r - R中长格式的行和列计算

Question

我在复杂数据集中的单元格计算中苦苦挣扎（参见下面的 dput() 示例）。

我需要将标准化平均差 (M1-M2/sqrt(s1^2+s2^2) 的公式应用于多行和多列（研究和测试）。M1 和 M2（平均值）值在 pr_cognm_列和 s1和 s2 （标准偏差）pr_cognsd_列中，它们应根据因子id& tx...3（处理）计算。

因此，例如对于pr_cognm_VV2_CRT_error列id336 有两行，在这种情况下！treat1需要从中减去行中的值VGT。但有时情况正好相反，例如需要减去id162以内（幸运的是，这个逻辑对于每个特定的比较都是相同的）。然后，标准偏差（即增强和加法）也需要发生同样的事情。最后，M和S需要分开。有许多测试（列）可以在（例如，等）上运行公式，并且通常不适用，因为具体没有这个测试。数据是长格式的，为了使它更复杂，一些dGTtreat1pr_cognm_BVMT_perc_retentionpr_cognm_VV2_CRT_erroridid有三行而不是两行（其中两行需要在特定方向上相互减去，例如task1）。

我最好的主意是

#make a dataset
a <- readxl::read_excel("C:/.../reprod.xlsx")
b<- a[!grepl("com", a$id),] #already omitted in example dataset
pr_cognm <- dplyr::select(b,contains("pr_cognm"))
pr_cognsd <- dplyr::select(b,contains("pr_cognsd"))

c <- cbind(b$tx...3, b$id ,pr_cognm, pr_cognsd)
c$`b$id` <- as.factor(c$`b$id`)

#turn var's into numerics and factors
#potentiate all standard deviations (s1^2 and s2^2)
c[,3:ncol(c)] <- sapply(c[,3:ncol(c)], as.numeric)
c[,grepl("pr_cognsd", colnames(c))] <- c[,grepl("pr_cognsd", colnames(c))]^2

#then reshape
require(reshape2)
c %>% 
  dcast(b$id ~ b$tx...3, value.var = c("pr_cognm_VV2_CRT_error"), fill = 0)

   b$id BF BL BT dGT H-TT  HFL LM-TT treat1  VGT
1    55  0  0  0   0    0 0.00  0.00   0.00 0.00
2   162  0  0  0   0    0 0.00  0.00   0.00 0.00
3   236  0  0  0   0    0 0.00  0.00   0.00 0.00
4   336  0  0  0   0    0 0.00  0.00   8.75 7.58
5   377  0  0  0   0    0 0.00  0.00   0.00 0.00
6   521  0  0  0   0    0 0.00  0.00   0.00 0.00
7   525  0  0  0   0    0 0.00  0.00   0.00 0.00
8   527  0  0  0   0    0 0.00  0.00   0.00 0.00
9   528  0  0  0   0    0 0.00  0.00   0.00 0.00
10  535  0  0  0   0    0 5.65  6.54   0.00 0.00
11  548  0  0  0   0    0 0.00  0.00   0.00 0.00
12  553  0  0  0   0    0 0.00  0.00   0.00 0.00

现在我可以定义应该添加哪些变量的规则，如c$sub <- c$treat1-c$VGT和c$sub <- c$HFL-c$LM-TT，以类似的方式使用 SD 进行添加，最后将两个变量相除以找到 SMD。但是，这只适用于一项测试。在这种情况下 value.var = c("pr_cognm_VV2_CRT_error")。我想通过例如循环为数据集中的每个测试获取这个矩阵，因为不止一个value.var不起作用：

require(reshape2)
c %>% 
   dcast(b$id ~ b$tx...3, value.var = c("pr_cognm_VV2_CRT_error", "pr_cognm_BNT_perc_retention"), fill = 0)

Error in .subset2(x, i, exact = exact) : subscript out of bounds
In addition: Warning message:
In if (!(value.var %in% names(data))) { :
  the condition has length > 1 and only the first element will be used

如果有办法循环通过

c %>% 
+   dcast(b$id ~ b$tx...3, value.var = c(names(c[,3:ncol(c)]), fill = 0)

然后我可以也许rbind他们并如上所述将减法运算到一个新变量中，并且在对 SD 执行相同操作之后，我最终能够进行除法以获得 SMD。

我无法得到任何解决方案。

复制示例（截断）：

a <- structure(list(checked = c("Y", "Y", NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA), id = c("55", "55", "162", "162", "236", "236", "336", "336", 
"377", "377", "521", "521", "525", "525", "527", "527", "528", 
"528", "535", "535", "548", "548", "548", "553", "553"), tx...3 = c("task1", 
"VGT", "dGT", "task1", "BT", "H-TT", "task1", "VGT", "BT", "H-TT", 
"task1", "VGT", "HFL", "H-TT", "BF", "BT", "HFL", "task1", "HFL", 
"LM-TT", "HFL", "BL", "task1", "HFL", "task1"), nta = c(2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 
2, 2), id2 = c(1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 
1, 2, 1, 2, 1, 2, 3, 1, 2), cross = c("N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N"), pre_post = c("N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N"), case_control = c("N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N"), expsy = c("Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"), hosp = c("Out", 
"Out", "Out", "Out", "In", "In", "NA", "NA", "In", "In", "NR", 
"NR", "NR", "NR", "Mx", "Mx", "NR", "NR", "Mx", "Mx", "Out", 
"Out", "Out", "Out", "Out"), tx...11 = c("task1", "VGT", "dGT", 
"task1", "BT", "H-TT", "task1", "VGT", "BT", "H-TT", "task1", 
"VGT", "HFL", "H-TT", "BF", "BT", "HFL", "task1", "HFL", "LM-TT", 
"HFL", "BL", "task1", "HFL", "task1"), vt_p = c("17", "17", "24", 
"24", "21", "21", "NR", "NR", "NR", "NR", "NA", "NA", "17", "17", 
"24", "24", "17", "17", "17", "17", "17", "17", "17", "17", "17"
), n_se = c("12", "12", "20", "20", "6", "6", "10", "10", "NR", 
"NR", "20", "20", "10", "6", "8", "8", "15", "15", "10", "6", 
"15", "15", "15", "10", "10"), cogn_name_AMI_K = c(NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA), cogn_cite_AMI_K = c(NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), cogn_last_stim_AMI_K = c(NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), n_bcogn_AMI_K = c(NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), n_pcogn_AMI_K = c(NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA), pr_cognm_AMI_K = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
    pr_cognsd_AMI_K = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
    ), po_cognm_n_AMI_K = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA), po_cognsd_n_AMI_K = c(NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA), cogn_name_BVMT_perc_retention = c(NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, "brief_visual_memory_test", "brief_visual_memory_test", 
    "brief_visual_memory_test", NA, NA), cogn_cite_BVMT_perc_retention = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "benedict_1997", "benedict_1997", "benedict_1997", 
    NA, NA), cogn_last_stim_BVMT_perc_retention = c(NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, "NR", "NR", "NR", NA, NA), n_bcogn_BVMT_perc_retention = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, 19, 24, 18, NA, NA), n_pcogn_BVMT_perc_retention = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, 19, 24, 18, NA, NA), pr_cognm_BVMT_perc_retention = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "91.6", "86.6", "90", NA, NA), pr_cognsd_BVMT_perc_retention = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "17", "36", "13.3", NA, NA), po_cognm_n_BVMT_perc_retention = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "86.7", "82.1", "71.7", NA, NA), po_cognsd_n_BVMT_perc_retention = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "20", "39.3", "24.2", NA, NA), cogn_name_BNT_naming = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, "boston_naming_task_naming", 
    "boston_naming_task_naming", NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA), cogn_cite_BNT_naming = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, "kaplan_1983", "kaplan_1983", 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
    ), cogn_last_stim_BNT_naming = c(NA, NA, NA, NA, NA, NA, 
    NA, NA, 30, 30, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA), n_bcogn_BNT_naming = c(NA, NA, NA, NA, NA, 
    NA, NA, NA, 14, 14, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA), n_pcogn_BNT_naming = c(NA, NA, NA, NA, 
    NA, NA, NA, NA, 14, 14, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA), pr_cognm_BNT_naming = c(NA, NA, 
    NA, NA, NA, NA, NA, NA, "19.64", "18.14", NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), pr_cognsd_BNT_naming = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, "9.15", "5.3", NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), po_cognm_n_BNT_naming = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, "20.21", "20.71", NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), po_cognsd_n_BNT_naming = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, "9.38", "6.34", NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), cogn_name_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, "VV2_crt_error", "VV2_crt_error", NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, "VV2_crt_error", "VV2_crt_error", 
    NA, NA, NA, NA, NA), cogn_cite_VV2_CRT_error = c(NA, NA, 
    NA, NA, NA, NA, "robbins_1994", "robbins_1994", NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, "robbins_1994", "robbins_1994", 
    NA, NA, NA, NA, NA), cogn_last_stim_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, "NR", "NR", NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, "NR", "NR", NA, NA, NA, NA, NA), n_bcogn_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, 12, 12, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, 12, 12, NA, NA, NA, NA, NA), n_pcogn_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, 12, 12, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, 12, 12, NA, NA, NA, NA, NA), pr_cognm_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, "8.75", "7.58", NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "5.65", "6.54", NA, NA, NA, NA, NA), pr_cognsd_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, "1.13", "2.84", NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "1.10", "1.89", NA, NA, NA, NA, NA), po_cognm_n_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, "7.50", "5.33", NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "7.50", "2.34", NA, NA, NA, NA, NA), po_cognsd_n_VV2_CRT_error = c(NA, 
    NA, NA, NA, NA, NA, "2.06", "2.42", NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, "2.06", "2", NA, NA, NA, NA, NA), cogn_name_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, "VV2_crt_latency", "VV2_crt_latency", 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA), cogn_cite_VV2_CRT_latency = c(NA, NA, NA, NA, NA, 
    NA, "robbins_1994", "robbins_1994", NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), cogn_last_stim_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, "NR", "NR", NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), n_bcogn_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, 12, 12, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA), n_pcogn_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, 12, 12, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA), pr_cognm_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, "476.05", "465.65", NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), pr_cognsd_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, "35.86", "37.54", NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), po_cognm_n_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, "460.66", "433.13", NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), po_cognsd_n_VV2_CRT_latency = c(NA, 
    NA, NA, NA, NA, NA, "34.75", "46.70", NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), row.names = c(NA, 
-25L), class = c("tbl_df", "tbl", "data.frame"))

r - R中长格式的行和列计算

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