Extract data after a string one each appearance R - r

Question

I have a data like this (named spectra):
#Milk spectra: 1234
##XYDATA=(X++(Y..Y))
649.025085449219
667.675231457819
686.325377466418
##XYDATA=(X++(Y..Y))
723.625669483618
742.275815492218
760.925961500818
##XYDATA=(X++(Y..Y))
872.826837552417
891.476983561017
910.127129569617
928.777275578216
In this data, each time the string ##XYDATA=(X++(Y..Y)), that is the data for each different animal.
So, I want to have the code that can help extract this sample into 3 pieces of data.
Animal 1: 3 lines after 1st ' ##XYDATA=(X++(Y..Y))'
Animal 2: 3 lines after 2nd ' ##XYDATA=(X++(Y..Y))'
And so on.
I tried this line of code but it only help to extract line 1 of all times the string '##XYDATA=(X++(Y..Y))' appeared together. Thus, it did not meet my expect to have three lines and to have a separate pieces of data after each appearance of the string.
bo<-data.frame(spectra$V1[which(spectra$V1 == '##XYDATA=(X++(Y..Y))')+1])

Answer 1

Okay I think you could do something along these lines. I'm sure this could be much better and more efficient but read it in as a character vector.
Then loop through to spread it out. However this assumes there are always the same number of measures and you have a way to identify the character values.
c_data<- c("split", 1, 2, 3,
"split", 4, 5, 6)
y<- c_data == "split"
df_wide <- data.frame("animal"= character(), "v1" = numeric(), "v2" = numeric(), "v3" = numeric(),
stringsAsFactors = FALSE)
names(df_wide)<- c("animal", "v1", "v2", "v3")
x <- 0
for (i in 1:length(c_data)){
if (y[i] == TRUE){
x <- x +1
df_wide[x,] <- rbind(c(c_data[i], c_data[i+1], c_data[i+2], c_data[i+3]))
}
}
yields
animal v1 v2 v3
1 split 1 2 3
2 split 4 5 6
If it is a one time thing, it may not be worth trying to write something nicer. If it is an ongoing thing then you may want to look at using an apply function that you could have to write a function for.

Answer 2

You can do either of the following with split and map:
library(dplyr)
library(purrr)
df %>%
mutate(Animal = cumsum(grepl("##XYDATA=(X++(Y..Y))", V1, fixed = TRUE))) %>%
split(.$Animal) %>%
map(~slice(., -1) %>% mutate(V1 = as.numeric(V1))) %>%
'['(-1)
This creates an indicator variable Animal, split by that indicator, remove the first row for each dataframe, convert V1 to numeric, and finally remove the first element of the list.
You can also do the following:
df %>%
mutate(Animal = cumsum(grepl("##XYDATA=(X++(Y..Y))", V1, fixed = TRUE))) %>%
filter(!grepl("^#.*$", V1)) %>%
mutate(V1 = as.numeric(V1)) %>%
split(.$Animal)
This also creates the indicator Animal, but it intead, filters out all rows with # signs in it and converts V1 to numeric before splitting into separate dataframes.
Result:
$`1`
# A tibble: 3 x 2
V1 Animal
<dbl> <int>
1 649.0251 1
2 667.6752 1
3 686.3254 1
$`2`
# A tibble: 3 x 2
V1 Animal
<dbl> <int>
1 723.6257 2
2 742.2758 2
3 760.9260 2
$`3`
# A tibble: 4 x 2
V1 Animal
<dbl> <int>
1 872.8268 3
2 891.4770 3
3 910.1271 3
4 928.7773 3
Note:
Here I assumed #Milk spectra: 1234 is also a row in your column, hence the subsetting at the end.
Data:
df = read.table(textConnection("'#Milk spectra: 1234'
##XYDATA=(X++(Y..Y))
649.025085449219
667.675231457819
686.325377466418
##XYDATA=(X++(Y..Y))
723.625669483618
742.275815492218
760.925961500818
##XYDATA=(X++(Y..Y))
872.826837552417
891.476983561017
910.127129569617
928.777275578216"),comment.char = "", stringsAsFactors = FALSE)