install.packages("readxl")
install.packages(c("haven", "readxl",
"readr", "xlsx",
"foreign", "sjPlot",
"psych", "tidyverse"))
cadernos
setwd("~/Dropbox (Personal)/Análise de dados/exercicios_2018")
library(readxl)
bd <- read_excel("ex3.xlsx", sheet = "ex3")
library(haven)
bd_dip <-read_sav("projeto_dip_2.sav")
save.image(file = "exercicio3.RData")
load("ex3.RData")
names(scores)
## [1] "banco" "cod_suj" "Data de nasc."
## [4] "data.aplic" "Idade0" "idade1"
## [7] "Termo" "sujeito" "serie"
## [10] "escola" "turma" "Sexo"
## [13] "Esc. Mãe" "port" "mat"
## [16] "cloze" "A_o" "C_o"
## [19] "E_o" "N_o" "O_o"
## [22] "A_c" "C_c" "E_c"
## [25] "N_c" "O_c" "A_z"
## [28] "C_z" "E_z" "N_z"
## [31] "O_z" "antonym.rc" "antonym.cntrst_A"
## [34] "antonym.cntrst_C" "antonym.cntrst_E" "antonym.cntrst_N"
## [37] "antonym.cntrst_O" "mean_A" "mean_C"
## [40] "mean_E" "mean_N" "mean_O"
## [43] "sd_A" "sd_C" "sd_E"
## [46] "sd_N" "sd_O" "antonym.rc_A"
## [49] "antonym.rc_C" "antonym.rc_E" "antonym.rc_N"
## [52] "antonym.rc_O" "nse" "A_1_o"
## [55] "C_1_o" "E_1_o" "N_1_o"
## [58] "O_1_o" "A_1_c" "C_1_c"
## [61] "E_1_c" "N_1_c" "O_1_c"
## [64] "A_1_z" "C_1_z" "E_1_z"
## [67] "N_1_z" "O_1_z" "A_0_o"
## [70] "C_0_o" "E_0_o" "N_0_o"
## [73] "O_0_o" "A_0_c" "C_0_c"
## [76] "E_0_c" "N_0_c" "O_0_c"
## [79] "A_0_z" "C_0_z" "E_0_z"
## [82] "N_0_z" "O_0_z" "cexp_o"
## [85] "comlrn_o" "coplrn_o" "cstrat_o"
## [88] "effper_o" "elab_o" "insmot_o"
## [91] "intmat_o" "intrea_o" "memor_o"
## [94] "scacad_o" "scmath_o" "scverb_o"
## [97] "selfef_o" "cexp_c" "comlrn_c"
## [100] "coplrn_c" "cstrat_c" "effper_c"
## [103] "elab_c" "insmot_c" "intmat_c"
## [106] "intrea_c" "memor_c" "scacad_c"
## [109] "scmath_c" "scverb_c" "selfef_c"
## [112] "cexp_z" "comlrn_z" "coplrn_z"
## [115] "cstrat_z" "effper_z" "elab_z"
## [118] "insmot_z" "intmat_z" "intrea_z"
## [121] "memor_z" "scacad_z" "scmath_z"
## [124] "scverb_z" "selfef_z" "means"
## [127] "sd"
names(dic)
## [1] "X__1" "test_ord" "teste" "coditem" "factor"
## [6] "factor0" "factor2" "factor3" "pole" "order"
## [11] "coditem2" "P_S" "domain" "facet" "pole2"
## [16] "seman_pairs" "ord_esc" "item_text" "CodItem3" "port_text1"
## [21] "engl_text1" "engl_text2" "Pairs" "carol_eng"
names(itens)
## [1] "p.1.13" "p.1.14" "p.1.15" "p.1.16" "p.1.1" "p.1.2" "p.1.3"
## [8] "p.1.4" "p.2.1" "p.2.2" "p.2.3" "p.2.4" "p.2.5" "p.2.6"
## [15] "p.2.7" "p.2.8" "p.2.9" "p.2.10" "p.2.11" "p.2.12" "p.2.13"
## [22] "p.2.14" "p.2.15" "p.2.16" "p.2.17" "p.2.18" "p.2.19" "p.2.20"
## [29] "p.2.21" "p.2.22" "p.2.23" "p.2.24" "p.2.25" "p.2.26" "p.2.27"
## [36] "p.2.28" "p.2.29" "p.2.30" "p.2.31" "p.2.32" "p.2.33" "p.3.1"
## [43] "p.3.2" "p.3.3" "p.3.4" "p.3.5" "p.3.6" "p.3.7" "p.3.8"
## [50] "p.3.9" "p.3.10" "p.3.11" "p.3.12" "p.3.13" "p.3.14" "p.3.15"
## [57] "p.3.16" "p.3.17" "p.3.18" "p.3.19" "p.3.20" "p.3.21" "p.3.22"
## [64] "p.3.23" "p.3.24" "p.3.25" "p.3.26" "p.4.1" "p.4.2" "p.4.3"
## [71] "p.4.4" "p.4.5" "p.4.6" "p.4.7" "p.4.8" "p.4.9" "p.4.10"
## [78] "p.4.11" "p.4.12" "p.4.13" "p.4.14" "p.4.15" "p.4.16" "p.4.17"
## [85] "p.4.18" "p.4.19" "p.4.20" "p.4.21" "p.4.22" "p.4.23" "p.4.24"
## [92] "p.4.25" "p.4.26" "p.4.27" "p.4.28" "p.5.1" "p.5.2" "p.5.3"
## [99] "p.5.4" "p.5.5" "p.5.6" "p.5.7" "p.5.8" "p.5.9" "p.5.10"
## [106] "p.5.11" "p.5.12" "p.5.13" "p.5.14" "p.5.15" "p.5.16" "p.5.17"
## [113] "p.5.18" "p.5.19" "p.5.20" "p.5.21" "p.5.22" "p.5.23" "p.5.24"
## [120] "CZ1_1" "CZ1_2" "CZ1_3" "CZ1_4" "CZ1_5" "CZ1_6" "CZ1_7"
## [127] "CZ1_8" "CZ1_9" "CZ1_10" "CZ1_11" "CZ1_12" "CZ1_13" "CZ1_14"
## [134] "CZ1_15" "CZ2_1" "CZ2_2" "CZ2_3" "CZ2_4" "CZ2_5" "CZ2_6"
## [141] "CZ2_7" "CZ2_8" "CZ2_9" "CZ2_10" "CZ2_11" "CZ2_12" "CZ2_13"
## [148] "CZ2_14" "CZ2_15" "rwn_id"
str(dic)
## Classes 'tbl_df', 'tbl' and 'data.frame': 124 obs. of 24 variables:
## $ X__1 : chr "1" "6" "11" "20" ...
## $ test_ord : num 1 1 1 1 1 1 1 1 1 1 ...
## $ teste : chr "senna" "senna" "senna" "senna" ...
## $ coditem : chr "p.2.1" "p.2.6" "p.2.11" "p.2.20" ...
## $ factor : chr "A_0" "A_0" "A_0" "A_0" ...
## $ factor0 : chr "A" "A" "A" "A" ...
## $ factor2 : chr "A_Cmp_0" "A_Cmp_0" "A_Mod_0" "A_Resp_0" ...
## $ factor3 : chr "A_Cmp" "A_Cmp" "A_Mod" "A_Resp" ...
## $ pole : num 1 1 0 0 1 1 1 1 1 1 ...
## $ order : num 1 6 11 20 16 25 34 39 44 49 ...
## $ coditem2 : chr "sv2.133" "Sv1.049" "sv2.139" "sv2.094" ...
## $ P_S : num 0 0 0 0 0 0 1 1 1 1 ...
## $ domain : chr "A" "A" "A" "A" ...
## $ facet : chr "Cmp" "Cmp" "Mod" "Resp" ...
## $ pole2 : num 1 1 0 0 1 1 1 1 1 1 ...
## $ seman_pairs: num 1 2 1 2 NA NA NA NA NA NA ...
## $ ord_esc : num 1 6 11 20 16 25 1 6 11 16 ...
## $ item_text : chr "Eu me preocupo com o que acontece com os outros." "Não sou egoísta e gosto de ajudar os outros." "Eu nunca estou satisfeito(a) com os outros." "Não me importo se tiver que magoar alguém para conseguir o que eu quero." ...
## $ CodItem3 : chr "sv2.133_0_A_Cmp_1" "Sv1.049_0_A_Cmp_1" "sv2.139_0_A_Mod_0" "sv2.094_0_A_Resp_0" ...
## $ port_text1 : chr NA NA NA NA ...
## $ engl_text1 : chr "I care about what happens to other people." "Is helpful and unselfish with others" "I'm never satisfied with other people." "I don't care if I have to hurt someone to get what I want." ...
## $ engl_text2 : chr NA NA NA NA ...
## $ Pairs : chr "L0_Cmp1" NA NA NA ...
## $ carol_eng : chr "Iget worried with what happens to others" NA NA NA ...
str(scores)
## 'data.frame': 168 obs. of 127 variables:
## $ banco : chr "josi" "josi" "josi" "josi" ...
## $ 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 ...
## $ 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. Mãe : 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 ...
## $ A_z : num -0.158 0.237 0.427 0.225 1.216 ...
## $ C_z : num -0.206 -0.192 0.324 0.504 0.164 ...
## $ E_z : num 0.3128 -0.0144 0.0554 0.5795 1.0489 ...
## $ N_z : num -0.1079 0.0189 0.4931 0.118 -0.2076 ...
## $ O_z : num -0.298 -0.4045 0.0435 0.1082 0.4202 ...
## $ 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_1_z : num -0.368 -0.21 0.21 -0.223 1.146 ...
## $ C_1_z : num 0.0295 -0.2097 0.4287 0.2574 0.6716 ...
## $ E_1_z : num 0.427 -0.329 0.429 0.498 0.988 ...
## $ N_1_z : num 0.228 0.15 0.21 -0.463 -0.119 ...
## $ O_1_z : num -0.36828 -0.44925 -0.00779 0.01716 0.19753 ...
## $ 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 ...
## $ A_0_z : num -0.0221 0.5491 0.6884 0.4862 1.3827 ...
## $ C_0_z : num -0.454 -0.25 0.325 0.486 -0.198 ...
## $ E_0_z : num 0.166 0.2 -0.182 0.4 1.185 ...
## $ N_0_z : num -0.374 -0.107 0.718 0.478 -0.254 ...
## $ O_0_z : num -0.3 -0.449 0.184 0.11 0.649 ...
## $ 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 ...
## [list output truncated]
library(tidyverse)
vars <- names(scores)
vars <- scores %>% names
class(scores)
## [1] "data.frame"
class(vars)
## [1] "character"
dic
## # A tibble: 124 x 24
## X__1 test… teste codi… fact… fact… fact… fact… pole order codi… P_S
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 1 1.00 senna p.2.1 A_0 A A_Cm… A_Cmp 1.00 1.00 sv2.… 0
## 2 6 1.00 senna p.2.6 A_0 A A_Cm… A_Cmp 1.00 6.00 Sv1.… 0
## 3 11 1.00 senna p.2.… A_0 A A_Mo… A_Mod 0 11.0 sv2.… 0
## 4 20 1.00 senna p.2.… A_0 A A_Re… A_Re… 0 20.0 sv2.… 0
## 5 16 1.00 senna p.2.… A_0 A A_Re… A_Re… 1.00 16.0 sv2.… 0
## 6 25 1.00 senna p.2.… A_0 A A_Tr… A_Tru 1.00 25.0 sv2.… 0
## 7 34 1.00 senna p.3.1 A_1 A A_Cm… A_Cmp 1.00 34.0 sv2.… 1.00
## 8 39 1.00 senna p.3.6 A_1 A A_Cm… A_Cmp 1.00 39.0 sv2.… 1.00
## 9 44 1.00 senna p.3.… A_1 A A_Cm… A_Cmp 1.00 44.0 sv2.… 1.00
## 10 49 1.00 senna p.3.… A_1 A A_Re… A_Re… 1.00 49.0 sv2.… 1.00
## # ... with 114 more rows, and 12 more variables: domain <chr>, facet
## # <chr>, pole2 <dbl>, seman_pairs <dbl>, ord_esc <dbl>, item_text <chr>,
## # CodItem3 <chr>, port_text1 <chr>, engl_text1 <chr>, engl_text2 <chr>,
## # Pairs <chr>, carol_eng <chr>
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(scores)
## [1] "banco" "cod_suj" "Data de nasc."
## [4] "data.aplic" "Idade0" "idade1"
## [7] "Termo" "sujeito" "serie"
## [10] "escola" "turma" "Sexo"
## [13] "Esc. Mãe" "port" "mat"
## [16] "cloze" "A_o" "C_o"
## [19] "E_o" "N_o" "O_o"
## [22] "A_c" "C_c" "E_c"
## [25] "N_c" "O_c" "A_z"
## [28] "C_z" "E_z" "N_z"
## [31] "O_z" "antonym.rc" "antonym.cntrst_A"
## [34] "antonym.cntrst_C" "antonym.cntrst_E" "antonym.cntrst_N"
## [37] "antonym.cntrst_O" "mean_A" "mean_C"
## [40] "mean_E" "mean_N" "mean_O"
## [43] "sd_A" "sd_C" "sd_E"
## [46] "sd_N" "sd_O" "antonym.rc_A"
## [49] "antonym.rc_C" "antonym.rc_E" "antonym.rc_N"
## [52] "antonym.rc_O" "nse" "A_1_o"
## [55] "C_1_o" "E_1_o" "N_1_o"
## [58] "O_1_o" "A_1_c" "C_1_c"
## [61] "E_1_c" "N_1_c" "O_1_c"
## [64] "A_1_z" "C_1_z" "E_1_z"
## [67] "N_1_z" "O_1_z" "A_0_o"
## [70] "C_0_o" "E_0_o" "N_0_o"
## [73] "O_0_o" "A_0_c" "C_0_c"
## [76] "E_0_c" "N_0_c" "O_0_c"
## [79] "A_0_z" "C_0_z" "E_0_z"
## [82] "N_0_z" "O_0_z" "cexp_o"
## [85] "comlrn_o" "coplrn_o" "cstrat_o"
## [88] "effper_o" "elab_o" "insmot_o"
## [91] "intmat_o" "intrea_o" "memor_o"
## [94] "scacad_o" "scmath_o" "scverb_o"
## [97] "selfef_o" "cexp_c" "comlrn_c"
## [100] "coplrn_c" "cstrat_c" "effper_c"
## [103] "elab_c" "insmot_c" "intmat_c"
## [106] "intrea_c" "memor_c" "scacad_c"
## [109] "scmath_c" "scverb_c" "selfef_c"
## [112] "cexp_z" "comlrn_z" "coplrn_z"
## [115] "cstrat_z" "effper_z" "elab_z"
## [118] "insmot_z" "intmat_z" "intrea_z"
## [121] "memor_z" "scacad_z" "scmath_z"
## [124] "scverb_z" "selfef_z" "means"
## [127] "sd"
names(scores[c(14, 15, 16, 22:26)])
## [1] "port" "mat" "cloze" "A_c" "C_c" "E_c" "N_c" "O_c"
vars <- names(scores[c(14, 15, 16, 22:26)])
# Subset de variáveis
dt <- scores[ , vars]
# ou com dplyr
dt <- scores %>% select(vars)
# Elimina
dt <- scores[ , -c(3:83)]
dt <- scores %>% select(-c(3:83))
# removendo da área de trabalho
rm(dt)
$
e |
# Seleção usando números
names(dic)
## [1] "X__1" "test_ord" "teste" "coditem" "factor"
## [6] "factor0" "factor2" "factor3" "pole" "order"
## [11] "coditem2" "P_S" "domain" "facet" "pole2"
## [16] "seman_pairs" "ord_esc" "item_text" "CodItem3" "port_text1"
## [21] "engl_text1" "engl_text2" "Pairs" "carol_eng"
dic[1:11 , c(6, 18)]
## # A tibble: 11 x 2
## factor0 item_text
## <chr> <chr>
## 1 A Eu me preocupo com o que acontece com os outros.
## 2 A Não sou egoísta e gosto de ajudar os outros.
## 3 A Eu nunca estou satisfeito(a) com os outros.
## 4 A Não me importo se tiver que magoar alguém para conseguir o que…
## 5 A Respeito autoridades (professores, diretores, etc.).
## 6 A Acredito no melhor das pessoas.
## 7 A Ser legal com os outros.
## 8 A Saber quando seus amigos precisam de ajuda mesmo que eles não …
## 9 A Perceber o que as outras pessoas estão sentindo.
## 10 A Ouvir respeitosamente a opinião dos outros?
## 11 A Tratar bem e respeitosamente as pessoas de que você não gosta.
# Seleção usando lógica
dic[dic$factor0 == "A", c(6, 18)]
## # A tibble: 11 x 2
## factor0 item_text
## <chr> <chr>
## 1 A Eu me preocupo com o que acontece com os outros.
## 2 A Não sou egoísta e gosto de ajudar os outros.
## 3 A Eu nunca estou satisfeito(a) com os outros.
## 4 A Não me importo se tiver que magoar alguém para conseguir o que…
## 5 A Respeito autoridades (professores, diretores, etc.).
## 6 A Acredito no melhor das pessoas.
## 7 A Ser legal com os outros.
## 8 A Saber quando seus amigos precisam de ajuda mesmo que eles não …
## 9 A Perceber o que as outras pessoas estão sentindo.
## 10 A Ouvir respeitosamente a opinião dos outros?
## 11 A Tratar bem e respeitosamente as pessoas de que você não gosta.
s <- dic$factor0 == "A"
v <- names(dic)[c(6, 18)]
dic[s, v]
## # A tibble: 11 x 2
## factor0 item_text
## <chr> <chr>
## 1 A Eu me preocupo com o que acontece com os outros.
## 2 A Não sou egoísta e gosto de ajudar os outros.
## 3 A Eu nunca estou satisfeito(a) com os outros.
## 4 A Não me importo se tiver que magoar alguém para conseguir o que…
## 5 A Respeito autoridades (professores, diretores, etc.).
## 6 A Acredito no melhor das pessoas.
## 7 A Ser legal com os outros.
## 8 A Saber quando seus amigos precisam de ajuda mesmo que eles não …
## 9 A Perceber o que as outras pessoas estão sentindo.
## 10 A Ouvir respeitosamente a opinião dos outros?
## 11 A Tratar bem e respeitosamente as pessoas de que você não gosta.
dic %>% filter(factor0 == "A") %>% select(factor0, item_text)
## # A tibble: 11 x 2
## factor0 item_text
## <chr> <chr>
## 1 A Eu me preocupo com o que acontece com os outros.
## 2 A Não sou egoísta e gosto de ajudar os outros.
## 3 A Eu nunca estou satisfeito(a) com os outros.
## 4 A Não me importo se tiver que magoar alguém para conseguir o que…
## 5 A Respeito autoridades (professores, diretores, etc.).
## 6 A Acredito no melhor das pessoas.
## 7 A Ser legal com os outros.
## 8 A Saber quando seus amigos precisam de ajuda mesmo que eles não …
## 9 A Perceber o que as outras pessoas estão sentindo.
## 10 A Ouvir respeitosamente a opinião dos outros?
## 11 A Tratar bem e respeitosamente as pessoas de que você não gosta.
names(scores)
## [1] "banco" "cod_suj" "Data de nasc."
## [4] "data.aplic" "Idade0" "idade1"
## [7] "Termo" "sujeito" "serie"
## [10] "escola" "turma" "Sexo"
## [13] "Esc. Mãe" "port" "mat"
## [16] "cloze" "A_o" "C_o"
## [19] "E_o" "N_o" "O_o"
## [22] "A_c" "C_c" "E_c"
## [25] "N_c" "O_c" "A_z"
## [28] "C_z" "E_z" "N_z"
## [31] "O_z" "antonym.rc" "antonym.cntrst_A"
## [34] "antonym.cntrst_C" "antonym.cntrst_E" "antonym.cntrst_N"
## [37] "antonym.cntrst_O" "mean_A" "mean_C"
## [40] "mean_E" "mean_N" "mean_O"
## [43] "sd_A" "sd_C" "sd_E"
## [46] "sd_N" "sd_O" "antonym.rc_A"
## [49] "antonym.rc_C" "antonym.rc_E" "antonym.rc_N"
## [52] "antonym.rc_O" "nse" "A_1_o"
## [55] "C_1_o" "E_1_o" "N_1_o"
## [58] "O_1_o" "A_1_c" "C_1_c"
## [61] "E_1_c" "N_1_c" "O_1_c"
## [64] "A_1_z" "C_1_z" "E_1_z"
## [67] "N_1_z" "O_1_z" "A_0_o"
## [70] "C_0_o" "E_0_o" "N_0_o"
## [73] "O_0_o" "A_0_c" "C_0_c"
## [76] "E_0_c" "N_0_c" "O_0_c"
## [79] "A_0_z" "C_0_z" "E_0_z"
## [82] "N_0_z" "O_0_z" "cexp_o"
## [85] "comlrn_o" "coplrn_o" "cstrat_o"
## [88] "effper_o" "elab_o" "insmot_o"
## [91] "intmat_o" "intrea_o" "memor_o"
## [94] "scacad_o" "scmath_o" "scverb_o"
## [97] "selfef_o" "cexp_c" "comlrn_c"
## [100] "coplrn_c" "cstrat_c" "effper_c"
## [103] "elab_c" "insmot_c" "intmat_c"
## [106] "intrea_c" "memor_c" "scacad_c"
## [109] "scmath_c" "scverb_c" "selfef_c"
## [112] "cexp_z" "comlrn_z" "coplrn_z"
## [115] "cstrat_z" "effper_z" "elab_z"
## [118] "insmot_z" "intmat_z" "intrea_z"
## [121] "memor_z" "scacad_z" "scmath_z"
## [124] "scverb_z" "selfef_z" "means"
## [127] "sd"
table(scores$Sexo)
##
## 1 2
## 72 96
table(scores$banco)
##
## josi maya
## 76 92
table(scores$`Esc. Mãe`)
##
## 0 1 2 3 4 5 6
## 2 8 10 19 9 3 22
table(scores$escola)
##
## 0 1 2
## 76 33 59
library(sjPlot)
sjt.frq(scores$Sexo)
value | N | raw % | valid % | cumulative % |
---|---|---|---|---|
1 | 72 | 42.86 | 42.86 | 42.86 |
2 | 96 | 57.14 | 57.14 | 100.00 |
missings | 0 | 0.00 | ||
total N=168 · valid N=168 · x̄=1.57 · σ=0.50 |
sjt.frq(scores$idade1)
value | N | raw % | valid % | cumulative % |
---|---|---|---|---|
8 | 26 | 15.48 | 15.76 | 15.76 |
9 | 33 | 19.64 | 20.00 | 35.76 |
10 | 37 | 22.02 | 22.42 | 58.18 |
11 | 41 | 24.40 | 24.85 | 83.03 |
12 | 1 | 0.60 | 0.61 | 83.64 |
14 | 23 | 13.69 | 13.94 | 97.58 |
15 | 4 | 2.38 | 2.42 | 100.00 |
missings | 3 | 1.79 | ||
total N=168 · valid N=165 · x̄=10.42 · σ=1.94 |
sjt.xtab(scores$idade1, scores$Sexo, show.row.prc = TRUE)
idade1 | Sexo | Total | |
---|---|---|---|
1 | 2 | ||
8 |
12 46.2 % |
14 53.8 % |
26 100 % |
9 |
17 51.5 % |
16 48.5 % |
33 100 % |
10 |
13 35.1 % |
24 64.9 % |
37 100 % |
11 |
18 43.9 % |
23 56.1 % |
41 100 % |
12 |
0 0 % |
1 100 % |
1 100 % |
14 |
7 30.4 % |
16 69.6 % |
23 100 % |
15 |
3 75 % |
1 25 % |
4 100 % |
Total |
70 42.4 % |
95 57.6 % |
165 100 % |
χ2=5.934 · df=6 · Cramer’s V=0.190 · Fisher’s p=0.431 |
# Veja
# http://www.strengejacke.de/sjPlot/
#
names(scores)
## [1] "banco" "cod_suj" "Data de nasc."
## [4] "data.aplic" "Idade0" "idade1"
## [7] "Termo" "sujeito" "serie"
## [10] "escola" "turma" "Sexo"
## [13] "Esc. Mãe" "port" "mat"
## [16] "cloze" "A_o" "C_o"
## [19] "E_o" "N_o" "O_o"
## [22] "A_c" "C_c" "E_c"
## [25] "N_c" "O_c" "A_z"
## [28] "C_z" "E_z" "N_z"
## [31] "O_z" "antonym.rc" "antonym.cntrst_A"
## [34] "antonym.cntrst_C" "antonym.cntrst_E" "antonym.cntrst_N"
## [37] "antonym.cntrst_O" "mean_A" "mean_C"
## [40] "mean_E" "mean_N" "mean_O"
## [43] "sd_A" "sd_C" "sd_E"
## [46] "sd_N" "sd_O" "antonym.rc_A"
## [49] "antonym.rc_C" "antonym.rc_E" "antonym.rc_N"
## [52] "antonym.rc_O" "nse" "A_1_o"
## [55] "C_1_o" "E_1_o" "N_1_o"
## [58] "O_1_o" "A_1_c" "C_1_c"
## [61] "E_1_c" "N_1_c" "O_1_c"
## [64] "A_1_z" "C_1_z" "E_1_z"
## [67] "N_1_z" "O_1_z" "A_0_o"
## [70] "C_0_o" "E_0_o" "N_0_o"
## [73] "O_0_o" "A_0_c" "C_0_c"
## [76] "E_0_c" "N_0_c" "O_0_c"
## [79] "A_0_z" "C_0_z" "E_0_z"
## [82] "N_0_z" "O_0_z" "cexp_o"
## [85] "comlrn_o" "coplrn_o" "cstrat_o"
## [88] "effper_o" "elab_o" "insmot_o"
## [91] "intmat_o" "intrea_o" "memor_o"
## [94] "scacad_o" "scmath_o" "scverb_o"
## [97] "selfef_o" "cexp_c" "comlrn_c"
## [100] "coplrn_c" "cstrat_c" "effper_c"
## [103] "elab_c" "insmot_c" "intmat_c"
## [106] "intrea_c" "memor_c" "scacad_c"
## [109] "scmath_c" "scverb_c" "selfef_c"
## [112] "cexp_z" "comlrn_z" "coplrn_z"
## [115] "cstrat_z" "effper_z" "elab_z"
## [118] "insmot_z" "intmat_z" "intrea_z"
## [121] "memor_z" "scacad_z" "scmath_z"
## [124] "scverb_z" "selfef_z" "means"
## [127] "sd"
ggplot(data = scores, aes(x = C_o)) +
geom_histogram()
ggplot(data = scores, aes(x = C_o)) +
geom_histogram(
binwidth = .5,
alpha = 1/2,
color = "gray",
fill = "blue"
)
ggplot(data = scores,
aes(x = C_o, y = effper_o, color = factor(Sexo))) +
geom_point(alpha = 1/2) +
geom_smooth(method = "lm")
scores %>%
ggplot(
aes(
x = C_o,
y = effper_o,
color = means)
) +
geom_point(alpha = 1/2) +
geom_smooth(method = "lm")
scores %>%
ggplot(aes(x = means)) +
geom_histogram(
binwidth = .25,
alpha = 1/2,
color = "gray",
fill = "red"
) +
scale_x_continuous(breaks = seq(1, 5, .5), limits = c(1, 5))
library(psych)
library(knitr)
describe(scores[, vars])
## vars n mean sd median trimmed mad min max range skew
## port 1 76 69.74 18.55 75.00 70.81 22.24 25.00 100.00 75.00 -0.46
## mat 2 76 61.05 23.31 65.00 62.02 22.24 0.00 100.00 100.00 -0.48
## cloze 3 92 46.44 21.57 46.67 47.10 23.77 0.00 86.67 86.67 -0.27
## A_c 4 168 1.09 0.54 1.13 1.12 0.52 -0.70 2.24 2.94 -0.53
## C_c 5 168 1.02 0.72 1.18 1.07 0.79 -0.99 2.39 3.38 -0.50
## E_c 6 168 0.66 0.51 0.68 0.66 0.47 -0.72 2.03 2.76 -0.10
## N_c 7 168 0.17 0.75 0.19 0.19 0.66 -1.63 2.00 3.63 -0.18
## O_c 8 168 0.71 0.62 0.64 0.70 0.61 -0.71 2.33 3.04 0.12
## kurtosis se
## port -0.64 2.13
## mat -0.10 2.67
## cloze -0.67 2.25
## A_c 0.12 0.04
## C_c -0.52 0.06
## E_c -0.11 0.04
## N_c -0.29 0.06
## O_c -0.39 0.05
describe.by(scores[, vars], scores$Sexo)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew
## port 1 35 70.43 20.70 75.00 71.90 22.24 25.00 100.00 75.00 -0.60
## mat 2 35 63.14 22.30 65.00 63.79 22.24 0.00 100.00 100.00 -0.50
## cloze 3 37 43.48 22.08 46.67 44.15 22.84 0.00 80.00 80.00 -0.40
## A_c 4 72 1.05 0.58 1.12 1.07 0.56 -0.70 2.15 2.85 -0.56
## C_c 5 72 0.99 0.71 1.06 1.02 0.91 -0.61 2.19 2.80 -0.23
## E_c 6 72 0.62 0.54 0.60 0.61 0.46 -0.60 2.03 2.63 0.18
## N_c 7 72 0.36 0.74 0.44 0.40 0.76 -1.36 1.86 3.22 -0.42
## O_c 8 72 0.80 0.64 0.81 0.79 0.68 -0.71 2.33 3.04 0.02
## kurtosis se
## port -0.64 3.50
## mat 0.00 3.77
## cloze -0.82 3.63
## A_c 0.11 0.07
## C_c -0.91 0.08
## E_c 0.04 0.06
## N_c -0.13 0.09
## O_c -0.43 0.08
## --------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew
## port 1 41 69.15 16.73 75.00 69.70 22.24 35.00 100.00 65.00 -0.24
## mat 2 41 59.27 24.28 60.00 60.30 29.65 0.00 100.00 100.00 -0.43
## cloze 3 55 48.42 21.20 50.00 48.81 24.71 3.33 86.67 83.33 -0.16
## A_c 4 96 1.12 0.51 1.17 1.14 0.52 -0.28 2.24 2.52 -0.42
## C_c 5 96 1.04 0.73 1.27 1.10 0.68 -0.99 2.39 3.38 -0.68
## E_c 6 96 0.68 0.50 0.70 0.70 0.50 -0.72 1.62 2.34 -0.35
## N_c 7 96 0.03 0.73 0.08 0.03 0.63 -1.63 2.00 3.63 -0.04
## O_c 8 96 0.64 0.60 0.60 0.62 0.64 -0.69 2.00 2.69 0.17
## kurtosis se
## port -1.01 2.61
## mat -0.33 3.79
## cloze -0.80 2.86
## A_c -0.19 0.05
## C_c -0.27 0.07
## E_c -0.25 0.05
## N_c -0.20 0.07
## O_c -0.41 0.06
scores %>%
select(vars) %>%
describe %>%
kable(digits = 2)
vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
port | 1 | 76 | 69.74 | 18.55 | 75.00 | 70.81 | 22.24 | 25.00 | 100.00 | 75.00 | -0.46 | -0.64 | 2.13 |
mat | 2 | 76 | 61.05 | 23.31 | 65.00 | 62.02 | 22.24 | 0.00 | 100.00 | 100.00 | -0.48 | -0.10 | 2.67 |
cloze | 3 | 92 | 46.44 | 21.57 | 46.67 | 47.10 | 23.77 | 0.00 | 86.67 | 86.67 | -0.27 | -0.67 | 2.25 |
A_c | 4 | 168 | 1.09 | 0.54 | 1.13 | 1.12 | 0.52 | -0.70 | 2.24 | 2.94 | -0.53 | 0.12 | 0.04 |
C_c | 5 | 168 | 1.02 | 0.72 | 1.18 | 1.07 | 0.79 | -0.99 | 2.39 | 3.38 | -0.50 | -0.52 | 0.06 |
E_c | 6 | 168 | 0.66 | 0.51 | 0.68 | 0.66 | 0.47 | -0.72 | 2.03 | 2.76 | -0.10 | -0.11 | 0.04 |
N_c | 7 | 168 | 0.17 | 0.75 | 0.19 | 0.19 | 0.66 | -1.63 | 2.00 | 3.63 | -0.18 | -0.29 | 0.06 |
O_c | 8 | 168 | 0.71 | 0.62 | 0.64 | 0.70 | 0.61 | -0.71 | 2.33 | 3.04 | 0.12 | -0.39 | 0.05 |
* Correl | ação e | anális | e de clu | ster via | heatmaps |
library(d3heatmap)
vars <- names(scores)[c(14:16, 59:63, 74:78, 98:111)]
scores %>%
select(vars) %>%
cor(use="pair") %>%
d3heatmap(
symn= TRUE,
symm = TRUE,
k_row = 5,
k_col = 5
)
vars <- names(scores)[c(14, 17:21)]
sjt.corr(scores[ , vars], na.deletion = "pairwise")
port | A_o | C_o | E_o | N_o | O_o | |
---|---|---|---|---|---|---|
port | 0.308** | 0.419*** | 0.250* | 0.156 | 0.355** | |
A_o | 0.308** | 0.508*** | 0.437*** | 0.366*** | 0.429*** | |
C_o | 0.419*** | 0.508*** | 0.475*** | 0.323*** | 0.474*** | |
E_o | 0.250* | 0.437*** | 0.475*** | 0.280*** | 0.517*** | |
N_o | 0.156 | 0.366*** | 0.323*** | 0.280*** | 0.432*** | |
O_o | 0.355** | 0.429*** | 0.474*** | 0.517*** | 0.432*** | |
Computed correlation used pearson-method with pairwise-deletion. |
vars <- names(scores)[c(14, 22:26)]
sjt.corr(scores[ , vars], na.deletion = "pairwise")
port | A_c | C_c | E_c | N_c | O_c | |
---|---|---|---|---|---|---|
port | 0.310** | 0.428*** | 0.257* | 0.160 | 0.386*** | |
A_c | 0.310** | 0.543*** | 0.447*** | 0.416*** | 0.446*** | |
C_c | 0.428*** | 0.543*** | 0.491*** | 0.365*** | 0.498*** | |
E_c | 0.257* | 0.447*** | 0.491*** | 0.316*** | 0.511*** | |
N_c | 0.160 | 0.416*** | 0.365*** | 0.316*** | 0.472*** | |
O_c | 0.386*** | 0.446*** | 0.498*** | 0.511*** | 0.472*** | |
Computed correlation used pearson-method with pairwise-deletion. |
fit1 <- lm(formula = port ~ A_o+C_o+E_o+N_o+O_o, data = scores)
fit2 <- lm(formula = port ~ A_c+C_c+E_c+N_c+O_c, data = scores)
fit3 <- lm(formula = port ~ A_o+C_o+E_o+N_o+O_o+means, data = scores)
fit4 <- lm(formula = port ~ A_c+C_c+E_c+N_c+O_c+means, data = scores)
fit1
##
## Call:
## lm(formula = port ~ A_o + C_o + E_o + N_o + O_o, data = scores)
##
## Coefficients:
## (Intercept) A_o C_o E_o N_o
## 5.6624 2.9452 7.5253 1.7771 0.8598
## O_o
## 3.8602
sjt.lm(fit1, show.std = TRUE)
port | ||||||
B | CI | std. Beta | CI | p | ||
(Intercept) | 5.66 | -30.20 – 41.53 | .754 | |||
A_o | 2.95 | -6.83 – 12.72 | 0.08 | -0.18 – 0.34 | .550 | |
C_o | 7.53 | 1.24 – 13.81 | 0.30 | 0.05 – 0.54 | .020 | |
E_o | 1.78 | -7.07 – 10.63 | 0.05 | -0.20 – 0.30 | .690 | |
N_o | 0.86 | -4.69 – 6.41 | 0.03 | -0.18 – 0.25 | .758 | |
O_o | 3.86 | -4.97 – 12.69 | 0.13 | -0.16 – 0.41 | .386 | |
Observations | 76 | |||||
R2 / adj. R2 | .213 / .157 |
sjt.lm(fit2, show.std = TRUE)
port | ||||||
B | CI | std. Beta | CI | p | ||
(Intercept) | 54.45 | 44.40 – 64.51 | <.001 | |||
A_c | 2.58 | -6.96 – 12.13 | 0.07 | -0.18 – 0.33 | .591 | |
C_c | 7.45 | 1.28 – 13.62 | 0.30 | 0.05 – 0.54 | .019 | |
E_c | 1.81 | -6.98 – 10.59 | 0.05 | -0.19 – 0.29 | .683 | |
N_c | -0.03 | -5.52 – 5.45 | -0.00 | -0.22 – 0.22 | .990 | |
O_c | 5.85 | -3.07 – 14.77 | 0.18 | -0.09 – 0.46 | .195 | |
Observations | 76 | |||||
R2 / adj. R2 | .232 / .177 |
sjt.lm(fit3, show.std = TRUE)
port | ||||||
B | CI | std. Beta | CI | p | ||
(Intercept) | 23.96 | -19.40 – 67.31 | .274 | |||
A_o | 3.44 | -6.28 – 13.16 | 0.09 | -0.17 – 0.35 | .482 | |
C_o | 7.76 | 1.52 – 14.00 | 0.31 | 0.06 – 0.55 | .016 | |
E_o | 1.92 | -6.86 – 10.70 | 0.06 | -0.19 – 0.30 | .665 | |
N_o | 0.16 | -5.42 – 5.75 | 0.01 | -0.21 – 0.23 | .954 | |
O_o | 5.18 | -3.76 – 14.12 | 0.17 | -0.12 – 0.46 | .252 | |
means | -8.43 | -19.85 – 2.99 | -0.17 | -0.39 – 0.05 | .145 | |
Observations | 76 | |||||
R2 / adj. R2 | .237 / .171 |
sjt.lm(fit4, show.std = TRUE)
port | ||||||
B | CI | std. Beta | CI | p | ||
(Intercept) | 44.47 | 8.65 – 80.28 | .016 | |||
A_c | 3.11 | -6.65 – 12.87 | 0.08 | -0.18 – 0.35 | .527 | |
C_c | 7.48 | 1.28 – 13.69 | 0.30 | 0.06 – 0.54 | .019 | |
E_c | 1.90 | -6.94 – 10.73 | 0.05 | -0.19 – 0.30 | .670 | |
N_c | 0.23 | -5.35 – 5.82 | 0.01 | -0.22 – 0.24 | .934 | |
O_c | 5.66 | -3.33 – 14.65 | 0.18 | -0.10 – 0.45 | .213 | |
means | 3.27 | -7.98 – 14.52 | 0.06 | -0.15 – 0.28 | .564 | |
Observations | 76 | |||||
R2 / adj. R2 | .236 / .169 |
https://cran.r-project.org/web/packages/apaTables/vignettes/apaTables.html
library(apaTables)
vars <- names(scores)[c(14, 17:21)]
scores %>%
select(vars) %>%
apa.cor.table(filename="Table1_APA.doc", table.number=1)
##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. port 69.74 18.55
##
## 2. A_o 4.02 0.52 .31**
## [.09, .50]
##
## 3. C_o 3.95 0.70 .42** .51**
## [.21, .59] [.39, .61]
##
## 4. E_o 3.61 0.52 .25* .44** .47**
## [.03, .45] [.31, .55] [.35, .58]
##
## 5. N_o 3.13 0.73 .16 .37** .32** .28**
## [-.07, .37] [.23, .49] [.18, .45] [.13, .41]
##
## 6. O_o 3.63 0.62 .36** .43** .47** .52**
## [.14, .54] [.30, .55] [.35, .58] [.40, .62]
##
## 5
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .43**
## [.30, .55]
##
##
## Note. * indicates p < .05; ** indicates p < .01.
## 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).
##
apa.reg.table(fit2, filename = "Table2_APA.doc", table.number = 2)
##
##
## Table 2
##
## Regression results using port as the criterion
##
##
## Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI
## (Intercept) 54.45** [44.40, 64.51]
## A_c 2.58 [-6.96, 12.13] 0.07 [-0.19, 0.33] .00 [-.02, .03]
## C_c 7.45* [1.28, 13.62] 0.30 [0.05, 0.54] .06 [-.03, .16]
## E_c 1.81 [-6.98, 10.59] 0.05 [-0.20, 0.30] .00 [-.02, .02]
## N_c -0.03 [-5.52, 5.45] -0.00 [-0.23, 0.22] .00 [-.00, .00]
## O_c 5.85 [-3.07, 14.77] 0.18 [-0.10, 0.46] .02 [-.03, .07]
##
##
##
## r Fit
##
## .31**
## .43**
## .26*
## .16
## .39**
## R2 = .232**
## 95% CI[.04,.34]
##
##
## Note. * indicates p < .05; ** indicates p < .01.
## 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.
##