Nhiệm vụ 2

1 Nhiệm vụ 2.1

1.1 Tóm tắt

Thao tác một số lệnh cơ bản và rút trích dữ liệu trên file excel “bwt_co_dieu_chinh.xlsx”, file này phân tích Trọng lượng lúc sinh của phụ nữ từ năm 1925 - 2004 thông qua các tiêu chí

• id: Khu vực (có 8 khu vực)

• year: Năm lấy số liệu

• bwt: Trọng lượng lúc sinh (tính bằng ounces, 1 ounce = 28.3495 gram)

• gestation: Thời gian mang thai (tính bằng ngày)

• parity: Số lần sinh (có hai giá trị, 1 = sinh lần đầu, 0 = không phải lần đầu)

• age: Tuổi của mẹ

• height: Chiều cao của mẹ (tính bằng inches, 1 inch = 2.54 cm)

• weight: Cân nặng của bà mẹ khi mang thai (tính bằng pounds, 1 pound = 0.453592 kg)

• smoke: Hút thuốc lúc mang thai (1 = có, 0 = không)

File này có 9 cột tưởng đương với 9 biến

Có 640 hàng tương đương với 640 quan sát

1.2 Đọc dữ liệu từ file excel

#Để vừa chạy dữ liệu, vừa hiển thị chữ ta nhấn tổ hợp phím Ctrl+Alt+I sau đó nhập {r message=TRUE, warning=FALSE}

#Load gói xlsx bằng câu lệnh
library(xlsx)

#Gán dữ liệu của file excel vào object bwt, sau đó đọc dữ liệu từ file excel bằng câu lệnh
bwt <- read.xlsx(file.choose(), sheetIndex = 1, header = T) #SheetIndex = 1 là lấy dữ liệu từ sheet 1 của file excel, header = T là lấy dữ liệu từ dòng đầu tiên của file excel 

1.3 Thông tin tổng quan

#Kiểm tra bwt có phải là khung dữ liệu hay không bằng câu lệnh
is.data.frame(bwt) #True chứng tỏ bwt là khung dữ liệu, False thì ngược lại

## [1] TRUE

#Hiển thị số cột của bwt bằng câu lệnh
length(bwt)

## [1] 9

#Hiển thị tên các cột của bwt bằng câu lệnh 
names(bwt)

## [1] "id"        "year"      "bwt"       "gestation" "parity"    "age"      
## [7] "height"    "weight"    "smoke"

#Hiển thị số hàng và số cột của bwt bằng câu lệnh
dim(bwt)

## [1] 640   9

#Hiển thị 10 dòng đầu tiên của bwt bằng câu lệnh 
head(bwt, 10)

##    id year bwt gestation parity age height weight smoke
## 1   1 1925 120       284      0  27     62    100     0
## 2   2 1925 112       267      1  22     62    138     0
## 3   3 1925 119       286      0  26     64    123     1
## 4   4 1925 124       287      0  27     62    105     1
## 5   5 1925 105       276      0  22     67    130     0
## 6   6 1925 120       289      1  31     59    102     0
## 7   7 1925  82       274      0  31     64    101     1
## 8   8 1925 111       278      0  29     65    145     1
## 9   1 1926 113       282      0  33     64    135     0
## 10  2 1926 134       297      0  27     67    170     1

#Hiển thị 10 dòng cuối cùng của bwt bằng câu lệnh 
tail(bwt, 10)

##     id year bwt gestation parity age height weight smoke
## 631  7 2003 100       264      0  29     64    120     1
## 632  8 2003 123       277      0  24     66    122     0
## 633  1 2004 139       292      0  25     68    135     0
## 634  2 2004 112       275      1  21     68    143     1
## 635  3 2004 114       289      0  36     60    115     0
## 636  4 2004 110       277      0  25     61    130     0
## 637  5 2004 120       271      1  17     64    142     1
## 638  6 2004 110       280      0  29     62    110     1
## 639  7 2004 160       271      0  32     67    215     0
## 640  8 2004 100       281      0  24     61    115     0

#Hiển thị cấu trúc của bwt bằng câu lệnh 
str(bwt)

## 'data.frame':    640 obs. of  9 variables:
##  $ id       : num  1 2 3 4 5 6 7 8 1 2 ...
##  $ year     : num  1925 1925 1925 1925 1925 ...
##  $ bwt      : num  120 112 119 124 105 120 82 111 113 134 ...
##  $ gestation: num  284 267 286 287 276 289 274 278 282 297 ...
##  $ parity   : num  0 1 0 0 0 1 0 0 0 0 ...
##  $ age      : num  27 22 26 27 22 31 31 29 33 27 ...
##  $ height   : num  62 62 64 62 67 59 64 65 64 67 ...
##  $ weight   : num  100 138 123 105 130 102 101 145 135 170 ...
##  $ smoke    : num  0 0 1 1 0 0 1 1 0 1 ...

#Kiểm tra xem bwt có chứa ô trống nào hay không bằng câu lệnh 
is.na(bwt) #True là ô trống, False là ô có dữ liệu 

##           id  year   bwt gestation parity   age height weight smoke
##   [1,] FALSE FALSE FALSE     FALSE  FALSE FALSE  FALSE  FALSE FALSE
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#Tính tổng số ô trống của bwt bằng câu lệnh 
sum(is.na(bwt))

## [1] 0

#Xác định vị trí các ô trống của bwt bằng câu lệnh
which(is.na(bwt))

## integer(0)

1.4 Thống kê mô tả

Để thống kê mô tả dữ liệu từ file excel, ta thực hiện câu lệnh

library(skimr)
skim(bwt)

Data summary

Name	bwt
Number of rows	640
Number of columns	9
_______________________
Column type frequency:
numeric	9
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
id	0	1	4.50	2.29	1	2.75	4.5	6.25	8	▇▃▇▃▇
year	0	1	1964.50	23.11	1925	1944.75	1964.5	1984.25	2004	▇▇▇▇▇
bwt	0	1	118.89	18.14	55	107.75	120.0	131.00	169	▁▂▇▆▁
gestation	0	1	279.33	15.80	181	273.00	280.0	288.00	351	▁▁▇▅▁
parity	0	1	0.31	0.46	0	0.00	0.0	1.00	1	▇▁▁▁▃
age	0	1	27.28	5.86	15	23.00	26.0	31.00	45	▃▇▅▂▁
height	0	1	64.10	2.50	53	62.00	64.0	66.00	71	▁▁▅▇▁
weight	0	1	128.22	19.49	87	115.00	126.0	137.00	217	▃▇▃▁▁
smoke	0	1	0.40	0.49	0	0.00	0.0	1.00	1	▇▁▁▁▆

Giải thích ý nghĩa:

• n_missing: số ô dữ liệu bị miss(trống)

• complete_rate: tỷ lệ ô có dữ liệu

• mean: trung bình

• sd: độ lệch chuẩn

• p0: giá trị nhỏ nhất

• p25: Phân vị thứ nhất

• p50: Phân vị thứ hai cũng chính là trung vị

• p75: phân vị thứ ba

• p100: giá trị lớn nhất

• hist: biểu đồ Histogram

1.5 Rút trích dữ liệu

Để rút trích dữ liệu ta có thể thực hiện các câu lệnh sau

#Đổi tên các biến của bwt bằng câu lệnh 
names(bwt) <- c('I','Y','B','G','P','A','H','W','S')
names(bwt)

## [1] "I" "Y" "B" "G" "P" "A" "H" "W" "S"

#Gán a để hiển thị giá trị của hàng 5 và cột 3 của bwt bằng câu lệnh
a <- bwt[5,3]
str(a)

##  num 105

#Gán H để hiển thị toàn bộ giá trị của cột height của bwt bằng câu lệnh
H <- bwt$H
str(H)

##  num [1:640] 62 62 64 62 67 59 64 65 64 67 ...

#Gán b để hiển thị toàn bộ giá trị của cột year và tất cả các hàng của bwt bằng câu lệnh 
b <- bwt[ ,2]
str(b)

##  num [1:640] 1925 1925 1925 1925 1925 ...

#Gán c để hiển thị giá trị của hàng 4 và tất cả các cột của bwt bằng câu lệnh
c <- bwt[4, ]
str(c)

## 'data.frame':    1 obs. of  9 variables:
##  $ I: num 4
##  $ Y: num 1925
##  $ B: num 124
##  $ G: num 287
##  $ P: num 0
##  $ A: num 27
##  $ H: num 62
##  $ W: num 105
##  $ S: num 1

#Gán bwt1 để hiển thị giá trị của cột id và cột bwt và tất cả các hàng của bwt bằng câu lệnh 
bwt1 <- bwt[ ,c(1,3)]
str(bwt1)

## 'data.frame':    640 obs. of  2 variables:
##  $ I: num  1 2 3 4 5 6 7 8 1 2 ...
##  $ B: num  120 112 119 124 105 120 82 111 113 134 ...

#Gán bwt2 để hiển thị giá trị từ hàng 3 đến hàng 9 và tất cả các cột của bwt bằng câu lệnh 
bwt2 <- bwt[3:9, ]
str(bwt2)

## 'data.frame':    7 obs. of  9 variables:
##  $ I: num  3 4 5 6 7 8 1
##  $ Y: num  1925 1925 1925 1925 1925 ...
##  $ B: num  119 124 105 120 82 111 113
##  $ G: num  286 287 276 289 274 278 282
##  $ P: num  0 0 0 1 0 0 0
##  $ A: num  26 27 22 31 31 29 33
##  $ H: num  64 62 67 59 64 65 64
##  $ W: num  123 105 130 102 101 145 135
##  $ S: num  1 1 0 0 1 1 0

#Gán bwt3 để hiển thị giá trị của các hàng 3,5,7,21 và tất cả các cột của bwt bằng câu lệnh 
bwt3 <- bwt[c(3,5,7,21), ] 
str(bwt3)

## 'data.frame':    4 obs. of  9 variables:
##  $ I: num  3 5 7 5
##  $ Y: num  1925 1925 1925 1927
##  $ B: num  119 105 82 122
##  $ G: num  286 276 274 281
##  $ P: num  0 0 0 0
##  $ A: num  26 22 31 42
##  $ H: num  64 67 64 63
##  $ W: num  123 130 101 103
##  $ S: num  1 0 1 1

#Gán bwt4 để hiển thị giá trị của hàng 2,3,6,18 và cột 2,3 của bwt bằng câu lệnh 
bwt4 <- bwt[c(2,3,6,18), c(2,3)]
str(bwt4)

## 'data.frame':    4 obs. of  2 variables:
##  $ Y: num  1925 1925 1925 1927
##  $ B: num  112 119 120 145

#Gán bwt5 để hiển thị giá trị các hàng height có dữ liệu >= 65 và tất cả các cột của bwt bằng câu lệnh 
bwt5  <- bwt[bwt$H >= 65, ]
str(bwt5)

## 'data.frame':    292 obs. of  9 variables:
##  $ I: num  5 8 2 3 4 5 4 1 2 3 ...
##  $ Y: num  1925 1925 1926 1926 1926 ...
##  $ B: num  105 111 134 97 125 93 110 108 116 115 ...
##  $ G: num  276 278 297 279 292 246 262 282 295 264 ...
##  $ P: num  0 0 0 0 0 0 0 0 0 1 ...
##  $ A: num  22 29 27 29 22 37 25 23 32 23 ...
##  $ H: num  67 65 67 68 65 65 66 67 65 67 ...
##  $ W: num  130 145 170 178 122 130 140 125 120 134 ...
##  $ S: num  0 1 1 1 0 0 0 1 0 1 ...

#Gán bwt6 để hiển thị giá trị của các hàng height >= 60 và <= 65 và tất cả các cột của bwt bằng câu lệnh
bwt6 <- bwt[bwt$H >= 60 & bwt$H <= 65, ]
str(bwt6)

## 'data.frame':    428 obs. of  9 variables:
##  $ I: num  1 2 3 4 7 8 1 4 5 6 ...
##  $ Y: num  1925 1925 1925 1925 1925 ...
##  $ B: num  120 112 119 124 82 111 113 125 93 146 ...
##  $ G: num  284 267 286 287 274 278 282 292 246 280 ...
##  $ P: num  0 1 0 0 0 0 0 0 0 0 ...
##  $ A: num  27 22 26 27 31 29 33 22 37 23 ...
##  $ H: num  62 62 64 62 64 65 64 65 65 61 ...
##  $ W: num  100 138 123 105 101 145 135 122 130 145 ...
##  $ S: num  0 0 1 1 1 1 0 0 0 0 ...

#Gán bwt7 để hiển thị giá trị của hàng height 66 hoặc giá trị của hàng 67 và tất cả các cột của bwt bằng câu lệnh 
bwt7 <- bwt[bwt$H == 66 | bwt$H == 67, ]
str(bwt7)

## 'data.frame':    154 obs. of  9 variables:
##  $ I: num  5 2 4 1 3 5 8 4 6 4 ...
##  $ Y: num  1925 1926 1927 1928 1928 ...
##  $ B: num  105 134 110 108 115 130 169 142 128 102 ...
##  $ G: num  276 297 262 282 264 296 296 284 292 280 ...
##  $ P: num  0 0 0 0 1 1 0 0 0 0 ...
##  $ A: num  22 27 25 23 23 22 33 39 32 38 ...
##  $ H: num  67 67 66 67 67 66 67 66 66 67 ...
##  $ W: num  130 170 140 125 134 117 185 132 130 140 ...
##  $ S: num  0 1 0 1 1 1 0 0 0 0 ...

1.6 Rút trích dữ liệu với filter và select

Để rút trích dữ liệu với filter và select ta có thể thực hiện các câu lệnh sau

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#Gán bwt8 để trích dữ liệu year >= 1925 và weight <= 150 bằng câu lệnh 
bwt8 <- filter(bwt, bwt$Y >= 1925 & bwt$W <= 150) 
str(bwt8)

## 'data.frame':    575 obs. of  9 variables:
##  $ I: num  1 2 3 4 5 6 7 8 1 4 ...
##  $ Y: num  1925 1925 1925 1925 1925 ...
##  $ B: num  120 112 119 124 105 120 82 111 113 125 ...
##  $ G: num  284 267 286 287 276 289 274 278 282 292 ...
##  $ P: num  0 1 0 0 0 1 0 0 0 0 ...
##  $ A: num  27 22 26 27 22 31 31 29 33 22 ...
##  $ H: num  62 62 64 62 67 59 64 65 64 65 ...
##  $ W: num  100 138 123 105 130 102 101 145 135 122 ...
##  $ S: num  0 0 1 1 0 0 1 1 0 0 ...

#Gán bwt9 để lấy 3 cột year, age, height 
bwt9 <- select(bwt,Y,A,H)
str(bwt9)

## 'data.frame':    640 obs. of  3 variables:
##  $ Y: num  1925 1925 1925 1925 1925 ...
##  $ A: num  27 22 26 27 22 31 31 29 33 27 ...
##  $ H: num  62 62 64 62 67 59 64 65 64 67 ...

#Gán bwt10 để trích dữ liệu age>27, weight>150 và chỉ lấy 5 cột id, year, bwt, age, weight   
bwt10 <- filter(bwt, A>27 & W>150) %>% select(I,Y,B,A,W)
str(bwt10)

## 'data.frame':    41 obs. of  5 variables:
##  $ I: num  3 8 1 2 3 3 4 5 4 1 ...
##  $ Y: num  1926 1929 1930 1933 1933 ...
##  $ B: num  97 169 138 136 89 129 127 116 119 114 ...
##  $ A: num  29 33 33 41 34 43 35 40 42 30 ...
##  $ W: num  178 185 178 191 170 160 165 159 156 154 ...

1.7 Tạo dữ liệu mới từ dữ liệu cũ

Để Tạo dữ liệu mới từ dữ liệu cũ ta có thể thực hiện các câu lệnh sau

#Gán bwt11 để thêm cột chiều cao với đơn vị là cm và cân nặng với đơn vị là kg 
bwt11 <- mutate(bwt, cm = H*2.54) %>% mutate( ,kg=W*0.453592) 
str(bwt11)

## 'data.frame':    640 obs. of  11 variables:
##  $ I : num  1 2 3 4 5 6 7 8 1 2 ...
##  $ Y : num  1925 1925 1925 1925 1925 ...
##  $ B : num  120 112 119 124 105 120 82 111 113 134 ...
##  $ G : num  284 267 286 287 276 289 274 278 282 297 ...
##  $ P : num  0 1 0 0 0 1 0 0 0 0 ...
##  $ A : num  27 22 26 27 22 31 31 29 33 27 ...
##  $ H : num  62 62 64 62 67 59 64 65 64 67 ...
##  $ W : num  100 138 123 105 130 102 101 145 135 170 ...
##  $ S : num  0 0 1 1 0 0 1 1 0 1 ...
##  $ cm: num  157 157 163 157 170 ...
##  $ kg: num  45.4 62.6 55.8 47.6 59 ...

1.8 Mã hóa dữ liệu

Để mã hóa dữ liệu ta có thể thực hiện các câu lệnh sau

#Thêm một cột xác định cân nặng có đạt tiêu chuẩn hay không bằng câu lệnh 
bwt11$cannang <- ifelse(bwt11$kg >= 58.5,'đạt tiêu chuẩn', 'không đạt tiêu chuẩn') #nếu cột kg có giá trị >= 58.5 thì cột cân nặng sẽ có giá trị là đạt tiêu chuẩn và ngược lại
str(bwt11)

## 'data.frame':    640 obs. of  12 variables:
##  $ I      : num  1 2 3 4 5 6 7 8 1 2 ...
##  $ Y      : num  1925 1925 1925 1925 1925 ...
##  $ B      : num  120 112 119 124 105 120 82 111 113 134 ...
##  $ G      : num  284 267 286 287 276 289 274 278 282 297 ...
##  $ P      : num  0 1 0 0 0 1 0 0 0 0 ...
##  $ A      : num  27 22 26 27 22 31 31 29 33 27 ...
##  $ H      : num  62 62 64 62 67 59 64 65 64 67 ...
##  $ W      : num  100 138 123 105 130 102 101 145 135 170 ...
##  $ S      : num  0 0 1 1 0 0 1 1 0 1 ...
##  $ cm     : num  157 157 163 157 170 ...
##  $ kg     : num  45.4 62.6 55.8 47.6 59 ...
##  $ cannang: chr  "không đạt tiêu chuẩn" "đạt tiêu chuẩn" "không đạt tiêu chuẩn" "không đạt tiêu chuẩn" ...

2 Nhiệm vụ 2.2

2.1 Tóm tắt

Thao tác một số lệnh cơ bản và rút trích dữ liệu trên file excel “NY-House-Dataset.xlsx”, file này phân tích các ngôi nhà được bán ở Mỹ năm 2023 bởi các mô giới thông qua các tiêu chí

• BROKERTITLE: tên của người mô giới

• TYPE: Loại căn nhà được bán

• PRICE: Giá bán

• BEDS: Số lượng giường

• BATH: Số lượng phòng tắm

• Year: Năm bán

• STREET_NAME: Tên đường

• ADDRESS: Địa chỉ

• STATE: Tiểu bang

• LOCALITY: Địa phương

File này có 10 cột tưởng đương với 10 biến

Có 1000 hàng tương đương với 1000 quan sát

2.2 Đọc dữ liệu từ file excel

#Để vừa chạy dữ liệu, vừa hiển thị chữ ta nhấn tổ hợp phím Ctrl+Alt+I sau đó nhập {r message=TRUE, warning=FALSE}

#Load gói xlsx bằng câu lệnh
library(xlsx)

#Gán dữ liệu của file excel vào object ny, sau đó đọc dữ liệu từ file excel bằng câu lệnh
ny <- read.xlsx(file.choose(), sheetIndex = 3, header = T) #SheetIndex = 3 là lấy dữ liệu từ sheet 3 của file excel, header = T là lấy dữ liệu từ dòng đầu tiên của file excel 

2.3 Thông tin tổng quan

#Kiểm tra ny có phải là khung dữ liệu hay không bằng câu lệnh
is.data.frame(ny) #True chứng tỏ ny là khung dữ liệu, False thì ngược lại

## [1] TRUE

#Hiển thị số cột của ny bằng câu lệnh
length(ny)

## [1] 10

#Hiển thị tên các cột của ny bằng câu lệnh 
names(ny)

##  [1] "BROKERTITLE" "TYPE"        "PRICE"       "BEDS"        "BATH"       
##  [6] "YEAR"        "STREET_NAME" "ADDRESS"     "STATE"       "LOCALITY"

#Hiển thị số hàng và số cột của ny bằng câu lệnh
dim(ny)

## [1] 999  10

#Hiển thị 10 dòng đầu tiên của ny bằng câu lệnh 
head(ny, 10)

##                                                                   BROKERTITLE
## 1                                 Brokered by Douglas Elliman  -111 Fifth Ave
## 2                                                         Brokered by Serhant
## 3                                                      Brokered by Sowae Corp
## 4                                                         Brokered by COMPASS
## 5  Brokered by Sotheby's International Realty - East Side Manhattan Brokerage
## 6                                                      Brokered by Sowae Corp
## 7                               Brokered by Douglas Elliman - 575 Madison Ave
## 8                                           Brokered by Connie Profaci Realty
## 9                                              Brokered by Pantiga Group Inc.
## 10                                           Brokered by CENTURY 21 MK Realty
##                  TYPE     PRICE BEDS BATH YEAR      STREET_NAME
## 1      Condo for sale    315000    2    2 2023 East 55th Street
## 2      Condo for sale 195000000    7   10 2023         New York
## 3      House for sale    260000    4    2 2023    Staten Island
## 4      Condo for sale     69000    3    1 2023         New York
## 5  Townhouse for sale  55000000    7    5 2023         New York
## 6      House for sale    690000    5    2 2023         Brooklyn
## 7      Condo for sale    899500    2    2 2023        Manhattan
## 8      House for sale  16800000    8   16 2023    Staten Island
## 9      Co-op for sale    265000    1    1 2023  Morrison Avenue
## 10     Co-op for sale    440000    2    1 2023          Midwood
##                                                               ADDRESS
## 1                                                2 E 55th St Unit 803
## 2  Central Park Tower Penthouse-217 W 57th New York St Unit Penthouse
## 3                                                    620 Sinclair Ave
## 4                                             2 E 55th St Unit 908W33
## 5                                                         5 E 64th St
## 6                                                         584 Park Pl
## 7                                              157 W 126th St Unit 1B
## 8                                                     177 Benedict Rd
## 9                                             875 Morrison Ave Apt 3M
## 10                                             1350 Ocean Pkwy Apt 5G
##                      STATE        LOCALITY
## 1       New York, NY 10022        New York
## 2       New York, NY 10019        New York
## 3  Staten Island, NY 10312        New York
## 4      Manhattan, NY 10022        New York
## 5       New York, NY 10065        New York
## 6       Brooklyn, NY 11238        New York
## 7       New York, NY 10027 New York County
## 8  Staten Island, NY 10304        New York
## 9          Bronx, NY 10473       The Bronx
## 10      Brooklyn, NY 11230    Kings County

#Hiển thị 10 dòng cuối cùng của ny bằng câu lệnh 
tail(ny, 10)

##                                     BROKERTITLE                       TYPE
## 990               Brokered by Ashford Homes LLC             House for sale
## 991           Brokered by Tiffany Moves You Inc Multi-family home for sale
## 992   Brokered by YourHomeSold Guarantee Realty Multi-family home for sale
## 993                         Brokered by Serhant         Townhouse for sale
## 994                         Brokered by Yoreevo             Co-op for sale
## 995   Brokered by Sold By Bart R E Partners Inc             Co-op for sale
## 996           Brokered by Ditommaso Real Estate             Condo for sale
## 997 Brokered by Douglas Elliman  -111 Fifth Ave             Co-op for sale
## 998    Brokered by Signature Premier Properties                    Pending
## 999                      Brokered by Exp Realty Multi-family home for sale
##       PRICE BEDS BATH YEAR     STREET_NAME                          ADDRESS
## 990 1250000    5    3 2023        Brooklyn                     1923 66th St
## 991 1275000    3    4 2023          Queens                 43-02 Orient Ave
## 992  749999    6    3 2023       The Bronx                 1817 Amethyst St
## 993 2650000    5    4 2023        Brooklyn                 259 New York Ave
## 994  999000    2    1 2023       Manhattan              139 W 85th St Apt 3
## 995  279000    2    1 2023     Kew Gardens           84-70 129th St Unit 1V
## 996  479000    2    2 2023 New Springville               57 Stone Ln Unit 2
## 997 2300000    2    2 2023        New York             242 E 7th St Unit Ph
## 998  294000    1    2 2023       Glen Oaks 26910 Grand Central Pkwy Apt 15A
## 999 1390000    6    5 2023          Queens                  104-30 219th St
##                       STATE        LOCALITY
## 990      Brooklyn, NY 11204        New York
## 991    Douglaston, NY 11363        New York
## 992         Bronx, NY 10462        New York
## 993      Brooklyn, NY 11216        New York
## 994      New York, NY 10024 New York County
## 995   Kew Gardens, NY 11415   Queens County
## 996 Staten Island, NY 10314 Richmond County
## 997      New York, NY 10009        New York
## 998   Floral Park, NY 11005   Queens County
## 999       Jamaica, NY 11429        New York

#Hiển thị cấu trúc của ny bằng câu lệnh 
str(ny)

## 'data.frame':    999 obs. of  10 variables:
##  $ BROKERTITLE: chr  "Brokered by Douglas Elliman  -111 Fifth Ave" "Brokered by Serhant" "Brokered by Sowae Corp" "Brokered by COMPASS" ...
##  $ TYPE       : chr  "Condo for sale" "Condo for sale" "House for sale" "Condo for sale" ...
##  $ PRICE      : num  3.15e+05 1.95e+08 2.60e+05 6.90e+04 5.50e+07 ...
##  $ BEDS       : num  2 7 4 3 7 5 2 8 1 2 ...
##  $ BATH       : num  2 10 2 1 5 2 2 16 1 1 ...
##  $ YEAR       : num  2023 2023 2023 2023 2023 ...
##  $ STREET_NAME: chr  "East 55th Street" "New York" "Staten Island" "New York" ...
##  $ ADDRESS    : chr  "2 E 55th St Unit 803" "Central Park Tower Penthouse-217 W 57th New York St Unit Penthouse" "620 Sinclair Ave" "2 E 55th St Unit 908W33" ...
##  $ STATE      : chr  "New York, NY 10022" "New York, NY 10019" "Staten Island, NY 10312" "Manhattan, NY 10022" ...
##  $ LOCALITY   : chr  "New York" "New York" "New York" "New York" ...

#Kiểm tra xem ny có chứa ô trống nào hay không bằng câu lệnh 
is.na(ny) #True là ô trống, False là ô có dữ liệu 

##         BROKERTITLE  TYPE PRICE  BEDS  BATH  YEAR STREET_NAME ADDRESS STATE
##    [1,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [2,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [3,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [4,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [5,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [6,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [7,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [8,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##    [9,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [10,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [11,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [12,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [13,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [14,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [15,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [16,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [17,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [18,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [19,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [20,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [21,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [22,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [23,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [24,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [25,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [26,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [27,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [28,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [29,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [30,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [31,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [32,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [33,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [34,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [35,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [36,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [37,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [38,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [39,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [40,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [41,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [42,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [43,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [44,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [45,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [46,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [47,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [48,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [49,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [50,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [51,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [52,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [53,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [54,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [55,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [56,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [57,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [58,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [59,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [60,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [61,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [62,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [63,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [64,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [65,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [66,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [67,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [68,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [69,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [70,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [71,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [72,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [73,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [74,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [75,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [76,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [77,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [78,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [79,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [80,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [81,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##   [82,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##   [84,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##   [90,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [100,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [107,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [514,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [515,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [516,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [517,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [518,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [519,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [520,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [525,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [526,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [527,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [528,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [531,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [532,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [533,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [534,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [536,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [537,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [538,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [540,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [541,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [543,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [544,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [547,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [550,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [551,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [553,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [555,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [569,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [578,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [610,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [615,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [632,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [664,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [680,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [684,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [686,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [690,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [693,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [697,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [700,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [704,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [706,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [710,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [714,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [856,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [857,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [866,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [875,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [876,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [880,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [886,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [892,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [904,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [907,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [909,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [910,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [912,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [913,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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##  [916,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [917,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
##  [918,]       FALSE FALSE FALSE FALSE FALSE FALSE       FALSE   FALSE FALSE
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#Tính tổng số ô trống của ny bằng câu lệnh 
sum(is.na(ny))

## [1] 0

#Xác định vị trí các ô trống của ny bằng câu lệnh
which(is.na(ny))

## integer(0)

2.4 Thống kê mô tả

Để thống kê mô tả dữ liệu từ file excel, ta thực hiện câu lệnh

library(skimr)
skim(ny)

Data summary

Name	ny
Number of rows	999
Number of columns	10
_______________________
Column type frequency:
character	6
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
BROKERTITLE	0	1	8	87	0	440	0
TYPE	0	1	7	26	0	11	0
STREET_NAME	0	1	5	18	0	95	0
ADDRESS	0	1	8	66	0	969	0
STATE	0	1	12	29	0	203	0
LOCALITY	0	1	8	15	0	9	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
PRICE	0	1	4226632.38	68348129.02	2494	469944	799000	1289000	2147483647	▇▁▁▁▁
BEDS	0	1	3.62	2.38	1	2	3	4	35	▇▁▁▁▁
BATH	0	1	2.53	2.00	1	1	2	3	32	▇▁▁▁▁
YEAR	0	1	2023.00	0.00	2023	2023	2023	2023	2023	▁▁▇▁▁

Giải thích ý nghĩa:

• n_missing: số ô dữ liệu bị miss(trống)

• complete_rate: tỷ lệ ô có dữ liệu

• mean: trung bình

• sd: độ lệch chuẩn

• p0: giá trị nhỏ nhất

• p25: Phân vị thứ nhất

• p50: Phân vị thứ hai cũng chính là trung vị

• p75: phân vị thứ ba

• p100: giá trị lớn nhất

• hist: biểu đồ Histogram

2.5 Rút trích dữ liệu

Để rút trích dữ liệu ta có thể thực hiện các câu lệnh sau

#Đổi tên các biến của ny bằng câu lệnh 
names(ny) <- c('BR','T','P','BE','BA','Y','SN','A','S','L')
names(ny)

##  [1] "BR" "T"  "P"  "BE" "BA" "Y"  "SN" "A"  "S"  "L"

#Gán a để hiển thị giá trị của hàng 5 và cột 3 của ny bằng câu lệnh
a <- ny[5,3]
str(a)

##  num 5.5e+07

#Gán T để hiển thị toàn bộ giá trị của cột TYPE của ny bằng câu lệnh
T <- ny$T
str(T)

##  chr [1:999] "Condo for sale" "Condo for sale" "House for sale" ...

#Gán b để hiển thị toàn bộ giá trị của cột PRICE và tất cả các hàng của ny bằng câu lệnh 
b <- ny[ ,3]
str(b)

##  num [1:999] 3.15e+05 1.95e+08 2.60e+05 6.90e+04 5.50e+07 ...

#Gán c để hiển thị giá trị của hàng 4 và tất cả các cột của ny bằng câu lệnh
c <- ny[4, ]
str(c)

## 'data.frame':    1 obs. of  10 variables:
##  $ BR: chr "Brokered by COMPASS"
##  $ T : chr "Condo for sale"
##  $ P : num 69000
##  $ BE: num 3
##  $ BA: num 1
##  $ Y : num 2023
##  $ SN: chr "New York"
##  $ A : chr "2 E 55th St Unit 908W33"
##  $ S : chr "Manhattan, NY 10022"
##  $ L : chr "New York"

#Gán ny1 để hiển thị giá trị của cột TYPE và cột BR và tất cả các hàng của P bằng câu lệnh 
ny1 <- ny[ ,c(1,3)]
str(ny1)

## 'data.frame':    999 obs. of  2 variables:
##  $ BR: chr  "Brokered by Douglas Elliman  -111 Fifth Ave" "Brokered by Serhant" "Brokered by Sowae Corp" "Brokered by COMPASS" ...
##  $ P : num  3.15e+05 1.95e+08 2.60e+05 6.90e+04 5.50e+07 ...

#Gán ny2 để hiển thị giá trị từ hàng 3 đến hàng 9 và tất cả các cột của ny bằng câu lệnh 
ny2 <- ny[3:9, ]
str(ny2)

## 'data.frame':    7 obs. of  10 variables:
##  $ BR: chr  "Brokered by Sowae Corp" "Brokered by COMPASS" "Brokered by Sotheby's International Realty - East Side Manhattan Brokerage" "Brokered by Sowae Corp" ...
##  $ T : chr  "House for sale" "Condo for sale" "Townhouse for sale" "House for sale" ...
##  $ P : num  2.6e+05 6.9e+04 5.5e+07 6.9e+05 9.0e+05 ...
##  $ BE: num  4 3 7 5 2 8 1
##  $ BA: num  2 1 5 2 2 16 1
##  $ Y : num  2023 2023 2023 2023 2023 ...
##  $ SN: chr  "Staten Island" "New York" "New York" "Brooklyn" ...
##  $ A : chr  "620 Sinclair Ave" "2 E 55th St Unit 908W33" "5 E 64th St" "584 Park Pl" ...
##  $ S : chr  "Staten Island, NY 10312" "Manhattan, NY 10022" "New York, NY 10065" "Brooklyn, NY 11238" ...
##  $ L : chr  "New York" "New York" "New York" "New York" ...

#Gán ny3 để hiển thị giá trị của các hàng 3,5,7,21 và tất cả các cột của ny bằng câu lệnh 
ny3 <- ny[c(3,5,7,21), ] 
str(ny3)

## 'data.frame':    4 obs. of  10 variables:
##  $ BR: chr  "Brokered by Sowae Corp" "Brokered by Sotheby's International Realty - East Side Manhattan Brokerage" "Brokered by Douglas Elliman - 575 Madison Ave" "Brokered by Radiant Estates LLC"
##  $ T : chr  "House for sale" "Townhouse for sale" "Condo for sale" "House for sale"
##  $ P : num  2.6e+05 5.5e+07 9.0e+05 6.4e+05
##  $ BE: num  4 7 2 3
##  $ BA: num  2 5 2 2
##  $ Y : num  2023 2023 2023 2023
##  $ SN: chr  "Staten Island" "New York" "Manhattan" "Staten Island"
##  $ A : chr  "620 Sinclair Ave" "5 E 64th St" "157 W 126th St Unit 1B" "27 Clove Way"
##  $ S : chr  "Staten Island, NY 10312" "New York, NY 10065" "New York, NY 10027" "Staten Island, NY 10301"
##  $ L : chr  "New York" "New York" "New York County" "New York"

#Gán ny4 để hiển thị giá trị của hàng 2,3,6,18 và cột 2,3 của ny bằng câu lệnh 
ny4 <- ny[c(2,3,6,18), c(2,3)]
str(ny4)

## 'data.frame':    4 obs. of  2 variables:
##  $ T: chr  "Condo for sale" "House for sale" "House for sale" "Co-op for sale"
##  $ P: num  1.95e+08 2.60e+05 6.90e+05 1.99e+05

#Gán ny5 để hiển thị giá trị các hàng TYPE có dữ liệu "Condo for sale" và tất cả các cột của ny bằng câu lệnh 
ny5  <- ny[ny$T == 'Condo for sale', ]
str(ny5)

## 'data.frame':    128 obs. of  10 variables:
##  $ BR: chr  "Brokered by Douglas Elliman  -111 Fifth Ave" "Brokered by Serhant" "Brokered by COMPASS" "Brokered by Douglas Elliman - 575 Madison Ave" ...
##  $ T : chr  "Condo for sale" "Condo for sale" "Condo for sale" "Condo for sale" ...
##  $ P : num  3.15e+05 1.95e+08 6.90e+04 9.00e+05 5.49e+05 ...
##  $ BE: num  2 7 3 2 2 2 2 1 1 1 ...
##  $ BA: num  2 10 1 2 2 2 2 1 2 1 ...
##  $ Y : num  2023 2023 2023 2023 2023 ...
##  $ SN: chr  "East 55th Street" "New York" "New York" "Manhattan" ...
##  $ A : chr  "2 E 55th St Unit 803" "Central Park Tower Penthouse-217 W 57th New York St Unit Penthouse" "2 E 55th St Unit 908W33" "157 W 126th St Unit 1B" ...
##  $ S : chr  "New York, NY 10022" "New York, NY 10019" "Manhattan, NY 10022" "New York, NY 10027" ...
##  $ L : chr  "New York" "New York" "New York" "New York County" ...

#Gán ny6 để hiển thị giá trị của các hàng BEDS >= 5 và <= 7 và tất cả các cột của ny bằng câu lệnh
ny6 <- ny[ny$BE >= 5 & ny$BE <= 7, ]
str(ny6)

## 'data.frame':    184 obs. of  10 variables:
##  $ BR: chr  "Brokered by Serhant" "Brokered by Sotheby's International Realty - East Side Manhattan Brokerage" "Brokered by Sowae Corp" "Brokered by Keller Williams Ny Realty" ...
##  $ T : chr  "Condo for sale" "Townhouse for sale" "House for sale" "Multi-family home for sale" ...
##  $ P : num  1.95e+08 5.50e+07 6.90e+05 1.10e+06 1.69e+06 ...
##  $ BE: num  7 7 5 6 6 5 5 6 6 7 ...
##  $ BA: num  10 5 2 3 4 2 4 5 2 5 ...
##  $ Y : num  2023 2023 2023 2023 2023 ...
##  $ SN: chr  "New York" "New York" "Brooklyn" "The Bronx" ...
##  $ A : chr  "Central Park Tower Penthouse-217 W 57th New York St Unit Penthouse" "5 E 64th St" "584 Park Pl" "1038 Throggs Neck Expy Unit Sr" ...
##  $ S : chr  "New York, NY 10019" "New York, NY 10065" "Brooklyn, NY 11238" "Bronx, NY 10465" ...
##  $ L : chr  "New York" "New York" "New York" "New York" ...

#Gán ny7 để hiển thị giá trị của hàng BATH = 4 hoặc = 9 và tất cả các cột của ny bằng câu lệnh 
ny7 <- ny[ny$BA == 4 | ny$BA == 9, ]
str(ny7)

## 'data.frame':    118 obs. of  10 variables:
##  $ BR: chr  "Brokered by CENTURY 21 Milestone Realty" "Brokered by RE MAX Edge" "Brokered by Ashford Homes" "Brokered by Century 21 Realty First" ...
##  $ T : chr  "Multi-family home for sale" "Multi-family home for sale" "Multi-family home for sale" "Multi-family home for sale" ...
##  $ P : num  1690000 848000 1299888 2150000 1049000 ...
##  $ BE: num  6 5 11 8 4 3 3 6 3 3 ...
##  $ BA: num  4 4 4 4 4 4 4 4 4 4 ...
##  $ Y : num  2023 2023 2023 2023 2023 ...
##  $ SN: chr  "Queens" "Brooklyn" "Clifton" "Brooklyn" ...
##  $ A : chr  "149-07 85 Rd" "544 Hinsdale St" "310-312 Hillside Ave" "2226 63rd St" ...
##  $ S : chr  "Briarwood, NY 11435" "Brooklyn, NY 11207" "Staten Island, NY 10304" "Brooklyn, NY 11204" ...
##  $ L : chr  "New York" "New York" "Richmond County" "New York" ...

2.6 Rút trích dữ liệu với filter và select

Để rút trích dữ liệu với filter và select ta có thể thực hiện các câu lệnh sau

library(dplyr)

#Gán ny8 để trích dữ liệu YEAR = 2023 và STREER_NAME = New York bằng câu lệnh 
ny8 <- filter(ny, ny$Y == 2023 & ny$SN == 'New York') 
str(ny8)

## 'data.frame':    57 obs. of  10 variables:
##  $ BR: chr  "Brokered by Serhant" "Brokered by COMPASS" "Brokered by Sotheby's International Realty - East Side Manhattan Brokerage" "Brokered by Sotheby's International Realty - East Side Manhattan Brokerage" ...
##  $ T : chr  "Condo for sale" "Condo for sale" "Townhouse for sale" "Townhouse for sale" ...
##  $ P : num  1.95e+08 6.90e+04 5.50e+07 6.50e+07 1.30e+07 ...
##  $ BE: num  7 3 7 3 5 1 5 8 2 6 ...
##  $ BA: num  10 1 5 1 4 1 1 8 2 5 ...
##  $ Y : num  2023 2023 2023 2023 2023 ...
##  $ SN: chr  "New York" "New York" "New York" "New York" ...
##  $ A : chr  "Central Park Tower Penthouse-217 W 57th New York St Unit Penthouse" "2 E 55th St Unit 908W33" "5 E 64th St" "4 E 79th St" ...
##  $ S : chr  "New York, NY 10019" "Manhattan, NY 10022" "New York, NY 10065" "New York, NY 10075" ...
##  $ L : chr  "New York" "New York" "New York" "New York" ...

#Gán ny9 để lấy 4 cột TYPE, PRICE, BEDS, BATH bằng câu lệnh 
ny9 <- select(ny, T,P,BE,BA)
str(ny9)

## 'data.frame':    999 obs. of  4 variables:
##  $ T : chr  "Condo for sale" "Condo for sale" "House for sale" "Condo for sale" ...
##  $ P : num  3.15e+05 1.95e+08 2.60e+05 6.90e+04 5.50e+07 ...
##  $ BE: num  2 7 4 3 7 5 2 8 1 2 ...
##  $ BA: num  2 10 2 1 5 2 2 16 1 1 ...

#Gán ny10 để trích dữ liệu BE>9, BA>7 và chỉ lấy 4 cột TYPE, PRICE, BEDS, BATH bằng câu lệnh 
ny10 <- filter(ny, BE>9 & BA>7) %>% select(T,P,BE,BA)
str(ny10)

## 'data.frame':    14 obs. of  4 variables:
##  $ T : chr  "Multi-family home for sale" "Multi-family home for sale" "Townhouse for sale" "House for sale" ...
##  $ P : num  2250000 1300000 5000000 56000000 29200000 ...
##  $ BE: num  12 10 10 11 12 15 10 12 14 10 ...
##  $ BA: num  8 8 9 10 10 9 10 8 8 13 ...

2.7 Tạo dữ liệu mới từ dữ liệu cũ

Để Tạo dữ liệu mới từ dữ liệu cũ ta có thể thực hiện các câu lệnh sau

#Gán ny11 để thêm cột TICH là tích của PRICE, BEDS, BATH bằng câu lệnh 
ny11 <- mutate(ny, TICH = P*BE*BA)
str(ny11)

## 'data.frame':    999 obs. of  11 variables:
##  $ BR  : chr  "Brokered by Douglas Elliman  -111 Fifth Ave" "Brokered by Serhant" "Brokered by Sowae Corp" "Brokered by COMPASS" ...
##  $ T   : chr  "Condo for sale" "Condo for sale" "House for sale" "Condo for sale" ...
##  $ P   : num  3.15e+05 1.95e+08 2.60e+05 6.90e+04 5.50e+07 ...
##  $ BE  : num  2 7 4 3 7 5 2 8 1 2 ...
##  $ BA  : num  2 10 2 1 5 2 2 16 1 1 ...
##  $ Y   : num  2023 2023 2023 2023 2023 ...
##  $ SN  : chr  "East 55th Street" "New York" "Staten Island" "New York" ...
##  $ A   : chr  "2 E 55th St Unit 803" "Central Park Tower Penthouse-217 W 57th New York St Unit Penthouse" "620 Sinclair Ave" "2 E 55th St Unit 908W33" ...
##  $ S   : chr  "New York, NY 10022" "New York, NY 10019" "Staten Island, NY 10312" "Manhattan, NY 10022" ...
##  $ L   : chr  "New York" "New York" "New York" "New York" ...
##  $ TICH: num  1.26e+06 1.36e+10 2.08e+06 2.07e+05 1.92e+09 ...

2.8 Mã hóa dữ liệu

Để mã hóa dữ liệu ta có thể thực hiện các câu lệnh sau

#Thêm một cột xác định giá bán có phù hợp hay không bằng câu lệnh 
ny11$giaban <- ifelse(ny11$TICH >= 10000000,'phù hợp', 'không phù hợp') #nếu cột PRICE có giá 10000000 thì cột giá bán sẽ có giá trị là phù hợp và ngược lại
str(ny11) 

## 'data.frame':    999 obs. of  12 variables:
##  $ BR    : chr  "Brokered by Douglas Elliman  -111 Fifth Ave" "Brokered by Serhant" "Brokered by Sowae Corp" "Brokered by COMPASS" ...
##  $ T     : chr  "Condo for sale" "Condo for sale" "House for sale" "Condo for sale" ...
##  $ P     : num  3.15e+05 1.95e+08 2.60e+05 6.90e+04 5.50e+07 ...
##  $ BE    : num  2 7 4 3 7 5 2 8 1 2 ...
##  $ BA    : num  2 10 2 1 5 2 2 16 1 1 ...
##  $ Y     : num  2023 2023 2023 2023 2023 ...
##  $ SN    : chr  "East 55th Street" "New York" "Staten Island" "New York" ...
##  $ A     : chr  "2 E 55th St Unit 803" "Central Park Tower Penthouse-217 W 57th New York St Unit Penthouse" "620 Sinclair Ave" "2 E 55th St Unit 908W33" ...
##  $ S     : chr  "New York, NY 10022" "New York, NY 10019" "Staten Island, NY 10312" "Manhattan, NY 10022" ...
##  $ L     : chr  "New York" "New York" "New York" "New York" ...
##  $ TICH  : num  1.26e+06 1.36e+10 2.08e+06 2.07e+05 1.92e+09 ...
##  $ giaban: chr  "không phù hợp" "phù hợp" "không phù hợp" "không phù hợp" ...