Introdução ao R e RStudio
Ricardo Primi
7 de abril de 20178
Importando dados no R
- Primeiro instale os pacotes
install.packages("readxl")Como trazer um arquivo para o R
- Aponte o Rstudio para o diretório de trabalho se ja nao estiver no diretório
- Importe o arquivo (ative o pacote e execute o comando)
- Note que a cada importação, você verá no Global Enviroment um novo objeto
# setwd("~/Dropbox (Personal)/AF_SEM/exercicios_2019")
library(readxl)
bd_sal_senna <- read_excel("bd_sal_senna.xlsx", sheet = "ex2")
library(haven)
bd_ie <-read_sav("ex1_ie_bpr_16pf_avdes.sav")- 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 = "ex2.RData")- Vamos agora abrir um arquivo em R
load("ex2.RData")Estrutura de dados (objetos) no R
Estruturas de dados no R
Explorando os dados nos objetos
- Vendo o nome das variáveis com o comando names()
names(bd_ie)## [1] "id" "idade" "sexo" "ai_br" "nf_br" "aq_br"
## [7] "a_br" "b_br" "c_br" "e_br" "f_br" "g_br"
## [13] "h_br" "i_br" "l_br" "m_br" "n_br" "o_br"
## [19] "q1_br" "q2_br" "q3_br" "q4_br" "fatoe_i" "fator_ii"
## [25] "fato_iii" "fator_iv" "fator_v" "ra" "rv" "rm"
## [31] "re" "rn" "eg" "epn_ra" "epn_rv" "epn_rm"
## [37] "epn_re" "epn_rn" "epn_eg" "av_des1" "av_des2" "av_des"
## [43] "etnia" "percep" "usofaci" "conemo" "regemo" "experi"
## [49] "etrateg" "ie1" "ie2" "ie" "ra_1" "rv_1"
## [55] "rm_1" "re_1" "rn_1" "faces" "paisag" "facilit"
## [61] "sensa" "transi" "mistur" "gerenc" "relac" names(bd_sal_senna)## [1] "...1" "banco" "cod_suj"
## [4] "Data de sc." "data.aplic" "Idade0"
## [7] "idade1" "Termo" "sujeito"
## [10] "serie" "escola" "turma"
## [13] "Sexo" "Esc. M√£e" "port"
## [16] "mat" "cloze" "A_o"
## [19] "C_o" "E_o" "N_o"
## [22] "O_o" "A_c" "C_c"
## [25] "E_c" "N_c" "O_c"
## [28] "antonym.rc" "antonym.cntrst_A" "antonym.cntrst_C"
## [31] "antonym.cntrst_E" "antonym.cntrst_N" "antonym.cntrst_O"
## [34] "mean_A" "mean_C" "mean_E"
## [37] "mean_N" "mean_O" "sd_A"
## [40] "sd_C" "sd_E" "sd_N"
## [43] "sd_O" "antonym.rc_A" "antonym.rc_C"
## [46] "antonym.rc_E" "antonym.rc_N" "antonym.rc_O"
## [49] "nse" "A_1_o" "C_1_o"
## [52] "E_1_o" "N_1_o" "O_1_o"
## [55] "A_1_c" "C_1_c" "E_1_c"
## [58] "N_1_c" "O_1_c" "A_0_o"
## [61] "C_0_o" "E_0_o" "N_0_o"
## [64] "O_0_o" "A_0_c" "C_0_c"
## [67] "E_0_c" "N_0_c" "O_0_c"
## [70] "cexp_o" "comlrn_o" "coplrn_o"
## [73] "cstrat_o" "effper_o" "elab_o"
## [76] "insmot_o" "intmat_o" "intrea_o"
## [79] "memor_o" "scacad_o" "scmath_o"
## [82] "scverb_o" "selfef_o" "cexp_c"
## [85] "comlrn_c" "coplrn_c" "cstrat_c"
## [88] "effper_c" "elab_c" "insmot_c"
## [91] "intmat_c" "intrea_c" "memor_c"
## [94] "scacad_c" "scmath_c" "scverb_c"
## [97] "selfef_c" "means" "sd"- Veja a estrutura dos arquivos
str(bd_ie)## Classes 'tbl_df', 'tbl' and 'data.frame': 159 obs. of 65 variables:
## $ id : chr "a001" "a002" "a003" "a004" ...
## ..- attr(*, "format.spss")= chr "A5"
## ..- attr(*, "display_width")= int 4
## $ idade : num 46 49 NA 38 42 36 31 52 39 NA ...
## ..- attr(*, "label")= chr "Questionário de Identificação - idade"
## ..- attr(*, "format.spss")= chr "F11.0"
## $ sexo : 'haven_labelled' num 1 1 2 1 1 1 1 1 1 1 ...
## ..- attr(*, "label")= chr "sexo"
## ..- attr(*, "format.spss")= chr "F11.0"
## ..- attr(*, "labels")= Named num 1 2
## .. ..- attr(*, "names")= chr "masculino" "feminino"
## $ ai_br : num 14 11 12 13 12 20 18 13 14 14 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ nf_br : num 7 11 0 3 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ aq_br : num 44 47 57 49 50 49 69 60 55 54 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ a_br : num 13 15 20 12 10 12 12 15 8 18 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ b_br : num 5 5 7 8 5 5 11 6 11 9 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ c_br : num 12 10 5 11 18 20 16 15 14 12 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ e_br : num 18 14 13 14 10 18 14 14 14 14 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ f_br : num 5 4 12 7 4 4 18 11 10 12 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ g_br : num 17 17 18 22 14 18 20 20 20 20 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ h_br : num 12 5 18 4 18 20 18 12 14 16 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ i_br : num 9 7 15 7 7 8 8 9 7 14 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ l_br : num 7 15 13 15 10 8 14 17 14 8 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ m_br : num 4 10 6 6 4 2 8 5 8 6 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ n_br : num 16 11 11 11 16 14 16 14 18 2 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ o_br : num 10 14 14 17 6 8 16 14 8 14 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ q1_br : num 16 17 23 16 6 24 16 12 24 22 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ q2_br : num 4 4 3 6 2 2 2 2 12 2 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ q3_br : num 15 16 14 14 12 18 20 18 16 18 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ q4_br : num 11 11 14 13 6 12 12 8 14 12 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ fatoe_i : num 4.1 4.6 7.5 4.1 4.5 5.3 6.6 5.9 2.8 8.2 ...
## ..- attr(*, "format.spss")= chr "F11.1"
## $ fator_ii: num 3.5 6.2 7.1 7 1.3 1.8 5.4 5.3 4.6 4.6 ...
## ..- attr(*, "format.spss")= chr "F11.1"
## $ fato_iii: num 7.1 6.3 3.3 7.8 10 6.1 7.5 7.4 5.9 4.2 ...
## ..- attr(*, "format.spss")= chr "F11.1"
## $ fator_iv: num 6.1 5.6 6.9 5 3.5 8.4 6.5 5.8 7.4 6.3 ...
## ..- attr(*, "format.spss")= chr "F11.1"
## $ fator_v : num 7.45 6.85 6.85 8.05 6.65 8.95 7.95 8.65 7.55 8.45 ...
## ..- attr(*, "format.spss")= chr "F11.2"
## $ ra : num 8 6 14 11 13 12 17 13 17 20 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ rv : num 9 12 14 11 18 19 17 12 21 21 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ rm : num 9 5 10 14 11 8 11 10 17 10 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ re : num 7 1 10 5 7 10 16 3 13 8 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ rn : num 4 5 6 11 12 13 14 7 7 13 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ eg : num 37 29 54 52 61 62 75 45 75 72 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ epn_ra : num 72 68 82 74 79 78 94 79 94 109 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ epn_rv : num 72 79 85 77 99 105 95 79 117 117 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ epn_rm : num 90 76 93 104 96 88 96 93 112 93 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ epn_re : num 80 64 88 74 80 88 107 65 96 84 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ epn_rn : num 75 78 81 93 96 98 103 83 83 98 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ epn_eg : num 72 63 81 80 87 87 96 75 96 95 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## $ av_des1 : num 4.5 2.2 4.5 4.1 2.9 5 3.4 3.3 5 4 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ av_des2 : num 3.1 4.1 5.1 2.8 4.6 3.8 3.5 4 3.8 4.5 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ av_des : num 3.8 3.15 4.8 3.45 3.75 4.4 3.45 3.65 4.4 4.25 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ etnia : num NA NA 2 1 NA 1 1 3 1 1 ...
## ..- attr(*, "format.spss")= chr "F6.0"
## ..- attr(*, "display_width")= int 6
## $ percep : num 21.4 26.5 40.4 33.9 40.8 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ usofaci : num 18.2 21.2 26.3 33.5 37.4 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ conemo : num 35.4 29 38.3 31.6 38.4 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ regemo : num 39.8 15.4 43.9 37 49.1 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ experi : num 19.8 23.9 33.4 33.7 39.1 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ etrateg : num 37.6 22.2 41.1 34.3 43.7 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ ie1 : num 31.7 30.2 36 33 43.5 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ ie2 : num 25.7 15.8 38.4 35 39.3 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ ie : num 28.7 23 37.2 34 41.4 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ ra_1 : num 8 6 14 11 13 12 17 13 17 20 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## ..- attr(*, "display_width")= int 13
## $ rv_1 : num 9 12 14 11 18 19 17 12 21 21 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## ..- attr(*, "display_width")= int 13
## $ rm_1 : num 9 5 10 14 11 8 11 10 17 10 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## ..- attr(*, "display_width")= int 13
## $ re_1 : num 7 1 10 5 7 10 16 3 13 8 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## ..- attr(*, "display_width")= int 13
## $ rn_1 : num 4 5 6 11 12 13 14 7 7 13 ...
## ..- attr(*, "format.spss")= chr "F11.0"
## ..- attr(*, "display_width")= int 13
## $ faces : num 27.1 38.3 39.7 24 29.7 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10
## $ paisag : num 15.7 14.8 41.1 43.8 51.9 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10
## $ facilit : num 25.1 24.8 17.7 31.3 47.8 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10
## $ sensa : num 11.2 17.6 34.9 35.6 26.9 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10
## $ transi : num 38.3 38.5 37.7 39.1 46.6 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10
## $ mistur : num 32.4 19.5 38.9 24.2 30.2 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10
## $ gerenc : num 36.2 19.4 49 37.6 50.1 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10
## $ relac : num 43.4 11.4 38.8 36.3 48.1 ...
## ..- attr(*, "label")= chr "SMEAN(relac)"
## ..- attr(*, "format.spss")= chr "F8.2"
## ..- attr(*, "display_width")= int 10 str(bd_sal_senna)## Classes 'tbl_df', 'tbl' and 'data.frame': 168 obs. of 99 variables:
## $ ...1 : num 1 2 3 4 5 6 7 8 9 10 ...
## $ banco : chr "josi" "josi" "josi" "josi" ...
## $ cod_suj : num 1 2 3 4 5 6 7 8 9 10 ...
## $ Data de sc. : 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 ...
## $ idade1 : num 14 NA 14 14 14 15 14 14 14 14 ...
## $ 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 ...
## $ escola : num 0 0 0 0 0 0 0 0 0 0 ...
## $ turma : num 91 91 91 91 91 91 91 91 91 91 ...
## $ Sexo : num 2 2 1 1 1 1 2 1 2 2 ...
## $ Esc. M√£e : num NA 6 6 1 4 5 6 6 3 3 ...
## $ port : num 35 50 65 65 30 80 55 25 80 70 ...
## $ mat : num 40 30 80 45 55 65 35 45 75 85 ...
## $ cloze : num NA NA NA NA NA NA NA NA NA NA ...
## $ A_o : num 2.3 3.18 3.18 3 4.45 ...
## $ C_o : num 2.36 2.45 3.09 3.18 3.09 ...
## $ E_o : num 3 2.82 2.91 3.27 4.27 ...
## $ N_o : num 2.67 2.92 3.33 3 2.67 ...
## $ O_o : num 2.27 2.08 2.83 2.92 3.42 ...
## $ A_c : num -0.156 0.402 0.378 0.147 1.577 ...
## $ C_c : num -0.204 -0.325 0.287 0.329 0.213 ...
## $ E_c : num 0.3091 -0.0245 0.049 0.3776 1.3601 ...
## $ N_c : num -0.1067 0.0321 0.4359 0.0769 -0.2692 ...
## $ O_c : num -0.2945 -0.6859 0.0385 0.0705 0.5449 ...
## $ antonym.rc : num 0.471 0.294 0.53 -0.385 -0.651 ...
## $ antonym.cntrst_A: num NA 0.302 0.577 0.707 0.98 ...
## $ antonym.cntrst_C: num -0.302 -0.577 0.577 1 -0.707 ...
## $ antonym.cntrst_E: num 0 0 -0.218 0.243 0.742 ...
## $ antonym.cntrst_N: num -0.405 -0.152 0.801 0.5 -0.729 ...
## $ antonym.cntrst_O: num 0 -0.457 -0.577 0.577 0.816 ...
## $ mean_A : num 2 2.25 2.5 3 2.75 3 2.75 4 3 3 ...
## $ mean_C : num 2.75 4.25 2.75 2.5 3 3 3.25 1.25 2.5 4.25 ...
## $ mean_E : num 3 2.67 3.5 3.17 2.83 ...
## $ mean_N : num 2.12 2.25 2.12 2.5 2.62 ...
## $ mean_O : num 1.5 2.25 2.75 2.75 3 ...
## $ sd_A : num 0 0.957 1 0.816 2.062 ...
## $ sd_C : num 0.957 1.5 0.5 0.577 0.816 ...
## $ sd_E : num 1.095 1.862 0.837 0.753 1.722 ...
## $ sd_N : num 0.991 1.753 0.835 0.535 0.916 ...
## $ sd_O : num 0.577 1.893 0.5 0.5 1.414 ...
## $ antonym.rc_A : num 1 1 NA -1 -0.949 ...
## $ antonym.rc_C : num 0.707 0.316 NA NA 0 ...
## $ antonym.rc_E : num NA 1 1 -0.707 -0.68 ...
## $ antonym.rc_N : num 0.167 -0.316 0.632 0 -0.289 ...
## $ antonym.rc_O : num 1 0.316 NA NA -0.707 ...
## $ nse : num 1.33 1.73 2.27 1.55 1.78 ...
## $ A_1_o : num 2 2.4 2.8 2.6 4.2 3.6 3 3.8 4.2 3.8 ...
## $ C_1_o : num 2.4 2.4 3 3 3.6 3.6 2.6 3.8 4 3.6 ...
## $ E_1_o : num 2.8 2.2 3 3.2 4 3.2 4 2.8 3.4 3.2 ...
## $ N_1_o : num 2.6 3 2.8 2.4 2.6 2.8 1.8 3.4 2.6 1 ...
## $ O_1_o : num 2 2 2.6 2.8 3 3.4 1.4 2.4 3.6 3 ...
## $ A_1_c : num -0.37 -0.35 0.193 -0.186 1.45 ...
## $ C_1_c : num 0.0296 -0.35 0.3929 0.2143 0.85 ...
## $ E_1_c : num 0.43 -0.55 0.393 0.414 1.25 ...
## $ N_1_c : num 0.23 0.25 0.193 -0.386 -0.15 ...
## $ O_1_c : num -0.37037 -0.75 -0.00714 0.01429 0.25 ...
## $ A_0_o : num 2.6 3.83 3.5 3.33 4.67 ...
## $ C_0_o : num 2.33 2.5 3.17 3.33 2.67 ...
## $ E_0_o : num 3.17 3.33 2.83 3.33 4.5 ...
## $ N_0_o : num 2.71 2.86 3.71 3.43 2.71 ...
## $ O_0_o : num 2.43 2.14 3 3 3.71 ...
## $ A_0_c : num -0.0222 0.9167 0.631 0.4048 1.75 ...
## $ C_0_c : num -0.457 -0.417 0.298 0.405 -0.25 ...
## $ E_0_c : num 0.167 0.333 -0.167 0.333 1.5 ...
## $ N_0_c : num -0.376 -0.179 0.658 0.398 -0.321 ...
## $ O_0_c : num -0.3016 -0.75 0.1684 0.0918 0.8214 ...
## $ cexp_o : num 2 2.25 2.5 2 3.25 ...
## $ comlrn_o : num 3 1.5 3 3.75 4 3.75 2.25 4.25 1.5 2.25 ...
## $ coplrn_o : num 3.33 2 3.6 3.2 4.6 ...
## $ cstrat_o : num 3.25 4.2 2 2 3.4 2.4 2 3.4 4.8 3.4 ...
## $ effper_o : num 3.5 2.25 2.5 2.5 4.25 ...
## $ elab_o : num 3 2.75 2 2 2.5 3.5 4 2.5 4.25 2.5 ...
## $ insmot_o : num 3.5 4 4 3.33 2.67 ...
## $ intmat_o : num 2.67 1.33 3.33 4 4 ...
## $ intrea_o : num 1.33 2.33 1.67 1.33 5 ...
## $ memor_o : num 1.67 1.75 2.25 2 3 ...
## $ scacad_o : num 2 2.67 3.33 2 2.67 ...
## $ scmath_o : num 2.33 1 4.33 2.67 2.67 ...
## $ scverb_o : num 3.33 2.67 3 4.67 2.67 ...
## $ selfef_o : num 2.5 1.75 3 2.5 3.5 3.75 2.75 3.75 5 3.5 ...
## $ cexp_c : num -0.37 -0.5 -0.107 -0.786 0.5 ...
## $ comlrn_c : num 0.63 -1.25 0.393 0.964 1.25 ...
## $ coplrn_c : num 0.963 -0.75 0.993 0.414 1.85 ...
## $ cstrat_c : num 0.88 1.45 -0.607 -0.786 0.65 ...
## $ effper_c : num 1.13 -0.5 -0.107 -0.286 1.5 ...
## $ elab_c : num 0.63 0 -0.607 -0.786 -0.25 ...
## $ insmot_c : num 1.1296 1.25 1.3929 0.5476 -0.0833 ...
## $ intmat_c : num 0.296 -1.417 0.726 1.214 1.25 ...
## $ intrea_c : num -1.037 -0.417 -0.94 -1.452 2.25 ...
## $ memor_c : num -0.704 -1 -0.357 -0.786 0.25 ...
## $ scacad_c : num -0.3704 -0.0833 0.7262 -0.7857 -0.0833 ...
## $ scmath_c : num -0.037 -1.75 1.7262 -0.119 -0.0833 ...
## $ scverb_c : num 0.543 -0.25 0.131 1.738 -0.25 ...
## $ selfef_c : num 0.13 -1 0.393 -0.286 0.75 ...
## $ means : num 2.37 2.75 2.61 2.79 2.75 ...
## $ sd : num 1.006 1.669 0.916 0.833 1.266 ...- Veja o que acontece se fizer isso
library(tidyverse)
bd_ie %>% names## [1] "id" "idade" "sexo" "ai_br" "nf_br" "aq_br"
## [7] "a_br" "b_br" "c_br" "e_br" "f_br" "g_br"
## [13] "h_br" "i_br" "l_br" "m_br" "n_br" "o_br"
## [19] "q1_br" "q2_br" "q3_br" "q4_br" "fatoe_i" "fator_ii"
## [25] "fato_iii" "fator_iv" "fator_v" "ra" "rv" "rm"
## [31] "re" "rn" "eg" "epn_ra" "epn_rv" "epn_rm"
## [37] "epn_re" "epn_rn" "epn_eg" "av_des1" "av_des2" "av_des"
## [43] "etnia" "percep" "usofaci" "conemo" "regemo" "experi"
## [49] "etrateg" "ie1" "ie2" "ie" "ra_1" "rv_1"
## [55] "rm_1" "re_1" "rn_1" "faces" "paisag" "facilit"
## [61] "sensa" "transi" "mistur" "gerenc" "relac" vars <- names(bd_ie)
vars <- bd_ie %>% names
class( bd_ie )## [1] "tbl_df" "tbl" "data.frame" class(vars)## [1] "character"Mais informação sobre o dplyr: https://www.curso-r.com/material/manipulacao/
Veja agora o que acontece quando você simplesmente digita e roda o nome do dataframe
bd_ie## # A tibble: 159 x 65
## id idade sexo ai_br nf_br aq_br a_br b_br c_br e_br f_br
## <chr> <dbl> <dbl+l> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 a001 46 1 [mas… 14 7 44 13 5 12 18 5
## 2 a002 49 1 [mas… 11 11 47 15 5 10 14 4
## 3 a003 NA 2 [fem… 12 0 57 20 7 5 13 12
## 4 a004 38 1 [mas… 13 3 49 12 8 11 14 7
## 5 a005 42 1 [mas… 12 0 50 10 5 18 10 4
## 6 a006 36 1 [mas… 20 0 49 12 5 20 18 4
## 7 a007 31 1 [mas… 18 0 69 12 11 16 14 18
## 8 a008 52 1 [mas… 13 0 60 15 6 15 14 11
## 9 a009 39 1 [mas… 14 0 55 8 11 14 14 10
## 10 a010 NA 1 [mas… 14 0 54 18 9 12 14 12
## # … with 149 more rows, and 54 more variables: g_br <dbl>, h_br <dbl>,
## # i_br <dbl>, l_br <dbl>, m_br <dbl>, n_br <dbl>, o_br <dbl>,
## # q1_br <dbl>, q2_br <dbl>, q3_br <dbl>, q4_br <dbl>, fatoe_i <dbl>,
## # fator_ii <dbl>, fato_iii <dbl>, fator_iv <dbl>, fator_v <dbl>,
## # ra <dbl>, rv <dbl>, rm <dbl>, re <dbl>, rn <dbl>, eg <dbl>,
## # epn_ra <dbl>, epn_rv <dbl>, epn_rm <dbl>, epn_re <dbl>, epn_rn <dbl>,
## # epn_eg <dbl>, av_des1 <dbl>, av_des2 <dbl>, av_des <dbl>, etnia <dbl>,
## # percep <dbl>, usofaci <dbl>, conemo <dbl>, regemo <dbl>, experi <dbl>,
## # etrateg <dbl>, ie1 <dbl>, ie2 <dbl>, ie <dbl>, ra_1 <dbl>, rv_1 <dbl>,
## # rm_1 <dbl>, re_1 <dbl>, rn_1 <dbl>, faces <dbl>, paisag <dbl>,
## # facilit <dbl>, sensa <dbl>, transi <dbl>, mistur <dbl>, gerenc <dbl>,
## # relac <dbl>Seleção de variáveis e observações em dataframes.
- 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_ie) ## [1] "id" "idade" "sexo" "ai_br" "nf_br" "aq_br"
## [7] "a_br" "b_br" "c_br" "e_br" "f_br" "g_br"
## [13] "h_br" "i_br" "l_br" "m_br" "n_br" "o_br"
## [19] "q1_br" "q2_br" "q3_br" "q4_br" "fatoe_i" "fator_ii"
## [25] "fato_iii" "fator_iv" "fator_v" "ra" "rv" "rm"
## [31] "re" "rn" "eg" "epn_ra" "epn_rv" "epn_rm"
## [37] "epn_re" "epn_rn" "epn_eg" "av_des1" "av_des2" "av_des"
## [43] "etnia" "percep" "usofaci" "conemo" "regemo" "experi"
## [49] "etrateg" "ie1" "ie2" "ie" "ra_1" "rv_1"
## [55] "rm_1" "re_1" "rn_1" "faces" "paisag" "facilit"
## [61] "sensa" "transi" "mistur" "gerenc" "relac" names(bd_ie[c(1, 28:33)])## [1] "id" "ra" "rv" "rm" "re" "rn" "eg" vars <- names(bd_ie[c(1, 28:33)])
# Subset de variáveis
dt <- bd_ie[ , vars]
# ou com dplyr
dt <- bd_ie %>% select(vars)
# Elimina
dt <- bd_ie[ , -1]
bd_16pf <- bd_ie %>% select(-c(28:65))
# removendo da área de trabalho
rm(dt, bd_16pf, vars)- Seleção de observações. É possível usar os números das linhas ou operações lógicas
$e|
# Seleção usando números
names(bd_ie)## [1] "id" "idade" "sexo" "ai_br" "nf_br" "aq_br"
## [7] "a_br" "b_br" "c_br" "e_br" "f_br" "g_br"
## [13] "h_br" "i_br" "l_br" "m_br" "n_br" "o_br"
## [19] "q1_br" "q2_br" "q3_br" "q4_br" "fatoe_i" "fator_ii"
## [25] "fato_iii" "fator_iv" "fator_v" "ra" "rv" "rm"
## [31] "re" "rn" "eg" "epn_ra" "epn_rv" "epn_rm"
## [37] "epn_re" "epn_rn" "epn_eg" "av_des1" "av_des2" "av_des"
## [43] "etnia" "percep" "usofaci" "conemo" "regemo" "experi"
## [49] "etrateg" "ie1" "ie2" "ie" "ra_1" "rv_1"
## [55] "rm_1" "re_1" "rn_1" "faces" "paisag" "facilit"
## [61] "sensa" "transi" "mistur" "gerenc" "relac" bd_ie[1:10 , 1:3] ## # A tibble: 10 x 3
## id idade sexo
## <chr> <dbl> <dbl+lbl>
## 1 a001 46 1 [masculino]
## 2 a002 49 1 [masculino]
## 3 a003 NA 2 [feminino]
## 4 a004 38 1 [masculino]
## 5 a005 42 1 [masculino]
## 6 a006 36 1 [masculino]
## 7 a007 31 1 [masculino]
## 8 a008 52 1 [masculino]
## 9 a009 39 1 [masculino]
## 10 a010 NA 1 [masculino]# Seleção usando lógica
bd_ie$sexo == 2## [1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE NA FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [23] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE
## [34] TRUE FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE TRUE
## [45] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
## [56] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [67] FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE
## [78] TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE
## [89] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [100] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE NA NA NA
## [111] NA NA NA TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
## [122] FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [144] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [155] TRUE FALSE FALSE FALSE FALSE s <- bd_ie$sexo == 2
bd_ie[s, 1:3]## # A tibble: 67 x 3
## id idade sexo
## <chr> <dbl> <dbl+lbl>
## 1 a003 NA 2 [feminino]
## 2 <NA> NA NA
## 3 a016 21 2 [feminino]
## 4 a017 19 2 [feminino]
## 5 a018 24 2 [feminino]
## 6 a019 24 2 [feminino]
## 7 a020 25 2 [feminino]
## 8 a023 29 2 [feminino]
## 9 a024 19 2 [feminino]
## 10 a025 25 2 [feminino]
## # … with 57 more rows# seleção usando dplyr
bd_ie %>% filter(sexo == 2) %>% select(1:3)## # A tibble: 59 x 3
## id idade sexo
## <chr> <dbl> <dbl+lbl>
## 1 a003 NA 2 [feminino]
## 2 a016 21 2 [feminino]
## 3 a017 19 2 [feminino]
## 4 a018 24 2 [feminino]
## 5 a019 24 2 [feminino]
## 6 a020 25 2 [feminino]
## 7 a023 29 2 [feminino]
## 8 a024 19 2 [feminino]
## 9 a025 25 2 [feminino]
## 10 a026 29 2 [feminino]
## # … with 49 more rowsAnálises estatísticas descritivas e figuras
- Análise de frequência
- Veja: http://www.strengejacke.de/sjPlot/ e também https://strengejacke.github.io/sjmisc
# devtools::install_github("strengejacke/sjPlot")
# usando R classico
names(bd_ie)## [1] "id" "idade" "sexo" "ai_br" "nf_br" "aq_br"
## [7] "a_br" "b_br" "c_br" "e_br" "f_br" "g_br"
## [13] "h_br" "i_br" "l_br" "m_br" "n_br" "o_br"
## [19] "q1_br" "q2_br" "q3_br" "q4_br" "fatoe_i" "fator_ii"
## [25] "fato_iii" "fator_iv" "fator_v" "ra" "rv" "rm"
## [31] "re" "rn" "eg" "epn_ra" "epn_rv" "epn_rm"
## [37] "epn_re" "epn_rn" "epn_eg" "av_des1" "av_des2" "av_des"
## [43] "etnia" "percep" "usofaci" "conemo" "regemo" "experi"
## [49] "etrateg" "ie1" "ie2" "ie" "ra_1" "rv_1"
## [55] "rm_1" "re_1" "rn_1" "faces" "paisag" "facilit"
## [61] "sensa" "transi" "mistur" "gerenc" "relac" table(bd_ie$sexo)##
## 1 2
## 92 59 table(bd_ie$idade)##
## 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42
## 1 3 4 11 12 9 9 9 15 8 5 5 5 2 6 4 4 1 4 2 2 1 5 3 1
## 43 44 45 46 49 50 51 52 55 60 64
## 1 3 1 2 3 1 3 1 1 1 1 table(bd_ie$idade, bd_ie$sexo)##
## 1 2
## 17 1 0
## 18 0 3
## 19 0 4
## 20 7 4
## 21 4 8
## 22 6 3
## 23 5 4
## 24 6 3
## 25 6 9
## 26 4 4
## 27 2 3
## 28 5 0
## 29 2 3
## 30 1 1
## 31 5 1
## 32 4 0
## 33 3 1
## 34 1 0
## 35 4 0
## 36 2 0
## 38 2 0
## 39 1 0
## 40 4 1
## 41 2 1
## 42 1 0
## 43 1 0
## 44 2 1
## 45 1 0
## 46 2 0
## 49 2 1
## 50 1 0
## 51 0 3
## 52 1 0
## 55 1 0
## 60 1 0
## 64 1 0# usando dplyr
bd_ie %>% select(idade, sexo) %>% table## sexo
## idade 1 2
## 17 1 0
## 18 0 3
## 19 0 4
## 20 7 4
## 21 4 8
## 22 6 3
## 23 5 4
## 24 6 3
## 25 6 9
## 26 4 4
## 27 2 3
## 28 5 0
## 29 2 3
## 30 1 1
## 31 5 1
## 32 4 0
## 33 3 1
## 34 1 0
## 35 4 0
## 36 2 0
## 38 2 0
## 39 1 0
## 40 4 1
## 41 2 1
## 42 1 0
## 43 1 0
## 44 2 1
## 45 1 0
## 46 2 0
## 49 2 1
## 50 1 0
## 51 0 3
## 52 1 0
## 55 1 0
## 60 1 0
## 64 1 0# usando sJlot
library(sjPlot)
library(sjmisc)
frq(bd_ie$sexo)##
## # sexo (x) <numeric>
## # total N=159 valid N=151 mean=1.39 sd=0.49
##
## val label frq raw.prc valid.prc cum.prc
## 1 masculino 92 57.86 60.93 60.93
## 2 feminino 59 37.11 39.07 100.00
## NA NA 8 5.03 NA NA sjt.xtab(bd_ie$idade, bd_ie$sexo, show.row.prc = TRUE)|
Questionário de Identificação - idade |
sexo | Total | |
|---|---|---|---|
| masculino | feminino | ||
| 17 |
1 100 % |
0 0 % |
1 100 % |
| 18 |
0 0 % |
3 100 % |
3 100 % |
| 19 |
0 0 % |
4 100 % |
4 100 % |
| 20 |
7 63.6 % |
4 36.4 % |
11 100 % |
| 21 |
4 33.3 % |
8 66.7 % |
12 100 % |
| 22 |
6 66.7 % |
3 33.3 % |
9 100 % |
| 23 |
5 55.6 % |
4 44.4 % |
9 100 % |
| 24 |
6 66.7 % |
3 33.3 % |
9 100 % |
| 25 |
6 40 % |
9 60 % |
15 100 % |
| 26 |
4 50 % |
4 50 % |
8 100 % |
| 27 |
2 40 % |
3 60 % |
5 100 % |
| 28 |
5 100 % |
0 0 % |
5 100 % |
| 29 |
2 40 % |
3 60 % |
5 100 % |
| 30 |
1 50 % |
1 50 % |
2 100 % |
| 31 |
5 83.3 % |
1 16.7 % |
6 100 % |
| 32 |
4 100 % |
0 0 % |
4 100 % |
| 33 |
3 75 % |
1 25 % |
4 100 % |
| 34 |
1 100 % |
0 0 % |
1 100 % |
| 35 |
4 100 % |
0 0 % |
4 100 % |
| 36 |
2 100 % |
0 0 % |
2 100 % |
| 38 |
2 100 % |
0 0 % |
2 100 % |
| 39 |
1 100 % |
0 0 % |
1 100 % |
| 40 |
4 80 % |
1 20 % |
5 100 % |
| 41 |
2 66.7 % |
1 33.3 % |
3 100 % |
| 42 |
1 100 % |
0 0 % |
1 100 % |
| 43 |
1 100 % |
0 0 % |
1 100 % |
| 44 |
2 66.7 % |
1 33.3 % |
3 100 % |
| 45 |
1 100 % |
0 0 % |
1 100 % |
| 46 |
2 100 % |
0 0 % |
2 100 % |
| 49 |
2 66.7 % |
1 33.3 % |
3 100 % |
| 50 |
1 100 % |
0 0 % |
1 100 % |
| 51 |
0 0 % |
3 100 % |
3 100 % |
| 52 |
1 100 % |
0 0 % |
1 100 % |
| 55 |
1 100 % |
0 0 % |
1 100 % |
| 60 |
1 100 % |
0 0 % |
1 100 % |
| 64 |
1 100 % |
0 0 % |
1 100 % |
| Total |
91 61.1 % |
58 38.9 % |
149 100 % |
χ2=46.712 · df=35 · Cramer’s V=0.560 · Fisher’s p=0.065 |
- Usando o Jamovi
library(jmv)
descriptives(data = bd_ie ,
vars = c("epn_ra" , "epn_rv", "epn_rm", "epn_re", "epn_rn", "epn_eg"),
hist = TRUE)##
## DESCRIPTIVES
##
## Descriptives
## ───────────────────────────────────────────────────────────────────────
## epn_ra epn_rv epn_rm epn_re epn_rn epn_eg
## ───────────────────────────────────────────────────────────────────────
## N 152 153 152 152 152 151
## Missing 7 6 7 7 7 8
## Mean 89.6 91.2 95.5 86.9 91.8 87.1
## Median 92.0 91.0 96.0 86.0 90.0 87.0
## Minimum 60.0 65.0 70.0 60.0 70.0 61.0
## Maximum 131 141 141 124 146 117
## ───────────────────────────────────────────────────────────────────────
corrMatrix(data = bd_ie , vars = c("epn_ra" , "epn_rv", "epn_rm", "epn_re", "epn_rn", "epn_eg"))##
## CORRELATION MATRIX
##
## Correlation Matrix
## ─────────────────────────────────────────────────────────────────────────────────────
## epn_ra epn_rv epn_rm epn_re epn_rn epn_eg
## ─────────────────────────────────────────────────────────────────────────────────────
## epn_ra Pearson's r — 0.464 0.531 0.559 0.492 0.788
## p-value — < .001 < .001 < .001 < .001 < .001
##
## epn_rv Pearson's r — 0.324 0.417 0.532 0.684
## p-value — < .001 < .001 < .001 < .001
##
## epn_rm Pearson's r — 0.552 0.456 0.767
## p-value — < .001 < .001 < .001
##
## epn_re Pearson's r — 0.540 0.799
## p-value — < .001 < .001
##
## epn_rn Pearson's r — 0.773
## p-value — < .001
##
## epn_eg Pearson's r —
## p-value —
## ─────────────────────────────────────────────────────────────────────────────────────- Correlação e análise de cluster via heatmaps
library(d3heatmap)
vars <- names(bd_ie)[c(23:32, 40:41, 44:47)]
bd_ie %>% select(vars) %>% cor(use="pair") %>%
d3heatmap(
symn= TRUE,
symm = TRUE,
k_row = 5,
k_col = 5
)- Explorando distribuições e correlações via ggplot (tidyverse)
ggplot(data = bd_ie, aes(x = epn_eg)) + geom_histogram()
ggplot(bd_ie, aes(x = epn_eg)) +
geom_histogram(
binwidth = 5, alpha = 1/2,
color = "gray", fill = "blue")
library(LaCroixColoR)
ggplot(bd_ie, aes(x = epn_eg, y =av_des, color = ie)) +
geom_point( ) +
geom_smooth() +
scale_color_gradientn(colours=lacroix_palette("PeachPear", type = "continuous", n=10)) +
theme_minimal() +
scale_x_continuous(breaks = seq(60, 120, 5), limits = c(60, 120)) 
Regressão múltipla
linReg(data= bd_ie,
dep = av_des1 ,
covs = vars(ra,rv, rm, re, rn, percep, usofaci, conemo, regemo),
blocks = list(list( "ra", "rv", "rm", "re", "rn", "percep","usofaci", "conemo", "regemo")),
stdEst = TRUE,
modelTest = TRUE,
anova = TRUE
)##
## LINEAR REGRESSION
##
## Model Fit Measures
## ──────────────────────────────────────────────────────────
## Model R R² F df1 df2 p
## ──────────────────────────────────────────────────────────
## 1 0.458 0.209 3.00 9 102 0.003
## ──────────────────────────────────────────────────────────
##
##
## MODEL SPECIFIC RESULTS
##
## MODEL 1
##
## Omnibus ANOVA Test
## ────────────────────────────────────────────────────────────────────────
## Sum of Squares df Mean Square F p
## ────────────────────────────────────────────────────────────────────────
## ra 0.0186 1 0.0186 0.0230 0.880
## rv 0.4239 1 0.4239 0.5246 0.471
## rm 0.0842 1 0.0842 0.1042 0.748
## re 2.4841 1 2.4841 3.0743 0.083
## rn 0.3346 1 0.3346 0.4141 0.521
## percep 0.4837 1 0.4837 0.5986 0.441
## usofaci 0.2219 1 0.2219 0.2746 0.601
## conemo 1.1565 1 1.1565 1.4313 0.234
## regemo 2.9383 1 2.9383 3.6364 0.059
## Residuals 82.4182 102 0.8080
## ────────────────────────────────────────────────────────────────────────
## Note. Type 3 sum of squares
##
##
## Model Coefficients
## ─────────────────────────────────────────────────────────────────────────
## Predictor Estimate SE t p Stand. Estimate
## ─────────────────────────────────────────────────────────────────────────
## Intercept 2.01019 0.8128 2.473 0.015
## ra 0.00425 0.0280 0.152 0.880 0.0189
## rv -0.02127 0.0294 -0.724 0.471 -0.0819
## rm 0.00808 0.0250 0.323 0.748 0.0390
## re 0.04797 0.0274 1.753 0.083 0.2146
## rn 0.01861 0.0289 0.643 0.521 0.0756
## percep -0.01240 0.0160 -0.774 0.441 -0.0823
## usofaci 0.00844 0.0161 0.524 0.601 0.0587
## conemo 0.02765 0.0231 1.196 0.234 0.1420
## regemo 0.02402 0.0126 1.907 0.059 0.2026
## ───────────────────────────────────────────────────────────────────────── linReg(data= bd_ie,
dep = av_des1 ,
covs = vars(ra,rv, rm, re, rn, percep, usofaci, conemo, regemo),
blocks = list(
m1 = list( "ra", "rv", "rm", "re", "rn"),
m2 = list("percep","usofaci", "conemo", "regemo")
),
stdEst = TRUE,
modelTest = TRUE,
)##
## LINEAR REGRESSION
##
## Model Fit Measures
## ──────────────────────────────────────────────────────────
## Model R R² F df1 df2 p
## ──────────────────────────────────────────────────────────
## 1 0.362 0.131 3.19 5 106 0.010
## 2 0.458 0.209 3.00 9 102 0.003
## ──────────────────────────────────────────────────────────
##
##
## Model Comparisons
## ────────────────────────────────────────────────────────────────
## Model Model ΔR² F df1 df2 p
## ────────────────────────────────────────────────────────────────
## 1 - 2 0.0786 2.53 4 102 0.045
## ────────────────────────────────────────────────────────────────
##
##
## MODEL SPECIFIC RESULTS
##
## MODEL 1
##
## Model Coefficients
## ─────────────────────────────────────────────────────────────────────────
## Predictor Estimate SE t p Stand. Estimate
## ─────────────────────────────────────────────────────────────────────────
## Intercept 3.38551 0.3961 8.546 < .001
## ra 0.00986 0.0269 0.366 0.715 0.0438
## rv 0.00325 0.0291 0.112 0.911 0.0125
## rm 0.00334 0.0243 0.138 0.891 0.0161
## re 0.06149 0.0264 2.331 0.022 0.2751
## rn 0.01828 0.0293 0.623 0.534 0.0743
## ─────────────────────────────────────────────────────────────────────────
##
##
## MODEL 2
##
## Model Coefficients
## ─────────────────────────────────────────────────────────────────────────
## Predictor Estimate SE t p Stand. Estimate
## ─────────────────────────────────────────────────────────────────────────
## Intercept 2.01019 0.8128 2.473 0.015
## ra 0.00425 0.0280 0.152 0.880 0.0189
## rv -0.02127 0.0294 -0.724 0.471 -0.0819
## rm 0.00808 0.0250 0.323 0.748 0.0390
## re 0.04797 0.0274 1.753 0.083 0.2146
## rn 0.01861 0.0289 0.643 0.521 0.0756
## percep -0.01240 0.0160 -0.774 0.441 -0.0823
## usofaci 0.00844 0.0161 0.524 0.601 0.0587
## conemo 0.02765 0.0231 1.196 0.234 0.1420
## regemo 0.02402 0.0126 1.907 0.059 0.2026
## ─────────────────────────────────────────────────────────────────────────Pacote APA tables
https://cran.r-project.org/web/packages/apaTables/vignettes/apaTables.html
library(apaTables)
vars <- c("av_des1", "ra", "rv", "rm", "re", "rn", "percep","usofaci", "conemo", "regemo")
apa.cor.table(data = bd_ie[ , vars], filename="t1_desfr.doc", table.number=1)##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. av_des1 4.38 0.98
##
## 2. ra 14.98 4.27 .25**
## [.08, .41]
##
## 3. rv 15.20 3.81 .22* .51**
## [.05, .38] [.38, .62]
##
## 4. rm 11.00 4.57 .20* .51** .32**
## [.02, .36] [.39, .62] [.17, .46]
##
## 5. re 9.54 4.38 .37** .51** .41** .55**
## [.20, .51] [.38, .62] [.27, .54] [.43, .66]
##
## 6. rn 10.07 4.11 .27** .49** .53** .41**
## [.10, .43] [.35, .60] [.40, .63] [.27, .54]
##
## 7. percep 41.61 6.40 .05 .32** .21* .06
## [-.13, .23] [.16, .47] [.03, .37] [-.12, .24]
##
## 8. usofaci 40.21 6.71 .19* .26** .22* -.01
## [.01, .36] [.09, .42] [.04, .38] [-.20, .17]
##
## 9. conemo 40.13 5.04 .30** .29** .37** .19*
## [.13, .45] [.12, .45] [.21, .52] [.01, .35]
##
## 10. regemo 40.00 8.14 .28** .25** .32** .12
## [.11, .44] [.08, .41] [.15, .47] [-.06, .29]
##
## 5 6 7 8 9
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .51**
## [.38, .62]
##
## .29** .26**
## [.12, .45] [.09, .42]
##
## .08 .14 .38**
## [-.10, .26] [-.05, .31] [.22, .53]
##
## .36** .29** .38** .49**
## [.19, .50] [.12, .45] [.22, .52] [.34, .62]
##
## .27** .22* .25** .37** .48**
## [.09, .42] [.05, .39] [.07, .41] [.20, .52] [.33, .60]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## fit <- lm(data = bd_ie, formula = av_des1 ~ ra + rv + rm + re + rn + percep + usofaci + conemo + regemo)
apa.reg.table( fit, filename = "t2_lm.doc", table.number = 2)##
##
## Table 2
##
## Regression results using av_des1 as the criterion
##
##
## Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI r
## (Intercept) 2.01* [0.40, 3.62]
## ra 0.00 [-0.05, 0.06] 0.02 [-0.23, 0.27] .00 [-.00, .00] .23*
## rv -0.02 [-0.08, 0.04] -0.08 [-0.31, 0.14] .00 [-.02, .03] .18
## rm 0.01 [-0.04, 0.06] 0.04 [-0.20, 0.28] .00 [-.01, .01] .22*
## re 0.05 [-0.01, 0.10] 0.21 [-0.03, 0.46] .02 [-.03, .07] .35**
## rn 0.02 [-0.04, 0.08] 0.08 [-0.16, 0.31] .00 [-.02, .02] .26**
## percep -0.01 [-0.04, 0.02] -0.08 [-0.29, 0.13] .00 [-.02, .03] .12
## usofaci 0.01 [-0.02, 0.04] 0.06 [-0.16, 0.28] .00 [-.01, .02] .20*
## conemo 0.03 [-0.02, 0.07] 0.14 [-0.09, 0.38] .01 [-.02, .05] .34**
## regemo 0.02 [-0.00, 0.05] 0.20 [-0.01, 0.41] .03 [-.03, .08] .33**
##
##
##
## Fit
##
##
##
##
##
##
##
##
##
##
## R2 = .209**
## 95% CI[.03,.28]
##
##
## Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
## b represents unstandardized regression weights. beta indicates the standardized regression weights.
## sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
## Square brackets are used to enclose the lower and upper limits of a confidence interval.
## * indicates p < .05. ** indicates p < .01.
## Exercício 2
- Crie um diretório
- Escolha um banco de dados próprio em excel ou SPSS
- Copie-o no diretório criado
- Abra o RSTUDIO
- Crie um R Script para escrever seus programas e salve no diretório criado chamando-o de dados.R
- Aponte o RSTUDIO para o diretório criado
- Importe o arquivo
- Estudo sua estrutura usando o str()
- Salve os dados no diretório
- Explore a base de dados do scores e explore a relação entre o desempenho as variáveis existentes na base.
- Rode uma análise de regressão múltipla.