I have a text file that only has one column. It's like:
sample1
color 12
length 34
validity 90
sample2
color 15
length 20
validity 120
sample3
color 34
validity 79
There are 3 lines between samples, and 1 line between sample id and its attribute. Also, for sample3, the length record is missing.
I want to read this file into an R data.frame so that it looks like:
sample1 sample2 sample3
color 12 15 34
length 34 20 NA
validity 90 120 79
You've got a data cleaning problem. Here is my solution for you.
I copied and pasted your "TXT" file into a blank TextEdit document on Mac, and saved it as file.txt. The order as shown in your "TXT" file is required:
data <- unlist(read.table("file.txt", header=F, sep="\t", stringsAsFactors=F), use.names=F)
data
sample_names <- data[grep("sample", data), drop=T]
sample_names
## [1] "sample1" "sample2" "sample3"
color <- data[grep("color", data), drop=T]
color
## "color 12" "color 15" "color 34"
length <- data[grep("length", data), drop=T]
length #note missing term, and requires manual coding
## [1] "length 34" "length 20"
length <- c(length, NA)
length
## [1] "length 34" "length 20" NA
validity <- data[grep("validity", data), drop=T]
validity
## [1] "validity 90" "validity 120" "validity 79"
## Assemble into data.frame:
assembled_df <- rbind(color, length, validity)
colnames(assembled_df) <- sample_names #update column names
assembled_df
## sample1 sample2 sample3
## color "color 12" "color 15" "color 34"
## length "length 34" "length 20" NA
## validity "validity 90" "validity 120" "validity 79"
Note that the code might not be generalizable. It is a matter of what the actual TXT file would look like. What's important is to learn to 1) know your data (which you do), 2) come up with a strategy, 3) and then a solution.