在r的帮助下给出不同类型变量的累计和的列

Question

我有一个看起来像这样的数据

    structure(c("Manufacturing Excell", "NPI Efficiencies", "NPI Efficiencies", 
"Manufacturing Excell", "Manufacturing Excell", "Material Efficiencie", 
"NPI Efficiencies", "Manufacturing Excell", "NPI Efficiencies", 
"NPI Efficiencies", "NPI Efficiencies", "Material Efficiencie", 
"NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"Manufacturing Excell", "NPI Efficiencies", "NPI Efficiencies", 
"NPI Efficiencies", "NPI Efficiencies", "Material Efficiencie", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"Manufacturing Excell", "Manufacturing Excell", "Manufacturing Excell", 
"NPI Efficiencies", "Manufacturing Excell", "Material Efficiencie", 
"Manufacturing Excell", "Manufacturing Excell", "NPI Efficiencies", 
"Manufacturing Excell", "Manufacturing Excell", "Manufacturing Excell", 
"NPI Efficiencies", "NPI Efficiencies", "Material Efficiencie", 
"NPI Efficiencies", "Manufacturing Excell", "NPI Efficiencies", 
"Manufacturing Excell", "NPI Efficiencies", "Manufacturing Excell", 
"Manufacturing Excell", "Manufacturing Excell", "NPI Efficiencies", 
"NPI Efficiencies", "Manufacturing Excell", "NPI Efficiencies", 
"NPI Efficiencies", "Manufacturing Excell", "Manufacturing Excell", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "Material Efficiencie", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", 
"Manufacturing Excell", "Manufacturing Excell", "Material Efficiencie", 
"NPI Efficiencies", "NPI Efficiencies", "Material Efficiencie", 
"Material Efficiencie", "NPI Efficiencies", "NPI Efficiencies", 
"Manufacturing Excell", "NPI Efficiencies", "NPI Efficiencies", 
"NPI Efficiencies", "Material Efficiencie", "NPI Efficiencies", 
"Material Efficiencie", "Manufacturing Excell", "Material Efficiencie", 
"NPI Efficiencies", "Manufacturing Excell", "Material Efficiencie", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"Manufacturing Excell", "NPI Efficiencies", "Manufacturing Excell", 
"Material Efficiencie", "NPI Efficiencies", "Material Efficiencie", 
"NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", 
"Manufacturing Excell", "Material Efficiencie", "Material Efficiencie", 
"Manufacturing Excell", "Material Efficiencie", "Manufacturing Excell", 
"Manufacturing Excell", "NPI Efficiencies", "NPI Efficiencies", 
"NPI Efficiencies", "Manufacturing Excell", "Material Efficiencie", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"Manufacturing Excell", "NPI Efficiencies", "NPI Efficiencies", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", 
"Manufacturing Excell", "NPI Efficiencies", "Manufacturing Excell", 
"NPI Efficiencies", "Material Efficiencie", "NPI Efficiencies", 
"NPI Efficiencies", "Manufacturing Excell", "Manufacturing Excell", 
"Manufacturing Excell", "NPI Efficiencies", "NPI Efficiencies", 
"Material Efficiencie", "Material Efficiencie", "Material Efficiencie", 
"Material Efficiencie", "NPI Efficiencies", "NPI Efficiencies", 
"Manufacturing Excell", "Manufacturing Excell", "Manufacturing Excell", 
"Manufacturing Excell", "Manufacturing Excell", "Manufacturing Excell", 
"NPI Efficiencies", "NPI Efficiencies", "NPI Efficiencies", "Manufacturing Excell", 
"Manufacturing Excell", "Material Efficiencie", "NPI Efficiencies", 
"NPI Efficiencies", "28859", "15134", "29429", "14214", "37988", 
"15328", "42679", "46206", "43311", "8158", "29937", "6021", 
"5581", "44627", "36779", "15888", "20088", "42170", "11560", 
"16401", "30293", "27682", "44574", "20240", "10176", "45920", 
"40615", "28510", "23527", "35717", "12608", "30585", "1344", 
"30179", "38589", "18135", "32662", "577", "47836", "36944", 
"8946", "36730", "6499", "47177", "31564", "17612", "19799", 
"43469", "780", "29003", "729", "39209", "8237", "12442", "40877", 
"45338", "44977", "2081", "47886", "19948", "38960", "27127", 
"33186", "36972", "29774", "24197", "47513", "21171", "10992", 
"2630", "39740", "38639", "8373", "7932", "44641", "8877", "4256", 
"47425", "4972", "11793", "48437", "15102", "30181", "23058", 
"27086", "11750", "32797", "33320", "42980", "2712", "3360", 
"18773", "34625", "48207", "18044", "16727", "36327", "38051", 
"39081", "35858", "11747", "32221", "45342", "25444", "27538", 
"3725", "29636", "37667", "24387", "43088", "49972", "39308", 
"17497", "26198", "42199", "20640", "26455", "42792", "36511", 
"16867", "34142", "10629", "15415", "38989", "24381", "45988", 
"19603", "40886", "16616", "13004", "8370", "34725", "17915", 
"29838", "38500", "10620", "45602", "11911", "38119", "308", 
"37473", "17560", "14887", "30872", "7622", "20169", "38494", 
"12728", "14816", "37183", "18602", "157", "49615", "12902", 
"31344", "15606", "30386", "49746", "26466", "19784", "9326", 
"33639", "25323", "31404", "20045", "45788", "49454", "13271", 
"44675", "44926", "33041"), .Dim = c(171L, 2L))

现在我要做的是创建3个不同的列，让我在材料效率、NPI效率和制造效率方面累积节省。有没有办法做到这一点。我所说的列是指3个不同的列，用于不同的储蓄类型。

Answer 1

一个选项是`

library(stringi)
stri_list2matrix(split(m1[,2], m1[,1]))

或使用base R

lst <- lapply(split(m1[,2], m1[,1]), as.numeric)
d1 <- data.frame(lapply(lst, `length<-`, max(lengths(lst))))
d1
#   Manufacturing.Excell Material.Efficiencie NPI.Efficiencies
#1                24387                 8877            44574
#2                   NA                   NA            19603
#3                   NA                   NA            29838
#4                   NA                   NA            20169

如果我们需要累加和

d1[] <- lapply(d1, function(x) cumsum(replace(x, is.na(x), 0)))
d1
#  Manufacturing.Excell Material.Efficiencie NPI.Efficiencies
#1                24387                 8877            44574
#2                24387                 8877            64177
#3                24387                 8877            94015
#4                24387                 8877           114184