https://github.com/stufield/wranglr
The wranglr package contains general functions necessary to manipulate and wrangle internal R representations of proteomic data into convenient forms for analysis.
https://github.com/stufield/wranglr
Last synced: about 1 year ago
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The wranglr package contains general functions necessary to manipulate and wrangle internal R representations of proteomic data into convenient forms for analysis.
- Host: GitHub
- URL: https://github.com/stufield/wranglr
- Owner: stufield
- License: other
- Created: 2024-09-29T18:37:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-18T16:27:04.000Z (about 1 year ago)
- Last Synced: 2025-03-18T17:36:52.699Z (about 1 year ago)
- Language: R
- Homepage: https://stufield.github.io/wranglr/
- Size: 4.59 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE
- Support: .github/SUPPORT.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r setup, include = FALSE}
options(width = 100)
Sys.setlocale("LC_COLLATE", "en_US.UTF-8") # ensure common sorting envir
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
ver <- desc::desc_get_version(".")
ver <- paste0("https://img.shields.io/badge/Version-", ver,
"-success.svg?style=flat&logo=github")
sample_df <- wranglr:::sample_df
```
# The `wranglr` package
[](https://cran.r-project.org/package=wranglr)
[](https://github.com/stufield/wranglr/actions)
[](https://cran.r-project.org/package=wranglr)
[](https://app.codecov.io/gh/stufield/wranglr?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://choosealicense.com/licenses/mit/)
## Overview
The `wranglr` package contains general functions
necessary to manipulate and wrangle internal `R` representations
of proteomic data into convenient forms for analysis.
----------------
## Installation
```{r install-git, eval = FALSE}
# current dev version
remotes::install_github("stufield/wranglr")
# or a specific version
remotes::install_github("stufield/wranglr@v0.0.1")
```
----------------
## Usage
To load `wranglr` simply make a call to `library()` as usual:
```{r load, message = FALSE}
library(wranglr)
```
## Help summary of the package
```{r help, eval = FALSE}
library(help = wranglr)
```
-------------
## Useful functions in `wranglr`
### Transforming Data
* `center_scale()`
* `create_recipe()`
```{r center-scale}
scaled <- center_scale(mtcars)
apply(feature_matrix(scaled), 2, mean) |> sum() # mean = 0
apply(feature_matrix(scaled), 2, sd) # sd = 1
# `create_recipe()`
rcp <- create_recipe(mtcars)
rcp
```
-----------
```{r helpr, echo = FALSE}
library(helpr) # needs get_outliers()
```
### Imputing Data
* `impute_outliers()`, `get_outliers()` (`helpr`)
* `imputeNAs()`
* `impute_predictors()`
```{r imputing}
# Outliers
x <- withr::with_seed(1, rnorm(10)) # normal
x <- c(x, 100) # add outlier
get_outliers(x) # index of the outlier
# parameters stored in attributes (parametric only)
attributes(get_outliers(x, type = "para"))
# Impute 11th value
impute_outliers(x)
# Impute NAs
x <- withr::with_seed(1, rnorm(6))
x[ c(3, 5) ] <- NA
median(x, na.rm = TRUE)
imputeNAs(x)
table(imputeNAs(x))
# Predictors
x <- data.frame(a = 1:3, b = 4:6, c = 7:9, d = c(1.23, 4.56, 7.89))
tbl <- tibble::tribble(
~ Feature, ~ xtrm_max, ~ impute_max, ~ xtrm_min, ~ impute_min,
"a", NA, NA, NA, NA,
"b", 5, 5, 0, 1,
"c", 9, 7, 7.1, 7.1
)
impute_predictors(x, tbl)
```
-----------
### Binding Data
* `bind_intersect()`
* `bind_union()`
```{r bind}
df1 <- data.frame(a = 1, b = 2, c = 3, row.names = "A")
df2 <- data.frame(a = 4, b = 5, d = 6, row.names = "B")
df3 <- data.frame(a = 7, b = 8, e = 9, row.names = "C")
list_df <- list(a = df1, b = df2, c = df3)
list_df
bind_intersect(list_df)
bind_union(list_df)
```
-----------
### Selecting Feature Data
* `refactor_data()`
* `feature_matrix()`
```{r refactor}
df <- data.frame(a = factor(c("a", "b")), b = 1:2L)
foo <- df[df$a == "a", ]
foo
levels(foo$a) # 2 levels! "b" is a ghost level
bar <- refactor_data(foo)
levels(bar$a) # 1 level now
```
-----------
### Sequence IDs and Annotations
* `lookup_anno()`
* `seq_lookup()`
* `seqify()`
```{r seq-lookup}
seqs <- withr::with_seed(101, sample(names(sample_df), 10L))
seqs
# NAs for those analytes dropped from menu
seq_lookup(seqs)
seqify(seqs)
# Pass `tbl` containing annotations
# to reconstitute those missing ones
anno <- attr(sample_df, "anno")
seq_lookup(seqs, tbl = anno)
```