使用r计算从数据库构造矩阵

Question

我试图在R上解决以下问题

我有一个像这样的data.framet（显然更大）：

Column_1     Column_2     Column_3
(0-1]        (15-25]      58
(2-3]        (35-45]      25
(4-5]        (35-45]      50
(0-1]        (15-25]      5
(2-3]        (25-35]      10
(1-2]        (25-35]      15
(1-2]        (15-25]      12
(3-4]        (25-35]      10
(4-5]        (35-45]      9

目标是从这个data.frame构造一个矩阵，其中Column_1作为列名，Column_2作为行名，并且矩阵内部具有Column_3中存在的每个值的平均值，与Column_1和Column_2中的相应值相关联。

结果矩阵应该是这样的：

      (15-25]    (25-35]     (35-45]
(0-1]   31.5      0             0
(1-2]   12        15            0
(2-3]   0         10            25     
(3-4]   0         10            0
(4-5]   0         0             29.5

我该怎么做？

Answer 1

xtabs()和aggregate()完成这项工作：

as.data.frame.matrix(xtabs(Column_3 ~ Column_1 + Column_2,
                           aggregate(Column_3 ~ Column_1 + Column_2, df, mean)))
# output
      (15-25] (25-35] (35-45]
(0-1]    31.5       0     0.0
(1-2]    12.0      15     0.0
(2-3]     0.0      10    25.0
(3-4]     0.0      10     0.0
(4-5]     0.0       0    29.5

# data
df <- structure(list(Column_1 = c("(0-1]", "(2-3]", "(4-5]", "(0-1]", 
"(2-3]", "(1-2]", "(1-2]", "(3-4]", "(4-5]"), Column_2 = c("(15-25]", 
"(35-45]", "(35-45]", "(15-25]", "(25-35]", "(25-35]", "(15-25]", 
"(25-35]", "(35-45]"), Column_3 = c(58L, 25L, 50L, 5L, 10L, 15L, 
12L, 10L, 9L)), .Names = c("Column_1", "Column_2", "Column_3"
), class = "data.frame", row.names = c(NA, -9L))

Answer 2

我们可以使用dcast的reshape2。调用您的数据dd：

wide = reshape2::dcast(data = dd, Column_1 ~ Column_2, fun.aggregate = mean, fill = 0)
wide
#   Column_1 (15-25] (25-35] (35-45]
# 1    (0-1]    31.5       0     0.0
# 2    (1-2]    12.0      15     0.0
# 3    (2-3]     0.0      10    25.0
# 4    (3-4]     0.0      10     0.0
# 5    (4-5]     0.0       0    29.5

这是一个数据框，我们当然可以转换为矩阵：

mat = as.matrix(wide[, -1])
row.names(mat) = wide[, 1]
mat
#       (15-25] (25-35] (35-45]
# (0-1]    31.5       0     0.0
# (1-2]    12.0      15     0.0
# (2-3]     0.0      10    25.0
# (3-4]     0.0      10     0.0
# (4-5]     0.0       0    29.5