library(tidyverse)

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## ✔ dplyr     1.2.1     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
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## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tibble)

tibble_karyawan <- tibble(
  `nama karyawan` = c("Andi", "Budi", "Cici"),
  gaji = c(5000000, 6000000, 5500000),
  tunjangan = list(c(100, 200), 150, c(150, 250, 50))
)

tibble_karyawan

## # A tibble: 3 × 3
##   `nama karyawan`    gaji tunjangan
##   <chr>             <dbl> <list>   
## 1 Andi            5000000 <dbl [2]>
## 2 Budi            6000000 <dbl [1]>
## 3 Cici            5500000 <dbl [3]>

Memanggil Isi Tibble

tibble_karyawan$tunjangan[[3]]

## [1] 150 250  50

Data Flights dengan Tibble

# install.packages("nycflights13")
library(nycflights13)

flights

## # A tibble: 336,776 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
##  1  2013     1     1      517            515         2      830            819
##  2  2013     1     1      533            529         4      850            830
##  3  2013     1     1      542            540         2      923            850
##  4  2013     1     1      544            545        -1     1004           1022
##  5  2013     1     1      554            600        -6      812            837
##  6  2013     1     1      554            558        -4      740            728
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # ℹ 336,766 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>

Merubah Tibble Menjadi Data Frame

df_flights <- as.data.frame(flights)

head(df_flights)

##   year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013     1   1      517            515         2      830            819
## 2 2013     1   1      533            529         4      850            830
## 3 2013     1   1      542            540         2      923            850
## 4 2013     1   1      544            545        -1     1004           1022
## 5 2013     1   1      554            600        -6      812            837
## 6 2013     1   1      554            558        -4      740            728
##   arr_delay carrier flight tailnum origin dest air_time distance hour minute
## 1        11      UA   1545  N14228    EWR  IAH      227     1400    5     15
## 2        20      UA   1714  N24211    LGA  IAH      227     1416    5     29
## 3        33      AA   1141  N619AA    JFK  MIA      160     1089    5     40
## 4       -18      B6    725  N804JB    JFK  BQN      183     1576    5     45
## 5       -25      DL    461  N668DN    LGA  ATL      116      762    6      0
## 6        12      UA   1696  N39463    EWR  ORD      150      719    5     58
##             time_hour
## 1 2013-01-01 05:00:00
## 2 2013-01-01 05:00:00
## 3 2013-01-01 05:00:00
## 4 2013-01-01 05:00:00
## 5 2013-01-01 06:00:00
## 6 2013-01-01 05:00:00

Memanggil Nama Kolom Secara Parsial

head(df_flights$y)

## [1] 2013 2013 2013 2013 2013 2013

Merubah Data Frame Menjadi Tibble

df_people <- data.frame(
  id = c(1, 2, 3, 4, 5),
  name = c("Adam", "Eva", "Miki", "Yola", "Jack"),
  age = c(46, 48, 21, 19, 17),
  gender = c("male", rep("female", 3), "male"),
  drives = c(TRUE, TRUE, FALSE, TRUE, FALSE)
)

tibble_people <- as_tibble(df_people)

tibble_people

## # A tibble: 5 × 5
##      id name    age gender drives
##   <dbl> <chr> <dbl> <chr>  <lgl> 
## 1     1 Adam     46 male   TRUE  
## 2     2 Eva      48 female TRUE  
## 3     3 Miki     21 female FALSE 
## 4     4 Yola     19 female TRUE  
## 5     5 Jack     17 male   FALSE

Melihat Summary

summary(tibble_people)

##        id        name                age          gender         
##  Min.   :1   Length:5           Min.   :17.0   Length:5          
##  1st Qu.:2   Class :character   1st Qu.:19.0   Class :character  
##  Median :3   Mode  :character   Median :21.0   Mode  :character  
##  Mean   :3                      Mean   :30.2                     
##  3rd Qu.:4                      3rd Qu.:46.0                     
##  Max.   :5                      Max.   :48.0                     
##    drives       
##  Mode :logical  
##  FALSE:2        
##  TRUE :3        
##                 
##                 
##

Melihat Tipe Variabel

glimpse(tibble_people)

## Rows: 5
## Columns: 5
## $ id     <dbl> 1, 2, 3, 4, 5
## $ name   <chr> "Adam", "Eva", "Miki", "Yola", "Jack"
## $ age    <dbl> 46, 48, 21, 19, 17
## $ gender <chr> "male", "female", "female", "female", "male"
## $ drives <lgl> TRUE, TRUE, FALSE, TRUE, FALSE

Merubah Gender Menjadi Factor

tibble_people$gender <- factor(tibble_people$gender)

summary(tibble_people)

##        id        name                age          gender    drives       
##  Min.   :1   Length:5           Min.   :17.0   female:3   Mode :logical  
##  1st Qu.:2   Class :character   1st Qu.:19.0   male  :2   FALSE:2        
##  Median :3   Mode  :character   Median :21.0              TRUE :3        
##  Mean   :3                      Mean   :30.2                             
##  3rd Qu.:4                      3rd Qu.:46.0                             
##  Max.   :5                      Max.   :48.0

Memanggil Kolom pada Tibble

tibble_people[,2]

## # A tibble: 5 × 1
##   name 
##   <chr>
## 1 Adam 
## 2 Eva  
## 3 Miki 
## 4 Yola 
## 5 Jack

Menyaring Baris pada Tibble

tibble_people[tibble_people$age < 30, ]

## # A tibble: 3 × 5
##      id name    age gender drives
##   <dbl> <chr> <dbl> <fct>  <lgl> 
## 1     3 Miki     21 female FALSE 
## 2     4 Yola     19 female TRUE  
## 3     5 Jack     17 male   FALSE

Readr

Package readr digunakan untuk membaca file data.

library(readr)

Membaca File CSV

data_csv <- read_csv("D:/r/iris_csv.csv")

## Rows: 150 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Species
## dbl (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data_csv

## # A tibble: 150 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            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           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           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 
## # ℹ 140 more rows

Merubah CSV Menjadi Tibble

data_tibble <- as_tibble(data_csv)

data_tibble

## # A tibble: 150 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            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           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           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 
## # ℹ 140 more rows

Melihat Summary Data CSV

summary(data_tibble)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##    Species         
##  Length:150        
##  Class :character  
##  Mode  :character  
##                    
##                    
##

Melihat Struktur Data CSV

glimpse(data_tibble)

## 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      <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa…

Mengubah Species Menjadi Factor

data_tibble$Species <- factor(data_tibble$Species)

summary(data_tibble)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

Memanggil Kolom Species

data_tibble$Species

##   [1] setosa     setosa     setosa     setosa     setosa     setosa    
##   [7] setosa     setosa     setosa     setosa     setosa     setosa    
##  [13] setosa     setosa     setosa     setosa     setosa     setosa    
##  [19] setosa     setosa     setosa     setosa     setosa     setosa    
##  [25] setosa     setosa     setosa     setosa     setosa     setosa    
##  [31] setosa     setosa     setosa     setosa     setosa     setosa    
##  [37] setosa     setosa     setosa     setosa     setosa     setosa    
##  [43] setosa     setosa     setosa     setosa     setosa     setosa    
##  [49] setosa     setosa     versicolor versicolor versicolor versicolor
##  [55] versicolor versicolor versicolor versicolor versicolor versicolor
##  [61] versicolor versicolor versicolor versicolor versicolor versicolor
##  [67] versicolor versicolor versicolor versicolor versicolor versicolor
##  [73] versicolor versicolor versicolor versicolor versicolor versicolor
##  [79] versicolor versicolor versicolor versicolor versicolor versicolor
##  [85] versicolor versicolor versicolor versicolor versicolor versicolor
##  [91] versicolor versicolor versicolor versicolor versicolor versicolor
##  [97] versicolor versicolor versicolor versicolor virginica  virginica 
## [103] virginica  virginica  virginica  virginica  virginica  virginica 
## [109] virginica  virginica  virginica  virginica  virginica  virginica 
## [115] virginica  virginica  virginica  virginica  virginica  virginica 
## [121] virginica  virginica  virginica  virginica  virginica  virginica 
## [127] virginica  virginica  virginica  virginica  virginica  virginica 
## [133] virginica  virginica  virginica  virginica  virginica  virginica 
## [139] virginica  virginica  virginica  virginica  virginica  virginica 
## [145] virginica  virginica  virginica  virginica  virginica  virginica 
## Levels: setosa versicolor virginica

Filter Data

filter_data <- data_tibble %>%
  filter(Sepal.Length > 6)

filter_data

## # A tibble: 61 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>     
##  1          7           3.2          4.7         1.4 versicolor
##  2          6.4         3.2          4.5         1.5 versicolor
##  3          6.9         3.1          4.9         1.5 versicolor
##  4          6.5         2.8          4.6         1.5 versicolor
##  5          6.3         3.3          4.7         1.6 versicolor
##  6          6.6         2.9          4.6         1.3 versicolor
##  7          6.1         2.9          4.7         1.4 versicolor
##  8          6.7         3.1          4.4         1.4 versicolor
##  9          6.2         2.2          4.5         1.5 versicolor
## 10          6.1         2.8          4           1.3 versicolor
## # ℹ 51 more rows

Menampilkan Nama Kolom

names(data_tibble)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

Menampilkan Data di Viewer

View(data_tibble)

Manajemen Data dengan Tidyverse

ADITYA PEBRIAN

2026-05-08

Memanggil Isi Tibble

Data Flights dengan Tibble

Merubah Tibble Menjadi Data Frame

Memanggil Nama Kolom Secara Parsial

Merubah Data Frame Menjadi Tibble

Melihat Summary

Melihat Tipe Variabel

Merubah Gender Menjadi Factor

Memanggil Kolom pada Tibble

Menyaring Baris pada Tibble

Readr

Membaca File CSV

Merubah CSV Menjadi Tibble

Melihat Summary Data CSV

Melihat Struktur Data CSV

Mengubah Species Menjadi Factor

Memanggil Kolom Species

Filter Data

Menampilkan Nama Kolom

Menampilkan Data di Viewer