In jmcascalheira/ProtoSolutreanVB: Research compedium for a report on the Proto-Solutrean occupation of Vale Boi
knitr::opts_chunk$set(
collapse = TRUE,
warning = FALSE,
message = FALSE,
echo = FALSE,
comment = "#>",
dpi = 300,
fig.path = "../figures/"
)
library(tidyverse)
library(googlesheets)
# Read lithics from Google sheets
gs_ls()
lithics <- gs_title("lithics_layers_4e_5")
lithics <- gs_read(ss = lithics, ws = "Sheet1")
lithics <- rename(lithics, id = ID)
dataset <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/derived_data/dataset.csv")
# Read CSV's with field data from NewPlot software
context <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/raw_data/context.csv")
xyz <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/raw_data/xyz.csv")
cols_to_concat <- c("Unit", "ID")
context$ID <- str_squish(context$ID)
xyz$ID <- str_squish(xyz$ID)
context <- context %>%
unite_(col='id', cols_to_concat, sep="-", remove=FALSE) %>%
select(id, Spit, Level, Code) %>%
distinct(id, .keep_all = TRUE)
xyz <- xyz %>%
rename(Unit = UNIT) %>%
unite_(col='id', cols_to_concat, sep="-", remove=FALSE) %>%
filter(Suffix == 0) %>%
select(id, X, Y, Z) %>%
distinct(id, .keep_all = TRUE)
# Join field tables
field_data <- full_join(xyz, context, by = "id")
# Calculate spit volume from Buckets
cols_to_concat <- c("Level", "Spit")
buckets_by_spit <- context %>%
filter(Level %in% c("4E", "5", "5B", "5C") & Code == "BUCKET" & !Spit %in% c("limp_corte", "profile", " ", NA)) %>%
unite_(col='depth', cols_to_concat, sep=".", remove=FALSE)
buckets_by_spit$depth <- str_replace(buckets_by_spit$depth, "B", "")
buckets_by_spit$depth <- str_replace(buckets_by_spit$depth, "C", "")
spit_vol <- buckets_by_spit %>%
group_by(depth) %>%
count(Code) %>%
mutate(spit_vol = (n*10)/1000)
# Calculate average depth of spits
spit_depths <- field_data %>%
filter(Level %in% c("4E", "5", "5B", "5C") & !Spit %in% c("limp_corte", "LIMP_CORTE", "profile", " ", NA, "0")) %>%
unite_(col='depth', cols_to_concat, sep=".", remove=FALSE)
spit_depths$depth <- str_replace(spit_depths$depth, "B", "")
spit_depths$depth <- str_replace(spit_depths$depth, "C", "")
spit_aver_depths <- spit_depths %>%
group_by(depth) %>%
summarise(avg = mean(Z)) %>%
mutate_if(is.numeric, format, 3)
## Data classes per spit
full_data <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/derived_data/dataset.csv")
cols_to_concat <- c("Level", "Spit")
lithics_by_spit <- full_data %>%
filter(Level %in% c("4E", "5", "5B", "5C") & !Spit %in% c("limp_corte", "profile", " ", NA)) %>%
select(id, Z, Spit, Level, Code, Classe, Fragmentacao_Artefacto, N_Esquirolas, Tipo_Retocado) %>%
unite_(col='depth', cols_to_concat, sep=".", remove=FALSE)
lithics_by_spit$depth <- str_replace(lithics_by_spit$depth, "B", "")
lithics_by_spit$depth <- str_replace(lithics_by_spit$depth, "C", "")
# Cores
cores_by_spit <- lithics_by_spit %>%
filter(Classe == "Núcleo") %>%
group_by(depth) %>%
count(Classe)
# Debitage
debitage_by_spit <- lithics_by_spit %>%
filter(Classe %in% c("Lasca", "Produto alongado"))
debitage_by_spit$Classe <- str_replace(debitage_by_spit$Classe, "Lasca", "Debitagem")
debitage_by_spit$Classe <- str_replace(debitage_by_spit$Classe, "Produto alongado", "Debitagem")
debitage_prox_by_spit <- lithics_by_spit %>%
filter(Classe %in% c("Fragmento Lasca", "Fragmento produto alongado") & Fragmentacao_Artefacto == "Proximal")
debitage_prox_by_spit$Classe <- str_replace(debitage_prox_by_spit$Classe, "Fragmento Lasca", "Debitagem")
debitage_prox_by_spit$Classe <- str_replace(debitage_prox_by_spit$Classe, "Fragmento produto alongado", "Debitagem")
debitage_by_spit_all <- bind_rows(debitage_by_spit, debitage_prox_by_spit, id = NULL)
debitage_by_spit_all <- debitage_by_spit_all %>%
group_by(depth) %>%
count(Classe)
# Retouched tools
retouched_tools_by_spit <- lithics_by_spit %>%
filter(Classe == "Produto retocado") %>%
group_by(depth) %>%
count(Classe)
# Chips
chips_by_spit <- lithics_by_spit %>%
filter(Classe == "Esquírola") %>%
select(depth, N_Esquirolas) %>%
group_by(depth) %>%
summarise(n = sum(N_Esquirolas))
chips_by_spit$Classe <- rep("Esquírola",nrow(chips_by_spit))
chips_by_spit <- select(chips_by_spit, depth, Classe, n)
# Burins
burins_by_spit <- lithics_by_spit %>%
filter(Tipo_Retocado %in% c("Buril", "Buril sobre truncatura"))
burins_by_spit$Tipo_Retocado <- str_replace(burins_by_spit$Tipo_Retocado, "Buril sobre truncatura", "Buril")
burins_by_spit <- burins_by_spit %>%
group_by(depth) %>%
count(Tipo_Retocado)
burins_by_spit <- select(burins_by_spit, depth, Classe = Tipo_Retocado, n)
# Endscrapers
endscrapers_by_spit <- lithics_by_spit %>%
filter(Tipo_Retocado == "Raspadeira") %>%
group_by(depth) %>%
count(Tipo_Retocado)
endscrapers_by_spit <- select(endscrapers_by_spit, depth, Classe = Tipo_Retocado, n)
# Calculate data classes per cubic meter !!Needs revision to reduce code!!
cores_by_spit$spit_vol <- spit_vol$spit_vol[match(cores_by_spit$depth, spit_vol$depth)]
cores_by_spit <- cores_by_spit %>%
mutate(artifacts_by_cubic_meter = n/spit_vol)
cores_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(cores_by_spit$depth, spit_aver_depths$depth)]
cores_by_spit$spit_aver_depth <- as.numeric(as.character(cores_by_spit$spit_aver_depth))
debitage_by_spit_all$spit_vol <- spit_vol$spit_vol[match(debitage_by_spit_all$depth, spit_vol$depth)]
debitage_by_spit_all <- debitage_by_spit_all %>%
mutate(artifacts_by_cubic_meter = n/spit_vol)
debitage_by_spit_all$spit_aver_depth <- spit_aver_depths$avg[match(debitage_by_spit_all$depth, spit_aver_depths$depth)]
debitage_by_spit_all$spit_aver_depth <- as.numeric(as.character(debitage_by_spit_all$spit_aver_depth))
retouched_tools_by_spit$spit_vol <- spit_vol$spit_vol[match(retouched_tools_by_spit$depth, spit_vol$depth)]
retouched_tools_by_spit <- retouched_tools_by_spit %>%
mutate(artifacts_by_cubic_meter = n/spit_vol)
retouched_tools_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(retouched_tools_by_spit$depth, spit_aver_depths$depth)]
retouched_tools_by_spit$spit_aver_depth <- as.numeric(as.character(retouched_tools_by_spit$spit_aver_depth))
chips_by_spit$spit_vol <- spit_vol$spit_vol[match(chips_by_spit$depth, spit_vol$depth)]
chips_by_spit <- chips_by_spit %>%
mutate(artifacts_by_cubic_meter = n/spit_vol) %>%
filter(!depth %in% c("5.9", "5.10"))
chips_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(chips_by_spit$depth, spit_aver_depths$depth)]
chips_by_spit$spit_aver_depth <- as.numeric(as.character(chips_by_spit$spit_aver_depth))
burins_by_spit$spit_vol <- spit_vol$spit_vol[match(burins_by_spit$depth, spit_vol$depth)]
burins_by_spit <- burins_by_spit %>%
mutate(artifacts_by_cubic_meter = n/spit_vol)
burins_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(burins_by_spit$depth, spit_aver_depths$depth)]
burins_by_spit$spit_aver_depth <- as.numeric(as.character(burins_by_spit$spit_aver_depth))
endscrapers_by_spit$spit_vol <- spit_vol$spit_vol[match(endscrapers_by_spit$depth, spit_vol$depth)]
endscrapers_by_spit <- endscrapers_by_spit %>%
mutate(artifacts_by_cubic_meter = n/spit_vol)
endscrapers_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(endscrapers_by_spit$depth, spit_aver_depths$depth)]
endscrapers_by_spit$spit_aver_depth <- as.numeric(as.character(endscrapers_by_spit$spit_aver_depth))
# Merge tables
artifacts_by_cubic_meter <- bind_rows(chips_by_spit, debitage_by_spit_all)
# Remove low volumetric levels
artifacts_by_cubic_meter <- filter(artifacts_by_cubic_meter, spit_vol > 0.10)
Introduction
Background
Methods
Results
# Plot artifact classes acros stratigraphy
ggplot(artifacts_by_cubic_meter, (aes(spit_aver_depth, artifacts_by_cubic_meter, colour = Classe))) +
geom_point() +
stat_smooth(span = 0.5, se = FALSE) +
xlab("Profundidade (m)") +
ylab("Artefactos por metro cúbico de sedimento (log10)") +
scale_y_log10()+
coord_flip() +
scale_color_manual(values = c("grey50", "grey20")) +
theme_minimal()
# Filter elongated debitage
alongados <- lithics %>%
filter(Classe == "Produto alongado" & Materia == "Silex") %>%
select(Materia, Espessura, Largura)
# Width Histogram
require(plyr)
mu <- ddply(alongados, "Materia", summarise, grp.mean=mean(Largura))
ggplot(alongados, aes(x=Largura, color=Materia, fill=Materia)) +
geom_histogram(aes(y=..density..), position="identity", alpha=0.5)+
geom_density(alpha=0.6)+
geom_vline(data=mu, aes(xintercept=grp.mean, color=Materia),
linetype="dashed")+
scale_color_manual(values = "grey50")+
scale_fill_manual(values = "grey50")+
labs(x="Largura(mm)", y = "Densidade")
alongados_all <- lithics %>%
filter(Classe == "Produto alongado" & Materia == "Silex") %>%
select(Materia, Comprimento, Largura)
# Width vs Length scaterplot
ggplot(alongados_all,aes(Largura, Comprimento)) +
geom_point() +
geom_smooth( method = lm, se = FALSE) +
scale_color_grey(start=0.8, end=0.2) +
xlab("Largura (mm)") +
ylab("Comprimento (mm)") +
theme_minimal()
# Filter data for raw materials
raw_materials <- lithics %>%
filter(!Classe %in% c("Fragmento", "Esquírola")) %>%
group_by(Materia) %>%
dplyr::summarise(n = n()) %>%
mutate(freq = (n / sum(n)) * 100)
# Barplot with raw materials
ggplot(raw_materials, aes(x = reorder(Materia, -freq), y = freq)) +
geom_bar(stat = "identity", width = 0.7, fill = "grey50") +
xlab("Matérias-primas") +
ylab("Percentagem") +
theme(axis.text=element_text(size=24),
axis.title.x = element_text(size = 26),
axis.title.y = element_text(size = 26),
legend.text = element_text(size= 24),
legend.title = element_text(size = 26)) +
theme_minimal()
# Filter data for cortex analysis
cortex <- lithics %>%
filter(Classe %in% c("Núcleo", "Produto alongado", "Lasca", "Produto retocado") & Cortex != "NA") %>%
mutate(Classe = replace(Classe, Classe == "Produto retocado", "Retocado")) %>%
mutate(Classe = replace(Classe, Classe == "Produto alongado", "Alongado")) %>%
group_by(Materia, Classe, Cortex) %>%
dplyr::summarise(n = n()) %>%
mutate(freq = (n / sum(n)) * 100)
cortex$Cortex = factor(cortex$Cortex, levels=c('0%','1-30%','31-60%','61-99%','100%'))
cortex$Materia = factor(cortex$Materia, levels=c('Silex','Grauvaque','Calcedonia','Dolerito','Outro'))
# Barplot of cortex by raw material and technological classes
ggplot() +
geom_bar(aes(y = freq, x = Classe, fill = Cortex), data = cortex, stat = "identity", position = "fill") +
facet_wrap( ~ Materia, ncol = 2, scales='free') +
xlab("Classes") +
ylab("Percentagem") +
scale_fill_grey(start=0.7, end=0.2) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90))
artifacts_by_cubic_meter <- bind_rows(burins_by_spit, endscrapers_by_spit)
artifacts_by_cubic_meter <- filter(artifacts_by_cubic_meter, spit_vol > 0.10) # removes low volumetric levels
ggplot(artifacts_by_cubic_meter, (aes(spit_aver_depth, artifacts_by_cubic_meter, colour = Classe))) +
geom_point() +
stat_smooth(span = 0.3, se = FALSE) +
xlab("Profundidade (m)") +
ylab("Número de artefactos por metro cúbico de sedimento (escala logarítmica)") +
scale_y_log10()+
coord_flip() +
scale_color_manual(values = c("grey50", "grey20")) +
theme_minimal()
chert_core_weight <- lithics %>%
filter(Classe == "Núcleo" & Materia != "Calcedonia") %>%
select(Peso, Materia)
ggplot(chert_core_weight, aes(x = Materia, y = Peso)) +
geom_boxplot() +
xlab("Matéria prima") +
ylab("Peso (g)")
Discussion
Conclusion
Acknowledgements
pagebreak
References
pagebreak
Colophon
This report was generated on r Sys.time() using the following computational environment and dependencies:
# which R packages and versions?
devtools::session_info()
The current Git commit details are:
# what commit is this file at? You may need to change the path value
# if your Rmd is not in analysis/paper/
git2r::repository("../..")
jmcascalheira/ProtoSolutreanVB documentation built on May 14, 2019, 5:14 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, warning = FALSE, message = FALSE, echo = FALSE, comment = "#>", dpi = 300, fig.path = "../figures/" )
library(tidyverse) library(googlesheets)
# Read lithics from Google sheets gs_ls() lithics <- gs_title("lithics_layers_4e_5") lithics <- gs_read(ss = lithics, ws = "Sheet1") lithics <- rename(lithics, id = ID)
dataset <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/derived_data/dataset.csv")
# Read CSV's with field data from NewPlot software context <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/raw_data/context.csv") xyz <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/raw_data/xyz.csv") cols_to_concat <- c("Unit", "ID") context$ID <- str_squish(context$ID) xyz$ID <- str_squish(xyz$ID) context <- context %>% unite_(col='id', cols_to_concat, sep="-", remove=FALSE) %>% select(id, Spit, Level, Code) %>% distinct(id, .keep_all = TRUE) xyz <- xyz %>% rename(Unit = UNIT) %>% unite_(col='id', cols_to_concat, sep="-", remove=FALSE) %>% filter(Suffix == 0) %>% select(id, X, Y, Z) %>% distinct(id, .keep_all = TRUE) # Join field tables field_data <- full_join(xyz, context, by = "id") # Calculate spit volume from Buckets cols_to_concat <- c("Level", "Spit") buckets_by_spit <- context %>% filter(Level %in% c("4E", "5", "5B", "5C") & Code == "BUCKET" & !Spit %in% c("limp_corte", "profile", " ", NA)) %>% unite_(col='depth', cols_to_concat, sep=".", remove=FALSE) buckets_by_spit$depth <- str_replace(buckets_by_spit$depth, "B", "") buckets_by_spit$depth <- str_replace(buckets_by_spit$depth, "C", "") spit_vol <- buckets_by_spit %>% group_by(depth) %>% count(Code) %>% mutate(spit_vol = (n*10)/1000) # Calculate average depth of spits spit_depths <- field_data %>% filter(Level %in% c("4E", "5", "5B", "5C") & !Spit %in% c("limp_corte", "LIMP_CORTE", "profile", " ", NA, "0")) %>% unite_(col='depth', cols_to_concat, sep=".", remove=FALSE) spit_depths$depth <- str_replace(spit_depths$depth, "B", "") spit_depths$depth <- str_replace(spit_depths$depth, "C", "") spit_aver_depths <- spit_depths %>% group_by(depth) %>% summarise(avg = mean(Z)) %>% mutate_if(is.numeric, format, 3) ## Data classes per spit full_data <- read.csv("C:/Users/jmcasca/Documents/R_DATA/compendiums/ProtoSolutreanVB/analysis/data/derived_data/dataset.csv") cols_to_concat <- c("Level", "Spit") lithics_by_spit <- full_data %>% filter(Level %in% c("4E", "5", "5B", "5C") & !Spit %in% c("limp_corte", "profile", " ", NA)) %>% select(id, Z, Spit, Level, Code, Classe, Fragmentacao_Artefacto, N_Esquirolas, Tipo_Retocado) %>% unite_(col='depth', cols_to_concat, sep=".", remove=FALSE) lithics_by_spit$depth <- str_replace(lithics_by_spit$depth, "B", "") lithics_by_spit$depth <- str_replace(lithics_by_spit$depth, "C", "") # Cores cores_by_spit <- lithics_by_spit %>% filter(Classe == "Núcleo") %>% group_by(depth) %>% count(Classe) # Debitage debitage_by_spit <- lithics_by_spit %>% filter(Classe %in% c("Lasca", "Produto alongado")) debitage_by_spit$Classe <- str_replace(debitage_by_spit$Classe, "Lasca", "Debitagem") debitage_by_spit$Classe <- str_replace(debitage_by_spit$Classe, "Produto alongado", "Debitagem") debitage_prox_by_spit <- lithics_by_spit %>% filter(Classe %in% c("Fragmento Lasca", "Fragmento produto alongado") & Fragmentacao_Artefacto == "Proximal") debitage_prox_by_spit$Classe <- str_replace(debitage_prox_by_spit$Classe, "Fragmento Lasca", "Debitagem") debitage_prox_by_spit$Classe <- str_replace(debitage_prox_by_spit$Classe, "Fragmento produto alongado", "Debitagem") debitage_by_spit_all <- bind_rows(debitage_by_spit, debitage_prox_by_spit, id = NULL) debitage_by_spit_all <- debitage_by_spit_all %>% group_by(depth) %>% count(Classe) # Retouched tools retouched_tools_by_spit <- lithics_by_spit %>% filter(Classe == "Produto retocado") %>% group_by(depth) %>% count(Classe) # Chips chips_by_spit <- lithics_by_spit %>% filter(Classe == "Esquírola") %>% select(depth, N_Esquirolas) %>% group_by(depth) %>% summarise(n = sum(N_Esquirolas)) chips_by_spit$Classe <- rep("Esquírola",nrow(chips_by_spit)) chips_by_spit <- select(chips_by_spit, depth, Classe, n) # Burins burins_by_spit <- lithics_by_spit %>% filter(Tipo_Retocado %in% c("Buril", "Buril sobre truncatura")) burins_by_spit$Tipo_Retocado <- str_replace(burins_by_spit$Tipo_Retocado, "Buril sobre truncatura", "Buril") burins_by_spit <- burins_by_spit %>% group_by(depth) %>% count(Tipo_Retocado) burins_by_spit <- select(burins_by_spit, depth, Classe = Tipo_Retocado, n) # Endscrapers endscrapers_by_spit <- lithics_by_spit %>% filter(Tipo_Retocado == "Raspadeira") %>% group_by(depth) %>% count(Tipo_Retocado) endscrapers_by_spit <- select(endscrapers_by_spit, depth, Classe = Tipo_Retocado, n) # Calculate data classes per cubic meter !!Needs revision to reduce code!! cores_by_spit$spit_vol <- spit_vol$spit_vol[match(cores_by_spit$depth, spit_vol$depth)] cores_by_spit <- cores_by_spit %>% mutate(artifacts_by_cubic_meter = n/spit_vol) cores_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(cores_by_spit$depth, spit_aver_depths$depth)] cores_by_spit$spit_aver_depth <- as.numeric(as.character(cores_by_spit$spit_aver_depth)) debitage_by_spit_all$spit_vol <- spit_vol$spit_vol[match(debitage_by_spit_all$depth, spit_vol$depth)] debitage_by_spit_all <- debitage_by_spit_all %>% mutate(artifacts_by_cubic_meter = n/spit_vol) debitage_by_spit_all$spit_aver_depth <- spit_aver_depths$avg[match(debitage_by_spit_all$depth, spit_aver_depths$depth)] debitage_by_spit_all$spit_aver_depth <- as.numeric(as.character(debitage_by_spit_all$spit_aver_depth)) retouched_tools_by_spit$spit_vol <- spit_vol$spit_vol[match(retouched_tools_by_spit$depth, spit_vol$depth)] retouched_tools_by_spit <- retouched_tools_by_spit %>% mutate(artifacts_by_cubic_meter = n/spit_vol) retouched_tools_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(retouched_tools_by_spit$depth, spit_aver_depths$depth)] retouched_tools_by_spit$spit_aver_depth <- as.numeric(as.character(retouched_tools_by_spit$spit_aver_depth)) chips_by_spit$spit_vol <- spit_vol$spit_vol[match(chips_by_spit$depth, spit_vol$depth)] chips_by_spit <- chips_by_spit %>% mutate(artifacts_by_cubic_meter = n/spit_vol) %>% filter(!depth %in% c("5.9", "5.10")) chips_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(chips_by_spit$depth, spit_aver_depths$depth)] chips_by_spit$spit_aver_depth <- as.numeric(as.character(chips_by_spit$spit_aver_depth)) burins_by_spit$spit_vol <- spit_vol$spit_vol[match(burins_by_spit$depth, spit_vol$depth)] burins_by_spit <- burins_by_spit %>% mutate(artifacts_by_cubic_meter = n/spit_vol) burins_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(burins_by_spit$depth, spit_aver_depths$depth)] burins_by_spit$spit_aver_depth <- as.numeric(as.character(burins_by_spit$spit_aver_depth)) endscrapers_by_spit$spit_vol <- spit_vol$spit_vol[match(endscrapers_by_spit$depth, spit_vol$depth)] endscrapers_by_spit <- endscrapers_by_spit %>% mutate(artifacts_by_cubic_meter = n/spit_vol) endscrapers_by_spit$spit_aver_depth <- spit_aver_depths$avg[match(endscrapers_by_spit$depth, spit_aver_depths$depth)] endscrapers_by_spit$spit_aver_depth <- as.numeric(as.character(endscrapers_by_spit$spit_aver_depth)) # Merge tables artifacts_by_cubic_meter <- bind_rows(chips_by_spit, debitage_by_spit_all) # Remove low volumetric levels artifacts_by_cubic_meter <- filter(artifacts_by_cubic_meter, spit_vol > 0.10)
Introduction
Background
Methods
Results
# Plot artifact classes acros stratigraphy ggplot(artifacts_by_cubic_meter, (aes(spit_aver_depth, artifacts_by_cubic_meter, colour = Classe))) + geom_point() + stat_smooth(span = 0.5, se = FALSE) + xlab("Profundidade (m)") + ylab("Artefactos por metro cúbico de sedimento (log10)") + scale_y_log10()+ coord_flip() + scale_color_manual(values = c("grey50", "grey20")) + theme_minimal()
# Filter elongated debitage alongados <- lithics %>% filter(Classe == "Produto alongado" & Materia == "Silex") %>% select(Materia, Espessura, Largura) # Width Histogram require(plyr) mu <- ddply(alongados, "Materia", summarise, grp.mean=mean(Largura)) ggplot(alongados, aes(x=Largura, color=Materia, fill=Materia)) + geom_histogram(aes(y=..density..), position="identity", alpha=0.5)+ geom_density(alpha=0.6)+ geom_vline(data=mu, aes(xintercept=grp.mean, color=Materia), linetype="dashed")+ scale_color_manual(values = "grey50")+ scale_fill_manual(values = "grey50")+ labs(x="Largura(mm)", y = "Densidade")
alongados_all <- lithics %>% filter(Classe == "Produto alongado" & Materia == "Silex") %>% select(Materia, Comprimento, Largura) # Width vs Length scaterplot ggplot(alongados_all,aes(Largura, Comprimento)) + geom_point() + geom_smooth( method = lm, se = FALSE) + scale_color_grey(start=0.8, end=0.2) + xlab("Largura (mm)") + ylab("Comprimento (mm)") + theme_minimal()
# Filter data for raw materials raw_materials <- lithics %>% filter(!Classe %in% c("Fragmento", "Esquírola")) %>% group_by(Materia) %>% dplyr::summarise(n = n()) %>% mutate(freq = (n / sum(n)) * 100) # Barplot with raw materials ggplot(raw_materials, aes(x = reorder(Materia, -freq), y = freq)) + geom_bar(stat = "identity", width = 0.7, fill = "grey50") + xlab("Matérias-primas") + ylab("Percentagem") + theme(axis.text=element_text(size=24), axis.title.x = element_text(size = 26), axis.title.y = element_text(size = 26), legend.text = element_text(size= 24), legend.title = element_text(size = 26)) + theme_minimal()
# Filter data for cortex analysis cortex <- lithics %>% filter(Classe %in% c("Núcleo", "Produto alongado", "Lasca", "Produto retocado") & Cortex != "NA") %>% mutate(Classe = replace(Classe, Classe == "Produto retocado", "Retocado")) %>% mutate(Classe = replace(Classe, Classe == "Produto alongado", "Alongado")) %>% group_by(Materia, Classe, Cortex) %>% dplyr::summarise(n = n()) %>% mutate(freq = (n / sum(n)) * 100) cortex$Cortex = factor(cortex$Cortex, levels=c('0%','1-30%','31-60%','61-99%','100%')) cortex$Materia = factor(cortex$Materia, levels=c('Silex','Grauvaque','Calcedonia','Dolerito','Outro')) # Barplot of cortex by raw material and technological classes ggplot() + geom_bar(aes(y = freq, x = Classe, fill = Cortex), data = cortex, stat = "identity", position = "fill") + facet_wrap( ~ Materia, ncol = 2, scales='free') + xlab("Classes") + ylab("Percentagem") + scale_fill_grey(start=0.7, end=0.2) + theme_minimal() + theme(axis.text.x = element_text(angle = 90))
artifacts_by_cubic_meter <- bind_rows(burins_by_spit, endscrapers_by_spit) artifacts_by_cubic_meter <- filter(artifacts_by_cubic_meter, spit_vol > 0.10) # removes low volumetric levels ggplot(artifacts_by_cubic_meter, (aes(spit_aver_depth, artifacts_by_cubic_meter, colour = Classe))) + geom_point() + stat_smooth(span = 0.3, se = FALSE) + xlab("Profundidade (m)") + ylab("Número de artefactos por metro cúbico de sedimento (escala logarítmica)") + scale_y_log10()+ coord_flip() + scale_color_manual(values = c("grey50", "grey20")) + theme_minimal()
chert_core_weight <- lithics %>% filter(Classe == "Núcleo" & Materia != "Calcedonia") %>% select(Peso, Materia) ggplot(chert_core_weight, aes(x = Materia, y = Peso)) + geom_boxplot() + xlab("Matéria prima") + ylab("Peso (g)")
Discussion
Conclusion
Acknowledgements
pagebreak
References
pagebreak
Colophon
This report was generated on r Sys.time() using the following computational environment and dependencies:
# which R packages and versions? devtools::session_info()
The current Git commit details are:
# what commit is this file at? You may need to change the path value # if your Rmd is not in analysis/paper/ git2r::repository("../..")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.