bettr
2024-03-04
Contents
1 Introduction
Method benchmarking is a core part of computational biology research, with an intrinsic power to establish best practices in method selection and application, as well as help identifying gaps and possibilities for improvement. A typical benchmark evaluates a set of methods using multiple different metrics, intended to capture different aspects of their performance. The best method to choose in any given situation can then be found, e.g., by averaging the different performance metrics, putting more emphasis on those that are more important to the specific situation.
Inspired by the
OECD ‘Better Life Index’,
the bettr package was developed to provide support for this last step. It
allows users to easily create performance summaries emphasizing the aspects
that are most important to them. bettr can be used interactively, via a
R/shiny application, or programmatically by calling the underlying functions.
In this vignette, we illustrate both alternatives, using example data
provided with the package.
2 Installation
bettr can be installed from GitHub:
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("federicomarini/bettr")3 Usage
suppressPackageStartupMessages({
library("bettr")
library("SummarizedExperiment")
library("tibble")
})The main input to bettr is a data.frame containing values of several
metrics for several methods. In addition, the user can provide additional
annotations and characteristics for the methods and metrics, which can be
used to group and filter them in the interactive application.
## Data for two metrics (metric1, metric2) for three methods (M1, M2, M3)
df <- data.frame(Method = c("M1", "M2", "M3"),
metric1 = c(1, 2, 3),
metric2 = c(3, 1, 2))
## More information for metrics
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
Group = c("G1", "G2", "G2"))
## More information for methods ('IDs')
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
Type = c("T1", "T1", "T2"))To simplify handling and sharing, the data can be combined into a
SummarizedExperiment (with methods as rows and metrics as columns) as
follows:
se <- assembleSE(df = df, idCol = "Method", metricInfo = metricInfo,
idInfo = idInfo)
se
#> class: SummarizedExperiment
#> dim: 3 2
#> metadata(1): bettrInfo
#> assays(1): values
#> rownames(3): M1 M2 M3
#> rowData names(2): Method Type
#> colnames(2): metric1 metric2
#> colData names(2): Metric GroupThe interactive application to explore the rankings can then be launched by
means of the bettr() function. The input can be either the assembled
SummarizedExperiment object or the individual components.
## Alternative 1
bettr(bettrSE = se)
## Alternative 2
bettr(df = df, idCol = "Method", metricInfo = metricInfo, idInfo = idInfo)4 Example - single-cell RNA-seq clustering benchmark
Next, we show a more elaborate example, visualizing data from the benchmark of
single-cell clustering methods performed by
Duo et al (2018). The benchmark
results are provided as an example data set in bettr:
duo2018 <- readRDS(system.file("extdata", "duo2018se.rds",
package = "bettr"))
duo2018
#> class: SummarizedExperiment
#> dim: 14 48
#> metadata(1): bettrInfo
#> assays(1): values
#> rownames(14): CIDR FlowSOM ... pcaReduce ascend
#> rowData names(0):
#> colnames(48): ARI_Koh ARI_KohTCC ... nclust.vs.true_Zhengmix4uneq
#> nclust.vs.true_Zhengmix8eq
#> colData names(2): Metric ClassHere, the summary data.frame (provided in the assay of the
SummarizedExperiment object) contains values for 48 performance
measures (4 different metrics, each for 12 data sets) for 14 clustering
methods. We also provide a metricInfo data.frame, assigning each metric
to a class, and two lists of colors that we would like to use for the
different metrics and data sets.
## Display the whole performance table
tibble(assay(duo2018, "values"))
#> # A tibble: 14 × 48
#> ARI_Koh ARI_KohTCC ARI_Kumar ARI_KumarTCC ARI_SimKumar4easy ARI_SimKumar4hard
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.672 0.805 0.989 0.977 1 1
#> 2 0.699 0.855 0.512 0.563 1 1
#> 3 0.869 0.891 1 1 1 1
#> 4 0.836 0.903 0.989 0.978 1 1
#> 5 0.280 0.276 0.949 1 0.644 0.194
#> 6 0.966 0.967 0.989 1 1 1
#> 7 0.613 0.950 0.989 1 0.952 NA
#> 8 0.939 0.939 1 1 1 1
#> 9 0.927 0.929 1 1 1 1
#> 10 0.862 0.902 0.988 0.989 1 1
#> 11 0.639 0.618 1 1 1 1
#> 12 0.855 0.963 0.988 1 0.995 0.992
#> 13 0.935 0.979 1 1 1 1
#> 14 NA NA 1 0.988 1 1
#> # ℹ 42 more variables: ARI_SimKumar8hard <dbl>, ARI_Trapnell <dbl>,
#> # ARI_TrapnellTCC <dbl>, ARI_Zhengmix4eq <dbl>, ARI_Zhengmix4uneq <dbl>,
#> # ARI_Zhengmix8eq <dbl>, elapsed_Koh <dbl>, elapsed_KohTCC <dbl>,
#> # elapsed_Kumar <dbl>, elapsed_KumarTCC <dbl>, elapsed_SimKumar4easy <dbl>,
#> # elapsed_SimKumar4hard <dbl>, elapsed_SimKumar8hard <dbl>,
#> # elapsed_Trapnell <dbl>, elapsed_TrapnellTCC <dbl>,
#> # elapsed_Zhengmix4eq <dbl>, elapsed_Zhengmix4uneq <dbl>, …
## Showing the first metric, evaluated on all datasets
head(colData(duo2018), 12)
#> DataFrame with 12 rows and 2 columns
#> Metric Class
#> <character> <character>
#> ARI_Koh ARI_Koh ARI
#> ARI_KohTCC ARI_KohTCC ARI
#> ARI_Kumar ARI_Kumar ARI
#> ARI_KumarTCC ARI_KumarTCC ARI
#> ARI_SimKumar4easy ARI_SimKumar4easy ARI
#> ... ... ...
#> ARI_Trapnell ARI_Trapnell ARI
#> ARI_TrapnellTCC ARI_TrapnellTCC ARI
#> ARI_Zhengmix4eq ARI_Zhengmix4eq ARI
#> ARI_Zhengmix4uneq ARI_Zhengmix4uneq ARI
#> ARI_Zhengmix8eq ARI_Zhengmix8eq ARI
## These are the color definitions (can mix character and hex values)
metadata(duo2018)$bettrInfo$idColors
#> $method
#> CIDR FlowSOM PCAHC PCAKmeans pcaReduce RtsneKmeans
#> "#332288" "#6699CC" "#88CCEE" "#44AA99" "#117733" "#999933"
#> Seurat SC3svm SC3 TSCAN ascend SAFE
#> "#DDCC77" "#661100" "#CC6677" "grey34" "orange" "black"
#> monocle RaceID2
#> "red" "blue"
metadata(duo2018)$bettrInfo$metricColors
#> $Class
#> ARI elapsed nclust.vs.true s.norm.vs.true
#> "purple" "forestgreen" "blue" "orange"Finally, we need to transform the metrics in
order to make them comparable (first, so that a high value is ‘good’, and
second, so that the numeric ranges of the metrics are comparable). In this
case, we have chosen to first flip the values (multiply by -1) of all
metrics representing elapsed time (elapsed) and the absolute deviation of
the number of clusters and Shannon entropies from the ground truth
(nclust.vs.true and s.norm.vs.true). Next, each of these metrics is scaled
linearly to the interval [0, 1]. These transformations are stored in
duo2018$initialTransforms.
names(metadata(duo2018)$bettrInfo$initialTransforms)
#> [1] "elapsed_Koh" "elapsed_KohTCC"
#> [3] "elapsed_Kumar" "elapsed_KumarTCC"
#> [5] "elapsed_SimKumar4easy" "elapsed_SimKumar4hard"
#> [7] "elapsed_SimKumar8hard" "elapsed_Trapnell"
#> [9] "elapsed_TrapnellTCC" "elapsed_Zhengmix4eq"
#> [11] "elapsed_Zhengmix4uneq" "elapsed_Zhengmix8eq"
#> [13] "nclust.vs.true_Koh" "nclust.vs.true_KohTCC"
#> [15] "nclust.vs.true_Kumar" "nclust.vs.true_KumarTCC"
#> [17] "nclust.vs.true_SimKumar4easy" "nclust.vs.true_SimKumar4hard"
#> [19] "nclust.vs.true_SimKumar8hard" "nclust.vs.true_Trapnell"
#> [21] "nclust.vs.true_TrapnellTCC" "nclust.vs.true_Zhengmix4eq"
#> [23] "nclust.vs.true_Zhengmix4uneq" "nclust.vs.true_Zhengmix8eq"
#> [25] "s.norm.vs.true_Koh" "s.norm.vs.true_KohTCC"
#> [27] "s.norm.vs.true_Kumar" "s.norm.vs.true_KumarTCC"
#> [29] "s.norm.vs.true_SimKumar4easy" "s.norm.vs.true_SimKumar4hard"
#> [31] "s.norm.vs.true_SimKumar8hard" "s.norm.vs.true_Trapnell"
#> [33] "s.norm.vs.true_TrapnellTCC" "s.norm.vs.true_Zhengmix4eq"
#> [35] "s.norm.vs.true_Zhengmix4uneq" "s.norm.vs.true_Zhengmix8eq"
## An example of a transformation - elapsed time for the Koh dataset
metadata(duo2018)$bettrInfo$initialTransforms$elapsed_Koh
#> $flip
#> [1] TRUE
#>
#> $transform
#> [1] "[0,1]"Now, we can launch the app for this data set:
bettr(bettrSE = duo2018, bstheme = "sandstone")The screenshot below illustrates the default view of the interactive interface.
We can choose to collapse the metric values to have a single value for each metric class, to reduce the redundancy. We can now also freely decide how to weight the respective metrics by means of the sliders in the left side bar. The bars on top of the heatmap show the current weight assignment.
bettr also provides alternative visualizations, e.g. a polar plot:
5 Programmatic interface
The interactive application showcased above, is the main entry point to using
bettr. However, we also provide a wrapper function to prepare the input data
for plotting (replicating the steps that are performed in the app), as well as
access to the plotting functions themselves. The following code replicates the
results for the example above.
## Assign a higher weight to one of the collapsed metric classes
metadata(duo2018)$bettrInfo$initialWeights["Class_ARI"] <- 0.55
prepData <- bettrGetReady(
bettrSE = duo2018, idCol = "method",
scoreMethod = "weighted mean", metricGrouping = "Class",
metricCollapseGroup = TRUE)
## This object is fairly verbose and detailed,
## but has the whole set of info needed
prepData
#> $plotdata
#> method metricGroup ScaledValue Weight Metric
#> 1 CIDR ARI 0.6512593 0.55 ARI
#> 2 CIDR elapsed 0.9889737 0.20 elapsed
#> 3 CIDR nclust.vs.true 0.7250000 0.20 nclust.vs.true
#> 4 CIDR s.norm.vs.true 0.7637276 0.20 s.norm.vs.true
#> 5 FlowSOM ARI 0.5211600 0.55 ARI
#> 6 FlowSOM elapsed 0.9743747 0.20 elapsed
#> 7 FlowSOM nclust.vs.true NA 0.20 nclust.vs.true
#> 8 FlowSOM s.norm.vs.true 0.4148342 0.20 s.norm.vs.true
#> 9 PCAHC ARI 0.7226564 0.55 ARI
#> 10 PCAHC elapsed 0.9737352 0.20 elapsed
#> 11 PCAHC nclust.vs.true NA 0.20 nclust.vs.true
#> 12 PCAHC s.norm.vs.true 0.8884335 0.20 s.norm.vs.true
#> 13 PCAKmeans ARI 0.7046547 0.55 ARI
#> 14 PCAKmeans elapsed 0.9725792 0.20 elapsed
#> 15 PCAKmeans nclust.vs.true NA 0.20 nclust.vs.true
#> 16 PCAKmeans s.norm.vs.true 0.9237374 0.20 s.norm.vs.true
#> 17 RaceID2 ARI 0.4842523 0.55 ARI
#> 18 RaceID2 elapsed 0.6875220 0.20 elapsed
#> 19 RaceID2 nclust.vs.true NA 0.20 nclust.vs.true
#> 20 RaceID2 s.norm.vs.true 0.5196616 0.20 s.norm.vs.true
#> 21 RtsneKmeans ARI 0.7832325 0.55 ARI
#> 22 RtsneKmeans elapsed 0.9450899 0.20 elapsed
#> 23 RtsneKmeans nclust.vs.true NA 0.20 nclust.vs.true
#> 24 RtsneKmeans s.norm.vs.true 0.9159085 0.20 s.norm.vs.true
#> 25 SAFE ARI 0.7146467 0.55 ARI
#> 26 SAFE elapsed 0.4037866 0.20 elapsed
#> 27 SAFE nclust.vs.true 0.6333333 0.20 nclust.vs.true
#> 28 SAFE s.norm.vs.true 0.7652029 0.20 s.norm.vs.true
#> 29 SC3 ARI 0.8533547 0.55 ARI
#> 30 SC3 elapsed 0.4596628 0.20 elapsed
#> 31 SC3 nclust.vs.true 0.3111111 0.20 nclust.vs.true
#> 32 SC3 s.norm.vs.true 0.8755450 0.20 s.norm.vs.true
#> 33 SC3svm ARI 0.8226663 0.55 ARI
#> 34 SC3svm elapsed 0.6680284 0.20 elapsed
#> 35 SC3svm nclust.vs.true 0.3111111 0.20 nclust.vs.true
#> 36 SC3svm s.norm.vs.true 0.8481785 0.20 s.norm.vs.true
#> 37 Seurat ARI 0.8470658 0.55 ARI
#> 38 Seurat elapsed 0.9871638 0.20 elapsed
#> 39 Seurat nclust.vs.true NA 0.20 nclust.vs.true
#> 40 Seurat s.norm.vs.true 0.9764823 0.20 s.norm.vs.true
#> 41 TSCAN ARI 0.6906276 0.55 ARI
#> 42 TSCAN elapsed 0.9119148 0.20 elapsed
#> 43 TSCAN nclust.vs.true 0.5833333 0.20 nclust.vs.true
#> 44 TSCAN s.norm.vs.true 0.8273006 0.20 s.norm.vs.true
#> 45 ascend ARI 0.6640133 0.55 ARI
#> 46 ascend elapsed 0.9434679 0.20 elapsed
#> 47 ascend nclust.vs.true 0.7592593 0.20 nclust.vs.true
#> 48 ascend s.norm.vs.true 0.8806625 0.20 s.norm.vs.true
#> 49 monocle ARI 0.7823963 0.55 ARI
#> 50 monocle elapsed 0.9494058 0.20 elapsed
#> 51 monocle nclust.vs.true NA 0.20 nclust.vs.true
#> 52 monocle s.norm.vs.true 0.8028666 0.20 s.norm.vs.true
#> 53 pcaReduce ARI 0.7639055 0.55 ARI
#> 54 pcaReduce elapsed 0.2947025 0.20 elapsed
#> 55 pcaReduce nclust.vs.true NA 0.20 nclust.vs.true
#> 56 pcaReduce s.norm.vs.true 0.9044682 0.20 s.norm.vs.true
#>
#> $scoredata
#> # A tibble: 14 × 2
#> method Score
#> <chr> <dbl>
#> 1 Seurat 0.904
#> 2 RtsneKmeans 0.845
#> 3 monocle 0.822
#> 4 PCAHC 0.810
#> 5 PCAKmeans 0.807
#> 6 ascend 0.767
#> 7 CIDR 0.742
#> 8 TSCAN 0.734
#> 9 SC3svm 0.711
#> 10 pcaReduce 0.695
#> 11 SC3 0.694
#> 12 SAFE 0.655
#> 13 FlowSOM 0.594
#> 14 RaceID2 0.535
#>
#> $idColors
#> $idColors$method
#> CIDR FlowSOM PCAHC PCAKmeans pcaReduce RtsneKmeans
#> "#332288" "#6699CC" "#88CCEE" "#44AA99" "#117733" "#999933"
#> Seurat SC3svm SC3 TSCAN ascend SAFE
#> "#DDCC77" "#661100" "#CC6677" "grey34" "orange" "black"
#> monocle RaceID2
#> "red" "blue"
#>
#>
#> $metricColors
#> $metricColors$Class
#> ARI elapsed nclust.vs.true s.norm.vs.true
#> "purple" "forestgreen" "blue" "orange"
#>
#> $metricColors$Metric
#> ARI_Koh ARI_KohTCC
#> "#F8766D" "#F37C58"
#> ARI_Kumar ARI_KumarTCC
#> "#ED813E" "#E68613"
#> ARI_SimKumar4easy ARI_SimKumar4hard
#> "#DE8C00" "#D69100"
#> ARI_SimKumar8hard ARI_Trapnell
#> "#CD9600" "#C29A00"
#> ARI_TrapnellTCC ARI_Zhengmix4eq
#> "#B79F00" "#ABA300"
#> ARI_Zhengmix4uneq ARI_Zhengmix8eq
#> "#9DA700" "#8EAB00"
#> elapsed_Koh elapsed_KohTCC
#> "#7CAE00" "#66B200"
#> elapsed_Kumar elapsed_KumarTCC
#> "#49B500" "#0CB702"
#> elapsed_SimKumar4easy elapsed_SimKumar4hard
#> "#00BA38" "#00BC52"
#> elapsed_SimKumar8hard elapsed_Trapnell
#> "#00BE67" "#00BF7A"
#> elapsed_TrapnellTCC elapsed_Zhengmix4eq
#> "#00C08B" "#00C19A"
#> elapsed_Zhengmix4uneq elapsed_Zhengmix8eq
#> "#00C1A9" "#00C0B7"
#> s.norm.vs.true_Koh s.norm.vs.true_KohTCC
#> "#00BFC4" "#00BDD1"
#> s.norm.vs.true_Kumar s.norm.vs.true_KumarTCC
#> "#00BBDC" "#00B8E7"
#> s.norm.vs.true_SimKumar4easy s.norm.vs.true_SimKumar4hard
#> "#00B4F0" "#00AFF8"
#> s.norm.vs.true_SimKumar8hard s.norm.vs.true_Trapnell
#> "#00A9FF" "#22A3FF"
#> s.norm.vs.true_TrapnellTCC s.norm.vs.true_Zhengmix4eq
#> "#619CFF" "#8494FF"
#> s.norm.vs.true_Zhengmix4uneq s.norm.vs.true_Zhengmix8eq
#> "#9F8CFF" "#B584FF"
#> nclust.vs.true_Koh nclust.vs.true_KohTCC
#> "#C77CFF" "#D674FD"
#> nclust.vs.true_Kumar nclust.vs.true_KumarTCC
#> "#E36EF6" "#ED68ED"
#> nclust.vs.true_SimKumar4easy nclust.vs.true_SimKumar4hard
#> "#F564E3" "#FB61D8"
#> nclust.vs.true_SimKumar8hard nclust.vs.true_Trapnell
#> "#FF61CC" "#FF62BF"
#> nclust.vs.true_TrapnellTCC nclust.vs.true_Zhengmix4eq
#> "#FF64B0" "#FF68A1"
#> nclust.vs.true_Zhengmix4uneq nclust.vs.true_Zhengmix8eq
#> "#FF6C91" "#FC7180"
#>
#>
#> $metricGrouping
#> [1] "Class"
#>
#> $metricCollapseGroup
#> [1] TRUE
#>
#> $idInfo
#> NULL
#>
#> $metricInfo
#> Metric Class
#> ARI_Koh ARI_Koh ARI
#> ARI_KohTCC ARI_KohTCC ARI
#> ARI_Kumar ARI_Kumar ARI
#> ARI_KumarTCC ARI_KumarTCC ARI
#> ARI_SimKumar4easy ARI_SimKumar4easy ARI
#> ARI_SimKumar4hard ARI_SimKumar4hard ARI
#> ARI_SimKumar8hard ARI_SimKumar8hard ARI
#> ARI_Trapnell ARI_Trapnell ARI
#> ARI_TrapnellTCC ARI_TrapnellTCC ARI
#> ARI_Zhengmix4eq ARI_Zhengmix4eq ARI
#> ARI_Zhengmix4uneq ARI_Zhengmix4uneq ARI
#> ARI_Zhengmix8eq ARI_Zhengmix8eq ARI
#> elapsed_Koh elapsed_Koh elapsed
#> elapsed_KohTCC elapsed_KohTCC elapsed
#> elapsed_Kumar elapsed_Kumar elapsed
#> elapsed_KumarTCC elapsed_KumarTCC elapsed
#> elapsed_SimKumar4easy elapsed_SimKumar4easy elapsed
#> elapsed_SimKumar4hard elapsed_SimKumar4hard elapsed
#> elapsed_SimKumar8hard elapsed_SimKumar8hard elapsed
#> elapsed_Trapnell elapsed_Trapnell elapsed
#> elapsed_TrapnellTCC elapsed_TrapnellTCC elapsed
#> elapsed_Zhengmix4eq elapsed_Zhengmix4eq elapsed
#> elapsed_Zhengmix4uneq elapsed_Zhengmix4uneq elapsed
#> elapsed_Zhengmix8eq elapsed_Zhengmix8eq elapsed
#> s.norm.vs.true_Koh s.norm.vs.true_Koh s.norm.vs.true
#> s.norm.vs.true_KohTCC s.norm.vs.true_KohTCC s.norm.vs.true
#> s.norm.vs.true_Kumar s.norm.vs.true_Kumar s.norm.vs.true
#> s.norm.vs.true_KumarTCC s.norm.vs.true_KumarTCC s.norm.vs.true
#> s.norm.vs.true_SimKumar4easy s.norm.vs.true_SimKumar4easy s.norm.vs.true
#> s.norm.vs.true_SimKumar4hard s.norm.vs.true_SimKumar4hard s.norm.vs.true
#> s.norm.vs.true_SimKumar8hard s.norm.vs.true_SimKumar8hard s.norm.vs.true
#> s.norm.vs.true_Trapnell s.norm.vs.true_Trapnell s.norm.vs.true
#> s.norm.vs.true_TrapnellTCC s.norm.vs.true_TrapnellTCC s.norm.vs.true
#> s.norm.vs.true_Zhengmix4eq s.norm.vs.true_Zhengmix4eq s.norm.vs.true
#> s.norm.vs.true_Zhengmix4uneq s.norm.vs.true_Zhengmix4uneq s.norm.vs.true
#> s.norm.vs.true_Zhengmix8eq s.norm.vs.true_Zhengmix8eq s.norm.vs.true
#> nclust.vs.true_Koh nclust.vs.true_Koh nclust.vs.true
#> nclust.vs.true_KohTCC nclust.vs.true_KohTCC nclust.vs.true
#> nclust.vs.true_Kumar nclust.vs.true_Kumar nclust.vs.true
#> nclust.vs.true_KumarTCC nclust.vs.true_KumarTCC nclust.vs.true
#> nclust.vs.true_SimKumar4easy nclust.vs.true_SimKumar4easy nclust.vs.true
#> nclust.vs.true_SimKumar4hard nclust.vs.true_SimKumar4hard nclust.vs.true
#> nclust.vs.true_SimKumar8hard nclust.vs.true_SimKumar8hard nclust.vs.true
#> nclust.vs.true_Trapnell nclust.vs.true_Trapnell nclust.vs.true
#> nclust.vs.true_TrapnellTCC nclust.vs.true_TrapnellTCC nclust.vs.true
#> nclust.vs.true_Zhengmix4eq nclust.vs.true_Zhengmix4eq nclust.vs.true
#> nclust.vs.true_Zhengmix4uneq nclust.vs.true_Zhengmix4uneq nclust.vs.true
#> nclust.vs.true_Zhengmix8eq nclust.vs.true_Zhengmix8eq nclust.vs.true
#>
#> $metricGroupCol
#> [1] "metricGroup"
#>
#> $methods
#> [1] "CIDR" "FlowSOM" "PCAHC" "PCAKmeans" "RaceID2"
#> [6] "RtsneKmeans" "SAFE" "SC3" "SC3svm" "Seurat"
#> [11] "TSCAN" "monocle" "pcaReduce" "ascend"
#>
#> $idCol
#> [1] "method"
#>
#> $metricCol
#> [1] "Metric"
#>
#> $valueCol
#> [1] "ScaledValue"
#>
#> $weightCol
#> [1] "Weight"
#>
#> $scoreCol
#> [1] "Score"
## Call the plotting routines specifying one single parameter
makeHeatmap(bettrList = prepData)
makePolarPlot(bettrList = prepData)
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_col()`).
6 Additional examples
bettr can also be adapted to represent more types of such collections of
metrics, other than the results of a benchmarking study in computational
biology. An examplem which is also included in the inst/scripts folder of
this package, presents the OECD Better Life Index
(https://stats.oecd.org/index.aspx?DataSetCode=BLI), spanning over 11 topics,
each represented by one to three indicators. These indicators are good measures
of the concepts of well-being, and well suited to display some
comparison across countries.
Additional examples can be added to the codebase upon interest, and we encourage users to contribute to that via a Pull Request to https://github.com/federicomarini/bettr.
Session info
sessionInfo()
#> R Under development (unstable) (2024-01-16 r85808)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] tibble_3.2.1 SummarizedExperiment_1.33.3
#> [3] Biobase_2.63.0 GenomicRanges_1.55.3
#> [5] GenomeInfoDb_1.39.7 IRanges_2.37.1
#> [7] S4Vectors_0.41.3 BiocGenerics_0.49.1
#> [9] MatrixGenerics_1.15.0 matrixStats_1.2.0
#> [11] bettr_0.99.1 BiocStyle_2.31.0
#>
#> loaded via a namespace (and not attached):
#> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3
#> [4] clue_0.3-65 GetoptLong_1.0.5 compiler_4.4.0
#> [7] png_0.1-8 vctrs_0.6.5 stringr_1.5.1
#> [10] pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.2
#> [13] fastmap_1.1.1 magick_2.8.3 backports_1.4.1
#> [16] XVector_0.43.1 ellipsis_0.3.2 labeling_0.4.3
#> [19] utf8_1.2.4 learnr_0.11.5 shinyjqui_0.4.1
#> [22] promises_1.2.1 rmarkdown_2.25 purrr_1.0.2
#> [25] xfun_0.42 zlibbioc_1.49.0 cachem_1.0.8
#> [28] jsonlite_1.8.8 highr_0.10 later_1.3.2
#> [31] DelayedArray_0.29.9 parallel_4.4.0 cluster_2.1.6
#> [34] R6_2.5.1 bslib_0.6.1 stringi_1.8.3
#> [37] RColorBrewer_1.1-3 rpart_4.1.23 jquerylib_0.1.4
#> [40] Rcpp_1.0.12 bookdown_0.37 assertthat_0.2.1
#> [43] iterators_1.0.14 knitr_1.45 base64enc_0.1-3
#> [46] httpuv_1.6.14 Matrix_1.6-5 nnet_7.3-19
#> [49] tidyselect_1.2.0 rstudioapi_0.15.0 abind_1.4-5
#> [52] yaml_2.3.8 doParallel_1.0.17 codetools_0.2-19
#> [55] lattice_0.22-5 withr_3.0.0 shiny_1.8.0
#> [58] evaluate_0.23 foreign_0.8-86 circlize_0.4.16
#> [61] pillar_1.9.0 BiocManager_1.30.22 checkmate_2.3.1
#> [64] DT_0.32 foreach_1.5.2 generics_0.1.3
#> [67] rprojroot_2.0.4 ggplot2_3.5.0 munsell_0.5.0
#> [70] scales_1.3.0 xtable_1.8-4 glue_1.7.0
#> [73] Hmisc_5.1-1 tools_4.4.0 data.table_1.15.2
#> [76] Cairo_1.6-2 cowplot_1.1.3 grid_4.4.0
#> [79] tidyr_1.3.1 sortable_0.5.0 colorspace_2.1-0
#> [82] GenomeInfoDbData_1.2.11 htmlTable_2.4.2 Formula_1.2-5
#> [85] cli_3.6.2 fansi_1.0.6 S4Arrays_1.3.5
#> [88] ComplexHeatmap_2.19.0 dplyr_1.1.4 gtable_0.3.4
#> [91] sass_0.4.8 digest_0.6.34 SparseArray_1.3.4
#> [94] farver_2.1.1 rjson_0.2.21 htmlwidgets_1.6.4
#> [97] htmltools_0.5.7 lifecycle_1.0.4 GlobalOptions_0.1.2
#> [100] mime_0.12