> #使用tibble
> library(tidyverse)
> a <- as_tibble(iris) #创建tibble
> tibble(
+   x=1:5,
+   y=1,
+   z=x^2+y
+ )
# A tibble: 5 x 3
     x     y     z
 <int> <dbl> <dbl>
1     1     1     2
2     2     1     5
3     3     1    10
4     4     1    17
5     5     1    26
> 
> x=1:5
> y=1
> data.frame(  #tibble可以使用刚刚创建的变量，而dataframe则需要先定义
+   x=1:5,
+   y=1,z=x+y
+ )
 x y z
1 1 1 2
2 2 1 3
3 3 1 4
4 4 1 5
5 5 1 6
> 
> 
> tb <- tibble(          #tibble可以用这些奇怪的符号，R不认识，需要用反引号
+   `:)` = "smile", 
+   ` ` = "space",
+   `2000` = "number"
+ )
> tb
# A tibble: 1 x 3
 `:)`  ` `   `2000`
 <chr> <chr> <chr> 
1 smile space number
> 
> 
> tribble(               #tribble 
+   ~x, ~y, ~z,
+   #--|--|----
+   "a", 2, 3.6,
+   "b", 1, 8.5
+ )
# A tibble: 2 x 3
 x         y     z
 <chr> <dbl> <dbl>
1 a         2   3.6
2 b         1   8.5
> 
> 
> data <- iris
> data[[3,1]] #第三行第一个
[1] 4.7
> 
> 
> #如何识别一个对象是否为tibble
> #is_tibble(),或者class()
> class(data)
[1] "data.frame"
> class(a)
[1] "tbl_df"     "tbl"        "data.frame"
> is_tibble(a)
[1] TRUE
> is_tibble(data)
[1] FALSE
> 
> #Tibbles has the classes c("tbl_df", "tbl", "data.frame")
> 
> tibble和dataframe的区别
Error: object 'tibble和dataframe的区别' not found
> 
> #Using $ a data.frame will partially complete the column.
> #So even though we wrote df$x it returned df$xyz.
> #This saves a few keystrokes, but can result in accidentally using a different variable than you thought you were using.
> 
> #With data.frames, with [ the type of object that is returned differs on the number of columns.
> #If it is one column, it won’t return a data.frame, but instead will return a vector.
> #With more than one column, then it will return a data.frame.
> #This is fine if you know what you are passing in, but suppose you did df[ , vars] where vars was a variable.
> #Then you what that code does depends on length(vars) and you’d have to write code to account for those situations or risk bugs.
> 
> 
> var <- "Species"  #如果你把变量保存在var里面，需要用[[]]提取，而不是$
> data[[var]]
 [1] setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa    
[11] setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa    
[21] setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa    
[31] setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa    
[41] setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa     setosa    
[51] versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor
[61] versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor
[71] versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor
[81] versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor
[91] versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor versicolor
[101] virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
[111] virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
[121] virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
[131] virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
[141] virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
Levels: setosa versicolor virginica
> 
> 
> 
> #建一个tibble
> 
> annoying <- tibble(
+   `1` = 1:10,
+   `2` = `1` * 2 + rnorm(length(`1`))
+ )
> #画个图查看关系
> ggplot(annoying, aes(x = `1`, y = `2`)) +
+   geom_point() 
> 
> #增加一列
> annoying[["var"]] <- annoying$`2` / annoying$`1`
> 
> annoying
# A tibble: 10 x 3
    `1`   `2`   var
  <int> <dbl> <dbl>
1     1  2.90  2.90
2     2  4.72  2.36
3     3  4.88  1.63
4     4  8.71  2.18
5     5  9.23  1.85
6     6 10.9   1.81
7     7 13.6   1.94
8     8 14.6   1.83
9     9 18.7   2.07
10    10 21.6   2.16
> #重命名
> annoying <- rename(annoying, one = `1`, two = `2`, three = "var")
> glimpse(annoying)
Observations: 10
Variables: 3
$ one   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
$ two   <dbl> 2.895630, 4.716864, 4.875581, 8.707593, 9.232054, 10.872596, 13.613199, 14.638820, 18.674116, 21.584095
$ three <dbl> 2.895630, 2.358432, 1.625194, 2.176898, 1.846411, 1.812099, 1.944743, 1.829853, 2.074902, 2.158410
> 
> 
> 
> enframe(c(a = 1, b = 2, c = 3))
# A tibble: 3 x 2
 name  value
 <chr> <dbl>
1 a         1
2 b         2
3 c         3
> #enframe可以转化vector为数据框