Skip to content

Commit

Permalink
update package description and readme
Browse files Browse the repository at this point in the history
  • Loading branch information
vbaliga committed Jan 14, 2021
1 parent 7e54c52 commit deffd9d
Show file tree
Hide file tree
Showing 13 changed files with 67 additions and 65 deletions.
6 changes: 3 additions & 3 deletions DESCRIPTION
Original file line number Diff line number Diff line change
Expand Up @@ -18,14 +18,14 @@ Authors@R:
email = "epress12@gmail.com",
comment = c(ORCID = "0000-0002-1944-3755"))
)
Description: Tools to import, clean, and visualize
animal movement data from motion capture systems such as Optitrack's
Description: Tools to import, clean, and visualize movement data,
particularly from motion capture systems such as Optitrack's
Motive, the Straw Lab's Flydra, or from other sources. We provide
functions to remove artifacts, standardize tunnel position and tunnel
axes, select a region of interest, isolate specific trajectories, fill
gaps in trajectory data, and calculate 3D and per-axis velocity. For
experiments of visual guidance, we also provide functions that use
animal position to estimate perception of visual stimuli.
subject position to estimate perception of visual stimuli.
Maintainer: Vikram B. Baliga <vbaliga87@gmail.com>
License: GPL-3
Encoding: UTF-8
Expand Down
30 changes: 15 additions & 15 deletions README.Rmd
Original file line number Diff line number Diff line change
Expand Up @@ -22,15 +22,15 @@ knitr::opts_chunk$set(
[![](https://badges.ropensci.org/409_status.svg)](https://github.com/ropensci/software-review/issues/409)
<!-- badges: end -->

`pathviewR` offers tools to import, clean, and visualize animal movement data
from motion capture systems such as
`pathviewR` offers tools to import, clean, and visualize movement data,
particularly from motion capture systems such as
[Optitrack's Motive](https://optitrack.com/software/motive/), the
[Straw Lab's Flydra](https://github.com/strawlab/flydra),
or other sources. We provide functions to remove artifacts, standardize
tunnel position and tunnel axes, select a region of interest, isolate specific
trajectories, fill gaps in trajectory data, and calculate 3D and per-axis
velocity. For experiments of visual guidance, we also provide functions that
use animal position to estimate perception of visual stimuli.
[Straw Lab's Flydra](https://github.com/strawlab/flydra), or other sources. We
provide functions to remove artifacts, standardize tunnel position and tunnel
axes, select a region of interest, isolate specific trajectories, fill gaps in
trajectory data, and calculate 3D and per-axis velocity. For experiments of
visual guidance, we also provide functions that use subject position to estimate
perception of visual stimuli.

## Installation

Expand All @@ -43,7 +43,8 @@ devtools::install_github("vbaliga/pathviewR")
## Example

#### Data import and cleaning via `pathviewR`
We'll also load two `tidyverse` packages for wrangling & plotting.
We'll also load two `tidyverse` packages for wrangling & plotting in this
readme.

```{r package_loading, message=FALSE, warning=FALSE}
library(pathviewR)
Expand All @@ -55,7 +56,7 @@ library(magrittr)
We will import and clean a sample data set from `.csv` files exported by
[Optitrack's Motive](https://optitrack.com/software/motive/) software. For
examples of how to import and clean other types of data,
[see the data import and cleaning vignette](https://vbaliga.github.io/pathviewR/articles/data-import-cleaning.html).
[see the Basics of data import and cleaning vignette](https://vbaliga.github.io/pathviewR/articles/data-import-cleaning.html).

```{r import_motive}
## Import the Motive example data included in
Expand All @@ -72,7 +73,7 @@ motive_data <-
Several functions to clean and wrangle data are available, and we have a
suggested pipeline for how these steps should be handled. For this example, we
will use one of two "all-in-one" functions: `clean_viewr()`.
[See the Data Import and Cleaning vignette](https://vbaliga.github.io/pathviewR/articles/data-import-cleaning.html)
[See the Basics of data import and cleaning vignette](https://vbaliga.github.io/pathviewR/articles/data-import-cleaning.html)
for the full pipeline and the other "all-in-one" function.

```{r all_in_one, fig.height=3, fig.width=6, dpi=300}
Expand Down Expand Up @@ -118,14 +119,14 @@ str(motive_allinone)

An important aspect of how `pathviewR` defines trajectories is by managing gaps
in the data.
[See the Managing Frame Gaps vignette](https://vbaliga.github.io/pathviewR/articles/managing-frame-gaps.html)
[See the vignette on Managing frame gaps](https://vbaliga.github.io/pathviewR/articles/managing-frame-gaps.html)
for more information on trajectory definition and frame gaps.

Now that the data is cleaned, `pathviewR` includes functions that estimate
visual perceptions based on the distance between the subject/observer and visual
stimuli on the walls of the experimental tunnel. For a complete description of
these functions,
[see the Visual Perception Functions vignette](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html).
[see the vignette on Estimating visual perceptions from tracking data](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html).


#### Add more info about experiments
Expand Down Expand Up @@ -175,8 +176,7 @@ motive_V_sf <-

Visualizing the calculations provides an more intuitive understanding of how
these visual perceptions change as the subject moves throughout the tunnel.
Please [see the Visual Perception Functions vignette](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html)
for more examples of visualizing calculations.
Please [see the vignette on Estimating visual perceptions from tracking data](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html) for more examples of visualizing calculations.

```{r motive_V_sf_pos, fig.height=3, fig.width=6, dpi=300}
ggplot(motive_V_sf, aes(x = position_width, y = position_height)) +
Expand Down
32 changes: 17 additions & 15 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -15,15 +15,15 @@ coverage](https://codecov.io/gh/vbaliga/pathviewR/graph/badge.svg)](https://code
[![](https://badges.ropensci.org/409_status.svg)](https://github.com/ropensci/software-review/issues/409)
<!-- badges: end -->

`pathviewR` offers tools to import, clean, and visualize animal movement
data from motion capture systems such as [Optitrack’s
`pathviewR` offers tools to import, clean, and visualize movement data,
particularly from motion capture systems such as [Optitrack’s
Motive](https://optitrack.com/software/motive/), the [Straw Lab’s
Flydra](https://github.com/strawlab/flydra), or other sources. We
provide functions to remove artifacts, standardize tunnel position and
tunnel axes, select a region of interest, isolate specific trajectories,
fill gaps in trajectory data, and calculate 3D and per-axis velocity.
For experiments of visual guidance, we also provide functions that use
animal position to estimate perception of visual stimuli.
subject position to estimate perception of visual stimuli.

## Installation

Expand All @@ -38,7 +38,8 @@ devtools::install_github("vbaliga/pathviewR")

#### Data import and cleaning via `pathviewR`

We’ll also load two `tidyverse` packages for wrangling & plotting.
We’ll also load two `tidyverse` packages for wrangling & plotting in
this readme.

``` r
library(pathviewR)
Expand All @@ -49,7 +50,7 @@ library(magrittr)
We will import and clean a sample data set from `.csv` files exported by
[Optitrack’s Motive](https://optitrack.com/software/motive/) software.
For examples of how to import and clean other types of data, [see the
data import and cleaning
Basics of data import and cleaning
vignette](https://vbaliga.github.io/pathviewR/articles/data-import-cleaning.html).

``` r
Expand All @@ -66,7 +67,7 @@ motive_data <-
Several functions to clean and wrangle data are available, and we have a
suggested pipeline for how these steps should be handled. For this
example, we will use one of two “all-in-one” functions: `clean_viewr()`.
[See the Data Import and Cleaning
[See the Basics of data import and cleaning
vignette](https://vbaliga.github.io/pathviewR/articles/data-import-cleaning.html)
for the full pipeline and the other “all-in-one” function.

Expand Down Expand Up @@ -139,7 +140,7 @@ str(motive_data)
#> - attr(*, ".internal.selfref")=<externalptr>
#> - attr(*, "pathviewR_steps")= chr "viewr"
#> - attr(*, "file_id")= chr "pathviewR_motive_example_data.csv"
#> - attr(*, "file_mtime")= POSIXct[1:1], format: "2021-01-09 16:14:48"
#> - attr(*, "file_mtime")= POSIXct[1:1], format: "2021-01-14 11:04:23"
#> - attr(*, "frame_rate")= num 100
#> - attr(*, "header")='data.frame': 11 obs. of 2 variables:
#> ..$ metadata: chr [1:11] "Format Version" "Take Name" "Take Notes" "Capture Frame Rate" ...
Expand Down Expand Up @@ -182,7 +183,7 @@ str(motive_allinone)
#> $ end_length_sign : num [1:449] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
#> $ direction : chr [1:449] "leftwards" "leftwards" "leftwards" "leftwards" ...
#> - attr(*, "file_id")= chr "pathviewR_motive_example_data.csv"
#> - attr(*, "file_mtime")= POSIXct[1:1], format: "2021-01-09 16:14:48"
#> - attr(*, "file_mtime")= POSIXct[1:1], format: "2021-01-14 11:04:23"
#> - attr(*, "frame_rate")= num 100
#> - attr(*, "header")='data.frame': 11 obs. of 2 variables:
#> ..$ metadata: chr [1:11] "Format Version" "Take Name" "Take Notes" "Capture Frame Rate" ...
Expand Down Expand Up @@ -213,16 +214,16 @@ str(motive_allinone)
```

An important aspect of how `pathviewR` defines trajectories is by
managing gaps in the data. [See the Managing Frame Gaps
vignette](https://vbaliga.github.io/pathviewR/articles/managing-frame-gaps.html)
managing gaps in the data. [See the vignette on Managing frame
gaps](https://vbaliga.github.io/pathviewR/articles/managing-frame-gaps.html)
for more information on trajectory definition and frame gaps.

Now that the data is cleaned, `pathviewR` includes functions that
estimate visual perceptions based on the distance between the
subject/observer and visual stimuli on the walls of the experimental
tunnel. For a complete description of these functions, [see the Visual
Perception Functions
vignette](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html).
tunnel. For a complete description of these functions, [see the vignette
on Estimating visual perceptions from tracking
data](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html).

#### Add more info about experiments

Expand Down Expand Up @@ -272,8 +273,9 @@ motive_V_sf <-

Visualizing the calculations provides an more intuitive understanding of
how these visual perceptions change as the subject moves throughout the
tunnel. Please [see the Visual Perception Functions
vignette](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html)
tunnel. Please [see the vignette on Estimating visual perceptions from
tracking
data](https://vbaliga.github.io/pathviewR/articles/visual-perception-functions.html)
for more examples of visualizing calculations.

``` r
Expand Down
4 changes: 2 additions & 2 deletions codemeta.json
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
],
"@type": "SoftwareSourceCode",
"identifier": "pathviewR",
"description": "Tools to import, clean, and visualize \n animal movement data from motion capture systems such as Optitrack's \n Motive, the Straw Lab's Flydra, or from other sources. We provide \n functions to remove artifacts, standardize tunnel position and tunnel \n axes, select a region of interest, isolate specific trajectories, fill\n gaps in trajectory data, and calculate 3D and per-axis velocity. For \n experiments of visual guidance, we also provide functions that use \n animal position to estimate perception of visual stimuli. ",
"description": "Tools to import, clean, and visualize movement data,\n particularly from motion capture systems such as Optitrack's \n Motive, the Straw Lab's Flydra, or from other sources. We provide \n functions to remove artifacts, standardize tunnel position and tunnel \n axes, select a region of interest, isolate specific trajectories, fill\n gaps in trajectory data, and calculate 3D and per-axis velocity. For \n experiments of visual guidance, we also provide functions that use \n subject position to estimate perception of visual stimuli. ",
"name": "pathviewR: Wrangle, Analyze, and Visualize Animal Movement Data",
"codeRepository": "https://github.com/vbaliga/pathviewR",
"issueTracker": "https://github.com/vbaliga/pathviewR/issues",
Expand Down Expand Up @@ -258,7 +258,7 @@
}
],
"readme": "https://github.com/vbaliga/pathviewR/blob/master/README.md",
"fileSize": "11559.219KB",
"fileSize": "11558.996KB",
"contIntegration": "https://codecov.io/gh/vbaliga/pathviewR?branch=master",
"developmentStatus": "https://www.repostatus.org/#active",
"citation": [
Expand Down
2 changes: 1 addition & 1 deletion docs/articles/data-import-cleaning.html

Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.

2 changes: 1 addition & 1 deletion docs/articles/managing-frame-gaps.html

Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.

6 changes: 3 additions & 3 deletions docs/articles/visual-perception-functions.html

Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.

0 comments on commit deffd9d

Please sign in to comment.