Assalamualaikum warahmatullahi wabarakatuh, Robbi Zidni Ilma Warzuqni Fahma, Aamiin Semoga Allah senantiasa memberkahi kita, dan menganugerahkan kepada kita, Ilmu yang bermanfaat. Dalam modul ini, teman-teman akan belajar Data Frame pada R.
Berbeda dengan matriks yang elemen-elemennya harus memiliki type data yang sama, data frame dapat berisikan elemen-elemen dengan type data yang berbeda. Dalam kehidupan sehari-hari kita akan lebih banyak menggunakan data frame daripada matriks. Data frame lebih relevan dengan banyak data yang ada.
Membuat DataFrame
nama <- c("Ana","Banu", "Cici", "Dido", "Erik")
tahun <- c(1992,1995,1993,1999,1994)
lahir <- data.frame(nama, tahun)
lahir## nama tahun
## 1 Ana 1992
## 2 Banu 1995
## 3 Cici 1993
## 4 Dido 1999
## 5 Erik 1994Memanggil DataFrame Bawaan
mtcars ## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2#Melihat profil Data Frame
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...#Statistik Data Numerik pada Data Frame
summary(mtcars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000#Mendefinisikan Data Frame df
df<-mtcars
print(df)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2#Mengakses kolom dengan nama kolom
pilih_kolom<-df$mpg
print(pilih_kolom)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4#Mengakses kolom dengan indeks baris
pilih_baris<-df[1:5]
print(pilih_baris)## mpg cyl disp hp drat
## Mazda RX4 21.0 6 160.0 110 3.90
## Mazda RX4 Wag 21.0 6 160.0 110 3.90
## Datsun 710 22.8 4 108.0 93 3.85
## Hornet 4 Drive 21.4 6 258.0 110 3.08
## Hornet Sportabout 18.7 8 360.0 175 3.15
## Valiant 18.1 6 225.0 105 2.76
## Duster 360 14.3 8 360.0 245 3.21
## Merc 240D 24.4 4 146.7 62 3.69
## Merc 230 22.8 4 140.8 95 3.92
## Merc 280 19.2 6 167.6 123 3.92
## Merc 280C 17.8 6 167.6 123 3.92
## Merc 450SE 16.4 8 275.8 180 3.07
## Merc 450SL 17.3 8 275.8 180 3.07
## Merc 450SLC 15.2 8 275.8 180 3.07
## Cadillac Fleetwood 10.4 8 472.0 205 2.93
## Lincoln Continental 10.4 8 460.0 215 3.00
## Chrysler Imperial 14.7 8 440.0 230 3.23
## Fiat 128 32.4 4 78.7 66 4.08
## Honda Civic 30.4 4 75.7 52 4.93
## Toyota Corolla 33.9 4 71.1 65 4.22
## Toyota Corona 21.5 4 120.1 97 3.70
## Dodge Challenger 15.5 8 318.0 150 2.76
## AMC Javelin 15.2 8 304.0 150 3.15
## Camaro Z28 13.3 8 350.0 245 3.73
## Pontiac Firebird 19.2 8 400.0 175 3.08
## Fiat X1-9 27.3 4 79.0 66 4.08
## Porsche 914-2 26.0 4 120.3 91 4.43
## Lotus Europa 30.4 4 95.1 113 3.77
## Ford Pantera L 15.8 8 351.0 264 4.22
## Ferrari Dino 19.7 6 145.0 175 3.62
## Maserati Bora 15.0 8 301.0 335 3.54
## Volvo 142E 21.4 4 121.0 109 4.11#Mengakses kolom dengan indeks baris dan kolom
pilih_df<-df[1:5, 2:3]
print(pilih_df)## cyl disp
## Mazda RX4 6 160
## Mazda RX4 Wag 6 160
## Datsun 710 4 108
## Hornet 4 Drive 6 258
## Hornet Sportabout 8 360#Mengakses data dengan kriteria tertentu [misal cyl=6] (tunggal)
pilih_df1<-df[df[, "cyl"]==6,]
print(pilih_df1)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6#Mengakses data dengan kriteria tertentu [misal cyl=6 dan disp=160.0] (lebih dari satu)
pilih_df2<-df[df[, "cyl"]==6 & df[, "disp"]==160.0,]
print(pilih_df2)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4#Mengakses data dengan kriteria tertentu dan untukkolom tertentu
pilih_df3<-df[df[, "cyl"]==6 & df[, "disp"]==160.0, "wt"]
print(pilih_df3)## [1] 2.620 2.875Menambah Kolom Baru
# Membuat Data Frame.
emp.data <- data.frame(
emp_id = c (1:5),
emp_name = c("Ricki","Daan","Megi","Ryan","Budi"),
salary = c(623.3,515.2,611.0,729.0,843.25),
start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
"2015-03-27")),
stringsAsFactors = FALSE
)
# Menambah kolom Departemen.
emp.data$dept <- c("IT","Operations","IT","HR","Finance")
v <- emp.data
print(v)## emp_id emp_name salary start_date dept
## 1 1 Ricki 623.30 2012-01-01 IT
## 2 2 Daan 515.20 2013-09-23 Operations
## 3 3 Megi 611.00 2014-11-15 IT
## 4 4 Ryan 729.00 2014-05-11 HR
## 5 5 Budi 843.25 2015-03-27 FinanceMenambah Baris Baru
# Membuat Data Frame.
emp.data <- data.frame(
emp_id = c (1:5),
emp_name = c("Ricki","Daan","Megi","Ryan","Budi"),
salary = c(623.3,515.2,611.0,729.0,843.25),
start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
"2015-03-27")),
dept = c("IT","Operations","IT","HR","Finance"),
stringsAsFactors = FALSE
)
# Membuat Data Frame kedua
emp.newdata <- data.frame(
emp_id = c (6:8),
emp_name = c("Rasmi","Pranab","Tusar"),
salary = c(578.0,722.5,632.8),
start_date = as.Date(c("2013-05-21","2013-07-30","2014-06-17")),
dept = c("IT","Operations","Finance"),
stringsAsFactors = FALSE
)
# Menambah baris Data.
emp.finaldata <- rbind(emp.data,emp.newdata)
print(emp.finaldata)## emp_id emp_name salary start_date dept
## 1 1 Ricki 623.30 2012-01-01 IT
## 2 2 Daan 515.20 2013-09-23 Operations
## 3 3 Megi 611.00 2014-11-15 IT
## 4 4 Ryan 729.00 2014-05-11 HR
## 5 5 Budi 843.25 2015-03-27 Finance
## 6 6 Rasmi 578.00 2013-05-21 IT
## 7 7 Pranab 722.50 2013-07-30 Operations
## 8 8 Tusar 632.80 2014-06-17 FinanceMengurutkan Isi Data Frame
# Membuat Data Frame.
df<-mtcars
print(df)
# Urutkan berdasar kolom mpg
newdata1 <- df[order(mpg),]
print(newdata1)
# Urutkan berdasar kolom mpg and cyl
newdata2 <- df[order(mpg, cyl),]
print(newdata2)
#Urutkan berdasar kolom mpg (menaik) and cyl (menurun)
newdata3 <- df[order(mpg, -cyl),]
print(newdata3)Syntax untuk menggabungkan dua Data Frame
merge(x, y, by.x = x, by.y = y)
Argumen:
-x: Data Frame Utama
-y: Data Frame yang akan digabungkan
-by.x: Kolom yang digunakan untuk menggabungkan dalam data frame x. Kolom x untuk digabungkan
-by.y: Kolom yang digunakan untuk menggabungkan dalam data frame y. Kolom y untuk digabungkanContoh
# Data Frame Utama {Producers}
producers <- data.frame(
surname = c("Spielberg","Scorsese","Hitchcock","Tarantino","Polanski"),
nationality = c("US","US","UK","US","Poland"),
stringsAsFactors=FALSE)
# Data Frame yang akan digabungkan
movies <- data.frame(
surname = c("Spielberg",
"Scorsese",
"Hitchcock",
"Hitchcock",
"Spielberg",
"Tarantino",
"Polanski"),
title = c("Super 8",
"Taxi Driver",
"Psycho",
"North by Northwest",
"Catch Me If You Can",
"Reservoir Dogs","Chinatown"),
stringsAsFactors=FALSE)
# Menggabungkan Dua Data Frame
m1 <- merge(producers, movies, by.x = "surname")
m1## surname nationality title
## 1 Hitchcock UK Psycho
## 2 Hitchcock UK North by Northwest
## 3 Polanski Poland Chinatown
## 4 Scorsese US Taxi Driver
## 5 Spielberg US Super 8
## 6 Spielberg US Catch Me If You Can
## 7 Tarantino US Reservoir Dogsdim(m1)## [1] 7 3# Mengganti nama kolom "movies"
colnames(movies)[colnames(movies) == 'surname'] <- 'name'
# Menggabungkan dengan kata kunci lainnya
m2 <- merge(producers, movies, by.x = "surname", by.y = "name")
head(m2)## surname nationality title
## 1 Hitchcock UK Psycho
## 2 Hitchcock UK North by Northwest
## 3 Polanski Poland Chinatown
## 4 Scorsese US Taxi Driver
## 5 Spielberg US Super 8
## 6 Spielberg US Catch Me If You Can# Menambah data producers
add_producer <- c('Lucas', 'US')
producers <- rbind(producers, add_producer)
# Melakukan gabungan parsial
m3 <-merge(producers, movies, by.x = "surname", by.y = "name", all.x = TRUE)
m3## surname nationality title
## 1 Hitchcock UK Psycho
## 2 Hitchcock UK North by Northwest
## 3 Lucas US <NA>
## 4 Polanski Poland Chinatown
## 5 Scorsese US Taxi Driver
## 6 Spielberg US Super 8
## 7 Spielberg US Catch Me If You Can
## 8 Tarantino US Reservoir Dogs