data-visualization

问题 I am working with a computer that does not have internet access or a USB port - I only have R with limited libraries (e.g. I do not have access to the "pec" library). I am following a github tutorial where a "survival analysis" statistical model is fit on some data, and then a ROC (Receiver Operator Curve) is plotted to measure the performance of the model. I am following this tutorial over here: https://gist.github.com/thomasmooon/6eb87964ea663f4a7441cc2b2b730bd4 Everything runs perfectly on

问题 I am working with a computer that does not have internet access or a USB port - I only have R with limited libraries (e.g. I do not have access to the "pec" library). I am following a github tutorial where a "survival analysis" statistical model is fit on some data, and then a ROC (Receiver Operator Curve) is plotted to measure the performance of the model. I am following this tutorial over here: https://gist.github.com/thomasmooon/6eb87964ea663f4a7441cc2b2b730bd4 Everything runs perfectly on

问题 Using R, I created and plotted a graph: library(igraph) library(igraphdata) data(karate) #cluster cfg <- cluster_fast_greedy(karate) #plot entire graph plot(cfg, karate) #plot first subgraph a = induced_subgraph(karate, cfg[[1]]) plot(a) #plot second subgraph b = induced_subgraph(karate, cfg[[2]]) plot(b) #plot third subgraph c = induced_subgraph(karate, cfg[[3]]) plot(c) Is it possible to write some code that shows which graph is connected to which graph? Example: the green graph is

问题 Using R, I created and plotted a graph: library(igraph) library(igraphdata) data(karate) #cluster cfg <- cluster_fast_greedy(karate) #plot entire graph plot(cfg, karate) #plot first subgraph a = induced_subgraph(karate, cfg[[1]]) plot(a) #plot second subgraph b = induced_subgraph(karate, cfg[[2]]) plot(b) #plot third subgraph c = induced_subgraph(karate, cfg[[3]]) plot(c) Is it possible to write some code that shows which graph is connected to which graph? Example: the green graph is

问题 Using R, I created and plotted a graph: library(igraph) library(igraphdata) data(karate) #cluster cfg <- cluster_fast_greedy(karate) #plot entire graph plot(cfg, karate) #plot first subgraph a = induced_subgraph(karate, cfg[[1]]) plot(a) #plot second subgraph b = induced_subgraph(karate, cfg[[2]]) plot(b) #plot third subgraph c = induced_subgraph(karate, cfg[[3]]) plot(c) Is it possible to write some code that shows which graph is connected to which graph? Example: the green graph is

问题 Using R, I created and plotted a graph: library(igraph) library(igraphdata) data(karate) #cluster cfg <- cluster_fast_greedy(karate) #plot entire graph plot(cfg, karate) #plot first subgraph a = induced_subgraph(karate, cfg[[1]]) plot(a) #plot second subgraph b = induced_subgraph(karate, cfg[[2]]) plot(b) #plot third subgraph c = induced_subgraph(karate, cfg[[3]]) plot(c) Is it possible to write some code that shows which graph is connected to which graph? Example: the green graph is

问题 Using R, I created and plotted a graph: library(igraph) library(igraphdata) data(karate) #cluster cfg <- cluster_fast_greedy(karate) #plot entire graph plot(cfg, karate) #plot first subgraph a = induced_subgraph(karate, cfg[[1]]) plot(a) #plot second subgraph b = induced_subgraph(karate, cfg[[2]]) plot(b) #plot third subgraph c = induced_subgraph(karate, cfg[[3]]) plot(c) Is it possible to write some code that shows which graph is connected to which graph? Example: the green graph is

问题 Using R, I created and plotted a graph: library(igraph) library(igraphdata) data(karate) #cluster cfg <- cluster_fast_greedy(karate) #plot entire graph plot(cfg, karate) #plot first subgraph a = induced_subgraph(karate, cfg[[1]]) plot(a) #plot second subgraph b = induced_subgraph(karate, cfg[[2]]) plot(b) #plot third subgraph c = induced_subgraph(karate, cfg[[3]]) plot(c) Is it possible to write some code that shows which graph is connected to which graph? Example: the green graph is

问题 I am running a LME (Linear Mixed-Effects regression) in R where the knot is determined by the time of diagnosis (t=0). So the model is now: lme(function ~ age+sex+timepre*marker+timepost*marker, random=~time|ID, data=data) Thus: timepre is where everything from t=0 is 0 and before that is 0-time, and timepost is where everything before diagnosis is 0 and afterwards is 0+time. The time is the combination of timepre and timepost. I now wanted to plot these effects using the sjPlot library as it

问题 i have a table like this order_id | user_id | createdAt | transaction_amount order_id as the id of the transaction, user_id as the user, createdAt as the dates, and transaction_amount is the transaction of each id order. so on tableau i want to find out users in time range '2020-01-01' until '2020-01-31' with 2 conditions the users are doing transaction before last date in range ('2020-01-31') and atleast doing more than 1 transaction and the users are at least doing 1 transaction in date