capesR

https://hugoavmedeiros.github.io/capesR/
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capesR is an R package designed to facilitate access to and manipulation of data from Brazil's...
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### Install capesR Package

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Install the capesR package from GitHub using devtools. Ensure devtools is installed first.

```r
devtools::install_github("hugoavmedeiros/capesR")
```

--------------------------------

### Example Usage of Synthetic CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Demonstrates loading and preparing to use the synthetic CAPES data for exploratory analyses or chart creation.

```r
# Load the data
data(capes_synthetic_df)

```

--------------------------------

### Install capesR

Source: https://hugoavmedeiros.github.io/capesR/index.html

Install the package from CRAN.

```R
# Install capesR from CRAN
install.packages('capesR')
```

--------------------------------

### Example: Search Capes Text Data

Source: https://hugoavmedeiros.github.io/capesR/reference/search_capes_text.html

Demonstrates how to download, combine, and search CAPES data for a specific term in the 'titulo' field. Requires prior data download and combination.

```R
# Download data for the years 1987 and 1990
capes_files <- download_capes_data(c(1987, 1990))
#> File already exists: C:\Users\HUGO~1.VAS\AppData\Local\Temp\Rtmpas8gKx/capes_1987.parquet
#> File already exists: C:\Users\HUGO~1.VAS\AppData\Local\Temp\Rtmpas8gKx/capes_1990.parquet
# Combine all selected data
combined_data <- read_capes_data(capes_files)
# Search data
results <- search_capes_text(
data = combined_data,
term = "Educação",
  field = "titulo"
)
#> No results found for the search.
# }
```

--------------------------------

### Accessing the years_osf dataset

Source: https://hugoavmedeiros.github.io/capesR/reference/years_osf.html

Load the dataset into the R environment and inspect the first few rows.

```R
years_osf
```

```R
data(years_osf)
head(years_osf)
```

--------------------------------

### View capesR Package Documentation

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Access documentation for various functions within the capesR package. Use the '?' operator followed by the function name.

```r
?years_osf
```

```r
?capes_synthetic_df
```

```r
?download_capes_data
```

```r
?search_capes_text
```

```r
?read_capes_data
```

--------------------------------

### Load capesR Package

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Load the capesR package into your R session to use its functions.

```r
library(capesR)
```

--------------------------------

### Download CAPES Data to Persistent Directory

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Download CAPES data for specified years to a custom persistent directory. This allows data reuse across sessions. The data is only downloaded once.

```r
# Define the directory to store the data
data_directory <- "/capes_data"

# Download data for 1987 and 1990 using a persistent directory
capes_files <- download_capes_data(
  c(1987, 1990),
  destination = data_directory)
```

--------------------------------

### Load and Preview Synthetic CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/reference/capes_synthetic_df.html

Load the dataset into the R environment and display the first few rows to inspect the structure.

```R
data(capes_synthetic_df)
head(capes_synthetic_df)
```

--------------------------------

### Download CAPES Data to Temporary Directory

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Download CAPES data for specified years to the system's temporary directory. The data will not persist after the session ends. Requires the dplyr package for glimpse().

```r
library(capesR)
library(dplyr)

# Download data for 1987 and 1990
capes_files <- download_capes_data(c(1987, 1990))

# View the list of downloaded files
capes_files %>% glimpse()
```

--------------------------------

### Access Synthetic Data

Source: https://hugoavmedeiros.github.io/capesR/index.html

Load and use the built-in synthetic dataset for quick analysis and prototyping.

```R
data(capes_synthetic_df)

# View the first rows of the data
head(capes_synthetic_df)
```

```R
# Load the data
data(capes_synthetic_df)

# Example: Count by year and type of work
library(dplyr)
capes_synthetic_df %>%
  group_by(base_year, type) %>%
  summarise(total = sum(n)) %>%
  arrange(desc(total))
```

--------------------------------

### Load Synthetic CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Load the synthetic CAPES dataset, 'capes_synthetic_df', which is included with the package for quick analyses and prototyping. Displays the first few rows.

```r
data(capes_synthetic_df)

# View the first rows of the data
head(capes_synthetic_df)
```

--------------------------------

### download_capes_data

Source: https://hugoavmedeiros.github.io/capesR/index.html

Downloads CAPES data files for specified years from OSF and saves them locally.

```APIDOC
## download_capes_data

### Description
Downloads CAPES data files hosted on OSF for the specified years.

### Parameters
#### Arguments
- **years** (vector) - Required - A vector of years to download.
- **destination** (string) - Optional - The directory path where files will be stored. Defaults to tempdir().

### Request Example
```r
capes_files <- download_capes_data(c(1987, 1990), destination = "/capes_data")
```

### Response
- **Returns** (vector) - A list of paths to the downloaded files.
```

--------------------------------

### download_capes_data

Source: https://hugoavmedeiros.github.io/capesR/reference/index.html

Downloads CAPES data from the source.

```APIDOC
## GET download_capes_data()

### Description
Downloads CAPES data. Also available as `baixar_dados_capes()`.

### Method
GET

### Endpoint
download_capes_data()
```

--------------------------------

### Combine Downloaded CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Combine all downloaded CAPES data files into a single data frame without applying any filters. Requires the dplyr package for glimpse().

```r
# Combine all selected data without using filters
combined_data <- read_capes_data(capes_files)

# View the combined data
combined_data %>% glimpse()
```

--------------------------------

### Download CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/index.html

Download data files from OSF. Use a persistent directory to avoid re-downloading files.

```R
library(capesR)
library(dplyr)

# Download data for the years 1987 and 1990
capes_files <- download_capes_data(c(1987, 1990))

# View the list of downloaded files
capes_files %>% glimpse()
```

```R
# Define the directory where the data will be stored
data_directory <- "/capes_data"

# Download data for 1987 and 1990 using a persistent directory
capes_files <- download_capes_data(
  c(1987, 1990),
  destination = data_directory)
```

--------------------------------

### Combine Filtered CAPES Data (Exact Filters)

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Combine CAPES data, applying exact filters for 'base_year' and 'state' before reading. This improves performance. Requires the dplyr package for glimpse().

```r
# Create an object with filters
exact_filter <- list(
  base_year = c(2021, 2022),
  state = c("PE", "CE")
)

# Combine filtered data
filtered_data <- read_capes_data(capes_files, exact_filter)

# View the filtered data
filtered_data %>% glimpse()
```

--------------------------------

### Count by Year and Type of Work

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Use this snippet to group data by year and type of work, then summarize the total count and arrange in descending order. Requires the dplyr package.

```r
library(dplyr)
capes_synthetic_df %>%
  group_by(base_year, type) %>%
  summarise(total = sum(n)) %>%
  arrange(desc(total))
```

--------------------------------

### Download CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/reference/download_capes_data.html

Downloads CAPES theses and dissertations data files from OSF for selected years.

```APIDOC
## Download CAPES Data

### Description
Downloads CAPES theses and dissertations data files from OSF for selected years.

### Method
GET (implied by download function)

### Endpoint
Not applicable (function-based download)

### Parameters
#### Arguments
- **years** (vector) - Required - A vector with the desired years.
- **destination** (string) - Optional - The directory where the files will be saved (default: temporary directory).
- **timeout** (numeric) - Optional - The timeout in seconds for the download process (default: 120 seconds).

### Request Example
```R
download_capes_data(years = c(1987, 1990), destination = "/path/to/save/data")
```

### Response
#### Success Response (200)
- **file_paths** (list) - A list of file paths for the downloaded or already existing files.

#### Response Example
```json
{
  "file_paths": [
    "/path/to/save/data/capes_1987.parquet",
    "/path/to/save/data/capes_1990.parquet"
  ]
}
```
```

--------------------------------

### Dataset: years_osf

Source: https://hugoavmedeiros.github.io/capesR/reference/years_osf.html

A data frame containing the years and the corresponding OSF IDs for downloading CAPES data files.

```APIDOC
## Dataset: years_osf

### Description
A data frame containing the years and the corresponding IDs for downloading the files from the Open Science Framework (OSF).

### Format
A data frame with the following columns:
- **year** (integer) - Year of the data (1987-2022).
- **osf_id** (string) - OSF ID corresponding to the year.

### Source
https://osf.io/

### Usage Example
```r
data(years_osf)
head(years_osf)
```
```

--------------------------------

### Download CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/reference/download_capes_data.html

Use this function to download CAPES theses and dissertations data for specified years. The data is saved to a destination directory, with a default timeout for the download process.

```R
download_capes_data(years, destination = tempdir(), timeout = 120)
```

```R
baixar_dados_capes(years, destination = tempdir(), timeout = 120)
```

```R
# Download data for the years 1987 and 1990
capes_files <- download_capes_data(c(1987, 1990))
#> Downloading: C:\Users\HUGO~1.VAS\AppData\Local\Temp\Rtmpas8gKx/capes_1987.parquet
#> Downloading: C:\Users\HUGO~1.VAS\AppData\Local\Temp\Rtmpas8gKx/capes_1990.parquet
```

--------------------------------

### Combine Filtered CAPES Data (Text Filters)

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Combine CAPES data, applying exact filters for 'base_year' and 'state', and a text filter for 'title'. Text filtering is optimized for faster searches. Requires the dplyr package for glimpse().

```r
# Create an object with filters
text_filter <- list(
  base_year = c(2018, 2019, 2020, 2021, 2022),
  state = c("PE", "CE"),
  title = "education"
)

# Combine filtered data
text_filtered_data <- read_capes_data(capes_files, text_filter)

# View the filtered data
text_filtered_data %>% glimpse()
```

--------------------------------

### Dataset: capes_synthetic_df

Source: https://hugoavmedeiros.github.io/capesR/reference/capes_synthetic_df.html

Details regarding the structure and columns of the synthetic CAPES data frame.

```APIDOC
## Dataset: capes_synthetic_df

### Description
Aggregated data from the CAPES Catalog of Theses and Dissertations, containing summarized information by year, institution, area, program, type, region, and state (UF).

### Format
A data frame with the following columns:
- **base_year** (double) - Reference year of the data.
- **institution** (string) - Higher Education Institution.
- **area** (string) - Area of Concentration.
- **program_name** (string) - Name of the Graduate Program.
- **type** (string) - Type of work (e.g., Master's, Doctorate).
- **region** (string) - Region of Brazil.
- **state** (string) - Federative Unit (state).
- **n** (integer) - Total number of works.

### Usage Example
```r
data(capes_synthetic_df)
head(capes_synthetic_df)
```
```

--------------------------------

### Combine and Filter CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/index.html

Read and combine downloaded files, optionally applying exact or text-based filters for performance.

```R
# Combine all selected data without filters
combined_data <- read_capes_data(capes_files)

# View the combined data
combined_data %>% glimpse()
```

```R
# Create a filter object
exact_filter <- list(
  ano_base = c(2021, 2022),
  uf = c("PE", "CE")
)

# Combine filtered data
filtered_data <- read_capes_data(capes_files, exact_filter)

# View the filtered data
filtered_data %>% glimpse()
```

```R
# Create a filter object
text_filter <- list(
  ano_base = c(2018, 2019, 2020, 2021, 2022),
  uf = c("PE", "CE"),
  titulo = "Educação"
)

# Combine filtered data
text_filtered_data <- read_capes_data(capes_files, text_filter)

# View the filtered data
text_filtered_data %>% glimpse()
```

--------------------------------

### read_capes_data

Source: https://hugoavmedeiros.github.io/capesR/index.html

Combines downloaded CAPES data files into a single dataset, with support for exact and text-based filtering.

```APIDOC
## read_capes_data

### Description
Reads and combines downloaded files. Filters can be applied to improve performance.

### Parameters
#### Arguments
- **files** (vector) - Required - List of file paths generated by download_capes_data.
- **filter** (list) - Optional - A list containing exact filters (e.g., ano_base, uf) or text filters (e.g., titulo).

### Request Example
```r
exact_filter <- list(ano_base = c(2021, 2022), uf = c("PE", "CE"))
combined_data <- read_capes_data(capes_files, exact_filter)
```

### Response
- **Returns** (data.frame) - The combined and filtered dataset.
```

--------------------------------

### Read and Filter CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/reference/read_capes_data.html

Combines data from multiple Parquet files and applies optional filters, including text-based searches. Use this function to load and preprocess data from the CAPES Catalog.

```R
read_capes_data(files, filters = list())
```

```R
ler_dados_capes(files, filters = list())
```

```R
# Download data for the years 1987 and 1990
capes_files <- download_capes_data(c(1987, 1990))
#> File already exists: C:\Users\HUGO~1.VAS\AppData\Local\Temp\Rtmpas8gKx/capes_1987.parquet
#> File already exists: C:\Users\HUGO~1.VAS\AppData\Local\Temp\Rtmpas8gKx/capes_1990.parquet
# Combine all selected data
combined_data <- read_capes_data(capes_files)
```

--------------------------------

### Search Text in Combined Data

Source: https://hugoavmedeiros.github.io/capesR/index.html

Search for specific terms within a text field of the combined dataset.

```R
results <- search_capes_text(
  data = combined_data,
  term = "Educação",
  field = "titulo"
)
```

--------------------------------

### Search Capes Text Function Signature

Source: https://hugoavmedeiros.github.io/capesR/reference/search_capes_text.html

Defines the usage and arguments for the search_capes_text function, which searches for a term in a specified text field of the CAPES data.

```R
search_capes_text(data, term, field)

buscar_texto_capes(data, term, field)
```

--------------------------------

### search_capes_text

Source: https://hugoavmedeiros.github.io/capesR/reference/index.html

Searches for specific terms within the text fields of the CAPES dataset.

```APIDOC
## GET search_capes_text()

### Description
Search for terms in text fields of the CAPES Catalog of Theses and Dissertations data. Also available as `buscar_texto_capes()`.

### Method
GET

### Endpoint
search_capes_text()
```

--------------------------------

### Search CAPES Text Data

Source: https://hugoavmedeiros.github.io/capesR/reference/search_capes_text.html

This function allows searching for specific terms in the text fields of a previously loaded data.frame.

```APIDOC
## POST /search/capes/text

### Description
Searches for specific terms within text fields of the CAPES Catalog of Theses and Dissertations data.

### Method
POST

### Endpoint
/search/capes/text

### Parameters
#### Request Body
- **data** (data.frame) - Required - A data.frame containing the CAPES Catalog of Theses and Dissertations data.
- **term** (string) - Required - The term to search for.
- **field** (string) - Required - The name of the field to search in (e.g., "resumo", "titulo").

### Request Example
{
  "data": "<data_frame_content>",
  "term": "Educação",
  "field": "titulo"
}

### Response
#### Success Response (200)
- **data** (data.frame) - A data.frame with rows matching the search.
#### Error Response (404)
- **message** (string) - A message indicating no results were found.

#### Response Example
{
  "data": [
    {
      "titulo": "Educação Infantil no Brasil",
      "resumo": "Análise sobre a educação infantil..."
    }
  ]
}

#### Error Response Example
{
  "message": "No results found for the search."
}
```

--------------------------------

### read_capes_data

Source: https://hugoavmedeiros.github.io/capesR/reference/read_capes_data.html

Reads and filters data from the CAPES Catalog of Theses and Dissertations stored in Parquet files.

```APIDOC
## read_capes_data

### Description
Combines data from multiple Parquet files and applies optional filters, including text-based searches.

### Parameters
#### Arguments
- **files** (vector/list) - Required - A vector or list of paths to Parquet files.
- **filters** (list) - Optional - A list of filters to apply (e.g., list(base_year = 1987, state = "SP", title = "education")).

### Value
A data.frame containing the combined and filtered data.

### Request Example
read_capes_data(files = c("data1.parquet", "data2.parquet"), filters = list(base_year = 1987))
```

--------------------------------

### Search Text in Combined CAPES Data

Source: https://hugoavmedeiros.github.io/capesR/articles/capesR.html

Search for a specific term within a specified text field (e.g., 'title') of an already combined CAPES data frame.

```r
results <- search_capes_text(
  data = combined_data,
  term = "education",
  field = "title"
)
```

--------------------------------

### read_capes_data

Source: https://hugoavmedeiros.github.io/capesR/reference/index.html

Reads and filters data from the CAPES Catalog of Theses and Dissertations.

```APIDOC
## GET read_capes_data()

### Description
Reads and filters data from the CAPES Catalog of Theses and Dissertations. Also available as `ler_dados_capes()`.

### Method
GET

### Endpoint
read_capes_data()
```
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