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
#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 #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, 15)## 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
## 11 3 1926 97 279 0 29 68 178 1
## 12 4 1926 125 292 0 22 65 122 0
## 13 5 1926 93 246 0 37 65 130 0
## 14 6 1926 146 280 0 23 61 145 0
## 15 7 1926 100 274 0 24 63 113 0#Hiển thị 10 dòng cuối cùng của bwt bằng câu lệnh
tail(bwt, 15)## id year bwt gestation parity age height weight smoke
## 626 2 2003 158 295 1 37 70 137 0
## 627 3 2003 78 258 1 24 66 115 1
## 628 4 2003 133 292 0 30 65 112 1
## 629 5 2003 140 251 0 28 63 210 0
## 630 6 2003 114 286 1 22 64 116 1
## 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 ... #dùng để tạo ra một bảng tóm tắt các thông tin cơ bản về bwt
library(skimr)
skim(bwt)| 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 ## Kiểm tra tính hoàn chỉnh của dữ liệu
# để tìm ô trống trong bwt dùng
is.na(bwt)## id year bwt gestation parity age height weight smoke
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## [640,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE# để xác định tổng các ô trống trong bwt dùng
sum(is.na(bwt))## [1] 0# tìm vị trí ô trrongs trong bwt dùng
which(is.na(bwt))## integer(0)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 cho giá trị của hàng 7 và cột 5 của bwt bằng câu lệnh
a <- bwt[7,5]
str(a)## num 0#Gán H cho 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 cho 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 cho 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 cho 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 cho 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 cho 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 cho 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 cho 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 cho 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 cho 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 ...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 ...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 ...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
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" ...Thao tác một số lệnh cơ bản và rút trích dữ liệu trên file excel ‘data2.xlsl’ ,file này phân tích thu nhập của người Mỹ
Age : tuổi
Workclass : lớp học nghề
education : giáo dục
educational-num : số giáo dục
marital-status : tình trạng hôn nhân
occupation : nghề nghiệp
relationship : mối quan hệ
gender : giới tính
capital-gain : tăng vốn
hours-per-week: giờ làm mỗi tuần
native-country: quốc gia
income : thu nhập
File này có 12 cột tưởng đương với 12 biến
Có 1211 hàng tương đương với 1211 quan sát
#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 data2, sau đó đọc dữ liệu từ file excel bằng câu lệnh
data2 <- read.xlsx(file.choose(), sheetIndex = 1, header = T) #Kiểm tra bwt có phải là khung dữ liệu hay không bằng câu lệnh
is.data.frame(data2) #True chứng tỏ data2 là khung dữ liệu, False thì ngược lại## [1] TRUE#Hiển thị số cột của data2 bằng câu lệnh
length(data2)## [1] 14#Hiển thị tên các cột của data2 bằng câu lệnh
names(data2)## [1] "age" "workclass" "education" "educational.num"
## [5] "marital.status" "occupation" "relationship" "race"
## [9] "gender" "capital.gain" "capital.loss" "hours.per.week"
## [13] "native.country" "income"#Hiển thị số hàng và số cột của data2 bằng câu lệnh
dim(data2)## [1] 1210 14#Hiển thị 10 dòng đầu tiên của data2 bằng câu lệnh
head(bwt, 10)## I Y B G P A H W S
## 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 data2 bằng câu lệnh
tail(data2, 10)## age workclass education educational.num marital.status
## 1201 20 Private Some-college 10 Never-married
## 1202 49 Private 10th 6 Never-married
## 1203 31 Private Prof-school 15 Married-civ-spouse
## 1204 41 Private Some-college 10 Never-married
## 1205 35 Private HS-grad 9 Married-civ-spouse
## 1206 20 ? Some-college 10 Never-married
## 1207 35 Private Assoc-voc 11 Married-civ-spouse
## 1208 46 Private Masters 14 Divorced
## 1209 35 Private HS-grad 9 Divorced
## 1210 21 Private Some-college 10 Never-married
## occupation relationship race gender capital.gain capital.loss
## 1201 Sales Own-child White Female 594 0
## 1202 Machine-op-inspct Not-in-family Black Female 0 0
## 1203 Prof-specialty Husband White Male 0 0
## 1204 Sales Not-in-family White Male 0 0
## 1205 Exec-managerial Husband White Male 0 0
## 1206 ? Own-child White Male 0 0
## 1207 Craft-repair Husband White Male 4386 0
## 1208 Exec-managerial Unmarried White Female 0 0
## 1209 Machine-op-inspct Unmarried White Female 0 0
## 1210 Tech-support Own-child White Male 0 0
## hours.per.week native.country income
## 1201 24 United-States <=50K
## 1202 40 United-States <=50K
## 1203 50 United-States >50K
## 1204 40 United-States <=50K
## 1205 60 United-States >50K
## 1206 30 United-States <=50K
## 1207 42 United-States >50K
## 1208 8 United-States <=50K
## 1209 60 United-States <=50K
## 1210 20 United-States <=50K#Hiển thị cấu trúc của bwt bằng câu lệnh
str(data2)## 'data.frame': 1210 obs. of 14 variables:
## $ age : num 25 38 28 44 18 34 29 63 24 55 ...
## $ workclass : chr "Private" "Private" "Local-gov" "Private" ...
## $ education : chr "11th" "HS-grad" "Assoc-acdm" "Some-college" ...
## $ educational.num: num 7 9 12 10 10 6 9 15 10 4 ...
## $ marital.status : chr "Never-married" "Married-civ-spouse" "Married-civ-spouse" "Married-civ-spouse" ...
## $ occupation : chr "Machine-op-inspct" "Farming-fishing" "Protective-serv" "Machine-op-inspct" ...
## $ relationship : chr "Own-child" "Husband" "Husband" "Husband" ...
## $ race : chr "Black" "White" "White" "Black" ...
## $ gender : chr "Male" "Male" "Male" "Male" ...
## $ capital.gain : num 0 0 0 7688 0 ...
## $ capital.loss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hours.per.week : num 40 50 40 40 30 30 40 32 40 10 ...
## $ native.country : chr "United-States" "United-States" "United-States" "United-States" ...
## $ income : chr "<=50K" "<=50K" ">50K" ">50K" ... #dùng để tạo ra một bảng tóm tắt các thông tin cơ bản về data2
library(skimr)
skim(data2)| Name | data2 |
| Number of rows | 1210 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| workclass | 0 | 1 | 1 | 16 | 0 | 7 | 0 |
| education | 0 | 1 | 3 | 12 | 0 | 16 | 0 |
| marital.status | 0 | 1 | 7 | 21 | 0 | 7 | 0 |
| occupation | 0 | 1 | 1 | 17 | 0 | 15 | 0 |
| relationship | 0 | 1 | 4 | 14 | 0 | 6 | 0 |
| race | 0 | 1 | 5 | 18 | 0 | 5 | 0 |
| gender | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| native.country | 0 | 1 | 1 | 18 | 0 | 31 | 0 |
| income | 0 | 1 | 4 | 5 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1 | 38.54 | 13.75 | 17 | 27 | 37 | 48 | 90 | ▇▇▅▂▁ |
| educational.num | 0 | 1 | 10.00 | 2.70 | 1 | 9 | 10 | 13 | 16 | ▁▂▇▃▁ |
| capital.gain | 0 | 1 | 1588.03 | 9815.22 | 0 | 0 | 0 | 0 | 99999 | ▇▁▁▁▁ |
| capital.loss | 0 | 1 | 86.87 | 400.45 | 0 | 0 | 0 | 0 | 3004 | ▇▁▁▁▁ |
| hours.per.week | 0 | 1 | 40.62 | 12.29 | 1 | 40 | 40 | 45 | 99 | ▁▇▃▁▁ |
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
# để tìm ô trống trong bwt dùng
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## [640,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE# để xác định tổng các ô trống trong bwt dùng
sum(is.na(bwt))## [1] 0# tìm vị trí ô trrongs trong bwt dùng
which(is.na(bwt))## integer(0)Để rút trích dữ liệu ta có thể thực hiện các câu lệnh sau
#Đổi tên các biến của data2 bằng câu lệnh
names(data2) <- c('A','Wo','Ed','Edn','Ms','Oc','Re','Ge','cg','Hp','Nc','In','Na')
names(data2)## [1] "A" "Wo" "Ed" "Edn" "Ms" "Oc" "Re" "Ge" "cg" "Hp" "Nc" "In"
## [13] "Na" NA#Gán a để hiển thị giá trị của hàng 5 và cột 3 của data2 bằng câu lệnh
a <- data2[5,3]
str(a)## chr "Some-college"#Gán T để hiển thị toàn bộ giá trị của cột age của data2 bằng câu lệnh
T <- data2$A
str(T)## num [1:1210] 25 38 28 44 18 34 29 63 24 55 ...#Gán b để hiển thị toàn bộ giá trị của cột marital-status
b <- data2[ ,5]
str(b)## chr [1:1210] "Never-married" "Married-civ-spouse" "Married-civ-spouse" ...#Gán c để hiển thị giá trị của hàng 4 và tất cả các cột của data2 bằng câu lệnh
c <- data2[4, ]
str(c)## 'data.frame': 1 obs. of 14 variables:
## $ A : num 44
## $ Wo : chr "Private"
## $ Ed : chr "Some-college"
## $ Edn: num 10
## $ Ms : chr "Married-civ-spouse"
## $ Oc : chr "Machine-op-inspct"
## $ Re : chr "Husband"
## $ Ge : chr "Black"
## $ cg : chr "Male"
## $ Hp : num 7688
## $ Nc : num 0
## $ In : num 40
## $ Na : chr "United-States"
## $ NA : chr ">50K"#Gán data21 cho giá trị của cột workclass và education
data21 <- data2[ ,c(2,3)]
str(data21)## 'data.frame': 1210 obs. of 2 variables:
## $ Wo: chr "Private" "Private" "Local-gov" "Private" ...
## $ Ed: chr "11th" "HS-grad" "Assoc-acdm" "Some-college" ...#Gán data22 cho giá trị từ hàng 3 đến hàng 9
data22 <- data2[3:9, ]
str(data22)## 'data.frame': 7 obs. of 14 variables:
## $ A : num 28 44 18 34 29 63 24
## $ Wo : chr "Local-gov" "Private" "?" "Private" ...
## $ Ed : chr "Assoc-acdm" "Some-college" "Some-college" "10th" ...
## $ Edn: num 12 10 10 6 9 15 10
## $ Ms : chr "Married-civ-spouse" "Married-civ-spouse" "Never-married" "Never-married" ...
## $ Oc : chr "Protective-serv" "Machine-op-inspct" "?" "Other-service" ...
## $ Re : chr "Husband" "Husband" "Own-child" "Not-in-family" ...
## $ Ge : chr "White" "Black" "White" "White" ...
## $ cg : chr "Male" "Male" "Female" "Male" ...
## $ Hp : num 0 7688 0 0 0 ...
## $ Nc : num 0 0 0 0 0 0 0
## $ In : num 40 40 30 30 40 32 40
## $ Na : chr "United-States" "United-States" "United-States" "United-States" ...
## $ NA : chr ">50K" ">50K" "<=50K" "<=50K" ...#Gán data23 cho giá trị của các hàng 3,5,7,21
data23 <- data2[c(3,5,7,21), ]
str(data23)## 'data.frame': 4 obs. of 14 variables:
## $ A : num 28 18 29 34
## $ Wo : chr "Local-gov" "?" "?" "Private"
## $ Ed : chr "Assoc-acdm" "Some-college" "HS-grad" "Bachelors"
## $ Edn: num 12 10 9 13
## $ Ms : chr "Married-civ-spouse" "Never-married" "Never-married" "Married-civ-spouse"
## $ Oc : chr "Protective-serv" "?" "?" "Tech-support"
## $ Re : chr "Husband" "Own-child" "Unmarried" "Husband"
## $ Ge : chr "White" "White" "Black" "White"
## $ cg : chr "Male" "Female" "Male" "Male"
## $ Hp : num 0 0 0 0
## $ Nc : num 0 0 0 0
## $ In : num 40 30 40 47
## $ Na : chr "United-States" "United-States" "United-States" "United-States"
## $ NA : chr ">50K" "<=50K" "<=50K" ">50K"#Gán data24 cho giá trị của hàng 2,3,6,18 và cột 2,3
data24 <- data2[c(2,3,6,18), c(2,3)]
str(data24)## 'data.frame': 4 obs. of 2 variables:
## $ Wo: chr "Private" "Local-gov" "Private" "Private"
## $ Ed: chr "HS-grad" "Assoc-acdm" "10th" "HS-grad"#Gán data25 để hiển thị gía trị Private của workclass
data25 <- data2[data2$Wo == 'Private', ]
str(data25)## 'data.frame': 810 obs. of 14 variables:
## $ A : num 25 38 44 34 24 55 65 26 48 43 ...
## $ Wo : chr "Private" "Private" "Private" "Private" ...
## $ Ed : chr "11th" "HS-grad" "Some-college" "10th" ...
## $ Edn: num 7 9 10 6 10 4 9 9 9 14 ...
## $ Ms : chr "Never-married" "Married-civ-spouse" "Married-civ-spouse" "Never-married" ...
## $ Oc : chr "Machine-op-inspct" "Farming-fishing" "Machine-op-inspct" "Other-service" ...
## $ Re : chr "Own-child" "Husband" "Husband" "Not-in-family" ...
## $ Ge : chr "Black" "White" "Black" "White" ...
## $ cg : chr "Male" "Male" "Male" "Male" ...
## $ Hp : num 0 0 7688 0 0 ...
## $ Nc : num 0 0 0 0 0 0 0 0 0 0 ...
## $ In : num 40 50 40 30 40 10 40 39 48 50 ...
## $ Na : chr "United-States" "United-States" "United-States" "United-States" ...
## $ NA : chr "<=50K" "<=50K" ">50K" "<=50K" ...#Gán data26 để hiển thị giá trị của age >= 15 và <= 43
data26 <- data2[data2$A >= 15 & data2$A <= 43, ]
str(data26)## 'data.frame': 795 obs. of 14 variables:
## $ A : num 25 38 28 18 34 29 24 36 26 43 ...
## $ Wo : chr "Private" "Private" "Local-gov" "?" ...
## $ Ed : chr "11th" "HS-grad" "Assoc-acdm" "Some-college" ...
## $ Edn: num 7 9 12 10 6 9 10 13 9 14 ...
## $ Ms : chr "Never-married" "Married-civ-spouse" "Married-civ-spouse" "Never-married" ...
## $ Oc : chr "Machine-op-inspct" "Farming-fishing" "Protective-serv" "?" ...
## $ Re : chr "Own-child" "Husband" "Husband" "Own-child" ...
## $ Ge : chr "Black" "White" "White" "White" ...
## $ cg : chr "Male" "Male" "Male" "Female" ...
## $ Hp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Nc : num 0 0 0 0 0 0 0 0 0 0 ...
## $ In : num 40 50 40 30 30 40 40 40 39 50 ...
## $ Na : chr "United-States" "United-States" "United-States" "United-States" ...
## $ NA : chr "<=50K" "<=50K" ">50K" "<=50K" ...#Gán data27 để hiển thị giá trị của age = 16 hoặc = 43
data27 <- data2[data2$A == 16 | data2$A == 43, ]
str(data27)## 'data.frame': 21 obs. of 14 variables:
## $ A : num 43 43 43 43 43 43 43 43 43 43 ...
## $ Wo : chr "Private" "Private" "Private" "Private" ...
## $ Ed : chr "Masters" "HS-grad" "HS-grad" "HS-grad" ...
## $ Edn: num 14 9 9 9 13 9 10 15 10 9 ...
## $ Ms : chr "Married-civ-spouse" "Married-civ-spouse" "Married-civ-spouse" "Separated" ...
## $ Oc : chr "Exec-managerial" "Adm-clerical" "Sales" "Machine-op-inspct" ...
## $ Re : chr "Husband" "Wife" "Husband" "Not-in-family" ...
## $ Ge : chr "White" "White" "White" "White" ...
## $ cg : chr "Male" "Female" "Male" "Female" ...
## $ Hp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Nc : num 0 0 0 0 0 ...
## $ In : num 50 30 48 44 40 45 60 25 70 40 ...
## $ Na : chr "United-States" "United-States" "United-States" "United-States" ...
## $ NA : chr ">50K" "<=50K" "<=50K" "<=50K" ...Để rút trích dữ liệu với filter và select ta có thể thực hiện các câu lệnh sau
library(dplyr)
#Gán data29 để lấy 4 cột age, workclass, education,occupation
data29 <- select(data2, A,Wo,Ed,Oc)
str(data29)## 'data.frame': 1210 obs. of 4 variables:
## $ A : num 25 38 28 44 18 34 29 63 24 55 ...
## $ Wo: chr "Private" "Private" "Local-gov" "Private" ...
## $ Ed: chr "11th" "HS-grad" "Assoc-acdm" "Some-college" ...
## $ Oc: chr "Machine-op-inspct" "Farming-fishing" "Protective-serv" "Machine-op-inspct" ...