R Notebook
In this notebook, I am only re-plotting some of the data that was previously re-analyzed by Helena Oakey and Mitch Morton.
list.files()## [1] "00-raw.0275.dat.rda"
## [2] "01-longi5.0275_MJLM_FINALFINAL.dat.csv"
## [3] "01-longi5.0275_MJLM_FINALFINAL.dat.rda"
## [4] "20221012_plots.nb.html"
## [5] "20221012_plots.Rmd"
## [6] "20230208_plots_and_figures.html"
## [7] "20230208_plots_and_figures.nb.html"
## [8] "20230208_plots_and_figures.Rmd"
## [9] "Analysis_Graphs"
## [10] "Annotated TPA outliers.numbers"
## [11] "emptyPots_adj.csv"
## [12] "emptyPots.csv"
## [13] "Evap.carts.rda"
## [14] "Fig.Interval.data_MMJ.pdf"
## [15] "Fig.Interval.data_MMJ.png"
## [16] "Fig.Interval.data.pdf"
## [17] "Fig.Interval.png"
## [18] "Fig.l_plots.pdf"
## [19] "Fig.S.ShootFreshMass_vs_PSA_MMJ.pdf"
## [20] "Fig.S.ShootFreshMass_vs_PSA_MMJ.png"
## [21] "Fig.S.ShootFreshMass_vs_PSA.pdf"
## [22] "Fig.S.ShootFreshMass_vs_PSA.png"
## [23] "Fig.SFM_indices.pdf"
## [24] "Fig.SFM_indices.png"
## [25] "Fig.SFM_ions_MMJ.pdf"
## [26] "Fig.SFM_ions.pdf"
## [27] "Fig.SFM_ions.png"
## [28] "Final_TPAScript_RawToSmooth.R"
## [29] "MMJ_Fig.Phenotype_TPA.pdf"
## [30] "MMJ_Fig.Phenotype_TPA.png"
## [31] "README_Data_prep_files.rtf"
## [32] "Regression_SFM.pdf"
## [33] "TPA_correlation_pearson.pdf"
## [34] "TPA_correlation_spearman.pdf"
## [35] "TPA_correlations.pdf"
## [36] "TPA_fig_long_MMJ.pdf"
## [37] "TPA_fig_long_MMJ.png"
## [38] "TPA_fig_long.pdf"
## [39] "TPA_terminal.harvest_MMJ.pdf"
## [40] "TPA_terminal.harvest.pdf"
## [41] "TPA_terminal.png"
## [42] "TPA.Adj.Rsq.csv"
## [43] "TPA.pval.csv"
## [44] "TPA.RSE.csv"
## [45] "TPA.Rsq.csv"
## [46] "TPA.stress.indices.all.traits.csv"
## [47] "TPAData_Corrected+SFM+FlameMeans_rmM104.csv"long <- read.csv("01-longi5.0275_MJLM_FINALFINAL.dat.csv")
longlibrary("ggplot2")
library("ggpubr")
library("ggsci")
long$Days.after.Salting <- as.factor(long$Days.after.Salting)
Area_lgraph <- ggplot(data=long, aes(x= Days.after.Salting, y=Area.smooth, group = Snapshot.ID.Tag, color = Treatment.1))
Area_lgraph <- Area_lgraph + geom_line(alpha = 0.1)
Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= Treatment.1), alpha=0.3)
Area_lgraph <- Area_lgraph + stat_summary(fun=mean, aes(group= Treatment.1), size=0.7, geom="line", linetype = "dashed")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.Area_lgraph <- Area_lgraph + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
Area_lgraph <- Area_lgraph + ylab("Projected Shoot Area (kpixels)") + xlab("Days after stress") + scale_color_aaas() + theme_bw()
Area_lgraph <- Area_lgraph + guides(color=guide_legend(title="Treatment")) + theme(legend.position = c(0.1, 0.7))
Area_lgraph## Warning: The dot-dot notation (`..p.signif..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(p.signif)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 532 rows containing non-finite values (`stat_summary()`).
## Removed 532 rows containing non-finite values (`stat_summary()`).## Warning: Removed 532 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 531 rows containing missing values (`geom_line()`).
AreaAGR_lgraph <- ggplot(data=long, aes(x= Days.after.Salting, y=Area.smooth.AGR, group = Snapshot.ID.Tag, color = Treatment.1))
AreaAGR_lgraph <- AreaAGR_lgraph + geom_line(alpha = 0.1)
AreaAGR_lgraph <- AreaAGR_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= Treatment.1), alpha=0.3)
AreaAGR_lgraph <- AreaAGR_lgraph + stat_summary(fun=mean, aes(group= Treatment.1), size=0.7, geom="line", linetype = "dashed")
AreaAGR_lgraph <- AreaAGR_lgraph + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
AreaAGR_lgraph <- AreaAGR_lgraph + ylab("Absolute Growth Rrate (kpixels / day)") + xlab("Days after stress") + scale_color_aaas() + theme_bw()
AreaAGR_lgraph <- AreaAGR_lgraph + guides(color=guide_legend(title="Treatment")) + theme(legend.position = c(0.1, 0.7))
AreaAGR_lgraph## Warning: Removed 533 rows containing non-finite values (`stat_summary()`).
## Removed 533 rows containing non-finite values (`stat_summary()`).## Warning: Removed 533 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 531 rows containing missing values (`geom_line()`).
AreaRGR_lgraph <- ggplot(data=long, aes(x= Days.after.Salting, y=Area.smooth.RGR, group = Snapshot.ID.Tag, color = Treatment.1))
AreaRGR_lgraph <- AreaRGR_lgraph + geom_line(alpha = 0.1)
AreaRGR_lgraph <- AreaRGR_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= Treatment.1), alpha=0.3)
AreaRGR_lgraph <- AreaRGR_lgraph + stat_summary(fun=mean, aes(group= Treatment.1), size=0.7, geom="line", linetype = "dashed")
AreaRGR_lgraph <- AreaRGR_lgraph + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
AreaRGR_lgraph <- AreaRGR_lgraph + ylab("Relative Growth Rrate (kpixels / day)") + xlab("Days after stress") + scale_color_aaas() + theme_bw()
AreaRGR_lgraph <- AreaRGR_lgraph + guides(color=guide_legend(title="Treatment")) + theme(legend.position = "none")
AreaRGR_lgraph## Warning: Removed 533 rows containing non-finite values (`stat_summary()`).
## Removed 533 rows containing non-finite values (`stat_summary()`).## Warning: Removed 533 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 531 rows containing missing values (`geom_line()`).
Transpiration_lgraph <- ggplot(data=long, aes(x= Days.after.Salting, y=Transpiration.smooth, group = Snapshot.ID.Tag, color = Treatment.1))
Transpiration_lgraph <- Transpiration_lgraph + geom_line(alpha = 0.1)
Transpiration_lgraph <- Transpiration_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= Treatment.1), alpha=0.3)
Transpiration_lgraph <- Transpiration_lgraph + stat_summary(fun=mean, aes(group= Treatment.1), size=0.7, geom="line", linetype = "dashed")
Transpiration_lgraph <- Transpiration_lgraph + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
Transpiration_lgraph <- Transpiration_lgraph + ylab("Transpiration Rrate (mL H2O / day)") + xlab("Days after stress") + scale_color_aaas() + theme_bw()
Transpiration_lgraph <- Transpiration_lgraph + guides(color=guide_legend(title="Treatment")) + theme(legend.position = "none")
Transpiration_lgraph## Warning: Removed 784 rows containing non-finite values (`stat_summary()`).
## Removed 784 rows containing non-finite values (`stat_summary()`).## Warning: Removed 784 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 564 rows containing missing values (`geom_line()`).
TUE_lgraph <- ggplot(data=long, aes(x= Days.after.Salting, y=Area.smooth.TUE, group = Snapshot.ID.Tag, color = Treatment.1))
TUE_lgraph <- TUE_lgraph + geom_line(alpha = 0.1)
TUE_lgraph <- TUE_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= Treatment.1), alpha=0.3)
TUE_lgraph <- TUE_lgraph + stat_summary(fun=mean, aes(group= Treatment.1), size=0.7, geom="line", linetype = "dashed")
TUE_lgraph <- TUE_lgraph + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
TUE_lgraph <- TUE_lgraph + ylab("Transpiration Use Efficiency (kpixels / mL H2O )") + xlab("Days after stress") + scale_color_aaas() + theme_bw()
TUE_lgraph <- TUE_lgraph + guides(color=guide_legend(title="Treatment")) + theme(legend.position = "none")
TUE_lgraph## Warning: Removed 784 rows containing non-finite values (`stat_summary()`).
## Removed 784 rows containing non-finite values (`stat_summary()`).## Warning: Removed 784 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 564 rows containing missing values (`geom_line()`).
OK - now let’s get to the destructivelly collected data:
list.files("Analysis_Graphs/Terminal_Violin_Plots/")## [1] "Final_TPAScript_TerminalViolinPlots.R"
## [2] "NaKratio violinplot.pdf"
## [3] "SDM violinplot.pdf"
## [4] "SFM violinplot_noleg.pdf"
## [5] "SFM violinplot.pdf"
## [6] "TissueK violinplot.pdf"
## [7] "TissueNa violinplot.pdf"
## [8] "TPAData_Corrected+SFM+FlameMeans_rmM104.csv"FW_data <- read.csv("Analysis_Graphs/Terminal_Violin_Plots/TPAData_Corrected+SFM+FlameMeans_rmM104.csv")
FW_data# install.packages("see")
library(see)##
## Attaching package: 'see'## The following objects are masked from 'package:ggsci':
##
## scale_color_material, scale_colour_material, scale_fill_materiallibrary(datasets)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionFW_violin_paired <- ggplot(data = FW_data, mapping = aes(x = Treatment.1, y = SFM_me, fill = Treatment.1))
FW_violin_paired <- FW_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
FW_violin_paired <- FW_violin_paired + geom_violindot(dots_size = 0.08, position_dots = position_dodge(0.1), flip = c(1)) ## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`FW_violin_paired <- FW_violin_paired + geom_point(mapping = aes(fill = Treatment.1, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
FW_violin_paired <- FW_violin_paired + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
FW_violin_paired <- FW_violin_paired + theme_bw() + scale_fill_aaas() + theme(legend.position = "none") + ylab("Shoot Fresh Mass (g)") + xlab("") + scale_color_aaas()
FW_violin_paired ## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1 rows containing missing values (`stat_bindot()`).## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).
summary(lm(SFM_me ~ Treatment.1, data = FW_data))##
## Call:
## lm(formula = SFM_me ~ Treatment.1, data = FW_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.424 -8.366 -1.514 7.522 45.907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.7741 0.8353 69.17 <2e-16 ***
## Treatment.1SALT -27.7767 1.1827 -23.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.39 on 437 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.558, Adjusted R-squared: 0.557
## F-statistic: 551.6 on 1 and 437 DF, p-value: < 2.2e-16DW_violin_paired <- ggplot(data = FW_data, mapping = aes(x = Treatment.1, y = SDM_me, fill = Treatment.1))
DW_violin_paired <- DW_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
DW_violin_paired <- DW_violin_paired + geom_violindot(dots_size = 0.08, position_dots = position_dodge(0.1), flip = c(1)) ## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`DW_violin_paired <- DW_violin_paired + geom_point(mapping = aes(fill = Treatment.1, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
DW_violin_paired <- DW_violin_paired + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
DW_violin_paired <- DW_violin_paired + scale_fill_aaas() + theme_bw()
DW_violin_paired <- DW_violin_paired + theme(legend.position = "none") + ylab("Shoot Dry Mass (g)") + xlab("")
DW_violin_paired ## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1 rows containing missing values (`stat_bindot()`).## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).
FW_data$TissueNa_me <- as.numeric(FW_data$TissueNa_me)
Na_violin_paired <- ggplot(data = FW_data, mapping = aes(x = Treatment.1, y = TissueNa_me, fill = Treatment.1))
Na_violin_paired <- Na_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
Na_violin_paired <- Na_violin_paired + geom_violindot(dots_size = 0.01, position_dots = position_dodge(0.1), flip = c(1)) ## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`Na_violin_paired <- Na_violin_paired + geom_point(mapping = aes(fill = Treatment.1, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
Na_violin_paired <- Na_violin_paired + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
Na_violin_paired <- Na_violin_paired + scale_fill_aaas() + theme_bw()
Na_violin_paired <- Na_violin_paired + theme(legend.position = "none") + ylab("Tissue [Na+] (mmol / g DM)") + xlab("")
Na_violin_paired## Warning: Removed 144 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 144 rows containing missing values (`stat_bindot()`).## Warning: Removed 144 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 144 rows containing missing values (`geom_line()`).## Warning: Removed 144 rows containing missing values (`geom_point()`).
summary(lm(TissueNa_me ~ Treatment.1, data = FW_data))##
## Call:
## lm(formula = TissueNa_me ~ Treatment.1, data = FW_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.26643 -0.06143 -0.00958 0.04732 0.56357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25940 0.01317 19.69 <2e-16 ***
## Treatment.1SALT 0.21702 0.01535 14.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1163 on 294 degrees of freedom
## (144 observations deleted due to missingness)
## Multiple R-squared: 0.4048, Adjusted R-squared: 0.4027
## F-statistic: 199.9 on 1 and 294 DF, p-value: < 2.2e-16FW_data$TissueK_me <- as.numeric(FW_data$TissueK_me)
K_violin_paired <- ggplot(data = FW_data, mapping = aes(x = Treatment.1, y = TissueK_me, fill = Treatment.1))
K_violin_paired <- K_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
K_violin_paired <- K_violin_paired + geom_violindot(dots_size = 0.01, position_dots = position_dodge(0.1), flip = c(1)) ## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`K_violin_paired <- K_violin_paired + geom_point(mapping = aes(fill = Treatment.1, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
K_violin_paired <- K_violin_paired + scale_color_aaas() + theme_bw()
K_violin_paired <- K_violin_paired + stat_compare_means(aes(group = Treatment.1), label = "p.signif", method = "aov", hide.ns = T)
K_violin_paired <- K_violin_paired + theme(legend.position = "none") + scale_fill_aaas() + ylab("Tissue [K+] (mmol / g DM)") + xlab("")
K_violin_paired## Warning: Removed 144 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 144 rows containing missing values (`stat_bindot()`).## Warning: Removed 144 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 144 rows containing missing values (`geom_line()`).## Warning: Removed 144 rows containing missing values (`geom_point()`).
summary(lm(TissueK_me ~ Treatment.1, data = FW_data))##
## Call:
## lm(formula = TissueK_me ~ Treatment.1, data = FW_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.42205 -0.11205 -0.01944 0.10222 0.60056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.57205 0.01948 80.697 <2e-16 ***
## Treatment.1SALT 0.02072 0.02270 0.913 0.362
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1721 on 294 degrees of freedom
## (144 observations deleted due to missingness)
## Multiple R-squared: 0.002827, Adjusted R-squared: -0.0005648
## F-statistic: 0.8335 on 1 and 294 DF, p-value: 0.362library(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggpubr':
##
## get_legendTPA_fig_long <- plot_grid(AreaAGR_lgraph, AreaRGR_lgraph, Transpiration_lgraph, TUE_lgraph, labels = "AUTO", ncol = 1)## Warning: Removed 533 rows containing non-finite values (`stat_summary()`).
## Removed 533 rows containing non-finite values (`stat_summary()`).## Warning: Removed 533 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 531 rows containing missing values (`geom_line()`).## Warning: Removed 533 rows containing non-finite values (`stat_summary()`).
## Removed 533 rows containing non-finite values (`stat_summary()`).## Warning: Removed 533 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 531 rows containing missing values (`geom_line()`).## Warning: Removed 784 rows containing non-finite values (`stat_summary()`).
## Removed 784 rows containing non-finite values (`stat_summary()`).## Warning: Removed 784 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 564 rows containing missing values (`geom_line()`).## Warning: Removed 784 rows containing non-finite values (`stat_summary()`).
## Removed 784 rows containing non-finite values (`stat_summary()`).## Warning: Removed 784 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 564 rows containing missing values (`geom_line()`).TPA_fig_long
pdf("TPA_fig_long.pdf", height = 10, width = 5)
plot(TPA_fig_long)
dev.off() ## quartz_off_screen
## 2TPA_terminal.harvest <- plot_grid(FW_violin_paired, DW_violin_paired, Na_violin_paired, K_violin_paired, ncol = 2, labels = "AUTO")## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1 rows containing missing values (`stat_bindot()`).## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 1 rows containing missing values (`stat_bindot()`).## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).## Warning: Removed 144 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 144 rows containing missing values (`stat_bindot()`).## Warning: Removed 144 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 144 rows containing missing values (`geom_line()`).## Warning: Removed 144 rows containing missing values (`geom_point()`).## Warning: Removed 144 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 144 rows containing missing values (`stat_bindot()`).## Warning: Removed 144 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 144 rows containing missing values (`geom_line()`).## Warning: Removed 144 rows containing missing values (`geom_point()`).TPA_terminal.harvest
pdf("TPA_terminal.harvest.pdf", height = 8, width = 8)
plot(TPA_terminal.harvest)
dev.off() ## quartz_off_screen
## 2Correlations
Let’s look if there is a correlation between the Projected shoot area and the FSM
longcolnames(long)## [1] "X" "Snapshot.ID.Tag"
## [3] "Days" "xDays"
## [5] "Smarthouse" "Evap.Cart"
## [7] "Lane" "Position"
## [9] "Snapshot.Time.Stamp" "xPosn"
## [11] "Reps" "Hour"
## [13] "Genotype.ID" "Treatment.1"
## [15] "Weight.Before" "Weight.After"
## [17] "Water.Amount" "Water.Loss"
## [19] "Area" "Area.SV1"
## [21] "Area.SV2" "Area.TV"
## [23] "Boundary.Points.To.Area.Ratio.SV1" "Boundary.Points.To.Area.Ratio.SV2"
## [25] "Boundary.Points.To.Area.Ratio.TV" "Caliper.Length.SV1"
## [27] "Caliper.Length.SV2" "Caliper.Length.TV"
## [29] "Compactness.SV1" "Compactness.SV2"
## [31] "Compactness.TV" "Convex.Hull.Area.SV1"
## [33] "Convex.Hull.Area.SV2" "Convex.Hull.Area.TV"
## [35] "Center.Of.Mass.Y.SV1" "Center.Of.Mass.Y.SV2"
## [37] "Max.Dist.Above.Horizon.Line.SV1" "Max.Dist.Above.Horizon.Line.SV2"
## [39] "Evaporation" "Days.after.Salting"
## [41] "Transpiration" "Days.diffs"
## [43] "Area.AGR" "Area.RGR"
## [45] "Area.TUE" "Transpiration.rate"
## [47] "TUE.cum" "Transpiration.Cum"
## [49] "Transpiration.rate.smooth" "Area.smooth"
## [51] "Area.smooth.AGR" "Area.smooth.RGR"
## [53] "Transpiration.smooth" "Area.smooth.TUE"cur <- read.csv("TPAData_Corrected+SFM+FlameMeans_rmM104.csv")
dim(cur)## [1] 440 34cur <- cur[,c(1:2, 33:34)]
curlong2 <- long[,c(13, 40,14,19)]
long2library(doBy)##
## Attaching package: 'doBy'## The following object is masked from 'package:dplyr':
##
## order_bylong2s <- summaryBy(Area ~ Genotype.ID + Treatment.1 + Days.after.Salting, data = long2)
long2s <- subset(long2s, long2s$Days.after.Salting == 14)
unique(long2s$Days)## [1] 14
## Levels: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14all_together <- merge(cur, long2s, by = c("Genotype.ID", "Treatment.1"))
all_together.nona <- na.omit(all_together)PSA_vs_SFM <- ggscatter(all_together, x = "SFM_me", y = "Area.mean",
add = "reg.line", xlab = "SFM (g)", ylab = "PSA (a.u.)",
color = "Treatment.1", palette = "aaas",
rug = TRUE) + stat_cor(aes(color = Treatment.1), label.x = 3)
PSA_vs_SFM## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 83 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 83 rows containing non-finite values (`stat_cor()`).## Warning: Removed 83 rows containing missing values (`geom_point()`).
PSA_vs_SDM <- ggscatter(all_together, x = "SDM_me", y = "Area.mean",
add = "reg.line", xlab = "SDM (g)", ylab = "PSA (a.u.)",
color = "Treatment.1", palette = "aaas",
rug = TRUE) + stat_cor(aes(color = Treatment.1), label.x = 3)
PSA_vs_SDM## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 83 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 83 rows containing non-finite values (`stat_cor()`).## Warning: Removed 83 rows containing missing values (`geom_point()`).
pdf("Fig.S.ShootFreshMass_vs_PSA.pdf", width = 10, height = 5)
plot_grid(PSA_vs_SFM, PSA_vs_SDM, labels = "AUTO", ncol = 2)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 83 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 83 rows containing non-finite values (`stat_cor()`).## Warning: Removed 83 rows containing missing values (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 83 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 83 rows containing non-finite values (`stat_cor()`).## Warning: Removed 83 rows containing missing values (`geom_point()`).dev.off()## quartz_off_screen
## 2INDICES and Best accessions
Start with the appropriate data:
TPA<-read.csv("TPAData_Corrected+SFM+FlameMeans_rmM104.csv", header=TRUE, sep=",")
TPA<-TPA[,c(1,2,4,5,7,9,10,12,14,15,17,19,20,22,25,27,33)]
TPAseparate into two files to calculate indices:
TPA.C <- subset(TPA, TPA$Treatment.1 == "CONTROL")
TPA.S <- subset(TPA, TPA$Treatment.1 == "SALT")
colnames(TPA.C)[3:17] <- paste(colnames(TPA.C)[3:17], ".C")
colnames(TPA.S)[3:17] <- paste(colnames(TPA.S)[3:17], ".S")
TPA.C <- TPA.C[2:17]
TPA.S <- TPA.S[2:17]
TPA.f <- merge(TPA.C, TPA.S, by = "Genotype.ID")
TPA.fthen - let’s calculate the indices:
ST1: STI1 = S/C ST2: TOL = C-S ST3: MP = (C+S)/2 ST4: GMP = sqrt(CxS) ST5: SSI = ( (C-S)/C ) / ( (C.me-S.me)/C.me ) ST6: STI2 = (C*S)/(C.me)^2 ST7: SWP = S/sqrt(C)
colnames(TPA.f) <- gsub(" ", "", colnames(TPA.f))
colnames(TPA.f)## [1] "Genotype.ID"
## [2] "Area.smooth.AGR.0to5.BLUE.C"
## [3] "Area.smooth.AGR.6to9.BLUE.C"
## [4] "Area.smooth.AGR.10to14.BLUE.C"
## [5] "Area.smooth.RGR.0to5.BLUE.C"
## [6] "Area.smooth.RGR.6to9.BLUE.C"
## [7] "Area.smooth.RGR.10to14.BLUE.C"
## [8] "Area.smooth.TUE.0to5.BLUE.C"
## [9] "Area.smooth.TUE.6to9.BLUE.C"
## [10] "Area.smooth.TUE.10to14.BLUE.C"
## [11] "Transpiration.smooth.rate.0to5.BLUE.C"
## [12] "Transpiration.smooth.rate.6to9.BLUE.C"
## [13] "Transpiration.smooth.rate.10to14.BLUE.C"
## [14] "TissueK_me.C"
## [15] "TissueNa_me.C"
## [16] "SFM_me.C"
## [17] "Area.smooth.AGR.0to5.BLUE.S"
## [18] "Area.smooth.AGR.6to9.BLUE.S"
## [19] "Area.smooth.AGR.10to14.BLUE.S"
## [20] "Area.smooth.RGR.0to5.BLUE.S"
## [21] "Area.smooth.RGR.6to9.BLUE.S"
## [22] "Area.smooth.RGR.10to14.BLUE.S"
## [23] "Area.smooth.TUE.0to5.BLUE.S"
## [24] "Area.smooth.TUE.6to9.BLUE.S"
## [25] "Area.smooth.TUE.10to14.BLUE.S"
## [26] "Transpiration.smooth.rate.0to5.BLUE.S"
## [27] "Transpiration.smooth.rate.6to9.BLUE.S"
## [28] "Transpiration.smooth.rate.10to14.BLUE.S"
## [29] "TissueK_me.S"
## [30] "TissueNa_me.S"
## [31] "SFM_me.S"TPA.f$AGR.0to5.ST1 <- TPA.f$Area.smooth.AGR.0to5.BLUE.S / TPA.f$Area.smooth.AGR.0to5.BLUE.C
TPA.f$AGR.0to5.ST2 <- TPA.f$Area.smooth.AGR.0to5.BLUE.C - TPA.f$Area.smooth.AGR.0to5.BLUE.S
TPA.f$AGR.0to5.ST3 <- (TPA.f$Area.smooth.AGR.0to5.BLUE.C + TPA.f$Area.smooth.AGR.0to5.BLUE.S)/2
TPA.f$AGR.0to5.ST4 <- sqrt(TPA.f$Area.smooth.AGR.0to5.BLUE.C * TPA.f$Area.smooth.AGR.0to5.BLUE.S)
TPA.f$AGR.0to5.ST5 <- ((TPA.f$Area.smooth.AGR.0to5.BLUE.C + TPA.f$Area.smooth.AGR.0to5.BLUE.S)/TPA.f$Area.smooth.AGR.0to5.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.AGR.0to5.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.AGR.0to5.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.AGR.0to5.BLUE.C)))
TPA.f$AGR.0to5.ST6 <- ((TPA.f$Area.smooth.AGR.0to5.BLUE.C * TPA.f$Area.smooth.AGR.0to5.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.AGR.0to5.BLUE.C))^2))
TPA.f$AGR.0to5.ST7 <- TPA.f$Area.smooth.AGR.0to5.BLUE.S / sqrt(TPA.f$Area.smooth.AGR.0to5.BLUE.C)
TPA.f$AGR.6to9.ST1 <- TPA.f$Area.smooth.AGR.6to9.BLUE.S / TPA.f$Area.smooth.AGR.6to9.BLUE.C
TPA.f$AGR.6to9.ST2 <- TPA.f$Area.smooth.AGR.6to9.BLUE.C - TPA.f$Area.smooth.AGR.6to9.BLUE.S
TPA.f$AGR.6to9.ST3 <- (TPA.f$Area.smooth.AGR.6to9.BLUE.C + TPA.f$Area.smooth.AGR.6to9.BLUE.S)/2
TPA.f$AGR.6to9.ST4 <- sqrt(TPA.f$Area.smooth.AGR.6to9.BLUE.C * TPA.f$Area.smooth.AGR.6to9.BLUE.S)
TPA.f$AGR.6to9.ST5 <- ((TPA.f$Area.smooth.AGR.6to9.BLUE.C + TPA.f$Area.smooth.AGR.6to9.BLUE.S)/TPA.f$Area.smooth.AGR.6to9.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.AGR.6to9.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.AGR.6to9.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.AGR.6to9.BLUE.C)))
TPA.f$AGR.6to9.ST6 <- ((TPA.f$Area.smooth.AGR.6to9.BLUE.C * TPA.f$Area.smooth.AGR.6to9.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.AGR.6to9.BLUE.C))^2))
TPA.f$AGR.6to9.ST7 <- TPA.f$Area.smooth.AGR.6to9.BLUE.S / sqrt(TPA.f$Area.smooth.AGR.6to9.BLUE.C)
TPA.f$AGR.10to14.ST1 <- TPA.f$Area.smooth.AGR.10to14.BLUE.S / TPA.f$Area.smooth.AGR.10to14.BLUE.C
TPA.f$AGR.10to14.ST2 <- TPA.f$Area.smooth.AGR.10to14.BLUE.C - TPA.f$Area.smooth.AGR.10to14.BLUE.S
TPA.f$AGR.10to14.ST3 <- (TPA.f$Area.smooth.AGR.10to14.BLUE.C + TPA.f$Area.smooth.AGR.10to14.BLUE.S)/2
TPA.f$AGR.10to14.ST4 <- sqrt(TPA.f$Area.smooth.AGR.10to14.BLUE.C * TPA.f$Area.smooth.AGR.10to14.BLUE.S)
TPA.f$AGR.10to14.ST5 <- ((TPA.f$Area.smooth.AGR.10to14.BLUE.C + TPA.f$Area.smooth.AGR.10to14.BLUE.S)/TPA.f$Area.smooth.AGR.10to14.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.AGR.10to14.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.AGR.10to14.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.AGR.10to14.BLUE.C)))
TPA.f$AGR.10to14.ST6 <- ((TPA.f$Area.smooth.AGR.10to14.BLUE.C * TPA.f$Area.smooth.AGR.10to14.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.AGR.10to14.BLUE.C))^2))
TPA.f$AGR.10to14.ST7 <- TPA.f$Area.smooth.AGR.10to14.BLUE.S / sqrt(TPA.f$Area.smooth.AGR.10to14.BLUE.C)
TPA.f$RGR.0to5.ST1 <- TPA.f$Area.smooth.RGR.0to5.BLUE.S / TPA.f$Area.smooth.RGR.0to5.BLUE.C
TPA.f$RGR.0to5.ST2 <- TPA.f$Area.smooth.RGR.0to5.BLUE.C - TPA.f$Area.smooth.RGR.0to5.BLUE.S
TPA.f$RGR.0to5.ST3 <- (TPA.f$Area.smooth.RGR.0to5.BLUE.C + TPA.f$Area.smooth.RGR.0to5.BLUE.S)/2
TPA.f$RGR.0to5.ST4 <- sqrt(TPA.f$Area.smooth.RGR.0to5.BLUE.C * TPA.f$Area.smooth.RGR.0to5.BLUE.S)
TPA.f$RGR.0to5.ST5 <- ((TPA.f$Area.smooth.RGR.0to5.BLUE.C + TPA.f$Area.smooth.RGR.0to5.BLUE.S)/TPA.f$Area.smooth.RGR.0to5.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.RGR.0to5.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.RGR.0to5.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.RGR.0to5.BLUE.C)))
TPA.f$RGR.0to5.ST6 <- ((TPA.f$Area.smooth.RGR.0to5.BLUE.C * TPA.f$Area.smooth.RGR.0to5.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.RGR.0to5.BLUE.C))^2))
TPA.f$RGR.0to5.ST7 <- TPA.f$Area.smooth.RGR.0to5.BLUE.S / sqrt(TPA.f$Area.smooth.RGR.0to5.BLUE.C)
TPA.f$RGR.6to9.ST1 <- TPA.f$Area.smooth.RGR.6to9.BLUE.S / TPA.f$Area.smooth.RGR.6to9.BLUE.C
TPA.f$RGR.6to9.ST2 <- TPA.f$Area.smooth.RGR.6to9.BLUE.C - TPA.f$Area.smooth.RGR.6to9.BLUE.S
TPA.f$RGR.6to9.ST3 <- (TPA.f$Area.smooth.RGR.6to9.BLUE.C + TPA.f$Area.smooth.RGR.6to9.BLUE.S)/2
TPA.f$RGR.6to9.ST4 <- sqrt(TPA.f$Area.smooth.RGR.6to9.BLUE.C * TPA.f$Area.smooth.RGR.6to9.BLUE.S)
TPA.f$RGR.6to9.ST5 <- ((TPA.f$Area.smooth.RGR.6to9.BLUE.C + TPA.f$Area.smooth.RGR.6to9.BLUE.S)/TPA.f$Area.smooth.RGR.6to9.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.RGR.6to9.BLUE.C)) - mean(TPA.f$Area.smooth.RGR.6to9.BLUE.S))/mean(na.omit(TPA.f$Area.smooth.RGR.6to9.BLUE.C)))
TPA.f$RGR.6to9.ST6 <- ((TPA.f$Area.smooth.RGR.6to9.BLUE.C * TPA.f$Area.smooth.RGR.6to9.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.RGR.6to9.BLUE.C))^2))
TPA.f$RGR.6to9.ST7 <- TPA.f$Area.smooth.RGR.6to9.BLUE.S / sqrt(TPA.f$Area.smooth.RGR.6to9.BLUE.C)
TPA.f$RGR.10to14.ST1 <- TPA.f$Area.smooth.RGR.10to14.BLUE.S / TPA.f$Area.smooth.RGR.10to14.BLUE.C
TPA.f$RGR.10to14.ST2 <- TPA.f$Area.smooth.RGR.10to14.BLUE.C - TPA.f$Area.smooth.RGR.10to14.BLUE.S
TPA.f$RGR.10to14.ST3 <- (TPA.f$Area.smooth.RGR.10to14.BLUE.C + TPA.f$Area.smooth.RGR.10to14.BLUE.S)/2
TPA.f$RGR.10to14.ST4 <- sqrt(TPA.f$Area.smooth.RGR.10to14.BLUE.C * TPA.f$Area.smooth.RGR.10to14.BLUE.S)
TPA.f$RGR.10to14.ST5 <- ((TPA.f$Area.smooth.RGR.10to14.BLUE.C + TPA.f$Area.smooth.RGR.10to14.BLUE.S)/TPA.f$Area.smooth.RGR.10to14.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.RGR.10to14.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.RGR.10to14.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.RGR.10to14.BLUE.C)))
TPA.f$RGR.10to14.ST6 <- ((TPA.f$Area.smooth.RGR.10to14.BLUE.C * TPA.f$Area.smooth.RGR.10to14.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.RGR.10to14.BLUE.C))^2))
TPA.f$RGR.10to14.ST7 <- TPA.f$Area.smooth.RGR.10to14.BLUE.S / sqrt(TPA.f$Area.smooth.RGR.10to14.BLUE.C)
TPA.f$TUE.0to5.ST1 <- TPA.f$Area.smooth.TUE.0to5.BLUE.S / TPA.f$Area.smooth.TUE.0to5.BLUE.C
TPA.f$TUE.0to5.ST2 <- TPA.f$Area.smooth.TUE.0to5.BLUE.C - TPA.f$Area.smooth.TUE.0to5.BLUE.S
TPA.f$TUE.0to5.ST3 <- (TPA.f$Area.smooth.TUE.0to5.BLUE.C + TPA.f$Area.smooth.TUE.0to5.BLUE.S)/2
TPA.f$TUE.0to5.ST4 <- sqrt(TPA.f$Area.smooth.TUE.0to5.BLUE.C * TPA.f$Area.smooth.TUE.0to5.BLUE.S)
TPA.f$TUE.0to5.ST5 <- ((TPA.f$Area.smooth.TUE.0to5.BLUE.C + TPA.f$Area.smooth.TUE.0to5.BLUE.S)/TPA.f$Area.smooth.TUE.0to5.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.TUE.0to5.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.TUE.0to5.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.TUE.0to5.BLUE.C)))
TPA.f$TUE.0to5.ST6 <- ((TPA.f$Area.smooth.TUE.0to5.BLUE.C * TPA.f$Area.smooth.TUE.0to5.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.TUE.0to5.BLUE.C))^2))
TPA.f$TUE.0to5.ST7 <- TPA.f$Area.smooth.TUE.0to5.BLUE.S / sqrt(TPA.f$Area.smooth.TUE.0to5.BLUE.C)
TPA.f$TUE.6to9.ST1 <- TPA.f$Area.smooth.TUE.6to9.BLUE.S / TPA.f$Area.smooth.TUE.6to9.BLUE.C
TPA.f$TUE.6to9.ST2 <- TPA.f$Area.smooth.TUE.6to9.BLUE.C - TPA.f$Area.smooth.TUE.6to9.BLUE.S
TPA.f$TUE.6to9.ST3 <- (TPA.f$Area.smooth.TUE.6to9.BLUE.C + TPA.f$Area.smooth.TUE.6to9.BLUE.S)/2
TPA.f$TUE.6to9.ST4 <- sqrt(TPA.f$Area.smooth.TUE.6to9.BLUE.C * TPA.f$Area.smooth.TUE.6to9.BLUE.S)
TPA.f$TUE.6to9.ST5 <- ((TPA.f$Area.smooth.TUE.6to9.BLUE.C + TPA.f$Area.smooth.TUE.6to9.BLUE.S)/TPA.f$Area.smooth.TUE.6to9.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.TUE.6to9.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.TUE.6to9.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.TUE.6to9.BLUE.C)))
TPA.f$TUE.6to9.ST6 <- ((TPA.f$Area.smooth.TUE.6to9.BLUE.C * TPA.f$Area.smooth.TUE.6to9.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.TUE.6to9.BLUE.C))^2))
TPA.f$TUE.6to9.ST7 <- TPA.f$Area.smooth.TUE.6to9.BLUE.S / sqrt(TPA.f$Area.smooth.TUE.6to9.BLUE.C)
TPA.f$TUE.10to14.ST1 <- TPA.f$Area.smooth.TUE.10to14.BLUE.S / TPA.f$Area.smooth.TUE.10to14.BLUE.C
TPA.f$TUE.10to14.ST2 <- TPA.f$Area.smooth.TUE.10to14.BLUE.C - TPA.f$Area.smooth.TUE.10to14.BLUE.S
TPA.f$TUE.10to14.ST3 <- (TPA.f$Area.smooth.TUE.10to14.BLUE.C + TPA.f$Area.smooth.TUE.10to14.BLUE.S)/2
TPA.f$TUE.10to14.ST4 <- sqrt(TPA.f$Area.smooth.TUE.10to14.BLUE.C * TPA.f$Area.smooth.TUE.10to14.BLUE.S)
TPA.f$TUE.10to14.ST5 <- ((TPA.f$Area.smooth.TUE.10to14.BLUE.C + TPA.f$Area.smooth.TUE.10to14.BLUE.S)/TPA.f$Area.smooth.TUE.10to14.BLUE.C) / ((mean(na.omit(TPA.f$Area.smooth.TUE.10to14.BLUE.C)) - mean(na.omit(TPA.f$Area.smooth.TUE.10to14.BLUE.S)))/mean(na.omit(TPA.f$Area.smooth.TUE.10to14.BLUE.C)))
TPA.f$TUE.10to14.ST6 <- ((TPA.f$Area.smooth.TUE.10to14.BLUE.C * TPA.f$Area.smooth.TUE.10to14.BLUE.S)/(mean(na.omit(TPA.f$Area.smooth.TUE.10to14.BLUE.C))^2))
TPA.f$TUE.10to14.ST7 <- TPA.f$Area.smooth.TUE.10to14.BLUE.S / sqrt(TPA.f$Area.smooth.TUE.10to14.BLUE.C)
TPA.f$TSR.0to5.ST1 <- TPA.f$Transpiration.smooth.rate.0to5.BLUE.S / TPA.f$Transpiration.smooth.rate.0to5.BLUE.C
TPA.f$TSR.0to5.ST2 <- TPA.f$Transpiration.smooth.rate.0to5.BLUE.C - TPA.f$Transpiration.smooth.rate.0to5.BLUE.S
TPA.f$TSR.0to5.ST3 <- (TPA.f$Transpiration.smooth.rate.0to5.BLUE.C + TPA.f$Transpiration.smooth.rate.0to5.BLUE.S)/2
TPA.f$TSR.0to5.ST4 <- sqrt(TPA.f$Transpiration.smooth.rate.0to5.BLUE.C * TPA.f$Transpiration.smooth.rate.0to5.BLUE.S)
TPA.f$TSR.0to5.ST5 <- ((TPA.f$Transpiration.smooth.rate.0to5.BLUE.C + TPA.f$Transpiration.smooth.rate.0to5.BLUE.S)/TPA.f$Transpiration.smooth.rate.0to5.BLUE.C) / ((mean(na.omit(TPA.f$Transpiration.smooth.rate.0to5.BLUE.C)) - mean(na.omit(TPA.f$Transpiration.smooth.rate.0to5.BLUE.S)))/mean(na.omit(TPA.f$Transpiration.smooth.rate.0to5.BLUE.C)))
TPA.f$TSR.0to5.ST6 <- ((TPA.f$Transpiration.smooth.rate.0to5.BLUE.C * TPA.f$Transpiration.smooth.rate.0to5.BLUE.S)/(mean(na.omit(TPA.f$Transpiration.smooth.rate.0to5.BLUE.C))^2))
TPA.f$TSR.0to5.ST7 <- TPA.f$Transpiration.smooth.rate.0to5.BLUE.S / sqrt(TPA.f$Transpiration.smooth.rate.0to5.BLUE.C)
TPA.f$TSR.6to9.ST1 <- TPA.f$Transpiration.smooth.rate.6to9.BLUE.S / TPA.f$Transpiration.smooth.rate.6to9.BLUE.C
TPA.f$TSR.6to9.ST2 <- TPA.f$Transpiration.smooth.rate.6to9.BLUE.C - TPA.f$Transpiration.smooth.rate.6to9.BLUE.S
TPA.f$TSR.6to9.ST3 <- (TPA.f$Transpiration.smooth.rate.6to9.BLUE.C + TPA.f$Transpiration.smooth.rate.6to9.BLUE.S)/2
TPA.f$TSR.6to9.ST4 <- sqrt(TPA.f$Transpiration.smooth.rate.6to9.BLUE.C * TPA.f$Transpiration.smooth.rate.6to9.BLUE.S)
TPA.f$TSR.6to9.ST5 <- ((TPA.f$Transpiration.smooth.rate.6to9.BLUE.C + TPA.f$Transpiration.smooth.rate.6to9.BLUE.S)/TPA.f$Transpiration.smooth.rate.6to9.BLUE.C) / ((mean(na.omit(TPA.f$Transpiration.smooth.rate.6to9.BLUE.C)) - mean(na.omit(TPA.f$Transpiration.smooth.rate.6to9.BLUE.S)))/mean(na.omit(TPA.f$Transpiration.smooth.rate.6to9.BLUE.C)))
TPA.f$TSR.6to9.ST6 <- ((TPA.f$Transpiration.smooth.rate.6to9.BLUE.C * TPA.f$Transpiration.smooth.rate.6to9.BLUE.S)/(mean(na.omit(TPA.f$Transpiration.smooth.rate.6to9.BLUE.C))^2))
TPA.f$TSR.6to9.ST7 <- TPA.f$Transpiration.smooth.rate.6to9.BLUE.S / sqrt(TPA.f$Transpiration.smooth.rate.6to9.BLUE.C)
TPA.f$TSR.10to14.ST1 <- TPA.f$Transpiration.smooth.rate.10to14.BLUE.S / TPA.f$Transpiration.smooth.rate.10to14.BLUE.C
TPA.f$TSR.10to14.ST2 <- TPA.f$Transpiration.smooth.rate.10to14.BLUE.C - TPA.f$Transpiration.smooth.rate.10to14.BLUE.S
TPA.f$TSR.10to14.ST3 <- (TPA.f$Transpiration.smooth.rate.10to14.BLUE.C + TPA.f$Transpiration.smooth.rate.10to14.BLUE.S)/2
TPA.f$TSR.10to14.ST4 <- sqrt(TPA.f$Transpiration.smooth.rate.10to14.BLUE.C * TPA.f$Transpiration.smooth.rate.10to14.BLUE.S)
TPA.f$TSR.10to14.ST5 <- ((TPA.f$Transpiration.smooth.rate.10to14.BLUE.C + TPA.f$Transpiration.smooth.rate.10to14.BLUE.S)/TPA.f$Transpiration.smooth.rate.10to14.BLUE.C) / ((mean(na.omit(TPA.f$Transpiration.smooth.rate.10to14.BLUE.C)) - mean(na.omit(TPA.f$Transpiration.smooth.rate.10to14.BLUE.S)))/mean(na.omit(TPA.f$Transpiration.smooth.rate.10to14.BLUE.C)))
TPA.f$TSR.10to14.ST6 <- ((TPA.f$Transpiration.smooth.rate.10to14.BLUE.C * TPA.f$Transpiration.smooth.rate.10to14.BLUE.S)/(mean(na.omit(TPA.f$Transpiration.smooth.rate.10to14.BLUE.C))^2))
TPA.f$TSR.10to14.ST7 <- TPA.f$Transpiration.smooth.rate.10to14.BLUE.S / sqrt(TPA.f$Transpiration.smooth.rate.10to14.BLUE.C)
TPA.f$TissueK.ST1 <- TPA.f$TissueK_me.S / TPA.f$TissueK_me.C
TPA.f$TissueK.ST2 <- TPA.f$TissueK_me.C - TPA.f$TissueK_me.S
TPA.f$TissueK.ST3 <- (TPA.f$TissueK_me.C + TPA.f$TissueK_me.S)/2
TPA.f$TissueK.ST4 <- sqrt(TPA.f$TissueK_me.C * TPA.f$TissueK_me.S)
TPA.f$TissueK.ST5 <- ((TPA.f$TissueK_me.C + TPA.f$TissueK_me.S)/TPA.f$TissueK_me.C) / ((mean(na.omit(TPA.f$TissueK_me.C)) - mean(na.omit(TPA.f$TissueK_me.S)))/mean(na.omit(TPA.f$TissueK_me.C)))
TPA.f$TissueK.ST6 <- ((TPA.f$TissueK_me.C * TPA.f$TissueK_me.S)/(mean(na.omit(TPA.f$TissueK_me.C))^2))
TPA.f$TissueK.ST7 <- TPA.f$TissueK_me.S / sqrt(TPA.f$TissueK_me.C)
TPA.f$TissueNa.ST1 <- TPA.f$TissueNa_me.S / TPA.f$TissueNa_me.C
TPA.f$TissueNa.ST2 <- TPA.f$TissueNa_me.C - TPA.f$TissueNa_me.S
TPA.f$TissueNa.ST3 <- (TPA.f$TissueNa_me.C + TPA.f$TissueNa_me.S)/2
TPA.f$TissueNa.ST4 <- sqrt(TPA.f$TissueNa_me.C * TPA.f$TissueNa_me.S)
TPA.f$TissueNa.ST5 <- ((TPA.f$TissueNa_me.C + TPA.f$TissueNa_me.S)/TPA.f$TissueNa_me.C) / ((mean(na.omit(TPA.f$TissueNa_me.C)) - mean(na.omit(TPA.f$TissueNa_me.S)))/mean(na.omit(TPA.f$TissueNa_me.C)))
TPA.f$TissueNa.ST6 <- ((TPA.f$TissueNa_me.C * TPA.f$TissueNa_me.S)/(mean(na.omit(TPA.f$TissueNa_me.C))^2))
TPA.f$TissueNa.ST7 <- TPA.f$TissueNa_me.S / sqrt(TPA.f$TissueNa_me.C)
TPA.f$SFM_me.ST1 <- TPA.f$SFM_me.S / TPA.f$SFM_me.C
TPA.f$SFM_me.ST2 <- TPA.f$SFM_me.C - TPA.f$SFM_me.S
TPA.f$SFM_me.ST3 <- (TPA.f$SFM_me.C + TPA.f$SFM_me.S)/2
TPA.f$SFM_me.ST4 <- sqrt(TPA.f$SFM_me.C * TPA.f$SFM_me.S)
TPA.f$SFM_me.ST5 <- ((TPA.f$SFM_me.C + TPA.f$SFM_me.S)/TPA.f$SFM_me.C) / ((mean(na.omit(TPA.f$SFM_me.C)) - mean(na.omit(TPA.f$SFM_me.S)))/mean(na.omit(TPA.f$SFM_me.C)))
TPA.f$SFM_me.ST6 <- ((TPA.f$SFM_me.C * TPA.f$SFM_me.S)/mean(na.omit(TPA.f$SFM_me.C))^2)
TPA.f$SFM_me.ST7 <- TPA.f$SFM_me.S / sqrt(TPA.f$SFM_me.C)
mean(na.omit(TPA.f$SFM_me.S))## [1] 29.98792TPA.fwrite.csv(TPA.f, "TPA.stress.indices.all.traits.csv", row.names = FALSE)now - let’s plot the indices for each trait over each interval
colnames(TPA.f)## [1] "Genotype.ID"
## [2] "Area.smooth.AGR.0to5.BLUE.C"
## [3] "Area.smooth.AGR.6to9.BLUE.C"
## [4] "Area.smooth.AGR.10to14.BLUE.C"
## [5] "Area.smooth.RGR.0to5.BLUE.C"
## [6] "Area.smooth.RGR.6to9.BLUE.C"
## [7] "Area.smooth.RGR.10to14.BLUE.C"
## [8] "Area.smooth.TUE.0to5.BLUE.C"
## [9] "Area.smooth.TUE.6to9.BLUE.C"
## [10] "Area.smooth.TUE.10to14.BLUE.C"
## [11] "Transpiration.smooth.rate.0to5.BLUE.C"
## [12] "Transpiration.smooth.rate.6to9.BLUE.C"
## [13] "Transpiration.smooth.rate.10to14.BLUE.C"
## [14] "TissueK_me.C"
## [15] "TissueNa_me.C"
## [16] "SFM_me.C"
## [17] "Area.smooth.AGR.0to5.BLUE.S"
## [18] "Area.smooth.AGR.6to9.BLUE.S"
## [19] "Area.smooth.AGR.10to14.BLUE.S"
## [20] "Area.smooth.RGR.0to5.BLUE.S"
## [21] "Area.smooth.RGR.6to9.BLUE.S"
## [22] "Area.smooth.RGR.10to14.BLUE.S"
## [23] "Area.smooth.TUE.0to5.BLUE.S"
## [24] "Area.smooth.TUE.6to9.BLUE.S"
## [25] "Area.smooth.TUE.10to14.BLUE.S"
## [26] "Transpiration.smooth.rate.0to5.BLUE.S"
## [27] "Transpiration.smooth.rate.6to9.BLUE.S"
## [28] "Transpiration.smooth.rate.10to14.BLUE.S"
## [29] "TissueK_me.S"
## [30] "TissueNa_me.S"
## [31] "SFM_me.S"
## [32] "AGR.0to5.ST1"
## [33] "AGR.0to5.ST2"
## [34] "AGR.0to5.ST3"
## [35] "AGR.0to5.ST4"
## [36] "AGR.0to5.ST5"
## [37] "AGR.0to5.ST6"
## [38] "AGR.0to5.ST7"
## [39] "AGR.6to9.ST1"
## [40] "AGR.6to9.ST2"
## [41] "AGR.6to9.ST3"
## [42] "AGR.6to9.ST4"
## [43] "AGR.6to9.ST5"
## [44] "AGR.6to9.ST6"
## [45] "AGR.6to9.ST7"
## [46] "AGR.10to14.ST1"
## [47] "AGR.10to14.ST2"
## [48] "AGR.10to14.ST3"
## [49] "AGR.10to14.ST4"
## [50] "AGR.10to14.ST5"
## [51] "AGR.10to14.ST6"
## [52] "AGR.10to14.ST7"
## [53] "RGR.0to5.ST1"
## [54] "RGR.0to5.ST2"
## [55] "RGR.0to5.ST3"
## [56] "RGR.0to5.ST4"
## [57] "RGR.0to5.ST5"
## [58] "RGR.0to5.ST6"
## [59] "RGR.0to5.ST7"
## [60] "RGR.6to9.ST1"
## [61] "RGR.6to9.ST2"
## [62] "RGR.6to9.ST3"
## [63] "RGR.6to9.ST4"
## [64] "RGR.6to9.ST5"
## [65] "RGR.6to9.ST6"
## [66] "RGR.6to9.ST7"
## [67] "RGR.10to14.ST1"
## [68] "RGR.10to14.ST2"
## [69] "RGR.10to14.ST3"
## [70] "RGR.10to14.ST4"
## [71] "RGR.10to14.ST5"
## [72] "RGR.10to14.ST6"
## [73] "RGR.10to14.ST7"
## [74] "TUE.0to5.ST1"
## [75] "TUE.0to5.ST2"
## [76] "TUE.0to5.ST3"
## [77] "TUE.0to5.ST4"
## [78] "TUE.0to5.ST5"
## [79] "TUE.0to5.ST6"
## [80] "TUE.0to5.ST7"
## [81] "TUE.6to9.ST1"
## [82] "TUE.6to9.ST2"
## [83] "TUE.6to9.ST3"
## [84] "TUE.6to9.ST4"
## [85] "TUE.6to9.ST5"
## [86] "TUE.6to9.ST6"
## [87] "TUE.6to9.ST7"
## [88] "TUE.10to14.ST1"
## [89] "TUE.10to14.ST2"
## [90] "TUE.10to14.ST3"
## [91] "TUE.10to14.ST4"
## [92] "TUE.10to14.ST5"
## [93] "TUE.10to14.ST6"
## [94] "TUE.10to14.ST7"
## [95] "TSR.0to5.ST1"
## [96] "TSR.0to5.ST2"
## [97] "TSR.0to5.ST3"
## [98] "TSR.0to5.ST4"
## [99] "TSR.0to5.ST5"
## [100] "TSR.0to5.ST6"
## [101] "TSR.0to5.ST7"
## [102] "TSR.6to9.ST1"
## [103] "TSR.6to9.ST2"
## [104] "TSR.6to9.ST3"
## [105] "TSR.6to9.ST4"
## [106] "TSR.6to9.ST5"
## [107] "TSR.6to9.ST6"
## [108] "TSR.6to9.ST7"
## [109] "TSR.10to14.ST1"
## [110] "TSR.10to14.ST2"
## [111] "TSR.10to14.ST3"
## [112] "TSR.10to14.ST4"
## [113] "TSR.10to14.ST5"
## [114] "TSR.10to14.ST6"
## [115] "TSR.10to14.ST7"
## [116] "TissueK.ST1"
## [117] "TissueK.ST2"
## [118] "TissueK.ST3"
## [119] "TissueK.ST4"
## [120] "TissueK.ST5"
## [121] "TissueK.ST6"
## [122] "TissueK.ST7"
## [123] "TissueNa.ST1"
## [124] "TissueNa.ST2"
## [125] "TissueNa.ST3"
## [126] "TissueNa.ST4"
## [127] "TissueNa.ST5"
## [128] "TissueNa.ST6"
## [129] "TissueNa.ST7"
## [130] "SFM_me.ST1"
## [131] "SFM_me.ST2"
## [132] "SFM_me.ST3"
## [133] "SFM_me.ST4"
## [134] "SFM_me.ST5"
## [135] "SFM_me.ST6"
## [136] "SFM_me.ST7"AGR.data <- TPA.f[,c(1:4, 17:19, 32:52)]
RGR.data <- TPA.f[,c(1, 5:7, 20:22, 53:73)]
TUE.data <- TPA.f[,c(1, 8:10, 23:25, 74:94)]
TSR.data <- TPA.f[,c(1, 11:13, 26:28, 95:115)]
ion.data <- TPA.f[,c(1, 14:15, 29:30, 116:129)]
SFM.data <- TPA.f[,c(1, 16, 31, 130:136)]Let’s start with SFM data because this is going to be the simplest one:
ST1: STI = S/C ST2: TOL = C-S ST3: MP = (C+S)/2 ST4: GMP = sqrt(CxS) ST5: SSI = ( (C-S)/C ) / ( (C.me-S.me)/C.me ) ST6: STI2 = (C*S)/(C.me)^2 ST7: STI3 = S/sqrt(C)
SFM.dataSFM.ST0 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S", , xlab = "SFM at Control (g)",
ylab = "SFM at Salt (g)")
SFM.ST0 <- SFM.ST0 + stat_cor(method = "spearman", label.x = 30, label.y = 50)
SFM.ST0## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.ST1 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S",
color = "SFM_me.ST1", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.ST1 <- SFM.ST1 + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.ST1 <- SFM.ST1 + guides(color=guide_legend(title="STI1 = C / S"))
SFM.ST1## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.ST2 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S",
color = "SFM_me.ST2", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.ST2 <- SFM.ST2 + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.ST2 <- SFM.ST2 + guides(color=guide_legend(title="TOL = C - S"))
SFM.ST2## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.ST3 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S",
color = "SFM_me.ST3", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.ST3 <- SFM.ST3 + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.ST3 <- SFM.ST3 + guides(color=guide_legend(title="MP = (C + S) / 2"))
SFM.ST3## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.ST4 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S",
color = "SFM_me.ST4", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.ST4 <- SFM.ST4 + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.ST4 <- SFM.ST4 + guides(color=guide_legend(title="GMP = sqrt(CxS)"))
SFM.ST4## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.ST5 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S",
color = "SFM_me.ST5", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.ST5 <- SFM.ST5 + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.ST5 <- SFM.ST5 + guides(color=guide_legend(title="SSI = ( (C-S)/C ) / ( (C.m-S.m)/C.m )"))
SFM.ST5## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.ST6 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S",
color = "SFM_me.ST6", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.ST6 <- SFM.ST6 + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.ST6 <- SFM.ST6 + guides(color=guide_legend(title="STI2 = (C*S)/(C.m)^2"))
SFM.ST6## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.ST7 <- ggscatter(SFM.data, x = "SFM_me.C", y = "SFM_me.S",
color = "SFM_me.ST7", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.ST7 <- SFM.ST7 + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.ST7 <- SFM.ST7 + guides(color=guide_legend(title="STI3 = S/sqrt(C)"))
SFM.ST7## Warning: Removed 1 rows containing missing values (`geom_point()`).
OK - now let’s save it all into one figure file:
pdf("Fig.SFM_indices.pdf", width = 18, height = 10)
plot_grid(SFM.ST0, SFM.ST1, SFM.ST2, SFM.ST3, SFM.ST4, SFM.ST5, SFM.ST6, SFM.ST7, cols = 4, labels = "AUTO")## Warning in plot_grid(SFM.ST0, SFM.ST1, SFM.ST2, SFM.ST3, SFM.ST4, SFM.ST5, :
## Argument 'cols' is deprecated. Use 'ncol' instead.## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).dev.off()## quartz_off_screen
## 2Let’s make another graph focusing on SFM C vs S with highlighting Na+ and K+ under salt stress:
colnames(TPA.f)## [1] "Genotype.ID"
## [2] "Area.smooth.AGR.0to5.BLUE.C"
## [3] "Area.smooth.AGR.6to9.BLUE.C"
## [4] "Area.smooth.AGR.10to14.BLUE.C"
## [5] "Area.smooth.RGR.0to5.BLUE.C"
## [6] "Area.smooth.RGR.6to9.BLUE.C"
## [7] "Area.smooth.RGR.10to14.BLUE.C"
## [8] "Area.smooth.TUE.0to5.BLUE.C"
## [9] "Area.smooth.TUE.6to9.BLUE.C"
## [10] "Area.smooth.TUE.10to14.BLUE.C"
## [11] "Transpiration.smooth.rate.0to5.BLUE.C"
## [12] "Transpiration.smooth.rate.6to9.BLUE.C"
## [13] "Transpiration.smooth.rate.10to14.BLUE.C"
## [14] "TissueK_me.C"
## [15] "TissueNa_me.C"
## [16] "SFM_me.C"
## [17] "Area.smooth.AGR.0to5.BLUE.S"
## [18] "Area.smooth.AGR.6to9.BLUE.S"
## [19] "Area.smooth.AGR.10to14.BLUE.S"
## [20] "Area.smooth.RGR.0to5.BLUE.S"
## [21] "Area.smooth.RGR.6to9.BLUE.S"
## [22] "Area.smooth.RGR.10to14.BLUE.S"
## [23] "Area.smooth.TUE.0to5.BLUE.S"
## [24] "Area.smooth.TUE.6to9.BLUE.S"
## [25] "Area.smooth.TUE.10to14.BLUE.S"
## [26] "Transpiration.smooth.rate.0to5.BLUE.S"
## [27] "Transpiration.smooth.rate.6to9.BLUE.S"
## [28] "Transpiration.smooth.rate.10to14.BLUE.S"
## [29] "TissueK_me.S"
## [30] "TissueNa_me.S"
## [31] "SFM_me.S"
## [32] "AGR.0to5.ST1"
## [33] "AGR.0to5.ST2"
## [34] "AGR.0to5.ST3"
## [35] "AGR.0to5.ST4"
## [36] "AGR.0to5.ST5"
## [37] "AGR.0to5.ST6"
## [38] "AGR.0to5.ST7"
## [39] "AGR.6to9.ST1"
## [40] "AGR.6to9.ST2"
## [41] "AGR.6to9.ST3"
## [42] "AGR.6to9.ST4"
## [43] "AGR.6to9.ST5"
## [44] "AGR.6to9.ST6"
## [45] "AGR.6to9.ST7"
## [46] "AGR.10to14.ST1"
## [47] "AGR.10to14.ST2"
## [48] "AGR.10to14.ST3"
## [49] "AGR.10to14.ST4"
## [50] "AGR.10to14.ST5"
## [51] "AGR.10to14.ST6"
## [52] "AGR.10to14.ST7"
## [53] "RGR.0to5.ST1"
## [54] "RGR.0to5.ST2"
## [55] "RGR.0to5.ST3"
## [56] "RGR.0to5.ST4"
## [57] "RGR.0to5.ST5"
## [58] "RGR.0to5.ST6"
## [59] "RGR.0to5.ST7"
## [60] "RGR.6to9.ST1"
## [61] "RGR.6to9.ST2"
## [62] "RGR.6to9.ST3"
## [63] "RGR.6to9.ST4"
## [64] "RGR.6to9.ST5"
## [65] "RGR.6to9.ST6"
## [66] "RGR.6to9.ST7"
## [67] "RGR.10to14.ST1"
## [68] "RGR.10to14.ST2"
## [69] "RGR.10to14.ST3"
## [70] "RGR.10to14.ST4"
## [71] "RGR.10to14.ST5"
## [72] "RGR.10to14.ST6"
## [73] "RGR.10to14.ST7"
## [74] "TUE.0to5.ST1"
## [75] "TUE.0to5.ST2"
## [76] "TUE.0to5.ST3"
## [77] "TUE.0to5.ST4"
## [78] "TUE.0to5.ST5"
## [79] "TUE.0to5.ST6"
## [80] "TUE.0to5.ST7"
## [81] "TUE.6to9.ST1"
## [82] "TUE.6to9.ST2"
## [83] "TUE.6to9.ST3"
## [84] "TUE.6to9.ST4"
## [85] "TUE.6to9.ST5"
## [86] "TUE.6to9.ST6"
## [87] "TUE.6to9.ST7"
## [88] "TUE.10to14.ST1"
## [89] "TUE.10to14.ST2"
## [90] "TUE.10to14.ST3"
## [91] "TUE.10to14.ST4"
## [92] "TUE.10to14.ST5"
## [93] "TUE.10to14.ST6"
## [94] "TUE.10to14.ST7"
## [95] "TSR.0to5.ST1"
## [96] "TSR.0to5.ST2"
## [97] "TSR.0to5.ST3"
## [98] "TSR.0to5.ST4"
## [99] "TSR.0to5.ST5"
## [100] "TSR.0to5.ST6"
## [101] "TSR.0to5.ST7"
## [102] "TSR.6to9.ST1"
## [103] "TSR.6to9.ST2"
## [104] "TSR.6to9.ST3"
## [105] "TSR.6to9.ST4"
## [106] "TSR.6to9.ST5"
## [107] "TSR.6to9.ST6"
## [108] "TSR.6to9.ST7"
## [109] "TSR.10to14.ST1"
## [110] "TSR.10to14.ST2"
## [111] "TSR.10to14.ST3"
## [112] "TSR.10to14.ST4"
## [113] "TSR.10to14.ST5"
## [114] "TSR.10to14.ST6"
## [115] "TSR.10to14.ST7"
## [116] "TissueK.ST1"
## [117] "TissueK.ST2"
## [118] "TissueK.ST3"
## [119] "TissueK.ST4"
## [120] "TissueK.ST5"
## [121] "TissueK.ST6"
## [122] "TissueK.ST7"
## [123] "TissueNa.ST1"
## [124] "TissueNa.ST2"
## [125] "TissueNa.ST3"
## [126] "TissueNa.ST4"
## [127] "TissueNa.ST5"
## [128] "TissueNa.ST6"
## [129] "TissueNa.ST7"
## [130] "SFM_me.ST1"
## [131] "SFM_me.ST2"
## [132] "SFM_me.ST3"
## [133] "SFM_me.ST4"
## [134] "SFM_me.ST5"
## [135] "SFM_me.ST6"
## [136] "SFM_me.ST7"SFM.ions.data <- TPA.f[,c(1, 14:16, 29:31)]
SFM.ions.dataSFM.Na.S <- ggscatter(SFM.ions.data, x = "SFM_me.C", y = "SFM_me.S",
color = "TissueNa_me.S", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.Na.S <- SFM.Na.S + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.Na.S <- SFM.Na.S + guides(color=guide_legend(title="Tissue [Na+] (mmol / g DW)"))
SFM.Na.S## Warning: Removed 1 rows containing missing values (`geom_point()`).
SFM.K.S <- ggscatter(SFM.ions.data, x = "SFM_me.C", y = "SFM_me.S",
color = "TissueK_me.S", xlab = "SFM at Control (g)", ylab = "SFM at Salt (g)")
SFM.K.S <- SFM.K.S + gradient_color(c("blue", "white", "red")) + theme(legend.position = c(0.9, 0.3))
SFM.K.S <- SFM.K.S + guides(color=guide_legend(title="Tissue [K+] (mmol / g DW)"))
SFM.K.S## Warning: Removed 1 rows containing missing values (`geom_point()`).
pdf("Fig.SFM_ions.pdf", width = 9, height = 5)
plot_grid(SFM.Na.S, SFM.K.S, cols = 2, labels = "AUTO")## Warning in plot_grid(SFM.Na.S, SFM.K.S, cols = 2, labels = "AUTO"): Argument
## 'cols' is deprecated. Use 'ncol' instead.
## Warning in plot_grid(SFM.Na.S, SFM.K.S, cols = 2, labels = "AUTO"): Removed 1
## rows containing missing values (`geom_point()`).
## Warning in plot_grid(SFM.Na.S, SFM.K.S, cols = 2, labels = "AUTO"): Removed 1
## rows containing missing values (`geom_point()`).dev.off()## quartz_off_screen
## 2regression analysis
TPA.reg <- TPA.f
colnames(TPA.reg) <- gsub("Area.smooth.AGR", "AGR", colnames(TPA.reg))
colnames(TPA.reg) <- gsub("Area.smooth.RGR", "RGR", colnames(TPA.reg))
colnames(TPA.reg) <- gsub("Area.smooth.TUE", "TUE", colnames(TPA.reg))
colnames(TPA.reg) <- gsub("Transpiration.smooth.rate", "TR", colnames(TPA.reg))
colnames(TPA.reg) <- gsub(".BLUE", "", colnames(TPA.reg))
colnames(TPA.reg) <- gsub("_me", "", colnames(TPA.reg))
TPA.reg <- TPA.reg[,c(1:7, 11:13, 8:10, 14:22, 26:28, 23:25, 29:31, 130:136)]
colnames(TPA.reg)## [1] "Genotype.ID" "AGR.0to5.C" "AGR.6to9.C" "AGR.10to14.C" "RGR.0to5.C"
## [6] "RGR.6to9.C" "RGR.10to14.C" "TR.0to5.C" "TR.6to9.C" "TR.10to14.C"
## [11] "TUE.0to5.C" "TUE.6to9.C" "TUE.10to14.C" "TissueK.C" "TissueNa.C"
## [16] "SFM.C" "AGR.0to5.S" "AGR.6to9.S" "AGR.10to14.S" "RGR.0to5.S"
## [21] "RGR.6to9.S" "RGR.10to14.S" "TR.0to5.S" "TR.6to9.S" "TR.10to14.S"
## [26] "TUE.0to5.S" "TUE.6to9.S" "TUE.10to14.S" "TissueK.S" "TissueNa.S"
## [31] "SFM.S" "SFM.ST1" "SFM.ST2" "SFM.ST3" "SFM.ST4"
## [36] "SFM.ST5" "SFM.ST6" "SFM.ST7"let’s calculate the correlation matrix between all of the measured variables:
library(ggcorrplot)
TPA.corr <- TPA.reg[,2:38]
TPA.corr <- na.omit(TPA.corr)
dim(TPA.corr)## [1] 74 37corr <- cor(TPA.corr, method = c("pearson"))
head(corr[, 1:6])## AGR.0to5.C AGR.6to9.C AGR.10to14.C RGR.0to5.C RGR.6to9.C
## AGR.0to5.C 1.0000000 0.95853135 0.89024119 -0.18994286 -0.17401076
## AGR.6to9.C 0.9585313 1.00000000 0.95219981 -0.05734557 0.07028892
## AGR.10to14.C 0.8902412 0.95219981 1.00000000 0.00312935 0.16512885
## RGR.0to5.C -0.1899429 -0.05734557 0.00312935 1.00000000 0.78688768
## RGR.6to9.C -0.1740108 0.07028892 0.16512885 0.78688768 1.00000000
## RGR.10to14.C -0.5800072 -0.45531806 -0.22143806 0.54064709 0.57856861
## RGR.10to14.C
## AGR.0to5.C -0.5800072
## AGR.6to9.C -0.4553181
## AGR.10to14.C -0.2214381
## RGR.0to5.C 0.5406471
## RGR.6to9.C 0.5785686
## RGR.10to14.C 1.0000000p.mat <- cor_pmat(corr)
head(p.mat[,1:5])## AGR.0to5.C AGR.6to9.C AGR.10to14.C RGR.0to5.C RGR.6to9.C
## AGR.0to5.C 0.000000e+00 2.938338e-35 8.237586e-25 2.217800e-06 1.933539e-06
## AGR.6to9.C 2.938338e-35 0.000000e+00 1.764016e-31 2.473231e-05 4.306389e-05
## AGR.10to14.C 8.237586e-25 1.764016e-31 0.000000e+00 3.702008e-04 5.372587e-04
## RGR.0to5.C 2.217800e-06 2.473231e-05 3.702008e-04 0.000000e+00 7.373627e-21
## RGR.6to9.C 1.933539e-06 4.306389e-05 5.372587e-04 7.373627e-21 0.000000e+00
## RGR.10to14.C 1.086871e-12 9.265774e-11 5.086937e-08 1.532074e-14 1.029376e-13Correlation_plot_TPA <- ggcorrplot(corr, p.mat = p.mat, type = "lower", outline.col = "white",
colors = c("#6D9EC1", "white", "#E46726"),
ggtheme = ggplot2::theme_gray)
Correlation_plot_TPA
pdf("TPA_correlation_pearson.pdf", width = 10, height= 10)
plot(Correlation_plot_TPA)
dev.off()## quartz_off_screen
## 2corr <- cor(TPA.corr, method = c("spearman"))
head(corr[, 1:6])## AGR.0to5.C AGR.6to9.C AGR.10to14.C RGR.0to5.C RGR.6to9.C
## AGR.0to5.C 1.0000000 0.95080341 0.86867086 -0.20927064 -0.13463162
## AGR.6to9.C 0.9508034 1.00000000 0.94730840 -0.11235839 0.09325435
## AGR.10to14.C 0.8686709 0.94730840 1.00000000 -0.03845983 0.20817475
## RGR.0to5.C -0.2092706 -0.11235839 -0.03845983 1.00000000 0.71684561
## RGR.6to9.C -0.1346316 0.09325435 0.20817475 0.71684561 1.00000000
## RGR.10to14.C -0.5619993 -0.44518327 -0.22582747 0.51378008 0.52319882
## RGR.10to14.C
## AGR.0to5.C -0.5619993
## AGR.6to9.C -0.4451833
## AGR.10to14.C -0.2258275
## RGR.0to5.C 0.5137801
## RGR.6to9.C 0.5231988
## RGR.10to14.C 1.0000000p.mat <- cor_pmat(corr)
head(p.mat[,1:5])## AGR.0to5.C AGR.6to9.C AGR.10to14.C RGR.0to5.C RGR.6to9.C
## AGR.0to5.C 0.000000e+00 1.235516e-35 1.577590e-24 2.082309e-06 2.907719e-05
## AGR.6to9.C 1.235516e-35 0.000000e+00 1.791738e-31 1.399338e-05 3.078169e-04
## AGR.10to14.C 1.577590e-24 1.791738e-31 0.000000e+00 2.631404e-04 3.158579e-03
## RGR.0to5.C 2.082309e-06 1.399338e-05 2.631404e-04 0.000000e+00 3.818209e-17
## RGR.6to9.C 2.907719e-05 3.078169e-04 3.158579e-03 3.818209e-17 0.000000e+00
## RGR.10to14.C 6.512834e-12 2.912931e-10 1.320133e-07 2.143801e-14 1.809468e-11Correlation_plot_TPA <- ggcorrplot(corr, p.mat = p.mat, type = "lower", outline.col = "white",
colors = c("#6D9EC1", "white", "#E46726"),
ggtheme = ggplot2::theme_gray)
Correlation_plot_TPA
pdf("TPA_correlation_spearman.pdf", width = 10, height= 10)
plot(Correlation_plot_TPA)
dev.off()## quartz_off_screen
## 2OK - I think that the Pearson’s correlations are looking better - a bit more insignificant correlations - and thus - somewhat stricter. Based on the nature of all measured traits - the normal distribution can be assumed.
OK - let’s get to the regression itself! But - if we calculate regression for each combination it will be easier. SO - let’s go!
colnames(TPA.reg)## [1] "Genotype.ID" "AGR.0to5.C" "AGR.6to9.C" "AGR.10to14.C" "RGR.0to5.C"
## [6] "RGR.6to9.C" "RGR.10to14.C" "TR.0to5.C" "TR.6to9.C" "TR.10to14.C"
## [11] "TUE.0to5.C" "TUE.6to9.C" "TUE.10to14.C" "TissueK.C" "TissueNa.C"
## [16] "SFM.C" "AGR.0to5.S" "AGR.6to9.S" "AGR.10to14.S" "RGR.0to5.S"
## [21] "RGR.6to9.S" "RGR.10to14.S" "TR.0to5.S" "TR.6to9.S" "TR.10to14.S"
## [26] "TUE.0to5.S" "TUE.6to9.S" "TUE.10to14.S" "TissueK.S" "TissueNa.S"
## [31] "SFM.S" "SFM.ST1" "SFM.ST2" "SFM.ST3" "SFM.ST4"
## [36] "SFM.ST5" "SFM.ST6" "SFM.ST7"TPA.Rsq = matrix(, nrow = 37, ncol = 37)
TPA.Adj.Rsq = matrix(, nrow = 37, ncol = 37)
TPA.pval = matrix(, nrow = 37, ncol = 37)
TPA.RSE = matrix(, nrow = 37, ncol = 37)
TPA.DF = matrix(, nrow = 37, ncol = 37)
colnames(TPA.Rsq) <- colnames(TPA.reg)[2:38]
rownames(TPA.Rsq) <- colnames(TPA.reg)[2:38]
colnames(TPA.Adj.Rsq) <- colnames(TPA.reg)[2:38]
rownames(TPA.Adj.Rsq) <- colnames(TPA.reg)[2:38]
colnames(TPA.pval) <- colnames(TPA.reg)[2:38]
rownames(TPA.pval) <- colnames(TPA.reg)[2:38]
colnames(TPA.RSE) <- colnames(TPA.reg)[2:38]
rownames(TPA.RSE) <- colnames(TPA.reg)[2:38]
colnames(TPA.DF) <- colnames(TPA.reg)[2:38]
rownames(TPA.DF) <- colnames(TPA.reg)[2:38]# I want to explain each trait -- colnames(TPA.reg)[1:38] -- by each trait -- colnames(TPA.reg)[1:38]
for(x in 2:38){
trait.y <- colnames(TPA.reg)[x]
for(y in 2:38){
trait.x <- colnames(TPA.reg)[y]
TPA.Adj.Rsq[y-1,x-1]<- summary(lm(paste(trait.y, "~", trait.x), data = TPA.reg))$adj.r.squared
TPA.Rsq[y-1,x-1]<- summary(lm(paste(trait.y, "~", trait.x), data = TPA.reg))$r.squared
pval_table <- summary(lm(paste(trait.y, "~", trait.x), data = TPA.reg))$coefficients
if(dim(pval_table)[1] > 1){
TPA.pval[y-1,x-1]<- summary(lm(paste(trait.y, "~", trait.x), data = TPA.reg))$coefficients[2,4]
} else {
TPA.pval[y-1,x-1]<- "N.A."
}
TPA.RSE[y-1,x-1]<- summary(lm(paste(trait.y, "~", trait.x), data = TPA.reg))$sigma
}}
trait.x## [1] "SFM.ST7"trait.y## [1] "SFM.ST7"TPA.Adj.Rsq## AGR.0to5.C AGR.6to9.C AGR.10to14.C RGR.0to5.C RGR.6to9.C
## AGR.0to5.C 0.000000000 0.8987103078 0.773388918 0.050512151 0.0371167575
## AGR.6to9.C 0.898710308 0.0000000000 0.888478982 0.017261002 0.0005222067
## AGR.10to14.C 0.773388918 0.8884789823 0.000000000 0.014734867 0.0017024029
## RGR.0to5.C 0.050512151 0.0172610021 0.014734867 0.000000000 0.3448872315
## RGR.6to9.C 0.037116757 0.0005222067 0.001702403 0.344887231 0.0000000000
## RGR.10to14.C 0.264517928 0.1422661900 0.021618288 0.150123864 0.3002890352
## TR.0to5.C 0.755527683 0.6729451049 0.569885135 0.277245627 0.0977046771
## TR.6to9.C 0.763335238 0.7725857388 0.674744456 0.170620397 0.0223698169
## TR.10to14.C 0.687405882 0.7424729464 0.727681157 0.143407121 0.0039030476
## TUE.0to5.C 0.200458036 0.1950838812 0.182865065 0.268717129 0.0322772817
## TUE.6to9.C 0.119958843 0.2036940541 0.189534112 0.267782517 0.2399393994
## TUE.10to14.C 0.015836882 0.0325205928 0.082310766 0.189327737 0.1010653545
## TissueK.C -0.013208806 -0.0053268144 -0.009831356 0.017300870 0.0418102993
## TissueNa.C 0.150011207 0.1839475898 0.213922217 0.009133451 -0.0093961572
## SFM.C 0.796820846 0.8528424605 0.787055529 0.049436279 -0.0040359042
## AGR.0to5.S 0.773844706 0.7259675878 0.667027340 0.047339657 0.0139507846
## AGR.6to9.S 0.665732609 0.7249864518 0.638999444 0.007089316 -0.0014905277
## AGR.10to14.S 0.583978876 0.6514184095 0.589106903 0.002714419 0.0023526448
## RGR.0to5.S 0.086943520 0.0315770032 0.024879354 0.535666029 0.3177206357
## RGR.6to9.S 0.016729274 -0.0046294431 -0.004502615 0.249658391 0.3207616562
## RGR.10to14.S 0.047289649 0.0063426057 0.004435643 0.134879540 0.1996670924
## TR.0to5.S 0.515375750 0.4809241521 0.446581673 0.248835885 0.0481976607
## TR.6to9.S 0.482151864 0.5067094618 0.450602768 0.152921585 0.0047285776
## TR.10to14.S 0.407311198 0.4456571729 0.411177268 0.086547737 -0.0034832749
## TUE.0to5.S 0.103388803 0.1061265268 0.091149900 0.211016834 0.0293720582
## TUE.6to9.S 0.175000567 0.2254024349 0.201208133 0.208614847 0.0977102796
## TUE.10to14.S 0.080118684 0.1119345445 0.103531501 0.203967064 0.1091013160
## TissueK.S 0.065875430 0.0480609064 0.044398940 0.080917206 0.0537848471
## TissueNa.S 0.029572027 0.0326415981 0.034130602 -0.003614275 -0.0046016210
## SFM.S 0.661483494 0.6888119202 0.610531878 0.051218530 0.0007505103
## SFM.ST1 0.001187499 0.0063092834 0.013894237 -0.004581188 0.0070043780
## SFM.ST2 0.550057435 0.6036872726 0.583591681 0.025227894 -0.0039335913
## SFM.ST3 0.802008805 0.8499714158 0.774641657 0.054317835 -0.0027701444
## SFM.ST4 0.781536783 0.8262739386 0.748289578 0.055767059 -0.0021951289
## SFM.ST5 0.001187499 0.0063092834 0.013894237 -0.004581188 0.0070043780
## SFM.ST6 0.765152311 0.8069955053 0.708012302 0.042940323 -0.0032268214
## SFM.ST7 0.353307563 0.3548202508 0.301731336 0.034264472 0.0069114260
## RGR.10to14.C TR.0to5.C TR.6to9.C TR.10to14.C
## AGR.0to5.C 0.2645179282 0.7555276829 0.7633352377 0.687405882
## AGR.6to9.C 0.1422661900 0.6729451049 0.7725857388 0.742472946
## AGR.10to14.C 0.0216182882 0.5698851346 0.6747444556 0.727681157
## RGR.0to5.C 0.1501238636 0.2772456267 0.1706203966 0.143407121
## RGR.6to9.C 0.3002890352 0.0977046771 0.0223698169 0.003903048
## RGR.10to14.C 0.0000000000 0.2917321694 0.2146817608 0.118174177
## TR.0to5.C 0.2917321694 0.0000000000 0.9216764434 0.752046207
## TR.6to9.C 0.2146817608 0.9216764434 0.0000000000 0.842797565
## TR.10to14.C 0.1181741772 0.7520462070 0.8427975649 0.000000000
## TUE.0to5.C -0.0004623195 -0.0043423864 -0.0003007496 0.009151506
## TUE.6to9.C 0.0031919092 -0.0001322575 -0.0045875163 0.001758795
## TUE.10to14.C 0.1655267047 0.0354661580 0.0235143232 0.014341270
## TissueK.C -0.0132360640 -0.0046163593 -0.0128925667 -0.012755466
## TissueNa.C 0.0029770642 0.1568878468 0.1836530228 0.241008654
## SFM.C 0.1419177497 0.6391353529 0.7375257634 0.753092529
## AGR.0to5.S 0.1702580898 0.5362039488 0.5507583851 0.531045339
## AGR.6to9.S 0.1193345868 0.4380399472 0.5235208374 0.496523648
## AGR.10to14.S 0.0853648764 0.3825196056 0.4723658296 0.437832433
## RGR.0to5.S 0.1437210628 0.2547594280 0.1585511591 0.131712624
## RGR.6to9.S 0.0678385489 0.0671346018 0.0146889028 0.010746605
## RGR.10to14.S 0.0969773898 0.0796542288 0.0282616514 0.024136349
## TR.0to5.S 0.1609331657 0.6540737497 0.6352542499 0.560681113
## TR.6to9.S 0.1335873098 0.5732628060 0.6189259038 0.557713090
## TR.10to14.S 0.0817656743 0.4561443864 0.5105131423 0.465340404
## TUE.0to5.S -0.0024281531 -0.0013524822 -0.0045640760 -0.003871124
## TUE.6to9.S -0.0021345382 -0.0013307273 0.0141830534 0.022714540
## TUE.10to14.S -0.0037106426 -0.0040971767 -0.0025930684 -0.004199981
## TissueK.S 0.0648016574 0.1419719704 0.1042699981 0.082330525
## TissueNa.S 0.0029997482 0.0234137136 0.0226174633 0.032430688
## SFM.S 0.1752756853 0.5187676674 0.5892381591 0.547623038
## SFM.ST1 0.0061275482 0.0040605388 0.0059829285 0.029649235
## SFM.ST2 0.0587083718 0.4537811414 0.5300218850 0.592316810
## SFM.ST3 0.1644935521 0.6400564518 0.7337629967 0.726477443
## SFM.ST4 0.1703843513 0.6226530113 0.7122071246 0.696345382
## SFM.ST5 0.0061275482 0.0040605388 0.0059829285 0.029649235
## SFM.ST6 0.1787933999 0.5789974036 0.6704051944 0.652078401
## SFM.ST7 0.1305580823 0.2740805703 0.3066791700 0.248531867
## TUE.0to5.C TUE.6to9.C TUE.10to14.C TissueK.C
## AGR.0to5.C 0.2004580359 0.1199588432 0.0158368820 -0.0132088056
## AGR.6to9.C 0.1950838812 0.2036940541 0.0325205928 -0.0053268144
## AGR.10to14.C 0.1828650650 0.1895341119 0.0823107664 -0.0098313561
## RGR.0to5.C 0.2687171286 0.2677825175 0.1893277375 0.0173008697
## RGR.6to9.C 0.0322772817 0.2399393994 0.1010653545 0.0418102993
## RGR.10to14.C -0.0004623195 0.0031919092 0.1655267047 -0.0132360640
## TR.0to5.C -0.0043423864 -0.0001322575 0.0354661580 -0.0046163593
## TR.6to9.C -0.0003007496 -0.0045875163 0.0235143232 -0.0128925667
## TR.10to14.C 0.0091515057 0.0017587949 0.0143412704 -0.0127554664
## TUE.0to5.C 0.0000000000 0.7363181016 0.4472242421 0.0205607081
## TUE.6to9.C 0.7363181016 0.0000000000 0.5520144762 0.0229645374
## TUE.10to14.C 0.4472242421 0.5520144762 0.0000000000 -0.0009291984
## TissueK.C 0.0205607081 0.0229645374 -0.0009291984 0.0000000000
## TissueNa.C -0.0091301418 -0.0014778551 -0.0132248444 0.0023425166
## SFM.C 0.1322033201 0.1119242467 0.0113037513 -0.0087955733
## AGR.0to5.S 0.1897128631 0.1483095246 0.0455140351 -0.0088432241
## AGR.6to9.S 0.2088475023 0.1994529125 0.0458640021 0.0024620408
## AGR.10to14.S 0.1941849318 0.1836091270 0.0419647033 0.0098879009
## RGR.0to5.S 0.1110198693 0.1620901466 0.1121437256 0.0052321534
## RGR.6to9.S 0.0553605712 0.1054512740 0.0317245851 0.0284691176
## RGR.10to14.S 0.0082172290 0.0266007344 0.0096382339 0.0327483989
## TR.0to5.S -0.0045836743 -0.0038006117 0.0080836942 -0.0117701010
## TR.6to9.S -0.0019455808 -0.0029547808 0.0045953395 -0.0097391721
## TR.10to14.S 0.0043451559 0.0034374898 -0.0001438407 0.0041901735
## TUE.0to5.S 0.6758243194 0.5446294786 0.3269037001 0.0120112354
## TUE.6to9.S 0.6398941444 0.6357078550 0.3265728113 0.0271557697
## TUE.10to14.S 0.4617996191 0.4727299834 0.3479560833 -0.0053832218
## TissueK.S 0.0170258613 0.0107186252 0.0261484892 0.2098799706
## TissueNa.S 0.0090438074 0.0036259669 -0.0028677856 -0.0106223444
## SFM.S 0.1271110199 0.0975143799 0.0131329315 0.0063852712
## SFM.ST1 -0.0037101924 -0.0043875710 -0.0032272633 0.0187621358
## SFM.ST2 0.0760042370 0.0714258824 0.0031414125 -0.0134510450
## SFM.ST3 0.1400582299 0.1146081107 0.0130529550 -0.0041620824
## SFM.ST4 0.1385140276 0.1125880146 0.0134284102 -0.0014448652
## SFM.ST5 -0.0037101924 -0.0043875710 -0.0032272633 0.0187621358
## SFM.ST6 0.1527685773 0.1216830019 0.0155757878 0.0044708056
## SFM.ST7 0.0752917537 0.0496400210 0.0081919281 0.0179209209
## TissueNa.C SFM.C AGR.0to5.S AGR.6to9.S AGR.10to14.S
## AGR.0to5.C 0.150011207 0.796820846 0.773844706 0.665732609 0.583978876
## AGR.6to9.C 0.183947590 0.852842460 0.725967588 0.724986452 0.651418410
## AGR.10to14.C 0.213922217 0.787055529 0.667027340 0.638999444 0.589106903
## RGR.0to5.C 0.009133451 0.049436279 0.047339657 0.007089316 0.002714419
## RGR.6to9.C -0.009396157 -0.004035904 0.013950785 -0.001490528 0.002352645
## RGR.10to14.C 0.002977064 0.141917750 0.170258090 0.119334587 0.085364876
## TR.0to5.C 0.156887847 0.639135353 0.536203949 0.438039947 0.382519606
## TR.6to9.C 0.183653023 0.737525763 0.550758385 0.523520837 0.472365830
## TR.10to14.C 0.241008654 0.753092529 0.531045339 0.496523648 0.437832433
## TUE.0to5.C -0.009130142 0.132203320 0.189712863 0.208847502 0.194184932
## TUE.6to9.C -0.001477855 0.111924247 0.148309525 0.199452913 0.183609127
## TUE.10to14.C -0.013224844 0.011303751 0.045514035 0.045864002 0.041964703
## TissueK.C 0.002342517 -0.008795573 -0.008843224 0.002462041 0.009887901
## TissueNa.C 0.000000000 0.211470710 0.196407159 0.197537775 0.185430816
## SFM.C 0.211470710 0.000000000 0.666029434 0.638316683 0.562100270
## AGR.0to5.S 0.196407159 0.666029434 0.000000000 0.814312270 0.717297346
## AGR.6to9.S 0.197537775 0.638316683 0.814312270 0.000000000 0.934504821
## AGR.10to14.S 0.185430816 0.562100270 0.717297346 0.934504821 0.000000000
## RGR.0to5.S -0.012626368 0.048280742 0.042625160 0.001028987 0.001058581
## RGR.6to9.S 0.005780608 -0.003007352 0.016328994 0.050432311 0.074018783
## RGR.10to14.S -0.012102650 0.009732131 0.045619202 0.004415238 0.061725864
## TR.0to5.S 0.168770640 0.482652367 0.703988109 0.573046639 0.505172746
## TR.6to9.S 0.168392880 0.483934186 0.639893725 0.720268820 0.668460333
## TR.10to14.S 0.151349534 0.417023260 0.540130196 0.691540885 0.717002376
## TUE.0to5.S 0.003288201 0.068607871 0.107567054 0.088223992 0.079777873
## TUE.6to9.S 0.045858998 0.158707697 0.160493776 0.298938196 0.301088733
## TUE.10to14.S 0.003398870 0.061461623 0.071787446 0.116819143 0.148064502
## TissueK.S 0.002009822 0.033104799 0.102904587 0.039409192 0.031339881
## TissueNa.S 0.358901026 0.032791303 0.044932618 0.022957593 0.034596932
## SFM.S 0.258072465 0.729764503 0.772735324 0.796436973 0.744203536
## SFM.ST1 -0.005463368 0.032736532 0.027859747 0.051391298 0.072223776
## SFM.ST2 0.122962398 0.776479092 0.305667535 0.262182276 0.208975832
## SFM.ST3 0.256348067 0.963721030 0.753169048 0.741820994 0.668309309
## SFM.ST4 0.261166090 0.925400631 0.775495181 0.773771568 0.703759118
## SFM.ST5 -0.005463368 0.032736532 0.027859747 0.051391298 0.072223776
## SFM.ST6 0.246891638 0.895791367 0.760198948 0.770632287 0.684802438
## SFM.ST7 0.198818100 0.333323982 0.570292336 0.618459367 0.614323371
## RGR.0to5.S RGR.6to9.S RGR.10to14.S TR.0to5.S
## AGR.0to5.C 0.086943520 0.0167292744 0.0472896493 0.515375750
## AGR.6to9.C 0.031577003 -0.0046294431 0.0063426057 0.480924152
## AGR.10to14.C 0.024879354 -0.0045026152 0.0044356431 0.446581673
## RGR.0to5.C 0.535666029 0.2496583907 0.1348795403 0.248835885
## RGR.6to9.C 0.317720636 0.3207616562 0.1996670924 0.048197661
## RGR.10to14.C 0.143721063 0.0678385489 0.0969773898 0.160933166
## TR.0to5.C 0.254759428 0.0671346018 0.0796542288 0.654073750
## TR.6to9.C 0.158551159 0.0146889028 0.0282616514 0.635254250
## TR.10to14.C 0.131712624 0.0107466048 0.0241363494 0.560681113
## TUE.0to5.C 0.111019869 0.0553605712 0.0082172290 -0.004583674
## TUE.6to9.C 0.162090147 0.1054512740 0.0266007344 -0.003800612
## TUE.10to14.C 0.112143726 0.0317245851 0.0096382339 0.008083694
## TissueK.C 0.005232153 0.0284691176 0.0327483989 -0.011770101
## TissueNa.C -0.012626368 0.0057806076 -0.0121026503 0.168770640
## SFM.C 0.048280742 -0.0030073519 0.0097321310 0.482652367
## AGR.0to5.S 0.042625160 0.0163289944 0.0456192018 0.703988109
## AGR.6to9.S 0.001028987 0.0504323106 0.0044152378 0.573046639
## AGR.10to14.S 0.001058581 0.0740187830 0.0617258641 0.505172746
## RGR.0to5.S 0.000000000 0.4298472513 0.1637779342 0.179179158
## RGR.6to9.S 0.429847251 0.0000000000 0.6423438592 0.052912372
## RGR.10to14.S 0.163777934 0.6423438592 0.0000000000 0.062162471
## TR.0to5.S 0.179179158 0.0529123720 0.0621624708 0.000000000
## TR.6to9.S 0.079483817 -0.0026086429 -0.0039498809 0.854220576
## TR.10to14.S 0.044044991 0.0250331798 0.0148339400 0.647245165
## TUE.0to5.S 0.142045741 0.0191141689 -0.0036780611 0.032013326
## TUE.6to9.S 0.157234994 0.2556087867 0.1237763197 -0.003909064
## TUE.10to14.S 0.095873840 0.0850917752 0.0885198104 0.004688455
## TissueK.S 0.076880947 0.1079387204 0.0843535266 0.204772835
## TissueNa.S -0.002794753 0.0059869425 -0.0044379392 0.014648834
## SFM.S 0.039845659 -0.0035995188 -0.0044950484 0.598756048
## SFM.ST1 -0.003676959 0.0168341773 0.0442341308 0.035941341
## SFM.ST2 0.031428248 0.0045910381 0.0308299299 0.199551853
## SFM.ST3 0.048751206 -0.0043996186 0.0024878318 0.559775227
## SFM.ST4 0.047308606 -0.0046249121 -0.0002476356 0.583509810
## SFM.ST5 -0.003676959 0.0168341773 0.0442341308 0.035941341
## SFM.ST6 0.036231626 -0.0042330707 -0.0001829948 0.535119293
## SFM.ST7 0.021745819 0.0009865449 0.0020788386 0.479309515
## TR.6to9.S TR.10to14.S TUE.0to5.S TUE.6to9.S
## AGR.0to5.C 0.4821518639 0.4073111976 0.103388803 0.1750005675
## AGR.6to9.C 0.5067094618 0.4456571729 0.106126527 0.2254024349
## AGR.10to14.C 0.4506027685 0.4111772678 0.091149900 0.2012081330
## RGR.0to5.C 0.1529215855 0.0865477367 0.211016834 0.2086148472
## RGR.6to9.C 0.0047285776 -0.0034832749 0.029372058 0.0977102796
## RGR.10to14.C 0.1335873098 0.0817656743 -0.002428153 -0.0021345382
## TR.0to5.C 0.5732628060 0.4561443864 -0.001352482 -0.0013307273
## TR.6to9.C 0.6189259038 0.5105131423 -0.004564076 0.0141830534
## TR.10to14.C 0.5577130903 0.4653404039 -0.003871124 0.0227145397
## TUE.0to5.C -0.0019455808 0.0043451559 0.675824319 0.6398941444
## TUE.6to9.C -0.0029547808 0.0034374898 0.544629479 0.6357078550
## TUE.10to14.C 0.0045953395 -0.0001438407 0.326903700 0.3265728113
## TissueK.C -0.0097391721 0.0041901735 0.012011235 0.0271557697
## TissueNa.C 0.1683928799 0.1513495340 0.003288201 0.0458589984
## SFM.C 0.4839341860 0.4170232601 0.068607871 0.1587076967
## AGR.0to5.S 0.6398937252 0.5401301962 0.107567054 0.1604937763
## AGR.6to9.S 0.7202688204 0.6915408853 0.088223992 0.2989381959
## AGR.10to14.S 0.6684603334 0.7170023757 0.079777873 0.3010887325
## RGR.0to5.S 0.0794838168 0.0440449913 0.142045741 0.1572349944
## RGR.6to9.S -0.0026086429 0.0250331798 0.019114169 0.2556087867
## RGR.10to14.S -0.0039498809 0.0148339400 -0.003678061 0.1237763197
## TR.0to5.S 0.8542205761 0.6472451648 0.032013326 -0.0039090639
## TR.6to9.S 0.0000000000 0.8693122212 0.012591398 -0.0005411012
## TR.10to14.S 0.8693122212 0.0000000000 -0.002147497 0.0161317995
## TUE.0to5.S 0.0125913980 -0.0021474965 0.000000000 0.5809116826
## TUE.6to9.S -0.0005411012 0.0161317995 0.580911683 0.0000000000
## TUE.10to14.S 0.0026466136 0.0022891416 0.452579773 0.6536937047
## TissueK.S 0.1446617337 0.1124247824 0.024579144 0.0390692213
## TissueNa.S 0.0105858431 0.0216284649 0.037543576 0.0117205440
## SFM.S 0.6692101611 0.6184974328 0.061946461 0.1538631718
## SFM.ST1 0.0735718648 0.0953071781 -0.004577459 -0.0030064213
## SFM.ST2 0.1661714107 0.1246661354 0.041336369 0.0905781055
## SFM.ST3 0.5852556548 0.5185704169 0.071245641 0.1684648450
## SFM.ST4 0.6214631847 0.5571556194 0.069881464 0.1682747775
## SFM.ST5 0.0735718648 0.0953071781 -0.004577459 -0.0030064213
## SFM.ST6 0.5755518383 0.5122011930 0.083325143 0.1902613435
## SFM.ST7 0.5758437196 0.5623963704 0.031767976 0.0915178824
## TUE.10to14.S TissueK.S TissueNa.S SFM.S SFM.ST1
## AGR.0to5.C 0.080118684 0.065875430 0.029572027 0.6614834938 0.0011874991
## AGR.6to9.C 0.111934544 0.048060906 0.032641598 0.6888119202 0.0063092834
## AGR.10to14.C 0.103531501 0.044398940 0.034130602 0.6105318785 0.0138942374
## RGR.0to5.C 0.203967064 0.080917206 -0.003614275 0.0512185298 -0.0045811875
## RGR.6to9.C 0.109101316 0.053784847 -0.004601621 0.0007505103 0.0070043780
## RGR.10to14.C -0.003710643 0.064801657 0.002999748 0.1752756853 0.0061275482
## TR.0to5.C -0.004097177 0.141971970 0.023413714 0.5187676674 0.0040605388
## TR.6to9.C -0.002593068 0.104269998 0.022617463 0.5892381591 0.0059829285
## TR.10to14.C -0.004199981 0.082330525 0.032430688 0.5476230378 0.0296492352
## TUE.0to5.C 0.461799619 0.017025861 0.009043807 0.1271110199 -0.0037101924
## TUE.6to9.C 0.472729983 0.010718625 0.003625967 0.0975143799 -0.0043875710
## TUE.10to14.C 0.347956083 0.026148489 -0.002867786 0.0131329315 -0.0032272633
## TissueK.C -0.005383222 0.209879971 -0.010622344 0.0063852712 0.0187621358
## TissueNa.C 0.003398870 0.002009822 0.358901026 0.2580724652 -0.0054633684
## SFM.C 0.061461623 0.033104799 0.032791303 0.7297645030 0.0327365315
## AGR.0to5.S 0.071787446 0.102904587 0.044932618 0.7727353238 0.0278597469
## AGR.6to9.S 0.116819143 0.039409192 0.022957593 0.7964369728 0.0513912984
## AGR.10to14.S 0.148064502 0.031339881 0.034596932 0.7442035362 0.0722237760
## RGR.0to5.S 0.095873840 0.076880947 -0.002794753 0.0398456589 -0.0036769589
## RGR.6to9.S 0.085091775 0.107938720 0.005986942 -0.0035995188 0.0168341773
## RGR.10to14.S 0.088519810 0.084353527 -0.004437939 -0.0044950484 0.0442341308
## TR.0to5.S 0.004688455 0.204772835 0.014648834 0.5987560481 0.0359413412
## TR.6to9.S 0.002646614 0.144661734 0.010585843 0.6692101611 0.0735718648
## TR.10to14.S 0.002289142 0.112424782 0.021628465 0.6184974328 0.0953071781
## TUE.0to5.S 0.452579773 0.024579144 0.037543576 0.0619464608 -0.0045774589
## TUE.6to9.S 0.653693705 0.039069221 0.011720544 0.1538631718 -0.0030064213
## TUE.10to14.S 0.000000000 0.043970909 0.006283877 0.0446406092 -0.0041663703
## TissueK.S 0.043970909 0.000000000 -0.004628996 0.0673013611 0.0216501888
## TissueNa.S 0.006283877 -0.004628996 0.000000000 0.0364629320 -0.0017031640
## SFM.S 0.044640609 0.067301361 0.036462932 0.0000000000 0.1029083416
## SFM.ST1 -0.004166370 0.021650189 -0.001703164 0.1029083416 0.0000000000
## SFM.ST2 0.045867697 0.001563499 0.014075712 0.2559197186 0.3785317730
## SFM.ST3 0.059476200 0.047405402 0.036825067 0.8792306881 -0.0045473425
## SFM.ST4 0.056951009 0.053379428 0.036549000 0.9284198298 0.0005158669
## SFM.ST5 -0.004166370 0.021650189 -0.001703164 0.1029083416 1.0000000000
## SFM.ST6 0.066541301 0.033644956 0.028625955 0.9080172091 0.0004763369
## SFM.ST7 0.017917394 0.081178908 0.030246409 0.8352648246 0.4557166832
## SFM.ST2 SFM.ST3 SFM.ST4 SFM.ST5
## AGR.0to5.C 0.550057435 0.802008805 0.7815367832 0.0011874991
## AGR.6to9.C 0.603687273 0.849971416 0.8262739386 0.0063092834
## AGR.10to14.C 0.583591681 0.774641657 0.7482895775 0.0138942374
## RGR.0to5.C 0.025227894 0.054317835 0.0557670588 -0.0045811875
## RGR.6to9.C -0.003933591 -0.002770144 -0.0021951289 0.0070043780
## RGR.10to14.C 0.058708372 0.164493552 0.1703843513 0.0061275482
## TR.0to5.C 0.453781141 0.640056452 0.6226530113 0.0040605388
## TR.6to9.C 0.530021885 0.733762997 0.7122071246 0.0059829285
## TR.10to14.C 0.592316810 0.726477443 0.6963453819 0.0296492352
## TUE.0to5.C 0.076004237 0.140058230 0.1385140276 -0.0037101924
## TUE.6to9.C 0.071425882 0.114608111 0.1125880146 -0.0043875710
## TUE.10to14.C 0.003141413 0.013052955 0.0134284102 -0.0032272633
## TissueK.C -0.013451045 -0.004162082 -0.0014448652 0.0187621358
## TissueNa.C 0.122962398 0.256348067 0.2611660898 -0.0054633684
## SFM.C 0.776479092 0.963721030 0.9254006307 0.0327365315
## AGR.0to5.S 0.305667535 0.753169048 0.7754951806 0.0278597469
## AGR.6to9.S 0.262182276 0.741820994 0.7737715682 0.0513912984
## AGR.10to14.S 0.208975832 0.668309309 0.7037591179 0.0722237760
## RGR.0to5.S 0.031428248 0.048751206 0.0473086060 -0.0036769589
## RGR.6to9.S 0.004591038 -0.004399619 -0.0046249121 0.0168341773
## RGR.10to14.S 0.030829930 0.002487832 -0.0002476356 0.0442341308
## TR.0to5.S 0.199551853 0.559775227 0.5835098096 0.0359413412
## TR.6to9.S 0.166171411 0.585255655 0.6214631847 0.0735718648
## TR.10to14.S 0.124666135 0.518570417 0.5571556194 0.0953071781
## TUE.0to5.S 0.041336369 0.071245641 0.0698814637 -0.0045774589
## TUE.6to9.S 0.090578106 0.168464845 0.1682747775 -0.0030064213
## TUE.10to14.S 0.045867697 0.059476200 0.0569510094 -0.0041663703
## TissueK.S 0.001563499 0.047405402 0.0533794281 0.0216501888
## TissueNa.S 0.014075712 0.036825067 0.0365490002 -0.0017031640
## SFM.S 0.255919719 0.879230688 0.9284198298 0.1029083416
## SFM.ST1 0.378531773 -0.004547342 0.0005158669 1.0000000000
## SFM.ST2 0.000000000 0.600430490 0.5162947992 0.3785317730
## SFM.ST3 0.600430490 0.000000000 0.9926119531 -0.0045473425
## SFM.ST4 0.516294799 0.992611953 0.0000000000 0.0005158669
## SFM.ST5 0.378531773 -0.004547342 0.0005158669 0.0000000000
## SFM.ST6 0.493424375 0.964380903 0.9733268505 0.0004763369
## SFM.ST7 0.012571926 0.520154808 0.6019915642 0.4557166832
## SFM.ST6 SFM.ST7
## AGR.0to5.C 0.7651523110 0.3533075631
## AGR.6to9.C 0.8069955053 0.3548202508
## AGR.10to14.C 0.7080123020 0.3017313363
## RGR.0to5.C 0.0429403230 0.0342644717
## RGR.6to9.C -0.0032268214 0.0069114260
## RGR.10to14.C 0.1787933999 0.1305580823
## TR.0to5.C 0.5789974036 0.2740805703
## TR.6to9.C 0.6704051944 0.3066791700
## TR.10to14.C 0.6520784015 0.2485318674
## TUE.0to5.C 0.1527685773 0.0752917537
## TUE.6to9.C 0.1216830019 0.0496400210
## TUE.10to14.C 0.0155757878 0.0081919281
## TissueK.C 0.0044708056 0.0179209209
## TissueNa.C 0.2468916377 0.1988180999
## SFM.C 0.8957913666 0.3333239821
## AGR.0to5.S 0.7601989476 0.5702923365
## AGR.6to9.S 0.7706322870 0.6184593672
## AGR.10to14.S 0.6848024378 0.6143233709
## RGR.0to5.S 0.0362316264 0.0217458185
## RGR.6to9.S -0.0042330707 0.0009865449
## RGR.10to14.S -0.0001829948 0.0020788386
## TR.0to5.S 0.5351192931 0.4793095155
## TR.6to9.S 0.5755518383 0.5758437196
## TR.10to14.S 0.5122011930 0.5623963704
## TUE.0to5.S 0.0833251429 0.0317679762
## TUE.6to9.S 0.1902613435 0.0915178824
## TUE.10to14.S 0.0665413006 0.0179173942
## TissueK.S 0.0336449561 0.0811789078
## TissueNa.S 0.0286259548 0.0302464093
## SFM.S 0.9080172091 0.8352648246
## SFM.ST1 0.0004763369 0.4557166832
## SFM.ST2 0.4934243745 0.0125719256
## SFM.ST3 0.9643809032 0.5201548080
## SFM.ST4 0.9733268505 0.6019915642
## SFM.ST5 0.0004763369 0.4557166832
## SFM.ST6 0.0000000000 0.5746511296
## SFM.ST7 0.5746511296 0.0000000000dim(summary(lm(paste(trait.y, "~", trait.x), data = TPA.reg))$coefficients)[1]## [1] 1OK - this is great. Now - I would like to remove the values in TPA.Adj.Rsq table for all the coordinates that have the p-value > 0.01.
for(x in 1:37){
for(y in 1:37){
if(TPA.pval[x,y] == "N.A." | as.numeric(TPA.pval[x,y]) > 0.01){
TPA.Adj.Rsq[x,y] <- NA
TPA.Rsq[x,y] <- NA
}else{
TPA.Adj.Rsq[x,y] <- as.numeric(TPA.Adj.Rsq[x,y])
TPA.Rsq[x,y] <- as.numeric(TPA.Rsq[x,y])}
}}## Warning: NAs introduced by coercion
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## Warning: NAs introduced by coercionwrite.csv(TPA.Adj.Rsq, "TPA.Adj.Rsq.csv")
write.csv(TPA.Rsq, "TPA.Rsq.csv")
write.csv(TPA.pval, "TPA.pval.csv")
write.csv(TPA.RSE, "TPA.RSE.csv")TPA.Adj.Rsq2 <- read.csv("TPA.Adj.Rsq.csv")
head(TPA.Adj.Rsq2)TPA.Adj.Rsq2$X## [1] "AGR.0to5.C" "AGR.6to9.C" "AGR.10to14.C" "RGR.0to5.C" "RGR.6to9.C"
## [6] "RGR.10to14.C" "TR.0to5.C" "TR.6to9.C" "TR.10to14.C" "TUE.0to5.C"
## [11] "TUE.6to9.C" "TUE.10to14.C" "TissueK.C" "TissueNa.C" "SFM.C"
## [16] "AGR.0to5.S" "AGR.6to9.S" "AGR.10to14.S" "RGR.0to5.S" "RGR.6to9.S"
## [21] "RGR.10to14.S" "TR.0to5.S" "TR.6to9.S" "TR.10to14.S" "TUE.0to5.S"
## [26] "TUE.6to9.S" "TUE.10to14.S" "TissueK.S" "TissueNa.S" "SFM.S"
## [31] "SFM.ST1" "SFM.ST2" "SFM.ST3" "SFM.ST4" "SFM.ST5"
## [36] "SFM.ST6" "SFM.ST7"TPA.Adj.Rsq2 <- TPA.Adj.Rsq2[1:30,1:37]
colnames(TPA.Adj.Rsq2)## [1] "X" "AGR.0to5.C" "AGR.6to9.C" "AGR.10to14.C" "RGR.0to5.C"
## [6] "RGR.6to9.C" "RGR.10to14.C" "TR.0to5.C" "TR.6to9.C" "TR.10to14.C"
## [11] "TUE.0to5.C" "TUE.6to9.C" "TUE.10to14.C" "TissueK.C" "TissueNa.C"
## [16] "SFM.C" "AGR.0to5.S" "AGR.6to9.S" "AGR.10to14.S" "RGR.0to5.S"
## [21] "RGR.6to9.S" "RGR.10to14.S" "TR.0to5.S" "TR.6to9.S" "TR.10to14.S"
## [26] "TUE.0to5.S" "TUE.6to9.S" "TUE.10to14.S" "TissueK.S" "TissueNa.S"
## [31] "SFM.S" "SFM.ST1" "SFM.ST2" "SFM.ST3" "SFM.ST4"
## [36] "SFM.ST5" "SFM.ST6"SFM.C <- TPA.Adj.Rsq2[,c(1,16)]
SFM.S <- TPA.Adj.Rsq2[,c(1,31)]
SFM.C <- na.omit(SFM.C)
SFM.S <- na.omit(SFM.S)
library(ggsci)
pal_aaas("default")(2)## [1] "#3B4992FF" "#EE0000FF"reg.plot_SFM_Salt <- ggbarplot(SFM.S, x = "X", y = "SFM.S", fill = "#EE0000FF",
sort.val = "desc", x.text.angle = 90, xlab = "", ylab = "Adjusted R square")
reg.plot_SFM_Control <- ggbarplot(SFM.C, x = "X", y = "SFM.C", fill = "#3B4992FF",
sort.val = "desc", x.text.angle = 90, xlab = "", ylab = "Adjusted R square")
TPA_SFM_regression <- plot_grid(reg.plot_SFM_Control, reg.plot_SFM_Salt, labels = "AUTO", ncol= 1)
TPA_SFM_regression
pdf("Regression_SFM.pdf", width = 10, height = 13)
plot(TPA_SFM_regression)
dev.off()## quartz_off_screen
## 2plot()
ploting interval data
library(reshape2)
colnames(AGR.data)## [1] "Genotype.ID" "Area.smooth.AGR.0to5.BLUE.C"
## [3] "Area.smooth.AGR.6to9.BLUE.C" "Area.smooth.AGR.10to14.BLUE.C"
## [5] "Area.smooth.AGR.0to5.BLUE.S" "Area.smooth.AGR.6to9.BLUE.S"
## [7] "Area.smooth.AGR.10to14.BLUE.S" "AGR.0to5.ST1"
## [9] "AGR.0to5.ST2" "AGR.0to5.ST3"
## [11] "AGR.0to5.ST4" "AGR.0to5.ST5"
## [13] "AGR.0to5.ST6" "AGR.0to5.ST7"
## [15] "AGR.6to9.ST1" "AGR.6to9.ST2"
## [17] "AGR.6to9.ST3" "AGR.6to9.ST4"
## [19] "AGR.6to9.ST5" "AGR.6to9.ST6"
## [21] "AGR.6to9.ST7" "AGR.10to14.ST1"
## [23] "AGR.10to14.ST2" "AGR.10to14.ST3"
## [25] "AGR.10to14.ST4" "AGR.10to14.ST5"
## [27] "AGR.10to14.ST6" "AGR.10to14.ST7"AGR.data.int <- AGR.data[,c(1:7)]
mAGR.data.int <- melt(AGR.data.int)## Using Genotype.ID as id variablescolnames(mAGR.data.int)[3] <- "AGR"
mAGR.data.int$treatment <- mAGR.data.int$variable
mAGR.data.int$treatment <- gsub("Area.smooth.AGR.0to5.BLUE.C", "Control", mAGR.data.int$treatment)
mAGR.data.int$treatment <- gsub("Area.smooth.AGR.0to5.BLUE.S", "Salt", mAGR.data.int$treatment)
mAGR.data.int$treatment <- gsub("Area.smooth.AGR.6to9.BLUE.C", "Control", mAGR.data.int$treatment)
mAGR.data.int$treatment <- gsub("Area.smooth.AGR.6to9.BLUE.S", "Salt", mAGR.data.int$treatment)
mAGR.data.int$treatment <- gsub("Area.smooth.AGR.10to14.BLUE.C", "Control", mAGR.data.int$treatment)
mAGR.data.int$treatment <- gsub("Area.smooth.AGR.10to14.BLUE.S", "Salt", mAGR.data.int$treatment)
mAGR.data.int$interval <- mAGR.data.int$variable
mAGR.data.int$interval <- gsub("Area.smooth.AGR.0to5.BLUE.C", "0.to.5", mAGR.data.int$interval)
mAGR.data.int$interval <- gsub("Area.smooth.AGR.0to5.BLUE.S", "0.to.5", mAGR.data.int$interval)
mAGR.data.int$interval <- gsub("Area.smooth.AGR.6to9.BLUE.C", "6.to.9", mAGR.data.int$interval)
mAGR.data.int$interval <- gsub("Area.smooth.AGR.6to9.BLUE.S", "6.to.9", mAGR.data.int$interval)
mAGR.data.int$interval <- gsub("Area.smooth.AGR.10to14.BLUE.C", "10.to.14", mAGR.data.int$interval)
mAGR.data.int$interval <- gsub("Area.smooth.AGR.10to14.BLUE.S", "10.to.14", mAGR.data.int$interval)
mAGR.data.int$interval <- factor(mAGR.data.int$interval, levels = c("0.to.5", "6.to.9", "10.to.14"))AGR.int_violin_paired <- ggplot(data = mAGR.data.int, mapping = aes(x = treatment, y = AGR, fill = treatment))
AGR.int_violin_paired <- AGR.int_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
AGR.int_violin_paired <- AGR.int_violin_paired + geom_violindot(dots_size = 0.0, position_dots = position_dodge(0.1), flip = c(1)) + facet_wrap(~ interval)## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`AGR.int_violin_paired <- AGR.int_violin_paired + geom_point(mapping = aes(fill = treatment, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
AGR.int_violin_paired <- AGR.int_violin_paired + stat_compare_means(aes(group = treatment), label = "p.signif", method = "aov", paired = TRUE)
AGR.int_violin_paired <- AGR.int_violin_paired + theme_bw() + theme(legend.position = "none") + ylab("AGR (pix. day)") + xlab("") + scale_fill_aaas()
AGR.int_violin_paired ## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).
AGR1 <- subset(mAGR.data.int, mAGR.data.int$interval == "0.to.5")
summary(lm(AGR ~ treatment, data = AGR1))##
## Call:
## lm(formula = AGR ~ treatment, data = AGR1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.035 -5.312 -0.612 4.518 35.619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.6019 0.5494 68.44 <2e-16 ***
## treatmentSalt -12.6929 0.7779 -16.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.131 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3797, Adjusted R-squared: 0.3782
## F-statistic: 266.2 on 1 and 435 DF, p-value: < 2.2e-16AGR2 <- subset(mAGR.data.int, mAGR.data.int$interval == "6.to.9")
summary(lm(AGR ~ treatment, data = AGR2))##
## Call:
## lm(formula = AGR ~ treatment, data = AGR2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.076 -11.110 -2.137 8.822 70.065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 85.228 1.177 72.41 <2e-16 ***
## treatmentSalt -44.318 1.666 -26.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.42 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6192, Adjusted R-squared: 0.6183
## F-statistic: 707.3 on 1 and 435 DF, p-value: < 2.2e-16AGR3 <- subset(mAGR.data.int, mAGR.data.int$interval == "10.to.14")
summary(lm(AGR ~ treatment, data = AGR3))##
## Call:
## lm(formula = AGR ~ treatment, data = AGR3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.824 -17.052 -1.479 15.584 97.847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 144.164 1.772 81.37 <2e-16 ***
## treatmentSalt -78.760 2.500 -31.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.04 on 432 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.6968, Adjusted R-squared: 0.6961
## F-statistic: 992.7 on 1 and 432 DF, p-value: < 2.2e-16colnames(RGR.data)## [1] "Genotype.ID" "Area.smooth.RGR.0to5.BLUE.C"
## [3] "Area.smooth.RGR.6to9.BLUE.C" "Area.smooth.RGR.10to14.BLUE.C"
## [5] "Area.smooth.RGR.0to5.BLUE.S" "Area.smooth.RGR.6to9.BLUE.S"
## [7] "Area.smooth.RGR.10to14.BLUE.S" "RGR.0to5.ST1"
## [9] "RGR.0to5.ST2" "RGR.0to5.ST3"
## [11] "RGR.0to5.ST4" "RGR.0to5.ST5"
## [13] "RGR.0to5.ST6" "RGR.0to5.ST7"
## [15] "RGR.6to9.ST1" "RGR.6to9.ST2"
## [17] "RGR.6to9.ST3" "RGR.6to9.ST4"
## [19] "RGR.6to9.ST5" "RGR.6to9.ST6"
## [21] "RGR.6to9.ST7" "RGR.10to14.ST1"
## [23] "RGR.10to14.ST2" "RGR.10to14.ST3"
## [25] "RGR.10to14.ST4" "RGR.10to14.ST5"
## [27] "RGR.10to14.ST6" "RGR.10to14.ST7"RGR.data.int <- RGR.data[,c(1:7)]
mRGR.data.int <- melt(RGR.data.int)## Using Genotype.ID as id variablescolnames(mRGR.data.int)[3] <- "RGR"
mRGR.data.int$treatment <- mRGR.data.int$variable
mRGR.data.int$treatment <- gsub("Area.smooth.RGR.0to5.BLUE.C", "Control", mRGR.data.int$treatment)
mRGR.data.int$treatment <- gsub("Area.smooth.RGR.0to5.BLUE.S", "Salt", mRGR.data.int$treatment)
mRGR.data.int$treatment <- gsub("Area.smooth.RGR.6to9.BLUE.C", "Control", mRGR.data.int$treatment)
mRGR.data.int$treatment <- gsub("Area.smooth.RGR.6to9.BLUE.S", "Salt", mRGR.data.int$treatment)
mRGR.data.int$treatment <- gsub("Area.smooth.RGR.10to14.BLUE.C", "Control", mRGR.data.int$treatment)
mRGR.data.int$treatment <- gsub("Area.smooth.RGR.10to14.BLUE.S", "Salt", mRGR.data.int$treatment)
mRGR.data.int$interval <- mRGR.data.int$variable
mRGR.data.int$interval <- gsub("Area.smooth.RGR.0to5.BLUE.C", "0.to.5", mRGR.data.int$interval)
mRGR.data.int$interval <- gsub("Area.smooth.RGR.0to5.BLUE.S", "0.to.5", mRGR.data.int$interval)
mRGR.data.int$interval <- gsub("Area.smooth.RGR.6to9.BLUE.C", "6.to.9", mRGR.data.int$interval)
mRGR.data.int$interval <- gsub("Area.smooth.RGR.6to9.BLUE.S", "6.to.9", mRGR.data.int$interval)
mRGR.data.int$interval <- gsub("Area.smooth.RGR.10to14.BLUE.C", "10.to.14", mRGR.data.int$interval)
mRGR.data.int$interval <- gsub("Area.smooth.RGR.10to14.BLUE.S", "10.to.14", mRGR.data.int$interval)
mRGR.data.int$interval <- factor(mRGR.data.int$interval, levels = c("0.to.5", "6.to.9", "10.to.14"))
mRGR.data.intRGR.int_violin_paired <- ggplot(data = mRGR.data.int, mapping = aes(x = treatment, y = RGR, fill = treatment))
RGR.int_violin_paired <- RGR.int_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
RGR.int_violin_paired <- RGR.int_violin_paired + geom_violindot(dots_size = 0.00, position_dots = position_dodge(0.1), flip = c(1)) + facet_wrap(~ interval)## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`RGR.int_violin_paired <- RGR.int_violin_paired + geom_point(mapping = aes(fill = treatment, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
RGR.int_violin_paired <- RGR.int_violin_paired + stat_compare_means(aes(group = treatment), label = "p.signif", method = "aov", paired = TRUE)
RGR.int_violin_paired <- RGR.int_violin_paired + theme_bw() + theme(legend.position = "none") + ylab("RGR (pix. day)") + xlab("") + scale_fill_aaas()
RGR.int_violin_paired ## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).
RGR1 <- subset(mRGR.data.int, mRGR.data.int$interval == "0.to.5")
summary(lm(RGR ~ treatment, data = RGR1))##
## Call:
## lm(formula = RGR ~ treatment, data = RGR1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053542 -0.012412 -0.000464 0.009860 0.065226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.255140 0.001173 217.47 <2e-16 ***
## treatmentSalt -0.052792 0.001661 -31.78 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01736 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.699, Adjusted R-squared: 0.6983
## F-statistic: 1010 on 1 and 435 DF, p-value: < 2.2e-16RGR2 <- subset(mRGR.data.int, mRGR.data.int$interval == "6.to.9")
summary(lm(RGR ~ treatment, data = RGR2))##
## Call:
## lm(formula = RGR ~ treatment, data = RGR2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.087547 -0.008505 0.000497 0.007944 0.054616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1956753 0.0009897 197.72 <2e-16 ***
## treatmentSalt -0.0526760 0.0014012 -37.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01465 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.7646, Adjusted R-squared: 0.7641
## F-statistic: 1413 on 1 and 435 DF, p-value: < 2.2e-16RGR3 <- subset(mRGR.data.int, mRGR.data.int$interval == "10.to.14")
summary(lm(RGR ~ treatment, data = RGR3))##
## Call:
## lm(formula = RGR ~ treatment, data = RGR3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061929 -0.005597 -0.000357 0.006418 0.031709
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1524814 0.0007028 216.95 <2e-16 ***
## treatmentSalt -0.0295306 0.0009917 -29.78 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01033 on 432 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.6724, Adjusted R-squared: 0.6717
## F-statistic: 886.8 on 1 and 432 DF, p-value: < 2.2e-16colnames(TSR.data)## [1] "Genotype.ID"
## [2] "Transpiration.smooth.rate.0to5.BLUE.C"
## [3] "Transpiration.smooth.rate.6to9.BLUE.C"
## [4] "Transpiration.smooth.rate.10to14.BLUE.C"
## [5] "Transpiration.smooth.rate.0to5.BLUE.S"
## [6] "Transpiration.smooth.rate.6to9.BLUE.S"
## [7] "Transpiration.smooth.rate.10to14.BLUE.S"
## [8] "TSR.0to5.ST1"
## [9] "TSR.0to5.ST2"
## [10] "TSR.0to5.ST3"
## [11] "TSR.0to5.ST4"
## [12] "TSR.0to5.ST5"
## [13] "TSR.0to5.ST6"
## [14] "TSR.0to5.ST7"
## [15] "TSR.6to9.ST1"
## [16] "TSR.6to9.ST2"
## [17] "TSR.6to9.ST3"
## [18] "TSR.6to9.ST4"
## [19] "TSR.6to9.ST5"
## [20] "TSR.6to9.ST6"
## [21] "TSR.6to9.ST7"
## [22] "TSR.10to14.ST1"
## [23] "TSR.10to14.ST2"
## [24] "TSR.10to14.ST3"
## [25] "TSR.10to14.ST4"
## [26] "TSR.10to14.ST5"
## [27] "TSR.10to14.ST6"
## [28] "TSR.10to14.ST7"TSR.data.int <- TSR.data[,c(1:7)]
mTSR.data.int <- melt(TSR.data.int)## Using Genotype.ID as id variablescolnames(mTSR.data.int)[3] <- "TSR"
mTSR.data.intmTSR.data.int$treatment <- mTSR.data.int$variable
mTSR.data.int$treatment <- gsub("Transpiration.smooth.rate.0to5.BLUE.C", "Control", mTSR.data.int$treatment)
mTSR.data.int$treatment <- gsub("Transpiration.smooth.rate.0to5.BLUE.S", "Salt", mTSR.data.int$treatment)
mTSR.data.int$treatment <- gsub("Transpiration.smooth.rate.6to9.BLUE.C", "Control", mTSR.data.int$treatment)
mTSR.data.int$treatment <- gsub("Transpiration.smooth.rate.6to9.BLUE.S", "Salt", mTSR.data.int$treatment)
mTSR.data.int$treatment <- gsub("Transpiration.smooth.rate.10to14.BLUE.C", "Control", mTSR.data.int$treatment)
mTSR.data.int$treatment <- gsub("Transpiration.smooth.rate.10to14.BLUE.S", "Salt", mTSR.data.int$treatment)
mTSR.data.int$interval <- mTSR.data.int$variable
mTSR.data.int$interval <- gsub("Transpiration.smooth.rate.0to5.BLUE.C", "0.to.5", mTSR.data.int$interval)
mTSR.data.int$interval <- gsub("Transpiration.smooth.rate.0to5.BLUE.S", "0.to.5", mTSR.data.int$interval)
mTSR.data.int$interval <- gsub("Transpiration.smooth.rate.6to9.BLUE.C", "6.to.9", mTSR.data.int$interval)
mTSR.data.int$interval <- gsub("Transpiration.smooth.rate.6to9.BLUE.S", "6.to.9", mTSR.data.int$interval)
mTSR.data.int$interval <- gsub("Transpiration.smooth.rate.10to14.BLUE.C", "10.to.14", mTSR.data.int$interval)
mTSR.data.int$interval <- gsub("Transpiration.smooth.rate.10to14.BLUE.S", "10.to.14", mTSR.data.int$interval)
mTSR.data.int$interval <- factor(mTSR.data.int$interval, levels = c("0.to.5", "6.to.9", "10.to.14"))
mTSR.data.intTSR.int_violin_paired <- ggplot(data = mTSR.data.int, mapping = aes(x = treatment, y = TSR, fill = treatment))
TSR.int_violin_paired <- TSR.int_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
TSR.int_violin_paired <- TSR.int_violin_paired + geom_violindot(dots_size = 0.00, position_dots = position_dodge(0.1), flip = c(1)) + facet_wrap(~ interval)## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`TSR.int_violin_paired <- TSR.int_violin_paired + geom_point(mapping = aes(fill = treatment, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
TSR.int_violin_paired <- TSR.int_violin_paired + stat_compare_means(aes(group = treatment), label = "p.signif", method = "aov", paired = TRUE)
TSR.int_violin_paired <- TSR.int_violin_paired + theme_bw() + theme(legend.position = "none") + ylab("Transpiration (g H2O / day)") + xlab("") + scale_fill_aaas()
TSR.int_violin_paired ## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).
TSR1 <- subset(mTSR.data.int, mTSR.data.int$interval == "0.to.5")
summary(lm(TSR ~ treatment, data = TSR1))##
## Call:
## lm(formula = TSR ~ treatment, data = TSR1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.7374 -3.6604 0.3365 3.3273 18.7399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.5464 0.3641 75.65 <2e-16 ***
## treatmentSalt -10.0261 0.5155 -19.45 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.388 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4651, Adjusted R-squared: 0.4639
## F-statistic: 378.2 on 1 and 435 DF, p-value: < 2.2e-16TSR2 <- subset(mTSR.data.int, mTSR.data.int$interval == "6.to.9")
summary(lm(TSR ~ treatment, data = TSR2))##
## Call:
## lm(formula = TSR ~ treatment, data = TSR2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.333 -7.250 -0.477 6.599 43.150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.9975 0.7894 83.61 <2e-16 ***
## treatmentSalt -34.5789 1.1176 -30.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.68 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6876, Adjusted R-squared: 0.6868
## F-statistic: 957.3 on 1 and 435 DF, p-value: < 2.2e-16TSR3 <- subset(mTSR.data.int, mTSR.data.int$interval == "10.to.14")
summary(lm(TSR ~ treatment, data = TSR3))##
## Call:
## lm(formula = TSR ~ treatment, data = TSR3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.761 -11.354 -0.388 11.489 75.193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 127.141 1.395 91.13 <2e-16 ***
## treatmentSalt -74.326 1.969 -37.76 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.5 on 432 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.7674, Adjusted R-squared: 0.7669
## F-statistic: 1426 on 1 and 432 DF, p-value: < 2.2e-16colnames(TUE.data)## [1] "Genotype.ID" "Area.smooth.TUE.0to5.BLUE.C"
## [3] "Area.smooth.TUE.6to9.BLUE.C" "Area.smooth.TUE.10to14.BLUE.C"
## [5] "Area.smooth.TUE.0to5.BLUE.S" "Area.smooth.TUE.6to9.BLUE.S"
## [7] "Area.smooth.TUE.10to14.BLUE.S" "TUE.0to5.ST1"
## [9] "TUE.0to5.ST2" "TUE.0to5.ST3"
## [11] "TUE.0to5.ST4" "TUE.0to5.ST5"
## [13] "TUE.0to5.ST6" "TUE.0to5.ST7"
## [15] "TUE.6to9.ST1" "TUE.6to9.ST2"
## [17] "TUE.6to9.ST3" "TUE.6to9.ST4"
## [19] "TUE.6to9.ST5" "TUE.6to9.ST6"
## [21] "TUE.6to9.ST7" "TUE.10to14.ST1"
## [23] "TUE.10to14.ST2" "TUE.10to14.ST3"
## [25] "TUE.10to14.ST4" "TUE.10to14.ST5"
## [27] "TUE.10to14.ST6" "TUE.10to14.ST7"TUE.data.int <- TUE.data[,c(1:7)]
mTUE.data.int <- melt(TUE.data.int)## Using Genotype.ID as id variablescolnames(mTUE.data.int)[3] <- "TUE"
mTUE.data.intmTUE.data.int$treatment <- mTUE.data.int$variable
mTUE.data.int$treatment <- gsub("Area.smooth.TUE.0to5.BLUE.C", "Control", mTUE.data.int$treatment)
mTUE.data.int$treatment <- gsub("Area.smooth.TUE.0to5.BLUE.S", "Salt", mTUE.data.int$treatment)
mTUE.data.int$treatment <- gsub("Area.smooth.TUE.6to9.BLUE.C", "Control", mTUE.data.int$treatment)
mTUE.data.int$treatment <- gsub("Area.smooth.TUE.6to9.BLUE.S", "Salt", mTUE.data.int$treatment)
mTUE.data.int$treatment <- gsub("Area.smooth.TUE.10to14.BLUE.C", "Control", mTUE.data.int$treatment)
mTUE.data.int$treatment <- gsub("Area.smooth.TUE.10to14.BLUE.S", "Salt", mTUE.data.int$treatment)
mTUE.data.int$interval <- mTUE.data.int$variable
mTUE.data.int$interval <- gsub("Area.smooth.TUE.0to5.BLUE.C", "0.to.5", mTUE.data.int$interval)
mTUE.data.int$interval <- gsub("Area.smooth.TUE.0to5.BLUE.S", "0.to.5", mTUE.data.int$interval)
mTUE.data.int$interval <- gsub("Area.smooth.TUE.6to9.BLUE.C", "6.to.9", mTUE.data.int$interval)
mTUE.data.int$interval <- gsub("Area.smooth.TUE.6to9.BLUE.S", "6.to.9", mTUE.data.int$interval)
mTUE.data.int$interval <- gsub("Area.smooth.TUE.10to14.BLUE.C", "10.to.14", mTUE.data.int$interval)
mTUE.data.int$interval <- gsub("Area.smooth.TUE.10to14.BLUE.S", "10.to.14", mTUE.data.int$interval)
mTUE.data.int$interval <- factor(mTUE.data.int$interval, levels = c("0.to.5", "6.to.9", "10.to.14"))
mTUE.data.intTUE.int_violin_paired <- ggplot(data = mTUE.data.int, mapping = aes(x = treatment, y = TUE, fill = treatment))
TUE.int_violin_paired <- TUE.int_violin_paired + geom_line(mapping = aes(group = Genotype.ID), alpha = 0.3, position = position_dodge(0.1))
TUE.int_violin_paired <- TUE.int_violin_paired + geom_violindot(dots_size = 0.00, position_dots = position_dodge(0.1), flip = c(1)) + facet_wrap(~ interval)## Warning in geom_dotplot(binaxis = "y", mapping = mapping, data = data, dotsize =
## size_dots, : Ignoring unknown parameters: `flip`TUE.int_violin_paired <- TUE.int_violin_paired + geom_point(mapping = aes(fill = treatment, group = Genotype.ID), size = 1.5, shape = 21, position = position_dodge(0.1))
TUE.int_violin_paired <- TUE.int_violin_paired + stat_compare_means(aes(group = treatment), label = "p.signif", method = "aov", paired = TRUE)
TUE.int_violin_paired <- TUE.int_violin_paired + theme_bw() + theme(legend.position = "none") + ylab("TUE (pix / g H2O)") + xlab("") + scale_fill_aaas()
TUE.int_violin_paired ## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).
TUE1 <- subset(mTUE.data.int, mTUE.data.int$interval == "0.to.5")
summary(lm(TUE ~ treatment, data = TUE1))##
## Call:
## lm(formula = TUE ~ treatment, data = TUE1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.40225 -0.12325 -0.00957 0.10675 0.56307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.33339 0.01178 113.233 <2e-16 ***
## treatmentSalt 0.02311 0.01667 1.386 0.167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1743 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.004396, Adjusted R-squared: 0.002107
## F-statistic: 1.921 on 1 and 435 DF, p-value: 0.1665TUE2 <- subset(mTUE.data.int, mTUE.data.int$interval == "6.to.9")
summary(lm(TUE ~ treatment, data = TUE2))##
## Call:
## lm(formula = TUE ~ treatment, data = TUE2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64051 -0.10082 -0.00144 0.11120 0.55113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2924344 0.0117164 110.310 <2e-16 ***
## treatmentSalt 0.0006801 0.0165885 0.041 0.967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1734 on 435 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 3.864e-06, Adjusted R-squared: -0.002295
## F-statistic: 0.001681 on 1 and 435 DF, p-value: 0.9673TUE3 <- subset(mTUE.data.int, mTUE.data.int$interval == "10.to.14")
summary(lm(TUE ~ treatment, data = TUE3))##
## Call:
## lm(formula = TUE ~ treatment, data = TUE3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41180 -0.10117 -0.00716 0.09021 0.54269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.17244 0.01110 105.62 <2e-16 ***
## treatmentSalt 0.18734 0.01566 11.96 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1631 on 432 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.2488, Adjusted R-squared: 0.247
## F-statistic: 143.1 on 1 and 432 DF, p-value: < 2.2e-16pdf("Fig.Interval.data.pdf", width=10, height = 10)
plot_grid(AGR.int_violin_paired, RGR.int_violin_paired, TSR.int_violin_paired, TUE.int_violin_paired,
cols = 2, labels = "AUTO")## Warning in plot_grid(AGR.int_violin_paired, RGR.int_violin_paired,
## TSR.int_violin_paired, : Argument 'cols' is deprecated. Use 'ncol' instead.## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 6 rows containing non-finite values (`stat_ydensity()`).## Warning: Removed 6 rows containing missing values (`stat_bindot()`).## Warning: Removed 6 rows containing non-finite values (`stat_compare_means()`).## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).dev.off()## quartz_off_screen
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