Replace NA between two values without loop - r

Question

I have the following data frame:
data <- structure(list(Date = structure(c(-17897, -17896, -17895, -17894,
-17893, -17892, -17891, -17890, -17889, -17888, -17887, -17887,
-17886, -17885, -17884, -17883, -17882, -17881, -17880, -17879,
-17878, -17877, -17876, -17875, -17874, -17873, -17872, -17871,
-17870, -17869, -17868, -17867, -17866, -17865, -17864), class = "Date"),
duration = c(NA, NA, NA, 5, NA, NA, NA, 5, NA, NA, 1, 1,
NA, NA, 3, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 4, NA, NA, 4, NA, NA), name = c(NA, NA, NA, "Date_beg",
NA, NA, NA, "Date_end", NA, NA, "Date_beg", "Date_end", NA,
NA, "Date_beg", NA, "Date_end", NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "Date_beg", NA, NA, "Date_end", NA, NA
)), row.names = c(NA, -35L), class = c("tbl_df", "tbl", "data.frame"
))
And looks like:
Date duration name
<date> <dbl> <chr>
1 1921-01-01 NA NA
2 1921-01-02 NA NA
3 1921-01-03 NA NA
4 1921-01-04 5 Date_beg
5 1921-01-05 NA NA
6 1921-01-06 NA NA
7 1921-01-07 NA NA
8 1921-01-08 5 Date_end
9 1921-01-09 NA NA
10 1921-01-10 NA NA
...
I want to replace the NA values in column name that are between rows with Date_beg and Date_end with the word "event".
I have tried this:
data %<>% mutate(name = ifelse(((lag(name) == 'Date_beg')|(lag(name) == 'event')) &
But only the first row after Date_beg changes. It is quite easy with a for-loop, but I wanted to use a more R-like method.

Answer 1

There is probably a better way using data.table::nafill, but as you're using tidyverse functions, I would do it by creating an extra event column using tidyr::fill and then pulling it through to the name column where name is NA:
library(tidyr)
data %>%
mutate(
events = ifelse(
fill(data, name)$name == "Date_beg",
"event",
NA),
name = coalesce(name, events)
) %>%
select(-events)

Answer 2

You can do it by looking at the indices where there have been more "Date_beg" than "Dat_end" with:
data$name[lag(cumsum(data$name == "Date_beg" & !is.na(data$name))) -
cumsum(data$name == "Date_end" & !is.na(data$name)) >0] <- "event"
print(data, n=20)
# # A tibble: 35 x 3
# Date duration name
# <date> <dbl> <chr>
# 1 1921-01-01 NA NA
# 2 1921-01-02 NA NA
# 3 1921-01-03 NA NA
# 4 1921-01-04 5 Date_beg
# 5 1921-01-05 NA event
# 6 1921-01-06 NA event
# 7 1921-01-07 NA event
# 8 1921-01-08 5 Date_end
# 9 1921-01-09 NA NA
# 10 1921-01-10 NA NA
# 11 1921-01-11 1 Date_beg
# 12 1921-01-11 1 Date_end
# 13 1921-01-12 NA NA
# 14 1921-01-13 NA NA
# 15 1921-01-14 3 Date_beg
# 16 1921-01-15 NA event
# 17 1921-01-16 3 Date_end
# 18 1921-01-17 NA NA
# 19 1921-01-18 NA NA
# 20 1921-01-19 NA NA
# # ... with 15 more rows
Lagging the first index by one is required so that you don't overwrite the "Date_beg" at the start of each run.

Answer 3

Another dplyr approach using the cumsum function.
If the row in the name column in NA, it'll add 0 to the cumsum, otherwise add 1. Therefore the values under Date_beg will always be odd numbers (0 + 1) and the values under Date_end will always be even numbers (0 + 1 + 1). Then replace values that are odd in the ref column AND not NA in the name column with "event".
library(dplyr)
data %>%
mutate(ref = cumsum(ifelse(is.na(name), 0, 1)),
name = ifelse(ref %% 2 == 1 & is.na(name), "event", name)) %>%
select(-ref)