美好的一天，
我将为你的出色评论提出两个[可能]非常微不足道的问题。
问题＃1
我有一个相对整洁的df（dat），暗淡的10299 x 563.两个数据集[创建] dat 共有的563个变量是＆＃39; subject＆＃39; （数字），＆＃39;标签＆＃39; （数字），3：563（来自文本文件的变量名）。观察1：2947来自一个＆＃39;测试＆＃39;数据集，而观察2948：10299来自培训＆＃39;数据集。
我想将一个列（header =＆＃39; type＆＃39;）插入到基本上由行 test 和行2948：10299组成的行1：2947的dat中字符串 train 的方式我可以稍后在dplyr / tidyr中对数据集或其他类似的聚合函数进行分组。
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
所以我真的不喜欢现在有一个X1.10299专栏。
问题：
问题＃2
我从上面到达我的[几乎]整洁的df（dat）的方式是两个分别采用dim（2947 x 563和7352 x 563）形式的dfs（测试和训练）和 rbinding 他们在一起。
我确认在绑定工作之后我的所有变量名都出现了这样的东西：
test.names <- names(test)
train.names <- names(train)
identical(test.names, train.names)
> TRUE
有趣且主要关注的是，如果我尝试使用来自＆＃39; dplyr＆＃39;的 bind_rows 函数执行相同的绑定练习：
dat <- bind_rows(test, train)
它返回一个显然保留了我所有观察结果的数据帧（x：10299），但现在我的变量计数从563减少到470！
问题：
tidyr？
感谢您抽出时间考虑这些事项。
用于审查的样本测试/训练dfs（最左边的数字是df索引）：
测试df 测试[1：10,1：5]
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
训练df 火车[1:10,1：5]
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
实际代码（忽略函数调用/我通过控制台执行大部分测试）。
[http://archive.ics.uci.edu/ml/machine-learning-databases/00240/]The数据集我正在使用此代码。 1
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)
}
答案 0 :(得分：1)
您遇到的问题源于您在用于创建数据框对象的变量列表中有重复名称的事实。如果确保列名称是唯一的，并且在代码将运行的对象之间共享。我已经包含了一个基于您上面使用的代码的完整工作示例（修复和评论中注明的各种编辑）：
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))