How to make a new tibble from an existing dataframe by searching within a column for contained words

[throwaway]

I have an existing dataframe. i want to search within this dataframes column for words that contain "x" words. Finally, i want to get a new tibble with rows and the columns which contain the the specific words im looking for.

example: This is my dataframe. i want a new tibble with abc, def, ghi, jkl and mno whenever pqr contains "leg"
abc def ghi jkl mno pqr
1 cracker 3 apple 5 blue
4 bubbles 5 cart 9 leg
7 andy 4 spud 3 leg

I want this tibble:
abc def ghi jkl mno pqr
4 bubbles 5 cart 9 leg
7 andy 4 spud 3 leg

[williaml]

It helps if you provide a reproducible example using reprex or something else:

FAQ: What's a reproducible example (`reprex`) and how do I create one?

Why reprex? Getting unstuck is hard. Your first step here is usually to create a reprex, or reproducible example. The goal of a reprex is to package your code, and information about your problem so that others can run it and feel your pain. Then, hopefully, folks can more easily provide a solution. What's in a Reproducible Example? Parts of a reproducible example: background information - Describe what you are trying to do. What have you already done? complete set up - include any library() calls and data to reproduce your issue. data for a reprex: Here's a discussion on setting up data for a reprex make it run - include the minimal code required to reproduce your error on the data…

I think you're after something like this though.

Example dataset (there are three different species):

library(tidyverse)

iris2 <- as_tibble(iris)
iris2

# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 
 4          4.6         3.1          1.5         0.2 setosa 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# ... with 140 more rows

Then, detect the text you want (in this case the string "set") - filtered down to 50 rows:

iris2 %>% 
  filter(str_detect(Species, "set"))

# A tibble: 50 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 
 4          4.6         3.1          1.5         0.2 setosa 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# ... with 40 more rows