| title |
descriptionMeta |
descriptionTop |
sectionText |
sectionLink |
DataToVizText |
DataToVizLink |
url |
output |
Correlogram with the corrgram library |
This post explains how to build a correlogram with the corrgram R package. It provides several reproducible examples with explanation and R code. |
This post explains how to build a [correlogram](correlogram.html) with the `corrgram` R package. It provides several reproducible examples with explanation and `R` code. |
Correlogram section |
correlogram.html |
Data to Viz |
data-to-viz.com/graph/correlogram.html |
199-correlation-matrix-with-ggally |
| html_document |
| self_contained |
mathjax |
lib_dir |
template |
css |
toc |
toc_float |
toc_depth |
df_print |
false |
default |
libs |
template_rgg.html |
style.css |
true |
true |
2 |
paged |
|
|
```{r global options, include = FALSE}
knitr::opts_chunk$set( warning=FALSE, message=FALSE)
```
# Scatterplot matrix with `ggpairs()`
***
```{r thecode, echo=FALSE, out.width = c("33%","33%","33%") , fig.height=7, fig.show="hold"}
# Corrgram library
library(corrgram)
# mtcars dataset is natively available in R
# head(mtcars)
# First
corrgram(mtcars, order=TRUE, lower.panel=panel.shade, upper.panel=panel.pie, text.panel=panel.txt, main="Car Milage Data in PC2/PC1 Order")
# Second
corrgram(mtcars, order=TRUE, lower.panel=panel.ellipse, upper.panel=panel.pts, text.panel=panel.txt, diag.panel=panel.minmax, main="Car Milage Data in PC2/PC1 Order")
# Third
corrgram(mtcars, order=NULL, lower.panel=panel.shade, upper.panel=NULL, text.panel=panel.txt, main="Car Milage Data (unsorted)")
```
The `corrgram` package allows to build [correlogram](correlogram.html). The output allows to check the relationship between each pair of a set of numeric variable.
Relationship can be visualized with different methods:
- `panel.ellipse` to display ellipses
- `panel.shade` for coloured squares
- `panel.pie` for pie charts
- `panel.pts` for scatterplots
```{r thecode, eval=FALSE}
```
# Visualize correlation with `ggcorr()`
***
The `ggcorr()` function allows to visualize the correlation of each pair of variable as a square. Note that the `method` argument allows to pick the correlation type you desire.
```{r thecode2, echo=FALSE, out.width = "100%", fig.height=7}
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# Create data
data
```{r thecode2, eval=FALSE}
```
# Split by group {#category}
***
It is possible to use [ggplot2](ggplot2-package.html) aesthetics on the chart, for instance to color each category.
```{r thecode3, echo=FALSE, out.width = "100%", fig.height=7}
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
data(flea)
ggpairs(flea, columns = 2:4, ggplot2::aes(colour=species))
```
```{r thecode3, eval=FALSE}
```
# Change plot types
***
Change the type of plot used on each part of the [correlogram](correlogram.html). This is done with the `upper` and `lower` argument.
```{r thecode4, echo=FALSE, out.width = "100%", fig.height=7}
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
data(tips, package = "reshape")
ggpairs(
tips[, c(1, 3, 4, 2)],
upper = list(continuous = "density", combo = "box_no_facet"),
lower = list(continuous = "points", combo = "dot_no_facet")
)
```
```{r thecode4, eval=FALSE}
```
```{r, echo=FALSE}
htmltools::includeHTML("htmlChunkRelatedCorrelation.html")
```
马建仓 AI 助手
