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包名: tibble 编码: UTF-8 最新版本: 1.2 标题: 简单数据框 描述: 构建一个 ‘tbl_df‘ 类，可以比传统的R数据框提供更好的检查和打印功能。 作者: Hadley Wickham , Romain Francois ,Kirill Müller, RStudio URL: https://github.com/hadley/tibble 要求: R (>= 3.1.2) Github: https://github.com/hadley/tibble
tibble包是一个轻量级的包，它实现的data.frame的重新塑造，保留了data.frame中经过实践证明有效的部分，吸取了专注于数据操作的dplyr包的基本思想。tibble包提供了更优于data.frame的性能，包括：打印，提取子集和因子操作。
tibble包内提供的主要函数：
|
名称 |
功能 |
|
as_tibble |
强制转换lists和matrices为数据框(data.frame) |
|
tibble |
创建数据框(data.frame)或列表(list) |
|
tribble |
智能行(Row-wise)创建tibble |
|
obj_sum/ type_sum/ tbl_sum |
给出对象的简明摘要：对象类型和数据框大小 |
|
rownames |
行名的操作工具（非常有用）：可以提取行名为列或列为行名 |
|
has_name |
检查命名元素的存在has_name(iris, "Species") |
|
repair_names |
修复对象的名称（如果没有命名则用V+i代替） |
|
all_equal |
数据框相等的柔性比较，忽略行和列的排列顺序 |
|
glimpse |
有点像str()，主要是查看数据集的结构 |
|
enframe |
将向量变为数据框 |
|
print.tbl_df |
print(x,n)打印数据集x的前n行，默认为10行，有点像head() |
|
add_column |
给数据框添加列 |
|
add_row |
给数据框添加行 |
|
is.tibble |
检测对象是否为tibble |
|
knit_print.trunc_mat |
截断显示 |
从CRAN安装：
install.packages("tibble")
从github安装：
# install.packages("devtools") devtools::install_github("hadley/tibble")
可以利用as_tibble()函数将已经存在的对象（data.frame,list,matrix,or table）强制转为tibble对象：
library(tibble) as_tibble(iris) #> # A tibble: 150 × 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fctr> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5.0 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # ... with 140 more rows
也可以利用tibble()函数创建：
tibble(x = 1:5, y = 1, z = x ^ 2 + y) #> # A tibble: 5 × 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 a <- 1:5 tibble(a, b = a * 2) ## # A tibble: 5 × 2 ## a b ## <int> <dbl> ## 1 1 2 ## 2 2 4 ## 3 3 6 ## 4 4 8 ## 5 5 10 tibble(a, b = a * 2, c = 1) ## # A tibble: 5 × 3 ## a b c ## <int> <dbl> <dbl> ## 1 1 2 1 ## 2 2 4 1 ## 3 3 6 1 ## 4 4 8 1 ## 5 5 10 1 tibble(x = runif(10), y = x * 2) # # A tibble: 10 × 2 # x y # <dbl> <dbl> # 1 0.7098188 1.4196377 # 2 0.2790267 0.5580533 # 3 0.2655437 0.5310874 # 4 0.1237294 0.2474587 # 5 0.9018147 1.8036293 # 6 0.1594413 0.3188827 # 7 0.2592028 0.5184056 # 8 0.6570324 1.3140648 # 9 0.8955551 1.7911102 # 10 0.1940897 0.3881794 tibble(x = letters) # # A tibble: 26 × 1 # x # <chr> # 1 a # 2 b # 3 c # 4 d # 5 e # 6 f # 7 g # 8 h # 9 i # 10 j # # ... with 16 more rows tibble(x = 1:3, y = list(1:5, 1:10, 1:20)) #> # A tibble: 3 × 2 #> x y #> <int> <list> #> 1 1 <int [5]> #> 2 2 <int [10]> #> 3 3 <int [20]>
也可以使用tribble()函数一行一行的定义一个tibble对象：
tribble( ~x, ~y, ~z, "a", 2, 3.6, "b", 1, 8.5 ) #> # A tibble: 2 × 3 #> x y z #> <chr> <dbl> <dbl> #> 1 a 2 3.6 #> 2 b 1 8.5
查看类型，最底层还是data.frame：
class(as_tibble(iris)) #> [1] "tbl_df" "tbl" "data.frame"
### 添加行 add_row(.data, ..., .before = NULL, .after = NULL) .data 要添加的数据框 .before , .after 在哪行之前或之后添加该数据
df <- tibble(x = 1:3, y = 3:1) df #> # A tibble: 3 × 2 #> x y #> <int> <int> #> 1 1 3 #> 2 2 2 #> 3 3 1
library(dplyr)
df %>% add_row(x = 4, y = 0, .before = 2)
#> # A tibble: 4 × 2
#> x y
#> <dbl> <dbl>
#> 1 1 3
#> 2 4 0
#> 3 2 2
#> 4 3 1
df %>% add_row(x = 4:5, y = 0:-1)
#> # A tibble: 5 × 2
#> x y
#> <int> <int>
#> 1 1 3
#> 2 2 2
#> 3 3 1
#> 4 4 0
#> 5 5 -1
add_row(df, x = 4)
#> # A tibble: 4 <U+00D7> 2
#> x y
#> <dbl> <int>
#> 1 1 3
#> 2 2 2
#> 3 3 1
#> 4 4 NA
### 添加列 add_column(.data, ..., .before = NULL, .after = NULL) .data 要添加的数据框 .before , .after 在哪行=列之前或之后添加该数据 df %>% add_column(z = -1:1, w = 0) #> # A tibble: 3 × 4 #> x y z w #> <int> <int> <int> <dbl> #> 1 1 3 -1 0 #> 2 2 2 0 0 #> 3 3 1 1 0 df %>% add_column(z = -1:1, .after = 1) #> # A tibble: 3 × 3 #> x z y #> <int> <int> <int> #> 1 1 -1 3 #> 2 2 0 2 #> 3 3 1 1 df %>% add_column(w = 0:2, .before = "x") #> # A tibble: 3 × 3 #> w x y #> <int> <int> <int> #> 1 0 1 3 #> 2 1 2 2 #> 3 2 3 1
df 数据框
var 用于rownames的列的名称
has_rownames(df) 确定数据框是否有行名
remove_rownames(df) 删除数据框的行名
library(tibble) head(mtcars) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 head(iris) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa has_rownames(mtcars) ## [1] TRUE has_rownames(iris) ## [1] FALSE has_rownames(remove_rownames(mtcars)) ## [1] FALSE head(remove_rownames(mtcars)) ## mpg cyl disp hp drat wt qsec vs am gear carb ## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
rownames_to_column(df, var = "rowname") 数据框的行名作为数据框的列，列名为rowname
column_to_rownames(df, var = "rowname") 数据框的某列作为行名
head(rownames_to_column(mtcars,"row2col")) ## row2col mpg cyl disp hp drat wt qsec vs am gear carb ## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 mtcars_tbl <- as_tibble(rownames_to_column(mtcars)) mtcars_tbl # # A tibble: 32 × 12 # rowname mpg cyl disp hp drat wt qsec vs am # <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 # 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 # 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 # 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 # 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 # 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 # 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 # 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 # 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 # 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 # # ... with 22 more rows, and 2 more variables: gear <dbl>, carb <dbl> head(column_to_rownames(as.data.frame(mtcars_tbl))) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 df <- rownames_to_column(mtcars,"row2col") column_to_rownames(df,"row2col")
has_name(x, name) x 数据框或其他命名对象 name 需检查的元素 has_name(iris, "Species") ## [1] TRUE has_name(mtcars, "gears") ## [1] FALSE
repair_names(x, prefix = "V", sep = "") x 命名的向量 prefix 字符串，前缀，该前缀用于新列名 sep 分隔符
list(3, 4, 5) # [[1]] # [1] 3 # # [[2]] # [1] 4 # # [[3]] # [1] 5 repair_names(list(3, 4, 5)) # works for lists, too # $V1 # [1] 3 # # $V2 # [1] 4 # # $V3 # [1] 5 tbl <- as_tibble(structure(list(3, 4, 5), class = "data.frame"),validate = FALSE) tbl # A tibble: 0 × 3 # ... with 3 variables: <dbl>, <dbl>, <dbl> repair_names(tbl) # A tibble: 0 <U+00D7> 3 # ... with 3 variables: V1 <dbl>, V2 <dbl>, V3 <dbl> repair_names(list(1,2,4),prefix = "new",sep = "-") # $`new-1` # [1] 1 # # $`new-2` # [1] 2 # # $`new-3` # [1] 4
obj_sum(x) # 如果is_s3_vector值为TRUE，也就是是S3类型的向量，同时返回对象的尺寸的对象数据类型 type_sum(x) # 给出对象类型简短摘要 tbl_sum(x) # 给出一个类似于表对象的简短的文字描述，包括维数，数据源，可能的组（for dplyr） is_vector_s3(x)
> obj_sum(1:10) # [1] "int [10]" > obj_sum(matrix(1:10)) # [1] "int [10 <U+00D7> 1]" > obj_sum(Sys.Date()) # [1] "date [1]" > obj_sum(Sys.time()) # [1] "dttm [1]" > obj_sum(mean) # [1] "fun"
当使用all.equal比较两个tbl_df，默认情况下行和列的顺序是被忽略的，并且类型也不是强制要求。
all_equal(target, current, ignore_col_order = TRUE, ignore_row_order = TRUE, convert = FALSE, ...) "all.equal"(target, current, ignore_col_order = TRUE, ignore_row_order = TRUE, convert = FALSE, ...) 参数： target, current 要比较的两个数据框 ignore_col_order 是否需要忽略列顺序，默认为TRUE ignore_row_order 是否需要忽略行顺序，默认为TRUE convert 是否需要转换为相似的类型，默认为FALSE，如果为TRUE，会将因子factor转为字符character，整型integer double转为双精度浮点型 ...
# 对行号和列号进行采样，打乱行列顺序 scramble <- function(x) x[sample(nrow(x)), sample(ncol(x))] # 转为tbl-df类型 mtcars_df <- as_tibble(mtcars) # 默认情况下行列顺序是忽略的 all.equal(mtcars_df, scramble(mtcars_df)) # [1] TRUE # 修改默认行列顺序不被忽略 all.equal(mtcars_df, scramble(mtcars_df), ignore_col_order = FALSE) # [1] TRUE all.equal(mtcars_df, scramble(mtcars_df), ignore_row_order = FALSE) # [1] "Component “mpg”: Mean relative difference: 0.3503521" # [2] "Component “cyl”: Mean relative difference: 0.4912281" # [3] "Component “disp”: Mean relative difference: 0.5690846" # [4] "Component “hp”: Mean relative difference: 0.5386953" # [5] "Component “drat”: Mean relative difference: 0.1387415" # [6] "Component “wt”: Mean relative difference: 0.3286861" # [7] "Component “qsec”: Mean relative difference: 0.1222072" # [8] "Component “vs”: Mean relative difference: 2" # [9] "Component “am”: Mean relative difference: 2" # [10] "Component “gear”: Mean relative difference: 0.32" # [11] "Component “carb”: Mean relative difference: 0.8" # 默认情况下all.equal对变量的差异很敏感 df1 <- tibble(x = "a") df2 <- tibble(x = factor("a")) all.equal(df1, df2) # [1] "Incompatible type for column x: x character, y factor" all.equal(df1, df2,convert = TRUE) # [1] "Factor levels not equal for column x" # Warning message: # Incompatible type for column x: x character, y factor
glimpse(x, width = NULL, ...) x glimpse的对象 width 输出宽度：默认为tibble.width设定的宽度（如果有限）或者是控制台显示的宽度 glimpse(mtcars) # Observations: 32 # Variables: 11 # $ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17... # $ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4,... # $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8,... # $ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, ... # $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.... # $ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150,... # $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90,... # $ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,... # $ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,... # $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3,... # $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1,... if (!requireNamespace("nycflights13", quietly = TRUE)) stop("Please install the nycflights13 package to run the rest of this example") # install.packages("nycflights13") glimpse(nycflights13::flights) # Observations: 336,776 # Variables: 19 # $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013... # $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1... # $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1... # $ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 55... # $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 60... # $ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2,... # $ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 8... # $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 8... # $ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7,... # $ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6"... # $ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301... # $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N... # $ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LG... # $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IA... # $ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149... # $ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 73... # $ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6... # $ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59... # $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-0...
将元向量或者列表转为两列的数据框，如果元向量没有命名，使用自然序列命名列。
enframe(x, name = "name", value = "value") x 元向量 name,value 两列命名，默认分别为name和value enframe(1:3) # # A tibble: 3 × 2 # name value # <int> <int> # 1 1 1 # 2 2 2 # 3 3 3 enframe(c(a = 5, b = 7)) # # A tibble: 2 × 2 # name value # <chr> <dbl> # 1 a 5 # 2 b 7
print(x,n)打印数据集x的前n行，默认为10行，有点像head()
描述矩阵的工具
"print"(x, ..., n = NULL, width = NULL, n_extra = NULL) trunc_mat(x, n = NULL, width = NULL, n_extra = NULL) x 展示的对象 n 要显示的行，如果为NULL(默认)并且行数小于tibble.print_max设定的值则会打印所有的行，否则会打印tibble.print_max设定的函数 width 生成的文本的宽度默认为NULL，此种情况下和使用getOption("tibble.width")或者getOption("width")设定值；后者只显示适应屏幕的列。也可以设定options(tibble.width = Inf)来显示所有的列 n_extra 整个tibble的宽度太小而打印的额外的信息，默认为NULL，会打印tibble.max_extra_cols作为额外的列信息
trunc_mat(mtcars) # # data.frame [32 × 11] # mpg cyl disp hp drat wt qsec vs am gear carb # * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 # 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 # 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 # 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 # 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 # 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 # 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 # 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 # 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 # 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 # ... with 22 more rows print(as_tibble(mtcars)) # # A tibble: 32 × 11 # mpg cyl disp hp drat wt qsec vs am gear carb # * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 # 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 # 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 # 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 # 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 # 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 # 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 # 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 # 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 # 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 # ... with 22 more rows print(as_tibble(mtcars), n = 1) # # A tibble: 32 × 11 # mpg cyl disp hp drat wt qsec vs am gear carb # * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 21 6 160 110 3.9 2.62 16.46 0 1 4 4 # # ... with 31 more rows print(as_tibble(mtcars), n = 3) # # A tibble: 32 × 11 # mpg cyl disp hp drat wt qsec vs am gear carb # * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 # 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 # 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 # # ... with 29 more rows print(as_tibble(mtcars), n = 100) # 全部打印 if (!requireNamespace("nycflights13", quietly = TRUE)) stop("Please install the nycflights13 package to run the rest of this example") print(nycflights13::flights, n_extra = 2) print(nycflights13::flights, width = Inf)
is.tibble(x) is_tibble(x)
参考链接：http://www.rdocumentation.org/packages/tibble/versions/1.2
本文链接：http://www.cnblogs.com/homewch/p/5827928.html
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原文地址：http://www.cnblogs.com/homewch/p/5827928.html
