String matching two dataframes with a sliding window - r

Question

I have two df.
df1
col1
1 a
2 b
3 c
4 c
df2
setID col1
1 1 a
2 1 b
3 1 b
4 1 a
5 2 w
6 2 v
7 2 c
8 2 b
9 3 a
10 3 a
11 3 b
12 3 a
13 4 a
14 4 b
15 4 c
16 4 a
I'm using the following code to match them.
scorematch <- function ()
{
require("dplyr")
#to make sure every element is preceded by the one before that element
combm <- rev(sapply(rev(seq_along(df1$col1)), function(i) paste0(df1$col1[i-1], df1$col1[i])));
tempdf <- df2
#group the history by their ID
tempdf <- group_by(tempdf, setID)
#collapse strings in history
tempdf <- summarise(tempdf, ss = paste(col1, collapse = ""))
tempdf <- rowwise(tempdf)
#add score based on how it matches compared to path
tempdf <- mutate(tempdf, score = sum(sapply(combm, function(x) sum(grepl(x, ss)))))
tempdf <- ungroup(tempdf)
#filter so that only IDs with scores more than 0 are available
tempdf <- filter(tempdf, score != 0)
tempdf <- pull(tempdf, setID)
#filter original history to reflect new history
tempdf2 <- filter(df2, setID %in% tempdf)
tempdf2
}
This code works great. But I want to take this further. I want to apply a sliding window function to get the df1 values I want to match against df2. So far I'm using this function as my sliding window.
slidingwindow <- function(data, window, step)
{
#data is dataframe with colname
total <- length(data)
#spots are start of each window
spots <- seq(from=1, to=(total-step), by=step)
result <- vector(length = length(spots))
for(i in 1:length(spots)){
...
}
return(result)
}
The scorematch function will be nested inside slidingwindow function. I'm unsure how to proceed from there though. Ideally df1 will be split into windows. Starting from the first window it will be matched against df2 using the scorematch function to get a filtered out df2. Then I want the second window of df1 to match against the newly filtered df2 and so on. The loop should end when df2 has been filtered down so that it contains only 1 distinct setID value. The final output can either be the whole filtered df2 or just the remaining setID.
Ideal output would be either
setID col1
1 4 a
2 4 b
3 4 c
4 4 a
or
[1] "4"

Answer 1

Here is a solution without using a for-loop. I use stringr because of its nice consistent syntax, purrr for map (although lapply would be sufficient in this case) and dplyr to group_by setID and collapse the strings for each group.
library(dplyr)
library(purrr)
library(stringr)
First I collapse the string for each group. This makes it easier to use pattern-matching with str_detect-later:
df2_collapse <- df2 %>%
group_by(setID) %>%
summarise(string = str_c(col1, collapse = ""))
df2_collapse
# A tibble: 4 x 2
# setID string
# <int> <chr>
# 1 1 abba
# 2 2 wvcb
# 3 3 aaba
# 4 4 abca
The "look-up" string is collapse as well and then the substrings (i.e. slding windows) are extract with str_sub. Here I work along the length of the string str_length and extract all possible groups following each letter in the string.
string <- str_c(df1$col1, collapse = "")
string
# [1] "abcc"
substrings <-
unlist(map(1:str_length(string), ~ str_sub(string, start = .x, end = .x:str_length(string))))
Store the substrings in a tibble with their length as score.
substrings
# [1] "a" "ab" "abc" "abcc" "b" "bc" "bcc" "c" "cc" "c"
substrings <- tibble(substring = substrings,
score = str_length(substrings))
substrings
# A tibble: 10 x 2
# substring score
# <chr> <int>
# 1 a 1
# 2 ab 2
# 3 abc 3
# 4 abcc 4
# 5 b 1
# 6 bc 2
# 7 bcc 3
# 8 c 1
# 9 cc 2
# 10 c 1
For each setID with extract the maximum score it matches in the substring-data and the filter out the row with the maximum score of all setIDs.
df2_collapse %>%
mutate(score = map_dbl(string,
~ max(substrings$score[str_detect(.x, substrings$substring)]))) %>%
filter(score == max(score))
# A tibble: 1 x 3
# setID string score
# <int> <chr> <dbl>
# 1 4 abca 3
Data
df1 <- structure(list(col1 = c("a", "b", "c", "c")),
class = "data.frame", row.names = c("1", "2", "3", "4"))
df2 <-
structure(list(setID = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L),
col1 = c("a", "b", "b", "a", "w", "v", "c", "b", "a", "a", "b", "a", "a", "b", "c", "a")),
class = "data.frame",
row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16"))