Prevalence of Delinquency- China’s Rural Adolescents
Siyang Ni
2022-12-05
Setting Environment
library(epiDisplay)## Warning: package 'epiDisplay' was built under R version 4.2.2library(tidyverse)## Warning: package 'readr' was built under R version 4.2.2df <- read_csv("D:/research/thesis/data/bugfix2_attach_caregiver.csv")Prevelance of delinquency by types
(1) “using indecent language in online chat or forum,”
Original
tab1(df$B24j, cum.percent = TRUE)
## df$B24j :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 1 1691 59.6 59.6 59.8 59.8
## 2 844 29.7 89.3 29.8 89.6
## 3 209 7.4 96.7 7.4 97.0
## 4 85 3.0 99.6 3.0 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b24j, cum.percent = TRUE)
## df$b24j :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 1691 59.6 59.6 59.8 59.8
## 1 1138 40.1 99.6 40.2 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(2) “using or distributing pirated software,”
Original
tab1(df$B24k, cum.percent = TRUE)
## df$B24k :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 1 2509 88.4 88.4 88.7 88.7
## 2 232 8.2 96.5 8.2 96.9
## 3 50 1.8 98.3 1.8 98.7
## 4 37 1.3 99.6 1.3 100.0
## <NA> 11 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b24j, cum.percent = TRUE)
## df$b24j :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 1691 59.6 59.6 59.8 59.8
## 1 1138 40.1 99.6 40.2 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(3) “using a fake identity to communicate with others frequently,”
Original
tab1(df$B24l, cum.percent = TRUE)
## df$B24l :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 1 2441 86.0 86.0 86.5 86.5
## 2 274 9.7 95.6 9.7 96.2
## 3 70 2.5 98.1 2.5 98.7
## 4 38 1.3 99.4 1.3 100.0
## <NA> 16 0.6 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b24l, cum.percent = TRUE)
## df$b24l :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2441 86.0 86.0 86.5 86.5
## 1 382 13.5 99.4 13.5 100.0
## <NA> 16 0.6 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(4) “logging into or using other’s online account without authorization,”
Original
tab1(df$B24m, cum.percent = TRUE)
## df$B24m :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 1 2539 89.4 89.4 89.7 89.7
## 2 189 6.7 96.1 6.7 96.4
## 3 55 1.9 98.0 1.9 98.4
## 4 46 1.6 99.6 1.6 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b24m, cum.percent = TRUE)
## df$b24m :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2539 89.4 89.4 89.7 89.7
## 1 290 10.2 99.6 10.3 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(5) “browsing or downloading pornography online intentionally,”
Original
tab1(df$B24n, cum.percent = TRUE)
## df$B24n :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 3 0.1 0.1 0.1 0.1
## 1 2668 94.0 94.1 94.3 94.4
## 2 92 3.2 97.3 3.3 97.7
## 3 32 1.1 98.5 1.1 98.8
## 4 34 1.2 99.6 1.2 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b24n, cum.percent = TRUE)
## df$b24n :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2668 94.0 94.0 94.3 94.3
## 1 161 5.7 99.6 5.7 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(6) “cigarette smoking,”
Original
tab1(df$B27a, cum.percent = TRUE)
## df$B27a :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2357 83.0 83.0 83.2 83.2
## 1 314 11.1 94.1 11.1 94.3
## 2 101 3.6 97.6 3.6 97.9
## 3 60 2.1 99.8 2.1 100.0
## <NA> 7 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27a, cum.percent = TRUE)
## df$b27a :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2357 83.0 83.0 83.2 83.2
## 1 475 16.7 99.8 16.8 100.0
## <NA> 7 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(7) “drinking alcohol,”
Original
tab1(df$B27b, cum.percent = TRUE)
## df$B27b :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 1704 60.0 60.0 60.3 60.3
## 1 738 26.0 86.0 26.1 86.4
## 2 321 11.3 97.3 11.4 97.7
## 3 64 2.3 99.6 2.3 100.0
## <NA> 12 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27b, cum.percent = TRUE)
## df$b27b :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 1704 60.0 60.0 60.3 60.3
## 1 1123 39.6 99.6 39.7 100.0
## <NA> 12 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(8) “getting drunk,”
Original
tab1(df$B27c, cum.percent = TRUE)
## df$B27c :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2559 90.1 90.1 90.4 90.4
## 1 194 6.8 97.0 6.9 97.2
## 2 50 1.8 98.7 1.8 99.0
## 3 28 1.0 99.7 1.0 100.0
## <NA> 8 0.3 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27c, cum.percent = TRUE)
## df$b27c :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2559 90.1 90.1 90.4 90.4
## 1 272 9.6 99.7 9.6 100.0
## <NA> 8 0.3 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(9) “skipping school,”
Original
tab1(df$B27d, cum.percent = TRUE)
## df$B27d :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2554 90.0 90.0 90.2 90.2
## 1 208 7.3 97.3 7.3 97.5
## 2 50 1.8 99.0 1.8 99.3
## 3 20 0.7 99.8 0.7 100.0
## <NA> 7 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27d, cum.percent = TRUE)
## df$b27d :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2554 90.0 90.0 90.2 90.2
## 1 278 9.8 99.8 9.8 100.0
## <NA> 7 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(10) “gambling,”
Original
tab1(df$B27e, cum.percent = TRUE)
## df$B27e :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2606 91.8 91.8 92.0 92.0
## 1 152 5.4 97.1 5.4 97.3
## 2 63 2.2 99.4 2.2 99.5
## 3 13 0.5 99.8 0.5 100.0
## <NA> 5 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27e, cum.percent = TRUE)
## df$b27e :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2606 91.8 91.8 92 92
## 1 228 8.0 99.8 8 100
## <NA> 5 0.2 100.0 0 100
## Total 2839 100.0 100.0 100 100
(11) “stealing,”
Original
tab1(df$B27f, cum.percent = TRUE)
## df$B27f :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2759 97.2 97.2 97.4 97.4
## 1 59 2.1 99.3 2.1 99.5
## 2 7 0.2 99.5 0.2 99.7
## 3 8 0.3 99.8 0.3 100.0
## <NA> 6 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27f, cum.percent = TRUE)
## df$b27f :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2759 97.2 97.2 97.4 97.4
## 1 74 2.6 99.8 2.6 100.0
## <NA> 6 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(12) “robbery,”
Original
tab1(df$B27g, cum.percent = TRUE)
## df$B27g :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2807 98.9 98.9 99.3 99.3
## 1 13 0.5 99.3 0.5 99.7
## 2 4 0.1 99.5 0.1 99.9
## 3 4 0.1 99.6 0.1 100.0
## <NA> 11 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27g, cum.percent = TRUE)
## df$b27g :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2807 98.9 98.9 99.3 99.3
## 1 21 0.7 99.6 0.7 100.0
## <NA> 11 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(13) “vandalism,” (including graffitiing)
Original
tab1(df$B27h, cum.percent = TRUE)
## df$B27h :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2216 78.1 78.1 78.3 78.3
## 1 530 18.7 96.7 18.7 97.1
## 2 66 2.3 99.0 2.3 99.4
## 3 17 0.6 99.6 0.6 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27h, cum.percent = TRUE)
## df$b27h :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2216 78.1 78.1 78.3 78.3
## 1 613 21.6 99.6 21.7 100.0
## <NA> 10 0.4 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(14) “fighting with others physically,”
Original
tab1(df$B27i, cum.percent = TRUE)
## df$B27i :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2219 78.2 78.2 78.4 78.4
## 1 443 15.6 93.8 15.6 94.0
## 2 134 4.7 98.5 4.7 98.8
## 3 35 1.2 99.7 1.2 100.0
## <NA> 8 0.3 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27i, cum.percent = TRUE)
## df$b27i :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2219 78.2 78.2 78.4 78.4
## 1 612 21.6 99.7 21.6 100.0
## <NA> 8 0.3 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0
(15) “deception for money or things.”
Original
tab1(df$B27j, cum.percent = TRUE)
## df$B27j :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2805 98.8 98.8 99.0 99.0
## 1 16 0.6 99.4 0.6 99.5
## 2 3 0.1 99.5 0.1 99.6
## 3 10 0.4 99.8 0.4 100.0
## <NA> 5 0.2 100.0 0.0 100.0
## Total 2839 100.0 100.0 100.0 100.0Dichotomous Recode
tab1(df$b27j, cum.percent = TRUE)
## df$b27j :
## Frequency %(NA+) cum.%(NA+) %(NA-) cum.%(NA-)
## 0 2805 98.8 98.8 99 99
## 1 29 1.0 99.8 1 100
## <NA> 5 0.2 100.0 0 100
## Total 2839 100.0 100.0 100 100Summary
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