make a condition when the function extract data from twitter

[clanyon]

Hi all, I work with academic twitter and trying to create a network between author tweets from a specific place with authors who replies to the conversation.

I have a data frame with different tweets for instance:

df_tweet_place

            id          Author id            Conversation Id                   Text

#> 1 5.1 3.5 1.4 Hola desde Perú
#> 2 4.9 3.0 1.9 Olas grandes
#> 3 4.7 3.2 1.3 Alerta costera ,@
#> 4 4.6 3.1 1.5 Quien sale hoy con esta lluvia
#> 5 5.0 3.6 1.8 Se viene la lluvia
.......
#> 206 5.4 3.9 1.7 Chao turistas

To get the reply of each twitter (id) I have to run a specific function from academic twitter using id conversation.

Code:

A loop to go through the 206 id conversation of df_tweets

for (i in 1:206){

df.reply <- get_all_tweets(build_query(conversation_id = df_tweets$conversation_id[i]), start_tweets = "2010-01-01T00:00:00Z", end_tweets = "2022-01-01T00:00:00Z"){

If the conversation (i) has a reply I will get something like this

            author id         Converstation  id Author id  Sensiivte

#> 1 45 3.5 1.4 False
#> 2 47 3.0 1.4 True
#> 3 89 3.2 1.4 False
#> 4 46 3.1 1.4 false
#> 5 53 3.6 1. 4 true

Wich means that 5 people answer to the conversation id of df. tweets. This function will have to run 206 times since I have 206 tweets with their I conversation

######## next steps are clean the data frame and add the result of each loop to an empty data already created.
df.reply_clean<-df.reply%>%select(author_id,conversation_id)
df.replytotal <- rbind(df.replytotal, df.reply_clean)
}

Having something like this as a results

df.replytotal<-
author id Converstation id
#> 1 45 1.4
#> 2 47 1.4
#> 3 89 1.4
#> 4 46 1.4
#> 5 53 1. 4
#>6 68 1.9
#>6 37 1.9
#>8 122 1.3

However, there is some conversation id that doesn't have a reply so the loop stops and can keep running. I don't know how to make the condition that say

if df.reply is null or empty ( 0 observation) go to the next conversation id from df.tweets_place run df.reply again until get some observation and continue in the next step that is:

df.replay_clean and df.replay.total

any suggestion would be great!!!

Thanks!!

[pieterjanvc]

Hi,

Welcome to the RStudio community!

In order for us to help you with your question, please provide us a minimal reproducible example where you provide a minimal (dummy) dataset and code that can recreate the issue. Once we have that, we can go from there. For help on creating a Reprex, see this guide:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

Good luck!
PJ