josephlewis

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    <teiHeader>
        <fileDesc>
            <titleStmt>
                <title>Grabinschrift auf Stele</title>
            </titleStmt>  
            <publicationStmt>
                <authority>Epigraphische Datenbank Heidelberg</authority>
                <idno type="URI">http://edh-www.adw.uni-heidelberg.de/edh/inschrift/HD008323</idno>
                <idno type="TM">178459</idno><idno type="localID">HD008323</idno>

josephlewis

ggplot(data = dist_travelled[c(TRUE, FALSE),], aes(x = factor(1), y = distance, label = paste0(place, ", ", cumulative, "km"))) + 
  geom_line(colour = "#C70039", position = "stack", stat = "identity", size = 3) + 
  geom_point(colour = "#C70039", fill = "#DDDDDD", pch = 21, position = "stack", stat = "identity", size = 6, stroke = 3) + 
  geom_text_repel(position = position_stack(vjust = .5), colour = "#666666", family="PT Sans", force=1, point.padding=unit(1,'lines')) + 
  coord_flip()

josephlewis

plaques$sex[grep("man", plaques$subjects, fixed = TRUE)] <- "Man"
plaques$sex[grep("woman", plaques$subjects, fixed = TRUE)] <- "Woman"

josephlewis

> head(xtestbuffer)
      battlefield.no distance decade
1200s             NA     1000  1200s
1300s     0.20000000     1000  1300s
1400s             NA     1000  1400s
1500s             NA     1000  1500s
1600s     0.05882353     1000  1600s
1700s     0.20000000     1000  1700s

decbuf <- ggplot(xtestbuffer) + 

josephlewis

for (i in levels(scot_battles_centroid_dates$century)) { # the levels are "1200s" to "1900s"
  battle_road_dist <- gDistance(scot_battles_centroid_dates[scot_battles_centroid_dates$century == i,], sp, byid = TRUE) # euclidean distance from battle point to road. 
  battle_road_dist_vect <- data.frame(as.vector(battle_road_dist)) # creates df of the distances
  dist_test <- rbind(battle_road_dist_vect) # adds to a df to create one df with all of the distances
}

josephlewis

road_pts <- list()

for (i in unique(wales_roads$Name)) {
  known_pts <- gInterpolate(wales_roads[wales_roads$Name == i,], d = seq(0, gLength(wales_roads[wales_roads$Name == i,]), by = gLength(wales_roads[wales_roads$Name == i,])), normalized = FALSE)
  known_pts@coords
  A <- known_pts@coords[1,]
  B <- known_pts@coords[2,]
  AtoB <- shortestPath(Conductance, A, B, output="SpatialLines")
  plot(AtoB, add = TRUE, col = "red")
  LCP_pts <- gInterpolate(AtoB, d = seq(0, gLength(AtoB), by = 250), normalized = FALSE)  

josephlewis

for (i in 1:(nrow(road_coords)/2)) {
  A <- unlist(road_coords[i,2:3])
  B <- unlist(road_coords[i + 1,2:3])
  

> head(road_coords)
          Name coords.x1 coords.x2
1 Margary 62aa  300409.3  229884.3
2 Margary 62aa  303385.8  231078.0
3  Margary 62b  300419.3  229884.6

josephlewis

xx$band[xx$dav_hue_vector >= (0/12) & xx$dav_hue_vector <= (1/12)] <- "Red"
xx$band[xx$dav_hue_vector > (1/12) & xx$dav_hue_vector <= (2/12)] <- "Red-Orange"
xx$band[xx$dav_hue_vector > (2/12) & xx$dav_hue_vector <= (3/12)] <- "Orange"
xx$band[xx$dav_hue_vector > (3/12) & xx$dav_hue_vector <= (4/12)] <- "Yellow-Orange"
xx$band[xx$dav_hue_vector > (4/12) & xx$dav_hue_vector <= (5/12)] <- "Yellow"
xx$band[xx$dav_hue_vector > (5/12) & xx$dav_hue_vector <= (6/12)] <- "Yellow-Green"
xx$band[xx$dav_hue_vector > (6/12) & xx$dav_hue_vector <= (7/12)] <- "Green"
xx$band[xx$dav_hue_vector > (7/12) & xx$dav_hue_vector <= (8/12)] <- "Blue-Green"
xx$band[xx$dav_hue_vector > (8/12) & xx$dav_hue_vector <= (9/12)] <- "Blue"
xx$band[xx$dav_hue_vector > (9/12) & xx$dav_hue_vector <= (10/12)] <- "Blue-Violet"

josephlewis

[1] "month"                                    
[2] "visits"                                   
[3] "tmax"                                     
[4] "tmin"                                     
[5] "rainfall"                                 
[6] "OS.visits.to.UK.Earnings..Â..Millions.NSA"
[7] "OS.visits.to.UK.All.visits.Thousands.NSA" 
[8] "season"   

josephlewis

ggplot(data = df.m, aes(x = month, group = 1)) + 
  geom_ribbon(data= subset(df.m, df.m$visits.raw > df.m$visits.mean), aes(ymin = pmin(visits.mean, visits.raw), ymax = pmax(visits.mean, visits.raw)),fill="#008FD5", alpha="0.2") + 
  geom_line(aes(y = visits.mean), size = 1.5, color = 'grey') +
  geom_line(aes(y = visits.raw), size = 1.5, colour = "#008FD5") + 
  facet_wrap(~year, ncol = 3) + 
  scale_x_discrete(limits = month.abb) +
  scale_y_continuous(labels=comma)