README.md

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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

Other algorithms



ManyLabsOpenScience/manylabRs documentation built on April 12, 2018, 8:22 p.m.