删除重复行并计算 R 中数据帧的平均值

我提供了一个我正在处理 >1,000 列和 >200 行的大型数据框的小示例。我想让每列的每个重复行成为单行而不是两行，同时取与第 1 列相关的每个值的平均值（平均值的列是第 6-8 列等） 。

Col1    Col2    Col3    Col4    Col5    Col6        Col7        Col8        Col9        Col10
A-001   7       40      1       J       3.985645    4.231623    2.36987     9.36545852  8.369663
A-001   7       40      1       J       1.458798    7.652123    1.236985    4.236987    1.22556633
B-002   8       50      0       K       5.00212     8.369562    7.4569852   5.36692     4.6632121
B-002   8       50      0       K       9.02336     1.2120145   3.0014588   8.214569    5.2223698
C-003   10      60      1       L       1.451203    5.321455    8.25963     2.03369878  4.3336988
C-003   10      60      1       L       1.65653     2.369898    8.2136999   7.21458777  5.3366
D-004   3       70      0       M       5.323211    1.147852    7.20014     5.36989     2.36555
D-004   3       70      0       M       4.36969     5.231478    4.23698     3.645478    9.214563
E-005   4       80      1       N       8.123256    9.2356478   5.3696      4.698889    7.366695
E-005   4       80      1       N       7.9632145   0.004555    1.24789     7.3696969   1.23655

预期输出：

col 1   col 2   col 3   col 4   col 5     col 6
A-001       7      40       1       J   2.49282
B-002       8      50       0       K   7.01274
C-003      10      60       1       L   1.55387
D-004       3      70       0       M   4.84645
E-005       4      80       1       N   8.04324

对于没有采用正确的格式，我提前表示歉意 预计感谢您提前提供的任何帮助

dd <- structure(list(col1 = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 
4L, 5L, 5L), .Label = c("A-001", "B-002", "C-003", "D-004", "E-005"
), class = "factor"), col2 = structure(c(1L, 1L, 2L, 2L, 3L, 
3L, 4L, 4L, 5L, 5L), .Label = c("7", "8", "10", "3", "4"), class = "factor"), 
    col3 = c(40L, 40L, 50L, 50L, 60L, 60L, 70L, 70L, 80L, 80L
    ), col4 = c(1, 1, 0, 0, 1, 1, 0, 0, 1, 1), col5 = c(JL, JL, KL, KL, LL, LL, ML, ML, NL, 
    NL), col6 = c(3.985645, 1.458798, 5.00212, 9.02336, 1.451203, 1.65653, 5.323211,  
    4.36969, 8.123256, 7.9632145), col7 = c(4.231623, 7.652123, 8.369562, 1.2120145,  
    5.321455, 2.369898, 1.147852, 5.231478, 9.2356478, 0.004555), col8 = c(2.36987,  
    1.236985, 7.4569852, 3.0014588, 8.25963, 8.2136999, 7.20014, 4.23698, 5.3696,  
    1.24789), col9 = c(9.36545852, 4.236987, 5.36692, 8.214569, 2.03369878, 7.21458777,  
    5.36989, 3.645478, 4.698889, 7.3696969), col10 = c(8.369663, 1.22556633, 4.6632121,  
    5.2223698, 4.3336988, 5.3366, 2.36555, 9.214563, 7.366695, 1.23655)), .Names =  
    c("col1", "col2", "col3", "col4", "col5", "col6", "col7", "col8", "col9", "col10"),  
    class = "data.frame", row.names = c(NA, -10L))

这使用dplyr这对于大数据来说会很快。第一部分提取未聚合的唯一列，第二部分进行聚合（通过 col1 组表示）。然后将两个部分绑定在一起（bind_cols）。我重命名了您的列以删除空格，如果您留下空格，则需要使用带有名称的反引号（即“col 1”）

library(dplyr)
cols <- 6:10  # columns to average
bind_cols(
    dat %>% distinct(col1) %>% .[,-cols],  # pull out columns we aren't aggregating
    dat[,c(1, cols)] %>% group_by(col1) %>%
        summarise_each(funs(mean)) %>% .[,-1]  # aggregate other columns
)

#    col1 col2 col3 col4 col5      col6       col7      col8      col9      col10
# 1 A-001    7   40    1    J   2.49282    5.94187   1.80343   6.80122    4.79762
# 2 B-002    8   50    0    K   7.01274    4.79079   5.22922   6.79075    4.94279
# 3 C-003   10   60    1    L   1.55387    3.84568   8.23667   4.62414    4.83515
# 4 D-004    3   70    0    M   4.84645    3.18967   5.71856   4.50768    5.79006
# 5 E-005    4   80    1    N   8.04324    4.62010   3.30875   6.03429    4.30162

Edit

这太复杂了，感谢@StevenBeaupré，简单的版本是（*_each 函数当然可以删除列！）

dat %>% group_by(col1) %>% mutate_each(funs(mean), -(1:5)) %>% distinct