【发布时间】：2019-06-19 18:26:10
【问题描述】：
我必须根据行的特定值对数据框进行子集化。这意味着如果必须以包含大于 10 的值的行作为条件来提取该行中满足条件的所有列。
这是我的数据样本。
structure(list(`Copper ores and concentrates; copper mattes, cemen` = c(200.53,
274.84, 1.37, 376.686907694609), `Fabrics, woven, of man-made fabrics` = c(4093.12,
1184.47, 0.29, 342.762777758776), Copper = c(44.76, 91.45, 2.04,
186.843219392315), Zinc = c(80.14, 110.73, 1.38, 152.996417519341
), `Waste, parings and scrap, of plastics` = c(590.3, 286.3,
0.49, 138.857682534305), `Fixed vegetable fats & oils, crude, refined, fract.` = c(864.14,
344.63, 0.4, 137.44281817761), `Sulphur and unroasted iron pyrites` = c(23.99,
55.11, 2.3, 126.599087119633), `Radio-actives and associated materials` = c(48.59,
76.67, 1.58, 120.977338958633), `Rails & railway track construction mat., iron, steel` = c(464.66,
214.76, 0.46, 99.259367279301), `Iron ore and concentrates` = c(46.91,
67.8, 1.45, 97.9927520784481), `Crude vegetable materials, n.e.s.` = c(164.46,
123.26, 0.75, 92.3812939316551), `Other plastics, in primary forms` = c(187.76,
124.21, 0.66, 82.169386983383), `Crude animal materials, n.e.s.` = c(43.08,
56.52, 1.31, 74.1529805013928), `Pig iron & spiegeleisen, sponge iron, powder & granu` = c(17.17,
33.03, 1.92, 63.5399475829936), `Ores and concentrates of base metals, n.e.s.` = c(15.7,
27.6, 1.76, 48.5197452229299), `Furskins, tanned or dressed, excluding those of 8483` = c(178.49,
75.12, 0.42, 31.6152972155303), `Metalworking machinery (excludingmachine-tools) & parts` = c(179.18,
71.69, 0.4, 28.6832018082375)), row.names = c("SD", "Mean", "INTENSITY",
"INTENSITY2"), class = "data.frame")
我希望数据框必须在名为 INTENSITY2 的行中将自身限制为大于 10 的值。
我试过这个tf4[, tf4[,"INTENSITY2" > 10, ]]，但它不起作用。
【问题讨论】：
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这个不一样，刚查了一下页面。
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试试看：
df[, df["INTENSITY2", ] > 10, FALSE]。与您的代码的唯一区别在于过滤：您打错字并按列值过滤，而不是按行值。 -
它给出的错误为
Error in[.data.frame(tf4, , tf4["INTENSITY2", ] > 10, FALSE) : undefined columns selected -
是的！使用整洁的数据要容易得多。我将提供另一个sn-p代码，所以你可以选择=）
library(tidyverse); tf4 %>% rownames_to_column("tmp") %>% gather(variable, val, -tmp) %>% spread(tmp, val)