-
Notifications
You must be signed in to change notification settings - Fork 0
/
README.Rmd
51 lines (35 loc) · 1.12 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
<!-- space for pandocs in future -->
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
eval = FALSE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
library(knitr)
```
# shlab.imgct
<!-- badges: start -->
<!-- badges: end -->
The goal of ``shlab.imgct`` is to streamline the processing and analysis
of participant category responses to blocks of images.
## Installation
You can install the ongoing development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("sokolhessnerlab/shlab.imgct")
```
## Example
This is a basic example which shows you how to solve a common problem:
```{r example, eval=FALSE}
library(shlab.imgct)
# Set your path to relevant task data
datapath <- "path/to/data"
# shlab.imgct::clean(datapath) # can be used as convenience
shlab.imgct::clean_qualtrics_export(datapath)
shlab.imgct::validate_all_participants(datapath)
shlab.imgct::categorize(datapath, threshold = 3)
shlab.imgct::analyze(datapath, "categorized_3_valid.tsv")
```