Import_Descriptive
2024-07-22
Техническая информация и библиотеки
rm(list = ls())
date()
## [1] "Fri Aug 9 14:43:27 2024"
sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-apple-darwin20
## Running under: macOS Ventura 13.6.8
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Moscow
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.36 R6_2.5.1 fastmap_1.2.0 xfun_0.46
## [5] cachem_1.1.0 knitr_1.48 htmltools_0.5.8.1 rmarkdown_2.27
## [9] lifecycle_1.0.4 cli_3.6.3 sass_0.4.9 jquerylib_0.1.4
## [13] compiler_4.4.1 rstudioapi_0.16.0 tools_4.4.1 evaluate_0.24.0
## [17] bslib_0.7.0 yaml_2.3.9 rlang_1.1.4 jsonlite_1.8.8
library(ggplot2)
library(dplyr)
library(tidyr)
library(psych)Описание переменных
Общие данные
subject_id номер
School школа
Class класс
Gender пол (1-м,2-д)
Age возраст
Шкала психологического благополучия Рифф (W)
W_PositiveRel Позитивные отношения
W_Autonomy Автономия
W_EnvMastery Управление средой
W_PersGrowth Личностный рост
W_PurposeLife Цели в жизни
W_SelfAcceptance Самопринятие
WellBeing Психологическое благополучие
Субъективное благополучие (SW)
SW_NapriCHuvstv Напр.иЧувств.
SW_PsiEmSim ПсиЭмСим
SW_Nastroenie Настроение
SW_SocOkr Соц.Окр.
SW_SamoocZdorov Самооц.Здоров.
SW_StUdovl Ст.Удовл.
SubWellBeing Субъективное благополучие
Психологическая безопасность (PB)
PB_1 1
PB_2 2
PB_3 3
PB_4a 4а
PB_4b 4б
PB_5 5
PB_6 6
PB_7a 07а
PB_b 07б
PB_c 07в
PB_d 07г
PB_e 07д
PB_f 07.е
PB_g 07ж
PB_h 07з
PB_8 8
PB_9-1a 9.1а
PB_9-1b 9.1б
PB_9-2a 9.2а
PB_9-2b 9.2б
PB_9-3a 9.3а
PB_9-3b 9.3б
PB_4a 9.4а
PB_4b 9.4б
PB_5a 9.5а
PB_5b 9.5б
PB_10 10
PB_O O
PB_U У
PB_Z З
Опросник самоотношения (SO)
SO_S-GlobSamootnoshenie Шкала S (глобальное самоотношение)
SO_I-Samouvazhenie Шкала самоуважения (I)
SO_II-Autosimpatiya Шкала аутосимпатии (II)
SO_III-OzhidOtnoshenie Шкала ожидаемого отношения от других (III)
SO_IV-Samointeres Шкала самоинтересов (IV)
SO_1-Samouverennost Шкала самоуверенности
SO_2-OtnoshenieDrugih Шкала отношения других
SO_3-Samoprinyatie Шкала самопринятия
SO_4-Samorukovodstvo Шкала саморуководства
SO_5-Samoobvinenie Шкала самообвинения
SO_6-Samointeres Шкала самоинтереса
SO_7-Samoponimanie Шкала самопонимания
Способы совладающего поведения (SP)
SP_Konfrontaciya Конфронтация
SP_Distancirovanie Дистанцирование
SP_Samokontrol Самоконтроль
SP_PoiskCocPodderzhki Поиск социальной поддержки
SP_PrinyatieOtvetstv Принятие ответственности
SP_BegstvoIzbeganie Бегство-избегание
SP_Planirovanie Планирование решения проблемы
SP_PolPereocenka Положительная переоценка
Тест жизнестойкости (ZhS)
ZhS_Vovlechennost Вовлеченность
ZhS_Kontrol Контроль
ZhS_PrinyatieRiska Принятие риска
ZhS_ZHiznestojkost Жизнестойкость
Стили познания (P)
P_AK АК
P_AE АЭ
P_KO КО
P_RN РН
Импорт данных и первичная проверка
Data <- read.delim2("../Data/SpecSchool.tsv", na.strings = c("", "-"))
Data$Gender <- factor(Data$Gender, levels = c(1,2), labels = c("М", "Ж"))
str(Data)## 'data.frame': 286 obs. of 77 variables:
## $ subject_id : chr "137" "140" "115" "116" ...
## $ School : chr "225" "225" "225" "225" ...
## $ Class : chr "10Б" "10Б" "8Б" "8Б" ...
## $ Gender : Factor w/ 2 levels "М","Ж": 1 2 2 1 1 2 1 1 1 2 ...
## $ Age : int 17 16 14 14 15 14 14 15 15 14 ...
## $ W_PositiveRel : int 47 76 57 34 45 66 62 61 62 51 ...
## $ W_Autonomy : int 73 56 48 26 45 43 61 77 43 50 ...
## $ W_EnvMastery : int 40 66 43 40 40 54 39 73 46 51 ...
## $ W_PersGrowth : int 73 71 70 37 56 79 52 59 57 75 ...
## $ W_PurposeLife : int 42 72 66 27 47 67 57 80 54 57 ...
## $ W_SelfAcceptance : int 36 72 58 14 31 60 54 72 29 40 ...
## $ WellBeing : int 311 413 342 178 264 369 325 422 291 324 ...
## $ SW_NapriCHuvstv : int 9 17 11 11 13 14 8 10 13 9 ...
## $ SW_PsiEmSim : int 7 25 16 7 9 18 12 20 11 12 ...
## $ SW_Nastroenie : int 5 14 12 5 5 11 8 13 7 9 ...
## $ SW_SocOkr : int 12 21 21 10 12 16 11 18 16 9 ...
## $ SW_SamoocZdorov : int 5 14 13 4 4 6 13 10 3 6 ...
## $ SW_StUdovl : int 13 20 17 3 5 13 3 20 9 13 ...
## $ SubWellBeing : int 51 111 90 40 48 78 55 91 59 58 ...
## $ PB_1 : int 2 0 2 2 2 2 2 2 2 2 ...
## $ PB_2 : int 2 2 2 2 2 2 0 2 2 0 ...
## $ PB_3 : int 2 2 2 2 2 2 0 0 2 2 ...
## $ PB_4a : int 2 2 2 2 2 2 2 2 2 2 ...
## $ PB_4b : int 2 2 2 2 0 0 1 1 0 2 ...
## $ PB_5 : int 2 2 0 2 2 2 0 2 2 1 ...
## $ PB_6 : int 0 2 2 1 1 2 1 2 2 2 ...
## $ PB_7a : int 4 5 3 5 3 5 3 5 4 3 ...
## $ PB_b : int 4 5 5 4 3 5 3 5 5 3 ...
## $ PB_c : int 5 4 4 2 4 4 3 5 3 NA ...
## $ PB_d : int 4 5 4 4 4 4 2 5 3 2 ...
## $ PB_e : int 5 5 3 5 4 4 1 5 4 NA ...
## $ PB_f : int 4 5 5 2 3 4 4 5 4 NA ...
## $ PB_g : int 5 5 5 5 4 4 3 5 4 3 ...
## $ PB_h : int 3 5 5 3 3 3 3 5 5 4 ...
## $ PB_8 : int 2 2 2 0 2 2 0 2 2 2 ...
## $ PB_9.1a : int 5 5 4 3 4 4 3 4 4 4 ...
## $ PB_9.1b : int 5 5 5 5 4 5 3 5 5 4 ...
## $ PB_9.2a : int 5 5 3 4 4 5 4 4 4 3 ...
## $ PB_9.2b : int 4 5 2 1 3 4 1 4 5 4 ...
## $ PB_9.3a : int 5 5 3 2 4 5 3 2 5 4 ...
## $ PB_9.3b : int 5 5 4 3 4 5 3 4 3 2 ...
## $ PB_4a.1 : int 5 5 5 5 4 5 3 5 3 5 ...
## $ PB_4b.1 : int 5 5 5 4 4 5 1 4 3 5 ...
## $ PB_5a : int 5 5 5 1 3 5 3 4 3 2 ...
## $ PB_5b : int 5 5 4 2 4 5 4 2 3 4 ...
## $ PB_10 : int 2 5 1 2 2 2 2 0 1 0 ...
## $ PB_O : int 8 8 6 6 5 8 1 5 7 5 ...
## $ PB_U : num 4.25 4.9 4.3 3.8 3.5 4.3 2.8 5 4 2.6 ...
## $ PB_Z : num 4.9 5 3.7 3 3.8 4.8 2.8 3.8 3.8 3.7 ...
## $ SO_S.GlobSamootnoshenie: int 14 27 19 15 10 15 16 20 11 19 ...
## $ SO_I.Samouvazhenie : int 9 13 9 8 5 9 6 13 4 11 ...
## $ SO_II.Autosimpatiya : int 0 14 8 8 2 9 9 7 2 9 ...
## $ SO_III.OzhidOtnoshenie : int 4 11 7 7 3 8 9 8 5 7 ...
## $ SO_IV.Samointeres : int 5 8 7 4 4 4 6 8 5 6 ...
## $ SO_1.Samouverennost : int 4 7 4 6 3 5 6 5 1 3 ...
## $ SO_2.OtnoshenieDrugih : int 2 6 5 3 2 6 4 4 4 4 ...
## $ SO_3.Samoprinyatie : int 2 7 4 2 0 7 6 5 1 5 ...
## $ SO_4.Samorukovodstvo : int 6 5 5 4 2 5 1 5 4 4 ...
## $ SO_5.Samoobvinenie : int 7 1 4 4 6 4 6 5 8 2 ...
## $ SO_6.Samointeres : int 3 7 7 5 2 5 7 7 5 5 ...
## $ SO_7.Samoponimanie : int 2 3 3 1 2 1 1 3 2 6 ...
## $ SP_Konfrontaciya : num 27.8 33.3 50 61.1 77.8 33.3 61.1 50 44.4 61.1 ...
## $ SP_Distancirovanie : num 11.1 27.8 72.2 38.9 66.7 38.9 55.6 27.8 61.1 66.7 ...
## $ SP_Samokontrol : num 57.1 52.4 61.9 57.1 81 61.9 47.6 76.2 90.5 81 ...
## $ SP_PoiskCocPodderzhki : num 50 72.2 66.7 27.8 66.7 66.7 38.9 27.8 66.7 44.4 ...
## $ SP_PrinyatieOtvetstv : num 41.7 50 58.3 91.7 91.7 83.3 50 75 100 41.7 ...
## $ SP_BegstvoIzbeganie : num 29.2 16.7 54.2 75 62.5 25 54.2 29.2 37.5 45.8 ...
## $ SP_Planirovanie : num 66.7 88.9 88.9 50 44.4 55.6 44.4 77.8 77.8 66.7 ...
## $ SP_PolPereocenka : num 47.6 52.4 90.5 23.8 66.7 61.9 42.9 76.2 61.9 57.1 ...
## $ ZhS_Vovlechennost : int 23 45 37 13 15 33 22 37 26 37 ...
## $ ZhS_Kontrol : int 58 37 34 14 15 24 18 55 26 38 ...
## $ ZhS_PrinyatieRiska : int 21 20 16 4 10 18 13 20 11 19 ...
## $ ZhS_ZHiznestojkost : int 102 102 87 31 40 75 53 112 63 94 ...
## $ P_AK : int 38 37 32 32 32 31 25 34 36 23 ...
## $ P_AE : int 27 28 29 27 24 27 30 33 26 37 ...
## $ P_KO : int 26 21 29 21 23 27 30 28 27 26 ...
## $ P_RN : int 28 36 32 38 37 37 31 26 29 33 ...save(Data, file = "Data.RData")Проверка всех переменных
summary(Data)## subject_id School Class Gender
## Length:286 Length:286 Length:286 М:175
## Class :character Class :character Class :character Ж:111
## Mode :character Mode :character Mode :character
##
##
##
##
## Age W_PositiveRel W_Autonomy W_EnvMastery
## Min. :13.00 Min. :19.00 Min. : 19.0 Min. :28.00
## 1st Qu.:14.00 1st Qu.:52.00 1st Qu.: 50.0 1st Qu.:49.00
## Median :15.00 Median :60.00 Median : 57.5 Median :55.00
## Mean :14.99 Mean :59.80 Mean : 57.6 Mean :55.73
## 3rd Qu.:16.00 3rd Qu.:68.75 3rd Qu.: 64.0 3rd Qu.:62.00
## Max. :17.00 Max. :84.00 Max. :101.0 Max. :83.00
##
## W_PersGrowth W_PurposeLife W_SelfAcceptance WellBeing
## Min. :22.00 Min. :19.00 Min. :14.00 Min. :124.0
## 1st Qu.:59.00 1st Qu.:54.00 1st Qu.:48.00 1st Qu.:323.0
## Median :65.00 Median :61.00 Median :58.00 Median :357.0
## Mean :64.55 Mean :60.56 Mean :56.48 Mean :354.7
## 3rd Qu.:71.00 3rd Qu.:68.00 3rd Qu.:66.75 3rd Qu.:385.8
## Max. :83.00 Max. :83.00 Max. :82.00 Max. :481.0
##
## SW_NapriCHuvstv SW_PsiEmSim SW_Nastroenie SW_SocOkr
## Min. : 3.00 Min. : 0.0 Min. : 0.000 Min. : 0.00
## 1st Qu.:10.00 1st Qu.:13.0 1st Qu.: 8.000 1st Qu.:13.00
## Median :12.00 Median :17.0 Median :10.000 Median :16.00
## Mean :12.11 Mean :17.2 Mean : 9.961 Mean :15.64
## 3rd Qu.:15.00 3rd Qu.:21.0 3rd Qu.:12.000 3rd Qu.:19.00
## Max. :20.00 Max. :28.0 Max. :14.000 Max. :21.00
## NA's :3 NA's :2 NA's :2 NA's :2
## SW_SamoocZdorov SW_StUdovl SubWellBeing PB_1
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. :0.000
## 1st Qu.: 6.000 1st Qu.: 9.00 1st Qu.: 63.75 1st Qu.:2.000
## Median : 9.000 Median :13.00 Median : 77.00 Median :2.000
## Mean : 8.898 Mean :12.43 Mean : 76.20 Mean :1.682
## 3rd Qu.:11.000 3rd Qu.:15.00 3rd Qu.: 90.00 3rd Qu.:2.000
## Max. :14.000 Max. :21.00 Max. :115.00 Max. :2.000
## NA's :2 NA's :2 NA's :2 NA's :6
## PB_2 PB_3 PB_4a PB_4b
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.750 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :3.819 Mean :1.354 Mean :1.796 Mean :1.354
## 3rd Qu.:7.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :9.000 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :5 NA's :6 NA's :6 NA's :6
## PB_5 PB_6 PB_7a PB_b
## Min. :0.000 Min. :0.000 Min. :1.000 Min. :1.000
## 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:4.000
## Median :2.000 Median :2.000 Median :4.000 Median :4.000
## Mean :1.221 Mean :1.364 Mean :3.944 Mean :4.029
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :3.000 Max. :2.000 Max. :5.000 Max. :5.000
## NA's :6 NA's :6 NA's :17 NA's :10
## PB_c PB_d PB_e PB_f
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.768 Mean :3.929 Mean :4.061 Mean :3.813
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :6.000
## NA's :19 NA's :18 NA's :24 NA's :24
## PB_g PB_h PB_8 PB_9.1a
## Min. :1.000 Min. :1.000 Min. :0.000 Min. :0.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:4.000
## Median :4.000 Median :3.000 Median :2.000 Median :4.000
## Mean :3.931 Mean :3.211 Mean :1.468 Mean :3.846
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:5.000
## Max. :7.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :27 NA's :30 NA's :8 NA's :6
## PB_9.1b PB_9.2a PB_9.2b PB_9.3a
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.500 Median :4.000 Median :4.000
## Mean :4.029 Mean :4.193 Mean :3.892 Mean :3.775
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :9 NA's :6 NA's :9 NA's :6
## PB_9.3b PB_4a.1 PB_4b.1 PB_5a
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.783 Mean :3.979 Mean :3.621 Mean :3.801
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :9 NA's :6 NA's :9 NA's :9
## PB_5b PB_10 PB_O PB_U
## Min. :1.000 Min. :0.00 Min. :-9.000 Min. :0.000
## 1st Qu.:3.000 1st Qu.:0.00 1st Qu.: 2.000 1st Qu.:3.375
## Median :4.000 Median :2.00 Median : 5.000 Median :3.875
## Mean :3.808 Mean :1.18 Mean : 4.147 Mean :3.756
## 3rd Qu.:5.000 3rd Qu.:2.00 3rd Qu.: 8.000 3rd Qu.:4.250
## Max. :5.000 Max. :5.00 Max. : 9.000 Max. :5.400
## NA's :10 NA's :14 NA's :1 NA's :1
## PB_Z SO_S.GlobSamootnoshenie SO_I.Samouvazhenie SO_II.Autosimpatiya
## Min. :0.000 Min. : 4.00 Min. : 0.000 Min. : 0.000
## 1st Qu.:3.400 1st Qu.:15.00 1st Qu.: 7.000 1st Qu.: 6.000
## Median :4.000 Median :20.00 Median : 9.000 Median : 8.000
## Mean :3.837 Mean :19.21 Mean : 9.241 Mean : 8.294
## 3rd Qu.:4.600 3rd Qu.:23.00 3rd Qu.:11.000 3rd Qu.:10.750
## Max. :5.000 Max. :29.00 Max. :15.000 Max. :14.000
## NA's :1
## SO_III.OzhidOtnoshenie SO_IV.Samointeres SO_1.Samouverennost
## Min. : 1.000 Min. :1.000 Min. :0.000
## 1st Qu.: 7.000 1st Qu.:5.000 1st Qu.:3.000
## Median : 9.000 Median :6.000 Median :5.000
## Mean : 8.374 Mean :5.748 Mean :4.462
## 3rd Qu.:10.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :12.000 Max. :8.000 Max. :8.000
##
## SO_2.OtnoshenieDrugih SO_3.Samoprinyatie SO_4.Samorukovodstvo
## Min. :1.000 Min. :0.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000
## Median :6.000 Median :5.000 Median :5.000
## Mean :5.332 Mean :4.563 Mean :4.276
## 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :8.000 Max. :7.000 Max. :7.000
##
## SO_5.Samoobvinenie SO_6.Samointeres SO_7.Samoponimanie SP_Konfrontaciya
## Min. :0.000 Min. :0.000 Min. :0.000 Min. : 0.00
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:2.000 1st Qu.: 38.90
## Median :4.000 Median :5.000 Median :3.000 Median : 50.00
## Mean :4.122 Mean :5.315 Mean :3.315 Mean : 49.86
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.: 61.10
## Max. :8.000 Max. :7.000 Max. :6.000 Max. :100.00
## NA's :44
## SP_Distancirovanie SP_Samokontrol SP_PoiskCocPodderzhki SP_PrinyatieOtvetstv
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 33.33 1st Qu.:52.40 1st Qu.: 45.83 1st Qu.: 43.77
## Median : 50.00 Median :61.90 Median : 61.10 Median : 58.30
## Mean : 48.69 Mean :59.33 Mean : 58.27 Mean : 60.30
## 3rd Qu.: 61.10 3rd Qu.:71.40 3rd Qu.: 72.20 3rd Qu.: 75.00
## Max. :100.00 Max. :95.20 Max. :100.00 Max. :100.00
## NA's :44 NA's :44 NA's :44 NA's :44
## SP_BegstvoIzbeganie SP_Planirovanie SP_PolPereocenka ZhS_Vovlechennost
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 9.00
## 1st Qu.: 33.30 1st Qu.: 55.60 1st Qu.: 47.6 1st Qu.:24.50
## Median : 45.80 Median : 66.70 Median : 57.1 Median :32.00
## Mean : 45.65 Mean : 66.62 Mean : 56.9 Mean :31.96
## 3rd Qu.: 58.30 3rd Qu.: 81.92 3rd Qu.: 66.7 3rd Qu.:38.50
## Max. :100.00 Max. :100.00 Max. :100.0 Max. :51.00
## NA's :44 NA's :44 NA's :44 NA's :75
## ZhS_Kontrol ZhS_PrinyatieRiska ZhS_ZHiznestojkost P_AK
## Min. : 5.00 Min. : 0.00 Min. : 23.00 Min. : 0.00
## 1st Qu.:23.00 1st Qu.:13.00 1st Qu.: 63.00 1st Qu.:27.00
## Median :28.00 Median :17.00 Median : 78.00 Median :31.00
## Mean :30.62 Mean :16.55 Mean : 79.12 Mean :28.98
## 3rd Qu.:37.00 3rd Qu.:20.00 3rd Qu.: 96.00 3rd Qu.:35.00
## Max. :64.00 Max. :29.00 Max. :134.00 Max. :45.00
## NA's :75 NA's :75 NA's :75 NA's :58
## P_AE P_KO P_RN
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:25.00 1st Qu.:22.00 1st Qu.:26.00
## Median :29.00 Median :26.00 Median :32.00
## Mean :26.39 Mean :24.68 Mean :28.58
## 3rd Qu.:32.00 3rd Qu.:30.00 3rd Qu.:35.00
## Max. :39.00 Max. :41.00 Max. :42.00
## NA's :58 NA's :58 NA's :58Пропущенные значения
Data |>
is.na() |>
colSums() |>
as.data.frame()## colSums(is.na(Data))
## subject_id 0
## School 0
## Class 0
## Gender 0
## Age 0
## W_PositiveRel 0
## W_Autonomy 0
## W_EnvMastery 0
## W_PersGrowth 0
## W_PurposeLife 0
## W_SelfAcceptance 0
## WellBeing 0
## SW_NapriCHuvstv 3
## SW_PsiEmSim 2
## SW_Nastroenie 2
## SW_SocOkr 2
## SW_SamoocZdorov 2
## SW_StUdovl 2
## SubWellBeing 2
## PB_1 6
## PB_2 5
## PB_3 6
## PB_4a 6
## PB_4b 6
## PB_5 6
## PB_6 6
## PB_7a 17
## PB_b 10
## PB_c 19
## PB_d 18
## PB_e 24
## PB_f 24
## PB_g 27
## PB_h 30
## PB_8 8
## PB_9.1a 6
## PB_9.1b 9
## PB_9.2a 6
## PB_9.2b 9
## PB_9.3a 6
## PB_9.3b 9
## PB_4a.1 6
## PB_4b.1 9
## PB_5a 9
## PB_5b 10
## PB_10 14
## PB_O 1
## PB_U 1
## PB_Z 1
## SO_S.GlobSamootnoshenie 0
## SO_I.Samouvazhenie 0
## SO_II.Autosimpatiya 0
## SO_III.OzhidOtnoshenie 0
## SO_IV.Samointeres 0
## SO_1.Samouverennost 0
## SO_2.OtnoshenieDrugih 0
## SO_3.Samoprinyatie 0
## SO_4.Samorukovodstvo 0
## SO_5.Samoobvinenie 0
## SO_6.Samointeres 0
## SO_7.Samoponimanie 0
## SP_Konfrontaciya 44
## SP_Distancirovanie 44
## SP_Samokontrol 44
## SP_PoiskCocPodderzhki 44
## SP_PrinyatieOtvetstv 44
## SP_BegstvoIzbeganie 44
## SP_Planirovanie 44
## SP_PolPereocenka 44
## ZhS_Vovlechennost 75
## ZhS_Kontrol 75
## ZhS_PrinyatieRiska 75
## ZhS_ZHiznestojkost 75
## P_AK 58
## P_AE 58
## P_KO 58
## P_RN 58Испытуемые
Data %>%
count(School, Class) %>%
arrange(School, Class)## School Class n
## 1 225 10Б 2
## 2 225 8Б 22
## 3 241 8А 17
## 4 564 10м 10
## 5 564 8 21
## 6 625 10а 59
## 7 625 10б 40
## 8 625 8а 21
## 9 625 8б 21
## 10 625 8в 11
## 11 А лицей 10а 16
## 12 А лицей 8А 27
## 13 А лицей 9А 19В исходнике были ошибки: 10м и 10М (разное написание) И 8а с пробелом - поправил в numbers
library(ggplot2)
# Распределение возраста
ggplot(Data, aes(x = Age)) +
geom_histogram(binwidth = 1, fill = "blue", color = "black") +
labs(title = "Распределение возраста", x = "Возраст", y = "Количество")
# Распределение по полу
ggplot(Data, aes(x = as.factor(Gender))) +
geom_bar(fill = "pink", color = "black") +
labs(title = "Распределение по полу", x = "Пол", y = "Количество")
Шкала психологического благополучия (Рифф)
Data |>
select(starts_with("W_"), WellBeing) |>
describe()## vars n mean sd median trimmed mad min max range skew
## W_PositiveRel 1 286 59.80 11.76 60.0 60.10 11.86 19 84 65 -0.29
## W_Autonomy 2 286 57.60 10.58 57.5 57.59 9.64 19 101 82 0.02
## W_EnvMastery 3 286 55.73 10.96 55.0 55.47 10.38 28 83 55 0.18
## W_PersGrowth 4 286 64.55 9.53 65.0 65.08 8.90 22 83 61 -0.73
## W_PurposeLife 5 286 60.56 11.26 61.0 60.87 10.38 19 83 64 -0.37
## W_SelfAcceptance 6 286 56.48 13.20 58.0 57.20 13.34 14 82 68 -0.55
## WellBeing 7 286 354.71 52.50 357.0 355.61 48.93 124 481 357 -0.37
## kurtosis se
## W_PositiveRel -0.05 0.70
## W_Autonomy 0.95 0.63
## W_EnvMastery -0.17 0.65
## W_PersGrowth 1.26 0.56
## W_PurposeLife 0.33 0.67
## W_SelfAcceptance 0.12 0.78
## WellBeing 1.04 3.10Графики
Data |>
ggplot(aes(x = WellBeing)) +
geom_histogram(binwidth = 20, fill = "lightblue", color = "black") +
labs(title = "Психологическог благополучие (Рифф)",
x = "Баллы", y = "Частота") +
theme_minimal()
Data |>
select(starts_with("W_")) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(binwidth = 5, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Гистограммы для шкалы психологического благополучия Рифф",
x = "Значения", y = "Частота") +
theme_minimal()
Data |>
select(starts_with("W_"), Gender) |>
pivot_longer(cols = -Gender, names_to = "variable", values_to = "value") |>
ggplot(aes(x = variable, y = value, fill = Gender)) +
geom_boxplot(color = "black") +
labs(title = "Шкалы психологического благополучия Рифф",
x = "Шкала", y = "Баллы") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Субъективное благополучие
Data |>
select(starts_with("SW_"), SubWellBeing) |>
describe()## vars n mean sd median trimmed mad min max range skew
## SW_NapriCHuvstv 1 283 12.11 3.51 12 12.15 4.45 3 20 17 -0.11
## SW_PsiEmSim 2 284 17.20 5.50 17 17.28 5.93 0 28 28 -0.15
## SW_Nastroenie 3 284 9.96 2.90 10 10.18 2.97 0 14 14 -0.68
## SW_SocOkr 4 284 15.64 4.03 16 15.96 4.45 0 21 21 -0.68
## SW_SamoocZdorov 5 284 8.90 3.35 9 9.01 4.45 0 14 14 -0.28
## SW_StUdovl 6 284 12.43 4.18 13 12.50 4.45 0 21 21 -0.21
## SubWellBeing 7 284 76.20 18.45 77 76.45 19.27 0 115 115 -0.26
## kurtosis se
## SW_NapriCHuvstv -0.60 0.21
## SW_PsiEmSim -0.48 0.33
## SW_Nastroenie 0.13 0.17
## SW_SocOkr 0.17 0.24
## SW_SamoocZdorov -0.74 0.20
## SW_StUdovl -0.38 0.25
## SubWellBeing 0.22 1.09Графики
Data |>
ggplot(aes(x = SubWellBeing)) +
geom_histogram(binwidth = 20, fill = "lightblue", color = "black") +
labs(title = "Субъективное благополучие",
x = "Баллы", y = "Частота") +
theme_minimal()
Шкалы субъектвного благополучия
Data |>
select(starts_with("SW_")) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(binwidth = 5, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Гистограммы для шкал субъективного благополучия",
x = "Значения", y = "Частота") +
theme_minimal()
Data |>
select(starts_with("SW_"), Gender) |>
pivot_longer(cols = -Gender, names_to = "variable", values_to = "value") |>
ggplot(aes(x = variable, y = value, fill = Gender)) +
geom_boxplot(color = "black") +
labs(title = "Шкалы субъективного благополучия",
x = "Шкала", y = "Баллы") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Психологическая безопасность (PB)
Data |>
select(starts_with("PB_"), SubWellBeing) |>
describe()## vars n mean sd median trimmed mad min max range skew
## PB_1 1 280 1.68 0.67 2.00 1.85 0.00 0 2.0 2.0 -1.83
## PB_2 2 281 3.82 3.35 2.00 3.65 2.97 0 9.0 9.0 0.27
## PB_3 3 280 1.35 0.86 2.00 1.44 0.00 0 2.0 2.0 -0.74
## PB_4a 4 280 1.80 0.54 2.00 1.95 0.00 0 2.0 2.0 -2.56
## PB_4b 5 280 1.35 0.78 2.00 1.44 0.00 0 2.0 2.0 -0.70
## PB_5 6 280 1.22 0.92 2.00 1.27 0.00 0 3.0 3.0 -0.42
## PB_6 7 280 1.36 0.84 2.00 1.46 0.00 0 2.0 2.0 -0.76
## PB_7a 8 269 3.94 0.88 4.00 4.01 1.48 1 5.0 4.0 -0.58
## PB_b 9 276 4.03 0.87 4.00 4.10 1.48 1 5.0 4.0 -0.68
## PB_c 10 267 3.77 1.01 4.00 3.87 1.48 1 5.0 4.0 -0.65
## PB_d 11 268 3.93 0.90 4.00 4.00 1.48 1 5.0 4.0 -0.53
## PB_e 12 262 4.06 0.89 4.00 4.14 1.48 1 5.0 4.0 -0.66
## PB_f 13 262 3.81 1.03 4.00 3.91 1.48 1 6.0 5.0 -0.50
## PB_g 14 259 3.93 1.01 4.00 4.03 1.48 1 7.0 6.0 -0.51
## PB_h 15 256 3.21 1.14 3.00 3.24 1.48 1 5.0 4.0 -0.12
## PB_8 16 278 1.47 0.89 2.00 1.54 0.00 0 5.0 5.0 -0.24
## PB_9.1a 17 280 3.85 1.09 4.00 4.00 1.48 0 5.0 5.0 -1.11
## PB_9.1b 18 277 4.03 1.19 4.00 4.23 1.48 1 5.0 4.0 -1.16
## PB_9.2a 19 280 4.19 1.04 4.50 4.38 0.74 1 5.0 4.0 -1.39
## PB_9.2b 20 277 3.89 1.28 4.00 4.08 1.48 1 5.0 4.0 -0.98
## PB_9.3a 21 280 3.78 1.20 4.00 3.92 1.48 1 5.0 4.0 -0.75
## PB_9.3b 22 277 3.78 1.19 4.00 3.93 1.48 1 5.0 4.0 -0.79
## PB_4a.1 23 280 3.98 1.14 4.00 4.17 1.48 1 5.0 4.0 -1.07
## PB_4b.1 24 277 3.62 1.29 4.00 3.77 1.48 1 5.0 4.0 -0.67
## PB_5a 25 277 3.80 1.19 4.00 3.96 1.48 1 5.0 4.0 -0.91
## PB_5b 26 276 3.81 1.18 4.00 3.96 1.48 1 5.0 4.0 -0.87
## PB_10 27 272 1.18 0.98 2.00 1.21 0.00 0 5.0 5.0 -0.13
## PB_O 28 285 4.15 4.31 5.00 4.72 4.45 -9 9.0 18.0 -1.13
## PB_U 29 285 3.76 0.79 3.88 3.81 0.63 0 5.4 5.4 -1.29
## PB_Z 30 285 3.84 0.93 4.00 3.94 0.89 0 5.0 5.0 -1.26
## SubWellBeing 31 284 76.20 18.45 77.00 76.45 19.27 0 115.0 115.0 -0.26
## kurtosis se
## PB_1 1.70 0.04
## PB_2 -1.53 0.20
## PB_3 -1.23 0.05
## PB_4a 5.24 0.03
## PB_4b -1.01 0.05
## PB_5 -1.64 0.05
## PB_6 -1.15 0.05
## PB_7a -0.03 0.05
## PB_b 0.16 0.05
## PB_c -0.08 0.06
## PB_d -0.36 0.06
## PB_e -0.10 0.06
## PB_f -0.41 0.06
## PB_g -0.21 0.06
## PB_h -0.70 0.07
## PB_8 0.77 0.05
## PB_9.1a 0.79 0.07
## PB_9.1b 0.39 0.07
## PB_9.2a 1.46 0.06
## PB_9.2b -0.22 0.08
## PB_9.3a -0.44 0.07
## PB_9.3b -0.29 0.07
## PB_4a.1 0.42 0.07
## PB_4b.1 -0.63 0.08
## PB_5a -0.07 0.07
## PB_5b -0.13 0.07
## PB_10 -1.08 0.06
## PB_O 1.01 0.26
## PB_U 3.92 0.05
## PB_Z 2.40 0.05
## SubWellBeing 0.22 1.09Графики
Data |>
select(PB_O, PB_U, PB_Z) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(bins = 10, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Гистограммы для шкал психологической безопасности",
x = "Значения", y = "Частота") +
theme_minimal()
#Опросник самоотношения (SO)
Data |>
select(starts_with("SO_")) |>
describe()## vars n mean sd median trimmed mad min max range
## SO_S.GlobSamootnoshenie 1 286 19.21 5.20 20 19.42 5.93 4 29 25
## SO_I.Samouvazhenie 2 286 9.24 3.13 9 9.33 2.97 0 15 15
## SO_II.Autosimpatiya 3 286 8.29 3.06 8 8.46 2.97 0 14 14
## SO_III.OzhidOtnoshenie 4 286 8.37 2.30 9 8.52 2.97 1 12 11
## SO_IV.Samointeres 5 286 5.75 1.88 6 5.93 1.48 1 8 7
## SO_1.Samouverennost 6 286 4.46 1.64 5 4.50 1.48 0 8 8
## SO_2.OtnoshenieDrugih 7 286 5.33 1.71 6 5.39 1.48 1 8 7
## SO_3.Samoprinyatie 8 286 4.56 2.00 5 4.71 1.48 0 7 7
## SO_4.Samorukovodstvo 9 286 4.28 1.19 5 4.33 1.48 1 7 6
## SO_5.Samoobvinenie 10 286 4.12 2.15 4 4.22 2.97 0 8 8
## SO_6.Samointeres 11 286 5.31 1.44 5 5.45 1.48 0 7 7
## SO_7.Samoponimanie 12 286 3.31 1.62 3 3.30 1.48 0 6 6
## skew kurtosis se
## SO_S.GlobSamootnoshenie -0.36 -0.50 0.31
## SO_I.Samouvazhenie -0.30 -0.40 0.19
## SO_II.Autosimpatiya -0.43 -0.27 0.18
## SO_III.OzhidOtnoshenie -0.47 -0.32 0.14
## SO_IV.Samointeres -0.69 -0.52 0.11
## SO_1.Samouverennost -0.15 -0.70 0.10
## SO_2.OtnoshenieDrugih -0.39 -0.76 0.10
## SO_3.Samoprinyatie -0.53 -0.88 0.12
## SO_4.Samorukovodstvo -0.54 -0.09 0.07
## SO_5.Samoobvinenie -0.24 -0.75 0.13
## SO_6.Samointeres -0.64 -0.02 0.09
## SO_7.Samoponimanie 0.05 -0.89 0.10Графики
Data |>
select(SO_S.GlobSamootnoshenie
, SO_I.Samouvazhenie
, SO_II.Autosimpatiya
, SO_III.OzhidOtnoshenie
, SO_IV.Samointeres) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(bins = 10, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Гистограммы для шкал субъективного благополучия",
x = "Значения", y = "Частота") +
theme_minimal()
Data |>
select(SO_1.Samouverennost
, SO_2.OtnoshenieDrugih
, SO_3.Samoprinyatie
, SO_4.Samorukovodstvo
, SO_5.Samoobvinenie
, SO_6.Samointeres
, SO_7.Samoponimanie) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(bins = 7, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Гистограммы для шкал субъективного благополучия",
x = "Значения", y = "Частота") +
theme_minimal()
Data |>
select(SO_S.GlobSamootnoshenie,
SO_I.Samouvazhenie,
SO_II.Autosimpatiya,
SO_III.OzhidOtnoshenie,
SO_IV.Samointeres,
Gender) |>
pivot_longer(cols = -Gender, names_to = "variable", values_to = "value") |>
ggplot(aes(x = variable, y = value, fill = Gender)) +
geom_boxplot(color = "black") +
labs(title = "Опросник самоотношения",
x = "Шкала", y = "Баллы") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Data |>
select(SO_1.Samouverennost
, SO_2.OtnoshenieDrugih
, SO_3.Samoprinyatie
, SO_4.Samorukovodstvo
, SO_5.Samoobvinenie
, SO_6.Samointeres
, SO_7.Samoponimanie) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = variable, y = value)) +
geom_boxplot(fill = "lightblue", color = "black") +
labs(title = "Опросник самоотношения",
x = "Шкала", y = "Баллы") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Способы совладающего поведения (SP)
Data |>
select(starts_with("SP_")) |>
describe()## vars n mean sd median trimmed mad min max range
## SP_Konfrontaciya 1 242 49.86 16.74 50.0 50.31 16.46 0 100.0 100.0
## SP_Distancirovanie 2 242 48.69 18.60 50.0 48.74 16.46 0 100.0 100.0
## SP_Samokontrol 3 242 59.33 16.85 61.9 60.23 14.08 0 95.2 95.2
## SP_PoiskCocPodderzhki 4 242 58.27 19.73 61.1 59.63 16.46 0 100.0 100.0
## SP_PrinyatieOtvetstv 5 242 60.30 20.73 58.3 60.74 24.61 0 100.0 100.0
## SP_BegstvoIzbeganie 6 242 45.65 18.78 45.8 45.66 18.53 0 100.0 100.0
## SP_Planirovanie 7 242 66.62 19.09 66.7 67.24 16.46 0 100.0 100.0
## SP_PolPereocenka 8 242 56.90 17.83 57.1 57.38 14.23 0 100.0 100.0
## skew kurtosis se
## SP_Konfrontaciya -0.24 0.36 1.08
## SP_Distancirovanie -0.01 0.04 1.20
## SP_Samokontrol -0.77 1.56 1.08
## SP_PoiskCocPodderzhki -0.65 0.30 1.27
## SP_PrinyatieOtvetstv -0.32 0.05 1.33
## SP_BegstvoIzbeganie 0.00 -0.04 1.21
## SP_Planirovanie -0.58 0.99 1.23
## SP_PolPereocenka -0.40 0.87 1.15Графики
Data |>
select(starts_with("SP_")) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(binwidth = 5, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Гистограммы для способов совладающего поведения",
x = "Значения", y = "Частота") +
theme_minimal()
Data |>
select(starts_with("SP_"), Gender) |>
pivot_longer(cols = -Gender, names_to = "variable", values_to = "value") |>
ggplot(aes(x = variable, y = value, fill = Gender)) +
geom_boxplot(color = "black") +
labs(title = "Шкалы способов совладающего поведения",
x = "Шкала", y = "Баллы") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Тест жизнестойкости (ZhS)
Data |>
select(starts_with("ZhS_")) |>
describe()## vars n mean sd median trimmed mad min max range
## ZhS_Vovlechennost 1 211 31.96 9.80 32 31.98 10.38 9 51 42
## ZhS_Kontrol 2 211 30.62 11.02 28 29.88 10.38 5 64 59
## ZhS_PrinyatieRiska 3 211 16.55 5.08 17 16.62 5.93 0 29 29
## ZhS_ZHiznestojkost 4 211 79.12 22.71 78 78.95 23.72 23 134 111
## skew kurtosis se
## ZhS_Vovlechennost -0.02 -0.73 0.67
## ZhS_Kontrol 0.63 0.15 0.76
## ZhS_PrinyatieRiska -0.18 -0.14 0.35
## ZhS_ZHiznestojkost 0.06 -0.47 1.56Графики
Data |>
select(starts_with("ZhS_")) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(binwidth = 5, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Тест жизнестойкости",
x = "Значения", y = "Частота") +
theme_minimal()
Data |>
select(starts_with("ZhS_"), Gender) |>
pivot_longer(cols = -Gender, names_to = "variable", values_to = "value") |>
ggplot(aes(x = variable, y = value, fill = Gender)) +
geom_boxplot(color = "black") +
labs(title = "Тест жизнестойкости",
x = "Шкала", y = "Баллы") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Стили познания (P)
Data |>
select(starts_with("P_")) |>
describe()## vars n mean sd median trimmed mad min max range skew kurtosis se
## P_AK 1 228 28.98 10.66 31 30.97 5.93 0 45 45 -1.71 2.54 0.71
## P_AE 2 228 26.39 9.42 29 28.45 4.45 0 39 39 -1.97 3.11 0.62
## P_KO 3 228 24.68 9.58 26 26.10 5.93 0 41 41 -1.33 1.69 0.63
## P_RN 4 228 28.58 10.45 32 30.74 5.93 0 42 42 -1.81 2.58 0.69Графики
Data |>
select(starts_with("P_")) |>
pivot_longer(everything(), names_to = "variable", values_to = "value") |>
ggplot(aes(x = value)) +
geom_histogram(binwidth = 5, fill = "lightblue", color = "black") +
facet_wrap(~ variable, scales = "free_x") +
labs(title = "Стили познания",
x = "Значения", y = "Частота") +
theme_minimal()
Data |>
select(starts_with("P_"), Gender) |>
pivot_longer(cols = -Gender, names_to = "variable", values_to = "value") |>
ggplot(aes(x = variable, y = value, fill = Gender)) +
geom_boxplot(color = "black") +
labs(title = "Стили познания",
x = "Шкала", y = "Баллы") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Сохранение объекта на диск.
saveRDS(Data, file = "DataRDS.RData")