PRACTICE D3
HOA NGUYEN
2022-04-19
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library(readxl);
library(tidyverse);## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2);
library(dplyr);
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractlibrary(gapminder)
library(ggthemes)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(base)
library(compareGroups)
library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-#arr=Arrest dataset, ins= Insurance dataset
arr=read.csv("C:\\Users\\hntn\\OneDrive - Sun Hydraulics\\Hoa\\Ftu\\DATA ANALYSIS\\Dataset for TDTU workshop 4-2022\\Arrest dataset.csv")
head(arr)## id age finance week arrest race work.exp married parole prior educ
## 1 1 27 no 20 1 black no not married yes 3 3
## 2 2 18 no 17 1 black no not married yes 8 4
## 3 3 19 no 25 1 other yes not married yes 13 3
## 4 4 23 yes 52 0 black yes married yes 1 5
## 5 5 19 no 52 0 other yes not married yes 3 3
## 6 6 24 no 52 0 black yes not married no 2 4
## employ1
## 1 no
## 2 no
## 3 no
## 4 no
## 5 no
## 6 noins=read_excel("C:\\Users\\hntn\\OneDrive - Sun Hydraulics\\Hoa\\Ftu\\DATA ANALYSIS\\Dataset for TDTU workshop 4-2022\\Insurance dataset.xlsx")
head(ins)## # A tibble: 6 x 7
## age sex bmi children smoker region charge
## <dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 19 female 27.9 0 yes southwest 16885.
## 2 18 male 33.8 1 no southeast 1726.
## 3 28 male 33 3 no southeast 4449.
## 4 33 male 22.7 0 no northwest 21984.
## 5 32 male 28.9 0 no northwest 3867.
## 6 31 female 25.7 0 no southeast 3757.summary(ins)## age sex bmi children
## Min. :18.00 Length:1338 Min. :15.96 Min. :0.000
## 1st Qu.:27.00 Class :character 1st Qu.:26.30 1st Qu.:0.000
## Median :39.00 Mode :character Median :30.40 Median :1.000
## Mean :39.21 Mean :30.66 Mean :1.095
## 3rd Qu.:51.00 3rd Qu.:34.69 3rd Qu.:2.000
## Max. :64.00 Max. :53.13 Max. :5.000
## smoker region charge
## Length:1338 Length:1338 Min. : 1122
## Class :character Class :character 1st Qu.: 4740
## Mode :character Mode :character Median : 9382
## Mean :13270
## 3rd Qu.:16640
## Max. :63770table1(~age+sex+bmi+children+smoker+charge|region,data=ins)| northeast (N=324) |
northwest (N=325) |
southeast (N=364) |
southwest (N=325) |
Overall (N=1338) |
|
|---|---|---|---|---|---|
| age | |||||
| Mean (SD) | 39.3 (14.1) | 39.2 (14.1) | 38.9 (14.2) | 39.5 (14.0) | 39.2 (14.0) |
| Median [Min, Max] | 39.5 [18.0, 64.0] | 39.0 [19.0, 64.0] | 39.0 [18.0, 64.0] | 39.0 [19.0, 64.0] | 39.0 [18.0, 64.0] |
| sex | |||||
| female | 161 (49.7%) | 164 (50.5%) | 175 (48.1%) | 162 (49.8%) | 662 (49.5%) |
| male | 163 (50.3%) | 161 (49.5%) | 189 (51.9%) | 163 (50.2%) | 676 (50.5%) |
| bmi | |||||
| Mean (SD) | 29.2 (5.94) | 29.2 (5.14) | 33.4 (6.48) | 30.6 (5.69) | 30.7 (6.10) |
| Median [Min, Max] | 28.9 [16.0, 48.1] | 28.9 [17.4, 42.9] | 33.3 [19.8, 53.1] | 30.3 [17.4, 47.6] | 30.4 [16.0, 53.1] |
| children | |||||
| Mean (SD) | 1.05 (1.20) | 1.15 (1.17) | 1.05 (1.18) | 1.14 (1.28) | 1.09 (1.21) |
| Median [Min, Max] | 1.00 [0, 5.00] | 1.00 [0, 5.00] | 1.00 [0, 5.00] | 1.00 [0, 5.00] | 1.00 [0, 5.00] |
| smoker | |||||
| no | 257 (79.3%) | 267 (82.2%) | 273 (75.0%) | 267 (82.2%) | 1064 (79.5%) |
| yes | 67 (20.7%) | 58 (17.8%) | 91 (25.0%) | 58 (17.8%) | 274 (20.5%) |
| charge | |||||
| Mean (SD) | 13400 (11300) | 12400 (11100) | 14700 (14000) | 12300 (11600) | 13300 (12100) |
| Median [Min, Max] | 10100 [1690, 58600] | 8970 [1620, 60000] | 9290 [1120, 63800] | 8800 [1240, 52600] | 9380 [1120, 63800] |
#DUNG COMPARE GROUPS TOM TAT DU LIEU arr
createTable(compareGroups(finance~age+race + prior + parole, data = arr))##
## --------Summary descriptives table by 'finance'---------
##
## ___________________________________________
## no yes p.overall
## N=216 N=216
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## age 24.2 (5.73) 25.0 (6.47) 0.203
## race: 0.241
## black 185 (85.6%) 194 (89.8%)
## other 31 (14.4%) 22 (10.2%)
## prior 2.99 (2.92) 2.98 (2.88) 0.987
## parole: 0.843
## no 81 (37.5%) 84 (38.9%)
## yes 135 (62.5%) 132 (61.1%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯#KIEM DINH SU KHAC BIET VE TI LE TAI PHAM GIUA NGUOI DA DEN VA NGUOI KO DA DEN
createTable(compareGroups(arrest~race, data = arr))##
## --------Summary descriptives table by 'arrest'---------
##
## ___________________________________________
## 0 1 p.overall
## N=318 N=114
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## race: 0.621
## black 277 (87.1%) 102 (89.5%)
## other 41 (12.9%) 12 (10.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯chisq.test(arr$race, arr$arrest)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: arr$race and arr$arrest
## X-squared = 0.24452, df = 1, p-value = 0.621BIEU DIEN DUOI THANH BAR
arr$arrest = as.factor(arr$arrest)
createTable(compareGroups(race~ arrest, data=arr))##
## --------Summary descriptives table by 'race'---------
##
## ________________________________________
## black other p.overall
## N=379 N=53
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## arrest: 0.621
## 0 277 (73.1%) 41 (77.4%)
## 1 102 (26.9%) 12 (22.6%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯arr %>% count(arrest,race) %>% group_by (arrest) %>% mutate (percent = n / sum(n) *100) %>% ggplot (aes(x=arrest,y= percent, fill = race)) + geom_bar(stat = "identity") + geom_text(aes(label=paste0 (sprintf ("%1.1f", percent), "%")), position = position_stack (vjust =0.5)) + theme (legend.position = "none") + labs (x = "Tái phạm", y= "Phần trăm (%)") 
# VIEC 5 - KIEM DINH GIA THIET VO HIEU RANG KHONG CO SU KHAC BIET VE TI
LE TAI PHAM GIUA NHOM DUOC HO TRO TAI CHANH VA KO
createTable(compareGroups(arrest~finance, data=arr))##
## --------Summary descriptives table by 'arrest'---------
##
## _________________________________________
## 0 1 p.overall
## N=318 N=114
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## finance: 0.063
## no 150 (47.2%) 66 (57.9%)
## yes 168 (52.8%) 48 (42.1%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯chisq.test(arr$finance, arr$arrest)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: arr$finance and arr$arrest
## X-squared = 3.4439, df = 1, p-value = 0.06349#TRINH DO HOC VAN CO LIEN QUAN DEN TI LE TAI PHAM
arr$edu = as.factor(arr$educ)
arr %>% count (arrest, edu) %>% group_by (arrest) %>% mutate (percent = n / sum(n) *100) %>% ggplot (aes(x= arrest, y = percent, fill = edu)) + geom_bar (stat = "identity") + geom_text(aes(label=paste0 (sprintf ("%1.1f", percent), "%")), position = position_stack (vjust =0.5)) + theme (legend.position = "none") + labs (x = "Tái phạm", y= "Phần trăm (%)") 
VIEC 6- KIEM DINH SU KHAC BIET VE DO TUOI VA TIEN SU TOI PHAM GIUA NGUOI TAI PHAM VA KO TAI PHAM
Nguoi tai pham tre hon Nguoi khong tai pham?
createTable(compareGroups(arrest~age,data=arr))##
## --------Summary descriptives table by 'arrest'---------
##
## _____________________________________
## 0 1 p.overall
## N=318 N=114
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## age 25.3 (6.31) 22.8 (5.12) <0.001
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯t.test (arr$age ~ arr$arrest)##
## Welch Two Sample t-test
##
## data: arr$age by arr$arrest
## t = 4.1789, df = 243.6, p-value = 4.086e-05
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## 1.317143 3.665975
## sample estimates:
## mean in group 0 mean in group 1
## 25.25472 22.76316createTable (compareGroups (arrest~ prior, data = arr))##
## --------Summary descriptives table by 'arrest'---------
##
## _______________________________________
## 0 1 p.overall
## N=318 N=114
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## prior 2.70 (2.55) 3.77 (3.59) 0.004
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯#t.test (arr$prior, arr$arrest)