2.1

Đọc dữ liệu từ file excel

#load tính năng đọc file excel bằng lệnh
library(xlsx)
#gán file vào biến d bằng lệnh 
d <- read.xlsx("D:\\Downloads\\bwt_co_dieu_chinh.xlsx", sheetIndex =1 ,header = T)

Thông tin tổng quan

#kiểm tra d có phải data frame không bằng lệnh
is.data.frame(d)#mếu kết quả trả về True là đúng, còn False thì không phải
## [1] TRUE
#kiểm tra số cột của d bằng lệnh
length(d)
## [1] 9
#kiểm tra tên các cột của d bằng lệnhl
names(d)
## [1] "id"        "year"      "bwt"       "gestation" "parity"    "age"      
## [7] "height"    "weight"    "smoke"
#kiểm tra số hàng và số cột của d bằng lệnh 
dim(d)
## [1] 640   9
#load gói thông kê mô tả bằng lệnh 
library(skimr)
#tạo bảng thống kê mô tả cho d bằng lệnh 
skim(d)
Data summary
Name d
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 ▇▁▁▁▆

Thông tin mở rộng

#lấy dữ liệu 5 hàng đầu của d bằng lệnh 
head(d,5)
##   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
#lấy dữ liệu 5 hàng cuối của d bằng lệnh 
tail(d,5)
##     id year bwt gestation parity age height weight smoke
## 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
#kiểm tra cấu trúc của d bằng lệnh 
str(d)
## '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 ...

Tính hoàn chỉnh của dữ liệu

#kiểm tra ô trống của d bằng lệnh 
is.na(d)#nếu ô nào trả kết quả FALSE là ô có dữ liệu, còn TRUE là ô trống 
##           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 các ô trống bằng lệnh 
sum(is.na(d))
## [1] 0
#xác định vị trí ô trống bằng lệnh 
which(is.na(d))
## integer(0)

Rút trích dữ liệu

#đổi tên cột của d bằng lệnh 
names(d) <- c('I', 'Y', 'B', 'G', 'P', 'A', 'H','W','S')
#lấy ô hàng 8 cột 3 bằng lệnh
a<-d[8,3]
str(a)
##  num 111
#hiển thị cột H bằng lệnh 
H <- d$H
str(H)
##  num [1:640] 62 62 64 62 67 59 64 65 64 67 ...
#hiển thị toàn bộ cột 8 bằng lệnh 
b <- d[,8]
str(b)
##  num [1:640] 100 138 123 105 130 102 101 145 135 170 ...
#hiển thị toàn bộ dòng 3 bằng lệnh
c <- d[3,]
str(c)
## 'data.frame':    1 obs. of  9 variables:
##  $ I: num 3
##  $ Y: num 1925
##  $ B: num 119
##  $ G: num 286
##  $ P: num 0
##  $ A: num 26
##  $ H: num 64
##  $ W: num 123
##  $ S: num 1
#hiển thị cột 1 và cột 3 của c bằng lệnh
d1 <- d[,c(1,3)]
str(d1)
## '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 ...
#hiển thị từ dòng 3 đến dòng 8 bằng lệnh
d2 <- d[3:8,]
str(d2)
## 'data.frame':    6 obs. of  9 variables:
##  $ I: num  3 4 5 6 7 8
##  $ Y: num  1925 1925 1925 1925 1925 ...
##  $ B: num  119 124 105 120 82 111
##  $ G: num  286 287 276 289 274 278
##  $ P: num  0 0 0 1 0 0
##  $ A: num  26 27 22 31 31 29
##  $ H: num  64 62 67 59 64 65
##  $ W: num  123 105 130 102 101 145
##  $ S: num  1 1 0 0 1 1
#hiển thị dòng 4, 8, 12 của d bằng lệnh 
d3 <- d[c(4,8,12),]
str(d3)
## 'data.frame':    3 obs. of  9 variables:
##  $ I: num  4 8 4
##  $ Y: num  1925 1925 1926
##  $ B: num  124 111 125
##  $ G: num  287 278 292
##  $ P: num  0 0 0
##  $ A: num  27 29 22
##  $ H: num  62 65 65
##  $ W: num  105 145 122
##  $ S: num  1 1 0
#hiển thị hàng 2, 3, 6, 8 của cột 2, 3 bằng lệnh 
d4 <- d[c(2,3,6,18),c(2,3)]
str(d4)
## 'data.frame':    4 obs. of  2 variables:
##  $ Y: num  1925 1925 1925 1927
##  $ B: num  112 119 120 145
#hiển thị tất cả các dòng có giá trị ở cột H lớn hơn hoặc bằng 70 
d5 <- d[d$H >=70,]
str(d5)
## 'data.frame':    9 obs. of  9 variables:
##  $ I: num  8 7 4 4 6 7 1 7 2
##  $ Y: num  1932 1945 1950 1956 1967 ...
##  $ B: num  144 113 148 121 117 113 124 143 158
##  $ G: num  248 285 279 276 281 287 278 270 295
##  $ P: num  0 0 0 1 1 1 0 1 1
##  $ A: num  30 22 27 23 21 29 26 27 37
##  $ H: num  70 70 71 71 70 70 70 70 70
##  $ W: num  145 145 189 152 141 145 145 148 137
##  $ S: num  0 0 0 1 1 1 1 0 0
#hiển thị tất cả các dòng có giá trị tại ở cột H lớn hơn bằng 62 và nhỏ hơn bằng 65
d6 <- d[d$H >=62 & d$H <=65,]
str(d6)
## 'data.frame':    343 obs. of  9 variables:
##  $ I: num  1 2 3 4 7 8 1 4 5 7 ...
##  $ Y: num  1925 1925 1925 1925 1925 ...
##  $ B: num  120 112 119 124 82 111 113 125 93 100 ...
##  $ G: num  284 267 286 287 274 278 282 292 246 274 ...
##  $ P: num  0 1 0 0 0 0 0 0 0 0 ...
##  $ A: num  27 22 26 27 31 29 33 22 37 24 ...
##  $ H: num  62 62 64 62 64 65 64 65 65 63 ...
##  $ W: num  100 138 123 105 101 145 135 122 130 113 ...
##  $ S: num  0 0 1 1 1 1 0 0 0 0 ...
d7 <- d[d$H == 63 | d$H == 66,]
str(d7)
## 'data.frame':    171 obs. of  9 variables:
##  $ I: num  7 4 5 4 5 4 3 6 3 5 ...
##  $ Y: num  1926 1927 1927 1928 1929 ...
##  $ B: num  100 110 122 125 130 142 99 128 105 120 ...
##  $ G: num  274 262 281 279 296 284 270 292 280 281 ...
##  $ P: num  0 0 0 0 1 0 0 0 1 0 ...
##  $ A: num  24 25 42 23 22 39 22 32 22 33 ...
##  $ H: num  63 66 63 63 66 66 63 66 63 63 ...
##  $ W: num  113 140 103 104 117 132 115 130 116 113 ...
##  $ S: num  0 0 1 1 1 0 1 0 0 0 ...

Rút trích dữ liệu bằng Filter và Select

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
#lọc những hàng có I lớn hơn bằng 3 và B lớn hơn bằng 112 bằng lệnh 
e<-filter(d, d$I >=3 & d$B >=112)
str(e)
## 'data.frame':    322 obs. of  9 variables:
##  $ I: num  3 4 6 4 6 5 6 7 8 3 ...
##  $ Y: num  1925 1925 1925 1926 1926 ...
##  $ B: num  119 124 120 125 146 122 112 114 114 115 ...
##  $ G: num  286 287 289 292 280 281 283 271 276 264 ...
##  $ P: num  0 0 1 0 0 0 1 0 0 1 ...
##  $ A: num  26 27 31 22 23 42 21 32 26 23 ...
##  $ H: num  64 62 59 65 61 63 62 61 62 67 ...
##  $ W: num  123 105 102 122 145 103 102 130 127 134 ...
##  $ S: num  1 1 0 0 0 1 1 0 0 1 ...
#chọn cột I,B,S của e bằng lệnh 
e1<-select(e,I,B,S)
str(e1)
## 'data.frame':    322 obs. of  3 variables:
##  $ I: num  3 4 6 4 6 5 6 7 8 3 ...
##  $ B: num  119 124 120 125 146 122 112 114 114 115 ...
##  $ S: num  1 1 0 0 0 1 1 0 0 1 ...

Tạo dữ liệu mới từ dữ liệu có sẵn

#tạo ra một cột mới tên 'tong' bằng tổng của 3 cột B, G, P 
d$tong <- d$B+d$G+d$P
str(d$tong)
##  num [1:640] 404 380 405 411 381 410 356 389 395 431 ...

Mã hóa dữ liệu

d$tong <- ifelse(d$tong >=400, "OK", "NOT OK")
str(d$tong)
##  chr [1:640] "OK" "NOT OK" "OK" "OK" "NOT OK" "OK" "NOT OK" "NOT OK" ...

2.2

Tóm tắt

Thao tác cơ bản và rút trích dữ liệu từ file “customer_shopping_data” - file cung cấp những thông tin cơ bản của khách hàng, bao gồm:

  • invoice_no: mã hóa đơn

  • customer_id: mã khách hàng

  • gender: giới tính

  • age: tuổi

  • category: loại hànghàng

  • quanity: số lượng

  • price: giá

  • payment_method: phương thức thanh toán

  • invoice_date: ngày xuất hóa đơn

  • shopping_mall: nơi mua

Bộ dữ liệu gồm có 10 đối tượng và 3003 quan sát

Đọc dữ liệu từ file excel

#gán file vào biến i bằng lệnh
i <- read.xlsx("D:\\Downloads\\customer_shopping_data.xlsx", sheetIndex =1 ,header = T)

Mô tả

#kiểm tra xem i có phải data frame không bằng lệnh
is.data.frame(i)
## [1] TRUE
#kiểm tra số cột của i bằng lệnh 
length(i)
## [1] 10
#hiển thị tên các cột của i bằng lệnh
names(i)
##  [1] "invoice_no"     "customer_id"    "gender"         "age"           
##  [5] "category"       "quantity"       "price"          "payment_method"
##  [9] "invoice_date"   "shopping_mall"
#hiển thị số hàng và số cột của i bằng lệnh
dim(i)
## [1] 3003   10
#tạo bảng thống kê mô tả cho i bằng lệnh 
skim(i)
Data summary
Name i
Number of rows 3003
Number of columns 10
_______________________
Column type frequency:
character 7
numeric 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
invoice_no 0 1 7 7 0 3003 0
customer_id 0 1 7 7 0 3003 0
gender 0 1 4 6 0 2 0
category 0 1 4 15 0 8 0
payment_method 0 1 4 11 0 3 0
invoice_date 0 1 5 10 0 782 0
shopping_mall 0 1 6 17 0 10 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
age 0 1 43.44 14.90 18.00 30.00 43.0 56.00 69 ▇▇▇▇▇
quantity 0 1 3.00 1.44 1.00 2.00 3.0 4.00 5 ▇▇▇▇▇
price 0 1 693.37 976.70 5.23 40.66 203.3 1200.32 5250 ▇▂▁▁▁ 
#lấy dứ liệu 5 hàng đầu tiên của i bằng lệnh
head(i,5)
##   invoice_no customer_id gender age category quantity   price payment_method
## 1    I138884     C241288 Female  28 Clothing        5 1500.40    Credit Card
## 2    I317333     C111565   Male  21    Shoes        3 1800.51     Debit Card
## 3    I127801     C266599   Male  20 Clothing        1  300.08           Cash
## 4    I173702     C988172 Female  66    Shoes        5 3000.85    Credit Card
## 5    I337046     C189076 Female  53    Books        4   60.60           Cash
##   invoice_date  shopping_mall
## 1        44689         Kanyon
## 2        44542 Forum Istanbul
## 3        44450      Metrocity
## 4   16/05/2021   Metropol AVM
## 5   24/10/2021         Kanyon
#lấy dữ liệu 5 hàng cuối cùng của i bằng lệnh
tail(i,5)
##      invoice_no customer_id gender age category quantity   price payment_method
## 2999    I262363     C251985   Male  32    Books        5   75.75           Cash
## 3000    I959479     C258369 Female  52 Clothing        5 1500.40     Debit Card
## 3001    I594574     C316999 Female  56 Clothing        2  600.16    Credit Card
## 3002    I248487     C129645 Female  59 Clothing        2  600.16    Credit Card
## 3003    I307338     C495855 Female  45    Shoes        2 1200.34    Credit Card
##      invoice_date shopping_mall
## 2999   28/07/2022  Istinye Park
## 3000   19/06/2022        Kanyon
## 3001   20/09/2022     Metrocity
## 3002   26/01/2023        Kanyon
## 3003   24/08/2021  Zorlu Center

Rút trích dữ liệu

#lấy ô hàng 8 cột 3 bằng lệnh
i1 <- i[8,3]
str(i1)
##  chr "Female"
#lấy hàng 2005 bằng lệnh
i2 <- i[2005,]
str(i2)
## 'data.frame':    1 obs. of  10 variables:
##  $ invoice_no    : chr "I248285"
##  $ customer_id   : chr "C294338"
##  $ gender        : chr "Female"
##  $ age           : num 54
##  $ category      : chr "Toys"
##  $ quantity      : num 2
##  $ price         : num 71.7
##  $ payment_method: chr "Cash"
##  $ invoice_date  : chr "44441"
##  $ shopping_mall : chr "Mall of Istanbul"
#lấy cột 4 bằng lệnh
i3 <- i[,4]
str(i3)
##  num [1:3003] 28 21 20 66 53 28 49 32 69 60 ...
#lấy hàng 3, 8, 200 của cột 5 bằng lệnh
i4 <- i[c(3,8,200),5]
str(i4)
##  chr [1:3] "Clothing" "Clothing" "Cosmetics"
#lọc những hàng của khách hàng lớn hơn bằng 40 bằng lệnh
i5<-filter(i, i$age >= 40)
str(i5)
## 'data.frame':    1734 obs. of  10 variables:
##  $ invoice_no    : chr  "I173702" "I337046" "I121056" "I293455" ...
##  $ customer_id   : chr  "C988172" "C189076" "C151197" "C159642" ...
##  $ gender        : chr  "Female" "Female" "Female" "Male" ...
##  $ age           : num  66 53 49 69 60 67 67 65 42 46 ...
##  $ category      : chr  "Shoes" "Books" "Cosmetics" "Clothing" ...
##  $ quantity      : num  5 4 1 3 2 4 2 2 3 2 ...
##  $ price         : num  3000.8 60.6 40.7 900.2 600.2 ...
##  $ payment_method: chr  "Credit Card" "Cash" "Cash" "Credit Card" ...
##  $ invoice_date  : chr  "16/05/2021" "24/10/2021" "13/03/2022" "44297" ...
##  $ shopping_mall : chr  "Metropol AVM" "Kanyon" "Istinye Park" "Metrocity" ...
#lọc những hàng có phương thức thanh toán bằng tiền mặt bằng lệnh
i6<-filter(i, i$payment_method == "Cash")
str(i6)
## 'data.frame':    1301 obs. of  10 variables:
##  $ invoice_no    : chr  "I127801" "I337046" "I121056" "I306368" ...
##  $ customer_id   : chr  "C266599" "C189076" "C151197" "C240286" ...
##  $ gender        : chr  "Male" "Female" "Female" "Female" ...
##  $ age           : num  20 53 49 36 25 46 24 27 44 42 ...
##  $ category      : chr  "Clothing" "Books" "Cosmetics" "Food & Beverage" ...
##  $ quantity      : num  1 4 1 2 2 2 4 1 5 5 ...
##  $ price         : num  300.1 60.6 40.7 10.5 600.2 ...
##  $ payment_method: chr  "Cash" "Cash" "Cash" "Cash" ...
##  $ invoice_date  : chr  "44450" "24/10/2021" "13/03/2022" "25/12/2022" ...
##  $ shopping_mall : chr  "Metrocity" "Kanyon" "Istinye Park" "Metrocity" ...
#chọn cột hóa đơn, tuổi, nơi mua hàng của i bằng lệnh
i7<-select(i,invoice_no,age,shopping_mall)
str(i7)
## 'data.frame':    3003 obs. of  3 variables:
##  $ invoice_no   : chr  "I138884" "I317333" "I127801" "I173702" ...
##  $ age          : num  28 21 20 66 53 28 49 32 69 60 ...
##  $ shopping_mall: chr  "Kanyon" "Forum Istanbul" "Metrocity" "Metropol AVM" ...

Tạo dữ liệu mới

#tạo cột mới có tên là "into_money" bằng tích của cột "quanity" và "price"
i$into_money <- i$quantity*i$price
str(i$into_money)
##  num [1:3003] 7502 5402 300 15004 242 ...
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