创建预定义文本的字符向量列，并使用rbind或bind_行将其绑定到现有数据帧

你好

我将提出两个[可能]非常微小的问题，供您进行出色的复习

问题#1

我有一个相对整洁的df（dat），尺寸为10299 x 563。两个数据集[创建]dat共有563个变量，分别为“主题”（数字）、“标签”（数字）、3:563（文本文件中的变量名）。观察值1:2947来自“测试”数据集，而观察值2948:10299来自“训练”数据集

我想在dat中插入一列（header='type'），它基本上是由字符串test的第1:2947行和字符串序列的第2948:10299行组成的，这样我以后就可以在dataset或dplyr/tidyr中的其他类似聚合函数上分组

我创建了一个测试df（testdf=1:10299:dim（testdf）=102499 x 1），然后：

testdat[1:2947 , "type"] <- c("test")
testdat[2948:10299, "type"] <- c("train")
> head(ds, 2);tail(ds, 2)
  X1.10299 type
1        1 test
2        2 test
      X1.10299  type
10298    10298 train
10299    10299 train

列车df 列车[1:10，1:5]

实际代码（忽略函数调用/我正在通过控制台进行大部分测试）

[我与此代码一起使用的数据集

---

run\u analysis您所面临的问题源于这样一个事实，即您在用于创建数据框对象的变量列表中有重复的名称。如果您确保列名是唯一的，并且在对象之间共享，那么代码将运行。基于您上面使用的代码，我提供了一个完整的工作示例（注释中注明了修复和各种编辑）：

vars Hi Zach，这确实很奇怪，因为我觉得你所做的一切都不会产生你所看到的东西。你能用你可以在这里复制的数据子集（可以是
dput（）的较小行/列数）复制行为吗
或read in？@forrest.Stevens用可复制的[希望]更新了问题dataset.非常有趣！您认为我在rbind和bind_行之间有一个列增量的原因是因为bind_行正在删除带有非字母数字的列？我不知道，因为我没有看到您的代码的实际摘录以及读取所有数据的方式。但是从您上面使用
idential（）进行的检查来看
情况似乎并非如此？正如我所说，我对这个结果感到困惑，看不出出现这种情况的明显原因，尤其是在所有数据类型都相同的情况下，等等（
bind_rows（）
如果列数据类型混合，则会呕吐）。更新了原始帖子，以包含我通过控制台测试编写的实际代码。好吧，我仍然无法解释您所观察到的内容，因为没有原始数据文件，我可能会被卡住。我编辑了上面的代码，以包含您的原始列名，认为这可能是
dplyr
名称检查中的奇怪之处，但是我仍然不明白为什么
bind_rows（）
会毫无错误地进行，但最终会删除列。通常，如果列名不匹配，则会保留列并填充NAs。Hi@Forrest我在代码块之前添加了指向我正在使用的数据集的链接。这是一种不寻常的行为。
test.names <- names(test)
train.names <- names(train)
identical(test.names, train.names)
> TRUE

dat <- bind_rows(test, train)

   subject labels tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z
1        2      5         0.2571778       -0.02328523       -0.01465376
2        2      5         0.2860267       -0.01316336       -0.11908252
3        2      5         0.2754848       -0.02605042       -0.11815167
4        2      5         0.2702982       -0.03261387       -0.11752018
5        2      5         0.2748330       -0.02784779       -0.12952716
6        2      5         0.2792199       -0.01862040       -0.11390197
7        2      5         0.2797459       -0.01827103       -0.10399988
8        2      5         0.2746005       -0.02503513       -0.11683085
9        2      5         0.2725287       -0.02095401       -0.11447249
10       2      5         0.2757457       -0.01037199       -0.09977589

   subject label tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z
1        1     5         0.2885845      -0.020294171        -0.1329051
2        1     5         0.2784188      -0.016410568        -0.1235202
3        1     5         0.2796531      -0.019467156        -0.1134617
4        1     5         0.2791739      -0.026200646        -0.1232826
5        1     5         0.2766288      -0.016569655        -0.1153619
6        1     5         0.2771988      -0.010097850        -0.1051373
7        1     5         0.2794539      -0.019640776        -0.1100221
8        1     5         0.2774325      -0.030488303        -0.1253604
9        1     5         0.2772934      -0.021750698        -0.1207508
10       1     5         0.2805857      -0.009960298        -0.1060652

run_analysis <- function () {
    #Vars available for use throughout the function that should be preserved
    vars <- read.table("features.txt", header = FALSE, sep = "")
    lookup_table <- data.frame(activitynum = c(1,2,3,4,5,6), 
                               activity_label = c("walking", "walking_up", 
                                                  "walking_down", "sitting", 
                                                  "standing", "laying"))
    test <- test_read_process(vars, lookup_table)
    train <- train_read_process(vars, lookup_table)
}

test_read_process <- function(vars, lookup_table) {
    #read in the three documents for cbinding later
    test.sub <- read.table("test/subject_test.txt", header = FALSE)
    test.labels <- read.table("test/y_test.txt", header = FALSE)
    test.obs <- read.table("test/X_test.txt", header = FALSE, sep = "")

    #cbind the cols together and set remaining colNames to var names in vars
    test.dat <- cbind(test.sub, test.labels, test.obs)  
    colnames(test.dat) <- c("subject", "labels", as.character(vars[,2]))

    #Use lookup_table to set the "test_labels" string values that correspond
    #to their integer IDs
    #test.lookup <- merge(test, lookup_table, by.x = "labels", 
    #               by.y ="activitynum", all.x = T)

    #Remove temporary symbols from globalEnv/memory
    rm(test.sub, test.labels, test.obs)

    #return
    return(test.dat)
}

train_read_process <- function(vars, lookup_table) {
    #read in the three documents for cbinding
    train.sub <- read.table("train/subject_train.txt", header = FALSE)
    train.labels <- read.table("train/y_train.txt", header = FALSE)
    train.obs <- read.table("train/X_train.txt", header = FALSE, sep = "")

    #cbind the cols together and set remaining colNames to var names in vars
    train.dat <- cbind(train.sub, train.labels, train.obs)    
    colnames(train.dat) <- c("subject", "label", as.character(vars[,2]))

    #Clean up temporary symbols from globalEnv/memory
    rm(train.sub, train.labels, train.obs, vars)

    return(train.dat)
}

vars <- read.table(file="features.txt", header=F, stringsAsFactors=F)

##  FRS: This is the source of original problem:
duplicated(vars[,2])
vars[317:340,2]
duplicated(vars[317:340,2])
vars[396:419,2]

##  FRS: I edited the following to both account for your data and variable
##    issues:
test_read_process <- function() {
  #read in the three documents for cbinding later
  test.sub <- read.table("test/subject_test.txt", header = FALSE)
  test.labels <- read.table("test/y_test.txt", header = FALSE)
  test.obs <- read.table("test/X_test.txt", header = FALSE, sep = "")

  #cbind the cols together and set remaining colNames to var names in vars
  test.dat <- cbind(test.sub, test.labels, test.obs)  
  #colnames(test.dat) <- c("subject", "labels", as.character(vars[,2]))
  colnames(test.dat) <- c("subject", "labels", paste0("V", 1:nrow(vars)))

  return(test.dat)
}

train_read_process <- function() {
  #read in the three documents for cbinding
  train.sub <- read.table("train/subject_train.txt", header = FALSE)
  train.labels <- read.table("train/y_train.txt", header = FALSE)
  train.obs <- read.table("train/X_train.txt", header = FALSE, sep = "")

  #cbind the cols together and set remaining colNames to var names in vars
  train.dat <- cbind(train.sub, train.labels, train.obs)    
  #colnames(train.dat) <- c("subject", "labels", as.character(vars[,2]))
  colnames(train.dat) <- c("subject", "labels", paste0("V", 1:nrow(vars)))

  return(train.dat)
}


test_df <- test_read_process()
train_df <- train_read_process()

identical(names(test_df), names(train_df))


library("dplyr")

## FRS: These could be piped together but I've kept them separate for clarity:
train_df %>%
  mutate(test="train") -> 
  train_df

test_df %>%
  mutate(test="test") -> 
  test_df

test_df %>% 
  bind_rows(train_df) -> 
  out_df

head(out_df)
out_df

##  FRS: You can set your column names to those of the original 
##    variable list but you still have duplicates to deal with:
names(out_df) <- c("subject", "labels", as.character(vars[,2]), "test")

duplicated(names(out_df))