r - networkD3 和 Shiny - 按节点数过滤

Question

我有一个闪亮的应用程序，可以从 df 生成网络图。

library(shiny)
library(dplyr)
library(tibble)
library(networkD3)
ui <- fluidPage(
sidebarPanel(
fluidRow(selectInput("nos","Mínimo de orientações",c(1:10),selected=c(1)))
),
fluidRow(simpleNetworkOutput(
"redes", width = "100%", height = "800px"
))
)
server <- function(input, output, session) {
df_orientadores <- data.frame(orientador=c("Chet Baker","Bill Evans","Miles Davis","Miles Davis","Dizzy Gillespie","Miles Davis"),
autor=c("Clifford Brown","Freddie Hubbard","Kenny Dorham","Kenny Burrell","Arturo Sandoval","Goku"))
output$redes <- renderSimpleNetwork({
sources <- df_orientadores %>%
select(orientador) %>%
dplyr::rename(label = orientador)
destination <- df_orientadores %>%
select(autor) %>%
dplyr::rename(label = autor)

nodes <- full_join(sources, destination, by = "label")
nodes <- nodes %>% group_by(label) %>% count(label) %>% rename(freq=n)

nodes <- nodes %>% rowid_to_column("id")
nodes$peso <- ((nodes$freq)^3)

orientacoes_network <- df_orientadores %>%  
group_by(orientador, autor) %>%
dplyr::summarise(weight = n()) %>% 
ungroup()
edges <- orientacoes_network %>% 
left_join(nodes, by = c("orientador" = "label")) %>% 
dplyr::rename(from = id)

edges <- edges %>% 
left_join(nodes, by = c("autor" = "label")) %>% 
dplyr::rename(to = id)
edges <- select(edges, from, to, weight)
nodes_d3 <- mutate(nodes, id = id - 1)
edges_d3 <- mutate(edges, from = from - 1, to = to - 1) 
filtro_nos <- nodes_d3

edges_d3$value <- 1  
forceNetwork(Links = edges_d3, Nodes = nodes_d3, Source = "from", Target = "to", 
NodeID = "label", Group = "id", Value = "value", 
opacity = 1, fontSize = 20, zoom = TRUE, Nodesize = "peso",
arrows = TRUE)
})
}
shinyApp(ui, server)

我想按用户选择的最小节点数(在nodes_d3数据帧中描述为freq)更新图形(在input$nos上)

我尝试按频率数过滤nodes_d3和edges_d3，但它返回错误Warning: Error in $<-.data.frame: replacement has 1 row, data has 0 [No stack trace available]

有什么想法怎么做吗？

我也尝试过使用reactiveValues，但它不行。我不知道在这种情况下，我是否必须对原始数据帧进行子集化并生成网络，或者只是简单地对原力网络中使用的 dfs 进行子集化(我认为我这样做了，但仍然不起作用。

Answer 1

创建数据后，需要过滤edges_d3和nodes_d3数据框，然后需要重新调整筛选后的edges_d3数据框中的from和to值，以反映它们在nodes_d3数据框中引用的节点的新位置。

# determine the nodes that have at least the minimum freq
nodes_d3_min_freq <-
nodes_d3 %>% 
filter(freq >= input$nos)
# filter the edge list to contain only links to or from the nodes that have
# the minimum or more freq
edges_d3_filtered <-
edges_d3 %>% 
filter(from %in% nodes_d3_min_freq$id | to %in% nodes_d3_filtered$id)
# filter the nodes list to contain only nodes that are in or are linked to 
# nodes in the filtered edge list
nodes_d3_filtered <-
nodes_d3 %>% 
filter(id %in% unlist(select(edges_d3_filtered, from, to)))
# re-adjust the from and to values to reflect the new positions of nodes in
# the filtered nodes list
edges_d3_filtered$from <- match(edges_d3_filtered$from, nodes_d3_filtered$id) - 1
edges_d3_filtered$to <- match(edges_d3_filtered$to, nodes_d3_filtered$id) - 1
forceNetwork(Links = edges_d3_filtered, Nodes = nodes_d3_filtered, 
Source = "from", Target = "to", NodeID = "label", 
Group = "id", Value = "value", opacity = 1, fontSize = 20, 
zoom = TRUE, Nodesize = "peso", arrows = TRUE)