Introduction to CSV and JSON Formats

CSV (Comma Separated Values) and JSON (JavaScript Object Notation) are two common data formats used for exchanging data between systems. While CSV is a simple, flat format, JSON is a more complex, hierarchical format that is widely used in web development and data exchange.

In this article, we will explore how to convert CSV data into a customized JSON format using R programming language.

Setting Up the Environment

Before we dive into the conversion process, let’s set up our environment. We will need to install and load the necessary libraries: tibble, dplyr, purrr, jsonlite.

# Install required libraries
install.packages("tibble")
install.packages("dplyr")
install.packages("purrr")
install.packages("jsonlite")

# Load libraries
library(tibble)
library(dplyr)
library(purrr)
library(jsonlite)

Understanding the CSV Data

The provided CSV data is a simple tibble object that represents a single record with several fields:

CERTIFICADO = tibble::tibble(
  type = "cuar_private_file",
  title = "Certificado Participacion CC FECHIC 2023 30207",
  excerpt = "Certificado Participacion CC FECHIC 2023 30207",
  post_date = "2024-01-21 21:00:00",
  owners = 1965,
  attachments = "Certificado_Participacion_CC_FECHIC_2023_30207.pdf",
  method = "copy"
)

Understanding the Desired JSON Format

The desired JSON format is a list of objects, where each object has several fields:

[
  {
    "type": "cuar_private_file",
    "title": "Certificado Participacion CC FECHIC 2023 30207",
    "excerpt": "Certificado de Participacion de Folio 30207 Cohorte FECHIC año 2023",
    "post_date": "2024-01-21 21:00:00",
    "owners": 
    { "usr": [1965] },
    "attachments":
    { "Certificado_Participacion_CC_FECHIC_2023_30207.pdf":
      { "method": "copy" }
    }
  }
]

Conversion Process

The conversion process involves several steps:

Step 1: Manipulate the Data

We will use the dplyr library to manipulate the data. Specifically, we will add extra lists to get the shape we want.

# Add extra lists to get the shape you want
library(dplyr)
library(purrr)
library(jsonlite)

CERTIFICADO %>% 
  mutate(owners = map(owners, ~list(usr=.x)),
         attachments = map2(attachments, method, ~setNames(list(list(method=unbox(.y))), .x)),
         method=NULL) %>% 
  jsonlite::toJSON(pretty=TRUE)

Step 2: Explanation of the Code

The code uses the map function from the purrr library to create new lists. The map function applies a function to each element of a vector and returns a list of results.

In this case, we use map to create two lists:

• owners: This list has a single element, which is another list with a single key-value pair: "usr" => 1965.
• attachments: This list also has a single element, which is another list with the same structure as before.

We use setNames from the purrr library to create the structure of the attachments list. The setNames function returns a named list, where the names are specified by the first argument and the values are the second argument.

The method=NULL line removes any existing method field from the data frame before converting it to JSON.

Conclusion

In this article, we explored how to convert CSV data into a customized JSON format using R programming language. We used the dplyr, purrr, and jsonlite libraries to manipulate the data and create the desired structure.

By understanding the basics of CSV and JSON formats, you can easily adapt this code to work with your own datasets.

Additional Resources

For more information on CSV and JSON formats, please refer to the following resources:

• CSV Format
• [JSON Format](https://en.wikipedia.org/wiki/JavaScript_Object Notation)

I hope this article helps you in understanding how to convert CSV data into a customized JSON format using R programming language.

Last modified on 2024-04-12