## 開始測(cè)試
using Gadfly, RDatasets, RCall
iris = dataset("datasets", "iris")
typeof(iris)
# DataFrame
#@ 查看數(shù)據(jù)的前六行內(nèi)容
print(first(iris, 6))
# 6×5 DataFrame
# │ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
# │? ? │ Float64? ? │ Float64? ? │ Float64? ? │ Float64? ? │ Cat…? ? │
# ├─────┼─────────────┼────────────┼─────────────┼────────────┼─────────┤
# │ 1? │ 5.1? ? ? ? │ 3.5? ? ? ? │ 1.4? ? ? ? │ 0.2? ? ? ? │ setosa? │
# │ 2? │ 4.9? ? ? ? │ 3.0? ? ? ? │ 1.4? ? ? ? │ 0.2? ? ? ? │ setosa? │
# │ 3? │ 4.7? ? ? ? │ 3.2? ? ? ? │ 1.3? ? ? ? │ 0.2? ? ? ? │ setosa? │
# │ 4? │ 4.6? ? ? ? │ 3.1? ? ? ? │ 1.5? ? ? ? │ 0.2? ? ? ? │ setosa? │
# │ 5? │ 5.0? ? ? ? │ 3.6? ? ? ? │ 1.4? ? ? ? │ 0.2? ? ? ? │ setosa? │
# │ 6? │ 5.4? ? ? ? │ 3.9? ? ? ? │ 1.7? ? ? ? │ 0.4? ? ? ? │ setosa? │
#@ 繪制第一張圖
plot(iris, x=:SepalLength, y=:SepalWidth)
#@ 保存繪制的圖形到本地
### 保存為SVG格式
p = plot(iris, x=:SepalLength, y=:SepalWidth, Geom.point);
img = SVG("iris_plot.svg", 14cm, 8cm)
draw(img, p)
### 保存為PDF格式
using Cairo
using Fontconfig
img = PDF("iris_plot.pdf", 14cm, 10cm)
draw(img, p)
### 保存為PNG格式
img = PNG("iris_plot.png", 14cm, 10cm)
draw(img, p)
# Alternatively one can manually call display on a Plot object. This workflow is necessary when display would not otherwise be called automatically.
function get_to_it(d)
? ppoint = plot(d, x=:SepalLength, y=:SepalWidth, Geom.point)
? pline = plot(d, x=:SepalLength, y=:SepalWidth, Geom.line)
? ppoint, pline
end
ps = get_to_it(iris)
map(display, ps)
p2 = plot(iris, x=:SepalLength, y=:SepalWidth, Geom.point, Geom.line)
img = PNG("iris_point_and_line_plot.png", 14cm, 10cm)
draw(img, p2)
img = PDF("iris_point_and_line_plot.pdf", 14cm, 10cm)
draw(img, p2)
## Arrays
SepalLength = iris.SepalLength
SepalWidth = iris.SepalWidth
plot(x=SepalLength, y=SepalWidth, Geom.point,
? ? Guide.xlabel("SepalLength"), Guide.ylabel("SepalWidth"))
## Color
plot(iris, x=:SepalLength, y=:SepalWidth, color=:Species, Geom.point);
# plot(iris, x=iris::SepalLength, y=iris::SepalWidth, color=iris::Species, Geom.point)
# TypeError: in typeassert, expected Type, got Array{Float64,1}
# plot(iris, x=iris:SepalLength, y=iris:SepalWidth, color=iris:Species, Geom.point);
# or equivalently for Arrays:
Color = iris.Species
p3 = plot(x=SepalLength, y=SepalWidth, color=Color, Geom.point,
? ? Guide.xlabel("SepalLength"), Guide.ylabel("SepalWidth"),
? ? Guide.colorkey(title="Species"))
img = PNG("iris_color_plot.png", 14cm, 10cm)
draw(img, p3)
img = PDF("iris_color_plot.pdf", 14cm, 10cm)
draw(img, p3)
## Continuous Scales
# Continuous scales can be transformed. In the next plot, the large animals are ruining things for us. Putting both axes on a log-scale clears things up.
#@ 圖片組合，使用函數(shù)hstack
set_default_plot_size(21cm ,8cm)
mammals = dataset("MASS", "mammals")
p1 = plot(mammals, x=:Body, y=:Brain, label=:Mammal, Geom.point, Geom.label)
img = PDF("mammals_body_and_brain.pdf", 14cm, 10cm)
draw(img, p1)
img = PNG("mammals_body_and_brain.png", 14cm, 10cm)
draw(img, p1)
p2 = plot(mammals, x=:Body, y=:Brain, label=:Mammal, Geom.point, Geom.label,
? ? Scale.x_log10, Scale.y_log10)
p12 = hstack(p1, p2)
img = PDF("mammals_comb.pdf", 15cm, 10cm)
draw(img, p12)
img = PNG("mammals_comb.png", 15cm, 10cm)
draw(img, p12)
print(first(mammals,10))
# 10×3 DataFrame
# │ Row │ Mammal? ? ? ? ? │ Body? ? │ Brain? │
# │? ? │ String? ? ? ? ? │ Float64 │ Float64 │
# ├─────┼─────────────────┼─────────┼─────────┤
# │ 1? │ Arctic fox? ? ? │ 3.385? │ 44.5? ? │
# │ 2? │ Owl monkey? ? ? │ 0.48? ? │ 15.5? ? │
# │ 3? │ Mountain beaver │ 1.35? ? │ 8.1? ? │
# │ 4? │ Cow? ? ? ? ? ? │ 465.0? │ 423.0? │
# │ 5? │ Grey wolf? ? ? │ 36.33? │ 119.5? │
# │ 6? │ Goat? ? ? ? ? ? │ 27.66? │ 115.0? │
# │ 7? │ Roe deer? ? ? ? │ 14.83? │ 98.2? ? │
# │ 8? │ Guinea pig? ? ? │ 1.04? ? │ 5.5? ? │
# │ 9? │ Verbet? ? ? ? ? │ 4.19? ? │ 58.0? ? │
# │ 10? │ Chinchilla? ? ? │ 0.425? │ 6.4? ? │
typeof(mammals)
# DataFrame
#@ 保存數(shù)據(jù)
using DelimitedFiles
new_data = open("mammals_data.csv", "w")
writedlm(new_data, mammals, ",")
# AbstractDataFrame is not iterable. Use eachrow(df) to get a row iterator or eachcol(df) to get a column iterator
close(new_data)
mammals
# 62×3 DataFrame
# │ Row │ Mammal? ? │ Body? ? │ Brain? │
# │? ? │ String? ? │ Float64 │ Float64 │
# ├─────┼────────────┼─────────┼─────────┤
# │ 1? │ Arctic fox │ 3.385? │ 44.5? ? │
# ?
# │ 61? │ Tree shrew │ 0.104? │ 2.5? ? │
# │ 62? │ Red fox? ? │ 4.235? │ 50.4? ? │
# Scale transformations include: _sqrt, _log, _log2, _log10, _asinh for the x, y, color aesthetics, and _area for the size aesthetic.
using Printf
Diamonds = dataset("ggplot2","diamonds")
Diamonds
# 53940×10 DataFrame
# │ Row? │ Carat? │ Cut? ? │ Color │ Clarity │ Depth? │ Table? │ Price │ X? ? ? │ Y? ? ? │ Z? ? ? │
# │? ? ? │ Float64 │ Cat…? ? │ Cat…? │ Cat…? ? │ Float64 │ Float64 │ Int32 │ Float64 │ Float64 │ Float64 │
# ├───────┼─────────┼─────────┼───────┼─────────┼─────────┼─────────┼───────┼─────────┼─────────┼─────────┤
# │ 1? ? │ 0.23? ? │ Ideal? │ E? ? │ SI2? ? │ 61.5? ? │ 55.0? ? │ 326? │ 3.95? ? │ 3.98? ? │ 2.43? ? │
# ?
# │ 53939 │ 0.86? ? │ Premium │ H? ? │ SI2? ? │ 61.0? ? │ 58.0? ? │ 2757? │ 6.15? ? │ 6.12? ? │ 3.74? ? │
# │ 53940 │ 0.75? ? │ Ideal? │ D? ? │ SI2? ? │ 62.2? ? │ 55.0? ? │ 2757? │ 5.83? ? │ 5.87? ? │ 3.64? ? │
p3= plot(Diamonds, x=:Price, y=:Carat, Geom.histogram2d(xbincount=25, ybincount=25),
? ? Scale.x_continuous(format=:engineering) )
img = PDF("diamonds_price_carat_histogram2d.pdf", 20cm, 15cm)
draw(img, p3)
img = PNG("diamonds_price_carat_histogram2d.png", 20cm, 15cm)
draw(img, p3)
p4= plot(Diamonds, x=:Price, y=:Carat, Geom.histogram2d(xbincount=25, ybincount=25),
? ? Scale.x_continuous(format=:plain),
? ? Scale.y_sqrt(labels=y->@sprintf("%i", y^2)),
? ? Scale.color_log10(minvalue=1.0, maxvalue=10^4),
? ? Guide.yticks(ticks=sqrt.(0:5)) )
img = PDF("diamonds_price_carat_histogram2d_2.pdf", 20cm, 15cm)
draw(img, p4)
img = PNG("diamonds_price_carat_histogram2d_2.png", 20cm, 15cm)
draw(img, p4)
p34 = hstack(p3, p4)
img = PDF("diamonds_comb.pdf", 30cm, 15cm)
draw(img, p34)
img = PNG("diamonds_comb.png", 30cm, 15cm)
draw(img, p34)
## Discrete Scales ##
mtcars = dataset("datasets","mtcars")
labeldict = Dict(4=>"four", 6=>"six", 8=>"eight")
p5 = plot(mtcars, x=:Cyl, color=:Cyl, Geom.histogram,
? ? Scale.x_discrete(levels=[4,6,8]), Scale.color_discrete(levels=[4,6,8]) )
p6 = plot(mtcars, x=:Cyl, color=:Cyl, Geom.histogram,
? ? Scale.x_discrete(labels=i->labeldict[i], levels=[8,6,4]),
? ? Scale.color_discrete(levels=[8,6,4]) )
p56 = hstack(p5, p6)
img = PDF("mtcars_cyl.pdf", 30cm, 15cm)
draw(img, p56)
# For discrete scales with a Theme palette, the order of levels and the order of the Theme palette match.
x, y = 0.55*rand(4), 0.55*rand(4)
print("x的內(nèi)容為：",x,"；y的內(nèi)容為：",y)
# x的內(nèi)容為：[0.026078572083341835, 0.38793499822192545, 0.38721812973576736, 0.31864486064946673]；y的內(nèi)容為：[0.31252641092827416, 0.535436887627406, 0.11158461909555882, 0.34650338563223454]
pp = plot( Coord.cartesian(xmin=0, ymin=0, xmax=1.0, ymax=1.0),
? ? layer(x=x, y=y, shape=["A"], alpha=["day","day","day","night"]),
? ? layer(x=1.0.-y[1:3], y=1.0.-x[1:3], shape=["B", "C","C"], alpha=["night"]),
? ? Scale.shape_discrete(levels=["A","B","C"]),
? ? Scale.alpha_discrete(levels=["day","night"]),
? ? Theme(discrete_highlight_color=identity, point_size=12pt,
? point_shapes=[Shape.circle, Shape.star1, Shape.star2], alphas=[0, 1.0],
? ? ? ? default_color="midnightblue" )
)
img = PDF("shape_and_color.pdf", 20cm, 15cm)
draw(img, pp)
img = PNG("shape_and_color.png", 20cm, 15cm)
draw(img, pp)
## Gadfly defaults ##
# If you don't supply Scales or Guides, Gadfly will make an educated guess.
gasoline = dataset("Ecdat", "Gasoline")
pp2 = plot(gasoline, x=:Year, y=:LGasPCar, color=:Country, Geom.point, Geom.line)
img = PDF("Gasoline_Ecdat.pdf", 20cm, 10cm)
draw(img, pp2)
img = PNG("Gasoline_Ecdat.png", 20cm, 10cm)
draw(img, pp2)
## Rendering ##
# Gadfly uses a custom graphics library called Compose, which is an attempt at a more elegant, purely functional take on the R grid package. It allows mixing of absolute and relative units and complex coordinate transforms. The primary backend is a native SVG generator (almost native: it uses pango to precompute text extents), though there is also a Cairo backend for PDF and PNG. See Backends for more details.
#
# Building graphics declaratively let's you do some fun things. Like stick two plots together:
fig1a = plot(iris, x=:SepalLength, y=:SepalWidth, Geom.point)
fig1b = plot(iris, x=:SepalWidth, Home)
fig1 = hstack(fig1a, fig1b)
mimg = PDF("iris_SepalLength_SepalWidth.pdf", 30cm, 15cm)
draw(mimg, fig1)
mimg = PNG("iris_SepalLength_SepalWidth.png", 30cm, 15cm)
draw(mimg, fig1)
# Ultimately this will make more complex visualizations easier to build. For example, facets, plots within plots, and so on. See Compositing for more details.
