使用mutate-over对每个列应用回归模型

这是我发布的一个后续问题

解决方案是使用以下代码：

groups <- c("group2", "group3", "group4")
dataGroups <- groups %>%
  purrr::map_dfr(~ data %>%
        filter(grp == "group1" | grp == .x) %>%
        mutate(!!.x := normaliseData(Y)))

唯一改变的是

Y

变量。如何映射列并运行回归模型

数据：

唯一改变的是

Y

变量

编辑：

整洁的解决方案：

linearRegFunction <- function(x){
  lm(get(x) ~ control + did + treatment, data = dataGroups)
}

groups %>% 
  map(., ~linearRegFunction(.x))

linearRegFunction%
映射（，~linearRegFunction（.x））

虽然可以使用

扫帚或
purr
想出一个
整洁的解决方案，但有时简单的解决方案也有它的优点。例如：

lappy（组，功能（x）总结（lm（get（x）~对照+did+治疗，数据=数据组2）））
您希望最终输出是什么？你想改变每个模型的结果，并且每个变量都有相同的3个预测值吗？有点困惑。。。这就是你想做的吗？我想为每个
groupX
列运行回归。我已经添加了一些代码编辑。
dataGroups2 <- dataGroups %>%
  rowwise %>%
  mutate(
    control = sample(c(0,1), 1),
    treatment = ifelse(grp == "group1", 1, 0),
    did = control * treatment
    )

dataGroups2 %>% 
  mutate(across(where(.) %in% groups),  ~lm(log(.x) ~ treatment + control + did ))

data <- structure(list(grp = c("group1", "group1", "group1", "group1", 
"group1", "group1", "group2", "group2", "group2", "group2", "group2", 
"group2", "group3", "group3", "group3", "group3", "group3", "group3", 
"group4", "group4", "group4", "group4", "group4", "group4"), 
    date = structure(c(1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400, 
    1598918400, 1598918400, 1598918400, 1598918400, 1598918400
    ), tzone = "UTC", class = c("POSIXct", "POSIXt")), id = c("04003", 
    "04006", "04011_AM", "0401301_AD", "0401303", "0401305", 
    "22017_AM", "22021_AM", "22039_AM", "22048", "22053_AM", 
    "22054_AM", "28002", "28004", "2800501", "2800502", "2800503", 
    "2800504", "31010_AM", "31015_AM", "31016", "31019_AM", "31023", 
    "31029_AM"), Y = c(17039.329, 13232.982, 7917.693, 22585.676, 
    20527.113, 29422.471, 7087.536, 8134.265, 15842.035, 16142.111, 
    11493.981, 6556.387, 22086.768, 11325.882, 53449.067, 83662.101, 
    78508.089, 66107.125, 5095.169, 5590.531, 17796.439, 6028.701, 
    39271.698, 3642.281)), row.names = c(NA, -24L), groups = structure(list(
    grp = c("group1", "group2", "group3", "group4"), .rows = structure(list(
        1:6, 7:12, 13:18, 19:24), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, 4L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

dataGroups2 %>% 
  lm(group2 ~ control + did + treatment + did, data = .) %>% 
  summary()

dataGroups2 %>% 
  lm(group3 ~ control + did + treatment + did, data = .) %>% 
  summary()

dataGroups2 %>% 
  lm(group4 ~ control + did + treatment + did, data = .) %>% 
  summary()

linearRegFunction <- function(x){
  lm(get(x) ~ control + did + treatment, data = dataGroups)
}

groups %>% 
  map(., ~linearRegFunction(.x))