Drop rows after criteria - r

Question

I have some data that I'm trying to clean up, and I noticed that I have 150 files that have rows that are subsets of previous rows. Is there a way that I can drop everything after certain criteria occur? Below I'm not sure how I'd write out sample data for this via code, so I've listed an example of the data as text. Below. I'd like to drop all rows at and below "section 2"
Name,Age,Address
Section 1,,
Abby,10,1 Baker St
Alice,12,3 Main St
Becky,13,156 F St
Ben,14,2 18th St
Cameron,15,4 Journey Road
Danny,16,123 North Ave
Eric,17,325 Hill Blvd
,,
Section 2,,
Abby,10,1 Baker St
Alice,12,3 Main St
Becky,13,156 F St
Ben,14,2 18th St
,,
Section 3,,
Becky,13,156 F St
Ben,14,2 18th St
Cameron,15,4 Journey Road
Danny,16,123 North Ave
,,
Section 5,,
Alice,12,3 Main St
Becky,13,156 F St
Ben,14,2 18th St
Cameron,15,4 Journey Road
Danny,16,123 North Ave
Eric,17,325 Hill Blvd
Expected output
Name,Age,Address
Section 1,,
Abby,10,1 Baker St
Alice,12,3 Main St
Becky,13,156 F St
Ben,14,2 18th St
Cameron,15,4 Journey Road
Danny,16,123 North Ave
Eric,17,325 Hill Blvd

Answer 1

Assuming your text file is called temp.txt you can use readLines to read it in, find the line with 'Section 2' in it and read all the lines above that.
tmp <- readLines('temp.txt')
inds <- grep('Section 2', tmp) - 2
data <- read.csv(text = paste0(tmp[1:inds], collapse = '\n'))
data
# Name Age Address
#1 Section 1 NA
#2 Abby 10 1 Baker St
#3 Alice 12 3 Main St
#4 Becky 13 156 F St
#5 Ben 14 2 18th St
#6 Cameron 15 4 Journey Road
#7 Danny 16 123 North Ave
#8 Eric 17 325 Hill Blvd

Answer 2

Here, I "read" in your data by calling strsplit and using the newline as the separator. If you were doing this from file, you could use readLines
I use grep to find the line number that contains "Section 2", use that to subset raw_data. I paste0(..., collapse="") the lines that do not start with "Section" and use read.table using sep="," with header=TRUE to parse as if I read just that section with read.csv.
raw_data <- strsplit(split = "\\n", "Name,Age,Address
Section 1,,
Abby,10,1 Baker St
Alice,12,3 Main St
Becky,13,156 F St
Ben,14,2 18th St
Cameron,15,4 Journey Road
Danny,16,123 North Ave
Eric,17,325 Hill Blvd
,,
Section 2,,
Abby,10,1 Baker St
Alice,12,3 Main St
Becky,13,156 F St
Ben,14,2 18th St
,,
Section 3,,
Becky,13,156 F St
Ben,14,2 18th St
Cameron,15,4 Journey Road
Danny,16,123 North Ave
,,
Section 5,,
Alice,12,3 Main St
Becky,13,156 F St
Ben,14,2 18th St
Cameron,15,4 Journey Road
Danny,16,123 North Ave
Eric,17,325 Hill Blvd")
section2_idx <- grep('Section 2', raw_data[[1]])
raw_data_clean <- trimws(raw_data[[1]][1:(section2_idx-2)])
allsect_idx <- grep('^Section', raw_data_clean)
if(length(allsect_idx > 0))
raw_data_clean <- raw_data_clean[-allsect_idx]
read.table(text = paste0(raw_data_clean, collapse="\n"), sep=",", header = TRUE)
#> Name Age Address
#> 1 Abby 10 1 Baker St
#> 2 Alice 12 3 Main St
#> 3 Becky 13 156 F St
#> 4 Ben 14 2 18th St
#> 5 Cameron 15 4 Journey Road
#> 6 Danny 16 123 North Ave
#> 7 Eric 17 325 Hill Blvd
Created on 2020-12-06 by the reprex package (v0.3.0)

Answer 3

Here is a made up example that avoids having to type in your starting data.
mixed_data is 500 elements long and each row is a string containing two commas. The string doesn't need to be broken apart if if looks like your example.
Create an empty vector to hold just one of each value. Then loop through the whole mixed list and add the unique entries to that vector. This example resulted in 444 unique items in one_of_each of the original 500 in mixed_data.
set.seed(101)
a <- sample(LETTERS,500, replace = TRUE)
b <- sample(letters,500, replace = TRUE)
d <- sample(c(1:3),500, replace = TRUE)
mixed_data <- paste0(a,",",b,",",d)
head(mixed_data)
one_of_each <- c() #starts empty
for (i in 1:length(mixed_data)){
if (mixed_data[i] %in% one_of_each == FALSE) {
one_of_each <- c(one_of_each,mixed_data[i]) #if not found, then add
}
}