README.Rmd

---
output: github_document
---

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```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# tidycode

The goal of tidycode is to allow users to analyze R expressions in a tidy way.

## Installation

You can install tidycode from CRAN with:

```{r cran-installation, eval = FALSE}
install.packages("tidycode")
```

You can install the development version of tidycode from github with:

```{r gh-installation, eval = FALSE}
# install.packages("remotes")
remotes::install_github("LucyMcGowan/tidycode")
```

## Example

### Read in existing code

Using the matahari package, we can read in existing code, either as a string or a file, and turn it into a matahari tibble using `matahari::dance_recital()`.

```{r}
code <- "
library(broom)
library(glue)
m <- lm(mpg ~ am, data = mtcars)
t <- tidy(m)
glue_data(t, 'The point estimate for term {term} is {estimate}.')
"

m <- matahari::dance_recital(code)
```

Alternatively, you may already have a matahari tibble that was recorded during an R session.

Load the tidycode library.

```{r}
library(tidycode)
```

We can use the expressions from this matahari tibble to extract the names of the packages included. We can also create a data frame that will include _all_ functions of the packages included.

```{r}
(pkg_names <- ls_packages(m$expr))
pkg_functions <- get_package_functions(m$expr)
```

Create a data frame of your expressions, splitting each into individual functions.

```{r}
u <- unnest_calls(m, expr)
```

Merge in the package names

```{r}
u <- u %>%
  dplyr::left_join(pkg_functions) %>%
  dplyr::select(func, args, line, package)
u
```


Add in the function classifications!

```{r}
u %>%
  dplyr::inner_join(
    get_classifications("crowdsource", include_duplicates = FALSE)
    )
```

We can also remove a list of "stopwords". We have a function, `get_stopfuncs()` that lists common "stopwords", frequently used operators, like `%>%` and `+`. 

```{r}
u %>%
  dplyr::inner_join(
    get_classifications("crowdsource", include_duplicates = FALSE)
    ) %>%
  dplyr::anti_join(get_stopfuncs()) %>%
  dplyr::select(func, classification)
  
```