How to check the data frame structure without using str function in R?

R ProgrammingServer Side ProgrammingProgramming

To check the data frame structure without using str function in R, we can follow the below steps −

  • First of all, load the data or create new data or use an in-built data set.
  • Then, use glimpse function of tibble package.

Example 1

Use in-built data set

Consider the mtcars data set, load the tibble package and use glimpse function to look at the structure of mtcars data −

library(tibble)
glimpse(mtcars)

Output

Rows: 32
Columns: 11
$ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,~
$ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,~
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16~
$ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180~
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,~
$ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.~
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18~
$ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,~
$ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,~
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,~
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,~

Example 2

Use in-built data set

Consider the CO2 data set, load the tibble package and use glimpse function to look at the structure of CO2 data −

library(tibble)
glimpse(CO2)

Output

Rows: 84
Columns: 5
$ Plant     <ord> Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn2, Qn2, Qn2, Qn2, Qn2, ~
$ Type      <fct> Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Queb~
$ Treatment <fct> nonchilled, nonchilled, nonchilled, nonchilled, nonchilled, ~
$ conc      <dbl> 95, 175, 250, 350, 500, 675, 1000, 95, 175, 250, 350, 500, 6~
$ uptake    <dbl> 16.0, 30.4, 34.8, 37.2, 35.3, 39.2, 39.7, 13.6, 27.3, 37.1, ~

Example 3

Use in-built data set

Consider the iris data set, load the tibble package and use glimpse function to look at the structure of iris data −

library(tibble)
glimpse((iris)

Output

Rows: 150
Columns: 5
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
$ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
$ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
$ Species      <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s~

Example 4

Use in-built data set

Consider the sleep data set, load the tibble package and use glimpse function to look at the structure of sleep data −

library(tibble)
glimpse(sleep)

Output

Rows: 20
Columns: 3
$ extra <dbl> 0.7, -1.6, -0.2, -1.2, -0.1, 3.4, 3.7, 0.8, 0.0, 2.0, 1.9, 0.8, ~
$ group <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
$ ID    <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

Example 5

Use in-built data set

Consider the ChickWeight data set, load the tibble package and use glimpse function to look at the structure of ChickWeight data −

library(tibble)
glimpse((ChickWeight)

Output

Rows: 578
Columns: 4
$ weight <dbl> 42, 51, 59, 64, 76, 93, 106, 125, 149, 171, 199, 205, 40, 49, 5~
$ Time   <dbl> 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 21, 0, 2, 4, 6, 8, 10, 1~
$ Chick  <ord> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, ~
$ Diet   <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~

Example 6

Use in-built data set

Consider the DNase data set, load the tibble package and use glimpse function to look at the structure of DNase data −

library(tibble)
glimpse((DNase)

Output

Rows: 176
Columns: 3
$ Run     <ord> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,~
$ conc    <dbl> 0.04882812, 0.04882812, 0.19531250, 0.19531250, 0.39062500, 0.~
$ density <dbl> 0.017, 0.018, 0.121, 0.124, 0.206, 0.215, 0.377, 0.374, 0.614,~

Example 7

Use in-built data set

Consider the Nile data set, load the tibble package and use glimpse function to look at the structure of Nile data −

library(tibble)
glimpse(Nile)

Output

Time-Series [1:100] from 1871 to 1970: 1120 1160 963 1210 1160 1160 813 1230 1370
1140 …

Example 8

Use in-built data set

Consider the HairEyeColor data set, load the tibble package and use glimpse function to look at the structure of HairEyeColor data −

library(tibble)
glimpse(HairEyeColor)

Output

‘table’ num [1:4, 1:4, 1:2] 32 53 10 3 11 50 10 30 10 25 …
- attr(*, “dimnames”)=List of 3
..$ Hair: chr [1:4] “Black” “Brown” “Red” “Blond”
..$ Eye : chr [1:4] “Brown” “Blue” “Hazel” “Green”
..$ Sex : chr [1:2] “Male” “Female”

Example 9

Use in-built data set

Consider the Indometh data set, load the tibble package and use glimpse function to look at the structure of Indometh data −

library(tibble)
glimpse(Indometh)

Output

Rows: 66
Columns: 3
$ Subject <ord> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,~
$ time    <dbl> 0.25, 0.50, 0.75, 1.00, 1.25, 2.00, 3.00, 4.00, 5.00, 6.00, 8.~
$ conc    <dbl> 1.50, 0.94, 0.78, 0.48, 0.37, 0.19, 0.12, 0.11, 0.08, 0.07, 0.~

Example 10

Use in-built data set

Consider the AirPassengers data set, load the tibble package and use glimpse function to look at the structure of AirPassengers data −

library(tibble)
glimpse(AirPassengers)

Output

Time-Series [1:144] from 1949 to 1961: 112 118 132 129 121 135 148 148 136 119 ...
raja
Published on 11-Aug-2021 07:10:32
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