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tibble包:高效显示表格数据的结构
1 tibble包简介
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, RStudioURL: 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 |
2 安装和使用
2.1 安装
从CRAN安装:
install.packages("tibble")
从github安装:
# install.packages("devtools")devtools::install_github("hadley/tibble")
2.2 创建tibbles对象
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 26a <- 1:5tibble(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 10tibble(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 1tibble(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 0.3881794tibble(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 rowstibble(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"
2.3 添加行和列
### 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 1df %>% 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 -1add_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 0df %>% 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 1df %>% 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
2.4 命名操作
2.4.1 rownames 行名的操作工具
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 1head(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 setosahas_rownames(mtcars)## [1] TRUEhas_rownames(iris)## [1] FALSEhas_rownames(remove_rownames(mtcars))## [1] FALSEhead(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 1mtcars_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 1df <- rownames_to_column(mtcars,"row2col")column_to_rownames(df,"row2col")
2.4.2 has_name 检查数据框或者其他对象中是否存在指定命名元素,返回逻辑值(TRUE or FALSE)
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] TRUEhas_name(mtcars, "gears")## [1] FALSE
2.4.3 repair_names 修复对象的名称(如果没有命名则用V+i代替)
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] 5repair_names(list(3, 4, 5)) # works for lists, too# $V1# [1] 3# # $V2# [1] 4# # $V3# [1] 5tbl <- 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
2.5 其他函数
2.5.1 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
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"
2.5.2 all_equal 数据框柔性比较,忽略行和列的排列顺序
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TRUEignore_row_order 是否需要忽略行顺序,默认为TRUEconvert 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] TRUEall.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
2.5.3 glimpse 有点像str(),主要是查看数据集的结构
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...
2.5.4 enframe 将向量变为数据框
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 = http://www.mamicode.com/"value")x 元向量name,value 两列命名,默认分别为name和valueenframe(1:3)# # A tibble: 3 × 2# name value# <int> <int># 1 1 1# 2 2 2# 3 3 3enframe(c(a = 5, b = 7))# # A tibble: 2 × 2# name value# <chr> <dbl># 1 a 5# 2 b 7
2.5.5 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()
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 rowsprint(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 rowsprint(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 rowsprint(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 rowsprint(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)
2.5.6 is.tibble 检测对象是否为tibble
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
tibble包:高效显示表格数据的结构