Merge pull request #121 from sunroofgod/effsize

update: library referencing for gh-pages and ci/cd

vignettes/plot_aesthetics.Rmd

@@ -48,7 +48,8 @@ The following parameters are responsible for adjusting the size of the text elem
 - `contrast_y_text`: Default 15. Numeric value determining the font size of the y-axis of the contrast plot.
 
 ```{r, include = FALSE}
-devtools::load_all(".")
+library(dabestr)
+
 data(twogroup_data)
 data(multigroup_data)
 data(my.data.proportional)

vignettes/tutorial_basics.Rmd

@@ -12,7 +12,6 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
-devtools::load_all(".")
 ```
 
 This vignette documents the basic functionalities of dabestr. It illustrates the 
@@ -22,7 +21,7 @@ The dataset is first processed into the dabestr format via `load()`. The effect
 calculated via `effect_size()`. Lastly, the estimation plots are produced via
 `dabest_plot()`
 
-```{r eval = FALSE}
+```{r setup}
 library(dabestr)
 ```
 

vignettes/tutorial_deltadelta.Rmd

@@ -12,7 +12,6 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
-devtools::load_all(".")
 ```
 
 This vignette documents how `dabestr` is able to compute the calculation of delta-delta, an experimental function that allows the comparison between two bootstrapped effect sizes computed from two independent categorical variables.
@@ -37,7 +36,7 @@ Effectively, we have 4 groups of subjects for comparison.
 
 There are 2 `Treatment` conditions, `Placebo` (control group) and `Drug` (test group). There are 2 `Genotypes`: `W` (wild type population) and `M` (mutant population). In addition, each experiment was done twice (`Rep1` and `Rep2`). We shall do a few analyses to visualise these differences in a simulated dataset.
 
-```{r eval = FALSE}
+```{r setup}
 library(dabestr)
 ```
 

vignettes/tutorial_minimeta.Rmd

@@ -12,7 +12,6 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
-devtools::load_all(".")
 ```
 
 When scientists perform replicates of the same experiment, the effect size of each replicate often varies, which complicates interpretation of the results. This vignette documents how `dabestr` is able to compute the meta-analyzed weighted effect size given multiple replicates of the same experiment. This can help resolve differences between replicates and simplify interpretation.
@@ -32,7 +31,7 @@ DABEST can only compute weighted effect size for _mean difference only_, and not
 
 For more information on meta-analysis, please refer to Chapter 10 of the Cochrane handbook: https://training.cochrane.org/handbook/current/chapter-10
 
-```{r eval = FALSE}
+```{r setup}
 library(dabestr)
 ```
 

vignettes/tutorial_proportion_plots.Rmd

@@ -12,14 +12,13 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
-devtools::load_all(".")
 ```
 
 This vignette documents how `dabestr` is able to generate proportion plots for binary data. 
 
 It’s important to note that the code we provide only supports numerical proportion data, where the values are limited to 0 (failure) and 1 (success). This means that the code is not suitable for analyzing proportion data that contains non-numeric values, such as strings like ‘yes’ and ‘no’.
 
-```{r eval = FALSE}
+```{r setup}
 library(dabestr)
 ```
 

vignettes/tutorial_repeated_measures.Rmd

@@ -12,7 +12,6 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
-devtools::load_all(".")
 ```
 
 This vignette documents how `dabestr` is able to generate estimation plots for experiments with repeated-measures designs. `dabestr` allows for the calculation and plotting of effect sizes for:
@@ -24,7 +23,7 @@ This is an improved version of paired data plotting in previous versions, which
 
 To use these features, you can simply declare the argument `paired = "sequential"` or `paired = "baseline"` correspondingly while running `load()`. You must also pass a column in the dataset that indicates the identity of each observation, using the `id_col` keyword.
 
-```{r eval = FALSE}
+```{r setup}
 library(dabestr)
 ```