plots are still in progress, but some examples:
mpg = pd.read_csv(data_path / "mpg.csv")
plot1 = ggplot(mpg, aes("class", fill="drv")) + geom_bar()
df = mpg.groupby(["class", "cyl"], as_index=False).agg(meanHwy=("hwy", "mean"))
plot2 = (
ggplot(df, aes("class", "cyl", fill="meanHwy"))
+ geom_tile()
+ geom_text(aes(text="meanHwy"))
+ scale_y_discrete()
)
plot3 = (
ggplot(mpg, aes(x="cty", fill="class"))
+ geom_freqpoly(alpha=0.3)
+ scale_x_continuous()
)
plot4 = ggplot(
mpg, aes(x="cty", fill="class")
) + geom_histogram() + scale_x_continuous()
mpg = mpg.copy(deep=True)
mpg["cty"] = mpg["cty"].astype(float)
plot5 = ggplot(mpg, aes(x="cty", y="displ", size="cyl", color="cty")) + geom_point()
ggmulti(
[plot1, plot2, plot3, plot4, plot5],
plots_path / "gg_multi_pmg.png",
)
mpg = pd.read_csv(data_path / "mpg.csv")
plot = ggplot(mpg, aes("class")) + geom_bar()
res = ggcreate(plot)
ggdraw_plot(res, data_path / "geom_bar.png")
mpg = pd.read_csv(data_path / "mpg.csv")
plot = ggplot(mpg, aes("class", fill="drv")) + geom_bar()
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_bar_fill.png")
rng = np.random.default_rng(42)
x_vals = np.repeat(np.arange(28), 28)
y_vals = np.tile(np.arange(28), 28)
z_vals = rng.random(28 * 28)
df = pd.DataFrame({
'xs': x_vals.astype(float),
'ys': y_vals.astype(float),
'zs': z_vals
})
plot = ggplot(df, aes("xs", "ys", fill="zs")) + geom_tile()
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_tile.png")
mpg = pd.read_csv(data_path / "mpg.csv")
df = mpg.groupby(["class", "cyl"], as_index=False).agg(meanHwy=("hwy", "mean"))
plot = (
ggplot(df, aes("class", "cyl", fill="meanHwy"))
+ geom_tile()
+ geom_text(aes(text="meanHwy"))
+ scale_y_discrete()
)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_tile_mpg.png")
mpg = pd.read_csv(data_path / "mpg.csv")
plot = (
ggplot(mpg, aes(x="cty", color="class")) + geom_freqpoly() + scale_x_continuous()
)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_freqpoly.png")
diamonds = pd.read_csv(data_path / "diamonds.csv")
plot = ggplot(
diamonds, aes("price", color="cut")
) + geom_freqpoly(
) + ylab(
label="custom label",
rotate=-45
) + xlab(
rotate=45,
tick_margin=2.5
)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_freqpoly.png")
mpg = pd.read_csv(data_path / "mpg.csv")
plot = (
ggplot(mpg, aes(x="cty", fill="class"))
+ geom_freqpoly(alpha=0.3)
+ scale_x_continuous()
)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "test_geom_freqpoly_cty_class_fill.png")
mpg = pd.read_csv(data_path / "mpg.csv")
plot = ggplot(mpg, aes(x = 'displ')) + geom_histogram()
res = ggcreate(plot)
ggdraw_plot(res, data_path / "geom_histogram.png")
mpg = pd.read_csv(data_path / "mpg.csv")
plot = ggplot(mpg, aes(x="cty", fill="class")) + geom_histogram() + scale_x_continuous()
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_histogram_fill.png")
df = create_dataframe()
plot = ggplot(
df, aes("tenors", "pathValues", color = "pathNames")
) + geom_line() + xlab(rotate=-90, tick_margin=3)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_line_With_color.png")
df = pd.DataFrame(
data={
"trt": pd.Categorical([1, 1, 2, 2]),
"resp": [1, 5, 3, 4],
"group": pd.Categorical([1, 2, 1, 2]),
"upper": [1.1, 5.3, 3.3, 4.2],
"lower": [0.8, 4.6, 2.4, 3.6],
}
)
plot = ggplot(df, aes(x="trt", y="resp", color="group")) + geom_linerange(
aes(ymin="lower", ymax="upper")
)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_linerange.png")
df = pd.DataFrame(
data={"dose": ["D0.5", "D1", "D2"], "bbb": [4.2, 10, 29.5]}
)
plot = ggplot(df, aes(x="dose", y="bbb")) + geom_line() + geom_point()
res = ggcreate(plot)
ggdraw_plot(res, data_path / "geom_line_and_point_with_linetype.png")
mpg = pd.read_csv(data_path / "mpg.csv")
mpg["cty"] = mpg["cty"].astype(float)
plot = ggplot(mpg, aes(x="displ", y="hwy", color="cty")) + geom_point()
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_point_with_continuous_color.png")
mpg = pd.read_csv(data_path / "mpg.csv")
mpg["cty"] = mpg["cty"].astype(float)
plot = ggplot(mpg, aes(x="cty", y="displ", size = "cyl", color="cty")) + geom_point()
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_point_with_continuous_color_and_size.png")
mpg = pd.read_csv(data_path / "mpg.csv")
mpg["cty"] = mpg["cty"].astype(float)
mpg["mpgMean"] = (mpg["cty"] + mpg["hwy"]) / 2.0
df_max = mpg.sort_values("mpgMean").tail(1)
plot = (
ggplot(mpg, aes("hwy", "displ"))
+ geom_point(aes(color="cty"))
+ geom_text(data=df_max, aes=aes(y=gg_col("displ") + 0.2, text="model"))
+ geom_text(data=df_max, aes=aes(y=gg_col("displ") - 0.2, text="mpgMean"))
)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_point_and_text.png")
df = pd.DataFrame({
'trt': [1, 1, 2, 2],
'resp': [1, 5, 3, 4],
'group': pd.Categorical([1, 2, 1, 2]),
'upper': [1.5, 5.0, 3.3, 4.2],
'lower': [1, 4.0, 2.4, 3.6]
})
plot = ggplot(df, aes(x="trt", y="resp", color="group")) + geom_error_bar(
aes(ymin="lower", ymax="upper"), size=20
)
res = ggcreate(plot)
ggdraw_plot(res, plots_path / "geom_error_bar.png")
mpg = pd.read_csv(data_path / "mpg.csv")
plot = ggplot(mpg, aes(x = 'displ', y = "cty", text = 'manufacturer')) + geom_text()
res = ggcreate(plot)
ggdraw_plot(res, data_path / "geom_text.png")
For ggmulti you can set where plots are empty using the 'empty_plots' variable. It can be either an int or list of ints. Note that the value is the index of the plot, starting from the top left and counting across. Also, as this is Python the indexes start at zero, so in a 2x3 grid, index no. 4 will be the bottom left hand side.
mpg = pd.read_csv(data_path / "mpg.csv")
plot1 = ggplot(mpg, aes("class", fill="drv")) + geom_bar()
df = mpg.groupby(["class", "cyl"], as_index=False).agg(meanHwy=("hwy", "mean"))
plot2 = (
ggplot(df, aes("class", "cyl", fill="meanHwy"))
+ geom_tile()
+ geom_text(aes(text="meanHwy"))
+ scale_y_discrete()
)
plot3 = (
ggplot(mpg, aes(x="cty", fill="class"))
+ geom_freqpoly(alpha=0.3)
+ scale_x_continuous()
)
plot4 = ggplot(
mpg, aes(x="cty", fill="class")
) + geom_histogram() + scale_x_continuous()
mpg = mpg.copy(deep=True)
mpg["cty"] = mpg["cty"].astype(float)
plot5 = ggplot(mpg, aes(x="cty", y="displ", size="cyl", color="cty")) + geom_point()
ggmulti(
[plot1, plot2, plot3, plot4, plot5],
plots_path / "gg_multi_pmg_with_one_empty.png",
empty_plots=4
)
You can also set the rows or cols
mpg = pd.read_csv(data_path / "mpg.csv")
plot1 = ggplot(mpg, aes("class", fill="drv")) + geom_bar()
df = mpg.groupby(["class", "cyl"], as_index=False).agg(meanHwy=("hwy", "mean"))
plot2 = (
ggplot(df, aes("class", "cyl", fill="meanHwy"))
+ geom_tile()
+ geom_text(aes(text="meanHwy"))
+ scale_y_discrete()
)
plot3 = (
ggplot(mpg, aes(x="cty", fill="class"))
+ geom_freqpoly(alpha=0.3)
+ scale_x_continuous()
)
ggmulti([plot1, plot2, plot3], plots_path / "gg_multi_pmg_set_rows1.png", rows=3)
