使用 dplyr 合并数据集和合并列 R

Using dplyr to merge datasets and consolidate columns R

我有两个要合并的数据集。它们不是完整的数据集,因此这意味着个人缺少记录。

这里是data1(示例是我真实数据的一个子集):

  squirrel_id   age ageclass trialdate   year   OFT1  MIS1
        10342     1 Y        2008-05-19  2008  0.605 -4.19
        10342     2 A        2009-05-31  2009 -1.85   1.14
        10342     3 A        2010-05-22  2010 -2.39   2.38

这里是data2(示例是我真实数据的一个子集):

   squirrel_id focal_age focal_ageclass focal_date focal_yr     PC1     PC2
         10342         1 Y              2008-07-14     2008    0.0932 -2.67  
         10342         3 A              2010-03-13     2010   -2.38    0.216 
         10342         3 A              2010-04-20     2010    0.0203  1.80  

我正在尝试做两件事:

  1. 合并这两个数据集,以便在记录不完整时保留 NA(即,data1age==3 有 1 条记录,而 data2 在 [=18= 时有 2 条记录])
  2. 合并列使数据集更精简(即数据集中不同名称的列代表相同的事物:age==focal_ageageclass==focal_ageclasstrialnumber==focalseqageclass==focal_ageclass, year==focal_yr)

所需的输出 - 我试图得到一个看起来像这样的最终数据集(其中 age==3data1 记录只显示一次,不是两次):

  squirrel_id   age ageclass date       year   OFT1  MIS1   PC1      PC2
        10342     1 Y        2008-05-19 2008  0.605 -4.19   NA       NA 
        10342     1 Y        2008-07-14 2008  NA     NA     0.0932  -2.67
        10342     2 A        2009-05-31 2009 -1.85   1.14   NA       NA
        10342     3 A        2010-05-22 2010 -2.39   2.38   NA       NA    
        10342     3 A        2010-03-13 2010  NA     NA    -2.38    0.216
        10342     3 A        2010-04-20 2010  NA     NA     0.0203  1.80  

我可以通过以下方式到达这里:

data3<-full_join(data1, data2, 
        by=c("squirrel_id"="squirrel_id", 
                    "year"="focal_yr", 
                     "age"="focal_age", 
                "ageclass"="focal_ageclass"))

但这会为 data2 中的两个 age==3 行重复 age==3data1 值(而不是仅匹配第一行),给出此 (不需要) 输出:

 squirrel_id   age ageclass trialdate   focal_date year   OFT1  MIS1   PC1      PC2
        10342     1 Y        2008-05-19  2008-07-14 2008  0.605 -4.19   0.0932  -2.67 
        10342     2 A        2009-05-31  NA         2009 -1.85   1.14   NA       NA
        10342     3 A        2010-05-22  2010-03-13 2010 -2.39   2.38   -2.38    0.216
        10342     3 A        2010-05-22  2010-04-20 2010 -2.39   2.38    0.0203  1.80  

更新问题: 如何让匹配记录在执行 full_join 时为所有行添加 NA? 注意我宁愿 dplyr 解决方案,因为我不在 data.table 工作(就像对 ) and I want to retain the rows that don't match (unlike 的回答)。

这是一个data.table方法

示例数据

library(data.table)
data1 <- fread("squirrel_id   age ageclass trialdate   year   OFT1  MIS1
10342     1 Y        2008-05-19  2008  0.605 -4.19
10342     2 A        2009-05-31  2009 -1.85   1.14
10342     3 A        2010-05-22  2010 -2.39   2.38")

data2 <- fread("squirrel_id focal_age focal_ageclass focal_date focal_yr     PC1     PC2
         10342         1 Y              2008-07-14     2008    0.0932 -2.67  
         10342         3 A              2010-03-13     2010   -2.38    0.216 
         10342         3 A              2010-04-20     2010    0.0203  1.80 ")

代码

# Assuming the first five columns can be rowbound without problem,
# melt them to long
L <- lapply(list(data1, data2), melt, id.vars = 1:5)

#    squirrel_id age ageclass  trialdate year variable  value
# 1:       10342   1        Y 2008-05-19 2008     OFT1  0.605
# 2:       10342   2        A 2009-05-31 2009     OFT1 -1.850
# 3:       10342   3        A 2010-05-22 2010     OFT1 -2.390
# 4:       10342   1        Y 2008-05-19 2008     MIS1 -4.190
# 5:       10342   2        A 2009-05-31 2009     MIS1  1.140
# 6:       10342   3        A 2010-05-22 2010     MIS1  2.380
# 
# [[2]]
#    squirrel_id focal_age focal_ageclass focal_date focal_yr variable   value
# 1:       10342         1              Y 2008-07-14     2008      PC1  0.0932
# 2:       10342         3              A 2010-03-13     2010      PC1 -2.3800
# 3:       10342         3              A 2010-04-20     2010      PC1  0.0203
# 4:       10342         1              Y 2008-07-14     2008      PC2 -2.6700
# 5:       10342         3              A 2010-03-13     2010      PC2  0.2160
# 6:       10342         3              A 2010-04-20     2010      PC2  1.8000

# Rowbind, ignore columnnames
DT <- data.table::rbindlist(L, use.names = FALSE, fill = FALSE)
#    squirrel_id age ageclass  trialdate year variable   value
# 1:       10342   1        Y 2008-05-19 2008     OFT1  0.6050
# 2:       10342   2        A 2009-05-31 2009     OFT1 -1.8500
# 3:       10342   3        A 2010-05-22 2010     OFT1 -2.3900
# 4:       10342   1        Y 2008-05-19 2008     MIS1 -4.1900
# 5:       10342   2        A 2009-05-31 2009     MIS1  1.1400
# 6:       10342   3        A 2010-05-22 2010     MIS1  2.3800
# 7:       10342   1        Y 2008-07-14 2008      PC1  0.0932
# 8:       10342   3        A 2010-03-13 2010      PC1 -2.3800
# 9:       10342   3        A 2010-04-20 2010      PC1  0.0203
#10:       10342   1        Y 2008-07-14 2008      PC2 -2.6700
#11:       10342   3        A 2010-03-13 2010      PC2  0.2160
#12:       10342   3        A 2010-04-20 2010      PC2  1.8000

# Cast to wide again
dcast(DT, ... ~ variable, value.var = "value")
#    squirrel_id age ageclass  trialdate year   OFT1  MIS1     PC1    PC2
# 1:       10342   1        Y 2008-05-19 2008  0.605 -4.19      NA     NA
# 2:       10342   1        Y 2008-07-14 2008     NA    NA  0.0932 -2.670
# 3:       10342   2        A 2009-05-31 2009 -1.850  1.14      NA     NA
# 4:       10342   3        A 2010-03-13 2010     NA    NA -2.3800  0.216
# 5:       10342   3        A 2010-04-20 2010     NA    NA  0.0203  1.800
# 6:       10342   3        A 2010-05-22 2010 -2.390  2.38      NA     NA