看来我会使用stat_function,但是失败了: #this fails pg <- ggplot(dd) + geom_density(aes(x=Predicted_value)) + stat_function(fun=dnorm) + facet_wrap(~State_CD) print(pg) 似乎stat_function与facet_wrap功能不兼容。我怎样才能使这两个打得更好? - - - - - - 编辑 - - - - - 我尝试从...
stat_bindot stat_ecdf stat_smooth stat_unique stat_binhex stat_function stat_spoke stat_vline stat_boxplot stat_hline stat_sum stat_ydensity 1. 2. 3. 4. 5. 6. 六 坐标系统(Coordinante) 坐标系统控制坐标轴,可以进行变换,例如XY轴翻转,笛卡尔坐标和极坐标转换,以满足我们的各种需求。 1 coord...
这时候,可以考虑使用stat_function()根据指定函数绘制拟合线。 如果已经提前计算出了回归式的各参数,则可以直接将已知的回归式指定给ggplot2函数stat_function()。stat_function()能够在作图时将自变量代入至已知的回归式中拟合响应变量的预测值,并使用平滑线连接响应变量的预测值获得回归线。在理论上,stat_function()...
# Load statip into the current R sesssion # Plot a histogram # Plot a box plot library(patchwork) # Get the variable to examine # Create a function that returns a density plot 无计算 计算 未连接 查看 内核未连接 下一单元: 检查现实世界数据 上一篇 下一步 需要...
stat_identity() -> p7 ggplot(df02, aes(x = x, y = n)) + stat_identity(geom = "bar") -> p8 p7 + p8 其他统计变换 ggplot2绘图系统中内置了许多类型的统计变换,还支持使用stat_function()函数进行自定义统计变换。下面简单列举几种,详细可查看官方帮助文档。
(p8<-ggplot(data.frame(x = c(-6,6)), aes(x = x)) + stat_function(fun = dt, args = list(df = 18), geom = "line", linewidth = 0.4) + labs(tag = "p8", x = expression(t[0]), y = "Probability Density", caption = "R_ggplot绘图:基础技能(1)-绘制第二坐标轴", title...
After the ggplot2 main function defines the mapping, you can directly use stat_summary to plot the graph. ggplot(.,aes(x=weight,y=species.coverage,fill=weight))+#geom_boxplot(outlier.size=1)+stat_summary(fun="mean",size=2,geom="bar",position=position_dodge(0.75))+## 绘制bar,数值来源...
qplot(x=carat, y=price, data=datax, color=cut, shape=cut, main="qplot function") 1. 如果不喜欢它默认的图形背景,要改变也相当简单,ggplot2预置了几个模板,这些内容我们在后面再详细说: theme_set(theme_bw()) qplot(x=carat, y=price, data=datax, color=cut, shape=cut, main="qplot function...
(x = month, ymin = seasonal_deviate - sem, ymax = seasonal_deviate + sem), width = 0.5, color = col) + stat_function(fun = cos_func, args = list(a = amplitude, phase = phase, omega = omega, intercept = 0), size = 0.7, color = col) + geom_ribbon(data = ci, aes(x =...
map_data: function params: list render_back: function render_front: function render_panels: function setup_data: function setup_params: function shrink: TRUE train: function train_positions: function train_scales: function vars: function super: <ggproto object: Class FacetNull, Facet> ...