Interactive map of Top 100 British Novels as decided by critics
Top 100 British Novels list: http://www.bbc.co.uk/culture/story/20151204-the-100-greatest-british-novels
Ceremonial County boundaries of England: https://data.gov.uk/dataset/ceremonial-county-boundaries-of-england
Future iterations include:
| ggplot() + geom_line(data = visits_1, aes(month, visits, group = 1,colour = "#008FD5", size=2)) + facet_wrap(~visits_1$year) + geom_line(data = x, aes(Month, Visits)) | |
| str(visits_1) | |
| 'data.frame': 164 obs. of 6 variables: | |
| $ museum: chr "NATIONAL GALLERY" "NATIONAL GALLERY" "NATIONAL GALLERY" "NATIONAL GALLERY" ... | |
| $ year : chr "2004" "2004" "2004" "2004" ... | |
| $ month : chr "4" "5" "6" "7" ... | |
| $ visits: num 437000 391000 319000 460000 443000 325000 425000 448000 410000 402000 ... | |
| $ season: chr "" "" "" "" ... | |
| $ date : Date, format: "2004-04-01" "2004-05-01" "2004-06-01" "2004-07-01" ... |
| 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) |
| [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" |
| 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" |
| 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 |
| 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) |
| 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 | |
| } | |
| > 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) + |
| plaques$sex[grep("man", plaques$subjects, fixed = TRUE)] <- "Man" | |
| plaques$sex[grep("woman", plaques$subjects, fixed = TRUE)] <- "Woman" | |
