20231128.csv"
## [5] "Maryam_2tomatoes_IAA_salt_soil_data.csv"
## [6] "Results_112123_IAA_half_deleted.csv"
## [7] "Results_112823_IAA.csv"
## [8] "Results_113023_IAA.csv"
## [9] "Results_120523_IAA.csv"
## [10] "Results_120723_IAA.csv"
Crop_early <- read.csv("all-data-Crop-IAA-20231121.csv")
Crop_early
Crop_early$All.ID<-paste(Crop_early$Genotype, Crop_early$condition, sep="_")
Crop_early
library(ggplot2)
library(ggpubr)
library(multcompView)
aov(Fv.Fm ~ All.ID, data = Crop_early)
## Call:
## aov(formula = Fv.Fm ~ All.ID, data = Crop_early)
##
## Terms:
## All.ID Residuals
## Sum of Squares 1.0273928 0.4411396
## Deg. of Freedom 11 108
##
## Residual standard error: 0.06391108
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(Fv.Fm ~ All.ID, data = Crop_early))
Output
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Fv.Fm ~ All.ID, data = Crop_early)
##
## $All.ID
## diff lwr upr p adj
## LA_c+mock-LA_c+IAA 0.2183 0.122809974 0.31379003 0.0000000
## LA_c+noIAA-LA_c+IAA 0.2264 0.130909974 0.32189003 0.0000000
## LA_s+IAA-LA_c+IAA 0.2254 0.129909974 0.32089003 0.0000000
## LA_s+mock-LA_c+IAA 0.2271 0.131609974 0.32259003 0.0000000
## LA_s+noIAA-LA_c+IAA 0.0878 -0.007690026 0.18329003 0.1028714
## m248_c+IAA-LA_c+IAA -0.0368 -0.132290026 0.05869003 0.9790624
## m248_c+mock-LA_c+IAA 0.2068 0.111309974 0.30229003 0.0000000
## m248_c+noIAA-LA_c+IAA 0.2113 0.115809974 0.30679003 0.0000000
## m248_s+IAA-LA_c+IAA 0.1993 0.103809974 0.29479003 0.0000000
## m248_s+mock-LA_c+IAA 0.2036 0.108109974 0.29909003 0.0000000
## m248_s+noIAA-LA_c+IAA 0.0654 -0.030090026 0.16089003 0.4913007
## LA_c+noIAA-LA_c+mock 0.0081 -0.087390026 0.10359003 1.0000000
## LA_s+IAA-LA_c+mock 0.0071 -0.088390026 0.10259003 1.0000000
## LA_s+mock-LA_c+mock 0.0088 -0.086690026 0.10429003 1.0000000
## LA_s+noIAA-LA_c+mock -0.1305 -0.225990026 -0.03500997 0.0007809
## m248_c+IAA-LA_c+mock -0.2551 -0.350590026 -0.15960997 0.0000000
## m248_c+mock-LA_c+mock -0.0115 -0.106990026 0.08399003 0.9999997
## m248_c+noIAA-LA_c+mock -0.0070 -0.102490026 0.08849003 1.0000000
## m248_s+IAA-LA_c+mock -0.0190 -0.114490026 0.07649003 0.9999464
## m248_s+mock-LA_c+mock -0.0147 -0.110190026 0.08079003 0.9999961
## m248_s+noIAA-LA_c+mock -0.1529 -0.248390026 -0.05740997 0.0000313
## LA_s+IAA-LA_c+noIAA -0.0010 -0.096490026 0.09449003 1.0000000
## LA_s+mock-LA_c+noIAA 0.0007 -0.094790026 0.09619003 1.0000000
## LA_s+noIAA-LA_c+noIAA -0.1386 -0.234090026 -0.04310997 0.0002542
## m248_c+IAA-LA_c+noIAA -0.2632 -0.358690026 -0.16770997 0.0000000
## m248_c+mock-LA_c+noIAA -0.0196 -0.115090026 0.07589003 0.9999269
## m248_c+noIAA-LA_c+noIAA -0.0151 -0.110590026 0.08039003 0.9999948
## m248_s+IAA-LA_c+noIAA -0.0271 -0.122590026 0.06839003 0.9983943
## m248_s+mock-LA_c+noIAA -0.0228 -0.118290026 0.07269003 0.9996796
## m248_s+noIAA-LA_c+noIAA -0.1610 -0.256490026 -0.06550997 0.0000091
## LA_s+mock-LA_s+IAA 0.0017 -0.093790026 0.09719003 1.0000000
## LA_s+noIAA-LA_s+IAA -0.1376 -0.233090026 -0.04210997 0.0002928
## m248_c+IAA-LA_s+IAA -0.2622 -0.357690026 -0.16670997 0.0000000
## m248_c+mock-LA_s+IAA -0.0186 -0.114090026 0.07689003 0.9999567
## m248_c+noIAA-LA_s+IAA -0.0141 -0.109590026 0.08139003 0.9999975
## m248_s+IAA-LA_s+IAA -0.0261 -0.121590026 0.06939003 0.9988601
## m248_s+mock-LA_s+IAA -0.0218 -0.117290026 0.07369003 0.9997921
## m248_s+noIAA-LA_s+IAA -0.1600 -0.255490026 -0.06450997 0.0000106
## LA_s+noIAA-LA_s+mock -0.1393 -0.234790026 -0.04380997 0.0002302
## m248_c+IAA-LA_s+mock -0.2639 -0.359390026 -0.16840997 0.0000000
## m248_c+mock-LA_s+mock -0.0203 -0.115790026 0.07519003 0.9998965
## m248_c+noIAA-LA_s+mock -0.0158 -0.111290026 0.07969003 0.9999918
## m248_s+IAA-LA_s+mock -0.0278 -0.123290026 0.06769003 0.9979801
## m248_s+mock-LA_s+mock -0.0235 -0.118990026 0.07199003 0.9995724
## m248_s+noIAA-LA_s+mock -0.1617 -0.257190026 -0.06620997 0.0000081
## m248_c+IAA-LA_s+noIAA -0.1246 -0.220090026 -0.02910997 0.0017116
## m248_c+mock-LA_s+noIAA 0.1190 0.023509974 0.21449003 0.0035055
## m248_c+noIAA-LA_s+noIAA 0.1235 0.028009974 0.21899003 0.0019748
## m248_s+IAA-LA_s+noIAA 0.1115 0.016009974 0.20699003 0.0087394
## m248_s+mock-LA_s+noIAA 0.1158 0.020309974 0.21129003 0.0052117
## m248_s+noIAA-LA_s+noIAA -0.0224 -0.117890026 0.07309003 0.9997297
## m248_c+mock-m248_c+IAA 0.2436 0.148109974 0.33909003 0.0000000
## m248_c+noIAA-m248_c+IAA 0.2481 0.152609974 0.34359003 0.0000000
## m248_s+IAA-m248_c+IAA 0.2361 0.140609974 0.33159003 0.0000000
## m248_s+mock-m248_c+IAA 0.2404 0.144909974 0.33589003 0.0000000
## m248_s+noIAA-m248_c+IAA 0.1022 0.006709974 0.19769003 0.0249787
## m248_c+noIAA-m248_c+mock 0.0045 -0.090990026 0.09999003 1.0000000
## m248_s+IAA-m248_c+mock -0.0075 -0.102990026 0.08799003 1.0000000
## m248_s+mock-m248_c+mock -0.0032 -0.098690026 0.09229003 1.0000000
## m248_s+noIAA-m248_c+mock -0.1414 -0.236890026 -0.04590997 0.0001705
## m248_s+IAA-m248_c+noIAA -0.0120 -0.107490026 0.08349003 0.9999995
## m248_s+mock-m248_c+noIAA -0.0077 -0.103190026 0.08779003 1.0000000
## m248_s+noIAA-m248_c+noIAA -0.1459 -0.241390026 -0.05040997 0.0000888
## m248_s+mock-m248_s+IAA 0.0043 -0.091190026 0.09979003 1.0000000
## m248_s+noIAA-m248_s+IAA -0.1339 -0.229390026 -0.03840997 0.0004905
## m248_s+noIAA-m248_s+mock -0.1382 -0.233690026 -0.04270997 0.0002690
P8 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P8)
stat.test
## LA_c+mock LA_c+noIAA LA_s+IAA LA_s+mock LA_s+noIAA m248_c+IAA
## "a" "a" "a" "a" "b" "c"
## m248_c+mock m248_c+noIAA m248_s+IAA m248_s+mock m248_s+noIAA LA_c+IAA
## "a" "a" "a" "a" "b" "bc"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
###################################################################
test$info <- strsplit(test$group1, "_")
test$info[[1]][2]
## [1] "c+mock"
test$Genotype <- "none"
test$condition<- "none"
test
for(i in 1:nrow(test)){
test$Genotype[i] <- test$info[[i]][1]
test$condition[i] <- test$info[[i]][2]
}
test2 <- test[,c(5:6,1)]
test2$group1 <- test2$Genotype
test2$group2 <- test2$Genotype
Crop_early
Crop_early$Genotype<- factor(Crop_early$Genotype, levels=c("LA", "m248"))
Crop_early$condition<- factor(Crop_early$condition, levels=c("c+noIAA", "c+mock", "c+IAA", "s+noIAA", "s+mock", "s+IAA"))
Crop_graph <- ggplot(data = Crop_early, mapping = aes(x = Genotype, y = Fv.Fm, colour = Genotype))
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(~ condition, scales = "free_y")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("Fv/Fm") + xlab("") + stat_pvalue_manual(test2, label = "Tukey", y.position = 0.83)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph
aov(Fqp.Fmp ~ All.ID, data = Crop_early)
## Call:
## aov(formula = Fqp.Fmp ~ All.ID, data = Crop_early)
##
## Terms:
## All.ID Residuals
## Sum of Squares 1.6498454 0.6930065
## Deg. of Freedom 11 108
##
## Residual standard error: 0.08010447
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(Fqp.Fmp ~ All.ID, data = Crop_early))
Output
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Fqp.Fmp ~ All.ID, data = Crop_early)
##
## $All.ID
## diff lwr upr p adj
## LA_c+mock-LA_c+IAA -0.3070 -0.426684702 -0.1873153 0.0000000
## LA_c+noIAA-LA_c+IAA -0.3711 -0.490784702 -0.2514153 0.0000000
## LA_s+IAA-LA_c+IAA -0.2425 -0.362184702 -0.1228153 0.0000000
## LA_s+mock-LA_c+IAA -0.2661 -0.385784702 -0.1464153 0.0000000
## LA_s+noIAA-LA_c+IAA -0.1947 -0.314384702 -0.0750153 0.0000216
## m248_c+IAA-LA_c+IAA -0.0194 -0.139084702 0.1002847 0.9999933
## m248_c+mock-LA_c+IAA -0.3276 -0.447284702 -0.2079153 0.0000000
## m248_c+noIAA-LA_c+IAA -0.3728 -0.492484702 -0.2531153 0.0000000
## m248_s+IAA-LA_c+IAA -0.3023 -0.421984702 -0.1826153 0.0000000
## m248_s+mock-LA_c+IAA -0.3016 -0.421284702 -0.1819153 0.0000000
## m248_s+noIAA-LA_c+IAA -0.2028 -0.322484702 -0.0831153 0.0000080
## LA_c+noIAA-LA_c+mock -0.0641 -0.183784702 0.0555847 0.8201742
## LA_s+IAA-LA_c+mock 0.0645 -0.055184702 0.1841847 0.8141796
## LA_s+mock-LA_c+mock 0.0409 -0.078784702 0.1605847 0.9919549
## LA_s+noIAA-LA_c+mock 0.1123 -0.007384702 0.2319847 0.0875585
## m248_c+IAA-LA_c+mock 0.2876 0.167915298 0.4072847 0.0000000
## m248_c+mock-LA_c+mock -0.0206 -0.140284702 0.0990847 0.9999876
## m248_c+noIAA-LA_c+mock -0.0658 -0.185484702 0.0538847 0.7940197
## m248_s+IAA-LA_c+mock 0.0047 -0.114984702 0.1243847 1.0000000
## m248_s+mock-LA_c+mock 0.0054 -0.114284702 0.1250847 1.0000000
## m248_s+noIAA-LA_c+mock 0.1042 -0.015484702 0.2238847 0.1527501
## LA_s+IAA-LA_c+noIAA 0.1286 0.008915298 0.2482847 0.0239163
## LA_s+mock-LA_c+noIAA 0.1050 -0.014684702 0.2246847 0.1449954
## LA_s+noIAA-LA_c+noIAA 0.1764 0.056715298 0.2960847 0.0001875
## m248_c+IAA-LA_c+noIAA 0.3517 0.232015298 0.4713847 0.0000000
## m248_c+mock-LA_c+noIAA 0.0435 -0.076184702 0.1631847 0.9867364
## m248_c+noIAA-LA_c+noIAA -0.0017 -0.121384702 0.1179847 1.0000000
## m248_s+IAA-LA_c+noIAA 0.0688 -0.050884702 0.1884847 0.7438760
## m248_s+mock-LA_c+noIAA 0.0695 -0.050184702 0.1891847 0.7315297
## m248_s+noIAA-LA_c+noIAA 0.1683 0.048615298 0.2879847 0.0004656
## LA_s+mock-LA_s+IAA -0.0236 -0.143284702 0.0960847 0.9999510
## LA_s+noIAA-LA_s+IAA 0.0478 -0.071884702 0.1674847 0.9726124
## m248_c+IAA-LA_s+IAA 0.2231 0.103415298 0.3427847 0.0000006
## m248_c+mock-LA_s+IAA -0.0851 -0.204784702 0.0345847 0.4314790
## m248_c+noIAA-LA_s+IAA -0.1303 -0.249984702 -0.0106153 0.0206329
## m248_s+IAA-LA_s+IAA -0.0598 -0.179484702 0.0598847 0.8779445
## m248_s+mock-LA_s+IAA -0.0591 -0.178784702 0.0605847 0.8861422
## m248_s+noIAA-LA_s+IAA 0.0397 -0.079984702 0.1593847 0.9937237
## LA_s+noIAA-LA_s+mock 0.0714 -0.048284702 0.1910847 0.6969828
## m248_c+IAA-LA_s+mock 0.2467 0.127015298 0.3663847 0.0000000
## m248_c+mock-LA_s+mock -0.0615 -0.181184702 0.0581847 0.8566064
## m248_c+noIAA-LA_s+mock -0.1067 -0.226384702 0.0129847 0.1295232
## m248_s+IAA-LA_s+mock -0.0362 -0.155884702 0.0834847 0.9971649
## m248_s+mock-LA_s+mock -0.0355 -0.155184702 0.0841847 0.9976145
## m248_s+noIAA-LA_s+mock 0.0633 -0.056384702 0.1829847 0.8318584
## m248_c+IAA-LA_s+noIAA 0.1753 0.055615298 0.2949847 0.0002125
## m248_c+mock-LA_s+noIAA -0.1329 -0.252584702 -0.0132153 0.0163935
## m248_c+noIAA-LA_s+noIAA -0.1781 -0.297784702 -0.0584153 0.0001543
## m248_s+IAA-LA_s+noIAA -0.1076 -0.227284702 0.0120847 0.1218711
## m248_s+mock-LA_s+noIAA -0.1069 -0.226584702 0.0127847 0.1277910
## m248_s+noIAA-LA_s+noIAA -0.0081 -0.127784702 0.1115847 1.0000000
## m248_c+mock-m248_c+IAA -0.3082 -0.427884702 -0.1885153 0.0000000
## m248_c+noIAA-m248_c+IAA -0.3534 -0.473084702 -0.2337153 0.0000000
## m248_s+IAA-m248_c+IAA -0.2829 -0.402584702 -0.1632153 0.0000000
## m248_s+mock-m248_c+IAA -0.2822 -0.401884702 -0.1625153 0.0000000
## m248_s+noIAA-m248_c+IAA -0.1834 -0.303084702 -0.0637153 0.0000834
## m248_c+noIAA-m248_c+mock -0.0452 -0.164884702 0.0744847 0.9820792
## m248_s+IAA-m248_c+mock 0.0253 -0.094384702 0.1449847 0.9999021
## m248_s+mock-m248_c+mock 0.0260 -0.093684702 0.1456847 0.9998719
## m248_s+noIAA-m248_c+mock 0.1248 0.005115298 0.2444847 0.0330049
## m248_s+IAA-m248_c+noIAA 0.0705 -0.049184702 0.1901847 0.7135242
## m248_s+mock-m248_c+noIAA 0.0712 -0.048484702 0.1908847 0.7006844
## m248_s+noIAA-m248_c+noIAA 0.1700 0.050315298 0.2896847 0.0003856
## m248_s+mock-m248_s+IAA 0.0007 -0.118984702 0.1203847 1.0000000
## m248_s+noIAA-m248_s+IAA 0.0995 -0.020184702 0.2191847 0.2047116
## m248_s+noIAA-m248_s+mock 0.0988 -0.020884702 0.2184847 0.2134116
P10 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P10)
stat.test
## LA_c+mock LA_c+noIAA LA_s+IAA LA_s+mock LA_s+noIAA m248_c+IAA
## "abc" "a" "bc" "abc" "b" "d"
## m248_c+mock m248_c+noIAA m248_s+IAA m248_s+mock m248_s+noIAA LA_c+IAA
## "ac" "a" "abc" "abc" "b" "d"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
###################################################################
test$info <- strsplit(test$group1, "_")
test$info[[1]][2]
## [1] "c+mock"
test$Genotype <- "none"
test$condition<- "none"
test
for(i in 1:nrow(test)){
test$Genotype[i] <- test$info[[i]][1]
test$condition[i] <- test$info[[i]][2]
}
test2 <- test[,c(5:6,1)]
test2$group1 <- test2$Genotype
test2$group2 <- test2$Genotype
Crop_early
Crop_early$condition<- factor(Crop_early$condition, levels=c("c+noIAA", "c+mock", "c+IAA", "s+noIAA", "s+mock", "s+IAA"))
Crop_early$Genotype<- factor(Crop_early$Genotype, levels=c("LA", "m248"))
Crop_graph <- ggplot(data = Crop_early, mapping = aes(x = Genotype, y = Fqp.Fmp, colour = Genotype))
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(~ condition, scales = "free_y")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("Fqp.Fmp") + xlab("") + stat_pvalue_manual(test2, label = "Tukey", y.position = 0.83)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph
aov(NPQ ~ All.ID, data = Crop_early)
## Call:
## aov(formula = NPQ ~ All.ID, data = Crop_early)
##
## Terms:
## All.ID Residuals
## Sum of Squares 88.36089 20.86777
## Deg. of Freedom 11 108
##
## Residual standard error: 0.4395681
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(NPQ ~ All.ID, data = Crop_early))
Output
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NPQ ~ All.ID, data = Crop_early)
##
## $All.ID
## diff lwr upr p adj
## LA_c+mock-LA_c+IAA -0.3586 -1.015362 0.298162 0.8011240
## LA_c+noIAA-LA_c+IAA 1.2129 0.556138 1.869662 0.0000008
## LA_s+IAA-LA_c+IAA -0.4283 -1.085062 0.228462 0.5683703
## LA_s+mock-LA_c+IAA -0.3602 -1.016962 0.296562 0.7965417
## LA_s+noIAA-LA_c+IAA 0.4048 -0.251962 1.061562 0.6521917
## m248_c+IAA-LA_c+IAA -0.0528 -0.709562 0.603962 1.0000000
## m248_c+mock-LA_c+IAA -0.2922 -0.948962 0.364562 0.9412140
## m248_c+noIAA-LA_c+IAA 2.5350 1.878238 3.191762 0.0000000
## m248_s+IAA-LA_c+IAA -0.2936 -0.950362 0.363162 0.9393008
## m248_s+mock-LA_c+IAA -0.3309 -0.987662 0.325862 0.8718606
## m248_s+noIAA-LA_c+IAA 0.8669 0.210138 1.523662 0.0014165
## LA_c+noIAA-LA_c+mock 1.5715 0.914738 2.228262 0.0000000
## LA_s+IAA-LA_c+mock -0.0697 -0.726462 0.587062 0.9999999
## LA_s+mock-LA_c+mock -0.0016 -0.658362 0.655162 1.0000000
## LA_s+noIAA-LA_c+mock 0.7634 0.106638 1.420162 0.0092752
## m248_c+IAA-LA_c+mock 0.3058 -0.350962 0.962562 0.9207847
## m248_c+mock-LA_c+mock 0.0664 -0.590362 0.723162 1.0000000
## m248_c+noIAA-LA_c+mock 2.8936 2.236838 3.550362 0.0000000
## m248_s+IAA-LA_c+mock 0.0650 -0.591762 0.721762 1.0000000
## m248_s+mock-LA_c+mock 0.0277 -0.629062 0.684462 1.0000000
## m248_s+noIAA-LA_c+mock 1.2255 0.568738 1.882262 0.0000006
## LA_s+IAA-LA_c+noIAA -1.6412 -2.297962 -0.984438 0.0000000
## LA_s+mock-LA_c+noIAA -1.5731 -2.229862 -0.916338 0.0000000
## LA_s+noIAA-LA_c+noIAA -0.8081 -1.464862 -0.151338 0.0042300
## m248_c+IAA-LA_c+noIAA -1.2657 -1.922462 -0.608938 0.0000002
## m248_c+mock-LA_c+noIAA -1.5051 -2.161862 -0.848338 0.0000000
## m248_c+noIAA-LA_c+noIAA 1.3221 0.665338 1.978862 0.0000001
## m248_s+IAA-LA_c+noIAA -1.5065 -2.163262 -0.849738 0.0000000
## m248_s+mock-LA_c+noIAA -1.5438 -2.200562 -0.887038 0.0000000
## m248_s+noIAA-LA_c+noIAA -0.3460 -1.002762 0.310762 0.8353801
## LA_s+mock-LA_s+IAA 0.0681 -0.588662 0.724862 0.9999999
## LA_s+noIAA-LA_s+IAA 0.8331 0.176338 1.489862 0.0026783
## m248_c+IAA-LA_s+IAA 0.3755 -0.281262 1.032262 0.7503264
## m248_c+mock-LA_s+IAA 0.1361 -0.520662 0.792862 0.9999196
## m248_c+noIAA-LA_s+IAA 2.9633 2.306538 3.620062 0.0000000
## m248_s+IAA-LA_s+IAA 0.1347 -0.522062 0.791462 0.9999274
## m248_s+mock-LA_s+IAA 0.0974 -0.559362 0.754162 0.9999974
## m248_s+noIAA-LA_s+IAA 1.2952 0.638438 1.951962 0.0000001
## LA_s+noIAA-LA_s+mock 0.7650 0.108238 1.421762 0.0090249
## m248_c+IAA-LA_s+mock 0.3074 -0.349362 0.964162 0.9181055
## m248_c+mock-LA_s+mock 0.0680 -0.588762 0.724762 0.9999999
## m248_c+noIAA-LA_s+mock 2.8952 2.238438 3.551962 0.0000000
## m248_s+IAA-LA_s+mock 0.0666 -0.590162 0.723362 1.0000000
## m248_s+mock-LA_s+mock 0.0293 -0.627462 0.686062 1.0000000
## m248_s+noIAA-LA_s+mock 1.2271 0.570338 1.883862 0.0000006
## m248_c+IAA-LA_s+noIAA -0.4576 -1.114362 0.199162 0.4638780
## m248_c+mock-LA_s+noIAA -0.6970 -1.353762 -0.040238 0.0273868
## m248_c+noIAA-LA_s+noIAA 2.1302 1.473438 2.786962 0.0000000
## m248_s+IAA-LA_s+noIAA -0.6984 -1.355162 -0.041638 0.0267985
## m248_s+mock-LA_s+noIAA -0.7357 -1.392462 -0.078938 0.0147581
## m248_s+noIAA-LA_s+noIAA 0.4621 -0.194662 1.118862 0.4482411
## m248_c+mock-m248_c+IAA -0.2394 -0.896162 0.417362 0.9864257
## m248_c+noIAA-m248_c+IAA 2.5878 1.931038 3.244562 0.0000000
## m248_s+IAA-m248_c+IAA -0.2408 -0.897562 0.415962 0.9857839
## m248_s+mock-m248_c+IAA -0.2781 -0.934862 0.378662 0.9581797
## m248_s+noIAA-m248_c+IAA 0.9197 0.262938 1.576462 0.0005028
## m248_c+noIAA-m248_c+mock 2.8272 2.170438 3.483962 0.0000000
## m248_s+IAA-m248_c+mock -0.0014 -0.658162 0.655362 1.0000000
## m248_s+mock-m248_c+mock -0.0387 -0.695462 0.618062 1.0000000
## m248_s+noIAA-m248_c+mock 1.1591 0.502338 1.815862 0.0000028
## m248_s+IAA-m248_c+noIAA -2.8286 -3.485362 -2.171838 0.0000000
## m248_s+mock-m248_c+noIAA -2.8659 -3.522662 -2.209138 0.0000000
## m248_s+noIAA-m248_c+noIAA -1.6681 -2.324862 -1.011338 0.0000000
## m248_s+mock-m248_s+IAA -0.0373 -0.694062 0.619462 1.0000000
## m248_s+noIAA-m248_s+IAA 1.1605 0.503738 1.817262 0.0000027
## m248_s+noIAA-m248_s+mock 1.1978 0.541038 1.854562 0.0000011
P12 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P12)
stat.test
## LA_c+mock LA_c+noIAA LA_s+IAA LA_s+mock LA_s+noIAA m248_c+IAA
## "a" "b" "a" "a" "cd" "ac"
## m248_c+mock m248_c+noIAA m248_s+IAA m248_s+mock m248_s+noIAA LA_c+IAA
## "a" "e" "a" "a" "bd" "ac"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
###################################################################
test$info <- strsplit(test$group1, "_")
test$info[[1]][2]
## [1] "c+mock"
test$Genotype <- "none"
test$condition<- "none"
test
for(i in 1:nrow(test)){
test$Genotype[i] <- test$info[[i]][1]
test$condition[i] <- test$info[[i]][2]
}
test2 <- test[,c(5:6,1)]
test2$group1 <- test2$Genotype
test2$group2 <- test2$Genotype
Crop_early
Crop_early$Genotype<- factor(Crop_early$Genotype, levels=c("LA", "m248"))
Crop_early$condition<- factor(Crop_early$condition, levels=c("c+noIAA", "c+mock", "c+IAA", "s+noIAA", "s+mock", "s+IAA"))
Crop_graph <- ggplot(data = Crop_early, mapping = aes(x = Genotype, y = NPQ, colour = Genotype))
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(~ condition, scales = "free_y")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("NPQ") + xlab("") + stat_pvalue_manual(test2, label = "Tukey", y.position = 4)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph
aov(ChlIdx ~ All.ID, data = Crop_early)
## Call:
## aov(formula = ChlIdx ~ All.ID, data = Crop_early)
##
## Terms:
## All.ID Residuals
## Sum of Squares 4.238529 0.615967
## Deg. of Freedom 11 108
##
## Residual standard error: 0.07552086
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(ChlIdx ~ All.ID, data = Crop_early))
Output
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ChlIdx ~ All.ID, data = Crop_early)
##
## $All.ID
## diff lwr upr p adj
## LA_c+mock-LA_c+IAA 0.4099 0.2970637141 0.52273629 0.0000000
## LA_c+noIAA-LA_c+IAA 0.2149 0.1020637141 0.32773629 0.0000003
## LA_s+IAA-LA_c+IAA 0.5218 0.4089637141 0.63463629 0.0000000
## LA_s+mock-LA_c+IAA 0.5052 0.3923637141 0.61803629 0.0000000
## LA_s+noIAA-LA_c+IAA 0.1385 0.0256637141 0.25133629 0.0043823
## m248_c+IAA-LA_c+IAA -0.0709 -0.1837362859 0.04193629 0.6243867
## m248_c+mock-LA_c+IAA 0.3255 0.2126637141 0.43833629 0.0000000
## m248_c+noIAA-LA_c+IAA 0.1181 0.0052637141 0.23093629 0.0317409
## m248_s+IAA-LA_c+IAA 0.3200 0.2071637141 0.43283629 0.0000000
## m248_s+mock-LA_c+IAA 0.3417 0.2288637141 0.45453629 0.0000000
## m248_s+noIAA-LA_c+IAA 0.0371 -0.0757362859 0.14993629 0.9941729
## LA_c+noIAA-LA_c+mock -0.1950 -0.3078362859 -0.08216371 0.0000048
## LA_s+IAA-LA_c+mock 0.1119 -0.0009362859 0.22473629 0.0540627
## LA_s+mock-LA_c+mock 0.0953 -0.0175362859 0.20813629 0.1859528
## LA_s+noIAA-LA_c+mock -0.2714 -0.3842362859 -0.15856371 0.0000000
## m248_c+IAA-LA_c+mock -0.4808 -0.5936362859 -0.36796371 0.0000000
## m248_c+mock-LA_c+mock -0.0844 -0.1972362859 0.02843629 0.3520054
## m248_c+noIAA-LA_c+mock -0.2918 -0.4046362859 -0.17896371 0.0000000
## m248_s+IAA-LA_c+mock -0.0899 -0.2027362859 0.02293629 0.2598831
## m248_s+mock-LA_c+mock -0.0682 -0.1810362859 0.04463629 0.6794182
## m248_s+noIAA-LA_c+mock -0.3728 -0.4856362859 -0.25996371 0.0000000
## LA_s+IAA-LA_c+noIAA 0.3069 0.1940637141 0.41973629 0.0000000
## LA_s+mock-LA_c+noIAA 0.2903 0.1774637141 0.40313629 0.0000000
## LA_s+noIAA-LA_c+noIAA -0.0764 -0.1892362859 0.03643629 0.5095259
## m248_c+IAA-LA_c+noIAA -0.2858 -0.3986362859 -0.17296371 0.0000000
## m248_c+mock-LA_c+noIAA 0.1106 -0.0022362859 0.22343629 0.0601723
## m248_c+noIAA-LA_c+noIAA -0.0968 -0.2096362859 0.01603629 0.1683947
## m248_s+IAA-LA_c+noIAA 0.1051 -0.0077362859 0.21793629 0.0929049
## m248_s+mock-LA_c+noIAA 0.1268 0.0139637141 0.23963629 0.0142006
## m248_s+noIAA-LA_c+noIAA -0.1778 -0.2906362859 -0.06496371 0.0000452
## LA_s+mock-LA_s+IAA -0.0166 -0.1294362859 0.09623629 0.9999976
## LA_s+noIAA-LA_s+IAA -0.3833 -0.4961362859 -0.27046371 0.0000000
## m248_c+IAA-LA_s+IAA -0.5927 -0.7055362859 -0.47986371 0.0000000
## m248_c+mock-LA_s+IAA -0.1963 -0.3091362859 -0.08346371 0.0000041
## m248_c+noIAA-LA_s+IAA -0.4037 -0.5165362859 -0.29086371 0.0000000
## m248_s+IAA-LA_s+IAA -0.2018 -0.3146362859 -0.08896371 0.0000019
## m248_s+mock-LA_s+IAA -0.1801 -0.2929362859 -0.06726371 0.0000337
## m248_s+noIAA-LA_s+IAA -0.4847 -0.5975362859 -0.37186371 0.0000000
## LA_s+noIAA-LA_s+mock -0.3667 -0.4795362859 -0.25386371 0.0000000
## m248_c+IAA-LA_s+mock -0.5761 -0.6889362859 -0.46326371 0.0000000
## m248_c+mock-LA_s+mock -0.1797 -0.2925362859 -0.06686371 0.0000355
## m248_c+noIAA-LA_s+mock -0.3871 -0.4999362859 -0.27426371 0.0000000
## m248_s+IAA-LA_s+mock -0.1852 -0.2980362859 -0.07236371 0.0000175
## m248_s+mock-LA_s+mock -0.1635 -0.2763362859 -0.05066371 0.0002628
## m248_s+noIAA-LA_s+mock -0.4681 -0.5809362859 -0.35526371 0.0000000
## m248_c+IAA-LA_s+noIAA -0.2094 -0.3222362859 -0.09656371 0.0000007
## m248_c+mock-LA_s+noIAA 0.1870 0.0741637141 0.29983629 0.0000139
## m248_c+noIAA-LA_s+noIAA -0.0204 -0.1332362859 0.09243629 0.9999796
## m248_s+IAA-LA_s+noIAA 0.1815 0.0686637141 0.29433629 0.0000282
## m248_s+mock-LA_s+noIAA 0.2032 0.0903637141 0.31603629 0.0000016
## m248_s+noIAA-LA_s+noIAA -0.1014 -0.2142362859 0.01143629 0.1222508
## m248_c+mock-m248_c+IAA 0.3964 0.2835637141 0.50923629 0.0000000
## m248_c+noIAA-m248_c+IAA 0.1890 0.0761637141 0.30183629 0.0000107
## m248_s+IAA-m248_c+IAA 0.3909 0.2780637141 0.50373629 0.0000000
## m248_s+mock-m248_c+IAA 0.4126 0.2997637141 0.52543629 0.0000000
## m248_s+noIAA-m248_c+IAA 0.1080 -0.0048362859 0.22083629 0.0741703
## m248_c+noIAA-m248_c+mock -0.2074 -0.3202362859 -0.09456371 0.0000009
## m248_s+IAA-m248_c+mock -0.0055 -0.1183362859 0.10733629 1.0000000
## m248_s+mock-m248_c+mock 0.0162 -0.0966362859 0.12903629 0.9999981
## m248_s+noIAA-m248_c+mock -0.2884 -0.4012362859 -0.17556371 0.0000000
## m248_s+IAA-m248_c+noIAA 0.2019 0.0890637141 0.31473629 0.0000019
## m248_s+mock-m248_c+noIAA 0.2236 0.1107637141 0.33643629 0.0000001
## m248_s+noIAA-m248_c+noIAA -0.0810 -0.1938362859 0.03183629 0.4163000
## m248_s+mock-m248_s+IAA 0.0217 -0.0911362859 0.13453629 0.9999619
## m248_s+noIAA-m248_s+IAA -0.2829 -0.3957362859 -0.17006371 0.0000000
## m248_s+noIAA-m248_s+mock -0.3046 -0.4174362859 -0.19176371 0.0000000
P14 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P14)
stat.test
## LA_c+mock LA_c+noIAA LA_s+IAA LA_s+mock LA_s+noIAA m248_c+IAA
## "ab" "cd" "a" "a" "ce" "f"
## m248_c+mock m248_c+noIAA m248_s+IAA m248_s+mock m248_s+noIAA LA_c+IAA
## "bd" "ce" "bd" "b" "ef" "f"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
###################################################################
test$info <- strsplit(test$group1, "_")
test$info[[1]][2]
## [1] "c+mock"
test$Genotype <- "none"
test$condition<- "none"
test
for(i in 1:nrow(test)){
test$Genotype[i] <- test$info[[i]][1]
test$condition[i] <- test$info[[i]][2]
}
test2 <- test[,c(5:6,1)]
test2$group1 <- test2$Genotype
test2$group2 <- test2$Genotype
Crop_early
Crop_early$Genotype<- factor(Crop_early$Genotype, levels=c("LA", "m248"))
Crop_early$condition<- factor(Crop_early$condition, levels=c("c+noIAA", "c+mock", "c+IAA", "s+noIAA", "s+mock", "s+IAA"))
Crop_graph <- ggplot(data = Crop_early, mapping = aes(x = Genotype, y = ChlIdx, colour = Genotype))
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(~ condition, scales = "free_y")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("ChlIdx") + xlab("") + stat_pvalue_manual(test2, label = "Tukey", y.position = 2)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph
aov(AriIdx ~ All.ID, data = Crop_early)
## Call:
## aov(formula = AriIdx ~ All.ID, data = Crop_early)
##
## Terms:
## All.ID Residuals
## Sum of Squares 18.442233 6.441141
## Deg. of Freedom 11 108
##
## Residual standard error: 0.2442134
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(AriIdx ~ All.ID, data = Crop_early))
Output
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AriIdx ~ All.ID, data = Crop_early)
##
## $All.ID
## diff lwr upr p adj
## LA_c+mock-LA_c+IAA 0.7972 0.432318869 1.16208113 0.0000000
## LA_c+noIAA-LA_c+IAA 0.6548 0.289918869 1.01968113 0.0000018
## LA_s+IAA-LA_c+IAA 1.0720 0.707118869 1.43688113 0.0000000
## LA_s+mock-LA_c+IAA 0.9988 0.633918869 1.36368113 0.0000000
## LA_s+noIAA-LA_c+IAA 0.2262 -0.138681131 0.59108113 0.6439520
## m248_c+IAA-LA_c+IAA -0.0102 -0.375081131 0.35468113 1.0000000
## m248_c+mock-LA_c+IAA 0.3710 0.006118869 0.73588113 0.0425896
## m248_c+noIAA-LA_c+IAA 0.1237 -0.241181131 0.48858113 0.9924698
## m248_s+IAA-LA_c+IAA 0.3477 -0.017181131 0.71258113 0.0770139
## m248_s+mock-LA_c+IAA 0.3986 0.033718869 0.76348113 0.0198437
## m248_s+noIAA-LA_c+IAA -0.2108 -0.575681131 0.15408113 0.7378270
## LA_c+noIAA-LA_c+mock -0.1424 -0.507281131 0.22248113 0.9769618
## LA_s+IAA-LA_c+mock 0.2748 -0.090081131 0.63968113 0.3415601
## LA_s+mock-LA_c+mock 0.2016 -0.163281131 0.56648113 0.7887938
## LA_s+noIAA-LA_c+mock -0.5710 -0.935881131 -0.20611887 0.0000527
## m248_c+IAA-LA_c+mock -0.8074 -1.172281131 -0.44251887 0.0000000
## m248_c+mock-LA_c+mock -0.4262 -0.791081131 -0.06131887 0.0087010
## m248_c+noIAA-LA_c+mock -0.6735 -1.038381131 -0.30861887 0.0000008
## m248_s+IAA-LA_c+mock -0.4495 -0.814381131 -0.08461887 0.0041566
## m248_s+mock-LA_c+mock -0.3986 -0.763481131 -0.03371887 0.0198437
## m248_s+noIAA-LA_c+mock -1.0080 -1.372881131 -0.64311887 0.0000000
## LA_s+IAA-LA_c+noIAA 0.4172 0.052318869 0.78208113 0.0114566
## LA_s+mock-LA_c+noIAA 0.3440 -0.020881131 0.70888113 0.0842105
## LA_s+noIAA-LA_c+noIAA -0.4286 -0.793481131 -0.06371887 0.0080775
## m248_c+IAA-LA_c+noIAA -0.6650 -1.029881131 -0.30011887 0.0000011
## m248_c+mock-LA_c+noIAA -0.2838 -0.648681131 0.08108113 0.2937571
## m248_c+noIAA-LA_c+noIAA -0.5311 -0.895981131 -0.16621887 0.0002405
## m248_s+IAA-LA_c+noIAA -0.3071 -0.671981131 0.05778113 0.1900123
## m248_s+mock-LA_c+noIAA -0.2562 -0.621081131 0.10868113 0.4515523
## m248_s+noIAA-LA_c+noIAA -0.8656 -1.230481131 -0.50071887 0.0000000
## LA_s+mock-LA_s+IAA -0.0732 -0.438081131 0.29168113 0.9999418
## LA_s+noIAA-LA_s+IAA -0.8458 -1.210681131 -0.48091887 0.0000000
## m248_c+IAA-LA_s+IAA -1.0822 -1.447081131 -0.71731887 0.0000000
## m248_c+mock-LA_s+IAA -0.7010 -1.065881131 -0.33611887 0.0000002
## m248_c+noIAA-LA_s+IAA -0.9483 -1.313181131 -0.58341887 0.0000000
## m248_s+IAA-LA_s+IAA -0.7243 -1.089181131 -0.35941887 0.0000001
## m248_s+mock-LA_s+IAA -0.6734 -1.038281131 -0.30851887 0.0000008
## m248_s+noIAA-LA_s+IAA -1.2828 -1.647681131 -0.91791887 0.0000000
## LA_s+noIAA-LA_s+mock -0.7726 -1.137481131 -0.40771887 0.0000000
## m248_c+IAA-LA_s+mock -1.0090 -1.373881131 -0.64411887 0.0000000
## m248_c+mock-LA_s+mock -0.6278 -0.992681131 -0.26291887 0.0000054
## m248_c+noIAA-LA_s+mock -0.8751 -1.239981131 -0.51021887 0.0000000
## m248_s+IAA-LA_s+mock -0.6511 -1.015981131 -0.28621887 0.0000021
## m248_s+mock-LA_s+mock -0.6002 -0.965081131 -0.23531887 0.0000166
## m248_s+noIAA-LA_s+mock -1.2096 -1.574481131 -0.84471887 0.0000000
## m248_c+IAA-LA_s+noIAA -0.2364 -0.601281131 0.12848113 0.5784330
## m248_c+mock-LA_s+noIAA 0.1448 -0.220081131 0.50968113 0.9738823
## m248_c+noIAA-LA_s+noIAA -0.1025 -0.467381131 0.26238113 0.9985364
## m248_s+IAA-LA_s+noIAA 0.1215 -0.243381131 0.48638113 0.9935173
## m248_s+mock-LA_s+noIAA 0.1724 -0.192481131 0.53728113 0.9130832
## m248_s+noIAA-LA_s+noIAA -0.4370 -0.801881131 -0.07211887 0.0062068
## m248_c+mock-m248_c+IAA 0.3812 0.016318869 0.74608113 0.0323579
## m248_c+noIAA-m248_c+IAA 0.1339 -0.230981131 0.49878113 0.9856851
## m248_s+IAA-m248_c+IAA 0.3579 -0.006981131 0.72278113 0.0597925
## m248_s+mock-m248_c+IAA 0.4088 0.043918869 0.77368113 0.0147307
## m248_s+noIAA-m248_c+IAA -0.2006 -0.565481131 0.16428113 0.7940383
## m248_c+noIAA-m248_c+mock -0.2473 -0.612181131 0.11758113 0.5079609
## m248_s+IAA-m248_c+mock -0.0233 -0.388181131 0.34158113 1.0000000
## m248_s+mock-m248_c+mock 0.0276 -0.337281131 0.39248113 1.0000000
## m248_s+noIAA-m248_c+mock -0.5818 -0.946681131 -0.21691887 0.0000345
## m248_s+IAA-m248_c+noIAA 0.2240 -0.140881131 0.58888113 0.6578408
## m248_s+mock-m248_c+noIAA 0.2749 -0.089981131 0.63978113 0.3410073
## m248_s+noIAA-m248_c+noIAA -0.3345 -0.699381131 0.03038113 0.1052624
## m248_s+mock-m248_s+IAA 0.0509 -0.313981131 0.41578113 0.9999986
## m248_s+noIAA-m248_s+IAA -0.5585 -0.923381131 -0.19361887 0.0000855
## m248_s+noIAA-m248_s+mock -0.6094 -0.974281131 -0.24451887 0.0000115
P16 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P16)
stat.test
## LA_c+mock LA_c+noIAA LA_s+IAA LA_s+mock LA_s+noIAA m248_c+IAA
## "ab" "ac" "b" "ab" "de" "df"
## m248_c+mock m248_c+noIAA m248_s+IAA m248_s+mock m248_s+noIAA LA_c+IAA
## "ce" "def" "cde" "ce" "f" "df"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
###################################################################
test$info <- strsplit(test$group1, "_")
test$info[[1]][2]
## [1] "c+mock"
test$Genotype <- "none"
test$condition<- "none"
test
for(i in 1:nrow(test)){
test$Genotype[i] <- test$info[[i]][1]
test$condition[i] <- test$info[[i]][2]
}
test2 <- test[,c(5:6,1)]
test2$group1 <- test2$Genotype
test2$group2 <- test2$Genotype
Crop_early
Crop_early$Genotype<- factor(Crop_early$Genotype, levels=c("LA", "m248"))
Crop_early$condition<- factor(Crop_early$condition, levels=c("c+noIAA", "c+mock", "c+IAA", "s+noIAA", "s+mock", "s+IAA"))
Crop_graph <- ggplot(data = Crop_early, mapping = aes(x = Genotype, y = AriIdx, colour = Genotype))
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(~ condition, scales = "free_y")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("AriIdx") + xlab("") + stat_pvalue_manual(test2, label = "Tukey", y.position = 3.8)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph
aov(Alpha ~ All.ID, data = Crop_early)
## Call:
## aov(formula = Alpha ~ All.ID, data = Crop_early)
##
## Terms:
## All.ID Residuals
## Sum of Squares 1.9002553 0.6705132
## Deg. of Freedom 11 108
##
## Residual standard error: 0.07879375
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(Alpha ~ All.ID, data = Crop_early))
Output
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Alpha ~ All.ID, data = Crop_early)
##
## $All.ID
## diff lwr upr p adj
## LA_c+mock-LA_c+IAA 0.3156 0.19787366 0.43332634 0.0000000
## LA_c+noIAA-LA_c+IAA 0.3214 0.20367366 0.43912634 0.0000000
## LA_s+IAA-LA_c+IAA 0.3017 0.18397366 0.41942634 0.0000000
## LA_s+mock-LA_c+IAA 0.2994 0.18167366 0.41712634 0.0000000
## LA_s+noIAA-LA_c+IAA 0.1320 0.01427366 0.24972634 0.0145913
## m248_c+IAA-LA_c+IAA -0.0229 -0.14062634 0.09482634 0.9999573
## m248_c+mock-LA_c+IAA 0.3172 0.19947366 0.43492634 0.0000000
## m248_c+noIAA-LA_c+IAA 0.3234 0.20567366 0.44112634 0.0000000
## m248_s+IAA-LA_c+IAA 0.3123 0.19457366 0.43002634 0.0000000
## m248_s+mock-LA_c+IAA 0.3030 0.18527366 0.42072634 0.0000000
## m248_s+noIAA-LA_c+IAA 0.1323 0.01457366 0.25002634 0.0141943
## LA_c+noIAA-LA_c+mock 0.0058 -0.11192634 0.12352634 1.0000000
## LA_s+IAA-LA_c+mock -0.0139 -0.13162634 0.10382634 0.9999998
## LA_s+mock-LA_c+mock -0.0162 -0.13392634 0.10152634 0.9999988
## LA_s+noIAA-LA_c+mock -0.1836 -0.30132634 -0.06587366 0.0000569
## m248_c+IAA-LA_c+mock -0.3385 -0.45622634 -0.22077366 0.0000000
## m248_c+mock-LA_c+mock 0.0016 -0.11612634 0.11932634 1.0000000
## m248_c+noIAA-LA_c+mock 0.0078 -0.10992634 0.12552634 1.0000000
## m248_s+IAA-LA_c+mock -0.0033 -0.12102634 0.11442634 1.0000000
## m248_s+mock-LA_c+mock -0.0126 -0.13032634 0.10512634 0.9999999
## m248_s+noIAA-LA_c+mock -0.1833 -0.30102634 -0.06557366 0.0000589
## LA_s+IAA-LA_c+noIAA -0.0197 -0.13742634 0.09802634 0.9999907
## LA_s+mock-LA_c+noIAA -0.0220 -0.13972634 0.09572634 0.9999715
## LA_s+noIAA-LA_c+noIAA -0.1894 -0.30712634 -0.07167366 0.0000281
## m248_c+IAA-LA_c+noIAA -0.3443 -0.46202634 -0.22657366 0.0000000
## m248_c+mock-LA_c+noIAA -0.0042 -0.12192634 0.11352634 1.0000000
## m248_c+noIAA-LA_c+noIAA 0.0020 -0.11572634 0.11972634 1.0000000
## m248_s+IAA-LA_c+noIAA -0.0091 -0.12682634 0.10862634 1.0000000
## m248_s+mock-LA_c+noIAA -0.0184 -0.13612634 0.09932634 0.9999954
## m248_s+noIAA-LA_c+noIAA -0.1891 -0.30682634 -0.07137366 0.0000291
## LA_s+mock-LA_s+IAA -0.0023 -0.12002634 0.11542634 1.0000000
## LA_s+noIAA-LA_s+IAA -0.1697 -0.28742634 -0.05197366 0.0002908
## m248_c+IAA-LA_s+IAA -0.3246 -0.44232634 -0.20687366 0.0000000
## m248_c+mock-LA_s+IAA 0.0155 -0.10222634 0.13322634 0.9999992
## m248_c+noIAA-LA_s+IAA 0.0217 -0.09602634 0.13942634 0.9999752
## m248_s+IAA-LA_s+IAA 0.0106 -0.10712634 0.12832634 1.0000000
## m248_s+mock-LA_s+IAA 0.0013 -0.11642634 0.11902634 1.0000000
## m248_s+noIAA-LA_s+IAA -0.1694 -0.28712634 -0.05167366 0.0003010
## LA_s+noIAA-LA_s+mock -0.1674 -0.28512634 -0.04967366 0.0003778
## m248_c+IAA-LA_s+mock -0.3223 -0.44002634 -0.20457366 0.0000000
## m248_c+mock-LA_s+mock 0.0178 -0.09992634 0.13552634 0.9999968
## m248_c+noIAA-LA_s+mock 0.0240 -0.09372634 0.14172634 0.9999317
## m248_s+IAA-LA_s+mock 0.0129 -0.10482634 0.13062634 0.9999999
## m248_s+mock-LA_s+mock 0.0036 -0.11412634 0.12132634 1.0000000
## m248_s+noIAA-LA_s+mock -0.1671 -0.28482634 -0.04937366 0.0003908
## m248_c+IAA-LA_s+noIAA -0.1549 -0.27262634 -0.03717366 0.0014932
## m248_c+mock-LA_s+noIAA 0.1852 0.06747366 0.30292634 0.0000469
## m248_c+noIAA-LA_s+noIAA 0.1914 0.07367366 0.30912634 0.0000220
## m248_s+IAA-LA_s+noIAA 0.1803 0.06257366 0.29802634 0.0000844
## m248_s+mock-LA_s+noIAA 0.1710 0.05327366 0.28872634 0.0002506
## m248_s+noIAA-LA_s+noIAA 0.0003 -0.11742634 0.11802634 1.0000000
## m248_c+mock-m248_c+IAA 0.3401 0.22237366 0.45782634 0.0000000
## m248_c+noIAA-m248_c+IAA 0.3463 0.22857366 0.46402634 0.0000000
## m248_s+IAA-m248_c+IAA 0.3352 0.21747366 0.45292634 0.0000000
## m248_s+mock-m248_c+IAA 0.3259 0.20817366 0.44362634 0.0000000
## m248_s+noIAA-m248_c+IAA 0.1552 0.03747366 0.27292634 0.0014462
## m248_c+noIAA-m248_c+mock 0.0062 -0.11152634 0.12392634 1.0000000
## m248_s+IAA-m248_c+mock -0.0049 -0.12262634 0.11282634 1.0000000
## m248_s+mock-m248_c+mock -0.0142 -0.13192634 0.10352634 0.9999997
## m248_s+noIAA-m248_c+mock -0.1849 -0.30262634 -0.06717366 0.0000486
## m248_s+IAA-m248_c+noIAA -0.0111 -0.12882634 0.10662634 1.0000000
## m248_s+mock-m248_c+noIAA -0.0204 -0.13812634 0.09732634 0.9999868
## m248_s+noIAA-m248_c+noIAA -0.1911 -0.30882634 -0.07337366 0.0000228
## m248_s+mock-m248_s+IAA -0.0093 -0.12702634 0.10842634 1.0000000
## m248_s+noIAA-m248_s+IAA -0.1800 -0.29772634 -0.06227366 0.0000875
## m248_s+noIAA-m248_s+mock -0.1707 -0.28842634 -0.05297366 0.0002594
P18 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P8)
stat.test
## LA_c+mock LA_c+noIAA LA_s+IAA LA_s+mock LA_s+noIAA m248_c+IAA
## "a" "a" "a" "a" "b" "c"
## m248_c+mock m248_c+noIAA m248_s+IAA m248_s+mock m248_s+noIAA LA_c+IAA
## "a" "a" "a" "a" "b" "bc"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
###################################################################
test$info <- strsplit(test$group1, "_")
test$info[[1]][2]
## [1] "c+mock"
test$Genotype <- "none"
test$condition<- "none"
test
for(i in 1:nrow(test)){
test$Genotype[i] <- test$info[[i]][1]
test$condition[i] <- test$info[[i]][2]
}
test2 <- test[,c(5:6,1)]
test2$group1 <- test2$Genotype
test2$group2 <- test2$Genotype
Crop_early
Crop_early$Genotype<- factor(Crop_early$Genotype, levels=c("LA", "m248"))
Crop_early$condition<- factor(Crop_early$condition, levels=c("c+noIAA", "c+mock", "c+IAA", "s+noIAA", "s+mock", "s+IAA"))
Crop_graph <- ggplot(data = Crop_early, mapping = aes(x = Genotype, y = Alpha, colour = Genotype))
Crop_graph <- Crop_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Crop_graph <- Crop_graph + facet_grid(~ condition, scales = "free_y")
Crop_graph <- Crop_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Crop_graph <- Crop_graph + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
Crop_graph <- Crop_graph + ylab("Alpha") + xlab("") + stat_pvalue_manual(test2, label = "Tukey", y.position = 0.83)
Crop_graph <- Crop_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Crop_graph <- Crop_graph + rremove("legend")
Crop_graph
