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data(Oxboys, package = "nlme")
head(Oxboys)library(ggplot2)
ggplot(Oxboys, aes(age, height, group = Subject)) +
geom_point() +
geom_line()
ggplot(Oxboys, aes(age, height)) +
geom_point() +
geom_line()
ggplot(Oxboys, aes(age, height, group = Subject)) +
geom_line() +
geom_smooth(method = "lm", se = FALSE)## `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'ggplot(Oxboys, aes(age, height)) +
geom_line(aes(group = Subject)) +
geom_smooth(method = "lm", size = 2, se = FALSE)## `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'ggplot(Oxboys, aes(Occasion, height)) +
geom_boxplot()
ggplot(Oxboys, aes(Occasion, height)) +
geom_boxplot() +
geom_line(colour = "#3366FF", alpha = 0.5)
ggplot(Oxboys, aes(Occasion, height)) +
geom_boxplot() +
geom_line(aes(group = Subject), colour = "#3366FF", alpha = 0.5)
df <- data.frame(x = 1:3, y = 1:3, colour = c(1,3,5))
ggplot(df, aes(x, y, colour = factor(colour))) +
geom_line(aes(group = 1), size = 2) +
geom_point(size = 5)
ggplot(df, aes(x, y, colour = colour)) +
geom_line(aes(group = 1), size = 2) +
geom_point(size = 5)
xgrid <- with(df, seq(min(x), max(x), length = 50))
interp <- data.frame(
x = xgrid,
y = approx(df$x, df$y, xout = xgrid)$y,
colour = approx(df$x, df$colour, xout = xgrid)$y
)
ggplot(interp, aes(x, y, colour = colour)) +
geom_line(size = 2) +
geom_point(data = df, size = 5)
ggplot(mpg, aes(class)) +
geom_bar()
ggplot(mpg, aes(class, fill = drv)) +
geom_bar()
ggplot(mpg, aes(class, fill = hwy)) +
geom_bar()
ggplot(mpg, aes(class, fill = hwy, group = hwy)) +
geom_bar()
ggplot(mpg, aes(displ, cty,group=displ)) +
geom_boxplot()
library(babynames)## Warning: package 'babynames' was built under R version 4.0.5hadley <- dplyr::filter(babynames, name == "Hadley")
ggplot(hadley, aes(year, n)) +
geom_line()
y <- c(18, 11, 16)
df <- data.frame(x = 1:3, y = y, se = c(1.2, 0.5, 1.0))
base <- ggplot(df, aes(x, y, ymin = y - se, ymax = y + se))
base + geom_crossbar()
base + geom_pointrange()
base + geom_smooth(stat = "identity")
base + geom_errorbar()
base + geom_linerange()
base + geom_ribbon()
# Unweighted
ggplot(midwest, aes(percwhite, percbelowpoverty)) +
geom_point()
# Weight by population
ggplot(midwest, aes(percwhite, percbelowpoverty)) +
geom_point(aes(size = poptotal / 1e6)) +
scale_size_area("Population\n(millions)", breaks = c(0.5, 1, 2, 4))
# Unweighted
ggplot(midwest, aes(percwhite, percbelowpoverty)) +
geom_point() +
geom_smooth(method = lm, size = 1)## `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
# Weighted by population
ggplot(midwest, aes(percwhite, percbelowpoverty)) +
geom_point(aes(size = poptotal / 1e6)) +
geom_smooth(aes(weight = poptotal), method = lm, size = 1) +
scale_size_area(guide = "none")## `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'ggplot(diamonds, aes(depth)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(diamonds, aes(depth)) +
geom_histogram(binwidth = 0.1) +
xlim(55, 70)## Warning: Removed 45 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_bar).
#> Warning: Removed 45 rows containing non-finite values (stat_bin).
#> Warning: Removed 2 rows containing missing values (geom_bar).ggplot(diamonds, aes(depth)) +
geom_freqpoly(aes(colour = cut), binwidth = 0.1, na.rm = TRUE) +
xlim(58, 68) +
theme(legend.position = "none")
ggplot(diamonds, aes(depth)) +
geom_histogram(aes(fill = cut), binwidth = 0.1, position = "fill",
na.rm = TRUE) +
xlim(58, 68) +
theme(legend.position = "none")
ggplot(diamonds, aes(depth)) +
geom_density(na.rm = TRUE) +
xlim(58, 68) +
theme(legend.position = "none")
ggplot(diamonds, aes(depth, fill = cut, colour = cut)) +
geom_density(alpha = 0.2, na.rm = TRUE) +
xlim(58, 68) +
theme(legend.position = "none")
ggplot(diamonds, aes(clarity, depth)) +
geom_boxplot()
ggplot(diamonds, aes(carat, depth)) +
geom_boxplot(aes(group = cut_width(carat, 0.1))) +
xlim(NA, 2.05)## Warning: Removed 997 rows containing missing values (stat_boxplot).
#> Warning: Removed 997 rows containing missing values (stat_boxplot).ggplot(diamonds, aes(clarity, depth)) +
geom_violin()
ggplot(diamonds, aes(carat, depth)) +
geom_violin(aes(group = cut_width(carat, 0.1))) +
xlim(NA, 2.05)## Warning: Removed 997 rows containing non-finite values (stat_ydensity).
#> Warning: Removed 997 rows containing non-finite values (stat_ydensity).df <- data.frame(x = rnorm(2000), y = rnorm(2000))
norm <- ggplot(df, aes(x, y)) + xlab(NULL) + ylab(NULL)
norm + geom_point()
norm + geom_point(shape = 1) # Hollow circles
norm + geom_point(shape = ".") # Pixel sized
norm + geom_point(alpha = 1 / 3)
norm + geom_point(alpha = 1 / 5)
norm + geom_point(alpha = 1 / 10)
norm + geom_bin2d()
norm + geom_bin2d(bins = 10)
norm + geom_hex()
norm + geom_hex(bins = 10)
ggplot(diamonds, aes(color)) +
geom_bar()
ggplot(diamonds, aes(color, price)) +
geom_bar(stat = "summary_bin", fun = mean)
ggplot(diamonds, aes(table, depth)) +
geom_bin2d(binwidth = 1, na.rm = TRUE) +
xlim(50, 70) +
ylim(50, 70)
ggplot(diamonds, aes(table, depth, z = price)) +
geom_raster(binwidth = 1, stat = "summary_2d", fun = mean,
na.rm = TRUE) +
xlim(50, 70) +
ylim(50, 70)## Warning: Raster pixels are placed at uneven horizontal intervals and will be
## shifted. Consider using geom_tile() instead.## Warning: Raster pixels are placed at uneven vertical intervals and will be
## shifted. Consider using geom_tile() instead.
#> Warning: Raster pixels are placed at uneven horizontal intervals and will be
#> shifted. Consider using geom_tile() instead.
#> Warning: Raster pixels are placed at uneven vertical intervals and will be
#> shifted. Consider using geom_tile() instead.ggplot(faithfuld, aes(eruptions, waiting)) +
geom_contour(aes(z = density, colour = ..level..))
ggplot(faithfuld, aes(eruptions, waiting)) +
geom_raster(aes(fill = density))
# Bubble plots work better with fewer observations
small <- faithfuld[seq(1, nrow(faithfuld), by = 10), ]
ggplot(small, aes(eruptions, waiting)) +
geom_point(aes(size = density), alpha = 1/3) +
scale_size_area()
library(ozmaps)## Warning: package 'ozmaps' was built under R version 4.0.5library(sf)## Warning: package 'sf' was built under R version 4.0.5## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1#> Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1
oz_states <- ozmaps::ozmap_states
oz_statesggplot(oz_states) +
geom_sf() +
coord_sf()
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.5## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.1 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
## v purrr 0.3.4## Warning: package 'tibble' was built under R version 4.0.5## Warning: package 'tidyr' was built under R version 4.0.5## Warning: package 'dplyr' was built under R version 4.0.5## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()oz_states <- ozmaps::ozmap_states %>% filter(NAME != "Other Territories")
oz_votes <- rmapshaper::ms_simplify(ozmaps::abs_ced)## Registered S3 method overwritten by 'geojsonlint':
## method from
## print.location dplyr#> Registered S3 method overwritten by 'geojsonlint':
#> method from
#> print.location dplyrggplot() +
geom_sf(data = oz_states, mapping = aes(fill = NAME), show.legend = FALSE) +
geom_sf(data = oz_votes, fill = NA) +
coord_sf()
# filter electorates in the Sydney metropolitan region
sydney_map <- ozmaps::abs_ced %>% filter(NAME %in% c(
"Sydney", "Wentworth", "Warringah", "Kingsford Smith", "Grayndler", "Lowe",
"North Sydney", "Barton", "Bradfield", "Banks", "Blaxland", "Reid",
"Watson", "Fowler", "Werriwa", "Prospect", "Parramatta", "Bennelong",
"Mackellar", "Greenway", "Mitchell", "Chifley", "McMahon"
))
# draw the electoral map of Sydney
ggplot(sydney_map) +
geom_sf(aes(fill = NAME), show.legend = FALSE) +
coord_sf(xlim = c(150.97, 151.3), ylim = c(-33.98, -33.79)) +
geom_sf_label(aes(label = NAME), label.padding = unit(1, "mm"))## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
#> Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
#> give correct results for longitude/latitude dataoz_capitals <- tibble::tribble(
~city, ~lat, ~lon,
"Sydney", -33.8688, 151.2093,
"Melbourne", -37.8136, 144.9631,
"Brisbane", -27.4698, 153.0251,
"Adelaide", -34.9285, 138.6007,
"Perth", -31.9505, 115.8605,
"Hobart", -42.8821, 147.3272,
"Canberra", -35.2809, 149.1300,
"Darwin", -12.4634, 130.8456,
)
ggplot() +
geom_sf(data = oz_votes) +
geom_sf(data = oz_states, colour = "black", fill = NA) +
geom_point(data = oz_capitals, mapping = aes(x = lon, y = lat), colour = "red") +
coord_sf()
st_crs(oz_votes)## Coordinate Reference System:
## User input: EPSG:4283
## wkt:
## GEOGCRS["GDA94",
## DATUM["Geocentric Datum of Australia 1994",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["Horizontal component of 3D system."],
## AREA["Australia including Lord Howe Island, Macquarie Islands, Ashmore and Cartier Islands, Christmas Island, Cocos (Keeling) Islands, Norfolk Island. All onshore and offshore."],
## BBOX[-60.56,93.41,-8.47,173.35]],
## ID["EPSG",4283]]#> Coordinate Reference System:
#> User input: EPSG:4283
#> wkt:
#> GEOGCRS["GDA94",
#> DATUM["Geocentric Datum of Australia 1994",
#> ELLIPSOID["GRS 1980",6378137,298.257222101,
#> LENGTHUNIT["metre",1]]],
#> PRIMEM["Greenwich",0,
#> ANGLEUNIT["degree",0.0174532925199433]],
#> CS[ellipsoidal,2],
#> AXIS["geodetic latitude (Lat)",north,
#> ORDER[1],
#> ANGLEUNIT["degree",0.0174532925199433]],
#> AXIS["geodetic longitude (Lon)",east,
#> ORDER[2],
#> ANGLEUNIT["degree",0.0174532925199433]],
#> USAGE[
#> SCOPE["unknown"],
#> AREA["Australia - GDA"],
#> BBOX[-60.56,93.41,-8.47,173.35]],
#> ID["EPSG",4283]]st_crs(oz_votes) == st_crs(4283)## [1] TRUE#> [1] TRUEggplot(oz_votes) + geom_sf()
ggplot(oz_votes) + geom_sf() + coord_sf(crs = st_crs(3112))
edenmonaro <- ozmaps::abs_ced %>% filter(NAME == "Eden-Monaro")
p <- ggplot(edenmonaro) + geom_sf()
p + coord_sf(xlim = c(147.75, 150.25), ylim = c(-37.5, -34.5)) 
p + coord_sf(xlim = c(150, 150.25), ylim = c(-36.3, -36)) 
edenmonaro <- edenmonaro %>% pull(geometry)st_bbox(edenmonaro)## xmin ymin xmax ymax
## 147.68741 -37.50503 150.23068 -34.53558edenmonaro ## Geometry set for 1 feature
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 147.6874 ymin: -37.50503 xmax: 150.2307 ymax: -34.53558
## Geodetic CRS: GDA94## MULTIPOLYGON (((150.2307 -36.24468, 150.2287 -3...st_cast(edenmonaro, "POLYGON")## Geometry set for 2 features
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 147.6874 ymin: -37.50503 xmax: 150.2307 ymax: -34.53558
## Geodetic CRS: GDA94## POLYGON ((150.2307 -36.24468, 150.2287 -36.2490...## POLYGON ((148.1345 -36.74374, 148.1366 -36.7393...dawson <- ozmaps::abs_ced %>%
filter(NAME == "Dawson") %>%
pull(geometry)
dawson## Geometry set for 1 feature
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 146.761 ymin: -21.21307 xmax: 149.9114 ymax: -19.18582
## Geodetic CRS: GDA94## MULTIPOLYGON (((147.8981 -19.85285, 147.899 -19...#> Geometry set for 1 feature
#> Geometry type: MULTIPOLYGON
#> MULTIPOLYGON (((148 -19.9, 148 -19.8, 148 -19.8...
ggplot(dawson) +
geom_sf() +
coord_sf()
dawson <- st_cast(dawson, "POLYGON")
which.max(st_area(dawson))## [1] 69ggplot(dawson[-69]) +
geom_sf() +
coord_sf()
library(tidygraph)## Warning: package 'tidygraph' was built under R version 4.0.5##
## Attaching package: 'tidygraph'## The following object is masked from 'package:stats':
##
## filtergraph <- play_erdos_renyi(n = 10, p = 0.2) %>%
activate(nodes) %>%
mutate(class = sample(letters[1:4], n(), replace = TRUE)) %>%
activate(edges) %>%
arrange(.N()$class[from])
graph## # A tbl_graph: 10 nodes and 16 edges
## #
## # A directed simple graph with 1 component
## #
## # Edge Data: 16 x 2 (active)
## from to
## <int> <int>
## 1 8 2
## 2 8 7
## 3 8 10
## 4 8 9
## 5 2 10
## 6 2 5
## # ... with 10 more rows
## #
## # Node Data: 10 x 1
## class
## <chr>
## 1 d
## 2 b
## 3 b
## # ... with 7 more rowsdata(highschool, package = "ggraph")
head(highschool)hs_graph <- as_tbl_graph(highschool, directed = FALSE)
hs_graph## # A tbl_graph: 70 nodes and 506 edges
## #
## # An undirected multigraph with 1 component
## #
## # Node Data: 70 x 1 (active)
## name
## <chr>
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## 6 6
## # ... with 64 more rows
## #
## # Edge Data: 506 x 3
## from to year
## <int> <int> <dbl>
## 1 1 13 1957
## 2 1 14 1957
## 3 1 20 1957
## # ... with 503 more rowsluv_clust <- hclust(dist(luv_colours[, 1:3]))
luv_graph <- as_tbl_graph(luv_clust)
luv_graph## # A tbl_graph: 1313 nodes and 1312 edges
## #
## # A rooted tree
## #
## # Node Data: 1,313 x 4 (active)
## height leaf label members
## <dbl> <lgl> <chr> <int>
## 1 0 TRUE "101" 1
## 2 0 TRUE "427" 1
## 3 778. FALSE "" 2
## 4 0 TRUE "571" 1
## 5 0 TRUE "426" 1
## 6 0 TRUE "424" 1
## # ... with 1,307 more rows
## #
## # Edge Data: 1,312 x 2
## from to
## <int> <int>
## 1 3 1
## 2 3 2
## 3 8 6
## # ... with 1,309 more rowsgraph %>%
activate(nodes) %>%
mutate(centrality = centrality_pagerank()) %>%
arrange(desc(centrality))## # A tbl_graph: 10 nodes and 16 edges
## #
## # A directed simple graph with 1 component
## #
## # Node Data: 10 x 2 (active)
## class centrality
## <chr> <dbl>
## 1 c 0.234
## 2 c 0.221
## 3 b 0.216
## 4 c 0.204
## 5 c 0.0259
## 6 d 0.0244
## # ... with 4 more rows
## #
## # Edge Data: 16 x 2
## from to
## <int> <int>
## 1 10 7
## 2 10 8
## 3 10 1
## # ... with 13 more rowslibrary(ggraph)## Warning: package 'ggraph' was built under R version 4.0.5ggraph(hs_graph) +
geom_edge_link() +
geom_node_point()## Using `stress` as default layout
ggraph(hs_graph, layout = "drl") +
geom_edge_link() +
geom_node_point()
hs_graph <- hs_graph %>%
activate(edges) %>%
mutate(edge_weights = runif(n()))
ggraph(hs_graph, layout = "stress", weights = edge_weights) +
geom_edge_link(aes(alpha = edge_weights)) +
geom_node_point() +
scale_edge_alpha_identity()
ggraph(luv_graph, layout = 'dendrogram', circular = TRUE) +
geom_edge_link() +
coord_fixed()
ggraph(luv_graph, layout = 'dendrogram') +
geom_edge_link() +
coord_polar() +
scale_y_reverse()
ggraph(hs_graph, layout = "stress") +
geom_edge_link() +
geom_node_point(
aes(filter = centrality_degree() > 2,
colour = centrality_power()),
size = 4
)
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