Download Xtable Package In R
Elegant correlation table using xtable R package
Correlation matrix analysis is an important method to find dependence between variables. Computing correlation matrix and drawing correlogram is explained here. The aim of this article is to show you how to get the lower and the upper triangular part of a correlation matrix. We will also use the xtable R package to display a nice correlation table in html or latex formats.
Note that online software is also available here to compute correlation matrix and to plot a correlogram without any installation.
Contents:
- Correlation matrix analysis
- Lower and upper triangular part of a correlation matrix
- Use xtable R package to display nice correlation table in html format
- Combine matrix of correlation coefficients and significance levels
- Conclusions
Correlation matrix analysis
The following R code computes a correlation matrix using mtcars data. Click here to read more.
mcor<-round(cor(mtcars),2) mcor mpg cyl disp hp drat wt qsec vs am gear carb mpg 1.00 -0.85 -0.85 -0.78 0.68 -0.87 0.42 0.66 0.60 0.48 -0.55 cyl -0.85 1.00 0.90 0.83 -0.70 0.78 -0.59 -0.81 -0.52 -0.49 0.53 disp -0.85 0.90 1.00 0.79 -0.71 0.89 -0.43 -0.71 -0.59 -0.56 0.39 hp -0.78 0.83 0.79 1.00 -0.45 0.66 -0.71 -0.72 -0.24 -0.13 0.75 drat 0.68 -0.70 -0.71 -0.45 1.00 -0.71 0.09 0.44 0.71 0.70 -0.09 wt -0.87 0.78 0.89 0.66 -0.71 1.00 -0.17 -0.55 -0.69 -0.58 0.43 qsec 0.42 -0.59 -0.43 -0.71 0.09 -0.17 1.00 0.74 -0.23 -0.21 -0.66 vs 0.66 -0.81 -0.71 -0.72 0.44 -0.55 0.74 1.00 0.17 0.21 -0.57 am 0.60 -0.52 -0.59 -0.24 0.71 -0.69 -0.23 0.17 1.00 0.79 0.06 gear 0.48 -0.49 -0.56 -0.13 0.70 -0.58 -0.21 0.21 0.79 1.00 0.27 carb -0.55 0.53 0.39 0.75 -0.09 0.43 -0.66 -0.57 0.06 0.27 1.00 The result is a table of correlation coefficients between all possible pairs of variables.
Lower and upper triangular part of a correlation matrix
To get the lower or the upper part of a correlation matrix, the R function lower.tri() or upper.tri() can be used. The formats of the functions are :
lower.tri(x, diag = FALSE) upper.tri(x, diag = FALSE) - x : is the correlation matrix - diag : if TRUE the diagonal are not included in the result.
The two functions above, return a matrix of logicals which has the same size of a the correlation matrix. The entries is TRUE in the lower or upper triangle :
upper.tri(mcor) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [1,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [2,] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [3,] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [4,] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [5,] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE [6,] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE [7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE [8,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE [9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE [10,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [11,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE # Hide upper triangle upper<-mcor upper[upper.tri(mcor)]<-"" upper<-as.data.frame(upper) upper mpg cyl disp hp drat wt qsec vs am gear carb mpg 1 cyl -0.85 1 disp -0.85 0.9 1 hp -0.78 0.83 0.79 1 drat 0.68 -0.7 -0.71 -0.45 1 wt -0.87 0.78 0.89 0.66 -0.71 1 qsec 0.42 -0.59 -0.43 -0.71 0.09 -0.17 1 vs 0.66 -0.81 -0.71 -0.72 0.44 -0.55 0.74 1 am 0.6 -0.52 -0.59 -0.24 0.71 -0.69 -0.23 0.17 1 gear 0.48 -0.49 -0.56 -0.13 0.7 -0.58 -0.21 0.21 0.79 1 carb -0.55 0.53 0.39 0.75 -0.09 0.43 -0.66 -0.57 0.06 0.27 1 #Hide lower triangle lower<-mcor lower[lower.tri(mcor, diag=TRUE)]<-"" lower<-as.data.frame(lower) lower mpg cyl disp hp drat wt qsec vs am gear carb mpg -0.85 -0.85 -0.78 0.68 -0.87 0.42 0.66 0.6 0.48 -0.55 cyl 0.9 0.83 -0.7 0.78 -0.59 -0.81 -0.52 -0.49 0.53 disp 0.79 -0.71 0.89 -0.43 -0.71 -0.59 -0.56 0.39 hp -0.45 0.66 -0.71 -0.72 -0.24 -0.13 0.75 drat -0.71 0.09 0.44 0.71 0.7 -0.09 wt -0.17 -0.55 -0.69 -0.58 0.43 qsec 0.74 -0.23 -0.21 -0.66 vs 0.17 0.21 -0.57 am 0.79 0.06 gear 0.27 carb Use xtable R package to display nice correlation table in html format
library(xtable) print(xtable(upper), type="html") | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| mpg | 1 | ||||||||||
| cyl | -0.85 | 1 | |||||||||
| disp | -0.85 | 0.9 | 1 | ||||||||
| hp | -0.78 | 0.83 | 0.79 | 1 | |||||||
| drat | 0.68 | -0.7 | -0.71 | -0.45 | 1 | ||||||
| wt | -0.87 | 0.78 | 0.89 | 0.66 | -0.71 | 1 | |||||
| qsec | 0.42 | -0.59 | -0.43 | -0.71 | 0.09 | -0.17 | 1 | ||||
| vs | 0.66 | -0.81 | -0.71 | -0.72 | 0.44 | -0.55 | 0.74 | 1 | |||
| am | 0.6 | -0.52 | -0.59 | -0.24 | 0.71 | -0.69 | -0.23 | 0.17 | 1 | ||
| gear | 0.48 | -0.49 | -0.56 | -0.13 | 0.7 | -0.58 | -0.21 | 0.21 | 0.79 | 1 | |
| carb | -0.55 | 0.53 | 0.39 | 0.75 | -0.09 | 0.43 | -0.66 | -0.57 | 0.06 | 0.27 | 1 |
Combine matrix of correlation coefficients and significance levels
Custom function corstars() is used to combine the correlation coefficients and the level of significance. The R code of the function is provided at the end of this article. It requires 2 packages :
- The Hmisc R package to compute the matrix of correlation coefficients and the corresponding p-values.
- The xtable R package for displaying in HTML or Latex format.
Before continuing with the following exercises, you should first copy and paste the source code the function corstars(), which you can find at the bottom of this article.
corstars(mtcars[,1:7], result="html") | mpg | cyl | disp | hp | drat | wt | |
|---|---|---|---|---|---|---|
| mpg | ||||||
| cyl | -0.85**** | |||||
| disp | -0.85**** | 0.90**** | ||||
| hp | -0.78**** | 0.83**** | 0.79**** | |||
| drat | 0.68**** | -0.70**** | -0.71**** | -0.45** | ||
| wt | -0.87**** | 0.78**** | 0.89**** | 0.66**** | -0.71**** | |
| qsec | 0.42* | -0.59*** | -0.43* | -0.71**** | 0.09 | -0.17 |
p < .0001 '****'; p < .001 '***', p < .01 '**', p < .05 '*'
The code of corstars function (The code is adapted from the one posted on this forum and on this blog ):
# x is a matrix containing the data # method : correlation method. "pearson"" or "spearman"" is supported # removeTriangle : remove upper or lower triangle # results : if "html" or "latex" # the results will be displayed in html or latex format corstars <-function(x, method=c("pearson", "spearman"), removeTriangle=c("upper", "lower"), result=c("none", "html", "latex")){ #Compute correlation matrix require(Hmisc) x <- as.matrix(x) correlation_matrix<-rcorr(x, type=method[1]) R <- correlation_matrix$r # Matrix of correlation coeficients p <- correlation_matrix$P # Matrix of p-value ## Define notions for significance levels; spacing is important. mystars <- ifelse(p < .0001, "****", ifelse(p < .001, "*** ", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))) ## trunctuate the correlation matrix to two decimal R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1] ## build a new matrix that includes the correlations with their apropriate stars Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x)) diag(Rnew) <- paste(diag(R), " ", sep="") rownames(Rnew) <- colnames(x) colnames(Rnew) <- paste(colnames(x), "", sep="") ## remove upper triangle of correlation matrix if(removeTriangle[1]=="upper"){ Rnew <- as.matrix(Rnew) Rnew[upper.tri(Rnew, diag = TRUE)] <- "" Rnew <- as.data.frame(Rnew) } ## remove lower triangle of correlation matrix else if(removeTriangle[1]=="lower"){ Rnew <- as.matrix(Rnew) Rnew[lower.tri(Rnew, diag = TRUE)] <- "" Rnew <- as.data.frame(Rnew) } ## remove last column and return the correlation matrix Rnew <- cbind(Rnew[1:length(Rnew)-1]) if (result[1]=="none") return(Rnew) else{ if(result[1]=="html") print(xtable(Rnew), type="html") else print(xtable(Rnew), type="latex") } } Conclusions
- Use cor() function to compute correlation matrix.
- Use lower.tri() and upper.tri() functions to get the lower or upper part of the correlation matrix
- Use xtable R function to display a nice correlation matrix in latex or html format.
Infos
This analysis was performed using R (ver. 3.3.2). Enjoyed this article? I'd be very grateful if you'd help it spread by emailing it to a friend, or sharing it on Twitter, Facebook or Linked In.
Show me some love with the like buttons below... Thank you and please don't forget to share and comment below!!
Avez vous aimé cet article? Je vous serais très reconnaissant si vous aidiez à sa diffusion en l'envoyant par courriel à un ami ou en le partageant sur Twitter, Facebook ou Linked In.
Montrez-moi un peu d'amour avec les like ci-dessous ... Merci et n'oubliez pas, s'il vous plaît, de partager et de commenter ci-dessous!
Recommended for You!
Recommended for you
This section contains best data science and self-development resources to help you on your path.
Want to Learn More on R Programming and Data Science?
Follow us by Email
On Social Networks:
Get involved :
Click to follow us on Facebook and Google+ :
Comment this article by clicking on "Discussion" button (top-right position of this page)
Source: http://www.sthda.com/english/wiki/elegant-correlation-table-using-xtable-r-package
Posted by: corneridee.blogspot.com