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corrplot.Rmd
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corrplot.Rmd
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---
title: "生成网页版"
author: "作者:**彭文波**"
date: "编译时间:`r Sys.time()`"
output:
html_document: default
rticles::ctex:
pdf_document: default
---
\newpage
```{r include=FALSE}
knitr::opts_chunk$set(comment = '',
# prompt = TRUE, #代码前面增加>号
# collapse = TRUE, #把代码块内的计算结果放在一起
message = FALSE,
warning = FALSE)
```
```{r,message = FALSE, include=FALSE}
library(EconGeo) # Computing Key Indicators of the Spatial Distribution of Economic Activities
library(tidyverse) # Easily Install and Load the 'Tidyverse'
library(export) # Streamlined Export of Graphs and Data Tables
library(corrplot) # Visualization of a Correlation Matrix # Visualization of a Correlation Matrix
# library(tidytext) # Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
# library(tidyquant) # Tidy Quantitative Financial Analysis
# library(knitr) # A General-Purpose Package for Dynamic Report Generation in R
# library(data.table) # Extension of `data.frame`
# library(RColorBrewer) # ColorBrewer Palettes
# library(reshape2) # Flexibly Reshape Data: A Reboot of the Reshape Package
# library(ggrepel) # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
# library(bruceR) # Broadly Useful Convenient and Efficient R Functions
# library(stargazer) # Well-Formatted Regression and Summary Statistics Tables
# library(foreign) # Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata','Systat', 'Weka', 'dBase', ...
# library(plm) # Linear Models for Panel Data
# library(AER) # Applied Econometrics with R
# library(igraph) # Network Analysis and Visualization
# library(networkD3) # D3 JavaScript Network Graphs from R
# library(texreg) # Conversion of R Regression Output to LaTeX or HTML Tables
# library(statnet) # CRAN v2019.6
# library(ggstatsplot)#躺着就能出图
# library(linkprediction) # CRAN v1.0-0
# rm(list = ls())
```
视频地址:[这么华丽的相关性热图居然这么容易画!零基础R语言之相关性分析 晴学R9_哔哩哔哩_bilibili](https://www.bilibili.com/video/BV1mK4y1N7g9/?spm_id_from=autoNext)
数据和代码[GitHub - linhesun/bilibiliRlearning: Use it with the bilibili vedios.](https://github.com/linhesun/bilibiliRlearning)
```{r}
# install.packages('corrplot')
# library('corrplot') # Visualization of a Correlation Matrix # Visualization of a Correlation Matrix
```
```{r}
dat <- read.table('cordat.txt', header = T, sep = '\t')
dat <- as.matrix(dat)
corr <- cor(dat)
# corr2 <- co.occurrence(t(dat)) %>% relatedness(.,method = "cosine")
## 此处不能二模网络的节点相似性来计算,因为这是求解变量之间的相关性。前者只可能是正直,这里可能是负值。
corrplot(corr)
corrplot(corr, tl.col = 'black')
corrplot(corr, tl.col = 'black', order = 'hclust')
corr1 <- cor.mtest(dat)
corr1$p
corrplot(corr, tl.col = 'black', order = 'hclust', p.mat = corr1$p, insig = 'blank')
corrplot(corr, tl.col = 'black', order = 'hclust', p.mat = corr1$p,
insig = 'label_sig', sig.level = c(0.001,0.01,0.05), pch.cex = 0.9,
pch.col = 'green')
corrplot(corr, tl.col = 'black', order = 'hclust', p.mat = corr1$p,
insig = 'label_sig', sig.level = c(0.001,0.01,0.05), pch.cex = 0.9,
pch.col = 'green', type = 'lower')
corrplot(corr, tl.col = 'black', order = 'hclust', p.mat = corr1$p,
insig = 'label_sig', sig.level = c(0.001,0.01,0.05), pch.cex = 0.9,
pch.col = 'green', type = 'lower', method = 'color')
```
```{r}
```