df <- read.csv("students.csv")
library(kableExtra)
df %>% kbl(format = "html") %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"bordered",
"responsive")) %>%
row_spec(0, bold = TRUE, align = "c", color = "white", background = "#1d6c00") %>%
kable_classic(full_width = TRUE, html_font = "arial") -> output
save_kable(output, file = "output.html")Các lệnh R thường dùng khi thực hiện thống kê mô tả
1 Tình huống thường gặp
Bạn có file dữ liệu df gồm rất nhiều hàng và cột (trong bài này ta sử dụng dummy dataset để làm ví dụ minh họa). Việc đầu tiên cần làm là kiểm tra đặc điểm dữ liệu để đánh giá tổng quát toàn bộ dataset như thế nào nhằm có một hình dung cụ thể về dữ liệu trước khi thực hiện các bước phân tích sâu hơn.
Để tạo ra 1 file HTML table show full dataset, bạn sử dụng package kableExtra1. View
df <- read.csv("students.csv")
head(df, n = 30) ### xem 30 dòng đầu của df no stud.id name gender age height weight religion nc.score semester major minor score1 score2 online.tutorial graduated salary
1 1 833917 Gonzales, Christina Female 19 160 64.8 Muslim 1.91 1st Political Science Social Sciences NA NA 0 0 NA
2 2 898539 Lozano, T'Hani Female 19 172 73.0 Other 1.56 2nd Social Sciences Mathematics and Statistics NA NA 0 0 NA
3 3 379678 Williams, Hanh Female 22 168 70.6 Protestant 1.24 3rd Social Sciences Mathematics and Statistics 45 46 0 0 NA
4 4 807564 Nem, Denzel Male 19 183 79.7 Other 1.37 2nd Environmental Sciences Mathematics and Statistics NA NA 0 0 NA
5 5 383291 Powell, Heather Female 21 175 71.4 Catholic 1.46 1st Environmental Sciences Mathematics and Statistics NA NA 0 0 NA
6 6 256074 Perez, Jadrian Male 19 189 85.8 Catholic 1.34 2nd Political Science Mathematics and Statistics NA NA 0 0 NA
7 7 754591 Clardy, Anita Female 21 156 65.9 Protestant 1.11 2nd Political Science Social Sciences NA NA 0 0 NA
8 8 146494 Allen, Rebecca Marie Female 21 167 65.7 Other 2.03 3rd Political Science Economics and Finance 58 62 0 0 NA
9 9 723584 Tracy, Robert Male 18 195 94.4 Other 1.29 3rd Economics and Finance Environmental Sciences 57 67 0 0 NA
10 10 314281 Nimmons, Laura Female 18 165 66.0 Orthodox 1.19 2nd Environmental Sciences Mathematics and Statistics NA NA 0 0 NA
11 11 200803 Lang, Mackenzie Female 22 162 66.8 Other 1.04 4th Economics and Finance Environmental Sciences 62 61 1 1 45254.11
12 12 444907 Rodriguez, Brianna Female 18 172 66.8 Other 3.81 3rd Environmental Sciences Economics and Finance 76 82 0 0 NA
13 13 354271 Covar Orendain, Christopher Male 23 185 84.6 Orthodox 1.00 4th Environmental Sciences Mathematics and Statistics 71 76 1 1 40552.79
14 14 317812 Lopez, Monique Female 20 158 64.4 Catholic 2.50 6th Environmental Sciences Social Sciences 66 70 1 1 27007.03
15 15 604115 Davis, Shagun Female 19 157 66.3 Orthodox 1.92 2nd Economics and Finance Political Science NA NA 0 0 NA
16 16 889551 Adams, Jose Male 20 172 73.9 Other 3.61 4th Mathematics and Statistics Political Science 87 91 1 0 NA
17 17 350040 Hines, Haileigh Female 22 156 61.7 Other 2.27 6th Political Science Biology 57 54 0 1 33969.16
18 18 240279 Daugherty, Jesus Male 22 182 82.1 Catholic 1.42 1st Economics and Finance Environmental Sciences NA NA 0 0 NA
19 19 865835 Roybal, Ebony Female 21 162 69.2 Catholic 1.32 3rd Political Science Environmental Sciences 69 46 1 0 NA
20 20 137196 Baysinger, Tanisha Female 22 168 70.9 Protestant 2.33 2nd Environmental Sciences Political Science NA NA 0 0 NA
21 21 708242 Phillips, Laiba Female 20 167 68.5 Other 1.79 4th Biology Economics and Finance 77 80 1 0 NA
22 22 499002 Culbertson, Deshawn Male 37 175 70.4 Protestant 1.97 2nd Political Science Environmental Sciences NA NA 0 0 NA
23 23 873149 O Reilly, Joshua Male 19 164 70.3 Protestant 1.68 2nd Political Science Environmental Sciences NA NA 0 0 NA
24 24 807361 Johnson, Stephanie Female 38 155 67.0 Catholic 2.30 2nd Environmental Sciences Biology NA NA 0 0 NA
25 25 531029 Mix, Aziel Male 23 183 81.8 Catholic 2.11 4th Economics and Finance Environmental Sciences 69 65 0 0 NA
26 26 970589 Gonzalez, Dixie Female 26 145 54.0 Other 1.14 1st Biology Environmental Sciences NA NA 0 0 NA
27 27 250298 Clark, Janelle Female 25 161 66.8 Other 1.45 3rd Social Sciences Economics and Finance 45 37 1 0 NA
28 28 763393 Woolsey, Bronson Male 24 182 80.1 Protestant 1.09 5th Economics and Finance Social Sciences 61 73 1 1 50617.64
29 29 544433 Diawara, Erica Female 54 169 71.4 Protestant 1.75 2nd Political Science Environmental Sciences NA NA 0 0 NA
30 30 252935 Lord, Benjamin Male 22 172 69.6 Protestant 3.94 3rd Mathematics and Statistics Social Sciences 89 90 1 0 NA2 Các lệnh kiểm tra dataset thường dùng
dim(df) ### Kiểm tra dataset gồm bao nhiêu hàng và cột[1] 8239 17str(df) ### Xem tổng quát cấu trúc dataset'data.frame': 8239 obs. of 17 variables:
$ no : int 1 2 3 4 5 6 7 8 9 10 ...
$ stud.id : int 833917 898539 379678 807564 383291 256074 754591 146494 723584 314281 ...
$ name : chr "Gonzales, Christina" "Lozano, T'Hani" "Williams, Hanh" "Nem, Denzel" ...
$ gender : chr "Female" "Female" "Female" "Male" ...
$ age : int 19 19 22 19 21 19 21 21 18 18 ...
$ height : int 160 172 168 183 175 189 156 167 195 165 ...
$ weight : num 64.8 73 70.6 79.7 71.4 85.8 65.9 65.7 94.4 66 ...
$ religion : chr "Muslim" "Other" "Protestant" "Other" ...
$ nc.score : num 1.91 1.56 1.24 1.37 1.46 1.34 1.11 2.03 1.29 1.19 ...
$ semester : chr "1st" "2nd" "3rd" "2nd" ...
$ major : chr "Political Science" "Social Sciences" "Social Sciences" "Environmental Sciences" ...
$ minor : chr "Social Sciences" "Mathematics and Statistics" "Mathematics and Statistics" "Mathematics and Statistics" ...
$ score1 : int NA NA 45 NA NA NA NA 58 57 NA ...
$ score2 : int NA NA 46 NA NA NA NA 62 67 NA ...
$ online.tutorial: int 0 0 0 0 0 0 0 0 0 0 ...
$ graduated : int 0 0 0 0 0 0 0 0 0 0 ...
$ salary : num NA NA NA NA NA NA NA NA NA NA ...summary(df) ### Tóm tắt đặc điểm từng cột trong dataset no stud.id name gender age height weight religion nc.score semester major minor score1 score2 online.tutorial graduated salary
Min. : 1 Min. :110250 Length:8239 Length:8239 Min. :18.00 Min. :135.0 Min. : 51.4 Length:8239 Min. :1.000 Length:8239 Length:8239 Length:8239 Min. :30.00 Min. :31.0 Min. :0.0000 Min. :0.0000 Min. :11444
1st Qu.:2060 1st Qu.:331223 Class :character Class :character 1st Qu.:20.00 1st Qu.:163.0 1st Qu.: 66.5 Class :character 1st Qu.:1.460 Class :character Class :character Class :character 1st Qu.:58.00 1st Qu.:59.0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:35207
Median :4120 Median :545132 Mode :character Mode :character Median :21.00 Median :171.0 Median : 71.8 Mode :character Median :2.040 Mode :character Mode :character Mode :character Median :70.00 Median :71.0 Median :0.0000 Median :0.0000 Median :41672
Mean :4120 Mean :548911 Mean :22.54 Mean :171.4 Mean : 73.0 Mean :2.166 Mean :68.17 Mean :69.5 Mean :0.3862 Mean :0.2128 Mean :42522
3rd Qu.:6180 3rd Qu.:770808 3rd Qu.:23.00 3rd Qu.:180.0 3rd Qu.: 78.5 3rd Qu.:2.780 3rd Qu.:78.00 3rd Qu.:80.0 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:49373
Max. :8239 Max. :989901 Max. :64.00 Max. :206.0 Max. :116.0 Max. :4.000 Max. :97.00 Max. :98.0 Max. :1.0000 Max. :1.0000 Max. :75597
NA's :3347 NA's :3347 NA's :6486 Vì giá trị NA missing values chỉ thể hiện ở numeric vector mà không thể hiện ở character vector. Vì vậy kết quả trong lệnh summary() chỉ để tham khảo sơ bộ khi đánh giá về tình trạng NA của bộ dữ liệu.
### Lệnh này xác nhận là toàn bộ dataset có `NA` hay không.
### Nếu FALSE là chắc chắn không có `NA`, còn TRUE thì chắc chắn là có `NA`.
any(is.na(df)) [1] TRUE2.1 Kiểm tra kỹ class từng cột
sapply(df, class) ### Kiểm tra class từng cột. no stud.id name gender age height weight religion nc.score semester major minor score1 score2 online.tutorial graduated salary
"integer" "integer" "character" "character" "integer" "integer" "numeric" "character" "numeric" "character" "character" "character" "integer" "integer" "integer" "integer" "numeric" Ta chuyển toàn bộ các cột ở class integer sang class numeric (nếu cần thiết). Ở đây mình làm ví dụ minh họa để có code mẫu áp dụng cho các trường hợp tương tự.
### Tách ra vector chứa kết quả kiểm tra class từng cột
class_tung_cot <- sapply(df, class, simplify = TRUE, USE.NAMES = TRUE)
### Tìm vị trí index của các cột có class là `integer`
integer_index_cot <- grep(pattern = "integer", ignore.case = FALSE, fixed = TRUE, x = class_tung_cot)
### Tách ra tên các cột có class `integer`
ten_cot_integer <- names(class_tung_cot)[integer_index_cot]
### Chuyển toàn bộ các cột `integer` này về dạng `numeric`
df[, ten_cot_integer] <- lapply(df[, ten_cot_integer], as.numeric)
### Kiểm tra lại thì thấy toàn bộ các cột `integer` đã chuyển qua dạng `numeric`
sapply(df, class, simplify = TRUE, USE.NAMES = TRUE) no stud.id name gender age height weight religion nc.score semester major minor score1 score2 online.tutorial graduated salary
"numeric" "numeric" "character" "character" "numeric" "numeric" "numeric" "character" "numeric" "character" "character" "character" "numeric" "numeric" "numeric" "numeric" "numeric" 2.2 Xác nhận mỗi dòng (observation, row) là một quan sát riêng biệt
Để tăng mức độ tự tin khi thao tác với dữ liệu, bạn cần xác nhận là mỗi dòng (ở đây là mỗi sinh viên) thì dữ liệu là duy nhất, không có lặp lại. Việc này rất quan trọng để tránh bị trùng lắp (trong trường hợp dataset ở dạng semi-long) để ta tìm cách trải dữ liệu ra làm sao thu được dạng true long hoặc true wide, tức là mỗi một dòng là một quan sát riêng biệt.
### Thông thường trong các dataset luôn có cột ID để làm cơ sở theo dõi thông tin,
### nếu không có cột ID thì bạn cần tạo ra cột ID để thuận tiện xử lý sau này (nếu cần)
### Kiểm tra thông tin ở cột ID sinh viên xem có trùng lắp hay không,
### Lệnh unique() trả về kết quả của toàn bộ các giá trị xuất hiện duy nhất trong cột ID
### Lệnh length() giúp đếm tổng chiều dài của vector
length(unique(df$stud.id)) [1] 8239Như vậy ta xác nhận xem tổng số hàng của dataset df có bằng với tổng số giá trị ID riêng biệt ở cột stud.id hay không. Nếu là TRUE thì chắc chắn là mỗi một dòng là một sinh viên riêng biệt.
identical(dim(df)[1], length(unique(df$stud.id)))[1] TRUECách 2 là ta dùng lệnh duplicated()
### Lệnh này trả về vector logical check cho mỗi giá trị trong vector stud.id
### Nếu có trùng lặp thì sẽ có TRUE, còn nếu không có trùng lặp sẽ là FALSE
check_duplicated <- duplicated(df$stud.id)
### Lệnh all này sẽ trả về kết quả TRUE nếu toàn bộ giá trị trong vector !check_duplicated
### là TRUE, nghĩa là không có trùng lặp
all(!check_duplicated)[1] TRUECách 3 là ta dùng lệnh anyDuplicated()
### Để thuận tiện thì ta dùng lệnh này, nếu kết quả trả về 0 nghĩa là không có trùng lặp
anyDuplicated(df$stud.id)[1] 02.3 Dùng lệnh table() để kiểm tra giá trị NA ở từng cột
2.3.1 Tham số gender ở vị trí hàng, tham số major ở vị trí cột
Lệnh table() là một lệnh rất mạnh trong R. Ta nên dùng tham số useNA = "ifany" nhằm thể hiện luôn số lượng NA nếu có trong vector đang kiểm tra.
options(width = 120)
check_na <- lapply(X = df[, !(names(df) %in% c("no", "stud.id", "name"))], ### Bỏ 3 cột đầu vì không cần thiết phải check `NA`
FUN = table,
### Tham số `gender` ở vị trí hàng, tham số `major` ở vị trí cột
useNA = "ifany")
check_na$gender
Female Male
4110 4129
$age
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
259 987 1497 1729 1508 948 485 252 106 39 21 15 9 11 13 11 12 15 9 12 8 10 7 18
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
16 14 17 11 9 15 5 11 12 10 9 6 14 16 10 12 9 11 16 10 9 13 3
$height
135 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
1 2 4 3 9 8 8 15 24 17 32 38 56 49 71 70 106 106 157 137 155 177 236 177 246 200 279 209 254 234
168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
287 246 293 241 294 224 298 227 292 198 269 210 237 189 243 185 183 140 166 112 146 80 89 57 63 39 43 38 24 12
198 199 200 201 202 203 206
8 5 7 6 4 2 2
$weight
51.4 54 54.3 54.6 54.7 54.8 55 55.2 55.3 55.4 55.5 55.7 55.9 56.1 56.2 56.3 56.4 56.5 56.6 56.7
1 1 1 1 1 1 2 1 1 2 1 1 2 5 5 1 4 5 2 3
56.8 56.9 57 57.1 57.2 57.3 57.4 57.5 57.6 57.7 57.8 57.9 58 58.1 58.2 58.3 58.4 58.5 58.6 58.7
2 3 4 2 2 2 2 4 5 4 7 6 7 3 8 7 4 10 8 9
58.8 58.9 59 59.1 59.2 59.3 59.4 59.5 59.6 59.7 59.8 59.9 60 60.1 60.2 60.3 60.4 60.5 60.6 60.7
8 13 5 7 5 8 8 10 12 14 17 13 9 17 11 12 16 14 12 15
60.8 60.9 61 61.1 61.2 61.3 61.4 61.5 61.6 61.7 61.8 61.9 62 62.1 62.2 62.3 62.4 62.5 62.6 62.7
20 16 11 19 11 22 28 16 24 18 15 30 24 28 20 19 19 24 36 20
62.8 62.9 63 63.1 63.2 63.3 63.4 63.5 63.6 63.7 63.8 63.9 64 64.1 64.2 64.3 64.4 64.5 64.6 64.7
30 25 37 23 34 35 40 34 25 32 33 26 28 20 31 24 30 39 29 36
64.8 64.9 65 65.1 65.2 65.3 65.4 65.5 65.6 65.7 65.8 65.9 66 66.1 66.2 66.3 66.4 66.5 66.6 66.7
43 36 41 28 34 41 31 30 35 38 49 41 47 52 39 38 32 33 39 36
66.8 66.9 67 67.1 67.2 67.3 67.4 67.5 67.6 67.7 67.8 67.9 68 68.1 68.2 68.3 68.4 68.5 68.6 68.7
36 47 30 57 35 43 36 34 34 54 38 30 42 37 36 51 34 44 48 40
68.8 68.9 69 69.1 69.2 69.3 69.4 69.5 69.6 69.7 69.8 69.9 70 70.1 70.2 70.3 70.4 70.5 70.6 70.7
49 37 36 40 46 34 38 33 38 36 38 37 40 34 42 34 25 23 47 48
70.8 70.9 71 71.1 71.2 71.3 71.4 71.5 71.6 71.7 71.8 71.9 72 72.1 72.2 72.3 72.4 72.5 72.6 72.7
43 32 42 32 31 27 37 32 54 41 38 35 31 41 30 44 30 29 29 33
72.8 72.9 73 73.1 73.2 73.3 73.4 73.5 73.6 73.7 73.8 73.9 74 74.1 74.2 74.3 74.4 74.5 74.6 74.7
51 28 32 41 37 34 41 29 32 36 36 31 34 30 30 49 37 30 29 35
74.8 74.9 75 75.1 75.2 75.3 75.4 75.5 75.6 75.7 75.8 75.9 76 76.1 76.2 76.3 76.4 76.5 76.6 76.7
32 26 30 31 28 23 18 41 35 30 33 30 24 38 22 29 25 27 22 22
76.8 76.9 77 77.1 77.2 77.3 77.4 77.5 77.6 77.7 77.8 77.9 78 78.1 78.2 78.3 78.4 78.5 78.6 78.7
38 30 30 28 25 24 26 28 34 28 26 30 25 24 31 21 18 24 14 32
78.8 78.9 79 79.1 79.2 79.3 79.4 79.5 79.6 79.7 79.8 79.9 80 80.1 80.2 80.3 80.4 80.5 80.6 80.7
30 22 27 32 25 22 26 18 26 28 11 18 21 25 29 19 20 19 25 24
80.8 80.9 81 81.1 81.2 81.3 81.4 81.5 81.6 81.7 81.8 81.9 82 82.1 82.2 82.3 82.4 82.5 82.6 82.7
21 26 27 16 23 25 21 21 20 14 19 16 19 27 20 11 22 15 17 24
82.8 82.9 83 83.1 83.2 83.3 83.4 83.5 83.6 83.7 83.8 83.9 84 84.1 84.2 84.3 84.4 84.5 84.6 84.7
19 15 18 19 13 14 13 14 19 11 14 16 12 17 15 8 12 12 14 11
84.8 84.9 85 85.1 85.2 85.3 85.4 85.5 85.6 85.7 85.8 85.9 86 86.1 86.2 86.3 86.4 86.5 86.6 86.7
17 18 10 12 12 15 15 10 18 17 14 7 12 10 10 8 10 15 11 12
86.8 86.9 87 87.1 87.2 87.3 87.4 87.5 87.6 87.7 87.8 87.9 88 88.1 88.2 88.3 88.4 88.5 88.6 88.7
9 14 6 8 6 9 6 6 8 8 6 8 13 8 8 6 7 10 11 5
88.8 88.9 89 89.1 89.2 89.3 89.4 89.5 89.6 89.7 89.8 89.9 90 90.1 90.2 90.3 90.4 90.5 90.6 90.7
8 8 8 10 8 12 3 6 8 11 11 7 11 8 9 7 6 4 5 7
90.8 90.9 91 91.1 91.2 91.3 91.4 91.5 91.6 91.7 91.8 91.9 92 92.1 92.2 92.3 92.4 92.5 92.6 92.7
4 10 6 5 4 4 5 5 4 4 4 7 8 4 6 1 2 2 1 5
92.8 92.9 93 93.1 93.2 93.3 93.4 93.5 93.6 93.7 93.8 94 94.1 94.2 94.3 94.4 94.5 94.6 94.7 94.8
3 5 3 4 3 4 2 1 1 3 4 1 9 2 4 6 2 2 1 1
94.9 95 95.1 95.2 95.3 95.4 95.7 95.8 95.9 96 96.1 96.2 96.3 96.4 96.5 96.6 96.7 96.8 96.9 97
2 3 1 6 2 2 2 2 2 4 4 1 3 2 3 3 3 3 2 2
97.2 97.4 97.6 97.8 97.9 98 98.1 98.2 98.3 98.4 98.6 98.7 98.8 99.1 99.2 99.4 99.8 99.9 100 100.2
2 4 2 3 4 1 3 1 1 1 5 1 2 3 2 1 1 1 1 1
100.4 100.7 101.1 101.2 101.5 101.8 102.1 102.2 102.5 103.1 103.2 103.6 104.6 104.8 105.3 105.5 105.7 106.2 106.3 109.3
1 1 1 3 2 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1
109.9 110.5 113.6 116
1 1 1 1
$religion
Catholic Muslim Orthodox Other Protestant
2797 330 585 2688 1839
$nc.score
1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.2 1.21 1.22 1.23
27 41 46 57 33 50 35 47 45 44 48 40 37 59 55 36 45 50 72 46 48 43 49 61
1.24 1.25 1.26 1.27 1.28 1.29 1.3 1.31 1.32 1.33 1.34 1.35 1.36 1.37 1.38 1.39 1.4 1.41 1.42 1.43 1.44 1.45 1.46 1.47
47 37 32 46 39 60 39 50 56 24 37 55 32 43 49 29 39 49 44 42 44 42 41 39
1.48 1.49 1.5 1.51 1.52 1.53 1.54 1.55 1.56 1.57 1.58 1.59 1.6 1.61 1.62 1.63 1.64 1.65 1.66 1.67 1.68 1.69 1.7 1.71
35 39 29 43 47 34 43 36 38 50 30 29 32 44 30 46 42 43 33 33 35 44 28 37
1.72 1.73 1.74 1.75 1.76 1.77 1.78 1.79 1.8 1.81 1.82 1.83 1.84 1.85 1.86 1.87 1.88 1.89 1.9 1.91 1.92 1.93 1.94 1.95
41 33 33 45 35 35 21 36 30 28 41 30 33 28 32 28 28 34 30 40 27 26 31 52
1.96 1.97 1.98 1.99 2 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.1 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19
26 31 51 40 32 34 29 31 31 35 28 28 23 39 30 27 39 29 19 30 32 33 27 41
2.2 2.21 2.22 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 2.4 2.41 2.42 2.43
22 22 47 34 32 16 27 46 32 25 34 25 34 27 35 19 17 31 30 29 28 28 23 18
2.44 2.45 2.46 2.47 2.48 2.49 2.5 2.51 2.52 2.53 2.54 2.55 2.56 2.57 2.58 2.59 2.6 2.61 2.62 2.63 2.64 2.65 2.66 2.67
35 42 23 27 35 26 20 27 24 16 32 20 44 18 34 29 27 15 26 42 25 21 23 18
2.68 2.69 2.7 2.71 2.72 2.73 2.74 2.75 2.76 2.77 2.78 2.79 2.8 2.81 2.82 2.83 2.84 2.85 2.86 2.87 2.88 2.89 2.9 2.91
23 17 36 33 20 31 30 26 23 15 25 24 20 26 25 23 24 19 24 20 12 36 31 25
2.92 2.93 2.94 2.95 2.96 2.97 2.98 2.99 3 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.1 3.11 3.12 3.13 3.14 3.15
20 21 19 18 24 18 15 16 22 21 15 25 25 11 32 27 27 18 23 14 17 20 26 21
3.16 3.17 3.18 3.19 3.2 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29 3.3 3.31 3.32 3.33 3.34 3.35 3.36 3.37 3.38 3.39
31 16 18 15 15 9 15 27 13 15 17 10 27 18 20 17 16 16 15 25 24 17 24 17
3.4 3.41 3.42 3.43 3.44 3.45 3.46 3.47 3.48 3.49 3.5 3.51 3.52 3.53 3.54 3.55 3.56 3.57 3.58 3.59 3.6 3.61 3.62 3.63
17 10 11 13 17 12 16 13 16 11 24 18 11 21 17 10 10 21 21 13 12 13 15 12
3.64 3.65 3.66 3.67 3.68 3.69 3.7 3.71 3.72 3.73 3.74 3.75 3.76 3.77 3.78 3.79 3.8 3.81 3.82 3.83 3.84 3.85 3.86 3.87
11 16 10 7 12 8 23 18 14 5 12 10 17 8 14 5 18 14 17 6 9 20 15 6
3.88 3.89 3.9 3.91 3.92 3.93 3.94 3.95 3.96 3.97 3.98 3.99 4
20 12 12 11 9 12 17 9 7 11 8 10 11
$semester
>6th 1st 2nd 3rd 4th 5th 6th
303 1709 1638 1641 1368 876 704
$major
Biology Economics and Finance Environmental Sciences Mathematics and Statistics
1597 1324 1626 1225
Political Science Social Sciences
1455 1012
$minor
Biology Economics and Finance Environmental Sciences Mathematics and Statistics
1318 1382 1318 1446
Political Science Social Sciences
1387 1388
$score1
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
2 2 5 3 2 6 10 15 19 23 46 32 54 54 56 60 60 64 62 56 57 66 78 79
54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77
64 67 72 79 79 58 62 71 85 97 78 103 124 132 132 156 148 168 158 162 161 175 139 141
78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 <NA>
130 98 81 91 60 65 51 73 77 81 94 122 86 82 29 26 10 10 2 2 3347
$score2
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
1 3 3 1 8 9 10 19 25 25 30 29 58 72 45 53 68 54 54 72 53 48 72 80
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1 1 1 1 1 1 1 1 1 1
47553.09478 47556.43909 47577.44261 47596.60773 47613.40818 47665.89303 47675.66337 47683.23657 47723.10304 47732.0466
1 1 1 1 1 1 1 1 1 1
47760.48273 47791.2197 47803.06667 47815.62218 47842.43633 47857.85541 47871.75373 47902.18725 47959.00645 47968.09999
1 1 1 1 1 1 1 1 1 1
47977.18725 47998.39682 48013.40457 48036.54779 48089.85329 48097.64417 48102.53104 48118.37091 48126.35525 48129.27732
1 1 1 1 1 1 1 1 1 1
48164.23128 48190.83336 48230.52783 48288.53006 48335.51625 48360.52231 48380.89957 48393.19056 48426.91185 48445.14113
1 1 1 1 1 1 1 1 1 1
48449.52772 48453.00423 48456.78746 48482.89362 48508.07265 48552.40533 48573.8517 48650.98376 48653.14679 48667.06794
1 1 1 1 1 1 1 1 1 1
48710.43154 48735.87871 48746.04396 48747.96861 48763.23599 48772.66215 48784.55228 48830.7323 48882.08317 48904.29012
1 1 1 1 1 1 1 1 1 1
48935.74115 48950.84288 48969.94787 48991.98355 48997.25119 49004.72712 49042.90145 49050.85828 49068.50218 49069.77774
1 1 1 1 1 1 1 1 1 1
49079.84406 49091.01011 49103.71669 49111.42749 49111.49115 49115.40783 49121.87015 49220.86576 49223.29964 49229.35336
1 1 1 1 1 1 1 1 1 1
49238.03702 49317.3985 49322.92839 49340.01328 49372.54091 49391.3757 49406.77509 49455.71771 49474.96574 49476.01035
1 1 1 1 1 1 1 1 1 1
49485.36939 49525.19765 49528.29047 49554.67915 49555.80823 49604.97441 49620.96813 49672.61226 49704.76729 49716.08996
1 1 1 1 1 1 1 1 1 1
49766.16166 49767.53874 49769.16984 49773.36109 49777.2153 49780.1771 49832.64746 49863.9024 49900.84249 49930.48873
1 1 1 1 1 1 1 1 1 1
49940.00059 49940.55353 49966.24335 49976.93715 49997.4875 50001.27282 50011.69106 50014.29006 50075.85001 50078.32817
1 1 1 1 1 1 1 1 1 1
50083.24151 50086.63515 50138.74108 50142.0207 50164.01044 50206.14187 50225.82045 50233.98463 50317.87484 50329.83591
1 1 1 1 1 1 1 1 1 1
50342.21241 50347.466 50350.42033 50360.62885 50363.35115 50365.09827 50373.10604 50392.36888 50402.74704 50416.15894
1 1 1 1 1 1 1 1 1 1
50474.56517 50474.89975 50496.18194 50508.15632 50533.83751 50617.64187 50670.71403 50704.7586 50732.37162 50739.36604
1 1 1 1 1 1 1 1 1 1
50756.55062 50763.65584 50765.31994 50790.13025 50827.34401 50860.48985 50861.45751 50865.40862 50883.82848 50910.60536
1 1 1 1 1 1 1 1 1 1
50910.6527 50913.03849 50949.08421 50965.81845 50988.39313 51017.43604 51032.83841 51050.36066 51107.29907 51126.52593
1 1 1 1 1 1 1 1 1 1
51132.67759 51206.52279 51222.99786 51239.72342 51332.4885 51353.46231 51367.37446 51401.33122 51416.20041 51439.25574
1 1 1 1 1 1 1 1 1 1
51465.42035 51467.27875 51513.53842 51518.15402 51528.68171 51552.63966 51564.03762 51568.10564 51584.53481 51687.75054
1 1 1 1 1 1 1 1 1 1
51708.81392 51715.69491 51752.34452 51828.65795 51836.25116 51866.92575 51898.02678 51907.27065 51907.97786 51963.77968
1 1 1 1 1 1 1 1 1 1
51975.48306 52046.95926 52062.54889 52083.60762 52102.9318 52111.61058 52183.85437 52229.81531 52238.89879 52259.07091
1 1 1 1 1 1 1 1 1 1
52275.27675 52316.11724 52335.11136 52343.76033 52366.89913 52383.91404 52416.59145 52433.73347 52452.11209 52541.23721
1 1 1 1 1 1 1 1 1 1
52582.45634 52587.05873 52631.1504 52645.28448 52675.52192 52683.89721 52691.50434 52703.29722 52721.56178 52738.10404
1 1 1 1 1 1 1 1 1 1
52856.89077 52875.73972 52930.7619 52979.80498 52997.80195 53051.92513 53064.78751 53074.26246 53086.63097 53113.69606
1 1 1 1 1 1 1 1 1 1
53116.83645 53178.73999 53205.78752 53242.38134 53268.11753 53282.24825 53294.84259 53297.19095 53300.02335 53304.27651
1 1 1 1 1 1 1 1 1 1
53314.99498 53319.9143 53339.86814 53341.43769 53344.13124 53371.55151 53418.19194 53462.19231 53470.06457 53471.52212
1 1 1 1 1 1 1 1 1 1
53485.50589 53511.77025 53527.42558 53565.89762 53575.58092 53632.22382 53637.18229 53682.70811 53704.74968 53800.46341
1 1 1 1 1 1 1 1 1 1
53834.89125 53876.87551 53905.96363 53908.5995 53928.99216 53973.05863 53988.03246 54008.33866 54022.73737 54077.54939
1 1 1 1 1 1 1 1 1 1
54083.05555 54141.45947 54145.98421 54206.22179 54234.84892 54267.01194 54335.24165 54404.83541 54410.43923 54416.31771
1 1 1 1 1 1 1 1 1 1
54441.25209 54455.96631 54494.9356 54543.16171 54590.09034 54595.39574 54631.11424 54647.32078 54677.30235 54680.65283
1 1 1 1 1 1 1 1 1 1
54803.98934 54851.41237 54885.28547 55056.01612 55057.93856 55068.7269 55082.50656 55139.19051 55191.82065 55273.77456
1 1 1 1 1 1 1 1 1 1
55300.01931 55484.64273 55623.29642 55656.78031 55660.20416 55664.49541 55693.3573 55726.0961 55794.58278 55800.95979
1 1 1 1 1 1 1 1 1 1
55860.90432 55882.64197 55888.36651 55899.4617 55938.63945 55980.19599 55993.91371 56015.67544 56036.51648 56110.42814
1 1 1 1 1 1 1 1 1 1
56147.73311 56157.26195 56181.52023 56182.71546 56208.93648 56216.77893 56251.62873 56261.95399 56301.17992 56308.85373
1 1 1 1 1 1 1 1 1 1
56317.58332 56494.33642 56532.11849 56535.24363 56558.16074 56640.59104 56717.13858 56720.38726 56720.61968 56722.28247
1 1 1 1 1 1 1 1 1 1
56732.15944 56756.0606 56853.81379 56869.77624 56899.71485 56954.08833 56960.16603 56975.03642 56996.09438 57010.1622
1 1 1 1 1 1 1 1 1 1
57029.39386 57047.99091 57083.24832 57091.46248 57131.61137 57164.60067 57196.49659 57247.82914 57318.04885 57318.4414
1 1 1 1 1 1 1 1 1 1
57332.58523 57403.47717 57415.39154 57420.1856 57427.14105 57479.75785 57480.48312 57527.17434 57559.89058 57628.75709
1 1 1 1 1 1 1 1 1 1
57710.71909 57765.70759 57805.1414 57841.89486 57849.34349 57897.6534 57905.8291 57924.64244 57928.76564 57948.65112
1 1 1 1 1 1 1 1 1 1
57994.47274 58024.40176 58071.53528 58080.50014 58110.74705 58113.05305 58133.20978 58178.58433 58316.01052 58380.59321
1 1 1 1 1 1 1 1 1 1
58449.33294 58511.54873 58533.49751 58568.91908 58612.23611 58612.84805 58613.80008 58614.71954 58670.84946 58683.51872
1 1 1 1 1 1 1 1 1 1
58692.96293 58760.81747 58824.49903 58913.76002 58970.41264 58973.79878 59001.48731 59288.03173 59404.19598 59472.34268
1 1 1 1 1 1 1 1 1 1
59480.842 59566.77875 59622.89682 59798.4239 59879.061 59885.00012 59920.48058 60085.76449 60101.44384 60177.53783
1 1 1 1 1 1 1 1 1 1
60187.9547 60216.82003 60407.31727 60414.20593 60492.32148 60502.36646 60576.84445 60675.75398 60675.79303 60679.98793
1 1 1 1 1 1 1 1 1 1
60847.56004 60958.21349 60996.3089 61023.75202 61067.56777 61102.02675 61260.51153 61382.06131 61384.54935 61432.2084
1 1 1 1 1 1 1 1 1 1
61615.84752 61663.58283 61778.79067 61819.84592 61828.73586 61853.83195 61861.8622 62038.35297 62052.20232 62091.43961
1 1 1 1 1 1 1 1 1 1
62192.25594 62307.04322 62360.01908 62478.72933 62858.83411 62887.0343 63154.33225 63183.62447 63199.06624 63375.37993
1 1 1 1 1 1 1 1 1 1
63426.3478 63633.9008 64012.32086 64050.00926 64052.1951 64271.11979 64486.40644 64525.11155 64539.88869 64548.11004
1 1 1 1 1 1 1 1 1 1
64592.71068 64707.81886 65013.0088 65107.53227 65353.38112 65389.89613 65397.21855 65422.04962 65441.30143 65635.9975
1 1 1 1 1 1 1 1 1 1
65776.22733 65956.82189 66365.12731 66416.6068 66473.13653 66607.29432 66636.72062 66922.11848 67230.09536 67278.11292
1 1 1 1 1 1 1 1 1 1
67357.92615 67362.35569 67379.49375 67893.55998 68324.0096 68576.68656 68980.28115 68985.59811 69348.60687 69351.41417
1 1 1 1 1 1 1 1 1 1
69518.73751 69521.13573 70094.84888 70324.83769 70744.63869 70931.61002 71467.89992 71537.22292 71612.7081 72787.28668
1 1 1 1 1 1 1 1 1 1
73980.21462 75530.38563 75596.79344 <NA>
1 1 1 6486 Kết quả trả về là bảng tần số ở từng cột, nếu có NA như ở trường hợp cột salaray thì sẽ hiện ra ở dòng cuối. Trong trường hợp kết quả này quá nhiều cột, ta chỉ quan tâm tách ra những cột nào có giá trị NA và muốn biết có bao nhiêu NA trong cột đó (dù là character hay numeric) thì ta sẽ làm thêm một bước kiểm tra sau.
### Lệnh này sẽ kiểm từng thành phần trong list `check_na` sau đó trả về kết quả
### Những thành phần nào (cột nào trong df) có bao nhiêu giá trị `NA`
check_1 <- lapply(check_na, function(x) {
x[which(is.na(names(x)))]
}
)
### Build function kiểm tra ruột của từng thành phần trong list có giá trị hay không
is.integer0 <- function(x) {
is.integer(x) && length(x) == 0L
}
### Kiểm tra từng thành phần trong check_1 có giá trị hay không
check_2 <- lapply(check_1, is.integer0)
### Trích xuất những cột có giá trị missing value
check_1[!unlist(check_2)]$score1
<NA>
3347
$score2
<NA>
3347
$salary
<NA>
6486 2.4 Thể hiện giá trị NA trên toàn bộ dataset qua heatmap
2.4.1 Cách 1: Sử dụng package mice
library(mice)
mice::md.pattern(df, plot = TRUE, rotate.names = TRUE)
no stud.id name gender age height weight religion nc.score semester major minor online.tutorial graduated score1 score2 salary
1753 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
3139 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
3347 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 3
0 0 0 0 0 0 0 0 0 0 0 0 0 0 3347 3347 6486 13180Kết quả này đọc là có 1753 sinh viên có đủ các thông tin ở tất cả các cột, 3139 sinh viên có đủ thông tin (chỉ trừ cột salary là có missing value), 3347 sinh viên có đủ thông tin (chỉ trừ các cột score1, score2, và salary). Có tổng cộng 13180 giá trị NA trong toàn bộ dataset. Cụ thể cách phân tích kết quả missing value theo package mice được trình bày ở đây
2.4.2 Cách 2: Sử dụng package VIM
Sử dụng đồ thị này để thể hiện tỷ lệ % giá trị NA trên toàn dataset.
library(VIM)
aggr_plot <- VIM::aggr(df,
col = c("navyblue", "red"),
numbers = TRUE,
sortVars = TRUE,
labels = names(data),
cex.axis = 1,
gap = 3,
ylab = c("Histogram of missing data", "Pattern"))
Variables sorted by number of missings:
Variable Count
salary 0.7872315
score1 0.4062386
score2 0.4062386
no 0.0000000
stud.id 0.0000000
name 0.0000000
gender 0.0000000
age 0.0000000
height 0.0000000
weight 0.0000000
religion 0.0000000
nc.score 0.0000000
semester 0.0000000
major 0.0000000
minor 0.0000000
online.tutorial 0.0000000
graduated 0.0000000Ta có thể so sánh từng cặp chỉ tiêu với nhau thông qua đồ thị này, với các biện luận tham khảo ở đây2.
VIM::marginplot(df[, c("salary", "score1")])
VIM::marginplot(df[, c("salary", "score2")])
VIM::marginplot(df[, c("score1", "score2")])
3 Các lệnh group và summary dữ liệu
Ở thời điểm này, ta đã có cái nhìn tổng quát về bộ dữ liệu. Lúc này việc phân tích thống kê mô tả sẽ đi theo câu hỏi nghiên cứu/chủ đề mà bạn quan tâm để làm cơ sở chọn ra những biến/cột/variable phân tích mô tả cụ thể hơn. Mình chọn quan tâm về số lượng nam và nữ ở cột giới tính gender theo học các ngành major trong bộ dữ liệu này.
3.1 Áp dụng các lệnh tạo bảng summary
3.1.1 Sử dụng lệnh table() cho hai tham số
### Tham số `gender` ở vị trí hàng, tham số `major` ở vị trí cột
table(df$gender, df$major, useNA = "ifany")
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 959 461 745 276 978 691
Male 638 863 881 949 477 321Nếu dùng lệnh table() cho ba tham số sẽ tạo ra dạng array 3 chiều, chẻ ra thành từng matrix 2 chiều.
### Tham số `religion` ở vị trí thứ 3 sẽ được tách ra tương ứng các matrix giữa `gender` và `major`
table(df$gender, df$major, df$religion, useNA = "ifany"), , = Catholic
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 322 162 244 95 307 226
Male 223 301 313 344 161 99
, , = Muslim
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 47 14 35 8 45 27
Male 22 28 38 32 21 13
, , = Orthodox
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 71 32 62 24 68 52
Male 29 55 70 59 38 25
, , = Other
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 313 157 229 86 328 246
Male 221 273 280 296 143 116
, , = Protestant
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 206 96 175 63 230 140
Male 143 206 180 218 114 683.1.2 Add margins vào table
Tạo bảng summary cho hai tham số gender và major
gender_major <- table(df$gender, df$major, useNA = "ifany")
names(dimnames(gender_major)) <- c("Gender", "Major")
addmargins(A = gender_major,
margin = seq_along(dim(gender_major)),
FUN = sum,
quiet = TRUE) Major
Gender Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 959 461 745 276 978 691 4110
Male 638 863 881 949 477 321 4129
sum 1597 1324 1626 1225 1455 1012 8239Tạo bảng summary cho ba tham số gender và major và religion
gender_major_religion <- table(df$gender, df$major, df$religion, useNA = "ifany")
names(dimnames(gender_major_religion)) <- c("Gender", "Major", "Religion")
addmargins(A = gender_major_religion,
margin = seq_along(dim(gender_major_religion)),
FUN = sum,
quiet = TRUE), , Religion = Catholic
Major
Gender Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 322 162 244 95 307 226 1356
Male 223 301 313 344 161 99 1441
sum 545 463 557 439 468 325 2797
, , Religion = Muslim
Major
Gender Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 47 14 35 8 45 27 176
Male 22 28 38 32 21 13 154
sum 69 42 73 40 66 40 330
, , Religion = Orthodox
Major
Gender Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 71 32 62 24 68 52 309
Male 29 55 70 59 38 25 276
sum 100 87 132 83 106 77 585
, , Religion = Other
Major
Gender Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 313 157 229 86 328 246 1359
Male 221 273 280 296 143 116 1329
sum 534 430 509 382 471 362 2688
, , Religion = Protestant
Major
Gender Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 206 96 175 63 230 140 910
Male 143 206 180 218 114 68 929
sum 349 302 355 281 344 208 1839
, , Religion = sum
Major
Gender Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 959 461 745 276 978 691 4110
Male 638 863 881 949 477 321 4129
sum 1597 1324 1626 1225 1455 1012 82393.1.3 Sử dụng lệnh prop.table()
### Lệnh này trả về tỷ lệ giữa nam và nữ trong cùng vector `gender`
prop.table(table(df$gender), margin = NULL)
Female Male
0.4988469 0.5011531 ### Nếu có 2 tham số trở lên thì các bạn chú ý tham số margin nhé
prop.table(table(df$gender, df$major, useNA = "ifany"), margin = NULL)
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 0.11639762 0.05595339 0.09042360 0.03349921 0.11870373 0.08386940
Male 0.07743658 0.10474572 0.10693045 0.11518388 0.05789538 0.03896104addmargins(A = prop.table(table(df$gender, df$major, useNA = "ifany"), margin = NULL),
margin = seq_along(dim(prop.table(table(df$gender, df$major, useNA = "ifany"), margin = NULL))),
FUN = sum,
quiet = TRUE)
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 0.11639762 0.05595339 0.09042360 0.03349921 0.11870373 0.08386940 0.49884695
Male 0.07743658 0.10474572 0.10693045 0.11518388 0.05789538 0.03896104 0.50115305
sum 0.19383420 0.16069911 0.19735405 0.14868309 0.17659910 0.12283044 1.00000000addmargins(A = prop.table(table(df$gender, df$major, useNA = "ifany"), margin = 1),
margin = seq_along(dim(prop.table(table(df$gender, df$major, useNA = "ifany"), margin = 1))),
FUN = sum,
quiet = TRUE)
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 0.23333333 0.11216545 0.18126521 0.06715328 0.23795620 0.16812652 1.00000000
Male 0.15451683 0.20900945 0.21336885 0.22983773 0.11552434 0.07774279 1.00000000
sum 0.38785017 0.32117490 0.39463406 0.29699102 0.35348054 0.24586932 2.00000000addmargins(A = prop.table(table(df$gender, df$major, useNA = "ifany"), margin = 2),
margin = seq_along(dim(prop.table(table(df$gender, df$major, useNA = "ifany"), margin = 2))),
FUN = sum,
quiet = TRUE)
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences sum
Female 0.6005009 0.3481873 0.4581796 0.2253061 0.6721649 0.6828063 2.9871452
Male 0.3994991 0.6518127 0.5418204 0.7746939 0.3278351 0.3171937 3.0128548
sum 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 6.00000003.2 Áp dụng các lệnh summary trong package dplyr
library(dplyr)
df |> dplyr::group_by(gender, religion) |>
dplyr::summarise(trung_binh_age = mean(age),
trung_vi_age = median(age),
do_lech_chuan_age = sd(age),
so_luong = n(),
max_age = max(age),
min_age = min(age),
tong_so_tuoi = sum(age)) -> summary_all
summary_all# A tibble: 10 × 9
# Groups: gender [2]
gender religion trung_binh_age trung_vi_age do_lech_chuan_age so_luong max_age min_age tong_so_tuoi
<chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 Female Catholic 22.4 21 5.65 1356 63 18 30435
2 Female Muslim 22.3 21 6.14 176 62 18 3932
3 Female Orthodox 22.5 21 5.95 309 63 18 6961
4 Female Other 22.6 21 6.27 1359 64 18 30653
5 Female Protestant 23.0 21 7.23 910 64 18 20933
6 Male Catholic 22.4 21 5.69 1441 63 18 32290
7 Male Muslim 22.3 21 4.56 154 55 18 3438
8 Male Orthodox 22.6 21 6.03 276 63 18 6232
9 Male Other 22.8 21 6.52 1329 63 18 30240
10 Male Protestant 22.2 21 5.17 929 63 18 20606library(dplyr)
df |> dplyr::group_by(major) |>
dplyr::summarise(so_luong_mon = n_distinct(major),
so_nguoi_theo_hoc = n()) |>
print(n = Inf)# A tibble: 6 × 3
major so_luong_mon so_nguoi_theo_hoc
<chr> <int> <int>
1 Biology 1 1597
2 Economics and Finance 1 1324
3 Environmental Sciences 1 1626
4 Mathematics and Statistics 1 1225
5 Political Science 1 1455
6 Social Sciences 1 1012library(dplyr)
df |> dplyr::group_by(major, gender) |>
dplyr::summarise(so_nguoi_theo_hoc = n()) -> df_major_gender
df_major_gender |> print(n = Inf)# A tibble: 12 × 3
# Groups: major [6]
major gender so_nguoi_theo_hoc
<chr> <chr> <int>
1 Biology Female 959
2 Biology Male 638
3 Economics and Finance Female 461
4 Economics and Finance Male 863
5 Environmental Sciences Female 745
6 Environmental Sciences Male 881
7 Mathematics and Statistics Female 276
8 Mathematics and Statistics Male 949
9 Political Science Female 978
10 Political Science Male 477
11 Social Sciences Female 691
12 Social Sciences Male 321# df |> dplyr::group_by(major, minor) |>
# dplyr::summarise(so_luong_mon = n_distinct(major),
# so_nguoi_theo_hoc = n()) |>
# print(n = Inf)
#
# df |> dplyr::group_by(major, minor) |>
# dplyr::summarise(so_luong_mon = n_distinct(minor),
# so_nguoi_theo_hoc = n())|>
# print(n = Inf)4 Vẽ đồ thị mô tả dữ liệu
4.1 Vẽ đồ thị cột
4.1.1 Đồ thị cột side-by-side
Nếu dataset ở dạng như sau thì khi ta vẽ đồ thị cột sẽ dùng theo dạng formula y ~ x1 + x2
df_major_gender# A tibble: 12 × 3
# Groups: major [6]
major gender so_nguoi_theo_hoc
<chr> <chr> <int>
1 Biology Female 959
2 Biology Male 638
3 Economics and Finance Female 461
4 Economics and Finance Male 863
5 Environmental Sciences Female 745
6 Environmental Sciences Male 881
7 Mathematics and Statistics Female 276
8 Mathematics and Statistics Male 949
9 Political Science Female 978
10 Political Science Male 477
11 Social Sciences Female 691
12 Social Sciences Male 321barplot(so_nguoi_theo_hoc ~ gender + major,
data = df_major_gender,
beside = TRUE,
col = c("blue", "red"),
angle = c(45, 135),
density = 20,
xlab = "",
ylab = "Sinh viên",
yaxs = "i",
ylim = c(0, 1000),
xaxs = "i",
xlim = c(0.5, 18.5),
# xaxt = "n",
main = "Số lượng sinh viên theo học ở các chuyên ngành khác nhau",
width = 1,
las = 1)
legend(x = "topright",
y = NULL,
title = "Chú thích",
legend = c("Nữ", "Nam"),
col = c("blue", "red"),
fill = c("blue", "red"),
angle = c(45, 135),
density = 20)
box()
Nếu dataset ở dạng table hay matrix thì ta vẽ trực tiếp bằng lệnh barplot() với chính table đó.
student_gender_major <- table(df$gender, df$major)
student_gender_major
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 959 461 745 276 978 691
Male 638 863 881 949 477 321barplot(height = student_gender_major,
beside = TRUE,
col = c("blue", "red"),
angle = c(45, 135),
density = 20,
xlab = "",
ylab = "Sinh viên",
yaxs = "i",
ylim = c(0, 1000),
xaxs = "i",
xlim = c(0.5, 18.5),
# xaxt = "n",
main = "Số lượng sinh viên theo học ở các chuyên ngành khác nhau",
width = 1,
las = 1)
legend(x = "topright",
y = NULL,
title = "Chú thích",
legend = c("Nữ", "Nam"),
col = c("blue", "red"),
fill = c("blue", "red"),
angle = c(45, 135),
density = 20)
box()
4.1.2 Đồ thị cột stacked barchart
barplot(height = student_gender_major,
beside = FALSE,
col = c("blue", "red"),
angle = c(45, 135),
density = 20,
xlab = "",
ylab = "Sinh viên",
yaxs = "i",
ylim = c(0, 2000),
xaxs = "i",
xlim = c(0, 7.4),
# xaxt = "n",
main = "Số lượng sinh viên theo học ở các chuyên ngành khác nhau",
width = 1,
space = 0.2,
las = 1)
par("usr")[1] 0.0 7.4 0.0 2000.0legend(x = "topright",
y = NULL,
title = "Chú thích",
legend = c("Nữ", "Nam"),
col = c("blue", "red"),
fill = c("blue", "red"),
angle = c(45, 135),
density = 20)
box()
4.1.3 Đồ thị cột percent stacked barchart
percent_1 <- prop.table(table(df$gender, df$major), margin = 2)
percent_1
Biology Economics and Finance Environmental Sciences Mathematics and Statistics Political Science Social Sciences
Female 0.6005009 0.3481873 0.4581796 0.2253061 0.6721649 0.6828063
Male 0.3994991 0.6518127 0.5418204 0.7746939 0.3278351 0.3171937barplot(height = percent_1,
beside = FALSE,
col = c("blue", "red"),
angle = c(45, 135),
density = 20,
xlab = "",
ylab = "Sinh viên (%)",
yaxs = "i",
ylim = c(0, 1),
xaxs = "i",
xlim = c(0, 7.4),
# xaxt = "n",
main = "Tỷ lệ sinh viên theo học ở các chuyên ngành khác nhau",
width = 1,
space = 0.2,
las = 1)
# par("usr")
legend(x = 6.461,
y = 1.16,
title = "Chú thích",
legend = c("Nữ", "Nam"),
horiz = TRUE,
col = c("blue", "red"),
fill = c("blue", "red"),
angle = c(45, 135),
density = 20,
xpd = TRUE)
box(which = "plot", col = "black")
# box(which = "figure", col = "red")4.2 Vẽ đồ thị đường
Ta quan tâm về salary và age xem có mối tương quan như thế nào.
### tách ra dataset clean
df_salary <- df[, c("age", "gender", "salary")]
df_salary <- na.omit(df_salary)
df_salary |> dplyr::arrange(age, salary) -> df_salary
head(df_salary, n = 30) age gender salary
1 18 Female 21000.25
2 18 Female 22313.33
3 18 Female 25979.07
4 18 Female 29178.47
5 18 Female 29550.79
6 18 Female 30062.71
7 18 Female 31266.92
8 18 Female 32038.41
9 18 Female 32943.41
10 18 Female 32966.91
11 18 Male 33908.18
12 18 Male 34460.44
13 18 Female 34492.08
14 18 Female 35507.83
15 18 Male 36031.25
16 18 Female 36589.21
17 18 Female 37245.87
18 18 Male 37691.93
19 18 Male 38422.71
20 18 Female 38738.01
21 18 Male 38764.40
22 18 Male 39298.01
23 18 Male 39994.14
24 18 Male 40495.26
25 18 Male 40590.54
26 18 Male 40899.32
27 18 Female 40964.11
28 18 Male 41816.70
29 18 Male 42388.60
30 18 Female 42539.95options(scipen = 1e9)
oldpar <- par(no.readonly = TRUE)
par(mar = c(6, 8, 4, 4))
par(mgp = c(4, 1, 0))
plot(x = df_salary$age,
y = df_salary$salary,
type = "o",
col = "darkblue",
xlim = c(0, 80),
ylim = c(0, 80000),
las = 1,
xaxs = "i",
yaxs = "i",
xlab = "Age",
ylab = "Salary",
main = paste0("Tương quan giữa tuổi và tiền lương (n = ",
dim(df_salary)[1], " sinh viên)"),
lwd = 1,
lty = 1,
bty = "o")
par(oldpar)4.3 Vẽ đồ thị hộp
Ta cắt vector age theo các độ tuổi khác nhau để thuận tiện vẽ đồ thị hộp.
df_salary$group_age <- cut(x = df_salary$age,
breaks = c(0, 20, 30, 40, 50, 60, 70),
labels = c("≤ 20",
"20 < age ≤ 30",
"30 < age ≤ 40",
"40 < age ≤ 50",
"50 < age ≤ 60",
"> 60"))
df_salary |> dplyr::arrange(age, salary) -> df_salary
as.data.frame(table(df_salary$group_age)) Var1 Freq
1 ≤ 20 577
2 20 < age ≤ 30 1093
3 30 < age ≤ 40 24
4 40 < age ≤ 50 25
5 50 < age ≤ 60 27
6 > 60 7# sample kiểu base R
# sample_x <- sample(1:nrow(df_salary), size = 30)
# df_salary[sample_x, ] -> df_sample
# sample kiểu dplyr (ngẫu nhiên)
# dplyr::sample_n(tbl = df_salary, size = 30, replace = FALSE) |> dplyr::arrange(age, salary)
set.seed(1)
# sample kiểu dplyr (ngẫu nhiên theo từng nhóm factor)
df_salary |> dplyr::group_by(group_age) |>
dplyr::sample_frac(size = 0.05, replace = FALSE) |>
dplyr::arrange(age, salary)-> sample_df
as.data.frame(sample_df) age gender salary group_age
1 18 Male 47490.25 ≤ 20
2 18 Male 48013.40 ≤ 20
3 19 Female 34791.43 ≤ 20
4 19 Male 36439.33 ≤ 20
5 19 Male 37929.52 ≤ 20
6 19 Female 38884.37 ≤ 20
7 19 Male 45582.76 ≤ 20
8 19 Female 46959.94 ≤ 20
9 19 Male 57527.17 ≤ 20
10 20 Female 23391.64 ≤ 20
11 20 Female 25706.96 ≤ 20
12 20 Female 29626.86 ≤ 20
13 20 Female 30656.10 ≤ 20
14 20 Female 33318.23 ≤ 20
15 20 Female 33606.85 ≤ 20
16 20 Male 35062.56 ≤ 20
17 20 Female 37653.17 ≤ 20
18 20 Male 38009.96 ≤ 20
19 20 Male 40151.72 ≤ 20
20 20 Male 41386.80 ≤ 20
21 20 Male 46127.31 ≤ 20
22 20 Male 47244.04 ≤ 20
23 20 Male 48036.55 ≤ 20
24 20 Female 49620.97 ≤ 20
25 20 Male 49773.36 ≤ 20
26 20 Male 53282.25 ≤ 20
27 20 Male 54234.85 ≤ 20
28 20 Male 58110.75 ≤ 20
29 20 Male 61819.85 ≤ 20
30 21 Female 26950.56 20 < age ≤ 30
31 21 Female 27884.85 20 < age ≤ 30
32 21 Male 29430.52 20 < age ≤ 30
33 21 Female 34371.45 20 < age ≤ 30
34 21 Female 34398.15 20 < age ≤ 30
35 21 Male 37554.01 20 < age ≤ 30
36 21 Male 41594.78 20 < age ≤ 30
37 21 Male 43989.75 20 < age ≤ 30
38 21 Female 45241.26 20 < age ≤ 30
39 21 Female 48089.85 20 < age ≤ 30
40 21 Male 48426.91 20 < age ≤ 30
41 21 Male 48573.85 20 < age ≤ 30
42 21 Male 52433.73 20 < age ≤ 30
43 21 Male 54008.34 20 < age ≤ 30
44 21 Male 54141.46 20 < age ≤ 30
45 21 Male 57994.47 20 < age ≤ 30
46 21 Male 61778.79 20 < age ≤ 30
47 21 Male 65441.30 20 < age ≤ 30
48 22 Female 30908.99 20 < age ≤ 30
49 22 Male 32394.79 20 < age ≤ 30
50 22 Male 33924.95 20 < age ≤ 30
51 22 Male 35881.21 20 < age ≤ 30
52 22 Male 37028.64 20 < age ≤ 30
53 22 Male 37668.89 20 < age ≤ 30
54 22 Female 38655.83 20 < age ≤ 30
55 22 Female 40466.69 20 < age ≤ 30
56 22 Male 41263.86 20 < age ≤ 30
57 22 Female 43552.60 20 < age ≤ 30
58 22 Female 45055.01 20 < age ≤ 30
59 22 Female 45254.11 20 < age ≤ 30
60 22 Male 48747.97 20 < age ≤ 30
61 22 Male 49068.50 20 < age ≤ 30
62 22 Male 49079.84 20 < age ≤ 30
63 22 Male 50988.39 20 < age ≤ 30
64 22 Male 51568.11 20 < age ≤ 30
65 22 Male 61260.51 20 < age ≤ 30
66 23 Male 33259.90 20 < age ≤ 30
67 23 Male 34103.23 20 < age ≤ 30
68 23 Male 43190.20 20 < age ≤ 30
69 23 Female 43287.32 20 < age ≤ 30
70 23 Male 45685.02 20 < age ≤ 30
71 23 Male 46434.88 20 < age ≤ 30
72 23 Male 47249.99 20 < age ≤ 30
73 23 Male 47871.75 20 < age ≤ 30
74 23 Male 49455.72 20 < age ≤ 30
75 24 Male 41309.07 20 < age ≤ 30
76 24 Female 52259.07 20 < age ≤ 30
77 24 Male 53462.19 20 < age ≤ 30
78 24 Male 54680.65 20 < age ≤ 30
79 24 Male 58133.21 20 < age ≤ 30
80 25 Male 39111.92 20 < age ≤ 30
81 25 Male 39765.06 20 < age ≤ 30
82 26 Female 31484.80 20 < age ≤ 30
83 26 Female 48830.73 20 < age ≤ 30
84 26 Male 75530.39 20 < age ≤ 30
85 39 Male 30375.19 30 < age ≤ 40
86 44 Male 55860.90 40 < age ≤ 50
87 56 Male 37119.18 50 < age ≤ 60oldpar <- par(no.readonly = TRUE)
par(mar = c(6, 8, 4, 4))
par(mgp = c(4, 1, 0))
boxplot(formula = salary ~ group_age,
data = df_salary,
col = rainbow(n = 6),
las = 1,
xlab = "Nhóm tuổi",
ylab = "Mức lương",
main = "Đồ thị thể hiện mức lương theo các nhóm tuổi")
par(oldpar)oldpar <- par(no.readonly = TRUE)
par(mar = c(6, 12, 4, 4))
par(mgp = c(4, 1, 0))
boxplot(formula = salary ~ group_age + gender,
data = df_salary,
col = c(rep("cyan", 6), rep("coral", 6)),
las = 1,
xlab = "Mức lương",
ylab = "",
horizontal = TRUE,
sep = "-",
lex.order = FALSE,
# names = c(letters[1:12]),
main = "Đồ thị thể hiện mức lương theo các nhóm tuổi và giới tính")
par(oldpar)oldpar <- par(no.readonly = TRUE)
par(mar = c(6, 12, 4, 4))
par(mgp = c(4, 1, 0))
boxplot(formula = salary ~ group_age + gender,
data = df_salary,
col = rep(c("cyan", "coral"), 6),
las = 1,
xlab = "Mức lương",
ylab = "",
horizontal = TRUE,
sep = "-",
lex.order = TRUE,
# names = c(letters[1:12]),
main = "Đồ thị thể hiện mức lương theo các nhóm tuổi và giới tính")
par(oldpar)df_salary |> dplyr::group_by(gender, group_age) |>
dplyr::summarise(trung_binh_salary = mean(salary),
trung_vi_salary = median(salary),
do_lech_chuan_salary = sd(salary),
max_salary = max(salary),
min_salary = min(salary),
so_luong = n()) -> summary_salary
as.data.frame(summary_salary) gender group_age trung_binh_salary trung_vi_salary do_lech_chuan_salary max_salary min_salary so_luong
1 Female ≤ 20 35559.89 35470.46 7704.974 54590.09 19636.59 232
2 Female 20 < age ≤ 30 36167.63 35987.41 7718.234 63154.33 11444.14 413
3 Female 30 < age ≤ 40 37033.79 34200.54 5025.071 43000.51 31806.92 7
4 Female 40 < age ≤ 50 39263.15 38688.85 9980.751 52691.50 23044.40 10
5 Female 50 < age ≤ 60 36698.65 35282.33 9103.857 53565.90 20690.65 8
6 Female > 60 35965.51 33467.47 6982.298 46130.95 30796.16 4
7 Male ≤ 20 46524.48 46306.62 9662.660 73980.21 14081.10 345
8 Male 20 < age ≤ 30 46510.91 45709.30 9695.876 75596.79 18571.24 680
9 Male 30 < age ≤ 40 46259.95 49103.72 8761.094 56853.81 27821.99 17
10 Male 40 < age ≤ 50 47586.86 47360.78 8112.779 58316.01 34491.28 15
11 Male 50 < age ≤ 60 50107.44 52875.74 10326.033 70094.85 35727.41 19
12 Male > 60 44729.42 39413.49 11353.582 57765.71 37009.08 34.4 Vẽ nhiều đồ thị con trong một hình
Vẽ đồ thị giữa height và weight
df_height <- df[, c("age", "gender", "major", "height", "weight", "salary")]
head(df_height, n = 30) age gender major height weight salary
1 19 Female Political Science 160 64.8 NA
2 19 Female Social Sciences 172 73.0 NA
3 22 Female Social Sciences 168 70.6 NA
4 19 Male Environmental Sciences 183 79.7 NA
5 21 Female Environmental Sciences 175 71.4 NA
6 19 Male Political Science 189 85.8 NA
7 21 Female Political Science 156 65.9 NA
8 21 Female Political Science 167 65.7 NA
9 18 Male Economics and Finance 195 94.4 NA
10 18 Female Environmental Sciences 165 66.0 NA
11 22 Female Economics and Finance 162 66.8 45254.11
12 18 Female Environmental Sciences 172 66.8 NA
13 23 Male Environmental Sciences 185 84.6 40552.79
14 20 Female Environmental Sciences 158 64.4 27007.03
15 19 Female Economics and Finance 157 66.3 NA
16 20 Male Mathematics and Statistics 172 73.9 NA
17 22 Female Political Science 156 61.7 33969.16
18 22 Male Economics and Finance 182 82.1 NA
19 21 Female Political Science 162 69.2 NA
20 22 Female Environmental Sciences 168 70.9 NA
21 20 Female Biology 167 68.5 NA
22 37 Male Political Science 175 70.4 NA
23 19 Male Political Science 164 70.3 NA
24 38 Female Environmental Sciences 155 67.0 NA
25 23 Male Economics and Finance 183 81.8 NA
26 26 Female Biology 145 54.0 NA
27 25 Female Social Sciences 161 66.8 NA
28 24 Male Economics and Finance 182 80.1 50617.64
29 54 Female Political Science 169 71.4 NA
30 22 Male Mathematics and Statistics 172 69.6 NAoldpar <- par(no.readonly = TRUE)
par(mar = c(6, 8, 4, 4))
par(mgp = c(4, 1, 0))
plot(x = df_height$weight,
y = df_height$height,
type = "p",
col = "darkgreen",
pch = 1,
cex = 0.5,
xlim = c(0, 120),
ylim = c(0, 250),
las = 1,
xaxs = "i",
yaxs = "i",
xlab = "Cân nặng (kg)",
ylab = "Chiều cao (cm)",
main = paste0("Tương quan giữa chiều cao và cân nặng (n = ",
dim(df_height)[1], " sinh viên)"),
lwd = 1,
lty = 1,
bty = "o")
par(oldpar)Tô màu theo gender
df_height <- df[, c("age", "gender", "religion", "height", "weight", "salary")]
col_1 <- factor(df_height$gender)
oldpar <- par(no.readonly = TRUE)
par(mar = c(6, 8, 4, 4))
# par(mgp = c(4, 1, 0))
plot(x = df_height$weight,
y = df_height$height,
type = "p",
col = c("blue", "red")[col_1],
pch = 1,
cex = 0.5,
xlim = c(0, 120),
ylim = c(0, 250),
las = 1,
xaxs = "i",
yaxs = "i",
xlab = "Cân nặng (kg)",
ylab = "Chiều cao (cm)",
main = paste0("Tương quan giữa chiều cao và cân nặng (n = ",
dim(df_height)[1], " sinh viên)"),
lwd = 1,
lty = 1,
bty = "o")
legend(x = "bottomleft",
y = NULL,
title = "Chú thích",
legend = c("Nữ", "Nam"),
# horiz = TRUE,
col = c("blue", "red"),
# fill = c("blue", "red"),
pch = 1,
xpd = TRUE)
par(oldpar)4.4.1 Sử dụng package lattice vẽ đồ thị scatterplot
library(lattice)
lattice::xyplot(x = height ~ weight | gender + religion,
groups = gender,
col = c("blue", "red"),
origin = 0,
xlim = c(30, 130),
ylim = c(50, 250),
xlab = "Cân nặng (kg)",
ylab = "Chiều cao (cm)",
main = paste0("Tương quan giữa chiều cao và cân nặng theo giới tính và tôn giáo (n = ",
dim(df_height)[1], " sinh viên)"),
data = df_height)
4.4.2 Vẽ đồ thị nhiều biến
df_clean <- df[, c("age", "gender", "religion", "height", "weight", "salary")]
df_clean <- na.omit(df_clean)
df_clean$group_age <- cut(x = df_clean$age,
breaks = c(0, 20, 30, 40, 50, 60, 70),
labels = c("≤ 20",
"20 < age ≤ 30",
"30 < age ≤ 40",
"40 < age ≤ 50",
"50 < age ≤ 60",
"> 60"))
df_clean |> dplyr::arrange(age) -> df_clean
as.data.frame(table(df_clean$group_age)) Var1 Freq
1 ≤ 20 577
2 20 < age ≤ 30 1093
3 30 < age ≤ 40 24
4 40 < age ≤ 50 25
5 50 < age ≤ 60 27
6 > 60 7library(lattice)
head(df_clean) age gender religion height weight salary group_age
1 18 Male Protestant 178 74.3 51564.04 ≤ 20
2 18 Male Catholic 190 91.4 43332.84 ≤ 20
3 18 Male Protestant 186 84.2 64525.11 ≤ 20
4 18 Male Catholic 187 84.4 37691.93 ≤ 20
5 18 Male Catholic 169 68.9 38422.71 ≤ 20
6 18 Male Other 192 92.2 34460.44 ≤ 20options(scipen = 10)
df_clean$bmi <- df_clean$weight / (df_clean$height * 0.01)^2
df_clean$religion <- factor(df_clean$religion)
labels <- levels(df_clean$religion)
lattice::xyplot(x = bmi ~ salary | group_age + gender,
groups = religion,
col = adjustcolor(col = c("red", "black", "darkgreen", "blue", "purple"),
alpha.f = 0.5),
pch = c(15, 17, 18, 19, 8),
origin = 0,
ylim = c(15, 31),
xlim = c(0, 80000),
xlab = "Salary (USD)",
ylab = "BMI index",
main = paste0("Phân bố mức lương theo chỉ số BMI, giới tính và nhóm tuổi (n = ",
dim(df_clean)[1], " sinh viên)"),
key = list(space = "top",
columns = 5,
title = "Chú thích",
points=list(pch = c(15, 17, 18, 19, 8),
col = adjustcolor(col = c("red", "black", "darkgreen", "blue", "purple"),
alpha.f = 1)),
text = list(labels)),
scale = list(alternating = 3, rot = 0),
panel = function(...) {
panel.abline(h = 30, col = "red", lty = 2)
panel.abline(h = 18.5, col = "red", lty = 2)
panel.abline(v = 20000, col = "blue", lty = 2)
panel.abline(v = 60000, col = "blue", lty = 2)
panel.xyplot(...)
},
data = df_clean)
4.4.3 Vẽ đồ thị histogram
height_data <- hist(df$height,
col = "lightyellow",
main = "Histogram of students' height",
xlab = "Height (cm)",
ylab = "Density",
xlim = c(120, 220),
ylim = c(0, 0.04),
probability = TRUE)
lines(density(df$height),
col = "red",
lty = 1,
lwd = 2)
day_so <- seq(from = min(df$height, na.rm = TRUE),
to = max(df$height, na.rm = TRUE),
length = 1000)
curve(expr = dnorm(day_so,
mean = mean(df$height, na.rm = TRUE),
sd = sd(df$height, na.rm = TRUE)),
type = "l",
add = TRUE,
lwd = 2,
xname = "day_so",
col = "blue",
lty = "dotted")
legend(x = "topright",
y = NULL,
legend = c("kernel density", "normal curve"),
col = c("red", "blue"),
lwd = 2,
lty = c(1, 2))
height_data$breaks
[1] 135 140 145 150 155 160 165 170 175 180 185 190 195 200 205 210
$counts
[1] 7 43 167 402 862 1111 1314 1284 1206 940 593 240 56 12 2
$density
[1] 1.699235e-04 1.043816e-03 4.053890e-03 9.758466e-03 2.092487e-02 2.696929e-02 3.189707e-02 3.116883e-02 2.927540e-02 2.281830e-02 1.439495e-02 5.825950e-03 1.359388e-03 2.912975e-04 4.854958e-05
$mids
[1] 137.5 142.5 147.5 152.5 157.5 162.5 167.5 172.5 177.5 182.5 187.5 192.5 197.5 202.5 207.5
$xname
[1] "df$height"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"