coco 21 august 2019
jihad alie mtsilizah
8/19/2019
library(readr)
GMCCoffstation <- read_csv("GMCCoffstation.csv")## Parsed with column specification:
## cols(
## season = col_double(),
## ward = col_double(),
## farmer = col_character(),
## crop = col_character(),
## treat = col_character(),
## rep = col_double(),
## biomass = col_double(),
## `biomass/1000` = col_double(),
## grain = col_double(),
## `grain/1000` = col_double()
## )View(GMCCoffstation)
attach(GMCCoffstation)season=as.numeric(season)
ward=as.factor(ward)
farmer=as.factor(farmer)
treat=as.factor(treat)
rep=as.factor(rep)library(ggplot2)
library(plyr)
library(maps)##
## Attaching package: 'maps'## The following object is masked from 'package:plyr':
##
## ozonelibrary(ggalt)## Registered S3 methods overwritten by 'ggalt':
## method from
## grid.draw.absoluteGrob ggplot2
## grobHeight.absoluteGrob ggplot2
## grobWidth.absoluteGrob ggplot2
## grobX.absoluteGrob ggplot2
## grobY.absoluteGrob ggplot2library(extrafontdb)
library(MASS)
library(pscl)## Classes and Methods for R developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University
## Simon Jackman
## hurdle and zeroinfl functions by Achim Zeileislibrary(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(gridExtra)
library(repr) ### adjusting the length and width of your plot
library(beanplot)
library("devtools")## Loading required package: usethislibrary("yarrr")## Loading required package: jpeg## Loading required package: BayesFactor## Loading required package: coda## Loading required package: Matrix## ************
## Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************## Loading required package: circlize## ========================================
## circlize version 0.4.6
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: http://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
## ========================================## yarrr v0.1.5. Citation info at citation('yarrr'). Package guide at yarrr.guide()## Email me at Nathaniel.D.Phillips.is@gmail.com##
## Attaching package: 'yarrr'## The following object is masked from 'package:ggplot2':
##
## diamondslibrary(agricolae)
library(easynls)
library(MVN)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## sROC 0.1-2 loadedlibrary(lme4)## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod carlibrary(ggsignif)
library(ggpubr)## Loading required package: magrittr##
## Attaching package: 'ggpubr'## The following object is masked from 'package:plyr':
##
## mutatelibrary(tidyverse)## -- Attaching packages --------------------------------------------------------- tidyverse 1.2.1 --## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 0.8.3 v stringr 1.4.0
## v purrr 0.3.2 v forcats 0.4.0## -- Conflicts ------------------------------------------------------------ tidyverse_conflicts() --
## x psych::%+%() masks ggplot2::%+%()
## x psych::alpha() masks ggplot2::alpha()
## x dplyr::arrange() masks plyr::arrange()
## x dplyr::combine() masks gridExtra::combine()
## x purrr::compact() masks plyr::compact()
## x dplyr::count() masks plyr::count()
## x tidyr::expand() masks Matrix::expand()
## x tidyr::extract() masks magrittr::extract()
## x dplyr::failwith() masks plyr::failwith()
## x dplyr::filter() masks stats::filter()
## x dplyr::id() masks plyr::id()
## x dplyr::lag() masks stats::lag()
## x purrr::map() masks maps::map()
## x dplyr::mutate() masks ggpubr::mutate(), plyr::mutate()
## x dplyr::rename() masks plyr::rename()
## x dplyr::select() masks MASS::select()
## x purrr::set_names() masks magrittr::set_names()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()library(ggalluvial)#################density plots#################
####################treat##############
ggplot(data = GMCCoffstation, mapping = aes(x = grain/1000, fill = treat)) + geom_density(alpha = 0.5)
ggplot(data = GMCCoffstation, mapping = aes(x = biomass/1000, fill = treat)) + geom_density(alpha = 0.5)
######################ANOVA grain###############
mod<-aov(grain/1000~rep+farmer+ward+season:rep+treat+season+farmer:ward+farmer:treat:ward+season:ward,data=GMCCoffstation)
anova(mod)## Analysis of Variance Table
##
## Response: grain/1000
## Df Sum Sq Mean Sq F value Pr(>F)
## rep 1 4.287 4.2872 4.5834 0.033816 *
## farmer 20 162.584 8.1292 8.6908 < 2.2e-16 ***
## treat 5 26.772 5.3545 5.7244 6.976e-05 ***
## season 1 27.098 27.0982 28.9704 2.612e-07 ***
## rep:season 1 5.067 5.0671 5.4172 0.021208 *
## ward:season 1 9.128 9.1284 9.7591 0.002123 **
## farmer:ward:treat 100 81.343 0.8134 0.8696 0.774441
## Residuals 158 147.789 0.9354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod)## Df Sum Sq Mean Sq F value Pr(>F)
## rep 1 4.29 4.287 4.583 0.03382 *
## farmer 20 162.58 8.129 8.691 < 2e-16 ***
## treat 5 26.77 5.354 5.724 6.98e-05 ***
## season 1 27.10 27.098 28.970 2.61e-07 ***
## rep:season 1 5.07 5.067 5.417 0.02121 *
## ward:season 1 9.13 9.128 9.759 0.00212 **
## farmer:ward:treat 100 81.34 0.813 0.870 0.77444
## Residuals 158 147.79 0.935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1y<-HSD.test(mod,"treat",group = TRUE)
y## $statistics
## MSerror Df Mean CV MSD
## 0.9353752 158 2.549194 37.93937 0.5695539
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey treat 6 4.080022 0.05
##
## $means
## grain/1000 std r Min Max Q25 Q50
## COWP_INT 2.505654 1.221901 48 0.3836 6.0609 1.767800 2.54715
## JCKBN_INT 2.418983 1.008070 48 0.2537 4.9269 1.800750 2.36910
## LABLAB_INT 2.418181 1.292090 48 0.4825 6.5697 1.510825 2.39455
## PGPEA_COWP_INT 2.172323 1.112348 48 0.5073 5.1913 1.366975 1.98030
## PGPEA_INT 2.618533 1.249121 48 0.6387 6.0833 1.640575 2.35075
## SOLE_MZ 3.161487 1.525724 48 0.5355 6.6848 2.090525 2.71220
## Q75
## COWP_INT 3.162225
## JCKBN_INT 2.917725
## LABLAB_INT 3.011300
## PGPEA_COWP_INT 2.917450
## PGPEA_INT 3.269675
## SOLE_MZ 4.337925
##
## $comparison
## NULL
##
## $groups
## grain/1000 groups
## SOLE_MZ 3.161487 a
## PGPEA_INT 2.618533 ab
## COWP_INT 2.505654 b
## JCKBN_INT 2.418983 b
## LABLAB_INT 2.418181 b
## PGPEA_COWP_INT 2.172323 b
##
## attr(,"class")
## [1] "group"x<-HSD.test(mod,"season",group = TRUE)
x## $statistics
## MSerror Df Mean CV MSD
## 0.9353752 158 2.549194 37.93937 0.330285
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey season 3 3.346043 0.05
##
## $means
## grain/1000 std r Min Max Q25 Q50 Q75
## 2017 2.843977 1.366597 96 0.5073 6.6848 1.778000 2.69950 3.754375
## 2018 2.738111 1.132469 96 0.9399 6.1279 1.937725 2.52270 3.135900
## 2019 2.065493 1.172418 96 0.2537 5.1913 1.082975 1.98715 2.822875
##
## $comparison
## NULL
##
## $groups
## grain/1000 groups
## 2017 2.843977 a
## 2018 2.738111 a
## 2019 2.065493 b
##
## attr(,"class")
## [1] "group"v<-HSD.test(mod,"farmer",group = TRUE)
v## $statistics
## MSerror Df Mean CV
## 0.9353752 158 2.549194 37.93937
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey farmer 21 5.135691 0.05
##
## $means
## grain/1000 std r Min Max Q25 Q50
## E. Maresa 2.478561 1.3101922 18 0.7193 4.8348 1.350725 2.46155
## E. Matanda 2.656083 0.5783633 12 1.9428 3.8277 2.065325 2.70585
## E. Pangwa 1.964733 0.6196082 12 0.7998 2.8411 1.651825 2.03390
## E.Matanda 1.806967 0.3775486 6 1.4516 2.3965 1.540900 1.66680
## E.Pangwa 1.079467 0.6002911 6 0.5384 2.2410 0.782175 0.93520
## F. Masango 2.564500 0.4976239 12 1.8057 3.4873 2.283400 2.52735
## F.Masango 3.014800 1.1298755 6 1.7877 5.0675 2.398425 2.86675
## J.Ackchomwe 2.232250 1.0063666 6 0.7087 3.1801 1.579375 2.66785
## J.Mukandi 1.817194 0.6088046 18 0.6928 3.1896 1.472750 1.82325
## Jakuchomwe 3.018175 0.6930173 12 2.0529 4.0870 2.447650 3.13120
## L. Kambarami 2.994161 1.9116775 18 0.4278 6.5697 1.534750 2.83300
## L. Masango 2.057233 0.8364770 12 0.9399 3.9311 1.637875 1.90390
## L.Chikaka 3.431733 1.5742558 18 0.7895 6.0833 2.346600 3.30985
## L.Masango 2.594133 1.0972136 6 0.5355 3.8251 2.626550 2.81755
## M. Masango 1.864950 0.9205786 18 0.5073 4.3559 1.244450 1.59845
## M.Chiweshe 3.637050 0.7211141 18 2.0781 4.9142 3.205700 3.69995
## S. Chikauro 4.383767 1.1424939 18 1.7859 6.6848 3.948925 4.36210
## S. Phiri 2.003256 1.0100625 18 0.2537 3.8888 1.190375 2.30385
## S.Manhembe 2.161783 0.8010658 18 0.7849 3.7792 1.684750 1.83680
## S.Vambe 2.492867 1.5323083 18 0.3836 5.3343 1.031150 2.45080
## T. Matanda 1.772089 0.3872743 18 1.0686 2.5193 1.547150 1.80515
## Q75
## E. Maresa 3.237825
## E. Matanda 3.026175
## E. Pangwa 2.446125
## E.Matanda 2.037575
## E.Pangwa 1.055675
## F. Masango 2.775075
## F.Masango 3.160025
## J.Ackchomwe 2.881300
## J.Mukandi 2.190225
## Jakuchomwe 3.481000
## L. Kambarami 4.379225
## L. Masango 2.413125
## L.Chikaka 4.440850
## L.Masango 2.959050
## M. Masango 2.297050
## M.Chiweshe 4.161150
## S. Chikauro 4.985475
## S. Phiri 2.602075
## S.Manhembe 2.398525
## S.Vambe 3.030775
## T. Matanda 1.950200
##
## $comparison
## NULL
##
## $groups
## grain/1000 groups
## S. Chikauro 4.383767 a
## M.Chiweshe 3.637050 ab
## L.Chikaka 3.431733 ab
## Jakuchomwe 3.018175 bc
## F.Masango 3.014800 bcd
## L. Kambarami 2.994161 bcd
## E. Matanda 2.656083 bcd
## L.Masango 2.594133 bcd
## F. Masango 2.564500 bcd
## S.Vambe 2.492867 bcd
## E. Maresa 2.478561 bcd
## J.Ackchomwe 2.232250 bcd
## S.Manhembe 2.161783 cd
## L. Masango 2.057233 cd
## S. Phiri 2.003256 cd
## E. Pangwa 1.964733 cd
## M. Masango 1.864950 cd
## J.Mukandi 1.817194 cd
## E.Matanda 1.806967 cd
## T. Matanda 1.772089 cd
## E.Pangwa 1.079467 d
##
## attr(,"class")
## [1] "group"################bar group chart##################
bar.group(y$groups, ylim=c(0,7.5), density=4,border="blue")
bar.group(v$groups, ylim=c(0,7.5), density=4,border="blue")
#####################grain#########################
m<-ggplot(GMCCoffstation, aes(x = treat, y = grain/1000))+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("grey","grey","grey","grey","grey","grey")) + geom_smooth(method=lm)+ ylab("Grain/1000 [kg/ha]") + xlab("treat")+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
#################facet by season###################
m<-ggplot(GMCCoffstation, aes(x = treat, y = grain/1000))+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey")) + geom_smooth(method=lm)+ ylab("Grain/1000 [kg/ha]") + xlab("treat")+facet_wrap(~season)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
#################facet by ward###################
m<-ggplot(GMCCoffstation, aes(x = treat, y = grain/1000))+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey")) + geom_smooth(method=lm)+ ylab("Grain/1000 [kg/ha]") + xlab("treat")+facet_wrap(~ward)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
############VIOLIN PLOTS EMBEDDED WITH BOX PLOTS#########
######################grain##################
theme_set(theme_gray(base_size =15))
m <- ggplot(data=GMCCoffstation,aes(x=treat, y=grain/1000))
m + geom_violin(size=1.3,shape=8) + geom_boxplot(width=.2, outlier.size=0,fill=c("grey","grey","grey", "grey","grey", "grey" ))+ylab("Grain [t/ha]") + xlab("treatment")+ theme(axis.title = element_text(size=20, face="bold"), axis.text.x = element_text(size=12, face="bold", angle = 90, hjust = 1), axis.text.y = element_text(size=16, face="bold"))+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: shape## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
######################ANOVA biomass###############
mod<-aov(biomass/1000~rep+farmer+ward+season:rep+treat+season+farmer:ward+farmer:treat:ward+season:ward,data=GMCCoffstation)
anova(mod)## Analysis of Variance Table
##
## Response: biomass/1000
## Df Sum Sq Mean Sq F value Pr(>F)
## rep 1 0.384 0.3836 0.4598 0.498688
## farmer 20 140.037 7.0019 8.3944 2.365e-16 ***
## treat 5 14.188 2.8377 3.4020 0.006021 **
## season 1 20.850 20.8501 24.9969 1.511e-06 ***
## rep:season 1 0.942 0.9421 1.1295 0.289514
## ward:season 1 0.254 0.2545 0.3051 0.581497
## farmer:ward:treat 100 48.821 0.4882 0.5853 0.997954
## Residuals 158 131.789 0.8341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mod)## Df Sum Sq Mean Sq F value Pr(>F)
## rep 1 0.38 0.384 0.460 0.49869
## farmer 20 140.04 7.002 8.394 2.36e-16 ***
## treat 5 14.19 2.838 3.402 0.00602 **
## season 1 20.85 20.850 24.997 1.51e-06 ***
## rep:season 1 0.94 0.942 1.129 0.28951
## ward:season 1 0.25 0.254 0.305 0.58150
## farmer:ward:treat 100 48.82 0.488 0.585 0.99795
## Residuals 158 131.79 0.834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1y<-HSD.test(mod,"treat",group = TRUE)
y## $statistics
## MSerror Df Mean CV MSD
## 0.8341075 158 1.902923 47.99432 0.5378398
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey treat 6 4.080022 0.05
##
## $means
## biomass/1000 std r Min Max Q25 Q50
## COWP_INT 1.837610 1.0866014 48 0.2418 4.9952 1.058000 1.46705
## JCKBN_INT 1.824623 0.9802905 48 0.1403 4.3502 1.128125 1.87145
## LABLAB_INT 1.842102 1.0706991 48 0.2634 5.2654 1.069000 1.80910
## PGPEA_COWP_INT 1.677294 1.0563908 48 0.4405 5.8978 0.878400 1.51545
## PGPEA_INT 1.855173 1.0519348 48 0.0832 6.1301 1.094125 1.86805
## SOLE_MZ 2.380735 1.3375108 48 0.3843 5.5742 1.464375 1.81830
## Q75
## COWP_INT 2.421375
## JCKBN_INT 2.310900
## LABLAB_INT 2.416300
## PGPEA_COWP_INT 2.120300
## PGPEA_INT 2.344275
## SOLE_MZ 3.340725
##
## $comparison
## NULL
##
## $groups
## biomass/1000 groups
## SOLE_MZ 2.380735 a
## PGPEA_INT 1.855173 ab
## LABLAB_INT 1.842102 b
## COWP_INT 1.837610 b
## JCKBN_INT 1.824623 b
## PGPEA_COWP_INT 1.677294 b
##
## attr(,"class")
## [1] "group"x<-HSD.test(mod,"season",group = TRUE)
x## $statistics
## MSerror Df Mean CV MSD
## 0.8341075 158 1.902923 47.99432 0.3118939
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey season 3 3.346043 0.05
##
## $means
## biomass/1000 std r Min Max Q25 Q50 Q75
## 2017 2.084092 1.216637 96 0.0832 6.1301 1.273125 1.85950 2.405800
## 2018 1.893193 1.084096 96 0.3843 5.5742 0.966925 1.75705 2.582075
## 2019 1.731484 1.020914 96 0.1403 5.1224 0.817450 1.67530 2.290725
##
## $comparison
## NULL
##
## $groups
## biomass/1000 groups
## 2017 2.084092 a
## 2018 1.893193 ab
## 2019 1.731484 b
##
## attr(,"class")
## [1] "group"v<-HSD.test(mod,"farmer",group = TRUE)
v## $statistics
## MSerror Df Mean CV
## 0.8341075 158 1.902923 47.99432
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey farmer 21 5.135691 0.05
##
## $means
## biomass/1000 std r Min Max Q25 Q50
## E. Maresa 1.971367 0.9920597 18 0.4808 3.8311 1.396850 1.99995
## E. Matanda 1.613258 0.5156470 12 1.0744 2.5788 1.270200 1.41170
## E. Pangwa 0.998725 0.3695461 12 0.4405 1.7128 0.736975 0.97155
## E.Matanda 2.048550 0.3256923 6 1.6839 2.4702 1.850450 1.93365
## E.Pangwa 0.777800 0.5235298 6 0.4171 1.8161 0.486125 0.60945
## F. Masango 1.516817 0.7424196 12 0.6844 2.7446 0.865600 1.35815
## F.Masango 2.310667 0.7456092 6 1.4317 3.2627 1.754475 2.21165
## J.Ackchomwe 2.956167 0.6949737 6 2.0057 3.7003 2.440575 3.27630
## J.Mukandi 1.241661 0.6919279 18 0.0832 2.5798 0.706275 1.30670
## Jakuchomwe 2.376500 0.8830212 12 0.2589 3.6919 2.156100 2.52015
## L. Kambarami 2.153700 1.4624386 18 0.5905 5.5742 1.085350 1.70740
## L. Masango 1.149950 0.5310115 12 0.5066 2.2907 0.773600 1.07645
## L.Chikaka 2.506017 1.1239497 18 1.2750 5.4052 1.654950 2.26560
## L.Masango 3.073267 0.8677583 6 2.2610 4.3242 2.537825 2.67030
## M. Masango 1.510161 0.8040185 18 0.4743 3.5882 0.899650 1.48950
## M.Chiweshe 2.436094 0.8971487 18 1.5590 4.6560 1.906375 2.03295
## S. Chikauro 3.627361 1.5377831 18 1.3183 6.1301 2.469775 3.28490
## S. Phiri 1.304128 0.8274181 18 0.1403 3.4984 0.754650 1.33900
## S.Manhembe 2.104378 0.6603780 18 0.9728 3.5676 1.735725 2.07055
## S.Vambe 1.464911 0.8229369 18 0.3843 3.3962 0.737150 1.45605
## T. Matanda 1.301339 0.6035300 18 0.5545 2.6747 0.776675 1.26135
## Q75
## E. Maresa 2.524075
## E. Matanda 1.818500
## E. Pangwa 1.188600
## E.Matanda 2.318800
## E.Pangwa 0.729625
## F. Masango 2.099500
## F.Masango 2.911075
## J.Ackchomwe 3.306375
## J.Mukandi 1.611800
## Jakuchomwe 2.805000
## L. Kambarami 2.952775
## L. Masango 1.398925
## L.Chikaka 2.887150
## L.Masango 3.682675
## M. Masango 1.886325
## M.Chiweshe 2.418300
## S. Chikauro 5.090600
## S. Phiri 1.615425
## S.Manhembe 2.448375
## S.Vambe 1.942975
## T. Matanda 1.670650
##
## $comparison
## NULL
##
## $groups
## biomass/1000 groups
## S. Chikauro 3.627361 a
## L.Masango 3.073267 ab
## J.Ackchomwe 2.956167 abc
## L.Chikaka 2.506017 bc
## M.Chiweshe 2.436094 bc
## Jakuchomwe 2.376500 bcd
## F.Masango 2.310667 bcde
## L. Kambarami 2.153700 bcde
## S.Manhembe 2.104378 bcde
## E.Matanda 2.048550 bcde
## E. Maresa 1.971367 bcde
## E. Matanda 1.613258 bcde
## F. Masango 1.516817 bcde
## M. Masango 1.510161 bcde
## S.Vambe 1.464911 cde
## S. Phiri 1.304128 de
## T. Matanda 1.301339 de
## J.Mukandi 1.241661 de
## L. Masango 1.149950 de
## E. Pangwa 0.998725 e
## E.Pangwa 0.777800 e
##
## attr(,"class")
## [1] "group"################bar group chart##################
bar.group(y$groups, ylim=c(0,7.5), density=4,border="blue")
bar.group(v$groups, ylim=c(0,7.5), density=4,border="blue")
#####################biomass#########################
m<-ggplot(GMCCoffstation, aes(x = treat, y = biomass/1000))+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("grey","grey","grey","grey","grey","grey")) + geom_smooth(method=lm)+ ylab("Biomass/1000 [kg/ha]") + xlab("treat")+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
#################facet by season###################
m<-ggplot(GMCCoffstation, aes(x = treat, y = biomass/1000))+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey")) + geom_smooth(method=lm)+ ylab("Biomass/1000 [kg/ha]") + xlab("treat")+facet_wrap(~season)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
#################facet by ward###################
m<-ggplot(GMCCoffstation, aes(x = treat, y = biomass/1000))+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey","grey")) + geom_smooth(method=lm)+ ylab("Biomass/1000 [kg/ha]") + xlab("treat")+facet_wrap(~ward)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
##########new ones box plots biomass##############
ggplot(GMCCoffstation, aes(x=treat, y=biomass/1000)) + geom_boxplot() + geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Biomass/1000 [kg/ha]") + xlab("treat")+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7)) ## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
##########reordered biomass#############
ggplot(GMCCoffstation, aes(x=reorder(treat, biomass/1000, FUN=median), y=biomass/1000)) + geom_boxplot()+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Biomass/1000 [kg/ha]") + xlab("treat")+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7)) ## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
#############################facets biomass################
##########reordered#############
ggplot(GMCCoffstation, aes(x=reorder(treat, biomass/1000, FUN=median), y=biomass/1000)) + geom_boxplot()+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Biomass/1000 [kg/ha]") + xlab("treat")+facet_wrap(~season)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
################################
##########reordered#############
ggplot(GMCCoffstation, aes(x=reorder(treat, biomass/1000, FUN=median), y=biomass/1000)) + geom_boxplot()+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Biomass/1000 [kg/ha]") + xlab("treat")+facet_wrap(~ward)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
Gmcc_summary <- GMCCoffstation %>% # the names of the new data frame and the data frame to be summarised
group_by(treat) %>% # the grouping variable
summarise(mean_B = mean(biomass/1000), # calculates the mean of each group
sd_B = sd(biomass/1000), # calculates the standard deviation of each group
n_B = n(), # calculates the sample size per group
SE_B = sd(biomass/1000)/sqrt(n())) # calculates the standard error of each group
########################
GMCCplot <- ggplot(Gmcc_summary, aes(treat, mean_B)) +
geom_col() +
geom_errorbar(aes(ymin = mean_B - sd_B, ymax = mean_B + sd_B), width=0.2)
GMCCplot + labs(y="Biomass/1000 ± s.d.", x = "Treat") + theme_classic()
Gmcc_summary <- GMCCoffstation %>% # the names of the new data frame and the data frame to be summarised
group_by(treat) %>% # the grouping variable
summarise(mean_G = mean(grain/1000), # calculates the mean of each group
sd_G = sd(grain/1000), # calculates the standard deviation of each group
n_G = n(), # calculates the sample size per group
SE_G = sd(grain/1000)/sqrt(n())) # calculates the standard error of each group
########################
GMCCplot <- ggplot(Gmcc_summary, aes(treat, mean_G)) +
geom_col() +
geom_errorbar(aes(ymin = mean_G - sd_G, ymax = mean_G + sd_G), width=0.2)
GMCCplot + labs(y="Grain/1000 ± s.d.", x = "Treat") + theme_classic()
##########new ones box plots grain##############
ggplot(GMCCoffstation, aes(x=treat, y=grain/1000)) + geom_boxplot() + geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Grain/1000 [kg/ha]") + xlab("treat")+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
##########reordered grain#############
ggplot(GMCCoffstation, aes(x=reorder(treat, grain/1000, FUN=median), y=grain/1000)) + geom_boxplot()+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Grain/1000 [kg/ha]") + xlab("treat")+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
#############################facets grain################
##########reordered#############
ggplot(GMCCoffstation, aes(x=reorder(treat, grain/1000, FUN=median), y=grain/1000)) + geom_boxplot()+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Grain/1000 [kg/ha]") + xlab("treat")+facet_wrap(~season)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
################################
##########reordered#############
ggplot(GMCCoffstation, aes(x=reorder(treat, grain/1000, FUN=median), y=grain/1000)) + geom_boxplot()+ geom_boxplot(size=0.8,varwidth =FALSE,outlier.colour = "red",outlier.shape = 2, shape=6,fill=c("gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold","gold")) + geom_smooth(method=lm)+ ylab("Grain/1000 [kg/ha]") + xlab("treat")+facet_wrap(~ward)+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
####################VIOLIN PLOTS EMBEDDED WITH BOXPLOTS####################
theme_set(theme_gray(base_size =15))
m <- ggplot(data=GMCCoffstation,aes(x=treat, y=biomass/1000))
m + geom_violin(size=1.3,shape=8) + geom_boxplot(width=.2, outlier.size=0,fill=c("grey","grey","grey", "grey","grey", "grey"))+ylab("Biomass [t/ha]") + xlab("treatment")+ theme(axis.title = element_text(size=20, face="bold"), axis.text.x = element_text(size=12, face="bold", angle = 90, hjust = 1), axis.text.y = element_text(size=16, face="bold"))+
stat_summary(fun.y=mean, geom="point", shape=10, size=2, color="blue", fill="red")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: shape## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
################# bar graphs biomass###################
m<-ggplot(GMCCoffstation, aes(x = treat, y = biomass/1000))+ geom_bar(stat="identity", width=0.5, position = "dodge")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 1.8, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 1.8, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 1.7, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 1.9, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 2.4, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
################# bar graphs grain###################
m<-ggplot(GMCCoffstation, aes(x = treat, y = grain/1000))+ geom_bar(stat="identity", width=0.5, position = "dodge")+
stat_summary(
fun.data = GMCCoffstation,
geom = "text",
hjust = 0.5,
vjust = 0.9,
size=3,
face="bold"
)+ theme_classic(base_size = 14)+ ylim(100, 7000)+ theme(axis.text.x = element_text(face="bold", angle = 75, hjust = 1, color="black",
size=6),
axis.text.y = element_text(face="bold", color="black",
size=10))+geom_text(data =GMCCoffstation, x = 1, y = 2.5, label = "b")+geom_text(data =GMCCoffstation, x = 2, y = 2.4, label = "b")+geom_text(data =GMCCoffstation, x = 3, y = 2.4, label = "b")+ geom_text(data =GMCCoffstation, x = 4, y = 2.2, label = "b")+geom_text(data =GMCCoffstation, x = 5, y = 2.6, label = "ab")+geom_text(data =GMCCoffstation, x = 6, y = 3.2, label = "a")+
ylim(100,7000) + scale_y_continuous(breaks = c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7))## Warning: Ignoring unknown parameters: face## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.m## Warning: Computation failed in `stat_summary()`:
## object 'fun.data' of mode 'function' was not found
#########aaluvials grain###########
ggplot(GMCCoffstation, aes(kg = grain/1000, axis1 = treat, axis2 = season)) +
geom_alluvium(aes(fill = treat), color = "black") + geom_stratum() 
########Add text##############
ggplot(GMCCoffstation, aes(kg = grain/1000, axis1 = treat, axis2 = season)) +
geom_alluvium(aes(fill = treat), color = "black") + geom_stratum() +
geom_text(stat = "stratum", label.strata = TRUE) +
theme(legend.position = "true",
axis.text = element_text(size=14))
#########aaluvials biomass###########
ggplot(GMCCoffstation, aes(kg = biomass/1000, axis1 = treat, axis2 = season)) +
geom_alluvium(aes(fill = treat), color = "black") + geom_stratum()
########Add text##############
ggplot(GMCCoffstation, aes(kg = biomass/1000, axis1 = treat, axis2 = season)) +
geom_alluvium(aes(fill = treat), color = "black") + geom_stratum() +
geom_text(stat = "stratum", label.strata = TRUE) +
theme(legend.position = "true",
axis.text = element_text(size=14))
ggplot(data = GMCCoffstation, mapping = aes(x = grain/1000, fill = treat)) + geom_density(alpha = 0.5)
mod<-aov(grain/1000~rep+farmer+ward+season:rep+treat+season+farmer:ward+farmer:treat:ward+season:ward,data=GMCCoffstation) anova(mod) ```