Tibble - tidyverse

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Get help with your R and Statistics assignments.

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Call the tidyverse package -

> library(tidyverse)

Using the built in sample dataset CO2. Let’s look at the data set -

> glimpse(CO2)

Rows: 84
Columns: 5
$ Plant     <ord> Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn2, Qn2, Qn2, Qn2, Qn...
$ Type      <fct> Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Q...
$ Treatment <fct> nonchilled, nonchilled, nonchilled, nonchilled, nonchille...
$ conc      <dbl> 95, 175, 250, 350, 500, 675, 1000, 95, 175, 250, 350, 500...
$ uptake    <dbl> 16.0, 30.4, 34.8, 37.2, 35.3, 39.2, 39.7, 13.6, 27.3, 37....

Turn the data set into a tibble -

> CO2_tibble <- as_tibble(CO2)
> print(CO2_tibble)

# A tibble: 84 x 5
   Plant Type   Treatment   conc uptake
   <ord> <fct>  <fct>      <dbl>  <dbl>
 1 Qn1   Quebec nonchilled    95   16  
 2 Qn1   Quebec nonchilled   175   30.4
 3 Qn1   Quebec nonchilled   250   34.8
 4 Qn1   Quebec nonchilled   350   37.2
 5 Qn1   Quebec nonchilled   500   35.3
 6 Qn1   Quebec nonchilled   675   39.2
 7 Qn1   Quebec nonchilled  1000   39.7
 8 Qn2   Quebec nonchilled    95   13.6
 9 Qn2   Quebec nonchilled   175   27.3
10 Qn2   Quebec nonchilled   250   37.1
# ... with 74 more rows

> print(CO2_tibble, n = 20) #shows first 20 rows

# A tibble: 84 x 5
   Plant Type   Treatment   conc uptake
   <ord> <fct>  <fct>      <dbl>  <dbl>
 1 Qn1   Quebec nonchilled    95   16  
 2 Qn1   Quebec nonchilled   175   30.4
 3 Qn1   Quebec nonchilled   250   34.8
 4 Qn1   Quebec nonchilled   350   37.2
 5 Qn1   Quebec nonchilled   500   35.3
 6 Qn1   Quebec nonchilled   675   39.2
 7 Qn1   Quebec nonchilled  1000   39.7
 8 Qn2   Quebec nonchilled    95   13.6
 9 Qn2   Quebec nonchilled   175   27.3
10 Qn2   Quebec nonchilled   250   37.1
11 Qn2   Quebec nonchilled   350   41.8
12 Qn2   Quebec nonchilled   500   40.6
13 Qn2   Quebec nonchilled   675   41.4
14 Qn2   Quebec nonchilled  1000   44.3
15 Qn3   Quebec nonchilled    95   16.2
16 Qn3   Quebec nonchilled   175   32.4
17 Qn3   Quebec nonchilled   250   40.3
18 Qn3   Quebec nonchilled   350   42.1
19 Qn3   Quebec nonchilled   500   42.9
20 Qn3   Quebec nonchilled   675   43.9
# ... with 64 more rows

Use n=Inf in print() to show all the observations or use View(CO2_tibble)

Creating Tibbles

Tibbles can be created in the same way as data frames -

> Name <- c("Sayem","Himel","Liuza","Ataur","Mohima","Joyes","Liuza","Champa")
> Gender <- as.factor(c("M","M","F","M","F","M","F","F"))
> Marks <- runif(8,40,100) %>% round() %>% as.integer()
> Stud <- tibble(Name, Gender, Marks)
> Stud

# A tibble: 8 x 3
  Name   Gender Marks
  <chr>  <fct>  <int>
1 Sayem  M         86
2 Himel  M         82
3 Liuza  F         96
4 Ataur  M         45
5 Mohima F         89
6 Joyes  M         43
7 Liuza  F         93
8 Champa F         67

So the tibble is created.

Selecting Columns or Variables

Using $
From our created data set -

> Stud$Name

[1] "Sayem"  "Himel"  "Liuza"  "Ataur"  "Mohima" "Joyes"  "Liuza"  "Champa"

Using [[]]

> Stud[["Name"]]

[1] "Sayem"  "Himel"  "Liuza"  "Ataur"  "Mohima" "Joyes"  "Liuza"  "Champa"

Both returns the same result in vector form.

Using select (returns a data frame/tibble)

> select(Stud, Name)

# A tibble: 8 x 1
  Name  
  <chr> 
1 Sayem 
2 Himel 
3 Liuza 
4 Ataur 
5 Mohima
6 Joyes 
7 Liuza 
8 Champa

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Filtering Rows or Observations

Using filter() -

> # Filtering students having marks more than 80
> filter(Stud, Marks > 80)

# A tibble: 5 x 3
  Name   Gender Marks
  <chr>  <fct>  <int>
1 Sayem  M         86
2 Himel  M         82
3 Liuza  F         96
4 Mohima F         89
5 Liuza  F         93

> # Filtering male students
> filter(Stud, Gender == "M")

# A tibble: 4 x 3
  Name  Gender Marks
  <chr> <fct>  <int>
1 Sayem M         86
2 Himel M         82
3 Ataur M         45
4 Joyes M         43

> # Multiple filtering using comma
> filter(Stud, Gender == "M", Marks > 80)

# A tibble: 2 x 3
  Name  Gender Marks
  <chr> <fct>  <int>
1 Sayem M         86
2 Himel M         82