Is it possible to merge rows in R data.frame? - r

Question

If I have the following data.frame:
> df <- data.frame(x = c('a', 'b*', 'c'), y = c('d', 'e', 'f'))
> df
x y
1 a d
2 b* e
3 c f
Is there a clear way to identify rows in which the df$x entries include the string value *, then use this condition to force the string entries of that row to be merged with the row preceding itself, resulting in a data.frame like the following:
> df
x y
1 a b* d e
2 c f
I assume that the first part of the problem (identifying the x row values that include `*) can be done in a fairly straightforward way using regular expressions. I'm having trouble identifying how to force a data.frame row merge with the row preceding it.
One particularly tricky challenge is if multiple entries in a row have the pattern, e.g.
> df <- data.frame(x = c('a', 'b*', 'c*'), y = c('d', 'e', 'f'))
> df
x y
1 a d
2 b* e
3 c* f
In this case, the resulting data.frame should look like this:
> df
x y
1 a b* c* d e f
The main issue that I find is that after running one iteration of a loop that pastes the strings from df[2,] into df[1,], the data.frame index does not adapt to the new data.frame size:
> df
x y
1 a b* d e
3 c* f
So, subsequent indexing is disrupted.

Answer 1

Here a initial solution:
# Creating the data frame
df <- data.frame(x = c('a', 'b*', 'c'), y = c('d', 'e', 'f'),stringsAsFactors = FALSE)
df
# Creating a vector of rows with *
ast <- grepl("\\*",df$x)
# For loop
for(i in seq(length(ast),1,-1)){
if(ast[i]){
df[i-1,"x"] <- paste(df[i-1,"x"],df[i,"x"],sep=" ")
df[i-1,"y"] <- paste(df[i-1,"y"],df[i,"y"],sep=" ")
df <- df[-i,]
}
}
That's an initial solution because you still have to manage when the first row has * and other situations like this. I hope that helps already.

Answer 2

Here are 3 alternatives (for the base R one, I assumed x and y are characters rather factor. I also made your data more complicated in order to cover different scenarios)
(A bit more complicated data set)
df <- data.frame(x = c('p','a', 'b*', 'c*', 'd', 'h*', 'j*', 'l*', 'n'),
y = c('r','d', 'e', 'f', 'g', 'i', 'k', 'm', 'o'),
stringsAsFactors = FALSE)
Base R
aggregate(. ~ ID,
transform(df, ID = cumsum(!grepl("*", x, fixed = TRUE))),
paste, collapse = " ")
# ID x y
# 1 1 p r
# 2 2 a b* c* d e f
# 3 3 d h* j* l* g i k m
# 4 4 n o
data.table
library(data.table)
setDT(df)[, lapply(.SD, paste, collapse = " "),
by = .(ID = cumsum(!grepl("*", df[["x"]], fixed = TRUE)))]
# ID x y
# 1: 1 p r
# 2: 2 a b* c* d e f
# 3: 3 d h* j* l* g i k m
# 4: 4 n o
dplyr
library(dplyr)
df %>%
group_by(ID = cumsum(!grepl("*", x, fixed = TRUE))) %>%
summarise_all(funs(paste(., collapse = " ")))
# # A tibble: 4 x 3
# ID x y
# <int> <chr> <chr>
# 1 1 p r
# 2 2 a b* c* d e f
# 3 3 d h* j* l* g i k m
# 4 4 n o

Answer 3

Not actually merging the rows, but for those rows that have a * it pastes the value of the previous row in, and then it gets rid of rows that had a * in the following row.
library(dplyr)
df <- data.frame(x = c('a', 'b*', 'c'), y = c('d', 'e', 'f'))
df <- mutate(df,
Operator = grepl("\\*",x), # Check for *
lagged.x = lag(x, n = 1), # Get x value from 1 row ago
lagged.y = lag(y, n = 1), # Get y value from 1 row ago
x = ifelse(Operator, paste(lagged.x, x),x), # if there is * paste lagged x
y = ifelse(Operator, paste(lagged.y, y),y), # if there is * paste lagged y
lead.Operator = lead(Operator, n = 1) # Check if next row has a *
)
# keep only rows that had no * in following row and that had no following row (last row)
df <- filter(df, !lead.Operator | is.na(lead.Operator))
# Select just the x and y columns
df <- select(df, x, y)