Appendix 14
C. Prévost & A. Suryanarayan
2023-03-24
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = 'C:\\Users\\camie\\Dropbox\\Dropbox\\Beg ar Loued\\Article AS_CP\\BAL R data and scripts\\clean_R_scripts\\data_for_figures')##This R Markdown document provides the code for all figures in Prevost and Suryanarayan et al. to ensure transparency and reproducibility of data.
INSTRUCTIONS
#1) Please ensure you have downloaded the latest version of R Studio.
#2) Download data files from the data repository.
#3) Change file pathway to the name of the folder where you have saved the data files.
#WARNING
#Order for publication of supplementary info is different than for the code of the plots
##publication order:
#Appendix 5.A
#Appendix 5.B
#Appendix 5.C
#Appendix 5.D
#Appendix 6.A
#Appendix 6.B
#Appendix 6.C
#Appendix 6.D
#Appendix 7.A
#Appendix 7.B
#Appendix 7.C
#Appendix 7.D
#Appendix 8.A
#Appendix 8.B
#Appendix 9.A
#Appendix 9.B
#Appendix 10
#Appendix 11
#Appendix 12
##code order :
#Appendix 10
#Appendix 12
#Appendix 11
#Appendix 5.A
#Appendix 7.D
#Appendix 6.D
#Appendix 5.D
#Appendix 7.B
#Appendix 9.A
#Appendix 9.B
#Appendix 8.A
#Appendix 8.B
#Appendix 5.C
#Appendix 6.C
#Appendix 7.C
#Appendix 5.B
#Appendix 6.B
#Appendix 6.A
#Appendix 7.A
Prepare Global environment
#Load packages
library(readr)
library(readxl)
library(ade4)
library(ggplot2)
library(dplyr)##
## Attachement du package : 'dplyr'## Les objets suivants sont masqués depuis 'package:stats':
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## filter, lag## Les objets suivants sont masqués depuis 'package:base':
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## intersect, setdiff, setequal, unionlibrary(plyr)## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attachement du package : 'plyr'## Les objets suivants sont masqués depuis 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarizelibrary(ggpubr)##
## Attachement du package : 'ggpubr'## L'objet suivant est masqué depuis 'package:plyr':
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## mutatelibrary(reshape2)
library(RColorBrewer)
library(httpuv)
library(caTools)
library(tidyr)##
## Attachement du package : 'tidyr'## L'objet suivant est masqué depuis 'package:reshape2':
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## smithslibrary(gridExtra)##
## Attachement du package : 'gridExtra'## L'objet suivant est masqué depuis 'package:dplyr':
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## combinelibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
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## ✖ purrr::compact() masks plyr::compact()
## ✖ plyr::count() masks dplyr::count()
## ✖ plyr::desc() masks dplyr::desc()
## ✖ plyr::failwith() masks dplyr::failwith()
## ✖ dplyr::filter() masks stats::filter()
## ✖ plyr::id() masks dplyr::id()
## ✖ dplyr::lag() masks stats::lag()
## ✖ ggpubr::mutate() masks plyr::mutate(), dplyr::mutate()
## ✖ plyr::rename() masks dplyr::rename()
## ✖ plyr::summarise() masks dplyr::summarise()
## ✖ plyr::summarize() masks dplyr::summarize()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsSupplementary for appendices 10-12
#Load data
Pathway <- "C:/Users/camie/Dropbox/Dropbox/Beg ar Loued/Article AS_CP/BAL R data and scripts/clean_R_scripts/data_for_figures"
setwd(Pathway)
dfA <- read_excel("data_app14.xls", sheet = "archeo2")
dfB <- read_excel("data_app14.xls", sheet = "archeo")## New names:
## • `` -> `...1`dfC <- read_excel("data_app14.xls", sheet = "ref")#Sorting data and verification
dfA_sorted <- arrange(dfA, ech)
str(dfA_sorted)## tibble [204 × 5] (S3: tbl_df/tbl/data.frame)
## $ mol : chr [1:204] "E34" "E36" "E38" "E40" ...
## $ qt : num [1:204] 0 0 0 0 100 ...
## $ id : chr [1:204] "Beeswax (potential)" "Beeswax (potential)" "Beeswax (potential)" "Beeswax (potential)" ...
## $ TLCpc: num [1:204] 6.31 6.31 6.31 6.31 6.31 ...
## $ ech : chr [1:204] "MR6506Re" "MR6506Re" "MR6506Re" "MR6506Re" ...plot(dfA_sorted)
dfB_sorted <- arrange(dfB, ech)
dfB_sorted$part = factor(dfB_sorted$part, levels = c("C26:0", "C24:0", "C22:0", "C20:0", "C18:0", "C16:0", "C14:0", "C12:0"))
str(dfB_sorted)## tibble [832 × 6] (S3: tbl_df/tbl/data.frame)
## $ ...1 : num [1:832] 1 2 3 4 5 6 7 8 1 2 ...
## $ ech : chr [1:832] "MR6506T" "MR6506T" "MR6506T" "MR6506T" ...
## $ mol : chr [1:832] "E34" "E34" "E34" "E34" ...
## $ part : Factor w/ 8 levels "C26:0","C24:0",..: 8 7 6 5 4 3 2 1 8 7 ...
## $ QT : num [1:832] 0 0 0 0 0 0 0 0 0 0 ...
## $ QT(MR): num [1:832] 0 0 0 0 0 0 0 0 0 0 ...plot(dfB_sorted)
dfC_sorted <- arrange(dfC, ech)
str(dfC_sorted)## tibble [60 × 3] (S3: tbl_df/tbl/data.frame)
## $ ech: chr [1:60] "Bercy; 4600-3000 BCE" "Bercy; 4600-3000 BCE" "Bercy; 4600-3000 BCE" "Bercy; 4600-3000 BCE" ...
## $ mol: chr [1:60] "E34" "E36" "E38" "E40" ...
## $ qt : num [1:60] 0 0 0 65.4 51.9 ...plot(dfC_sorted)
Appendix 10
n1A<-ggplot(dfA_sorted, aes (x=mol, y=qt, fill="Esters"))+
geom_bar(stat= "identity", show.legend = FALSE, width = 0.90)+
scale_fill_manual(values=c("#534b62"))+
facet_wrap(~ech, ncol=6, scales = "free") +
labs(title = "Ester profiles of archaeological samples", y = "Intensity")+
xlab("")+
theme_minimal()
leg<-element_text(color = "black", size = 6, angle=90)
APP10<-n1A+theme(axis.text.x =leg, panel.grid.minor = element_line(colour="white"))
APP10
Appendix 12
n1B<-ggplot(dfB_sorted, aes (x=mol, y=QT, fill=part))+
geom_bar(stat= "identity", position="fill", show.legend = TRUE, width = 0.90)+
facet_wrap(~ech, ncol=6) +
labs(title = "Percent composition of wax ester profiles", y = "Percentage")+
guides(fill = guide_legend(title = "Molecules"))+
xlab("") +
scale_fill_brewer(palette = "Set2")+
theme_minimal()
leg<-element_text(color = "black", size = 6, angle=90)
APP12<-n1B+theme(axis.text.x =leg, panel.grid.minor = element_line(colour="white"))
APP12## Warning: Removed 346 rows containing missing values (`geom_bar()`).
Appendix 11
n1C<-ggplot(dfC_sorted, aes (x=mol, y=qt, fill="C16:0"))+
geom_bar(stat= "identity", show.legend = TRUE, width = 0.90)+
facet_wrap(~ech, ncol=6,labeller = label_wrap_gen(15)) +
labs(title = "Published ester profiles of archaeological and reference beeswaxes", y = "Intensity")+
xlab("")+
scale_fill_manual(values=c("#efca00"))+
guides(fill = guide_legend(title = "Molecules"))+
theme_minimal()
leg<-element_text(color = "black", size = 6, angle=90)
APP11<-n1C+theme(axis.text.x =leg, panel.grid.minor = element_line(colour="white"))
APP11
Supplementary for appendices 5-9
#Load data and verification
Pathway <- "C:/Users/camie/Dropbox/Dropbox/Beg ar Loued/Article AS_CP/BAL R data and scripts/clean_R_scripts/data_for_figures"
setwd(Pathway)
dt <- droplevels(read_excel("data_app14_2.xlsx"))#Convert variables to factors
dt$new_chrono<- factor(dt$new_chrono,
levels = c("FN","EBA1", "EBA(1&2)","EBA2", "EBA3","Undet."))
dt$Rim_Base_body <- factor(dt$Rim_Base_body)
dt$Sample_type <- factor(dt$Sample_type,
levels = c("Charred residue", "Slip", "Absorbed residue"))
dt$Residue_type<- factor(dt$Residue_type,
levels = c("Internal residue","Internal slip","Absorbed residue", "External slip", "External residue"))#Convert variables to numeric values
dt$Thickness_max_mm <- as.numeric(dt$Thickness_max_mm)
dt$Thickness_min_mm <- as.numeric(dt$Thickness_min_mm)
dt$Av_thickness<- as.numeric(dt$Av_thickness)Subsetting data
#Create separate groups for all samples with TLE superior to 5 ug/g
dt_lip <- droplevels(subset(dt, TLE >= 5))
summary(dt_lip$TLE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 16.16 63.80 226.59 217.10 4368.00#Subset absorbed residues
absorbedr <- droplevels(subset (dt,Sample_type == "Absorbed residue"))
summary(absorbedr$TLE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.93 26.92 106.79 92.10 1331.30#Create separate groups for absorbed residue samples with TLE superior to 5 ug/g
abs_lip <- droplevels(subset(absorbedr, TLE>= 5))
summary(abs_lip$TLE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 15.30 53.25 140.02 138.09 1331.30#Subset SLIPS and subset external and internal slips
slip <- droplevels(subset(dt, Sample_type == "Slip"))
slip_ext <- droplevels(subset(slip, Residue_type == "External slip"))
slip_int <- droplevels(subset(slip, Residue_type == "Internal slip")) #Create separate groups for charred residue samples with TLE superior to 5 ug/g
slip_lip <- droplevels(subset(slip, TLE >= 5))#Subset charred residues and subset external and internal charred residues
residue <- droplevels(subset (dt, Sample_type == "Charred residue"))
residue_ext <- droplevels(subset(residue, Residue_type == "External residue"))
residue_int <- droplevels(subset(residue, Residue_type == "Internal residue")) #Create separate groups for charred residue samples with TLE superior to 5 ug/g
residue_lip <- droplevels (subset(residue, TLE >= 5))Creating boxplots for comparisons
Appendix 5.A
#Boxplot of absorbed vs. charred vs. slips
#Removing outliers in order to plot without a log scale
#Remove samples with TLE above 1000 ug/g
dt_minushighlip <- subset(dt, TLE < 1000)
TLEn<-as.numeric(dt_minushighlip$TLE)#Plotting
APP5.A <-
ggplot(data = dt_minushighlip,
aes(x = Sample_type, y = TLEn, colour = Sample_type)) +
geom_boxplot(aes(x=Sample_type, y=TLEn),
position = position_dodge(0.5), width = .2, outlier.shape = NA) +
labs(x = "Sample type",
y = "TLE (continous scale ; values > 5µg.g-1)",
colour = "Sample type",
caption = "Values > 1000µg.g-1 not inclued") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10(breaks = c(5, 25, 100, 200, 500, 1000)) +
scale_colour_manual(values = c("#1b9e77", "#d95f02", "#984ea3")) +
geom_jitter(aes(y = TLEn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt_minushighlip$Sample_type),
"\n(N=",table(dt_minushighlip$Sample_type),")",sep="")) +
theme_classic(base_size = 10) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP5.A## Warning: Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis## Warning: Removed 13 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 13 rows containing non-finite values (`stat_summary()`).
Appendix 7.D
#Create boxplot comparing TLEs of samples that have multiple residue types
#Create subset paired data
paireddata <- droplevels(subset(dt, Paired >= 1))#Subset paired data with values above 5 ug/g only
paireddata_lip <- subset(paireddata, TLE> 5)
TLEn<-as.numeric(paireddata$TLE)#Create boxplots comparing TLEs of rim, body and base of vessels (absorbed residues)
presentstudy <- droplevels(subset(dt, Rim_Base_body_3 >= 1))
TLEn<-as.numeric(presentstudy$TLE)#Order variables
presentstudy$Rim_Base_body_2<-factor(presentstudy$Rim_Base_body_2, levels = c("Rim", "Body", "Base"))#Heat marker plots
#Organise data
presentstudy <- droplevels(subset(dt, Rim_Base_body_3 >= 1))
presentstudy$Rim_Base_body_2<-factor(presentstudy$Rim_Base_body_2, levels = c("Rim", "Body", "Base"))
ourstudysherdthickness <- droplevels(subset(presentstudy, Av_thickness >= 1 ))
ourstudysherdthickness$Presence_heat_marker<- factor(ourstudysherdthickness$Heat)
Av_thicknessn<-as.numeric(ourstudysherdthickness$Av_thickness)#Plotting
AAP7.D <-
ggplot(data = ourstudysherdthickness,
aes(x = Rim_Base_body_2, y = Av_thicknessn)) +
labs(x = "Fragment type",
y = "Average sherd thickness (log scale ; all samples of the present study)",
colour = "Presence of heat marker") +
scale_y_log10(breaks = c(1, 5, 7, 10)) +
scale_colour_manual(values = c("grey", "red")) +
geom_point(position=position_jitterdodge
(jitter.width= 0.2, dodge.width = 0.3),
aes(y = Av_thicknessn, color=factor(Heat))) +
scale_x_discrete(labels =paste(levels(ourstudysherdthickness$Rim_Base_body_2), "\n(N=",table(ourstudysherdthickness$Rim_Base_body_2),")",sep="")) +
theme_classic(base_size = 10) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
AAP7.D
Appendix 6.D
#Organise data
presentstudy$Residue_type<-factor(presentstudy$Residue_type)
ourstudysherdthickness <- droplevels(subset(presentstudy, Av_thickness >= 1 ))
ourstudysherdthickness$Presence_heat_marker<- factor(ourstudysherdthickness$Heat)
Av_thicknessn<-as.numeric(ourstudysherdthickness$Av_thickness)#Plotting
APP6.D <-
ggplot(data = ourstudysherdthickness,
aes(x = Residue_type, y = Av_thicknessn)) +
labs(x = "Residue type",
y = "Average sherd thickness (log scale ; all samples of the present study)",
colour = "Presence of heat marker") +
scale_y_log10(breaks = c(1, 5, 7, 10)) +
geom_point(position=position_jitterdodge(jitter.width= 0.2, dodge.width = 0.3),aes(y = Av_thicknessn, color=factor(Heat))) +
scale_colour_manual(values = c("grey", "red")) +
scale_x_discrete(labels =paste(levels(ourstudysherdthickness$Residue_type),
"\n(N=",table(ourstudysherdthickness$Residue_type),")",sep="")) +
theme_classic(base_size = 10) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP6.D
Appendix 5.D
APP5.D <- ggplot(data = ourstudysherdthickness,
aes(x = Sample_type, y = Av_thicknessn)) +
labs(x = "Heat markers", y = "Average sherd thickness (log scale ; all samples of the present study)", colour = "Presence of heat marker") +
scale_y_log10(breaks = c(1, 5, 7, 10)) +
scale_colour_manual(values = c("grey", "red")) +
geom_point(position=position_jitterdodge(jitter.width= 0.2, dodge.width = 0.3),aes(y = Av_thicknessn, color=factor(Heat))) +
scale_x_discrete(labels = paste(levels(ourstudysherdthickness$Sample_type),"\n(N=",table(ourstudysherdthickness$Sample_type),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP5.D
Appendix 7.B
#Morpho vs TAG
#Order variables
presentstudy <- droplevels(subset(dt, Rim_Base_body_3 >= 1))
presentstudy$Rim_Base_body_2<- factor(presentstudy$Rim_Base_body_2, levels = c("Rim","Body", "Base"))
TAGn<-as.numeric(presentstudy$`TAG%`)#Plotting
APP7.B <-
ggplot(data = presentstudy,
aes(x = Rim_Base_body_2, y = TAGn, colour=Rim_Base_body_2)) +
geom_boxplot(aes(x=Rim_Base_body_2, y=TAGn),
position = position_dodge(0.5), width = .2, outlier.shape = NA) +
labs(x = "Fragment type",
y = "TAG% (log scale ; all samples)",
colour = "Residues type")+
scale_colour_manual(values = c("#2b5be2", "#186d4d", "#db2297")) +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="red")+
scale_y_log10(breaks = c(1, 5, 25, 50, 100, 200, 500, 1000)) +
geom_jitter(aes(y = TAGn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(presentstudy$Rim_Base_body_2),
"\n(N=",table(presentstudy$Rim_Base_body_2),")",sep="")) +
theme_classic(base_size = 10) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP7.B## Warning: Removed 87 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 87 rows containing non-finite values (`stat_summary()`).## Warning: Removed 87 rows containing missing values (`geom_point()`).
Appendix 9.A
#Create boxplots comparing TLEs of rim, body and base of vessels (charred residues)
#Exploratory plots comparing time period and TLE
#Order variables
thisstudyresidues <- droplevels(subset(residue, Study == "present"))
summary(thisstudyresidues$Rim_Base_body)## Base-External residue Base-Internal residue Body-External residue
## 1 3 5
## Body-Internal residue Rim-External residue Rim-Internal residue
## 16 1 1thisstudyresidues$Rim_Base_body<- factor(thisstudyresidues$Rim_Base_body,
levels = c("Rim-Internal residue",
"Body-Internal residue",
"Base-Internal residue",
"Rim-External residue",
"Body-External residue",
"Base-External residue"))
TLEn<-as.numeric(thisstudyresidues$TLE)
TLEn<-as.numeric(dt$TLE)#Plotting
##colors indications
#FN=#b2a153
#FNBA=#9d7338
#EBA=#99520d
#MBA=#705230
#U=#999691
APP9.A <- ggplot(data = dt,
aes(x = new_chrono, y = TLEn, colour=new_chrono)) +
geom_boxplot(aes(x=new_chrono, y=TLEn), position = position_dodge(0.5), width = .2, outlier.shape = NA) +
xlab("Chronological period") + ylab("TLE (log scale ; all samples)") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10(breaks = c(1, 5, 25, 50, 100, 200, 500, 2000)) +
scale_colour_manual(values = c("#c5ba85", "#b2a153","#9d7338","#705230", "#463014", "#999691")) +
labs(color = "Chronology")+
geom_jitter(aes(y = TLEn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt$new_chrono),"\n(N=",table(dt$new_chrono),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP9.A## Warning: Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 15 rows containing non-finite values (`stat_summary()`).## Warning: Removed 2 rows containing missing values (`geom_point()`).
Appendix 9.B
#Exploratory plots comparing time period and TAG%
#Order variables
TAGn<-as.numeric(dt$`TAG%`)#Plotting
APP9.B <- ggplot(data = dt,
aes(x = new_chrono, y = TAGn, colour=new_chrono)) +
geom_boxplot(aes(x=new_chrono, y=TAGn), position = position_dodge(0.5), width = .2, outlier.shape = NA) +
xlab("Chronological period") + ylab("TAG% (log scale ; all samples)") +
labs(color = "Chronology")+
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10(breaks = c(1, 5, 25, 50, 100, 200, 500, 2000)) +
scale_colour_manual(values = c("#c5ba85", "#b2a153","#9d7338","#705230", "#463014", "#999691")) +
geom_jitter(aes(y = TAGn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt$new_chrono),"\n(N=",table(dt$new_chrono),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP9.B## Warning: Removed 104 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 104 rows containing non-finite values (`stat_summary()`).## Warning: Removed 104 rows containing missing values (`geom_point()`).
Appendix 8.A
#Comparing excavation years and TLE
#Order variables
TLEn<-as.numeric(dt$TLE)
dt$Mission <- factor(dt$Mission)#Plotting
APP8.A <- ggplot(data = dt,
aes(x = Mission, y = TLEn)) +
geom_boxplot(aes(x=Mission, y=TLEn), position = position_dodge(0.5), width = .2, outlier.shape = NA) +
xlab("Year of discovery") + ylab("TLE (log scale ; all samples)") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="red")+
scale_y_log10(breaks = c(1, 5, 25, 50, 100, 200, 500, 2000)) +
geom_jitter(aes(y = TLEn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt$Mission),"\n(N=",table(dt$Mission),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP8.A## Warning: Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 15 rows containing non-finite values (`stat_summary()`).## Warning: Removed 2 rows containing missing values (`geom_point()`).
Appendix 8.B
#Comparing excavation years and TAG%
#Order variables
TAGn<-as.numeric(dt$`TAG%`)
dt$Mission <- factor(dt$Mission)#Plotting
APP8.B <- ggplot(data = dt,
aes(x = Mission, y = TAGn)) +
geom_boxplot(aes(x=Mission, y=TAGn), position = position_dodge(0.5), width = .2, outlier.shape = NA) +
xlab("Year of discovery") + ylab("TAG% (log scale ; all samples)") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="red")+
scale_y_log10(breaks = c(1, 5, 25, 50, 100, 200, 500, 2000)) +
geom_jitter(aes(y = TAGn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt$Mission),"\n(N=",table(dt$Mission),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP8.B## Warning: Removed 104 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 104 rows containing non-finite values (`stat_summary()`).## Warning: Removed 104 rows containing missing values (`geom_point()`).
Appendix 5.C
#Comparing thickness vs Sample_type
#Order variables
TLEn<-as.numeric(dt$TLE)
dt$Sample_type<- factor(dt$Sample_type)#Plotting
APP5.C <-
ggplot(data = dt,
aes(x = Sample_type,
y = Av_thickness, colour = Sample_type)) +
geom_boxplot(aes(x=Sample_type, y=Av_thickness),
position = position_dodge(0.5),
width = .2, outlier.shape = NA) +
labs(x = "Sample type",
y = "Average sherd thickness (log scale ; all samples of the pr. study)",
colour = "Sample type") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10 () +
scale_colour_manual(values = c("#1b9e77","#d95f02","#984ea3")) +
geom_jitter(aes(y = Av_thickness), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt$Sample_type),
"\n(N=",table(dt$Sample_type),")",sep="")) +
theme_classic(base_size = 10) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP5.C ## Warning: Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis## Warning: Removed 28 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 28 rows containing non-finite values (`stat_summary()`).## Warning: Removed 24 rows containing missing values (`geom_point()`).
Appendix 6.C
#Comparing thickness vs Residue_Type
#Order variables
presentstudy$Residue_type<-factor(presentstudy$Residue_type)
ourstudysherdthickness <- droplevels(subset(presentstudy, Av_thickness >= 1 ))
Av_thicknessn<-as.numeric(ourstudysherdthickness$Av_thickness)#Plotting
APP6.C <-
ggplot(data = ourstudysherdthickness,
aes(x = Residue_type,
y = Av_thicknessn, colour = Residue_type)) +
geom_boxplot(aes(x=Residue_type, y=Av_thicknessn),
position = position_dodge(0.5),
width = .2, outlier.shape = NA) +
labs(x = "Residue type",
y = "Average sherd thickness (log scale ; all samples of the pr. study)",
colour = "Residue type") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_colour_manual(values = c("#ff6a33", "#adbf3b", "#06ae81", "#1dd7f6", "#ac81fb")) +
scale_y_log10 () +
geom_jitter(aes(y = Av_thicknessn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(ourstudysherdthickness$Residue_type),
"\n(N=",table(ourstudysherdthickness$Residue_type),")",sep="")) +
theme_classic(base_size = 10) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP6.C
Appendix 7.C
#Comparing thickness vs Morpho-Fragment type
#Order variables
#dt$Rim_Base_body<- factor(dt$Rim_Base_body)
presentstudy <- droplevels(subset(dt, Rim_Base_body_3 >= 1))
presentstudy$Rim_Base_body_2<-factor(presentstudy$Rim_Base_body_2, levels = c("Rim", "Body", "Base"))
ourstudysherdthickness <- droplevels(subset(presentstudy, Av_thickness >= 1 ))
Av_thicknessn<-as.numeric(ourstudysherdthickness$Av_thickness)#Plotting
APP7.C <- ggplot(data = ourstudysherdthickness,
aes(x = Rim_Base_body_2, y = Av_thicknessn, colour = Rim_Base_body_2)) +
geom_boxplot(aes(x=Rim_Base_body_2, y=Av_thicknessn), position = position_dodge(0.5), width = .2, outlier.shape = NA) +
labs(x = "Fragment type", y = "Average sherd thickness (log scale ; all samples of the pr. study)", colour = "Fragment type") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10 () +
scale_colour_manual(values = c("#2b5be2", "#186d4d", "#db2297")) +
geom_jitter(aes(y = Av_thicknessn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(ourstudysherdthickness$Rim_Base_body_2),"\n(N=",table(ourstudysherdthickness$Rim_Base_body_2),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP7.C
Appendix 5.B
#Comparing TAG% vs Sample_type
#Order variables
TAGn<-as.numeric(dt$`TAG%`)
dt$Sample_type<- factor(dt$Sample_type)#Plotting
APP5.B <- ggplot(data = dt,
aes(x = Sample_type, y = TAGn, colour = Sample_type)) +
geom_boxplot(aes(x=Sample_type, y=TAGn), position = position_dodge(0.5), width = .2, outlier.shape = NA) +
labs(x = "Sample type", y = "TAG% (continunous scale ; all samples)", colour = "Sample type") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_colour_manual(values = c("#984ea3", "#d95f02", "#1b9e77")) +
geom_jitter(aes(y = TAGn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt$Sample_type),"\n(N=",table(dt$Sample_type),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP5.B## Warning: Removed 104 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 104 rows containing non-finite values (`stat_summary()`).## Warning: Removed 104 rows containing missing values (`geom_point()`).
Appendix 6.B
#Comparing TAG% vs Residues_type
#Order variables
TAGn<-as.numeric(dt$`TAG%`)
dt$Residue_type<- factor(dt$Residue_type)#Plotting
APP6.B <- ggplot(data = dt,
aes(x = Residue_type, y = TAGn, colour = Residue_type)) +
geom_boxplot(aes(x=Residue_type, y=TAGn), position = position_dodge(0.5), width = .2, outlier.shape = NA) +
labs(x = "Residue type", y = "TAG% (log scale ; all samples)", colour = "Residue type") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10 () +
scale_colour_manual(values = c("#ff6a33", "#adbf3b", "#06ae81", "#1dd7f6", "#ac81fb")) +
geom_jitter(aes(y = TAGn), position = position_jitter(0.1)) +
scale_x_discrete(labels = paste(levels(dt$Residue_type),"\n(N=",table(dt$Residue_type),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP6.B## Warning: Removed 104 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 104 rows containing non-finite values (`stat_summary()`).## Warning: Removed 104 rows containing missing values (`geom_point()`).
Appendix 6.A
#Comparing TLE vs Residue_type
#Plotting
APP6.A <- ggplot(data = dt,
aes(x = Residue_type, y = TLE, colour = Residue_type))+
geom_boxplot(aes(x=Residue_type, y=TLE), position = position_dodge(0.5), width = .2, outlier.shape = NA)+
labs(x = "Profile", y = "TLE (log scale; all values; µg.g-1)", colour = "Residues_type") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10(breaks = c(5, 25, 100, 200, 500, 1000))+
geom_jitter(aes(y = TLE), position = position_jitter(0.1))+
scale_x_discrete(labels = paste(levels(dt$Residue_type),"\n(N=",table(dt$Residue_type),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP6.A## Warning: Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 15 rows containing non-finite values (`stat_summary()`).## Warning: Removed 2 rows containing missing values (`geom_point()`).
Appendix 7.A
#Comparing TLE vs Rim_Base_Body
#Plotting
APP7.A <- ggplot(data = presentstudy,
aes(x = Rim_Base_body_2, y = TLE, colour = Rim_Base_body_2))+
geom_boxplot(aes(x=Rim_Base_body_2, y=TLE), position = position_dodge(0.5), width = .2, outlier.shape = NA)+
labs(x = "Profile", y = "TLE (log scale; all values; µg.g-1)", colour = "Profile") +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black")+
scale_y_log10(breaks = c(5, 25, 100, 200, 500, 1000))+
scale_colour_manual(values = c("#2b5be2", "#186d4d", "#db2297")) +
geom_jitter(aes(y = TLE), position = position_jitter(0.1))+
scale_x_discrete(labels = paste(levels(presentstudy$Rim_Base_body_2),"\n(N=",table(presentstudy$Rim_Base_body_2),")",sep="")) +
theme_classic(base_size = 10) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
APP7.A## Warning: Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis
## Transformation introduced infinite values in continuous y-axis## Warning: Removed 12 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 12 rows containing non-finite values (`stat_summary()`).