问题描述
structure(list(drug = c("Chlorambucil","Fludarabine","FludarabineMafosfamide","NDI031301","CMPB","Tofacitinib","Peficitinib","PDB","Filgotinib","Dexamethasone","CMPA","Lenalidomide","Gandotinib","Ruxolitinib","CC122","Atovaquone","SAR20347","Momelotinib","Cerdulatinib","Chlorambucil","NDI031301"),dose = c(1,1,10,0.1,100,3,30,0.01,0.3,0.001,0.03,1),drug.dose = c("Chlorambucil_1uM","Fludarabine_1uM","FludarabineMafosfamide_10ug/mlplus1ug/ml","NDI031301_1uM","CMPB_0.1uM","Tofacitinib_1uM","Peficitinib_1uM","FludarabineMafosfamide_1ug/mlplus1ug/ml","PDB_100ng/ml","Filgotinib_1uM","Dexamethasone_10uM","CMPA_1uM","Lenalidomide_10uM","Dexamethasone_100uM","Gandotinib_1uM","NDI031301_10uM","Filgotinib_10uM","PDB_10ng/ml","CMPB_1uM","Ruxolitinib_1uM","CC122_0.1uM","Atovaquone_3uM","CC122_1uM","SAR20347_1uM","Momelotinib_1uM","Momelotinib_0.1uM","Tofacitinib_10uM","Fludarabine_1ug/ml","Fludarabine_10ug/ml","Cerdulatinib_1uM","Lenalidomide_1uM","Atovaquone_30uM","Chlorambucil_30uM","CMPA_0.1uM","FludarabineMafosfamide_0.01ug/mlplus1ug/ml","FludarabineMafosfamide_0.1ug/mlplus1ug/ml","Fludarabine_0.01ug/ml","Atovaquone_0.3uM","Momelotinib_0.001uM","PDB_1ng/ml","Filgotinib_0.01uM","Chlorambucil_0.3uM","Dexamethasone_0.1uM","Tofacitinib_0.01uM","SAR20347_0.1uM","CMPB_0.001uM","Momelotinib_0.01uM","Fludarabine_0.1ug/ml","Cerdulatinib_0.01uM","Peficitinib_0.1uM","Atovaquone_0.03uM","CC122_0.01uM","CMPA_0.01uM","NDI031301_0.01uM","PDB_0.1ng/ml","CMPA_0.001uM","Lenalidomide_0.01uM","SAR20347_0.01uM","Tofacitinib_0.1uM","Gandotinib_0.01uM","Lenalidomide_0.1uM","Peficitinib_0.01uM","CMPB_0.01uM","CC122_0.001uM","Dexamethasone_1uM","Ruxolitinib_10uM","SAR20347_10uM","Peficitinib_10uM","NDI031301_1uM"),combo = c("none","none","none"),cluster = c(3L,3L,4L,5L,6L,6L),dosage = c("1uM","1uM","10ug/mlplus1ug/ml","0.1uM","1ug/mlplus1ug/ml","100ng/ml","10uM","100uM","10ng/ml","3uM","1ug/ml","10ug/ml","30uM","0.01ug/mlplus1ug/ml","0.1ug/mlplus1ug/ml","0.01ug/ml","0.3uM","0.001uM","1ng/ml","0.01uM","0.1ug/ml","0.03uM","0.1ng/ml","1uM")),row.names = c(NA,-95L),class = "data.frame")
抱歉新手问题，我有这个复杂的药物集群数据，如屏幕截图所示。
我想将它们显示成一个堆叠的 geom_col 类型的图，x 轴是“药物”，Y 轴是出现的次数，并按簇对它进行分面。
到目前为止，这很容易。但我也想通过使用颜色填充来匹配它们的剂量来查看这些药物和剂量在每个集群中的分布。实际用量有不同的单位等。
我将数字剂量提取到它自己的立柱中。我想分配一个因子向量（“min”、“low”、“high”、“max”）来反映剂量水平，因为我知道每种药物有 4 种不同的剂量。
问题是不同药物的数字剂量不同，所以我不能简单地使用等级
例如有些药物剂量范围从 0.03 到 30，有些等级从 0.3 到 300，有些范围从 0.01 到 10。
那么如何使用数字药物剂量列将药物水平分配给每种药物？
解决方法
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