我之前的帖子被删除了，是针对我的其他帖子，但实际上这对我的目的没有用处，因为这篇帖子只有2列可以比较
我的代码
data_samp <-
structure(list(paciente = structure(c(6036, 6299, 6324, 6457,
6201, 6092, 6409, 6268, 6126, 6306, 6119, 6297, 6467, 6123, 6249,
6304, 6496, 6210, 6306, 6197), label = "Paciente", format.spss = "F6.0"),
sexo_s1 = structure(c(2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L), .Label = c("Hombre",
"Mujer"), label = "Sexo", class = "factor"), edad_s1 = structure(c(61,
60, 61, 61, 64, 67, 71, 60, 62, 62, 65, 74, 62, 61, 74, 69,
62, 69, 62, 62), label = "Edad", format.spss = "F3.0"), grupo_int_v00 = structure(c(1L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
1L, 2L, 1L, 1L), .Label = c("A", "B"), label = "Grupo de intervención", class = "factor"),
peso1_v00 = structure(c(93.6, 78.8, 94.5, 99, 72.1, 86.2,
91.6, 88.2, 78.6, 82.5, 72, 90.6, 103.2, 117.2, 98, 90.9,
78.4, 83, 82.5, 98), label = "Peso: 1a determinación", format.spss = "F5.1"),
cintura1_v00 = structure(c(111.6, 105, 111, 107, 92.5, 102,
115.5, 103, 102, 106.5, 101.5, 111.5, 114, 134, 123, 107,
104, 114.5, 106.5, 132), label = "Cintura: 1a determinación", format.spss = "F5.1"),
tasis2_e_v00 = structure(c(125, 137, 137, 136, 124, 146,
162, 123, 131, 124, 115, 154, 122, 122, 158, 130, 152, 137,
124, 152), label = "TA: tensión arterial 2: sistólica", format.spss = "F4.0"),
tadias2_e_v00 = structure(c(81, 79, 83, 84, 62, 76, 95, 65,
65, 49, 72, 73, 71, 84, 65, 76, 69, 72, 49, 90), label = "TA: tensión arterial 2: diastólica", format.spss = "F4.0"),
p17_total_v00 = structure(c(4, 7, 8, 5, 5, 10, 8, 7, 9, 10,
10, 6, 14, 5, 6, 6, 9, 9, 10, 6), label = "Cuestionario de 17 puntos: Suma de puntuación de P17", format.spss = "F3.0"),
geaf_tot_v00 = structure(c(1090.91, 1398.6, 4895.1, 5734.27,
447.55, 1622.38, 3356.64, 923.08, 1678.32, 1976.69, 3184.15,
3251.75, 2293.71, 671.33, 7370.63, 335.66, 993.01, 466.2,
1976.69, 0), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2"),
peso1_v66 = structure(c(91.6, 78.7, 89.5, 91.5, 68.2, 82.4,
87, 89.5, 75.6, 79, 70.3, 84.5, 96.5, 118, 86.2, 88.7, 73.2,
80, 79, 90.5), label = "Peso: 1a determinación", format.spss = "F5.1"),
cintura1_v66 = structure(c(109, 105, 105.2, 99, 86, 95, 112,
104, 95, 103, 98, 107, NA, 135.5, 106.5, 105.8, 105, 108,
103, 122.5), label = "Cintura: 1a determinación", format.spss = "F5.1"),
tasis2_e_v66 = structure(c(137, 138, 129, 119, 136, 146,
144, 138, 131, 131, 111, 144, NA, 132, 172, 106, 142, 129,
131, 131), label = "TA: tensión arterial 2: sistólica", format.spss = "F4.0"),
tadias2_e_v66 = structure(c(65, 72, 76, 74, 71, 69, 63, 56,
69, 69, 77, 80, NA, 66, 92, 67, 71, 63, 69, 85), label = "TA: tensión arterial 2: diastólica", format.spss = "F4.0", display_width = 13L),
p17_total_v66 = structure(c(2, 14, 11, 10, 7, 15, 11, 8,
13, 13, 12, 12, 10, 10, 15, 7, 15, 12, 13, 11), label = "Cuestionario de 17 puntos: Suma total de P17", format.spss = "F3.0"),
geaf_tot_v66 = structure(c(223.78, 406.53, 2797.2, 4055.94,
1678.32, 1566.43, 1860.14, 1657.34, 2349.65, 4195.8, 4461.54,
4090.91, 1538.46, 938, 6153.85, 769.23, 3286.71, 2517.48,
4195.8, 139.86), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2"),
peso1_v01 = structure(c(87.2, 77.4, 88, 95, 66.5, 79.2, 86.1,
93, 73, 71.7, 71.5, 83, 97, 117.7, 83, 90.5, 76.5, 77.5,
71.7, 93), label = "Peso: 1a determinación", format.spss = "F5.1"),
cintura1_v01 = structure(c(106.5, 106, 104, 103.5, 85, 94,
112, NA, 94, 95, 98, 106, NA, 135, 105, 108, 105.5, 105,
95, 124.5), label = "Cintura: 1a determinación", format.spss = "F5.1"),
tasis2_e_v01 = structure(c(115, 136, 126, 131, 145, 150,
138, NA, 144, 119, 115, 143, NA, 115, 145, 128, 146, 129,
119, 139), label = "TA: tensión arterial 2: sistólica", format.spss = "F4.0"),
tadias2_e_v01 = structure(c(58, 67, 75, 80, 72, 79, 64, NA,
71, 47, 80, 69, NA, 65, 80, 75, 62, 60, 47, 88), label = "TA: tensión arterial 2: diastólica", format.spss = "F4.0"),
geaf_tot_v01 = structure(c(839.16, 499.77, 3804.2, 1230.77,
223.78, 2582.75, 4055.94, 1132.87, 2769.23, 1734.27, 2526.81,
3575.76, 839.16, 3169.23, 5616.78, 1048.95, 2284.38, 2307.69,
1734.27, 559.44), label = "AF: Gasto energético en actividad física total (MET•min/sem)", format.spss = "F8.2")), row.names = c(NA,
-20L), class = c("tbl_df", "tbl", "data.frame"))
标识变量：paciente性别：Sex_s1变量1_v00：基线变量1变量1_v66：6个月时的变量1变量1_v01：12个月时的变量1变量2_v00：基线变量2变量2_v66：6个月时的变量2变量2_v01：变量2在12个月时。我的目的是比较受试者内部比较(患者1比较不同时间点(00、66和01(之间的每个变量(变量1..(，例如
患者1variable1_v00与variable1_v46variable1_v00与variable1_v01variable1_v66与variable1_v21
我有20多个变量要比较，还有200多个人。我已经将数据修改为纵向数据，以时间作为一个因素，但我不能用ANOVA运行它(记住分析必须成对！！(
建议的代码是这样的(只是最后的一部分(
do(tidy(t.test(.$Price_Online,
.$Price_Offline,
但我有两个以上的变量。我当然可以手动完成，但这是编程的任务
提前感谢
编辑1:
编程方法以获得目标。运行时必须考虑发布的数据集(data_samp(的结构
# Both groups of intervention: A and B
c("p17_total", "geaf_tot", "glucosa", "homa", "coltot", "peso1",
"cintura1", "tadias2_e", "tasis2_e", "hba1c", "trigli", "col_rema",
"ldl_calc", "hdl", "i_huglp1", "i_hugip", "i_hupai1", "i_huvisfatin",
"i_huresistin", "i_huleptin", "i_huglucagon", "i_huinsulin",
"i_hucpeptide", "i_hughrelin", "i_pcr", "i_ratiolg", "albumi"
)
# Represents: baseline, 6months, 6m-change, 12m, 12m-change
visit <- c("_v00", "_v66", "_v66", "_v01", "_v01")
i=1
tab <- NA
for(i in 1:length(rowvars)){
#For each variable in 'rowvars', writes the corresponding to each visit.
#eg: "p17_total_v00","p17_total_v66","d_p17_total_v66","p17_total_v01","d_p17_total_v01"
#eg: "i_hugip_v00","i_hugip_v66","d_hugip_v66","i_hugip_v01","d_hugip_v01"
v <- paste0(rowvars[i], visit)
v[c(3,5)] <- paste0("d_", v[c(3,5)])
v <- sub("d_i_", "d_", v)
现在我们得到了整个数据集的一些描述性统计，或者根据一些分组变量进行分离
#Computes "mean (n)" of each variable in 'v'. Both groups of int.
MEANs <- sapply(dat[,v], mean, na.rm = TRUE)
MEANs <- sprintf("%.2f", round(MEANs, digits = 2))
SD <- sapply(dat[,v], sd, na.rm = TRUE)
SD <- sprintf("%.2f", round(SD, digits = 2))
#Computes "mean (n)" of each variable in 'v' considering both groups of intervention.
MEANsI <- sapply(subset(dat, grupo_int_v00 == "A")[,v], mean, na.rm = TRUE)
MEANsI <- sprintf("%.2f", round(MEANsI, digits = 2))
MEANsC <- sapply(subset(dat, grupo_int_v00 == "B")[,v], mean, na.rm = TRUE)
MEANsC <- sprintf("%.2f", round(MEANsC, digits = 2))
SDI <- sapply(subset(dat, grupo_int_v00 == "A")[,v], sd, na.rm = TRUE)
SDI <- sprintf("%.2f", round(SDI, digits = 2))
SDC <- sapply(subset(dat, grupo_int_v00 == "B")[,v], sd, na.rm = TRUE)
SDC <- sprintf("%.2f", round(SDC, digits = 2))
我的问题来了。首先，我可以在没有干预组的情况下建立比较
#Computes paired T-test between pairs of v00, v66, v01
P1 <- t.test(x=dat[,v[1]], y=dat[,v[2]], paired = TRUE)$p.value #v00 vs v66
P2 <- t.test(x=dat[,v[1]], y=dat[,v[4]], paired = TRUE)$p.value #v00 vs v01
P3 <- t.test(x=dat[,v[2]], y=dat[,v[4]], paired = TRUE)$p.value #v66 vs v01
选择A组或B组(grupo_int_v00(
t.test(subset(dat, grupo_int_v00 == "A")[,v[1]], subset(dat, grupo_int_v00 == "A")[,v[2]], paired = T , na.rm = T)$p.value
# Error: Must subset columns with a valid subscript vector.
i Logical subscripts must match the size of the indexed input.
x Input has size 1 but subscript `yok` has size 206.
如果我将paired改为F，它会给我一个值。但我需要配对。在选择grupo_int_V00之前，我检查了循环中的值是否一致。它工作
这就是我尝试的一种方法
第二种方法
dplyr包sthg不那么彻底错误：问题需要组合和交互00-66-01
dat %>%
select(paciente, matches("_v00|_v01|_v66")) %>%
pivot_longer(!c(grupo_int_v00, paciente)) %>%
separate(name, into=c("name", "time"), sep="_v") %>%
group_by(time) %>% mutate(time = factor(time))
group_by(...) %>%
## and here I get errors
1 - group_by(paciente) %>%
+ purrr::map2_dbl(where(is.numeric), ~ t.test(.x ~ time, paired = T))
2 - do(tidy(t.test...
我很确定我已经在路上尝试了其他东西来获得
如果还需要sthg来澄清，我将非常感谢再次编辑
最后，在该领域有更多专业知识的人给了我一些启示不知怎么的，数据是一个tibble而不是data.frame。我被告知tibble对基本语法有不同的行为
class(dat)
"tbl_df" "tbl" "data.frame"
dat <- as.data.frame(dat)
#Computes paired T-test between pairs of v00, v66, v01
P1 <- t.test(subset(dat, grupo_int_v00 == "A")[,v[1]], subset(dat, grupo_int_v00 == "A")[,v[2]], paired = TRUE)$p.value
1.695603e-32