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+---
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+title: "table analysis"
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+author: "hungerzs"
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+date: "2016年8月1日"
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+output: html_document
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+---
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+
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+```{r setup, include=FALSE}
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+knitr::opts_chunk$set(echo = TRUE)
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+```
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+
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+## 交叉表统计 cross tabulation
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+
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+使用table进行一半的描述性统计
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+
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+```{r}
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+#加载数据框
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+lake.df<-readRDS("data/lake.df.rds")
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+# 生成列联表contingency table
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+lake.table<-xtabs(EVI~year+yday, lake.df, sparse = T)
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+# 计算边界平均值，1 代表dim1，计算列，2代表dim2，计算行
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+addmargins(lake.table, c(1,2), mean)
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+```
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+
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+## summary
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+```{r}
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+summary(lake.df)
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+```
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+
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+## sapply
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+
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+simple lapply(list apply)
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+```{r}
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+#sapply(lake.df, sd)
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+
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+descstats <- function(x, na.omit=TRUE){
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+ if (na.omit)
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+ x <- x[!is.na(x)]
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+ m <- mean(x)
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+ n <- length(x)
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+ s <- sd(x)
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+ #skew <- sum((x-m)^3/s^3)/n
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+ #kurt <- sum((x-m)^4/s^4)/n - 3
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+ return(c(n=n, mean=m, stdev=s))
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+ }
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+
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+sapply(lake.df, descstats)
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+```
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+
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+```{r}
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+library(Hmisc)
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+describe(lake.df)
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+```
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+
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+# 描述性统计分析
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+```{r}
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+library(pastecs)
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+stat.desc(lake.df, basic = T, desc = T, norm = F, p=0.95)
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+```
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+
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+```{r}
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+library(psych)
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+describe(lake.df)
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+```
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+
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+## 分组统计(Descriptive statistics by group)
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+```{r}
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+aggregate(lake.df, by=list(y=lake.df$year), mean, na.rm=T)
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+aggregate(lake.df, by=list(y=lake.df$yday), mean, na.rm=T)
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+aggregate(lake.df, by=list(y=lake.df$year, d=lake.df$yday), mean, na.rm=T)
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+```
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+
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+## using by
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+可返回多个统计数
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+```{r}
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+dstats<-function(x) (c(mean=mean(x), sd=sd(x)))
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+by(lake.df$EVI, lake.df$year, dstats)
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+```
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+
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+
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+```{r}
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+library(psych)
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+describe.by(lake.df$EVI, lake.df$year)
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+
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+```
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+
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+## reshape
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+
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+```{r}
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+library(reshape)
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+dstats <- function(x)(c(n=length(x), mean=mean(x), sd=sd(x)))
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+dfm <- melt(mtcars, measure.vars=c("mpg", "hp", "wt"), id.vars=c("am", "cyl"))
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+dfm
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+cast(dfm, am + cyl + variable ~ ., dstats)
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+```
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+
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+## crosstable
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+```{r}
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+library(gmodels)
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+CrossTable(Arthritis$Treatment, Arthritis$Improved)
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+CrossTable(lake.df$year, lake.df$yday)
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+```
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+
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+## tapply
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+```{r}
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+tapply(lake.df$EVI, lake.df$year, dstats)
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+
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+```
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