在 R 中按类别划分矩阵值
Divide matrix values by category means in R
我有一个矩阵 (A) 包含 211 行和 6 列（每个时间段一个）和一个不同的矩阵 (B)包含211行2列，其中第二列包含分类信息(1-9)。
我的目标是创建一个新矩阵 (C)，其中矩阵 A 中的每个值都是 value(A) 除以 (value (A) 按类别 (B))。我设法使用聚合函数计算每列每个类别的均值。这些存储在单独的数据框中，column_means，每个时间波都在单独的列中。这也包含有关 column_means[1].
中的组的信息
我不知道如何从这里开始，我正在寻找一个优雅的解决方案，以便我可以将这些知识转移到未来的项目中（并可能改进我现有的代码）。我的猜测是解决方案隐藏在 dplyr 的某个地方，一旦你知道它就相当简单。
感谢您的任何建议。
数据示例：
##each column here represents a wave:
initialmatrix <- structure(c(0.882647671948723, 0.847932241438909, 0.753052308699317,
0.754977233408875, NA, 0.886095543329695, 0.849625252682829,
0.78893884364632, 0.77111113840682, NA, 0.887255207679895, 0.851503493865384,
0.812107856411831, 0.793982699495818, NA, 0.885212452552841,
0.854894065774315, 0.815265718290737, 0.806766276556325, NA,
0.882027335190646, 0.85386634818439, 0.818052477777012, 0.815997781565393,
NA, 0.88245957310107, 0.855819521951304, 0.830425687228663, 0.820857689847061,
NA), .Dim = 5:6, .Dimnames = list(NULL, c("V1", "V2", "V3", "V4",
"V5", "V6")))
##the first column is unique ID, the 2nd the category:
categories <- structure(c(1L, 2L, 3L, 4L, 5L, 2L, 1L, 2L, 2L, 4L), .Dim = c(5L,
2L), .Dimnames = list(NULL, c("V1", "V2")))
##the first column represents the category, column 1-6 the mean per category for each corresponding wave in "initialmatrix"
column.means <- structure(list(Group.1 = 1:5, x = c(0.805689153058216, 0.815006230419524,
0.832326976776262, 0.794835253329865, 0.773041961434791), asset_means_2...2. = c(0.80050960343197,
0.81923553710203, 0.833814773618545, 0.797834687980729, 0.780028077018158
), asset_means_3...2. = c(0.805053341257357, 0.828691564900149,
0.833953165695685, 0.799381078569563, 0.785813047374534), asset_means_4...2. = c(0.806116664276125,
0.832439754757116, 0.835982197159582, 0.801702200401293, 0.788814840753852
), asset_means_5...2. = c(0.807668548993891, 0.83801834926905,
0.836036508152776, 0.803433961863399, 0.79014026195926), asset_means_6...2. = c(0.808800359101212,
0.840923947682599, 0.839660313992458, 0.804901773257962, 0.793165113115977
)), row.names = c(NA, 5L), class = "data.frame")
这看起来像是 Superma 的工作......不等等......map2。
library(dplyr)
library(purrr)
as_tibble(initialmatrix) %>%
mutate(category = as.double(as_tibble(categories)$V2),
across(starts_with('V'),
~ unlist(map2(., category, ~ .x/mean(c(.x, .y)))))) %>%
select(-category)
# V1 V2 V3 V4 V5 V6
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 0.612 0.614 0.615 0.614 0.612 0.612
# 2 0.918 0.919 0.920 0.922 0.921 0.922
# 3 0.547 0.566 0.578 0.579 0.581 0.587
# 4 0.548 0.557 0.568 0.575 0.580 0.582
# 5 NA NA NA NA NA NA
这是你想要做的吗？
options(digits=3)
divisor <- column.means[categories[, 2], -1]
divisor
# x asset_means_2...2. asset_means_3...2. asset_means_4...2. asset_means_5...2. asset_means_6...2.
# 2 0.815 0.819 0.829 0.832 0.838 0.841
# 1 0.806 0.801 0.805 0.806 0.808 0.809
# 2.1 0.815 0.819 0.829 0.832 0.838 0.841
# 2.2 0.815 0.819 0.829 0.832 0.838 0.841
# 4 0.795 0.798 0.799 0.802 0.803 0.805
initialmatrix/divisor
# x asset_means_2...2. asset_means_3...2. asset_means_4...2. asset_means_5...2. asset_means_6...2.
# 2 1.083 1.082 1.071 1.063 1.053 1.049
# 1 1.052 1.061 1.058 1.061 1.057 1.058
# 2.1 0.924 0.963 0.980 0.979 0.976 0.988
# 2.2 0.926 0.941 0.958 0.969 0.974 0.976
# 4 NA NA NA NA NA NA
我有一个矩阵 (A) 包含 211 行和 6 列（每个时间段一个）和一个不同的矩阵 (B)包含211行2列，其中第二列包含分类信息(1-9)。
我的目标是创建一个新矩阵 (C)，其中矩阵 A 中的每个值都是 value(A) 除以 (value (A) 按类别 (B))。我设法使用聚合函数计算每列每个类别的均值。这些存储在单独的数据框中，column_means，每个时间波都在单独的列中。这也包含有关 column_means[1].
中的组的信息我不知道如何从这里开始，我正在寻找一个优雅的解决方案，以便我可以将这些知识转移到未来的项目中（并可能改进我现有的代码）。我的猜测是解决方案隐藏在 dplyr 的某个地方，一旦你知道它就相当简单。
感谢您的任何建议。
数据示例：
##each column here represents a wave:
initialmatrix <- structure(c(0.882647671948723, 0.847932241438909, 0.753052308699317,
0.754977233408875, NA, 0.886095543329695, 0.849625252682829,
0.78893884364632, 0.77111113840682, NA, 0.887255207679895, 0.851503493865384,
0.812107856411831, 0.793982699495818, NA, 0.885212452552841,
0.854894065774315, 0.815265718290737, 0.806766276556325, NA,
0.882027335190646, 0.85386634818439, 0.818052477777012, 0.815997781565393,
NA, 0.88245957310107, 0.855819521951304, 0.830425687228663, 0.820857689847061,
NA), .Dim = 5:6, .Dimnames = list(NULL, c("V1", "V2", "V3", "V4",
"V5", "V6")))
##the first column is unique ID, the 2nd the category:
categories <- structure(c(1L, 2L, 3L, 4L, 5L, 2L, 1L, 2L, 2L, 4L), .Dim = c(5L,
2L), .Dimnames = list(NULL, c("V1", "V2")))
##the first column represents the category, column 1-6 the mean per category for each corresponding wave in "initialmatrix"
column.means <- structure(list(Group.1 = 1:5, x = c(0.805689153058216, 0.815006230419524,
0.832326976776262, 0.794835253329865, 0.773041961434791), asset_means_2...2. = c(0.80050960343197,
0.81923553710203, 0.833814773618545, 0.797834687980729, 0.780028077018158
), asset_means_3...2. = c(0.805053341257357, 0.828691564900149,
0.833953165695685, 0.799381078569563, 0.785813047374534), asset_means_4...2. = c(0.806116664276125,
0.832439754757116, 0.835982197159582, 0.801702200401293, 0.788814840753852
), asset_means_5...2. = c(0.807668548993891, 0.83801834926905,
0.836036508152776, 0.803433961863399, 0.79014026195926), asset_means_6...2. = c(0.808800359101212,
0.840923947682599, 0.839660313992458, 0.804901773257962, 0.793165113115977
)), row.names = c(NA, 5L), class = "data.frame")
这看起来像是 Superma 的工作......不等等......map2。
library(dplyr)
library(purrr)
as_tibble(initialmatrix) %>%
mutate(category = as.double(as_tibble(categories)$V2),
across(starts_with('V'),
~ unlist(map2(., category, ~ .x/mean(c(.x, .y)))))) %>%
select(-category)
# V1 V2 V3 V4 V5 V6
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 0.612 0.614 0.615 0.614 0.612 0.612
# 2 0.918 0.919 0.920 0.922 0.921 0.922
# 3 0.547 0.566 0.578 0.579 0.581 0.587
# 4 0.548 0.557 0.568 0.575 0.580 0.582
# 5 NA NA NA NA NA NA
这是你想要做的吗？
options(digits=3)
divisor <- column.means[categories[, 2], -1]
divisor
# x asset_means_2...2. asset_means_3...2. asset_means_4...2. asset_means_5...2. asset_means_6...2.
# 2 0.815 0.819 0.829 0.832 0.838 0.841
# 1 0.806 0.801 0.805 0.806 0.808 0.809
# 2.1 0.815 0.819 0.829 0.832 0.838 0.841
# 2.2 0.815 0.819 0.829 0.832 0.838 0.841
# 4 0.795 0.798 0.799 0.802 0.803 0.805
initialmatrix/divisor
# x asset_means_2...2. asset_means_3...2. asset_means_4...2. asset_means_5...2. asset_means_6...2.
# 2 1.083 1.082 1.071 1.063 1.053 1.049
# 1 1.052 1.061 1.058 1.061 1.057 1.058
# 2.1 0.924 0.963 0.980 0.979 0.976 0.988
# 2.2 0.926 0.941 0.958 0.969 0.974 0.976
# 4 NA NA NA NA NA NA