docs: Update README

README.Rrst

@@ -3,24 +3,24 @@ ledger
 
 .. {r setup, echo = FALSE}
 knitr::opts_chunk$set(fig.path = "man/figures/README-")
+options(width=80)
 .. ..
 
-.. image:: https://www.r-pkg.org/badges/version/ledger
+.. |CRAN-status| image:: https://www.r-pkg.org/badges/version/ledger
     :target: https://cran.r-project.org/package=ledger
     :alt: CRAN Status Badge
-
-.. image:: https://github.com/trevorld/r-ledger/workflows/R-CMD-check/badge.svg
+.. |R-CMD-check| image:: https://github.com/trevorld/r-ledger/workflows/R-CMD-check/badge.svg
     :target: https://github.com/trevorld/r-ledger/actions
     :alt: R-CMD-check
-
-.. image:: https://codecov.io/github/trevorld/r-ledger/branch/master/graph/badge.svg
+.. |codecov| image:: https://codecov.io/github/trevorld/r-ledger/branch/master/graph/badge.svg
     :target: https://app.codecov.io/github/trevorld/r-ledger?branch=master
     :alt: Coverage Status
-
-.. image:: https://cranlogs.r-pkg.org/badges/ledger
+.. |downloads| image:: https://cranlogs.r-pkg.org/badges/ledger
     :target: https://cran.r-project.org/package=ledger
     :alt: RStudio CRAN mirror downloads
 
+|CRAN-status| |R-CMD-check| |codecov| |downloads|
+
 ``ledger`` is an R package to import data from `plain text accounting <https://plaintextaccounting.org/>`_ software like `Ledger <https://www.ledger-cli.org/>`_, `HLedger <https://hledger.org/>`_, and `Beancount <https://github.com/beancount/beancount>`_ into an R data frame for convenient analysis, plotting, and export.
 
 Right now it supports reading in the register from ``ledger``, ``hledger``, and ``beancount`` files.  
@@ -78,14 +78,13 @@ API
 
 The main function of this package is ``register`` which reads in the register of a plaintext accounting file.  This package also registers S3 methods so one can use ``rio::import`` to read in a register, a ``net_worth`` convenience function, and a ``prune_coa`` convenience function.
 
-register
-~~~~~~~~
+``register()``
+~~~~~~~~~~~~~~
 
 Here are some examples of very basic files stored within the package:
 
 .. {r register}
 library("ledger")
-options(width=180)
 ledger_file <- system.file("extdata", "example.ledger", package = "ledger") 
 register(ledger_file)
 hledger_file <- system.file("extdata", "example.hledger", package = "ledger") 
@@ -100,16 +99,15 @@ Here is an example reading in a beancount file generated by ``bean-example``:
 bean_example_file <- tempfile(fileext = ".beancount")
 system(paste("bean-example -o", bean_example_file), ignore.stderr=TRUE)
 df <- register(bean_example_file)
-options(width=240)
 print(df)
 suppressPackageStartupMessages(library("dplyr"))
 dplyr::filter(df, grepl("Expenses", account), grepl("trip", tags)) %>% 
     group_by(trip = tags, account) %>% 
-    summarise(trip_total = sum(amount))
+    summarize(trip_total = sum(amount), .groups = "drop")
 .. ..
 
-Using rio::import and rio::convert
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Using ``rio::import()`` and ``rio::convert()``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 If one has loaded in the ``ledger`` package one can also use ``rio::import`` to read in the register:
 
@@ -125,8 +123,8 @@ The main advantage of this is that it allows one to use ``rio::convert`` to easi
     bean-example -o example.beancount
     Rscript --default-packages=ledger,rio -e 'convert("example.beancount", "example.csv")'
 
-net_worth
-~~~~~~~~~
+``net_worth()``
+~~~~~~~~~~~~~~~
 
 Some examples of using the ``net_worth`` function using the example files from the ``register`` examples:
 
@@ -135,11 +133,12 @@ dates <- seq(as.Date("2016-01-01"), as.Date("2018-01-01"), by="years")
 net_worth(ledger_file, dates)
 net_worth(hledger_file, dates)
 net_worth(beancount_file, dates)
+dates <- seq(min(as.Date(df$date)), max(as.Date(df$date)), by="years")
 net_worth(bean_example_file, dates)
 .. ..
 
-prune_coa
-~~~~~~~~~
+``prune_coa()``
+~~~~~~~~~~~~~~~
 
 Some examples using the ``prune_coa`` function to simplify the "Chart of Account" names to a given maximum depth:
 
@@ -164,7 +163,6 @@ beancount example generated by ``bean-example``.
 First we load the (mainly tidyverse) libraries we'll be using and adjusting terminal output:
 
 .. {r setup_things, message=FALSE}
-options(width=240) # tibble output looks better in wide terminal output
 library("ledger")
 library("dplyr")
 filter <- dplyr::filter