R with paws

The dcdcr (deprecated) page explains how to use the DataCamp Data Connector using a convenient R package created by DataCamp. However, if you cannot install the package, you can also use the opensource paws package instead.

The dcdcr package is actually just a wrapper of the paws package custom built to support our Data Model.

This page describes how you can easily use the paws package to use R to analyze the data from the Data Connector.

Before you get started, please make sure your credentials are set up properly. How to do this is explained in the Storing your Credentials article.

Examples

Get a list of all users

This script retrieves a list of all users from the Data Connector and stores it in a tibble.

library(dplyr)

library(paws)



S3_BUCKET_NAME <- "<your bucket name here>"



# Create client, authentication is done through environment variables

s3 <- paws::s3()



# Utility method to get a file and load it into a df

get_data_frame_from_s3 <- function(table){
  
    key <- paste0("latest/", table, ".csv")
  
    response <- s3$get_object(Bucket = S3_BUCKET_NAME,
     
                         Key = key)
  
    df <- response$Body %>% 
     rawToChar %>%
     read.csv(text = .) %>%
     as_tibble()
     


     return(df)

}




user_dim <- get_data_frame_from_s3('user_dim')



user_dim 

Get time spent on courses for a single user

This code will print a dataframe with all course activity for a single user, it lists the course, the amount of time spent (in seconds) and date for all their learning sessions.

library(dplyr)

library(paws)



S3_BUCKET_NAME <- "<your bucket name here>"
USER_EMAIL = '[e-mail]'




# Create client, authentication is done through environment variables
s3 <- paws::s3()


# Utility method to get a file and load it into a df

get_data_frame_from_s3 <- function(table){
  
    key <- paste0("latest/", table, ".csv")
  
    response <- s3$get_object(Bucket = S3_BUCKET_NAME,
     
                         Key = key)
  
    df <- response$Body %>% 
     rawToChar %>%
     read.csv(text = .) %>%
     as_tibble()
     

     return(df)
}



# Get required data frames

course_fact <- get_data_frame_from_s3('course_fact')

course_dim <- get_data_frame_from_s3('course_dim')

user_dim <- get_data_frame_from_s3('user_dim')



# Merge the dataframes

result <- course_fact %>%
 left_join(course_dim, by = "course_id") %>%
 left_join(user_dim, by = "user_id") %>%
 select(course_id, title, time_spent, date_id, email)



# Filter results on a single user

result <- result %>% filter(email == USER_EMAIL)



result

More examples?

Reach out to your customer success manager and we are happy to help you get the data you need using our R library or SQL.