data frame after unseating has multiple rows with na values that can be summarized into one row. All text/character data. Example:
link feature-1 feature-2 feature-3
link_1 a. NA NA
link_1. NA NA b
link_1. NA. c NA
link2 NA. a NA
link_2 NA NA d
link_2 x NA NA
Assuming that you are only ever combining NA values and text, then I recommend the following:
library(dplyr)
# here is a mock dataset
df = data.frame(grp = c('a','a','a','b','b','b'),
value1 = c(NA,NA,'text','text',NA,NA),
value2 = c(NA,'txt',NA,NA,'txt',NA),
stringsAsFactors = FALSE)
df %>%
# convert NA values to empty text strings
mutate(value1 = ifelse(is.na(value1), "", value1),
value2 = ifelse(is.na(value2), "", value2)) %>%
# specify the groups
group_by(grp) %>%
# append all the text in each group into a single row
summarise(val1 = paste(value1, collapse = ""),
val2 = paste(value2, collapse = ""))
Based on this answer.
Looking at the data in your question, you might need to first standardize some values. Because "link_1" vs "link_1." and "NA" vs "NA." will be treated as different.
You can use across to get first non-NA value by group in multiple columns.
library(dplyr)
df %>% group_by(link) %>% summarise(across(starts_with('feature'), ~na.omit(.)[1]))
# link feature.1 feature.2 feature.3
# <chr> <chr> <chr> <chr>
#1 link_1 a c b
#2 link_2 x a d
data
df <- structure(list(link = c("link_1", "link_1", "link_1", "link_2",
"link_2", "link_2"), feature.1 = c("a", NA, NA, NA, NA, "x"),
feature.2 = c(NA, NA, "c", "a", NA, NA), feature.3 = c(NA,
"b", NA, NA, "d", NA)), class = "data.frame", row.names = c(NA, -6L))