Iterating through dataframes for creating and filling new data frames - r

Question

I have the following large dataframes:
Jan_Feb2019
Mar_Apr2019
May_Jun2019
Jul_Aug2019
Sep_Oct2019
Nov_Dec2019
Jan_Feb2020
Mar_2020
And i use the following code to generate other dataframes and fill the columns with the data i want.
#Jan_Feb2019
Jan_Feb2019_df <- as.data.frame(Jan_Feb2019$reactions$summary$total_count)
colnames(Jan_Feb2019_df)[1] <- "Reactions"
Jan_Feb2019_df$Shares <- Jan_Feb2019$shares$count
Jan_Feb2019_df$Comments <- Jan_Feb2019$comments$summary$total_count
Jan_Feb2019_df$Message <- Jan_Feb2019$message
Jan_Feb2019_df$Likes <- Jan_Feb2019$likes$summary$total_count
Jan_Feb2019_df$CreatedDate <- Jan_Feb2019$created_time
Jan_Feb2019_df$PostID <- Jan_Feb2019$id
Jan_Feb2019_df$Love <- Jan_Feb2019$reacts_love$summary$total_count
Jan_Feb2019_df$Angry <- Jan_Feb2019$reacts_angry$summary$total_count
Jan_Feb2019_df$Sad <- Jan_Feb2019$reacts_sad$summary$total_count
Jan_Feb2019_df$HAHA <- Jan_Feb2019$reacts_haha$summary$total_count
Jan_Feb2019_df$WOW <- Jan_Feb2019$reacts_wow$summary$total_count
Jan_Feb2019_df$CreatedDate <- anytime(Jan_Feb2019_df[,6])
Jan_Feb2019_df$insights.data <- Jan_Feb2019$insights$data
Jan_Feb2019_df <- Jan_Feb2019_df %>%
unnest(insights.data) %>%
unnest(values) %>%
select(Message,Shares,Comments,Reactions,Likes,CreatedDate,PostID,Love,Angry,Sad,HAHA,WOW,name,value) %>%
pivot_wider(names_from = name, values_from = value)
Is there a way to iterate between all the above dataframes, so i won't have to repeat the process 8 times?
Thanks

Answer 1

The code below is untested. I have tried to follow the code in the question, making it general. There are 2 functions.
fillNewDf takes the old object as only argument and creates and fills the new data frame.
makeNewDf takes the old object name as an argument and calls fillNewDf returning its value.
If the objects are in the global environment then makeNewDf argument envir default value is used.
fillNewDf <- function(X){
vec <- X[['reactions']][['summary']][['total_count']]
Y <- data.frame(Reactions = vec)
Y[['Shares']] <- X[['shares']][['count']]
Y[['Comments']] <- X[['comments']][['summary']][['total_count']]
Y[['Message']] <- X[['message']]
Y[['Likes']] <- X[['likes']][['summary']][['total_count']]
Y[['CreatedDate']] <- X[['created_time']]
Y[['PostID']] <- X[['id']]
Y[['Love']] <- X[['reacts_love']][['summary']][['total_count']]
Y[['Angry']] <- X[['reacts_angry']][['summary']][['total_count']]
Y[['Sad']] <- X[['reacts_sad']][['summary']][['total_count']]
Y[['HAHA']] <- X[['reacts_haha']][['summary']][['total_count']]
Y[['WOW']] <- X[['reacts_wow']][['summary']][['total_count']]
Y[['CreatedDate']] <- anytime(Y[, 6])
Y[['insights.data']] <- X[['insights']][['data']]
Y %>%
unnest(insights.data) %>%
unnest(values) %>%
select(Message, Shares, Comments, Reactions, Likes, CreatedDate, PostID, Love, Angry, Sad, HAHA, WOW, name, value) %>%
pivot_wider(names_from = name, values_from = value)
}
makeNewDf <- function(X, envir = .GlobalEnv){
DF <- get(X, envir = envir)
filNewDf(DF)
}
Now get the names of the objects to be processed with ls() and create a list with the new data frames.
old_names <- ls(pattern = '\\d{4}$')
new_list <- lapply(old_list, makeNewDf)
names(new_list) <- paste(old_names, "df", sep = "_")
If these new data frames are to become objects in the global environment, list2env(new_list) will create them with the same names as the names attribute of new_list.