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index.Rmd
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---
title: "<span style='font-size: 40px;font-family:Luminari, fantasy'><b>Sauri Pallars</b></style> <span style='font-size: 30px'> <br>art rupestre medieval al Pirineu català</style>"
author: "Universitat Autonoma de Barcelona (UAB) <br> Grup d'Arqueologia de l'Alta Muntanya (GAAM)"
# date: "11/12/2020"
bibliography: data/references.bib
output:
# ioslides_presentation:
# css: css.css
html_document:
toc: true
toc_float:
collapsed: false
smooth_scroll: false
---
```{r, echo=FALSE}
url.root <- "https://raw.githubusercontent.com/zoometh/thomashuet/main/img/"
htmltools::img(src = paste0(url.root, "prj_sauri.png"),
alt = 'logo',
width = '120px',
style = 'position:absolute; top:0; right:0; padding:10px;')
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, fig.width = 10, fig.height = 10)
# opts_chunk$set(fig.width=12, fig.height=8)
library(htmlwidgets)
library(kableExtra)
library(dplyr)
library(knitr)
library(magick)
library(leaflet)
library(RPostgreSQL)
library(rpostgis)
library(rdrop2)
library(sp)
library(sf)
library(plotly)
library(openxlsx)
library(ggplot2)
library(stringr)
library(ggrepel)
library(gridExtra)
library(readr)
library(shapefiles)
library(rgdal)
library(raster)
chm <- "D:/Sites_10/Sauri/"
roches <- st_read(dsn = paste0(chm, "GIS/roches_pts"), layer = "roches_all")
# roches <- st_read(dsn = "D:/Sites_10/Sauri/GIS/roches_pts", layer = "roches_all")
roches <- st_transform(roches, 4236) # rdev.reproj
zones <- st_read(dsn = paste0(chm, "GIS"), layer = "zonas_3")
zones <- st_transform(zones, 4236) # rdev.reproj
# zones <- as(zones, "Spatial")
chm_photos <- paste0(chm,"fotos")
chm_pts <- paste0(chm,"GIS/roches_pts")
#fich <- "fiche_19_R114.xlsx"
# fich <- "fiche_2020_part1.xlsx" # campagne 2020 part 1
# fich <- "fiche_2020_part2.xlsx" # campagne 2020 part 2
fich <- "fiche_2019_20.xlsx" # le fichie source
list_grav_xt <- "list_gravures_xt" # le decodage des gravures
fich_fotos <- "fiche_fotos.xlsx" # TODO
shape.name.roches <- "roques" # dans le dossier 'chm_pts'
fields_roches <- c("roche", "x", "y", "z", "topo")
fields_fig <- c("num", "roche", "FG", "description", "doss_fotos")
lzon_etud <- c(1, 2, 3, 4, 5)
mycolo <- c("red", "orange", "yellow", "green", "blue") # zone colors
# lit une 1e fois
fiche_a <- openxlsx::read.xlsx(paste0(chm, fich),
rowNames = F,
skipEmptyRows = TRUE)
# trouve l'indice 'row' après quoi commence l'inventaire
start_row <- as.numeric(rownames(fiche_a[match('num', fiche_a$THM), ])) + 1
# lit une 2e fois en commençant àaprès 'row'
fiche <- openxlsx::read.xlsx(paste0(chm, fich),
startRow = start_row,
rowNames = F,
skipEmptyRows=TRUE)
# renomme les roches
fiche$roche <- sub("^", "R", fiche$R)
# regroupe les FG
fiche$FG <- NA
fiche$FG <- ifelse(!is.na(fiche$G), fiche$G, fiche$F)
# doss photos
fiche$doss_fotos <- NA # dossier photo
fiche <- subset(fiche,select=c("num", "jour", "roche", "FG", "description", "doss_fotos"))
doss_fotos <- as.data.frame(list.files(chm_photos),
stringsAsFactors=F) # list photos folders
colnames(doss_fotos)[1] <- "id"
doss_fotos$doss_fotos <- doss_fotos$id
# merge liste roches & photos
fiche_p <- merge(fiche,doss_fotos,by.x = "roche", by.y="id", all.x = T)
fiche_p$doss_fotos.x <- NULL
names(fiche_p)[names(fiche_p) == "doss_fotos.y"] <- "doss_fotos"
zones.areas.m2 <- st_area(zones)
zones.areas.ha <- as.numeric(zones.areas.m2/10000) # by zones
zones.areas.ha <- round(sum(zones.areas.ha), 0) # total area
```
Sauri (Pallars Sobirà, Lleida), al Pirineu català, és una important concentració de art rupestre medieval amb aproximadament 4,000 gravats [@Gassiot20]. Fins allà la recerca ha permès documentar 372 roques amb petroglifs que contenen de 3940 gravats. La majoria d'aquests gravats (~ 3,000) i roques gravades (~ 300) s'han localitzat, fotografiat i descrit breument
# El jaciment arqueològic {#ra}
El jaciment es divideix en 5 zones i ocupa `r zones.areas.ha` hectàres
```{r df.roche.z, warning=F}
zones.list <- as.data.frame(zones)
zones.list <- as.data.frame(zones.list[ ,"zona"])
names(zones.list) <- "zones"
# kable(zones.list,"html", row.names = F) %>%
# kable_styling(full_width = FALSE, position = "center", font_size=12)
```
```{r df.map.z, warning=F}
# roches <- st_crs(roches, 2531)
leaflet(height = "500px") %>%
addWMSTiles(
# "http://geoserveis.icgc.cat/icc_ortohistorica/wms/service?",
"http://www.ign.es/wms-inspire/pnoa-ma?",
layers = "OI.OrthoimageCoverage",
options = WMSTileOptions(format = "image/png", transparent = TRUE),
attribution = "") %>%
# addTiles() %>% # Add default OpenStreetMap map tiles
# all rocks
addPolygons(data = zones,
popup= ~paste0("zone: ",zona),
stroke = TRUE,
color = "#000000",
weight = 2,
fillOpacity = 0,
smoothFactor = 0.5) %>%
addCircleMarkers(data =roches,
popup=~roche,
radius = 1,
opacity = 0.5)
```
## Descripció de l'art rupestre {#A}
```{r g.dist.desc, warning=F, echo=FALSE, message = F}
# list_grav_xt <- "list_gravures_xt_2019_2020_1" # le fichier .xlsx
# thm_xt <-read.xlsx(paste0(chm,'/', list_grav_xt, '.xlsx'))
rocas_z <- st_read(paste0(chm_pts,"/", shape.name.roches,".shp"), quiet = T)
list_grav_trad <- read.xlsx(paste0(chm,"/", list_grav_xt, ".xlsx"))
n.roches <- length(unique(list_grav_trad$roche))
n.gravures <- nrow(list_grav_trad)
thm_xt <- subset(list_grav_trad, select=c("roche","thm_xt"))
thm_xt <- merge(thm_xt, rocas_z, by = "roche", all.x = T) # merge list and shapes
thm_xt <- subset(thm_xt, select=c("roche", "thm_xt", "geometry", "z", "zona"))
thm_xt_z <- subset(thm_xt,select=c("roche", "thm_xt", "zona"))
```
Es descriuen `r n.roches` roques gravades i `r n.gravures` gravats
### Nombre de gravats per roca {#Anb}
```{r g.dist.nbgrav.calc, warning=F, echo=FALSE, message = F}
# list_grav_xt <- "list_gravures_xt_2019_2020_1" # le fichier .xlsx
# thm_xt <-read.xlsx(paste0(chm,'/', list_grav_xt, '.xlsx'))
# rocas_z <- st_read(paste0(chm_pts,"/", shape.name.roches,".shp"), quiet = T)
# list_grav_trad <- read.xlsx(paste0(chm,"/", list_grav_xt, ".xlsx"))
# thm_xt <- subset(list_grav_trad, select=c("roche","thm_xt"))
# thm_xt <- merge(thm_xt, rocas_z, by="roche", all.x=T) # merge list and shapes
# thm_xt <- subset(thm_xt, select=c("roche","thm_xt","geometry","z","zona"))
# thm_xt_z <- subset(thm_xt,select=c("roche","thm_xt","zona"))
# # export avec num zona
# openxlsx::write.xlsx(thm_xt_z,
# paste0("D:/Sites_10/Sauri/", list_grav_xt,"_z.xlsx"),
# row.names = F,
# col.names=T,
# append = TRUE)
# nb gravures roches
# chm_shp <- "D:/Sites_10/Sauri/GIS/roches_pts"
# roches_sp <- st_read(roches, shape.name.roches)
nb.grav.roche <- as.data.frame(roches)
nb.grav.roche <- subset(nb.grav.roche, select=c("roche","num"))
nb.grav.roche <- thm_xt %>%
count(roche)
# stats
mean.gr <- mean(nb.grav.roche$n)
median.gr <- median(nb.grav.roche$n)
mode.gr <- as.integer(names(sort(-table(nb.grav.roche$n)))[1])
#
g.dist.nbgrav <- ggplot(nb.grav.roche, aes(n)) +
geom_freqpoly(binwidth = 1, size=.3)+
# mode
geom_vline(xintercept=mode.gr,
linetype="dotted",
color = "orange",
size=.3)+
annotate("text", x=mode.gr, y=40,
label= paste("modo:", mode.gr),
angle=90,size=3,
color = "red") +
# mean
geom_vline(xintercept=mean.gr,
color = "orange",
size=.3)+
annotate("text", x=mean.gr, y=40,
label= paste("mitjana:", round(mean.gr, 1)),
angle=90,size=3,
color = "red") +
# median
geom_vline(xintercept=median.gr,
linetype="dashed",
color = "orange",
size=.3)+
annotate("text", x=median.gr, y=40,
label= paste("mediana:", median.gr),
angle=90,size=3,
color = "red") +
xlab("nb gravats") +
ylab("nb roques gravades") +
scale_x_continuous(breaks=c(1, 5, 10, 20, 30, 50, 75, max(nb.grav.roche$n))) +
# xlim(0.9, 100)+
theme_bw()
```
La distribució del nombre de gravats per les roques gravades és propera a una distribució logarítmica amb una majoria de roques amb `r mode.gr` gravats i, de mitjana, amb `r round(mean.gr, 1)` gravats
```{r g.dist.nbgrav.graph, warning=F, echo=FALSE, out.height="50%"}
g.dist.nbgrav
```
### Altituds
#### Altituds generals
```{r dist_alti, warning=F, echo=FALSE}
z.min <- round(min(thm_xt$z, na.rm = T), 0)
z.max <- round(max(thm_xt$z, na.rm = T), 0)
# altitudes histogrammes
altis.zonas <- thm_xt %>%
group_by(zona) %>%
mutate(alt.min = min(z),
alt.max = max(z),
alt.moy = mean(z))
# rm NA
altis.zonas <- altis.zonas[!is.na(altis.zonas$zona), ]
altis.zonas <- subset(altis.zonas,select=c("zona",
"alt.min",
"alt.max",
"alt.moy"))
altis.zonas <- altis.zonas[!duplicated(altis.zonas), ]
thm_xt_sp <- st_as_sf(thm_xt) # -> sf
# nprosp.alt <- c(1440, 1600) # no prospected
# regroupements
thm_xt_sp <- thm_xt_sp[order(thm_xt_sp$thm_xt),]
# min.mean.sd.max <- function(x) {
# r <- c(min(x), mean(x) - sd(x), mean(x), mean(x) + sd(x), max(x))
# names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
# r
# }
g.hist.thm <- ggplot(thm_xt_sp) +
ggtitle("distribució d'altituds del gravat") +
# annotate("rect",
# xmin=nprosp.alt[1],
# xmax=nprosp.alt[2],
# ymin=-Inf, ymax=Inf,
# alpha=0.1, fill="black")+
# annotate("text", x=1500, y=100, label= "zone 4 \n no prospectada",angle=90,size=3) +
geom_rect(data=altis.zonas,
mapping=aes(xmin=alt.min, xmax=alt.max,
ymin=0, ymax=Inf),
fill=mycolo, alpha=0.1)+
geom_histogram(aes(x = z), colour = "black",
fill = "white", binwidth = 10) +
geom_text(data=altis.zonas,
mapping=aes(x = alt.moy, y=300,
label=paste0("zona ",zona)),
angle=90,size=3,vjust=-1,hjust=1) +
theme(axis.text.y = element_text(angle=90)) +
xlab("altituds (msnm)") +
ylab("total") +
theme_bw()
# ggsave(paste0(chm,"/publi/_alti_thm_hist.svg"),g.hist.thm, width = 16, height = 10, units ="cm")
# ggsave(paste0(chm,"/publi/_alti_thm_hist.pdf"),g.hist.thm, width = 16, height = 10, units ="cm")
```
Les roques gravades es distribueixen entre els `r z.min` msnm i els `r z.max` msnm
```{r dist_alti.graph, warning=F, echo=FALSE, out.height="50%"}
g.hist.thm
```
## Altituds de temes gravats
```{r dist_alti_thm, warning=F, echo=FALSE, results='asis', out.width="100%"}
# boxplot des altitudes par themes
# rocas_z <- st_read(paste0(chm_pts,"/", shape.name.roches,".shp"))
# list_grav_trad <- read.xlsx(paste0(chm,"/", list_grav_xt, ".xlsx"))
# thm_xt <- subset(list_grav_trad, select=c("roche","thm_xt"))
# thm_xt <- merge(thm_xt, rocas_z, by="roche", all.x=T) # merge list and shapes
# thm_xt <- subset(thm_xt, select=c("roche","thm_xt","geometry","z","zona"))
# create graph of boxplots for themes altitudes (z)
# order by thm z mean
# df <- df.patts
df <- thm_xt
df <- df[!is.na(df$thm_xt), ]
# zmean
thm_xt_zmean <- df %>%
group_by(thm_xt) %>%
summarize(z_mean = mean(z, na.rm=TRUE))
# count
thm_xt_count <- df %>%
group_by(thm_xt) %>%
summarise(n = n())
thm_xt_zmean <- merge(thm_xt_zmean, thm_xt_count, by = "thm_xt")
thm_xt_zmean_order <- thm_xt_zmean[order(thm_xt_zmean$z_mean),"thm_xt"]
# thm_xt_zmean_order <- thm_xt_zmean_order$thm_xt
# reorder on factors/zmean
# thm_xt_ordered_by_zmean <- thm_xt[match(thm_xt_zmean_order$thm_xt,
# thm_xt$thm_xt),]
df$thm_xt <- factor(df$thm_xt, levels = thm_xt_zmean_order)
df <- merge(df, thm_xt_zmean, by="thm_xt", all.x = T)
df_sp <- st_as_sf(df) # -> sf
# max/min for ggplot
z.min <- plyr::round_any(min(df$z, na.rm = T)-50, 50, f = ceiling)
z.max <- plyr::round_any(max(df$z, na.rm = T), 50, f = ceiling)
# size depend from nb thms (dim output, y labels)
siz.rec <- length(unique(df$thm_xt))
if(siz.rec < 33){by <- 100} else {by <- 50}
#
a.marg <- z.max + 25
ggplot(data = df_sp, aes(y = z, x = thm_xt)) +
geom_boxplot(fatten = 1.5, width=0.7, lwd=0.1, outlier.shape = NA) +
#stat_summary(fun.data = min.mean.sd.max, geom = "boxplot",position=position_dodge(width=.5), size=.5)+
geom_jitter(position=position_jitter(width=.5),
size=.2, alpha = 0.5, aes(colour = df_sp$zona)) +
geom_point(y = df_sp$z_mean, x = df_sp$thm_xt,
shape= 3, color = "red", cex = .5) + # mean
geom_text(y = z.max, x = df_sp$thm_xt, label = df_sp$n, size = 1.5, hjust = 0.5) + # numbers
# annotate("rect",ymin=nprosp.alt[1],ymax=nprosp.alt[2],xmin=-Inf,xmax=Inf, alpha=0.1, fill="black")+
# annotate("text", y=1500, x=25, label= "zone 4 \n no prospectada",size=3,hjust=0) +
labs(x="temas", y="altituds (msnm)", colour="zonas")+
expand_limits(y = c(z.min, a.marg)) +
scale_y_continuous(breaks = seq(z.min, z.max, by = by)) +
# scale_y_continuous(breaks = seq(z.min, z.max, by = by)) +
#xlab("temas") + ylab("altituds (msnm)")+
theme_bw()+
theme(legend.title = element_text(size = 8),
legend.text = element_text(size = 6),
#legend.key.size = unit(6,"line"),
axis.title=element_text(size=7),
axis.text.y = element_text(size=6),
axis.text.x = element_text(size=6))+
# axis.text.y = element_text(angle=90, size=5),
# axis.text.x = element_text(angle=90, size=5, hjust=1, vjust=0))+
coord_flip()+
scale_colour_manual(values = mycolo) +
guides(color = guide_legend(override.aes = list(size=2)))
# ggsave(paste0(chm,"/publi/_alti_thm_boxplot.svg"),g.bx.thm, width = 18, height = 24, units ="cm")
# width = 18, height = 24, units ="cm")
# format.out <- ".pdf"
# name.out <- paste0("_alti_det_thm_boxplot_1", format.out)
#
# f.thm.z.boxplot(thm_xt, name.out) # all theme
# f.thm.z.boxplot(df.patts, paste0("_alti_main_thm_boxplot", format.out)) # all theme
```
# Difusió
## Tribuna d'Arqueologia
El 21 d'abril 2021, a la "Tribuna d'Arqueologia" de Barcelona, Ermengol Gassiot Balbè, Oscar Augé i Thomas Huet van presentar un primer estat de recerca sobre els gravats de Sauri
<div style="width:600px; height:400px; margin: auto">
[](https://www.youtube.com/watch?v=4b7gLw4NV_E)
<center>Tribuna d'Arqueologia video (21/04/21, Barcelona)</center>
</div>
# References