Reputation: 29
I've been working in R with some IMDB data that they publish, and have been stuck on this for embarrassingly long today.
primaryName tconst primaryTitle knownForTitles
1 Aaron Lim tt2317744 My Friend Bernard tt0268228,tt0891369,tt2317744,tt3709694
2 Aaron Woodley tt3228088 Spark: A Space Tail tt0326065,tt1650535,tt4426464,tt3228088
3 Abdelkader Belhedi tt11069302 The Carthage Castaways tt11698758,tt11069302,tt0485746
I am struggling to come up with a way to match the knownForTitles
IDs with the ID in the tconst
column. After matching, I would like to replace the IDs in knownForTitles
with the actual title names from primaryTitle
, like below.
primaryName tconst primaryTitle knownForTitles
1 Aaron Lim tt2317744 My Friend Bernard Movie Title,Movie Title,Movie Title,Movie Title
2 Aaron Woodley tt3228088 Spark: A Space Tail Movie Title,Movie Title,Movie Title,Movie Title
3 Abdelkader Belhedi tt11069302 The Carthage Castaways Movie Title,Movie Title,Movie Title
I can only think of using a bunch of for loops which may be very inefficient for thousands of rows. If anyone can point me in a better direction that'd be awesome.
Upvotes: 0
Views: 456
Reputation: 389235
We can get the data in separate_rows
, match
knownForTitles
with tconst
get the corresponding primaryTitle
value and collapse data together for each Name
.
library(dplyr)
df %>%
tidyr::separate_rows(knownForTitles, sep = ',') %>%
mutate(knownForTitles = primaryTitle[match(knownForTitles, tconst)]) %>%
group_by(primaryName) %>%
summarise(knownForTitles = toString(na.omit(knownForTitles)))
In base R, we can split the string and match
df$knownForTitles <- sapply(strsplit(df$knownForTitles, ','), function(x)
with(df, toString(na.omit(primaryTitle[match(x, tconst)]))))
Upvotes: 2
Reputation: 1577
The code is this. The explanation is below.
Code.
df = data.frame(primaryName = c("Aaron Lim", "Aaron Woodley"), tconst = c("tt2317744", "tt3228088"), primaryTitle = c("My friend Ron", "Spark: Some Title"), knownForTitles = c("tt0268228,tt0891369,tt2317744,tt3709694", "tt0326065,tt1650535,tt4426464,tt3228088"))
df$tconst = as.character(df$tconst)
Names = df %>%
mutate(V2 = strsplit(as.character(knownForTitles), ",")) %>%
tidyr::unnest(V2) %>%
select(-knownForTitles) %>%
as.data.frame(.)
Movies = df[,2:3]
Modi = left_join(Names, Movies, by = c("V2" = "tconst"))
Modi$primaryTitle.y = as.character(Modi$primaryTitle.y)
Modi[is.na(Modi$primaryTitle.y), "primaryTitle.y"] = "Test"
Modi %>%
group_by(tconst) %>%
summarise(primNew = stringr::str_c(primaryTitle.y, collapse = ", ")) %>%
inner_join(df, .)
Output.
primaryName tconst primaryTitle knownForTitles
1 Aaron Lim tt2317744 My friend Ron tt0268228,tt0891369,tt2317744,tt3709694
2 Aaron Woodley tt3228088 Spark: Some Title tt0326065,tt1650535,tt4426464,tt3228088
primNew
1 Test, Test, My friend Ron, Test
2 Test, Test, Test, Spark: Some Title
Explanation.
Let's define some toy data.
df = data.frame(primaryName = c("Aaron Lim", "Aaron Woodley"),
tconst = c("tt2317744", "tt3228088"),
primaryTitle = c("My friend", "Spark"),
knownForTitles = c("tt0268228,tt0891369,tt2317744,tt3709694", "tt0326065,tt1650535,tt4426464,tt3228088"))
df$tconst = as.character(df$tconst)
Then you can take tidyr's unnest
function to split all the column strings into rows, like this
Names = df %>%
mutate(V2 = strsplit(as.character(knownForTitles), ",")) %>%
tidyr::unnest(V2) %>%
select(-knownForTitles) %>%
as.data.frame(.)
with the result
> Names
primaryName tconst primaryTitle V2
1 Aaron Lim tt2317744 My friend Ron tt0268228
2 Aaron Lim tt2317744 My friend Ron tt0891369
3 Aaron Lim tt2317744 My friend Ron tt2317744
4 Aaron Lim tt2317744 My friend Ron tt3709694
5 Aaron Woodley tt3228088 Spark: Some Title tt0326065
6 Aaron Woodley tt3228088 Spark: Some Title tt1650535
7 Aaron Woodley tt3228088 Spark: Some Title tt4426464
8 Aaron Woodley tt3228088 Spark: Some Title tt3228088
Then you get the movie names for all tconstants
with
Movies = df[,2:3]
Modi = left_join(Names, Movies, by = c("V2" = "tconst"))
and the result
primaryName tconst primaryTitle.x V2 primaryTitle.y
1 Aaron Lim tt2317744 My friend Ron tt0268228 <NA>
2 Aaron Lim tt2317744 My friend Ron tt0891369 <NA>
3 Aaron Lim tt2317744 My friend Ron tt2317744 My friend Ron
4 Aaron Lim tt2317744 My friend Ron tt3709694 <NA>
5 Aaron Woodley tt3228088 Spark: Some Title tt0326065 <NA>
6 Aaron Woodley tt3228088 Spark: Some Title tt1650535 <NA>
7 Aaron Woodley tt3228088 Spark: Some Title tt4426464 <NA>
8 Aaron Woodley tt3228088 Spark: Some Title tt3228088 Spark: Some Title
As this is toy data there are NA
values which cause some trouble, so we do
Modi$primaryTitle.y = as.character(Modi$primaryTitle.y)
Modi[is.na(Modi$primaryTitle.y), "primaryTitle.y"] = "Test"
to cope with that.
Finally, you modify the matched movies and collapse them in one row with
Modi %>%
group_by(tconst) %>%
summarise(primNew = stringr::str_c(primaryTitle.y, collapse = ", ")) %>%
inner_join(df, .)
and the result
primaryName tconst primaryTitle knownForTitles
1 Aaron Lim tt2317744 My friend Ron tt0268228,tt0891369,tt2317744,tt3709694
2 Aaron Woodley tt3228088 Spark: Some Title tt0326065,tt1650535,tt4426464,tt3228088
primNew
1 Test, Test, My friend Ron, Test
2 Test, Test, Test, Spark: Some Title
Upvotes: 2