## list()列表
```{r}
findwords <- function(tf){     #tf short for:text file
  txt <- scan(tf,"")           #读取文本文件，空格间隔的英文内容，汉字不适用
  print(txt)                   #输出文本列表
  wl <- list()                 #创建word（单词）列表list --wl
  for (i in 1:length(txt)){    # 循环读取txt内单词
    wrd <- txt[i]
    wl[[wrd]] <- c(wl[[wrd]],i)  # wl[[wrd]是个单词位置，把位置向量存起来
  }
  return(wl)
}
#orders the output of findwords() by word frequwncy
freqwl <- function(wordlist){
  freqs <- sapply(wordlist,length)
  return(wordlist[order(freqs)])   #order()按位次排序，sort() 按值排序
}

x <- findwords("tf.txt")
# names(x)     #显示列表标签名，提取x中的单词
# unlisted(x)  #获取列表的值
# unname(x)    #去掉列表元素名
#sort(names(x)) #列表标签名（单词)排序
#提取词频较高的10%单词作图
#----------------------------
snyt <- freqwl(x)
nword <- length(snyt)
freqs <- sapply(snyt[round(0.9*nword):nword],length)
barplot(freqs)

```

## 列表应用lapply（） & sapply（）
```{r}
lapply(list(1:6,20:36),median) #输出中位数1:6，20:36列表
sapply(list(1:6,20:36),median) #输出中位数1:6，20:36矩阵

不同自由的的t分布图

par(mfrow=c(2,2))
#opar=par(no.readonly = TRUE)
y=seq(-4,4,length.out=100)
par(fig=c(0,1,0,1))
plot(y,dt(y,1),type = "l",ylim = c(0,0.5))
plot(y,dt(y,3),type = "l",ylim=c(0,0.5))
plot(y,dt(y,5),type = "l",ylim=c(0,0.5))
plot(y,dt(y,10),type = "l",ylim=c(0,0.5))
plot(y,dt(y,30),type = "l",ylim=c(0,0.5))

Cluster聚类分析

means <- sample(c(-3,0,3),replace = T)
x <- rnorm(99,mean = means)
d <- dist(x)
hc <- hclust(d)
plot(hc,hang=-1)
clust <- cutree(hc,k=3)
plot(x~factor(clust),main = "分类均值")

widgets <- c(179,153,183,153,154)
gadgets <- c(167,193,190,161,181)
thingys <- c(182,166,170,171,186)
daily.prod <- data.frame(widgets,gadgets,thingys,row.names = c('Mon','Tue','Wed','Thu','Fri'))
rbind(daily.prod,Total=colSums(daily.prod))
cbind(daily.prod,Totol=rowSums(daily.prod))

https://bookdown.org/xiangyun/masr/

stack函数将列表合并为一个两列数据框，该数据框的列名称分别为：values，ind

第一列包含数据，第二列包含平行因子-即一个分组变量。

v1 <- c(0.60,0.35,0.44,.62,.60)
v2 <- c(.70,.601,.63,.87,.85,.70,.64)
v3 <- c(.76,.71,.92,.87)
comb <- stack(list(v1=v1,v2=v2,v3=v3)) #向量列表标签不能省略，用做平行因子水平
print(comb)
aov(values~ind,comb)
summary(aov(values~ind,data=comb))

 2022年4月1日cnliutz  0 CommentsEdit

A Advanced R Programming by Roger Peng
C ConoceR by David Duncan
D Data Science and R by Wush Wu Daten einlesen und kennenlernen by RLab-Team Daten visualisieren mit ggplot2 by RLab-Team Deskriptive Statistik Gelaendeklimatologie by RLab-Team Deskriptive Statistik mit bodenkundlichen Daten by RLab-Team Deskriptive Statistik und Vergleiche meteorologischer Zeitreihen by RLab-Team
E
Einfuehrung in Datenaufbereitung mit tidyR by RLab-Team
Einfuehrung in Datenhandling und Visualisieren von Klimadaten by RLab-Team
Exploratory Data Analysis by Team swirl
G
Getting and Cleaning Data by Team swirl Google Forms Course by Sean Kross
M
MARSYS Data analysis with R by RLab-Team MARSYS Statistik und Programmierung mit R by RLab-Team
P
Programacion Estadistica R by Ismael Fernández Programando en R by José R Sosa
Q
qss-swirl by Kosuke Imai
R
R-Grundlagen by RLab-Team R Programming by Team swirl
R Programmieren by Stephan Weibelzahl
The R Programming Environment by Roger Peng
Regular Expressions by Jon Calder
Regression Models by Team swirl
S
Statistical Inference by Team swirl
V
A (Very) Short Introduction to R by Claudia Brauer

---

Advanced R Programming

Author Roger Peng Co-Authors Brooke Anderson Sean Kross Description The second course in the Mastering Software Development in R series.

Installation

swirl::install_course("Advanced R Programming")

Manual Installation Download this file. Run swirl::install_course() in the R console. Select the file you just downloaded. Website https://github.com/swirldev/Advanced_R_Programming

The R Graph Gallery – Help and inspiration for R charts (r-graph-gallery.com)

Example：

Library

library(fmsb)

Create data: note in High school for Jonathan:

data <- as.data.frame(matrix( sample( 2:20 , 10 , replace=T) , ncol=10))
colnames(data) <- c(” 数学” , “english” , “biology” , “music” , “R-coding”, “data-viz” , “french” , “physic”, “statistic”, “sport” )

To use the fmsb package, I have to add 2 lines to the dataframe: the max and min of each topic to show on the plot!

data <- rbind(rep(20,10) , rep(0,10) , data)

Check your data, it has to look like this!

head(data)

The default radar chart

radarchart(data)

网址： bookdown.org

Write HTML, PDF, ePub, and Kindle books with R Markdown,and put it in bookdown.org