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Heatmap for Correlation by \(R^2\)

• The correlation matrix of \(R^2\) is calculated by linear regression or Cramér’s V.
• The matrix is presented using R plotly package.

library(data.table)
dt <- data.table(
    var1 = 1:20 + rnorm(20)
  , var2 = 1:20 + rnorm(20) * 2
  , var3 = 1:20 + rnorm(20) * 4
  , var4 = 1:20 + rnorm(20) * 8
  , var5 = 1:20 + rnorm(20) * 16
  , var6 = sample(c("A", "B"), size = 20, replace = TRUE)
  , var7 = sample(c("I", "II", "III"), size = 20, replace = TRUE)
)

Wu::heatmap(dt)

Heatmap for Spearman’s Correlation Coefficient

• Use ggcorrplot package to present the heatmap of a correlation matrix of Spearman’s \(\rho\)

library(ggcorrplot)
library(data.table)

dt <- data.table(
    var1 = 1:20 + rnorm(20)
  , var2 = 1:20 + rnorm(20) * 2
  , var3 = 1:20 + rnorm(20) * 4
  , var4 = 1:20 + rnorm(20) * 8
  , var5 = 1:20 + rnorm(20) * 16
)


NVars <- c("var1"
         , "var2"
         , "var3"
         , "var4"
         , "var5"
           )

mtx <- cor(dt[, ..NVars]
         ## , na.rm=FALSE
         , use="na.or.complete"
         , method="spearman")


ggcorrplot(mtx, 
           hc.order = FALSE,
           outline.color = "white", 
           type = "lower",
           ## colors = c("red", "white", "blue"), 
           show.diag = TRUE, 
           lab = TRUE
         , tl.srt=30
         , lab_size=10
         , tl.cex=10
           , show.legend = FALSE
         ## , ggtheme = ggplot2::theme_gray
         , colors = c("#6D9EC1", "white", "#E46726")
         ## , method="circle"
           )

library(corrplot)
library(corrgram)


corrplot::corrplot(mtx)

corrgram::corrgram(mtx)

treemap

library(treemap)
data(GNI2014)
treemap(GNI2014,
       index=c("continent", "iso3"),
       vSize="population",
       vColor="GNI",
       type="value")

Variable Clustering

library(data.table)

dt <- data.table(
    var1 = 1:20 + rnorm(20)
  , var2 = 1:20 + rnorm(20) * 2
  , var3 = 1:20 + rnorm(20) * 4
  , var4 = 1:20 + rnorm(20) * 8
  , var5 = 1:20 + rnorm(20) * 16
)

library(ClustOfVar)

tree <- hclustvar(X.quanti = as.data.frame(dt))
plot(tree)

Contour

n <- 200
x <- runif(n)
x <- unique(x)
x <- sort(x)
y <- runif(n)
y <- unique(y)
y <- sort(y)

## dt <- dt[, trt := factor(trt)]


z1 <- x ^ 2 %*% t(rep(1, times = length(y)))
z2 <- rep(1, times = length(x)) %*% t(y ^ 2)
z <- z1 + z2

filled.contour(x, y, z)

R sessionInfo

sessionInfo()

R version 4.2.0 (2022-04-22) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.3 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale: [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] ClustOfVar_1.1 treemap_2.4-3 corrgram_1.14
[4] corrplot_0.92 ggcorrplot_0.1.3 Wu_0.0.0.9000
[7] flexdashboard_0.5.2 lme4_1.1-29 Matrix_1.4-0
[10] mgcv_1.8-38 nlme_3.1-152 png_0.1-7
[13] scales_1.2.0 nnet_7.3-16 labelled_2.9.1
[16] kableExtra_1.3.4 plotly_4.10.0 gridExtra_2.3
[19] ggplot2_3.3.6 DT_0.23 tableone_0.13.2
[22] magrittr_2.0.3 lubridate_1.8.0 dplyr_1.0.9
[25] plyr_1.8.7 data.table_1.14.2 rmdformats_1.0.4
[28] knitr_1.39

loaded via a namespace (and not attached): [1] webshot_0.5.3 RColorBrewer_1.1-3 httr_1.4.3 tools_4.2.0
[5] bslib_0.3.1 utf8_1.2.2 R6_2.5.1 DBI_1.1.2
[9] lazyeval_0.2.2 colorspace_2.0-3 withr_2.5.0 tidyselect_1.1.2
[13] compiler_4.2.0 cli_3.3.0 rvest_1.0.2 xml2_1.3.3
[17] bookdown_0.27 sass_0.4.1 systemfonts_1.0.4 stringr_1.4.0
[21] digest_0.6.29 minqa_1.2.4 rmarkdown_2.14 svglite_2.1.0
[25] pkgconfig_2.0.3 htmltools_0.5.3 fastmap_1.1.0 highr_0.9
[29] htmlwidgets_1.5.4 rlang_1.0.4 rstudioapi_0.13 shiny_1.7.1
[33] jquerylib_0.1.4 generics_0.1.3 farver_2.1.1 jsonlite_1.8.0
[37] crosstalk_1.2.0 Rcpp_1.0.9 munsell_0.5.0 fansi_1.0.3
[41] lifecycle_1.0.1 stringi_1.7.8 yaml_2.3.5 MASS_7.3-54
[45] grid_4.2.0 promises_1.2.0.1 forcats_0.5.1 lattice_0.20-45
[49] haven_2.5.0 splines_4.2.0 hms_1.1.1 klippy_0.0.0.9500 [53] pillar_1.8.0 igraph_1.3.4 boot_1.3-28 reshape2_1.4.4
[57] glue_1.6.2 evaluate_0.15 mitools_2.4 httpuv_1.6.5
[61] vctrs_0.4.1 nloptr_2.0.3 gtable_0.3.0 purrr_0.3.4
[65] tidyr_1.2.0 assertthat_0.2.1 xfun_0.31 gridBase_0.4-7
[69] mime_0.12 xtable_1.8-4 survey_4.1-1 later_1.3.0
[73] survival_3.2-13 viridisLite_0.4.0 tibble_3.1.8 PCAmixdata_3.1
[77] ellipsis_0.3.2