如何使用R将单叶独立测量数据汇总为单株总数据集

你的需求核心是按植株（Plant列）为分组维度，对Leaf.Mass列的叶片重量数值进行求和，以下是两种常用实现方案：

方法1：使用tidyverse生态的dplyr包（适配常规数据分析流程） #

# 未安装dplyr的话先运行 install.packages("dplyr")
library(dplyr)

# 读入你的数据集，示例数据赋值给df
df <- structure(list(Tray = c(1, 1, 1, 1, 1, 1), Plant = c(2, 2, 2, 
2, 3, 3), Treatment = structure(c(4L, 4L, 4L, 4L, 4L, 4L), .Label = c("2TLH", 
"E2TL", "EH", "WL"), class = "factor"), PreSwitch = c("Soil", 
"Soil", "Soil", "Soil", "Soil", "Soil"), PostSwitch = c("Soil", 
"Soil", "Soil", "Soil", "Soil", "Soil"), Pellet = c(1, 1, 1, 
1, 1, 1), Rep = c(1, 1, 1, 1, 1, 1), Date = structure(c(1618963200, 
1618963200, 1618963200, 1618963200, 1618963200, 1618963200), tzone = "UTC", class = c("POSIXct", 
"POSIXt")), DAP = c(60, 60, 60, 60, 60, 60), Position = c(2, 
1, 3, 4, 4, 3), Whorl = structure(c(1L, 1L, 2L, 2L, 2L, 2L), .Label = c("1", 
"2", "3", "4", "5"), class = "factor"), PetioleLength = c(1.229, 
1.365, 1.713, 1.02, 0, 1.408), BladeLength = c(1.604, 1.755, 
2.466, 2.672, 0.267, 2.662), BladeWidth = c(1.023, 1.185, 1.803, 
1.805, 0.077, 1.771), BladeArea = c(1.289, 1.634, 3.492, 3.789, 
0.016, 3.704), BladePerimeter = c(6.721, 7.812, 11.61, 12.958, 
1.019, 14.863), BladeCircularity = c(0.359, 0.336, 0.326, 0.284, 
0.196, 0.211), BPR = c(1.30512611879577, 1.28571428571429, 1.43957968476357, 
2.61960784313725, NA, 1.890625), Leaf.Mass = c(9, 11, 31, 33, 
32, 33), BladeAR = c(1.56793743890518, 1.48101265822785, 1.36772046589018, 
1.4803324099723, 3.46753246753247, 1.50310559006211), Subirrigation = c(0, 
0, 0, 0, 0, 0), Genotype = c(1, 1, 1, 1, 1, 1), Location = c(0, 
0, 0, 0, 0, 0)), row.names = c(NA, -6L), class = c("tbl_df", 
"tbl", "data.frame"))

# 按Plant分组求和叶片总质量
plant_total_mass <- df %>%
  group_by(Plant) %>%
  summarise(total_leaf_mass = sum(Leaf.Mass, na.rm = TRUE))

group_by用于指定分组字段，summarise对每个分组执行汇总计算，na.rm = TRUE参数可以避免叶片重量存在缺失值时返回NA的问题。用你提供的示例数据运行后，会得到Plant=2的总叶片质量为84，Plant=3的总叶片质量为65。

方法2：使用基础R实现（无需安装额外包） #

用内置aggregate函数即可完成分组求和：

plant_total_mass <- aggregate(Leaf.Mass ~ Plant, data = df, FUN = sum, na.rm = TRUE)

运行效果和tidyverse方案一致。

相关搜索关键词 #

如果需要查找更多相关解决方案，可以搜索以下关键词：

• R 分组求和
• R aggregate 函数用法
• R dplyr group_by summarise 汇总
• R 按列分组计算统计量

内容的提问来源于stack exchange，提问作者Joe Ballenger