Create unique list of names - r

Question

I have a list of actors:
name <- c('John Doe','Peter Gynn','Jolie Hope')
age <- c(26 , 32, 56)
postcode <- c('4011', '5600', '7700')
actors <- data.frame(name, age, postcode)
name age postcode
1 John Doe 26 4011
2 Peter Gynn 32 5600
3 Jolie Hope 56 7700
I also have an edge list of relations:
from <- c('John Doe','John Doe','John Doe', 'Peter Gynn', 'Peter Gynn', 'Jolie Hope')
to <- c('John Doe', 'John Doe', 'Peter Gynn', 'Jolie Hope', 'Peter Gynn', 'Frank Smith')
edge <- data.frame(from, to)
from to
1 John Doe John Doe
2 John Doe John Doe
3 John Doe Peter Gynn
4 Peter Gynn Jolie Hope
5 Peter Gynn Peter Gynn
6 Jolie Hope Frank Smith
First, I want to eliminate self references in my edge list i.e. rows 1,2,5 in my 'edge' dataframe.
non.self.ref <- edge[!(edge$from == edge$to),]
does not produce the desired result.
Second, edge includes a name not in the 'actor' dataframe ('Frank Smith'). I want to add 'Frank Smith' to my 'actor' dataframe, even though I do not have age or postcode data for 'Frank Smith'. For example:
name age postcode
1 John Doe 26 4011
2 Peter Gynn 32 5600
3 Jolie Hope 56 7700
4 Frank Smith NA NA
I would be grateful for a tidy solution!

Answer 1

Here is a tidyverse solution to both parts, though in general try not to ask multiple questions per question.
The first part is fairly simple. filter allows a very intuitive syntax that just specifies you want to keep rows where from isn't equal to to.
The second part is a little more complicated. First we gather up the from and to columns, so all the actors are in one column. Then we use distinct to leave us with a one column tbl with unique actor names. Finally, we can use full_join to combine the tables. A full_join keeps all rows and columns from both tables, matching on shared name column by default, and fills NA if there is no data (as there isn't for Frank).
library(tidyverse)
actors <- tibble(
name = c('John Doe','Peter Gynn','Jolie Hope'),
age = c(26 , 32, 56),
postcode = c('4011', '5600', '7700')
)
edge <- tibble(
from = c('John Doe','John Doe','John Doe', 'Peter Gynn', 'Peter Gynn', 'Jolie Hope'),
to = c('John Doe', 'John Doe', 'Peter Gynn', 'Jolie Hope', 'Peter Gynn', 'Frank Smith')
)
edge %>%
filter(from != to)
#> # A tibble: 3 x 2
#> from to
#> <chr> <chr>
#> 1 John Doe Peter Gynn
#> 2 Peter Gynn Jolie Hope
#> 3 Jolie Hope Frank Smith
edge %>%
gather("to_from", "name", from, to) %>%
distinct(name) %>%
full_join(actors)
#> Joining, by = "name"
#> # A tibble: 4 x 3
#> name age postcode
#> <chr> <dbl> <chr>
#> 1 John Doe 26.0 4011
#> 2 Peter Gynn 32.0 5600
#> 3 Jolie Hope 56.0 7700
#> 4 Frank Smith NA <NA>
Created on 2018-03-02 by the reprex package (v0.2.0).

Answer 2

I discovered by including stringsAsFactors = FALSE e.g.
edge <- data.frame(from, to, stringsAsFactors = F)
then:
non.self.ref <- edge[!(edge$from == edge$to),]
works!

Answer 3

An option with dplyr would be to filter the rows by comparing 'from' and 'to' (to get the first output - it is not needed if we are interested only at the second output), unlist, get the unique values, convert it to a tibble and do a left_join
library(dplyr)
edge %>%
filter(from != to) %>% #get the results for the first question
unlist %>%
unique %>%
tibble(name = .) %>%
left_join(actors) # second output
# A tibble: 4 x 3
# name age postcode
# <chr> <dbl> <fctr>
#1 John Doe 26.0 4011
#2 Peter Gynn 32.0 5600
#3 Jolie Hope 56.0 7700
#4 Frank Smith NA <NA>