TRI com R em Exercício 1
Ricardo Primi
31 de agosto de 2019
Importando dados no R
- Primeiro instale os pacotes
install.packages("readxl")
pacotes <-c("tidyverse", "corrr", "psych", "readxl", "d3heatmap", "sjmisc")
install.packages(pacotes)Como trazer um arquivo para o R
- Aponte o Rstudio para o diretório de trabalho
- Importe o arquivo (ative o pacote e execute o comando)
- Note que a cada importação, você verá no Global Enviroment um novo objeto como
cadernos - Faça o dwonload do arquivo de http://www.labape.com.br/rprimi/TRI/2019_slides_exerc/senna_sal_ex1.xlsx
setwd("~/Dropbox (Personal)/TRI/2019_slides_exerc")
library(readxl)
bd <- read_excel("senna_sal_ex1.xlsx", sheet = "bd_exp")
dic <- read_excel("senna_sal_ex1.xlsx", sheet = "dic_itens")- Salve os dados do projeto. Note que o R salva todos os arquivos ou objetos listados no Global Enviroment
- Salvando os objetos em arquivos que podem sere lidos posteriormente
save.image(file = "ex1.RData")- Abrindo arquivos em novas sessões de análise
load("ex1.RData")Estrutura de dados (objetos) no R
Estruturas de dados no R
Explorando objetos
- Vendo o nome das variáveis com o comando names()
names(bd)## [1] "cod_suj" "Data de nasc."
## [3] "data.aplic" "Idade0"
## [5] "Termo" "sujeito"
## [7] "serie" "turma"
## [9] "Sexo" "Mãe (1)"
## [11] "Pai (2)" "Avô (ó) (3)"
## [13] "Tios (as) (4)" "Irmão (as) (5)"
## [15] "Filhos (as) (6)" "Meio irmão (7)"
## [17] "Madrasta (8)" "Padastro (9)"
## [19] "Moro só (10)" "Outr.parentes (11)"
## [21] "Não parets (12)" "Quantas moram"
## [23] "Irmão mais novos" "Irmãos mais velhos"
## [25] "Mais velhos que moram" "Esc. Mãe"
## [27] "R. asfaltada" "E. elétrica"
## [29] "Água torneira" "c. de lixo"
## [31] "bol. familia" "E. doméstica"
## [33] "carro" "geladeira"
## [35] "máq. Lav roupa" "computador"
## [37] "microondas" "televisão"
## [39] "foi reprovado?" "faço tarefa casa"
## [41] "L. de exerc.?" "Pai (1)"
## [43] "Mãe (2)" "Irmão (3)"
## [45] "Sozinho (4)" "Outro lugar (5)"
## [47] "Não estudo (6)" "nível esc. Pret"
## [49] "p.1.13" "p.1.14"
## [51] "p.1.15" "p.1.16"
## [53] "p.1.1" "p.1.2"
## [55] "p.1.3" "p.1.4"
## [57] "sal_cexp_i03_1" "sal_cexp_i08_1"
## [59] "sal_cexp_i17_1" "sal_cexp_i23_1"
## [61] "sal_comlrn_i31_1" "sal_comlrn_i36_1"
## [63] "sal_comlrn_i45_1" "sal_comlrn_i47_1"
## [65] "sal_coplrn_i34_1" "sal_coplrn_i39_1"
## [67] "sal_coplrn_i40_1" "sal_coplrn_i43_1"
## [69] "sal_coplrn_i51_1" "sal_cstrat_i05_1"
## [71] "sal_cstrat_i11_1" "sal_cstrat_i16_1"
## [73] "sal_cstrat_i20_1" "sal_cstrat_i22_1"
## [75] "sal_effper_i04_1" "sal_effper_i13_1"
## [77] "sal_effper_i21_1" "sal_effper_i26_1"
## [79] "sal_elab_i09_1" "sal_elab_i14_1"
## [81] "sal_elab_i18_1" "sal_elab_i28_1"
## [83] "sal_insmot_i06_1" "sal_insmot_i15_1"
## [85] "sal_insmot_i25_1" "sal_intmat_i29_1"
## [87] "sal_intmat_i30_1" "sal_intmat_i42_1"
## [89] "sal_intrea_i33_1" "sal_intrea_i37_1"
## [91] "sal_intrea_i49_1" "sal_memor_i01_1"
## [93] "sal_memor_i02_1" "sal_memor_i07_1"
## [95] "sal_memor_i24_1" "sal_scacad_i41_1"
## [97] "sal_scacad_i46_1" "sal_scacad_i50_1"
## [99] "sal_scmath_i32_1" "sal_scmath_i35_1"
## [101] "sal_scmath_i38_1" "sal_scverb_i44_1"
## [103] "sal_scverb_i48_0" "sal_scverb_i52_1"
## [105] "sal_selfef_i10_1" "sal_selfef_i12_1"
## [107] "sal_selfef_i19_1" "sal_selfef_i27_1"
## [109] "senna_A_Cmp_i01_1" "senna_A_Cmp_i30_1"
## [111] "senna_A_Cmp_i34_1" "senna_A_Cmp_i36_1"
## [113] "senna_A_Cmp_i56_1" "senna_A_Mod_i03_0"
## [115] "senna_A_Resp_i08_1" "senna_A_Resp_i13_0"
## [117] "senna_A_Resp_i41_1" "senna_A_Resp_i47_1"
## [119] "senna_A_Tru_i18_1" "senna_C_Achv_i12_1"
## [121] "senna_C_Achv_i31_1" "senna_C_Achv_i45_1"
## [123] "senna_C_Achv_i57_1" "senna_C_Conc_i37_1"
## [125] "senna_C_Conc_i42_1" "senna_C_Ord_i04_1"
## [127] "senna_C_Ord_i09_0" "senna_C_SD_i14_1"
## [129] "senna_C_SD_i19_0" "senna_C_SD_i49_1"
## [131] "senna_chk_chk_i21_0" "senna_chk_chk_i48_0"
## [133] "senna_E_Act_i23_1" "senna_E_Act_i53_1"
## [135] "senna_E_Assr_i32_0" "senna_E_Assr_i38_1"
## [137] "senna_E_Assr_i43_1" "senna_E_Assr_i58_1"
## [139] "senna_E_Soc_i05_1" "senna_E_Soc_i15_0"
## [141] "senna_E_Soc_i20_0" "senna_E_Soc_i24_1"
## [143] "senna_E_Soc_i50_1" "senna_N_LAngrVol_i06_0"
## [145] "senna_N_LAngrVol_i28_1" "senna_N_LAngrVol_i33_0"
## [147] "senna_N_LAngrVol_i54_1" "senna_N_LAnx_i10_1"
## [149] "senna_N_LAnx_i16_1" "senna_N_LAnx_i22_0"
## [151] "senna_N_LAnx_i39_1" "senna_N_LAnx_i44_1"
## [153] "senna_N_LAnx_i59_1" "senna_N_LDep_i26_0"
## [155] "senna_N_LDep_i51_1" "senna_O_Aes_i29_1"
## [157] "senna_O_Aes_i55_1" "senna_O_CrImg_i02_1"
## [159] "senna_O_CrImg_i07_1" "senna_O_CrImg_i11_0"
## [161] "senna_O_CrImg_i17_0" "senna_O_CrImg_i35_1"
## [163] "senna_O_CrImg_i40_1" "senna_O_IntCur_i25_1"
## [165] "senna_O_IntCur_i27_1" "senna_O_IntCur_i46_1"
## [167] "senna_O_IntCur_i52_1" "A.M. Port."
## [169] "A.M. Mat."- Veja a estrutura dos arquivos
str(bd)## Classes 'tbl_df', 'tbl' and 'data.frame': 76 obs. of 169 variables:
## $ cod_suj : num 1 2 3 4 5 6 7 8 9 10 ...
## $ Data de nasc. : POSIXct, format: "2002-11-09" NA ...
## $ data.aplic : POSIXct, format: "2016-07-01" "2016-07-01" ...
## $ Idade0 : num 13.6 NA 14.2 14.4 14.1 ...
## $ Termo : num 0 1 1 0 0 0 0 0 1 0 ...
## $ sujeito : num 47 48 49 50 51 52 53 54 55 56 ...
## $ serie : num 9 9 9 9 9 9 9 9 9 9 ...
## $ turma : num 91 91 91 91 91 91 91 91 91 91 ...
## $ Sexo : num 2 2 1 1 1 1 2 1 2 2 ...
## $ Mãe (1) : num 1 1 1 1 1 0 1 1 1 1 ...
## $ Pai (2) : num 0 0 1 1 0 0 0 1 1 0 ...
## $ Avô (ó) (3) : num 1 0 0 0 0 0 0 0 0 0 ...
## $ Tios (as) (4) : num 1 0 0 0 0 1 0 0 0 0 ...
## $ Irmão (as) (5) : num 1 1 1 1 1 0 1 0 1 1 ...
## $ Filhos (as) (6) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Meio irmão (7) : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Madrasta (8) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Padastro (9) : num 1 1 0 0 1 0 1 0 0 1 ...
## $ Moro só (10) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Outr.parentes (11) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Não parets (12) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Quantas moram : num 7 3 5 3 3 4 3 4 4 3 ...
## $ Irmão mais novos : num 1 0 0 0 2 0 1 1 1 2 ...
## $ Irmãos mais velhos : num 3 1 1 3 2 2 0 1 5 0 ...
## $ Mais velhos que moram : num 1 NA 1 1 1 0 0 0 2 0 ...
## $ Esc. Mãe : num NA 6 6 1 4 5 6 6 3 3 ...
## $ R. asfaltada : num 1 1 1 1 1 0 1 1 1 1 ...
## $ E. elétrica : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Água torneira : num 1 1 1 1 1 1 1 1 1 1 ...
## $ c. de lixo : num 1 1 1 1 1 1 1 1 1 1 ...
## $ bol. familia : num 1 1 0 0 0 0 0 0 0 0 ...
## $ E. doméstica : num 0 0 0 0 0 1 0 0 0 0 ...
## $ carro : num NA 1 2 2 1 2 1 1 1 1 ...
## $ geladeira : num 2 1 2 1 1 2 1 1 1 1 ...
## $ máq. Lav roupa : num 0 1 1 0 1 1 1 1 1 1 ...
## $ computador : num 0 1 1 1 1 3 2 2 1 2 ...
## $ microondas : num 1 1 2 1 NA 1 0 0 1 1 ...
## $ televisão : num 2 1 3 3 NA 2 1 2 2 2 ...
## $ foi reprovado? : num 0 0 0 0 0 1 0 0 0 0 ...
## $ faço tarefa casa : num 2 2 2 2 2 1 2 2 1 1 ...
## $ L. de exerc.? : num 2 2 2 2 2 2 2 1 1 2 ...
## $ Pai (1) : num 0 0 1 1 0 0 0 0 0 0 ...
## $ Mãe (2) : num 0 0 1 0 0 1 0 0 0 1 ...
## $ Irmão (3) : num 0 1 1 1 1 1 0 0 0 0 ...
## $ Sozinho (4) : num 1 0 0 0 0 1 1 1 1 0 ...
## $ Outro lugar (5) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Não estudo (6) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nível esc. Pret : num 4 4 3 4 3 4 4 4 4 4 ...
## $ p.1.13 : num 4 5 2 4 4 5 5 5 5 4 ...
## $ p.1.14 : num 2 3 3 3 2 3 3 2 3 3 ...
## $ p.1.15 : num 1 2 1 1 1 1 1 2 1 2 ...
## $ p.1.16 : num 2 3 3 3 3 4 4 5 5 5 ...
## $ p.1.1 : num 1 1 1 1 3 2 2 2 1 1 ...
## $ p.1.2 : num 2 4 2 3 5 3 3 1 2 3 ...
## $ p.1.3 : num 5 5 4 5 2 5 5 4 5 5 ...
## $ p.1.4 : num 2 4 2 2 4 3 3 5 4 4 ...
## $ sal_cexp_i03_1 : num 2 1 2 2 2 4 3 3 4 1 ...
## $ sal_cexp_i08_1 : num 2 2 2 2 4 1 4 2 3 2 ...
## $ sal_cexp_i17_1 : num NA 4 3 2 2 4 5 4 NA 2 ...
## $ sal_cexp_i23_1 : num 2 1 2 2 3 3 2 3 4 2 ...
## $ sal_comlrn_i31_1 : num 2 1 3 3 3 3 2 4 2 2 ...
## $ sal_comlrn_i36_1 : num 3 2 2 5 3 4 2 2 2 4 ...
## $ sal_comlrn_i45_1 : num 3 2 3 2 3 3 1 4 1 1 ...
## $ sal_comlrn_i47_1 : num 2 1 2 3 3 2 3 4 1 1 ...
## $ sal_coplrn_i34_1 : num NA 1 3 2 4 4 5 3 3 4 ...
## $ sal_coplrn_i39_1 : num 3 1 3 3 3 3 4 3 4 4 ...
## $ sal_coplrn_i40_1 : num 2 2 3 2 4 4 4 4 4 4 ...
## $ sal_coplrn_i43_1 : num 3 3 2 3 3 4 3 3 4 4 ...
## $ sal_coplrn_i51_1 : num NA 2 3 3 4 4 1 3 4 3 ...
## $ sal_cstrat_i05_1 : num 3 2 2 2 2 2 1 3 4 3 ...
## $ sal_cstrat_i11_1 : num 4 4 2 2 3 2 2 2 4 2 ...
## $ sal_cstrat_i16_1 : num 2 3 2 2 4 2 2 3 3 4 ...
## $ sal_cstrat_i20_1 : num NA 4 2 2 2 3 3 2 4 2 ...
## $ sal_cstrat_i22_1 : num 2 4 2 2 3 2 1 4 4 3 ...
## $ sal_effper_i04_1 : num 2 2 2 2 4 3 2 4 4 3 ...
## $ sal_effper_i13_1 : num 4 1 2 2 2 2 2 4 4 2 ...
## $ sal_effper_i21_1 : num 4 4 3 2 4 3 3 3 4 2 ...
## $ sal_effper_i26_1 : num 2 1 2 3 4 3 3 NA 4 3 ...
## $ sal_elab_i09_1 : num 2 3 2 2 2 3 4 2 3 2 ...
## $ sal_elab_i14_1 : num 2 2 2 NA 3 3 4 2 3 2 ...
## $ sal_elab_i18_1 : num 3 3 2 2 2 2 2 2 3 2 ...
## $ sal_elab_i28_1 : num 3 1 2 2 2 3 3 3 4 3 ...
## $ sal_insmot_i06_1 : num NA 4 3 3 3 4 5 4 4 3 ...
## $ sal_insmot_i15_1 : num 4 4 3 3 2 3 4 4 4 4 ...
## $ sal_insmot_i25_1 : num 2 2 3 2 2 4 3 4 4 4 ...
## $ sal_intmat_i29_1 : num 1 1 3 3 3 2 1 4 3 3 ...
## $ sal_intmat_i30_1 : num 2 1 2 3 3 2 1 2 3 2 ...
## $ sal_intmat_i42_1 : num 4 2 3 3 3 3 5 4 3 4 ...
## $ sal_intrea_i33_1 : num 1 1 2 1 4 3 1 1 3 3 ...
## $ sal_intrea_i37_1 : num 2 4 2 2 4 4 2 2 4 4 ...
## $ sal_intrea_i49_1 : num 1 1 1 1 4 4 1 3 4 3 ...
## $ sal_memor_i01_1 : num 2 1 2 2 2 2 2 2 4 4 ...
## $ sal_memor_i02_1 : num NA 1 3 2 3 2 2 3 3 3 ...
## $ sal_memor_i07_1 : num 2 3 2 2 2 3 1 3 3 NA ...
## $ sal_memor_i24_1 : num 1 1 1 2 3 3 1 2 3 3 ...
## $ sal_scacad_i41_1 : num 2 4 3 2 2 4 1 2 4 2 ...
## $ sal_scacad_i46_1 : num 2 1 2 2 2 3 1 2 4 2 ...
## $ sal_scacad_i50_1 : num 2 2 3 2 3 3 5 3 4 3 ...
## $ sal_scmath_i32_1 : num 1 1 3 3 3 3 1 3 4 3 ...
## [list output truncated]- Veja o que acontece se fizer isso
vars <- names(bd)
vars## [1] "cod_suj" "Data de nasc."
## [3] "data.aplic" "Idade0"
## [5] "Termo" "sujeito"
## [7] "serie" "turma"
## [9] "Sexo" "Mãe (1)"
## [11] "Pai (2)" "Avô (ó) (3)"
## [13] "Tios (as) (4)" "Irmão (as) (5)"
## [15] "Filhos (as) (6)" "Meio irmão (7)"
## [17] "Madrasta (8)" "Padastro (9)"
## [19] "Moro só (10)" "Outr.parentes (11)"
## [21] "Não parets (12)" "Quantas moram"
## [23] "Irmão mais novos" "Irmãos mais velhos"
## [25] "Mais velhos que moram" "Esc. Mãe"
## [27] "R. asfaltada" "E. elétrica"
## [29] "Água torneira" "c. de lixo"
## [31] "bol. familia" "E. doméstica"
## [33] "carro" "geladeira"
## [35] "máq. Lav roupa" "computador"
## [37] "microondas" "televisão"
## [39] "foi reprovado?" "faço tarefa casa"
## [41] "L. de exerc.?" "Pai (1)"
## [43] "Mãe (2)" "Irmão (3)"
## [45] "Sozinho (4)" "Outro lugar (5)"
## [47] "Não estudo (6)" "nível esc. Pret"
## [49] "p.1.13" "p.1.14"
## [51] "p.1.15" "p.1.16"
## [53] "p.1.1" "p.1.2"
## [55] "p.1.3" "p.1.4"
## [57] "sal_cexp_i03_1" "sal_cexp_i08_1"
## [59] "sal_cexp_i17_1" "sal_cexp_i23_1"
## [61] "sal_comlrn_i31_1" "sal_comlrn_i36_1"
## [63] "sal_comlrn_i45_1" "sal_comlrn_i47_1"
## [65] "sal_coplrn_i34_1" "sal_coplrn_i39_1"
## [67] "sal_coplrn_i40_1" "sal_coplrn_i43_1"
## [69] "sal_coplrn_i51_1" "sal_cstrat_i05_1"
## [71] "sal_cstrat_i11_1" "sal_cstrat_i16_1"
## [73] "sal_cstrat_i20_1" "sal_cstrat_i22_1"
## [75] "sal_effper_i04_1" "sal_effper_i13_1"
## [77] "sal_effper_i21_1" "sal_effper_i26_1"
## [79] "sal_elab_i09_1" "sal_elab_i14_1"
## [81] "sal_elab_i18_1" "sal_elab_i28_1"
## [83] "sal_insmot_i06_1" "sal_insmot_i15_1"
## [85] "sal_insmot_i25_1" "sal_intmat_i29_1"
## [87] "sal_intmat_i30_1" "sal_intmat_i42_1"
## [89] "sal_intrea_i33_1" "sal_intrea_i37_1"
## [91] "sal_intrea_i49_1" "sal_memor_i01_1"
## [93] "sal_memor_i02_1" "sal_memor_i07_1"
## [95] "sal_memor_i24_1" "sal_scacad_i41_1"
## [97] "sal_scacad_i46_1" "sal_scacad_i50_1"
## [99] "sal_scmath_i32_1" "sal_scmath_i35_1"
## [101] "sal_scmath_i38_1" "sal_scverb_i44_1"
## [103] "sal_scverb_i48_0" "sal_scverb_i52_1"
## [105] "sal_selfef_i10_1" "sal_selfef_i12_1"
## [107] "sal_selfef_i19_1" "sal_selfef_i27_1"
## [109] "senna_A_Cmp_i01_1" "senna_A_Cmp_i30_1"
## [111] "senna_A_Cmp_i34_1" "senna_A_Cmp_i36_1"
## [113] "senna_A_Cmp_i56_1" "senna_A_Mod_i03_0"
## [115] "senna_A_Resp_i08_1" "senna_A_Resp_i13_0"
## [117] "senna_A_Resp_i41_1" "senna_A_Resp_i47_1"
## [119] "senna_A_Tru_i18_1" "senna_C_Achv_i12_1"
## [121] "senna_C_Achv_i31_1" "senna_C_Achv_i45_1"
## [123] "senna_C_Achv_i57_1" "senna_C_Conc_i37_1"
## [125] "senna_C_Conc_i42_1" "senna_C_Ord_i04_1"
## [127] "senna_C_Ord_i09_0" "senna_C_SD_i14_1"
## [129] "senna_C_SD_i19_0" "senna_C_SD_i49_1"
## [131] "senna_chk_chk_i21_0" "senna_chk_chk_i48_0"
## [133] "senna_E_Act_i23_1" "senna_E_Act_i53_1"
## [135] "senna_E_Assr_i32_0" "senna_E_Assr_i38_1"
## [137] "senna_E_Assr_i43_1" "senna_E_Assr_i58_1"
## [139] "senna_E_Soc_i05_1" "senna_E_Soc_i15_0"
## [141] "senna_E_Soc_i20_0" "senna_E_Soc_i24_1"
## [143] "senna_E_Soc_i50_1" "senna_N_LAngrVol_i06_0"
## [145] "senna_N_LAngrVol_i28_1" "senna_N_LAngrVol_i33_0"
## [147] "senna_N_LAngrVol_i54_1" "senna_N_LAnx_i10_1"
## [149] "senna_N_LAnx_i16_1" "senna_N_LAnx_i22_0"
## [151] "senna_N_LAnx_i39_1" "senna_N_LAnx_i44_1"
## [153] "senna_N_LAnx_i59_1" "senna_N_LDep_i26_0"
## [155] "senna_N_LDep_i51_1" "senna_O_Aes_i29_1"
## [157] "senna_O_Aes_i55_1" "senna_O_CrImg_i02_1"
## [159] "senna_O_CrImg_i07_1" "senna_O_CrImg_i11_0"
## [161] "senna_O_CrImg_i17_0" "senna_O_CrImg_i35_1"
## [163] "senna_O_CrImg_i40_1" "senna_O_IntCur_i25_1"
## [165] "senna_O_IntCur_i27_1" "senna_O_IntCur_i46_1"
## [167] "senna_O_IntCur_i52_1" "A.M. Port."
## [169] "A.M. Mat." str(vars)## chr [1:169] "cod_suj" "Data de nasc." "data.aplic" "Idade0" "Termo" ...Manipulação dos dados
- Seleção de variáveis. Use o número das variáveis entre [linhas , colunas] logo após o nome do objeto, nesse caso, um dataframe
1:6## [1] 1 2 3 4 5 6 c(1:6)## [1] 1 2 3 4 5 6 c(1, 3, 10:20)## [1] 1 3 10 11 12 13 14 15 16 17 18 19 20 names(bd) ## [1] "cod_suj" "Data de nasc."
## [3] "data.aplic" "Idade0"
## [5] "Termo" "sujeito"
## [7] "serie" "turma"
## [9] "Sexo" "Mãe (1)"
## [11] "Pai (2)" "Avô (ó) (3)"
## [13] "Tios (as) (4)" "Irmão (as) (5)"
## [15] "Filhos (as) (6)" "Meio irmão (7)"
## [17] "Madrasta (8)" "Padastro (9)"
## [19] "Moro só (10)" "Outr.parentes (11)"
## [21] "Não parets (12)" "Quantas moram"
## [23] "Irmão mais novos" "Irmãos mais velhos"
## [25] "Mais velhos que moram" "Esc. Mãe"
## [27] "R. asfaltada" "E. elétrica"
## [29] "Água torneira" "c. de lixo"
## [31] "bol. familia" "E. doméstica"
## [33] "carro" "geladeira"
## [35] "máq. Lav roupa" "computador"
## [37] "microondas" "televisão"
## [39] "foi reprovado?" "faço tarefa casa"
## [41] "L. de exerc.?" "Pai (1)"
## [43] "Mãe (2)" "Irmão (3)"
## [45] "Sozinho (4)" "Outro lugar (5)"
## [47] "Não estudo (6)" "nível esc. Pret"
## [49] "p.1.13" "p.1.14"
## [51] "p.1.15" "p.1.16"
## [53] "p.1.1" "p.1.2"
## [55] "p.1.3" "p.1.4"
## [57] "sal_cexp_i03_1" "sal_cexp_i08_1"
## [59] "sal_cexp_i17_1" "sal_cexp_i23_1"
## [61] "sal_comlrn_i31_1" "sal_comlrn_i36_1"
## [63] "sal_comlrn_i45_1" "sal_comlrn_i47_1"
## [65] "sal_coplrn_i34_1" "sal_coplrn_i39_1"
## [67] "sal_coplrn_i40_1" "sal_coplrn_i43_1"
## [69] "sal_coplrn_i51_1" "sal_cstrat_i05_1"
## [71] "sal_cstrat_i11_1" "sal_cstrat_i16_1"
## [73] "sal_cstrat_i20_1" "sal_cstrat_i22_1"
## [75] "sal_effper_i04_1" "sal_effper_i13_1"
## [77] "sal_effper_i21_1" "sal_effper_i26_1"
## [79] "sal_elab_i09_1" "sal_elab_i14_1"
## [81] "sal_elab_i18_1" "sal_elab_i28_1"
## [83] "sal_insmot_i06_1" "sal_insmot_i15_1"
## [85] "sal_insmot_i25_1" "sal_intmat_i29_1"
## [87] "sal_intmat_i30_1" "sal_intmat_i42_1"
## [89] "sal_intrea_i33_1" "sal_intrea_i37_1"
## [91] "sal_intrea_i49_1" "sal_memor_i01_1"
## [93] "sal_memor_i02_1" "sal_memor_i07_1"
## [95] "sal_memor_i24_1" "sal_scacad_i41_1"
## [97] "sal_scacad_i46_1" "sal_scacad_i50_1"
## [99] "sal_scmath_i32_1" "sal_scmath_i35_1"
## [101] "sal_scmath_i38_1" "sal_scverb_i44_1"
## [103] "sal_scverb_i48_0" "sal_scverb_i52_1"
## [105] "sal_selfef_i10_1" "sal_selfef_i12_1"
## [107] "sal_selfef_i19_1" "sal_selfef_i27_1"
## [109] "senna_A_Cmp_i01_1" "senna_A_Cmp_i30_1"
## [111] "senna_A_Cmp_i34_1" "senna_A_Cmp_i36_1"
## [113] "senna_A_Cmp_i56_1" "senna_A_Mod_i03_0"
## [115] "senna_A_Resp_i08_1" "senna_A_Resp_i13_0"
## [117] "senna_A_Resp_i41_1" "senna_A_Resp_i47_1"
## [119] "senna_A_Tru_i18_1" "senna_C_Achv_i12_1"
## [121] "senna_C_Achv_i31_1" "senna_C_Achv_i45_1"
## [123] "senna_C_Achv_i57_1" "senna_C_Conc_i37_1"
## [125] "senna_C_Conc_i42_1" "senna_C_Ord_i04_1"
## [127] "senna_C_Ord_i09_0" "senna_C_SD_i14_1"
## [129] "senna_C_SD_i19_0" "senna_C_SD_i49_1"
## [131] "senna_chk_chk_i21_0" "senna_chk_chk_i48_0"
## [133] "senna_E_Act_i23_1" "senna_E_Act_i53_1"
## [135] "senna_E_Assr_i32_0" "senna_E_Assr_i38_1"
## [137] "senna_E_Assr_i43_1" "senna_E_Assr_i58_1"
## [139] "senna_E_Soc_i05_1" "senna_E_Soc_i15_0"
## [141] "senna_E_Soc_i20_0" "senna_E_Soc_i24_1"
## [143] "senna_E_Soc_i50_1" "senna_N_LAngrVol_i06_0"
## [145] "senna_N_LAngrVol_i28_1" "senna_N_LAngrVol_i33_0"
## [147] "senna_N_LAngrVol_i54_1" "senna_N_LAnx_i10_1"
## [149] "senna_N_LAnx_i16_1" "senna_N_LAnx_i22_0"
## [151] "senna_N_LAnx_i39_1" "senna_N_LAnx_i44_1"
## [153] "senna_N_LAnx_i59_1" "senna_N_LDep_i26_0"
## [155] "senna_N_LDep_i51_1" "senna_O_Aes_i29_1"
## [157] "senna_O_Aes_i55_1" "senna_O_CrImg_i02_1"
## [159] "senna_O_CrImg_i07_1" "senna_O_CrImg_i11_0"
## [161] "senna_O_CrImg_i17_0" "senna_O_CrImg_i35_1"
## [163] "senna_O_CrImg_i40_1" "senna_O_IntCur_i25_1"
## [165] "senna_O_IntCur_i27_1" "senna_O_IntCur_i46_1"
## [167] "senna_O_IntCur_i52_1" "A.M. Port."
## [169] "A.M. Mat." vars <- names(bd)
e_vars<- vars[c(133:143)]
# Subset de variáveis
dt <- bd[ , vars]
# removendo da área de trabalho
rm(dt)- Tabelas e seleção de observações. É possível usar os números das linhas ou operações lógicas
$e|
# R raiz
bd[1:5 , ]
table(bd$serie)
table(bd$serie, bd$Sexo)
dt <- bd[bd$serie==5 , ]
# R nutela: dplyr
# https://strengejacke.github.io/sjmisc/index.html
library(sjmisc)
frq(bd$serie)
flat_table(bd, serie, Sexo)
library(tidyverse)
bd %>% select(serie, Sexo) %>% flat_table()
dt <- bd %>% filter(Sexo==1)Explorando estatísticas descritivas
library(psych)
describe(bd[ , e_vars])## vars n mean sd median trimmed mad min max range
## senna_E_Act_i23_1 1 75 4.40 0.77 5 4.51 0.00 2 5 3
## senna_E_Act_i53_1 2 75 3.95 1.16 4 4.07 1.48 1 5 4
## senna_E_Assr_i32_0 3 75 2.72 1.44 3 2.66 1.48 1 5 4
## senna_E_Assr_i38_1 4 76 2.83 1.26 3 2.79 1.48 1 5 4
## senna_E_Assr_i43_1 5 75 3.21 1.35 3 3.26 1.48 1 5 4
## senna_E_Assr_i58_1 6 75 3.17 1.37 3 3.21 1.48 1 5 4
## senna_E_Soc_i05_1 7 75 4.43 0.90 5 4.61 0.00 1 5 4
## senna_E_Soc_i15_0 8 75 2.88 1.48 3 2.85 1.48 1 5 4
## senna_E_Soc_i20_0 9 75 2.32 1.28 2 2.18 1.48 1 5 4
## senna_E_Soc_i24_1 10 75 3.63 1.14 4 3.72 1.48 1 5 4
## senna_E_Soc_i50_1 11 75 3.37 1.40 4 3.46 1.48 1 5 4
## skew kurtosis se
## senna_E_Act_i23_1 -0.98 -0.07 0.09
## senna_E_Act_i53_1 -0.61 -0.98 0.13
## senna_E_Assr_i32_0 0.30 -1.26 0.17
## senna_E_Assr_i38_1 0.24 -0.96 0.14
## senna_E_Assr_i43_1 -0.22 -1.15 0.16
## senna_E_Assr_i58_1 0.06 -1.32 0.16
## senna_E_Soc_i05_1 -1.69 2.46 0.10
## senna_E_Soc_i15_0 0.06 -1.43 0.17
## senna_E_Soc_i20_0 0.59 -0.72 0.15
## senna_E_Soc_i24_1 -0.56 -0.46 0.13
## senna_E_Soc_i50_1 -0.41 -1.08 0.16 corr.test(bd[ , e_vars])## Call:corr.test(x = bd[, e_vars])
## Correlation matrix
## senna_E_Act_i23_1 senna_E_Act_i53_1 senna_E_Assr_i32_0
## senna_E_Act_i23_1 1.00 0.05 0.05
## senna_E_Act_i53_1 0.05 1.00 -0.14
## senna_E_Assr_i32_0 0.05 -0.14 1.00
## senna_E_Assr_i38_1 0.03 -0.01 -0.29
## senna_E_Assr_i43_1 -0.06 0.04 0.00
## senna_E_Assr_i58_1 0.07 -0.01 -0.08
## senna_E_Soc_i05_1 -0.03 0.24 -0.16
## senna_E_Soc_i15_0 -0.06 0.02 0.32
## senna_E_Soc_i20_0 -0.13 0.08 0.07
## senna_E_Soc_i24_1 0.21 0.00 0.13
## senna_E_Soc_i50_1 -0.05 0.21 0.03
## senna_E_Assr_i38_1 senna_E_Assr_i43_1
## senna_E_Act_i23_1 0.03 -0.06
## senna_E_Act_i53_1 -0.01 0.04
## senna_E_Assr_i32_0 -0.29 0.00
## senna_E_Assr_i38_1 1.00 0.43
## senna_E_Assr_i43_1 0.43 1.00
## senna_E_Assr_i58_1 0.20 0.30
## senna_E_Soc_i05_1 0.11 0.07
## senna_E_Soc_i15_0 -0.31 0.02
## senna_E_Soc_i20_0 0.00 0.06
## senna_E_Soc_i24_1 0.23 0.06
## senna_E_Soc_i50_1 0.12 0.16
## senna_E_Assr_i58_1 senna_E_Soc_i05_1 senna_E_Soc_i15_0
## senna_E_Act_i23_1 0.07 -0.03 -0.06
## senna_E_Act_i53_1 -0.01 0.24 0.02
## senna_E_Assr_i32_0 -0.08 -0.16 0.32
## senna_E_Assr_i38_1 0.20 0.11 -0.31
## senna_E_Assr_i43_1 0.30 0.07 0.02
## senna_E_Assr_i58_1 1.00 0.12 -0.14
## senna_E_Soc_i05_1 0.12 1.00 -0.04
## senna_E_Soc_i15_0 -0.14 -0.04 1.00
## senna_E_Soc_i20_0 -0.01 -0.11 0.12
## senna_E_Soc_i24_1 0.26 0.33 -0.05
## senna_E_Soc_i50_1 0.21 0.17 -0.06
## senna_E_Soc_i20_0 senna_E_Soc_i24_1 senna_E_Soc_i50_1
## senna_E_Act_i23_1 -0.13 0.21 -0.05
## senna_E_Act_i53_1 0.08 0.00 0.21
## senna_E_Assr_i32_0 0.07 0.13 0.03
## senna_E_Assr_i38_1 0.00 0.23 0.12
## senna_E_Assr_i43_1 0.06 0.06 0.16
## senna_E_Assr_i58_1 -0.01 0.26 0.21
## senna_E_Soc_i05_1 -0.11 0.33 0.17
## senna_E_Soc_i15_0 0.12 -0.05 -0.06
## senna_E_Soc_i20_0 1.00 -0.04 0.11
## senna_E_Soc_i24_1 -0.04 1.00 0.20
## senna_E_Soc_i50_1 0.11 0.20 1.00
## Sample Size
## senna_E_Act_i23_1 senna_E_Act_i53_1 senna_E_Assr_i32_0
## senna_E_Act_i23_1 75 74 74
## senna_E_Act_i53_1 74 75 74
## senna_E_Assr_i32_0 74 74 75
## senna_E_Assr_i38_1 75 75 75
## senna_E_Assr_i43_1 74 74 74
## senna_E_Assr_i58_1 74 74 74
## senna_E_Soc_i05_1 74 74 74
## senna_E_Soc_i15_0 74 74 74
## senna_E_Soc_i20_0 74 74 74
## senna_E_Soc_i24_1 74 74 74
## senna_E_Soc_i50_1 74 74 74
## senna_E_Assr_i38_1 senna_E_Assr_i43_1
## senna_E_Act_i23_1 75 74
## senna_E_Act_i53_1 75 74
## senna_E_Assr_i32_0 75 74
## senna_E_Assr_i38_1 76 75
## senna_E_Assr_i43_1 75 75
## senna_E_Assr_i58_1 75 74
## senna_E_Soc_i05_1 75 74
## senna_E_Soc_i15_0 75 74
## senna_E_Soc_i20_0 75 74
## senna_E_Soc_i24_1 75 74
## senna_E_Soc_i50_1 75 74
## senna_E_Assr_i58_1 senna_E_Soc_i05_1 senna_E_Soc_i15_0
## senna_E_Act_i23_1 74 74 74
## senna_E_Act_i53_1 74 74 74
## senna_E_Assr_i32_0 74 74 74
## senna_E_Assr_i38_1 75 75 75
## senna_E_Assr_i43_1 74 74 74
## senna_E_Assr_i58_1 75 74 74
## senna_E_Soc_i05_1 74 75 74
## senna_E_Soc_i15_0 74 74 75
## senna_E_Soc_i20_0 74 74 74
## senna_E_Soc_i24_1 74 74 74
## senna_E_Soc_i50_1 74 74 74
## senna_E_Soc_i20_0 senna_E_Soc_i24_1 senna_E_Soc_i50_1
## senna_E_Act_i23_1 74 74 74
## senna_E_Act_i53_1 74 74 74
## senna_E_Assr_i32_0 74 74 74
## senna_E_Assr_i38_1 75 75 75
## senna_E_Assr_i43_1 74 74 74
## senna_E_Assr_i58_1 74 74 74
## senna_E_Soc_i05_1 74 74 74
## senna_E_Soc_i15_0 74 74 74
## senna_E_Soc_i20_0 75 74 74
## senna_E_Soc_i24_1 74 75 74
## senna_E_Soc_i50_1 74 74 75
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## senna_E_Act_i23_1 senna_E_Act_i53_1 senna_E_Assr_i32_0
## senna_E_Act_i23_1 0.00 1.00 1.00
## senna_E_Act_i53_1 0.70 0.00 1.00
## senna_E_Assr_i32_0 0.66 0.22 0.00
## senna_E_Assr_i38_1 0.77 0.95 0.01
## senna_E_Assr_i43_1 0.62 0.71 0.98
## senna_E_Assr_i58_1 0.54 0.94 0.48
## senna_E_Soc_i05_1 0.80 0.04 0.18
## senna_E_Soc_i15_0 0.63 0.88 0.01
## senna_E_Soc_i20_0 0.27 0.47 0.53
## senna_E_Soc_i24_1 0.07 0.98 0.28
## senna_E_Soc_i50_1 0.65 0.07 0.81
## senna_E_Assr_i38_1 senna_E_Assr_i43_1
## senna_E_Act_i23_1 1.00 1.00
## senna_E_Act_i53_1 1.00 1.00
## senna_E_Assr_i32_0 0.66 1.00
## senna_E_Assr_i38_1 0.00 0.01
## senna_E_Assr_i43_1 0.00 0.00
## senna_E_Assr_i58_1 0.08 0.01
## senna_E_Soc_i05_1 0.34 0.55
## senna_E_Soc_i15_0 0.01 0.84
## senna_E_Soc_i20_0 0.97 0.62
## senna_E_Soc_i24_1 0.05 0.60
## senna_E_Soc_i50_1 0.30 0.17
## senna_E_Assr_i58_1 senna_E_Soc_i05_1 senna_E_Soc_i15_0
## senna_E_Act_i23_1 1.00 1.00 1.00
## senna_E_Act_i53_1 1.00 1.00 1.00
## senna_E_Assr_i32_0 1.00 1.00 0.28
## senna_E_Assr_i38_1 1.00 1.00 0.38
## senna_E_Assr_i43_1 0.46 1.00 1.00
## senna_E_Assr_i58_1 0.00 1.00 1.00
## senna_E_Soc_i05_1 0.30 0.00 1.00
## senna_E_Soc_i15_0 0.24 0.74 0.00
## senna_E_Soc_i20_0 0.94 0.34 0.32
## senna_E_Soc_i24_1 0.02 0.00 0.70
## senna_E_Soc_i50_1 0.07 0.15 0.59
## senna_E_Soc_i20_0 senna_E_Soc_i24_1 senna_E_Soc_i50_1
## senna_E_Act_i23_1 1.00 1.00 1
## senna_E_Act_i53_1 1.00 1.00 1
## senna_E_Assr_i32_0 1.00 1.00 1
## senna_E_Assr_i38_1 1.00 1.00 1
## senna_E_Assr_i43_1 1.00 1.00 1
## senna_E_Assr_i58_1 1.00 1.00 1
## senna_E_Soc_i05_1 1.00 0.25 1
## senna_E_Soc_i15_0 1.00 1.00 1
## senna_E_Soc_i20_0 0.00 1.00 1
## senna_E_Soc_i24_1 0.72 0.00 1
## senna_E_Soc_i50_1 0.36 0.08 0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionPorque você vai querer mudar para o R
r <- cor(bd[ , e_vars],use="pair")
library(d3heatmap)
d3heatmap(r, xaxis_font_size = 10, yaxis_font_size = 10) library(corrr)
network_plot(r, min_cor=.10)
x <- correlate(bd[ , e_vars])
fashion(x)## rowname senna_E_Act_i23_1 senna_E_Act_i53_1
## 1 senna_E_Act_i23_1 .05
## 2 senna_E_Act_i53_1 .05
## 3 senna_E_Assr_i32_0 .05 -.14
## 4 senna_E_Assr_i38_1 .03 -.01
## 5 senna_E_Assr_i43_1 -.06 .04
## 6 senna_E_Assr_i58_1 .07 -.01
## 7 senna_E_Soc_i05_1 -.03 .24
## 8 senna_E_Soc_i15_0 -.06 .02
## 9 senna_E_Soc_i20_0 -.13 .08
## 10 senna_E_Soc_i24_1 .21 .00
## 11 senna_E_Soc_i50_1 -.05 .21
## senna_E_Assr_i32_0 senna_E_Assr_i38_1 senna_E_Assr_i43_1
## 1 .05 .03 -.06
## 2 -.14 -.01 .04
## 3 -.29 -.00
## 4 -.29 .43
## 5 -.00 .43
## 6 -.08 .20 .30
## 7 -.16 .11 .07
## 8 .32 -.31 .02
## 9 .07 -.00 .06
## 10 .13 .23 .06
## 11 .03 .12 .16
## senna_E_Assr_i58_1 senna_E_Soc_i05_1 senna_E_Soc_i15_0
## 1 .07 -.03 -.06
## 2 -.01 .24 .02
## 3 -.08 -.16 .32
## 4 .20 .11 -.31
## 5 .30 .07 .02
## 6 .12 -.14
## 7 .12 -.04
## 8 -.14 -.04
## 9 -.01 -.11 .12
## 10 .26 .33 -.05
## 11 .21 .17 -.06
## senna_E_Soc_i20_0 senna_E_Soc_i24_1 senna_E_Soc_i50_1
## 1 -.13 .21 -.05
## 2 .08 .00 .21
## 3 .07 .13 .03
## 4 -.00 .23 .12
## 5 .06 .06 .16
## 6 -.01 .26 .21
## 7 -.11 .33 .17
## 8 .12 -.05 -.06
## 9 -.04 .11
## 10 -.04 .20
## 11 .11 .20Análise psicométrica com o Psych
e_vars## [1] "senna_E_Act_i23_1" "senna_E_Act_i53_1" "senna_E_Assr_i32_0"
## [4] "senna_E_Assr_i38_1" "senna_E_Assr_i43_1" "senna_E_Assr_i58_1"
## [7] "senna_E_Soc_i05_1" "senna_E_Soc_i15_0" "senna_E_Soc_i20_0"
## [10] "senna_E_Soc_i24_1" "senna_E_Soc_i50_1"alpha(bd[ , e_vars],keys = e_vars[c(3, 8, 9)])##
## Reliability analysis
## Call: alpha(x = bd[, e_vars], keys = e_vars[c(3, 8, 9)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.54 0.54 0.6 0.096 1.2 0.077 3.5 0.53 0.071
##
## lower alpha upper 95% confidence boundaries
## 0.39 0.54 0.69
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## senna_E_Act_i23_1 0.55 0.55 0.61 0.110 1.24 0.077
## senna_E_Act_i53_1 0.54 0.54 0.59 0.105 1.18 0.077
## senna_E_Assr_i32_0- 0.52 0.52 0.57 0.099 1.10 0.080
## senna_E_Assr_i38_1 0.46 0.46 0.51 0.080 0.86 0.092
## senna_E_Assr_i43_1 0.52 0.52 0.56 0.097 1.08 0.082
## senna_E_Assr_i58_1 0.48 0.49 0.55 0.087 0.95 0.087
## senna_E_Soc_i05_1 0.50 0.49 0.55 0.089 0.97 0.083
## senna_E_Soc_i15_0- 0.51 0.51 0.57 0.095 1.05 0.083
## senna_E_Soc_i20_0- 0.57 0.56 0.61 0.113 1.27 0.073
## senna_E_Soc_i24_1 0.51 0.50 0.54 0.090 0.99 0.082
## senna_E_Soc_i50_1 0.52 0.52 0.58 0.097 1.07 0.081
## var.r med.r
## senna_E_Act_i23_1 0.017 0.113
## senna_E_Act_i53_1 0.016 0.084
## senna_E_Assr_i32_0- 0.015 0.071
## senna_E_Assr_i38_1 0.013 0.063
## senna_E_Assr_i43_1 0.014 0.084
## senna_E_Assr_i58_1 0.016 0.061
## senna_E_Soc_i05_1 0.017 0.061
## senna_E_Soc_i15_0- 0.016 0.072
## senna_E_Soc_i20_0- 0.016 0.084
## senna_E_Soc_i24_1 0.015 0.071
## senna_E_Soc_i50_1 0.016 0.071
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## senna_E_Act_i23_1 75 0.22 0.29 0.124 0.073 4.4 0.77
## senna_E_Act_i53_1 75 0.31 0.34 0.196 0.118 3.9 1.16
## senna_E_Assr_i32_0- 75 0.44 0.40 0.311 0.211 3.3 1.44
## senna_E_Assr_i38_1 76 0.61 0.59 0.585 0.444 2.8 1.26
## senna_E_Assr_i43_1 75 0.44 0.42 0.344 0.227 3.2 1.35
## senna_E_Assr_i58_1 75 0.53 0.52 0.443 0.337 3.2 1.37
## senna_E_Soc_i05_1 75 0.44 0.50 0.430 0.311 4.4 0.90
## senna_E_Soc_i15_0- 75 0.49 0.44 0.344 0.256 3.1 1.48
## senna_E_Soc_i20_0- 75 0.25 0.26 0.083 0.037 3.7 1.28
## senna_E_Soc_i24_1 75 0.43 0.48 0.432 0.259 3.6 1.14
## senna_E_Soc_i50_1 75 0.45 0.42 0.304 0.216 3.4 1.40
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## senna_E_Act_i23_1 0.00 0.01 0.13 0.29 0.56 0.01
## senna_E_Act_i53_1 0.01 0.13 0.21 0.17 0.47 0.01
## senna_E_Assr_i32_0 0.27 0.23 0.20 0.13 0.17 0.01
## senna_E_Assr_i38_1 0.16 0.28 0.28 0.16 0.13 0.00
## senna_E_Assr_i43_1 0.15 0.16 0.24 0.24 0.21 0.01
## senna_E_Assr_i58_1 0.11 0.27 0.24 0.12 0.27 0.01
## senna_E_Soc_i05_1 0.01 0.04 0.08 0.24 0.63 0.01
## senna_E_Soc_i15_0 0.27 0.16 0.19 0.20 0.19 0.01
## senna_E_Soc_i20_0 0.36 0.21 0.25 0.09 0.08 0.01
## senna_E_Soc_i24_1 0.05 0.11 0.25 0.33 0.25 0.01
## senna_E_Soc_i50_1 0.16 0.09 0.24 0.23 0.28 0.01alpha(bd[ , e_vars], check.keys=TRUE)##
## Reliability analysis
## Call: alpha(x = bd[, e_vars], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.54 0.54 0.6 0.096 1.2 0.077 3.5 0.53 0.071
##
## lower alpha upper 95% confidence boundaries
## 0.39 0.54 0.69
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## senna_E_Act_i23_1 0.55 0.55 0.61 0.110 1.24 0.077
## senna_E_Act_i53_1 0.54 0.54 0.59 0.105 1.18 0.077
## senna_E_Assr_i32_0- 0.52 0.52 0.57 0.099 1.10 0.080
## senna_E_Assr_i38_1 0.46 0.46 0.51 0.080 0.86 0.092
## senna_E_Assr_i43_1 0.52 0.52 0.56 0.097 1.08 0.082
## senna_E_Assr_i58_1 0.48 0.49 0.55 0.087 0.95 0.087
## senna_E_Soc_i05_1 0.50 0.49 0.55 0.089 0.97 0.083
## senna_E_Soc_i15_0- 0.51 0.51 0.57 0.095 1.05 0.083
## senna_E_Soc_i20_0- 0.57 0.56 0.61 0.113 1.27 0.073
## senna_E_Soc_i24_1 0.51 0.50 0.54 0.090 0.99 0.082
## senna_E_Soc_i50_1 0.52 0.52 0.58 0.097 1.07 0.081
## var.r med.r
## senna_E_Act_i23_1 0.017 0.113
## senna_E_Act_i53_1 0.016 0.084
## senna_E_Assr_i32_0- 0.015 0.071
## senna_E_Assr_i38_1 0.013 0.063
## senna_E_Assr_i43_1 0.014 0.084
## senna_E_Assr_i58_1 0.016 0.061
## senna_E_Soc_i05_1 0.017 0.061
## senna_E_Soc_i15_0- 0.016 0.072
## senna_E_Soc_i20_0- 0.016 0.084
## senna_E_Soc_i24_1 0.015 0.071
## senna_E_Soc_i50_1 0.016 0.071
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## senna_E_Act_i23_1 75 0.22 0.29 0.124 0.073 4.4 0.77
## senna_E_Act_i53_1 75 0.31 0.34 0.196 0.118 3.9 1.16
## senna_E_Assr_i32_0- 75 0.44 0.40 0.311 0.211 3.3 1.44
## senna_E_Assr_i38_1 76 0.61 0.59 0.585 0.444 2.8 1.26
## senna_E_Assr_i43_1 75 0.44 0.42 0.344 0.227 3.2 1.35
## senna_E_Assr_i58_1 75 0.53 0.52 0.443 0.337 3.2 1.37
## senna_E_Soc_i05_1 75 0.44 0.50 0.430 0.311 4.4 0.90
## senna_E_Soc_i15_0- 75 0.49 0.44 0.344 0.256 3.1 1.48
## senna_E_Soc_i20_0- 75 0.25 0.26 0.083 0.037 3.7 1.28
## senna_E_Soc_i24_1 75 0.43 0.48 0.432 0.259 3.6 1.14
## senna_E_Soc_i50_1 75 0.45 0.42 0.304 0.216 3.4 1.40
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## senna_E_Act_i23_1 0.00 0.01 0.13 0.29 0.56 0.01
## senna_E_Act_i53_1 0.01 0.13 0.21 0.17 0.47 0.01
## senna_E_Assr_i32_0 0.27 0.23 0.20 0.13 0.17 0.01
## senna_E_Assr_i38_1 0.16 0.28 0.28 0.16 0.13 0.00
## senna_E_Assr_i43_1 0.15 0.16 0.24 0.24 0.21 0.01
## senna_E_Assr_i58_1 0.11 0.27 0.24 0.12 0.27 0.01
## senna_E_Soc_i05_1 0.01 0.04 0.08 0.24 0.63 0.01
## senna_E_Soc_i15_0 0.27 0.16 0.19 0.20 0.19 0.01
## senna_E_Soc_i20_0 0.36 0.21 0.25 0.09 0.08 0.01
## senna_E_Soc_i24_1 0.05 0.11 0.25 0.33 0.25 0.01
## senna_E_Soc_i50_1 0.16 0.09 0.24 0.23 0.28 0.01Tente você
- Faça as mesmas análises (descritivas, correlação e alfa para a escala de neuroticismo
Organize um projeto crianco um diretório e salvando os dados e código. Você consegue fechar o projeto e abri-lo de novo ?
Resposta do exercício
vars## [1] "cod_suj" "Data de nasc."
## [3] "data.aplic" "Idade0"
## [5] "Termo" "sujeito"
## [7] "serie" "turma"
## [9] "Sexo" "Mãe (1)"
## [11] "Pai (2)" "Avô (ó) (3)"
## [13] "Tios (as) (4)" "Irmão (as) (5)"
## [15] "Filhos (as) (6)" "Meio irmão (7)"
## [17] "Madrasta (8)" "Padastro (9)"
## [19] "Moro só (10)" "Outr.parentes (11)"
## [21] "Não parets (12)" "Quantas moram"
## [23] "Irmão mais novos" "Irmãos mais velhos"
## [25] "Mais velhos que moram" "Esc. Mãe"
## [27] "R. asfaltada" "E. elétrica"
## [29] "Água torneira" "c. de lixo"
## [31] "bol. familia" "E. doméstica"
## [33] "carro" "geladeira"
## [35] "máq. Lav roupa" "computador"
## [37] "microondas" "televisão"
## [39] "foi reprovado?" "faço tarefa casa"
## [41] "L. de exerc.?" "Pai (1)"
## [43] "Mãe (2)" "Irmão (3)"
## [45] "Sozinho (4)" "Outro lugar (5)"
## [47] "Não estudo (6)" "nível esc. Pret"
## [49] "p.1.13" "p.1.14"
## [51] "p.1.15" "p.1.16"
## [53] "p.1.1" "p.1.2"
## [55] "p.1.3" "p.1.4"
## [57] "sal_cexp_i03_1" "sal_cexp_i08_1"
## [59] "sal_cexp_i17_1" "sal_cexp_i23_1"
## [61] "sal_comlrn_i31_1" "sal_comlrn_i36_1"
## [63] "sal_comlrn_i45_1" "sal_comlrn_i47_1"
## [65] "sal_coplrn_i34_1" "sal_coplrn_i39_1"
## [67] "sal_coplrn_i40_1" "sal_coplrn_i43_1"
## [69] "sal_coplrn_i51_1" "sal_cstrat_i05_1"
## [71] "sal_cstrat_i11_1" "sal_cstrat_i16_1"
## [73] "sal_cstrat_i20_1" "sal_cstrat_i22_1"
## [75] "sal_effper_i04_1" "sal_effper_i13_1"
## [77] "sal_effper_i21_1" "sal_effper_i26_1"
## [79] "sal_elab_i09_1" "sal_elab_i14_1"
## [81] "sal_elab_i18_1" "sal_elab_i28_1"
## [83] "sal_insmot_i06_1" "sal_insmot_i15_1"
## [85] "sal_insmot_i25_1" "sal_intmat_i29_1"
## [87] "sal_intmat_i30_1" "sal_intmat_i42_1"
## [89] "sal_intrea_i33_1" "sal_intrea_i37_1"
## [91] "sal_intrea_i49_1" "sal_memor_i01_1"
## [93] "sal_memor_i02_1" "sal_memor_i07_1"
## [95] "sal_memor_i24_1" "sal_scacad_i41_1"
## [97] "sal_scacad_i46_1" "sal_scacad_i50_1"
## [99] "sal_scmath_i32_1" "sal_scmath_i35_1"
## [101] "sal_scmath_i38_1" "sal_scverb_i44_1"
## [103] "sal_scverb_i48_0" "sal_scverb_i52_1"
## [105] "sal_selfef_i10_1" "sal_selfef_i12_1"
## [107] "sal_selfef_i19_1" "sal_selfef_i27_1"
## [109] "senna_A_Cmp_i01_1" "senna_A_Cmp_i30_1"
## [111] "senna_A_Cmp_i34_1" "senna_A_Cmp_i36_1"
## [113] "senna_A_Cmp_i56_1" "senna_A_Mod_i03_0"
## [115] "senna_A_Resp_i08_1" "senna_A_Resp_i13_0"
## [117] "senna_A_Resp_i41_1" "senna_A_Resp_i47_1"
## [119] "senna_A_Tru_i18_1" "senna_C_Achv_i12_1"
## [121] "senna_C_Achv_i31_1" "senna_C_Achv_i45_1"
## [123] "senna_C_Achv_i57_1" "senna_C_Conc_i37_1"
## [125] "senna_C_Conc_i42_1" "senna_C_Ord_i04_1"
## [127] "senna_C_Ord_i09_0" "senna_C_SD_i14_1"
## [129] "senna_C_SD_i19_0" "senna_C_SD_i49_1"
## [131] "senna_chk_chk_i21_0" "senna_chk_chk_i48_0"
## [133] "senna_E_Act_i23_1" "senna_E_Act_i53_1"
## [135] "senna_E_Assr_i32_0" "senna_E_Assr_i38_1"
## [137] "senna_E_Assr_i43_1" "senna_E_Assr_i58_1"
## [139] "senna_E_Soc_i05_1" "senna_E_Soc_i15_0"
## [141] "senna_E_Soc_i20_0" "senna_E_Soc_i24_1"
## [143] "senna_E_Soc_i50_1" "senna_N_LAngrVol_i06_0"
## [145] "senna_N_LAngrVol_i28_1" "senna_N_LAngrVol_i33_0"
## [147] "senna_N_LAngrVol_i54_1" "senna_N_LAnx_i10_1"
## [149] "senna_N_LAnx_i16_1" "senna_N_LAnx_i22_0"
## [151] "senna_N_LAnx_i39_1" "senna_N_LAnx_i44_1"
## [153] "senna_N_LAnx_i59_1" "senna_N_LDep_i26_0"
## [155] "senna_N_LDep_i51_1" "senna_O_Aes_i29_1"
## [157] "senna_O_Aes_i55_1" "senna_O_CrImg_i02_1"
## [159] "senna_O_CrImg_i07_1" "senna_O_CrImg_i11_0"
## [161] "senna_O_CrImg_i17_0" "senna_O_CrImg_i35_1"
## [163] "senna_O_CrImg_i40_1" "senna_O_IntCur_i25_1"
## [165] "senna_O_IntCur_i27_1" "senna_O_IntCur_i46_1"
## [167] "senna_O_IntCur_i52_1" "A.M. Port."
## [169] "A.M. Mat." c_vars<- vars[c(120:130)]
r <- cor(bd[ , c_vars],use="pair")
library(d3heatmap)
d3heatmap(r, xaxis_font_size = 10, yaxis_font_size = 10) library(corrr)
network_plot(r, min_cor=.10)
x <- correlate(bd[ , c_vars])
fashion(x)## rowname senna_C_Achv_i12_1 senna_C_Achv_i31_1
## 1 senna_C_Achv_i12_1 .63
## 2 senna_C_Achv_i31_1 .63
## 3 senna_C_Achv_i45_1 .58 .50
## 4 senna_C_Achv_i57_1 .33 .30
## 5 senna_C_Conc_i37_1 .38 .46
## 6 senna_C_Conc_i42_1 .40 .49
## 7 senna_C_Ord_i04_1 .61 .72
## 8 senna_C_Ord_i09_0 -.13 -.42
## 9 senna_C_SD_i14_1 .40 .38
## 10 senna_C_SD_i19_0 -.27 -.44
## 11 senna_C_SD_i49_1 .48 .49
## senna_C_Achv_i45_1 senna_C_Achv_i57_1 senna_C_Conc_i37_1
## 1 .58 .33 .38
## 2 .50 .30 .46
## 3 .22 .43
## 4 .22 .26
## 5 .43 .26
## 6 .44 .28 .46
## 7 .52 .42 .38
## 8 -.15 .14 -.24
## 9 .43 .18 .41
## 10 -.28 -.02 -.32
## 11 .37 .11 .37
## senna_C_Conc_i42_1 senna_C_Ord_i04_1 senna_C_Ord_i09_0 senna_C_SD_i14_1
## 1 .40 .61 -.13 .40
## 2 .49 .72 -.42 .38
## 3 .44 .52 -.15 .43
## 4 .28 .42 .14 .18
## 5 .46 .38 -.24 .41
## 6 .45 -.41 .30
## 7 .45 -.29 .37
## 8 -.41 -.29 -.26
## 9 .30 .37 -.26
## 10 -.51 -.29 .61 -.25
## 11 .66 .50 -.53 .34
## senna_C_SD_i19_0 senna_C_SD_i49_1
## 1 -.27 .48
## 2 -.44 .49
## 3 -.28 .37
## 4 -.02 .11
## 5 -.32 .37
## 6 -.51 .66
## 7 -.29 .50
## 8 .61 -.53
## 9 -.25 .34
## 10 -.53
## 11 -.53 alpha(bd[ , c_vars], check.keys=TRUE)##
## Reliability analysis
## Call: alpha(x = bd[, c_vars], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.9 0.38 6.7 0.023 3.9 0.74 0.39
##
## lower alpha upper 95% confidence boundaries
## 0.82 0.87 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## senna_C_Achv_i12_1 0.85 0.85 0.88 0.37 5.8 0.026
## senna_C_Achv_i31_1 0.84 0.85 0.87 0.35 5.5 0.027
## senna_C_Achv_i45_1 0.85 0.86 0.88 0.37 6.0 0.025
## senna_C_Achv_i57_1 0.87 0.88 0.90 0.42 7.2 0.022
## senna_C_Conc_i37_1 0.85 0.86 0.89 0.38 6.1 0.025
## senna_C_Conc_i42_1 0.85 0.85 0.88 0.36 5.7 0.026
## senna_C_Ord_i04_1 0.84 0.85 0.88 0.36 5.7 0.027
## senna_C_Ord_i09_0- 0.86 0.87 0.88 0.40 6.6 0.023
## senna_C_SD_i14_1 0.86 0.86 0.89 0.39 6.4 0.024
## senna_C_SD_i19_0- 0.86 0.86 0.89 0.38 6.2 0.024
## senna_C_SD_i49_1 0.85 0.85 0.88 0.36 5.7 0.026
## var.r med.r
## senna_C_Achv_i12_1 0.026 0.38
## senna_C_Achv_i31_1 0.025 0.37
## senna_C_Achv_i45_1 0.028 0.39
## senna_C_Achv_i57_1 0.017 0.42
## senna_C_Conc_i37_1 0.030 0.39
## senna_C_Conc_i42_1 0.028 0.37
## senna_C_Ord_i04_1 0.026 0.39
## senna_C_Ord_i09_0- 0.020 0.39
## senna_C_SD_i14_1 0.029 0.41
## senna_C_SD_i19_0- 0.025 0.39
## senna_C_SD_i49_1 0.026 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## senna_C_Achv_i12_1 75 0.70 0.72 0.70 0.64 3.8 0.94
## senna_C_Achv_i31_1 75 0.80 0.81 0.80 0.75 4.2 1.05
## senna_C_Achv_i45_1 76 0.66 0.68 0.64 0.58 4.0 1.03
## senna_C_Achv_i57_1 74 0.39 0.41 0.33 0.27 3.8 1.10
## senna_C_Conc_i37_1 76 0.64 0.65 0.59 0.56 3.8 1.02
## senna_C_Conc_i42_1 74 0.75 0.74 0.72 0.67 4.2 1.03
## senna_C_Ord_i04_1 75 0.76 0.76 0.75 0.68 4.1 1.24
## senna_C_Ord_i09_0- 74 0.56 0.54 0.50 0.44 4.0 1.24
## senna_C_SD_i14_1 74 0.61 0.58 0.51 0.48 3.7 1.26
## senna_C_SD_i19_0- 74 0.64 0.63 0.59 0.54 3.9 1.26
## senna_C_SD_i49_1 76 0.74 0.74 0.73 0.67 4.1 1.09
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## senna_C_Achv_i12_1 0.01 0.07 0.28 0.39 0.25 0.01
## senna_C_Achv_i31_1 0.01 0.07 0.20 0.19 0.53 0.01
## senna_C_Achv_i45_1 0.00 0.09 0.25 0.24 0.42 0.00
## senna_C_Achv_i57_1 0.04 0.07 0.27 0.30 0.32 0.03
## senna_C_Conc_i37_1 0.01 0.09 0.26 0.32 0.32 0.00
## senna_C_Conc_i42_1 0.03 0.04 0.16 0.24 0.53 0.03
## senna_C_Ord_i04_1 0.04 0.12 0.13 0.16 0.55 0.01
## senna_C_Ord_i09_0 0.47 0.22 0.18 0.07 0.07 0.03
## senna_C_SD_i14_1 0.05 0.14 0.26 0.18 0.38 0.03
## senna_C_SD_i19_0 0.43 0.24 0.14 0.14 0.05 0.03
## senna_C_SD_i49_1 0.04 0.05 0.16 0.29 0.46 0.00