# 添加次轴 p + scale_y_continuous(sec.axis = sec_axis(~ . / 1000, name = "Displacement (x1000)")) 在这个示例中,我们首先加载了ggplot2库并创建了一个散点图,其中x轴表示mpg(每加仑英里数),y轴表示disp(排量)。然后,我们使用scale_y_continuous()函数添加了一个次轴,该次轴将disp的值除以1000。
This problem comes from here in CoordTrans$setup_panel_params(); out is redefined so the params for the secondary axis (out$sec.range, out$sec.major, out$sec.minor, etc) are lost. ggplot2/R/coord-transform.r Lines 192 to 195 in 868fdb7 out <- list( range = out$range, labels...
sec_axis用于为二级轴创建规范。除了反式参数外,任何参数都可以被设置为derive(),这将导致二级坐标轴从主轴继承设置。dup_axis提供了一个简写,用于创建一个副轴,这是主轴的复制,有效地称为主轴的镜像。 Examples p <-ggplot(mtcars,aes(cyl, mpg)) +geom_point() # Create a simple secondary axisp +scale_...
In plots with a secondary y-axis, the grid lines of the second axis aren't below the data (as opposed to the grid lines of the primary axis). This becomes very visible with stackplots and styles like ggplot or seaborn. MWE: from matplotl...
在ggh4x包中使用help_secondary函数时，是否可以添加点并更改两个y轴的名称？要沿着线添加点，请添加...
Selected scatter plots of GFP fluorescence (y-axis) and cell forward scatter (x-axis), showing gating for GFP fluorescence for HEK293T cells transfected with plasmids encoding dsDNA donor template, Cas9, and non-targeting gRNA only (top), primary and non-targeting gRNAs (middle), or primary ...
The generation of volcano plots and boxplots for differentially expressed genes could be accomplished using the "ggplot2" package in R, while a heatmap of differential gene expression could be created using the "heatmap" package in R. After converting the obtained differentially expressed genes ...
Categorical variables were compared by χ2 and adjusted for multiple comparisons). All metabolomics analysis was conducted in R [34] using GNU Emacs v26.2 [35] on Windows 10. Plots were constructed using the R package ggplot2 [36]. Data analysis scripts documenting the complete statistical ...
In other words, one end of this axis is characterized by high LOM and LOC, while another end is characterized by low RLWC. In one extreme, negative values with the high values of traits (LTN and LTP) were positively correlated with one another and representative of the resource acquisition ...
[38], and the results are shown inSupplementary Table S4. A principal component analysis (PCA) was carried out using R and visualized using the ggplot2 [39] and ggbiplot packages [40]. For this analysis, 62 different TAP families were used, for which at least one gene occurs in one of...