过滤数据集中的行以获取 r 中的不同单词

Question

Goal : To filter rows in dataset so that only distinct words remain At the moment, I have used inner_join to retain rows in 2 datasets which has made my rows in this dataset duplicate.

Attempt 1: I have tried to use distinct to retain only those rows which are unique, but this has not worked. I may be using it incorrectly.

This is my code so far; output attached in png format :

# join warriner emotion lemmas by `word` column in collocations data frame to see how many word matches there are

warriner2 <- dplyr::inner_join(warriner, coll, by = "word") # join data; retain only rows in both sets (works both ways)
warriner2 <- distinct(warriner2)
warriner2

coll2 <- dplyr::semi_join(coll, warriner, by = "word") # join all rows in a that have a match in b

# There are 8166 lemma matches (including double-ups)
# There are XXX unique lemma matches

Answer 1

You can try:

library(dplyr)

warriner2 <- inner_join(warriner, coll, by = "word") %>%
                distinct(word, .keep_all = TRUE)

Answer 2

To even further clarify Ronak's answer, here is an example with some mock data. Note that you can just use distinct() at the end of the pipe to keep distinct columns if that's what you want. Your error might very well have occurred because you performed two operations, and assigned the result to the same name both times (warriner2).

library(dplyr)

# Here's a couple sample tibbles
name <- c("cat", "dog", "parakeet")

df1 <- tibble(
        x = sample(5, 99, rep = TRUE),
        y = sample(5, 99, rep = TRUE),
        name = rep(name, times = 33))
df2 <- tibble(
        x = sample(5, 99, rep = TRUE),
        y = sample(5, 99, rep = TRUE),
        name = rep(name, times = 33))

# It's much less confusing if you do this in one pipe
p <- df1 %>%
        inner_join(df2, by = "name") %>%
        distinct()