README.md

In ManyLabsOpenScience/manylabRs: Analysis scripts and data from the Many Labs projects

Install the package

Several ways to install the package.

Source from GitHub

Use the code below to install the manylabRs package directly from GitHub.

```{r, eval=FALSE} library(devtools) install_github("ManyLabsOpenScience/manylabRs")

### Download tarball from GitHub

First [download the tarball](https://github.com/ManyLabsOpenScience/manylabRs/pkg/), then install the package locally through the RStudio package installer: `Tools` >> `Install Packages...`


## Main function

The main function to inspect is `get.analyses()`.  

It will take one or more take analysis (`studies`) from the `masteRkey` sheet and an indication of whether the analysis is:
1. `global` - will disregard the clusters in the data and use all valid caes for analyses, both `primary` and `secondary` analyses have a `global` variant.
2. `primary`- target analysis of replication study conducted for each lab seperately.
3. `secondary` - additional analyses conducted for each lab seperately.
4. `order` - presentation order analyses disregard the clusters int he data, each order is analysed seperately

> Have a look at [`saveConsole.R`](https://github.com/ManyLabsOpenScience/manylabRs/blob/master/inst/saveConsole.R) which calls the `testScript()` function and creates a log file with lots of info about the analysis steps.


The example below runs a global analysis for `Huang.1`
```{r}
library(manylabRs)
library(tidyverse)

df <- get.analyses(studies = 1, analysis.type = 1)

The object df contains two named lists:^[these names correspond to the analysis name in the masteRkey spreadsheet]

raw.case

This list contains dataframes with the relevant variables for each analysis, but before the analysis specific variable functions (varfun) are applied. There is a Boolean variable case.include which indicates whther a case is valid and should be included for analysis.

df$raw.case$Huang.1

aggregated

The dataframe in aggregated contains the data as is was analysed, after the varfun is applied.

df$aggregated$Huang.1

Other algorithms

• Get information from masteRkey on the analyses to run: get.info()
• Get a data filter based on exclusion criteria: get.chain()
• Select the appropriate variables: get.sourceData()
• Apply the analysis-specific variable function: varfun.ABC.#()
• Apply the analysis listed in column stat.test to the data
• Organise the output get.desriptives()
• Calculate confidence intervals for effect sizes any2any()
• Return the ouput

Other algorithms

• Get information from masteRkey on the analyses to run: get.info()
• Get a data filter based on exclusion criteria: get.chain()
• Select the appropriate variables: get.sourceData()
• Apply the analysis-specific variable function: varfun.ABC.#()
• Apply the analysis listed in column stat.test to the data
• Organise the output get.desriptives()
• Calculate confidence intervals for effect sizes any2any()
• Return the ouput

ManyLabsOpenScience/manylabRs documentation built on Oct. 15, 2018, 7:59 a.m.