Update dataframe B with values from dataframe A in R - r

Question

I am doing social network analysis and working with two data frames. Dataframe A (or "nodes") has the information related to each node of the network (i.e. id and name). Dataframe B (or "links") has two columns: "from" and "to" which basically shows how the nodes are connected between them. Each row represents a link "from" one node "to" the other.
I want to use the package networkD3 to visualize the network but it has some requirements: id's should start from zero and they have to be consecutive (0,1,2, etc). Because my nodes and links are a random subset from a larger database, they are not consecutive.
I sorted the "nodes" data frame based on the id and created a new column (new_id) starting from zero and with consecutive numbers. But now, I don't know how to update the "links" data frame based on the new_id's.
Currently, I am converting the values in the "links" data frame to characters and then revaluing them using the plyr package. But I need to do this for a larger dataset.
I am copying a sample of the two data frame that I have now:
set.seed(10)
nodes_df <- data.frame(id = c(1,3,5,6,8,10),
name = c("Agriculture", "Agriculture_in_Mesoamerica", "Agriculture_in_ancient_Greece",
"Agriculture_in_ancient_Rome", "Agriculture_in_India", "Agriculture_in_China"),
new_id = seq(0,5))
links_df <- data.frame(from = c(3,3,5,6,8,10),
to = c(1,5,6,8,10,3))
In summary, I need to update the values in the links_df to correspond to the new_id values from the nodes_df.
Thank you so much in advance. I hope I was clear enough.
Best regards,

Answer 1

In base you just need to use merge and extract your required column
links_df$new_to <- merge(links_df, nodes_df,
by.x = "to", by.y = "id",
all.x = TRUE)$new_id
links_df$new_from <- merge(links_df, nodes_df,
by.x = "from", by.y = "id",
all.x = TRUE)$new_id
links_df <- links_df[,c(1,2,4,3)] # Reordering columns
links_df
from to new_from new_to
1 3 1 1 0
2 3 5 1 1
3 5 6 2 2
4 6 8 3 3
5 8 10 4 4
6 10 3 5 5

Answer 2

An alternative to merging or joining could be to use recode. A solution (based in the tidyverse) could look as follows.
library(dplyr)
library(tibble)
swap <- deframe(tibble(id = nodes_df$id, new_id = nodes_df$new_id))
links_df %>%
mutate(new_from = recode(from, !!!swap),
new_to = recode(to, !!!swap))
# from to new_from new_to
# 1 3 1 1 0
# 2 3 5 1 2
# 3 5 6 2 3
# 4 6 8 3 4
# 5 8 10 4 5
# 6 10 3 5 1

Answer 3

Technically speaking, networkD3 expects the values in the links data frame to be the (zero-based) index of the nodes they refer to in the nodes data frame. So the first row/node in the nodes data frame is 0, and so forth.
You can use match() to determine the 1-based index of each element in a vector in a target vector, and subtract 1 to get a 0-based index.
links_df$from
#> [1] 3 3 5 6 8 10
nodes_df$id
#> [1] 1 3 5 6 8 10
match(links_df$from, nodes_df$id) - 1
#> [1] 1 1 2 3 4 5
links_df$to
#> [1] 1 5 6 8 10 3
nodes_df$id
#> [1] 1 3 5 6 8 10
match(links_df$to, nodes_df$id) - 1
#> [1] 0 2 3 4 5 1
Created on 2021-03-28 by the reprex package (v1.0.0)