mirror of
https://github.com/adambard/learnxinyminutes-docs.git
synced 2025-04-27 15:43:58 +00:00
translate to Chinese 100%
This commit is contained in:
alswl
parent
3ff90ab9eb
commit
bfd1293c4f
@ -1,3 +1,18 @@
|
||||
---
|
||||
language: R
|
||||
contributors:
|
||||
- ["e99n09", "http://github.com/e99n09"]
|
||||
- ["isomorphismes", "http://twitter.com/isomorphisms"]
|
||||
translators:
|
||||
- ["小柒", "http://weibo.com/u/2328126220"]
|
||||
- ["alswl", "https://github.com/alswl"]
|
||||
filename: learnr.r
|
||||
---
|
||||
|
||||
R 是一门统计语言。它有很多数据分析和挖掘程序包。可以用来统计、分析和制图。
|
||||
你也可以在 LaTeX 文档中运行 `R` 命令。
|
||||
|
||||
```python
|
||||
# 评论以 # 开始
|
||||
|
||||
# R 语言原生不支持 多行注释
|
||||
@ -397,7 +412,6 @@ dat
|
||||
# 4 4 dog
|
||||
class(dat$number) # "numeric"
|
||||
class(dat[,2]) # "factor"
|
||||
# The data.frame() function converts character vectors to factor vectors
|
||||
# data.frame() 会将字符向量转换为 factor 向量
|
||||
|
||||
# 有很多精妙的方法来获取 data frame 的子数据集
|
||||
@ -407,16 +421,14 @@ dat[,"number"] # 5 2 1 4
|
||||
|
||||
# 多维（相同元素类型）
|
||||
|
||||
# 利用数组创造一个 n 维的表格
|
||||
# 使用 arry 创造一个 n 维的表格
|
||||
# You can make a two-dimensional table (sort of like a matrix)
|
||||
#你可以建立一个2维表格（类型和矩阵相似）
|
||||
# 你可以建立一个 2 维表格（有点像矩阵）
|
||||
array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
|
||||
#数组(c(c(1,2,4,5),c(8,9,3,6)),有前两个向量组成，2行4列
|
||||
# =>
|
||||
# [,1] [,2] [,3] [,4]
|
||||
# [1,] 1 4 8 3
|
||||
# [2,] 2 5 9 6
|
||||
# You can use array to make three-dimensional matrices too
|
||||
#你也可以利用数组建立一个三维的矩阵
|
||||
array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
|
||||
# =>
|
||||
@ -434,29 +446,25 @@ array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
|
||||
# [2,] 60 7
|
||||
# [3,] 0 847
|
||||
|
||||
# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)
|
||||
#列表（多维的，不同类型的）
|
||||
|
||||
# Finally, R has lists (of vectors)
|
||||
# R语言有列表的形式
|
||||
list1 <- list(time = 1:40)
|
||||
list1$price = c(rnorm(40,.5*list1$time,4)) # random
|
||||
list1$price = c(rnorm(40,.5*list1$time,4)) # 随机
|
||||
list1
|
||||
|
||||
# You can get items in the list like so
|
||||
#你可以获得像上面列表的形式
|
||||
# 你可以这样获得列表的元素
|
||||
list1$time
|
||||
# You can subset list items like vectors
|
||||
#你也可以获取他们的子集，一种类似于矢量的形式
|
||||
# 你也可以和矢量一样获取他们的子集
|
||||
list1$price[4]
|
||||
|
||||
#########################
|
||||
# The apply() family of functions
|
||||
#apply()函数家族的应用
|
||||
# apply()函数家族
|
||||
#########################
|
||||
|
||||
# Remember mat?
|
||||
#输出mat矩阵
|
||||
# 还记得 mat 么？
|
||||
mat
|
||||
# =>
|
||||
# [,1] [,2]
|
||||
@ -464,90 +472,69 @@ mat
|
||||
# [2,] 2 5
|
||||
# [3,] 3 6
|
||||
# Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X
|
||||
#使用(X, MARGIN, FUN)将一个function功能函数根据其特征应用到矩阵x中
|
||||
# over rows (MAR = 1) or columns (MAR = 2)
|
||||
#规定行列，其边界分别为1,2
|
||||
# 使用(X, MARGIN, FUN)将函数 FUN 应用到矩阵 X 的行 (MAR = 1) 或者 列 (MAR = 2)
|
||||
# That is, R does FUN to each row (or column) of X, much faster than a
|
||||
#即就是，R定义一个function使每一行/列的x快于一个for或者while循环
|
||||
# for or while loop would do
|
||||
# R 在 X 的每一行/列使用 FUN，比循环要快很多
|
||||
apply(mat, MAR = 2, myFunc)
|
||||
# =>
|
||||
# [,1] [,2]
|
||||
# [1,] 3 15
|
||||
# [2,] 7 19
|
||||
# [3,] 11 23
|
||||
# Other functions: ?lapply, ?sapply
|
||||
其他的功能函数，
|
||||
# 还有其他家族函数 ?lapply, ?sapply
|
||||
|
||||
# Don't feel too intimidated; everyone agrees they are rather confusing
|
||||
#不要被这些吓到，许多人在此都会容易混淆
|
||||
# The plyr package aims to replace (and improve upon!) the *apply() family.
|
||||
#plyr程序包的作用是用来改进family函数家族
|
||||
# 不要被吓到，虽然许多人在此都被搞混
|
||||
# plyr 程序包的作用是用来改进 apply() 函数家族
|
||||
|
||||
install.packages("plyr")
|
||||
require(plyr)
|
||||
?plyr
|
||||
|
||||
#########################
|
||||
# Loading data
|
||||
# 载入数据
|
||||
#########################
|
||||
|
||||
# "pets.csv" is a file on the internet
|
||||
# "pets.csv" 是网上的一个文本
|
||||
pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
|
||||
#首先读取这个文本
|
||||
pets
|
||||
head(pets, 2) # first two rows
|
||||
#显示前两行
|
||||
tail(pets, 1) # last row
|
||||
#显示最后一行
|
||||
head(pets, 2) # 前两行
|
||||
tail(pets, 1) # 最后一行
|
||||
|
||||
# To save a data frame or matrix as a .csv file
|
||||
# 以 .csv 格式来保存数据集或者矩阵
|
||||
write.csv(pets, "pets2.csv") # to make a new .csv file
|
||||
#输出新的文本pets2.csv
|
||||
write.csv(pets, "pets2.csv") # 保存到新的文件 pets2.csv
|
||||
# set working directory with setwd(), look it up with getwd()
|
||||
#改变工作路径setwd()，查找工作路径getwd()
|
||||
# 使用 setwd() 改变工作目录，使用 getwd() 查看当前工作目录
|
||||
|
||||
# Try ?read.csv and ?write.csv for more information
|
||||
#试着做一做以上学到的，或者运行更多的信息
|
||||
# 尝试使用 ?read.csv 和 ?write.csv 来查看更多信息
|
||||
|
||||
#########################
|
||||
# Plots
|
||||
# 画图
|
||||
#########################
|
||||
|
||||
# Scatterplots!
|
||||
# 散点图
|
||||
plot(list1$time, list1$price, main = "fake data")
|
||||
#作图，横轴list1$time，纵轴list1$price，主题fake data
|
||||
# Regressions!
|
||||
#退回
|
||||
linearModel <- lm(price ~ time, data = list1)
|
||||
# 线性模型，数据集为list1，以价格对时间做相关分析模型
|
||||
linearModel # outputs result of regression
|
||||
#输出拟合结果，并退出
|
||||
# Plot regression line on existing plot
|
||||
plot(list1$time, list1$price, main = "fake data") # 译者注：横轴 list1$time，纵轴 wlist1$price，标题 fake data
|
||||
# 回归图
|
||||
linearModel <- lm(price ~ time, data = list1) # 译者注：线性模型，数据集为list1，以价格对时间做相关分析模型
|
||||
linearModel # 拟合结果
|
||||
# 将拟合结果展示在图上，颜色设为红色
|
||||
abline(linearModel, col = "red")
|
||||
# Get a variety of nice diagnostics
|
||||
# 也可以获取各种各样漂亮的分析图
|
||||
plot(linearModel)
|
||||
|
||||
# Histograms!
|
||||
# 直方图
|
||||
hist(rpois(n = 10000, lambda = 5), col = "thistle")
|
||||
#统计频数直方图（）
|
||||
hist(rpois(n = 10000, lambda = 5), col = "thistle") # 译者注：统计频数直方图
|
||||
|
||||
# Barplots!
|
||||
# 柱状图
|
||||
barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
|
||||
#作图，柱的高度负值c(1,4,5,1,2），各个柱子的名称"red","blue","purple","green","yellow"
|
||||
|
||||
# Try the ggplot2 package for more and better graphics
|
||||
# 可以尝试着使用 ggplot2 程序包来美化图片
|
||||
install.packages("ggplot2")
|
||||
require(ggplot2)
|
||||
?ggplot2
|
||||
|
||||
```
|
||||
|
||||
## 获得 R
|
||||
|
||||
* 从 [http://www.r-project.org/](http://www.r-project.org/) 获得安装包和图形化界面
|
||||
* [RStudio](http://www.rstudio.com/ide/) 是另一个图形化界面
|
||||
|
||||
Loading…
Reference in New Issue
Block a user