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Title: Tidy Fundamental Financial Data from 'SEC's 'EDGAR' 'API'
Version: 1.0.1
Description: Streamline the process of accessing fundamental financial data from the United States Securities and Exchange Commission's ('SEC') Electronic Data Gathering, Analysis, and Retrieval system ('EDGAR') 'API' https://www.sec.gov/edgar/sec-api-documentation, transforming it into a tidy, analysis-ready format.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: dplyr, jsonlite, parallel, tidyr, httr
URL: https://gerardgimenezadsuar.github.io/tidyedgar/
NeedsCompilation: no
Packaged: 2024-02-09 11:12:56 UTC; gerard
Author: Gerard Gimenez-Adsuar [aut, cre]
Maintainer: Gerard Gimenez-Adsuar <gerard@solucionsdedades.cat>
Repository: CRAN
Date/Publication: 2024-02-09 15:20:02 UTC

Getting quarterly data from all public companies from EDGAR

Description

Getting quarterly data from all public companies from EDGAR

Usage

get_qdata(
  account = "Revenues",
  years = 2020:2023,
  quarters = c("Q3"),
  max_cores = TRUE
)

Arguments

account

A string representing the account (eg NetIncomeLoss, Revenues, OperatingIncomeLoss, ...)

years

A sequence of numeric values representing the years.

quarters

A string representing the quarter.

max_cores

Boolean limiting the number of cores to 1.

Value

A dataframe

Examples

get_qdata(account = "NetIncomeLoss", years = 2022:2023, quarters = c("Q4"))


Getting yearly data from all public companies from EDGAR

Description

Getting yearly data from all public companies from EDGAR

Usage

get_ydata(account = "Revenues", years = 2020:2023)

Arguments

account

A string representing the account (eg NetIncomeLoss, Revenues, OperatingIncomeLoss, ...)

years

A sequence of numeric values representing the years.

Value

A dataframe

Examples

get_ydata(account = "NetIncomeLoss", years = 2022:2023)

Data wrangling for tidy fundamental data from EDGAR

Description

Data wrangling for tidy fundamental data from EDGAR

Usage

prepare_data(df = NULL, quarterly = TRUE, ...)

Arguments

df

A dataframe, output from get_qdata() or get_ydata().

quarterly

Boolean indicating if quarterly data is present.

...

Additional dataframes to be combined from other accounts (NetIncomeLoss, OperatingIncomeLoss, etc).

Value

A dataframe

Examples

revenue <- data.frame(
taxonomy = rep("us-gaap", 3),
tag = rep("Revenues", 3),
ccp = rep("CY2020", 3),
uom = rep("USD", 3),
label = rep("Revenues", 3),
description = rep("Amount of revenue recognized from goods sold, services rendered, ...", 3),
pts = rep(2762, 3),
data.accn = c("0001564590-22-012597", "0000002178-23-000038", "0001654954-22-005679"),
data.cik = c(2098, 2178, 2186),
data.entityName = c("ACME CORP", "ADAMS RESOURCES, INC.", "BK TECHNOLOGIES"),
data.loc = c("US-CT", "US-TX", "US-FL"),
data.start = rep("2020-01-01", 3),
data.end = rep("2020-12-31", 3),
data.val = c(164003040, 1022422000, 44139000),
year = rep(2020, 3))
netincome <- data.frame(
taxonomy = rep("us-gaap", 3),
tag = rep("NetIncomeLoss", 3),
ccp = rep("CY2020", 3),
uom = rep("USD", 3),
label = rep("NetIncomeLoss", 3),
description = rep("Net Income from operating activities", 3),
pts = rep(2762, 3),
data.accn = c("0001564590-22-012597", "0000002178-23-000038", "0001654954-22-005679"),
data.cik = c(2098, 2178, 2186),
data.entityName = c("ACME CORP", "ADAMS RESOURCES, INC.", "BK TECHNOLOGIES"),
data.loc = c("US-CT", "US-TX", "US-FL"),
data.start = rep("2020-01-01", 3),
data.end = rep("2020-12-31", 3),
data.val = c(100000, 200000, 4000000),
year = rep(2020, 3))
prepare_data(revenue,netincome, quarterly = FALSE)

Helper function for quarterly financial data retrieval

Description

Helper function for quarterly financial data retrieval

Usage

retrieve_data(account, year, quarter)

Arguments

account

A string representing the account.

year

A numeric value representing the year.

quarter

A string representing the quarter.

Value

A dataframe


Safely calculating the max.

Description

Safely calculating the max.

Usage

safe_max(x, na.rm = FALSE)

Arguments

x

A number.

na.rm

Boolean.

Value

A number.


Getting a summary with the basic financials for all companies

Description

Getting a summary with the basic financials for all companies

Usage

yearly_data(years = 2020:2023)

Arguments

years

A sequence of numeric values representing the years.

Value

A dataframe

Examples

yearly_data(years = 2022:2023)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.