CODEWITHSUNDEEP
r
lostinsql
lostinsql

Reputation: 111

R - For loop to run code against data frame list

I have this piece of code that I can run against one data frame. However, I'd like to be able to loop it over a data frame list.

This is the base code:

# Run RFM Analysis on df_0
df_1 <- rfm_table_order(df_0, customer = customer_id, order_date = txn_date, revenue = txn_price, analysis_date = analysis_date, 
                             recency_bins=3, frequency_bins=3, monetary_bins=3)

df_2 <- as.data.frame(df_1$rfm)

# Add weighting to the scores    
df_2$finalscore <- (df_2 $recency_score*3 + df_2 $frequency_score*2 + df_2 $monetary_score*3)/8

# Add labels according to weighted score
df_2<- df_2 %>%
mutate(segment = case_when(
  .$finalscore >= 2.5 ~ "Loyal",
  .$finalscore <= 1.5 ~ "Lapsed",
  TRUE ~ "Regular"
))

# Add the analysis date
df_2$analysis_date <- rep(analysis_date,nrow(df_2))

# Output the final dataset with required rows
df_final <- df_2[,c("customer_id","segment","analysis_date")]

df_0 looks like this:

customer_id    txn_date    txn_price   category   
123            01/01/2019  12          a
456            01/02/2019  7           b
...

After running the above code, df_final looks like this:

customer_id    segment     analysis_date
123            Loyals      01/05/2019
456            Loyals      01/05/2019
...

I wanted to see how the results would differ if I use category as a factor. Because of that, I made a data frame list.

cat_list <- split(df_0, as.factor(df_0$category))

I need to add a loop that runs against a dataframe list. The last step in the loop should also append the name of the data frame into the result.

Desired output:

customer_id    segment   category    analysis_date
123            Loyals    a           01/05/2019
456            Loyals    b           01/05/2019
...

Upvotes: 1

Views: 87

Answers (1)

Parfait
Parfait

Reputation: 107652

Simply generalize your process that takes a data frame as input and run by (roughly equivalent to split + lapply) to subset main data frame by category and pass subsets into function. Consider also within and ifelse for adding needed columns (base R or tinyverse version of mutate and case_when)

Function

my_func <- function(sub_df) {

    # Run RFM Analysis on df
    df_1 <- rfm_table_order(sub_df, customer = customer_id, order_date = txn_date,     
                            revenue = txn_price, analysis_date = analysis_date, 
                            recency_bins=3, frequency_bins=3, monetary_bins=3)

    df_2 <- within(as.data.frame(df_1$rfm), {
                # Add weighting to the scores  
               finalscore <- (recency_score*3 + frequency_score*2 + monetary_score*3)/8

               # Add labels according to weighted score
               segment <- ifelse(finalscore >= 2.5, "Loyal", 
                                 ifelse(finalscore <= 1.5, "Lapsed", "Regular")
                          )

               # Add the analysis date
               analysis_date <- analysis_date

               # Add category
               category <- sub_df$category[[1]]
          })

    # Output the final dataset with required rows
    df_final <- df_2[,c("customer_id", "segment", "category", "analysis_date")]

    return(df_final)
}

Call

cat_list <- by(df_0, df_0$category, my_func)

# cat_list <- lapply(split(df_0, df_0$category), my_func)

Upvotes: 3

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