如何基于多个条件求和-R? [英] How to sum rows based on multiple conditions - R?
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问题描述
我有一个数据框,其中包含一个地块ID(plotID),一个树种代码(物种)和一个覆盖值(封面)。您可以看到其中一个地块中有多种树种记录。如果每个图中有重复的种类行,我该如何对覆盖字段求和?
I have a dataframe that contains a plot ID (plotID), tree species code (species), and a cover value (cover). You can see there are multiple records of tree species within one of the plots. How can I sum the "cover" field if there are duplicate "species" rows within each plot?
例如,下面是一些示例数据:
# Sample Data
plotID = c( "SUF200001035014", "SUF200001035014", "SUF200001035014", "SUF200001035014", "SUF200001035014", "SUF200046012040",
"SUF200046012040", "SUF200046012040", "SUF200046012040", "SUF200046012040", "SUF200046012040", "SUF200046012040")
species = c("ABBA", "BEPA", "PIBA2", "PIMA", "PIRE", "PIBA2", "PIBA2", "PIMA", "PIMA", "PIRE", "POTR5", "POTR5")
cover = c(26.893939, 5.681818, 9.469697, 16.287879, 1.893939, 16.287879, 4.166667, 10.984848, 16.666667, 11.363636, 18.181818,
13.257576)
df_original = data.frame(plotID, species, cover)
这是预期的输出:
# Intended Output
plotID2 = c( "SUF200001035014", "SUF200001035014", "SUF200001035014", "SUF200001035014", "SUF200001035014", "SUF200046012040",
"SUF200046012040", "SUF200046012040", "SUF200046012040")
species2 = c("ABBA", "BEPA", "PIBA2", "PIMA", "PIRE", "PIBA2", "PIMA", "PIRE", "POTR5")
cover2 = c(26.893939, 5.681818, 9.469697, 16.287879, 1.893939, 20.454546, 18.651515, 11.363636, 31.439394)
df_intended_output = data.frame(plotID2, species2, cover2)
推荐答案
易于使用聚合
aggregate(cover~species+plotID, data=df_original, FUN=sum)
更容易使用 data.table
as.data.table(df_original)[, sum(cover), by = .(plotID, species)]
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