# Import
master.df <- read.csv("Synopsis_Data.csv")

1 Study Overview

1.1 Status summary of each subject in the study

glimpse(master.df)

Observations: 77 Variables: 307 $ X 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1… $ ID 34, 431, 1121, 2121, 3421, 4400,… $ CRFvp NA, NA, 1, 2, 3, 4, 5, 6, 7, 8, … $ CRFgroup NA, NA, HC, HC, PAT, PAT, HC, HC… $ CRFstatus NA, NA, dropout, dropout, not in… $ CRFdropout NA, NA, Yes, Yes, Yes, Yes, No, … $ CRFscree NA, NA, Yes, Yes, Yes, Yes, Yes,… $ CRFscreeDate NA, NA, 2017-05-16, 2017-05-18, … $ CRFscreeTime NA, NA, 1904-01-01 16:00:00, 190… $ CRFscreeVL NA, NA, MP, MP, MP/FZ, MP, AS/SF… $ CRFscreeOK NA, NA, Yes, Yes, Yes, No, Yes, … $ CRFrandList NA, NA, NA, NA, NA, NA, NA, 12, … $ CRFmri NA, NA, Yes, Yes, No, No, No, Ye… $ CRFmriDate NA, NA, 41438, 41438, NA, NA, NA… $ CRFmriTime NA, NA, 0.344, 0.375, NA, NA, NA… $ CRFmriVL NA, NA, AS/FZ/SF/MP, AS/FZ/SF/MP… $ CRFmriLOK NA, NA, No, No, NA, NA, NA, No, … $ CRFmriAOK NA, NA, No, No, NA, NA, NA, Yes,… $ CRFmriMPedalOK NA, NA, No, No, NA, NA, NA, Yes,… $ CRFmriMPlwOK NA, NA, No, No, NA, NA, NA, Yes,… $ CRFeeg NA, NA, Yes, Yes, No, No, No, Ye… $ CRFeegDate NA, NA, 41423, 41423, 41457, NA,… $ CRFeegTime NA, NA, 0.333, 0.333, 0.625, NA,… $ CRFeegVL NA, NA, AS/FZ, AS/FZ, AS/FZ/SF/M… $ CRFeegLOK NA, NA, No, No, NA, NA, NA, Yes,… $ CRFeegAOK NA, NA, No, No, NA, NA, NA, Yes,… $ CRFeegMPedalOK NA, NA, No, No, NA, NA, NA, Yes,… $ CRFeegMPlwOK NA, NA, No, No, NA, NA, NA, Yes,… $ SES_VP NA, NA, NA, NA, NA, NA, NA, 6, 7… $ SES_Group NA, NA, NA, NA, NA, NA, NA, HC, … $ SES_Date NA, NA, NA, NA, NA, NA, NA, 2017… $ SES_Time NA, NA, NA, NA, NA, NA, NA, 1904… $ SES_Day NA, NA, NA, NA, NA, NA, NA, PM, … $ SES_VL NA, NA, NA, NA, NA, NA, NA, AS, … $ SES_1_Gender NA, NA, NA, NA, NA, NA, NA, Male… $ SES_2_Age NA, NA, NA, NA, NA, NA, NA, 29, … $ SES_3a_Nationality1 NA, NA, NA, NA, NA, NA, NA, DE, … $ SES_3b_Nationality2 NA, NA, NA, NA, NA, NA, NA, NA, … $ SES_4_Language1 NA, NA, NA, NA, NA, NA, NA, DE, … $ SES_4_Language2 NA, NA, NA, NA, NA, NA, NA, NA, … $ SES_5_Immigration NA, NA, NA, NA, NA, NA, NA, Yes,… $ SES_6_SozialStatus NA, NA, NA, NA, NA, NA, NA, Marr… $ SES_7_Education NA, NA, NA, NA, NA, NA, NA, 6, 5… $ SES_8_Workstatus NA, NA, NA, NA, NA, NA, NA, Full… $ SES_9a_Job1 NA, NA, NA, NA, NA, NA, NA, Verk… $ SES_9a_Job2 NA, NA, NA, NA, NA, NA, NA, Stud… $ SES_9b_JobPensum NA, NA, NA, NA, NA, NA, NA, 100,… $ SES_9c_LastJob NA, NA, NA, NA, NA, NA, NA, NA, … $ SES_9d_LastJobPesnum NA, NA, NA, NA, NA, NA, NA, NA, … $ SES_10a_PsychFamily NA, NA, NA, NA, NA, NA, NA, No, … $ SES_10b_PsychFamilyDiagnosis1 NA, NA, NA, NA, NA, NA, NA, NA, … $ SES_10b_PsychFamilyDiagnosis2 NA, NA, NA, NA, NA, NA, NA, NA, … $ SES_10b_PsychFamilyDiagnosis3 NA, NA, NA, NA, NA, NA, NA, NA, … $ SES_10c_PsychFamilyMember NA, NA, NA, NA, NA, NA, NA, NA, … $ Gender NA, NA, NA, NA, NA, NA, NA, Male… $ Age NA, NA, NA, NA, NA, NA, NA, 29, … $ Education NA, NA, NA, NA, NA, NA, NA, 6, 5… $ SAM_VP NA, NA, NA, NA, NA, NA, NA, 6, 7… $ SAM_Group NA, NA, NA, NA, NA, NA, NA, HC, … $ SAM_Date NA, NA, NA, NA, NA, NA, NA, 2017… $ SAM_Time NA, NA, NA, NA, NA, NA, NA, 1904… $ SAM_Day NA, NA, NA, NA, NA, NA, NA, AM, … $ SAM_A_Main NA, NA, NA, NA, NA, NA, NA, 3, 5… $ SAM_A_Pos_01 NA, NA, NA, NA, NA, NA, NA, 6, 7… $ SAM_A_Pos_02 NA, NA, NA, NA, NA, NA, NA, 2, 6… $ SAM_A_Pos_03 NA, NA, NA, NA, NA, NA, NA, 3, 3… $ SAM_A_Pos_04 NA, NA, NA, NA, NA, NA, NA, 4, 3… $ SAM_A_Neg_01 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ SAM_A_Neg_02 NA, NA, NA, NA, NA, NA, NA, 3, 4… $ SAM_A_Neg_03 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ SAM_A_Neg_04 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ SAM_M_Main NA, NA, NA, NA, NA, NA, NA, 4, 5… $ SAM_M_Pos_01 NA, NA, NA, NA, NA, NA, NA, 3, 5… $ SAM_M_Pos_02 NA, NA, NA, NA, NA, NA, NA, 4, 3… $ SAM_M_Pos_03 NA, NA, NA, NA, NA, NA, NA, 4, 3… $ SAM_M_Pos_04 NA, NA, NA, NA, NA, NA, NA, 3, 2… $ SAM_M_Neg_01 NA, NA, NA, NA, NA, NA, NA, 3, 7… $ SAM_M_Neg_02 NA, NA, NA, NA, NA, NA, NA, 3, 5… $ SAM_M_Neg_03 NA, NA, NA, NA, NA, NA, NA, 4, 4… $ SAM_M_Neg_04 NA, NA, NA, NA, NA, NA, NA, 1, 7… $ SAM_S_Main NA, NA, NA, NA, NA, NA, NA, 7, 6… $ SAM_S_Pos_01 NA, NA, NA, NA, NA, NA, NA, 2, 5… $ SAM_S_Pos_02 NA, NA, NA, NA, NA, NA, NA, 4, 6… $ SAM_S_Pos_03 NA, NA, NA, NA, NA, NA, NA, 7, 6… $ SAM_S_Pos_04 NA, NA, NA, NA, NA, NA, NA, 7, 6… $ SAM_S_Neg_01 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ SAM_S_Neg_02 NA, NA, NA, NA, NA, NA, NA, 6, 5… $ SAM_S_Neg_03 NA, NA, NA, NA, NA, NA, NA, 6, 4… $ SAM_S_Neg_04 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ Affect NA, NA, NA, NA, NA, NA, NA, 4.67… $ Motoric NA, NA, NA, NA, NA, NA, NA, 3.22… $ Language NA, NA, NA, NA, NA, NA, NA, NA, … $ PANAS_MRI_VP NA, NA, NA, NA, NA, NA, NA, 6, 7… $ PANAS_MRI_Group NA, NA, NA, NA, NA, NA, NA, HC, … $ PANAS_MRI_Date NA, NA, NA, NA, NA, NA, NA, 2017… $ PANAS_MRI_Time NA, NA, NA, NA, NA, NA, NA, 1904… $ PANAS_MRI_Day NA, NA, NA, NA, NA, NA, NA, AM, … $ PANAS_MRI_aktiv NA, NA, NA, NA, NA, NA, NA, 4, 4… $ PANAS_MRI_bekuemmert NA, NA, NA, NA, NA, NA, NA, 1, 2… $ PANAS_MRI_interessiert NA, NA, NA, NA, NA, NA, NA, 4, 4… $ PANAS_MRI_freudig NA, NA, NA, NA, NA, NA, NA, 3, 4… $ PANAS_MRI_veraergert NA, NA, NA, NA, NA, NA, NA, 1, 1… $ PANAS_MRI_stark NA, NA, NA, NA, NA, NA, NA, 3, 4… $ PANAS_MRI_schuldig NA, NA, NA, NA, NA, NA, NA, 1, 1… $ PANAS_MRI_erschrocken NA, NA, NA, NA, NA, NA, NA, 1, 1… $ PANAS_MRI_feindselig NA, NA, NA, NA, NA, NA, NA, 1, 1… $ PANAS_MRI_angeregt NA, NA, NA, NA, NA, NA, NA, 2, 4… $ PANAS_MRI_stolz NA, NA, NA, NA, NA, NA, NA, 1, 2… $ PANAS_MRI_gereizt NA, NA, NA, NA, NA, NA, NA, 1, 1… $ PANAS_MRI_begeistert NA, NA, NA, NA, NA, NA, NA, 2, 4… $ PANAS_MRI_beschaemt NA, NA, NA, NA, NA, NA, NA, 1, 1… $ PANAS_MRI_wach NA, NA, NA, NA, NA, NA, NA, 4, 4… $ PANAS_MRI_nervoes NA, NA, NA, NA, NA, NA, NA, 1, 2… $ PANAS_MRI_entschlossen NA, NA, NA, NA, NA, NA, NA, 2, 4… $ PANAS_MRI_aufmerksam NA, NA, NA, NA, NA, NA, NA, 4, 4… $ PANAS_MRI_durcheinander NA, NA, NA, NA, NA, NA, NA, 1, 1… $ PANAS_MRI_aengstlich NA, NA, NA, NA, NA, NA, NA, 1, 1… $ TEIQue_VP NA, NA, NA, NA, NA, NA, NA, 6, 7… $ TEIQue_Group NA, NA, NA, NA, NA, NA, NA, HC, … $ TEIQue_01 NA, NA, NA, NA, NA, NA, NA, 7, 7… $ TEIQue_02 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ TEIQue_03 NA, NA, NA, NA, NA, NA, NA, 7, 6… $ TEIQue_04 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_05 NA, NA, NA, NA, NA, NA, NA, 3, 5… $ TEIQue_06 NA, NA, NA, NA, NA, NA, NA, 7, 6… $ TEIQue_07 NA, NA, NA, NA, NA, NA, NA, 6, 6… $ TEIQue_08 NA, NA, NA, NA, NA, NA, NA, 7, 6… $ TEIQue_09 NA, NA, NA, NA, NA, NA, NA, 6, 6… $ TEIQue_10 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_11 NA, NA, NA, NA, NA, NA, NA, 6, 3… $ TEIQue_12 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_13 NA, NA, NA, NA, NA, NA, NA, 7, 6… $ TEIQue_14 NA, NA, NA, NA, NA, NA, NA, 7, 3… $ TEIQue_15 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ TEIQue_16 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ TEIQue_17 NA, NA, NA, NA, NA, NA, NA, 6, 6… $ TEIQue_18 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_19 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ TEIQue_20 NA, NA, NA, NA, NA, NA, NA, 5, 5… $ TEIQue_21 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_22 NA, NA, NA, NA, NA, NA, NA, 7, 5… $ TEIQue_23 NA, NA, NA, NA, NA, NA, NA, 2, 5… $ TEIQue_24 NA, NA, NA, NA, NA, NA, NA, 6, 6… $ TEIQue_25 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_26 NA, NA, NA, NA, NA, NA, NA, 7, 3… $ TEIQue_27 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_28 NA, NA, NA, NA, NA, NA, NA, 6, 5… $ TEIQue_29 NA, NA, NA, NA, NA, NA, NA, 7, 6… $ TEIQue_30 NA, NA, NA, NA, NA, NA, NA, 7, 4… $ TEIQue_SUM NA, NA, NA, NA, NA, NA, NA, 193,… $ Well_Being NA, NA, NA, NA, NA, NA, NA, 5.67… $ Self_Control NA, NA, NA, NA, NA, NA, NA, 6.83… $ Emotionality NA, NA, NA, NA, NA, NA, NA, 6.12… $ Sociability NA, NA, NA, NA, NA, NA, NA, 6.83… $ TEIQue_Total NA, NA, NA, NA, NA, NA, NA, 6.43… $ Row.Number 1, 33, NA, NA, NA, NA, NA, 8, 9,… $ Synopsis.Project SYNOPSIS, SYNOPSIS, NA, NA, NA, … $ Site default-site, default-site, NA, … $ Site.Name default-site, default-site, NA, … $ subject.ID.x 34, 431, NA, NA, NA, NA, NA, 611… $ Date 30/06/17, 30/01/18, NA, NA, NA, … $ Cohort All, All, NA, NA, NA, NA, NA, Al… $ Visit.Identifier All, All, NA, NA, NA, NA, NA, Al… $ Visit.Name all, all, NA, NA, NA, NA, NA, al… $ Rater Francilia Zengaffinen, Antje Sta… $ Visit.Start..Local. 2017.06.30 15:30:35, 2018.01.30 … $ Visit.Start..GMT. 2017.06.30 13:30:35 UTC, 2018.01… $ Visit.Start.Offset 2, 1, NA, NA, NA, NA, NA, 2, 2, … $ Visit.End..Local. 2017.06.30 15:59:07, 2018.01.30 … $ Visit.End..GMT. 2017.06.30 13:59:07 UTC, 2018.01… $ Visit.End.Offset 2, 1, NA, NA, NA, NA, NA, 2, 2, … $ ERT.Long.Status COMPLETED, COMPLETED, NA, NA, NA… $ ERTMDCRTA 2680, 1558, NA, NA, NA, NA, NA, … $ ERTMDCRTD 1295, 809, NA, NA, NA, NA, NA, 1… $ ERTMDCRTF 862, 2024, NA, NA, NA, NA, NA, 1… $ ERTMDCRTH 1418, 952, NA, NA, NA, NA, NA, 9… $ ERTMDCRTS 820, 2120, NA, NA, NA, NA, NA, 2… $ ERTMDCRTSU 1629, 746, NA, NA, NA, NA, NA, 1… $ ERTMDRTA 2680, 1558, NA, NA, NA, NA, NA, … $ ERTMDRTD 1598, 955, NA, NA, NA, NA, NA, 2… $ ERTMDRTF 2598, 2447, NA, NA, NA, NA, NA, … $ ERTMDRTH 614, 960, NA, NA, NA, NA, NA, 10… $ ERTMDRTS 912, 1589, NA, NA, NA, NA, NA, 2… $ ERTMDRTSU 1616, 763, NA, NA, NA, NA, NA, 2… $ ERTOCRTSD 1647, 1077, NA, NA, NA, NA, NA, … $ ERTOMDCRT 1246, 970, NA, NA, NA, NA, NA, 1… $ ERTOMDRT 1396, 1016, NA, NA, NA, NA, NA, … $ ERTORTSD 1505, 1027, NA, NA, NA, NA, NA, … $ ERTTFAA 10, 0, NA, NA, NA, NA, NA, 11, 2… $ ERTTFAD 12, 13, NA, NA, NA, NA, NA, 12, … $ ERTTFAF 5, 4, NA, NA, NA, NA, NA, 3, 5, … $ ERTTFAH 11, 3, NA, NA, NA, NA, NA, 3, 11… $ ERTTFAS 13, 4, NA, NA, NA, NA, NA, 6, 9,… $ ERTTFASU 6, 8, NA, NA, NA, NA, NA, 3, 15,… $ ERTTH 33, 58, NA, NA, NA, NA, NA, 52, … $ ERTTHA 5, 7, NA, NA, NA, NA, NA, 9, 3, … $ ERTTHD 7, 13, NA, NA, NA, NA, NA, 11, 6… $ ERTTHF 3, 2, NA, NA, NA, NA, NA, 3, 3, … $ ERTTHH 4, 14, NA, NA, NA, NA, NA, 10, 1… $ ERTTHS 8, 10, NA, NA, NA, NA, NA, 10, 9… $ ERTTHSU 6, 12, NA, NA, NA, NA, NA, 9, 10… $ ERTUHRA 0.11, 0.47, NA, NA, NA, NA, NA, … $ ERTUHRD 0.17, 0.43, NA, NA, NA, NA, NA, … $ ERTUHRF 0.08, 0.04, NA, NA, NA, NA, NA, … $ ERTUHRH 0.07, 0.77, NA, NA, NA, NA, NA, … $ ERTUHRS 0.20, 0.48, NA, NA, NA, NA, NA, … $ ERTUHRSU 0.20, 0.48, NA, NA, NA, NA, NA, … $ nr.x 0, 0, NA, NA, NA, NA, NA, 6, 7, … $ group.x none, PAT, NA, NA, NA, NA, NA, H… $ visit.x 34, 31, NA, NA, NA, NA, NA, 12, … $ ERT_percent 36.7, 64.4, NA, NA, NA, NA, NA, … $ AST.Standard.Status COMPLETED, COMPLETED, NA, NA, NA… $ ASTCCMD 63.0, 54.5, NA, NA, NA, NA, NA, … $ ASTCCOST 52.87, 79.29, NA, NA, NA, NA, NA… $ ASTCE 0, 0, NA, NA, NA, NA, NA, 0, 5, … $ ASTDBE 1, 0, NA, NA, NA, NA, NA, 2, 4, … $ ASTDE 12, 1, NA, NA, NA, NA, NA, 4, 22… $ ASTICE 14, 2, NA, NA, NA, NA, NA, 6, 20… $ ASTLCM 651, 548, NA, NA, NA, NA, NA, 74… $ ASTLCMD 602, 503, NA, NA, NA, NA, NA, 67… $ ASTLCSD 158, 155, NA, NA, NA, NA, NA, 28… $ ASTLDBM 611, 462, NA, NA, NA, NA, NA, 61… $ ASTLDBMD 602, 469, NA, NA, NA, NA, NA, 60… $ ASTLDBSD 84.4, 47.5, NA, NA, NA, NA, NA, … $ ASTLDM 675, 593, NA, NA, NA, NA, NA, 83… $ ASTLDMD 647, 518, NA, NA, NA, NA, NA, 71… $ ASTLDSD 158, 199, NA, NA, NA, NA, NA, 33… $ ASTLICM 704, 628, NA, NA, NA, NA, NA, 85… $ ASTLICMD 665, 558, NA, NA, NA, NA, NA, 71… $ ASTLICSD 197, 195, NA, NA, NA, NA, NA, 39… $ ASTLM 677, 588, NA, NA, NA, NA, NA, 79… $ ASTLMD 645, 523, NA, NA, NA, NA, NA, 68… $ ASTLNBM 615, 472, NA, NA, NA, NA, NA, 60… $ ASTLNBMD 584, 466, NA, NA, NA, NA, NA, 55… $ ASTLNBSD 118.8, 62.1, NA, NA, NA, NA, NA,… $ ASTLSBM 619, 481, NA, NA, NA, NA, NA, 58… $ ASTLSBMD 564, 466, NA, NA, NA, NA, NA, 49… $ ASTLSBSD 146.3, 73.2, NA, NA, NA, NA, NA,… $ ASTLSD 180, 180, NA, NA, NA, NA, NA, 34… $ ASTLSDM 679, 582, NA, NA, NA, NA, NA, 76… $ ASTLSDMD 639, 530, NA, NA, NA, NA, NA, 66… $ ASTLSDSD 201.6, 159.6, NA, NA, NA, NA, NA… $ ASTLSWM 740, 704, NA, NA, NA, NA, NA, 10… $ ASTLSWMD 697, 688, NA, NA, NA, NA, NA, 92… $ ASTLSWSD 209, 184, NA, NA, NA, NA, NA, 34… $ ASTNBE 1, 0, NA, NA, NA, NA, NA, 2, 4, … $ ASTSBE 0, 0, NA, NA, NA, NA, NA, 0, 0, … $ ASTSE 2, 1, NA, NA, NA, NA, NA, 2, 3, … $ ASTSWBE 13, 2, NA, NA, NA, NA, NA, 4, 21… $ ASTSWCM 124.75, 232.51, NA, NA, NA, NA, … $ ASTSWCMD 113.5, 222.0, NA, NA, NA, NA, NA… $ ASTTC 145, 158, NA, NA, NA, NA, NA, 15… $ ASTTCE 0, 0, NA, NA, NA, NA, NA, 0, 0, … $ ASTTIC 14, 2, NA, NA, NA, NA, NA, 6, 25… $ ASTTOE 1, 0, NA, NA, NA, NA, NA, 1, 1, … $ subject.ID.y 34, 431, NA, NA, NA, NA, NA, 611… $ nr.y 0, 0, NA, NA, NA, NA, NA, 6, 7, … $ group.y none, PAT, NA, NA, NA, NA, NA, H… $ visit.y 34, 31, NA, NA, NA, NA, NA, 12, … $ gender_FE_AS NA, NA, NA, NA, NA, NA, NA, 7.00… $ gender_FE_UN NA, NA, NA, NA, NA, NA, NA, 3.83… $ gender_MA_AS NA, NA, NA, NA, NA, NA, NA, 1.00… $ gender_MA_UN NA, NA, NA, NA, NA, NA, NA, 1.00… $ attractiveness_FE_AS NA, NA, NA, NA, NA, NA, NA, 6.58… $ attractiveness_FE_UN NA, NA, NA, NA, NA, NA, NA, 1.17… $ attractiveness_MA_AS NA, NA, NA, NA, NA, NA, NA, 6.50… $ attractiveness_MA_UN NA, NA, NA, NA, NA, NA, NA, 1.12… $ dominance_FE_AS NA, NA, NA, NA, NA, NA, NA, 4.54… $ dominance_FE_UN NA, NA, NA, NA, NA, NA, NA, 1.50… $ dominance_MA_AS NA, NA, NA, NA, NA, NA, NA, 4.58… $ dominance_MA_UN NA, NA, NA, NA, NA, NA, NA, 1.46… $ health_FE_AS NA, NA, NA, NA, NA, NA, NA, 6.62… $ health_FE_UN NA, NA, NA, NA, NA, NA, NA, 1.12… $ health_MA_AS NA, NA, NA, NA, NA, NA, NA, 6.79… $ health_MA_UN NA, NA, NA, NA, NA, NA, NA, 1.17… $ friendliness_FE_AS NA, NA, NA, NA, NA, NA, NA, 5.58… $ friendliness_FE_UN NA, NA, NA, NA, NA, NA, NA, 3.04… $ friendliness_MA_AS NA, NA, NA, NA, NA, NA, NA, 5.71… $ friendliness_MA_UN NA, NA, NA, NA, NA, NA, NA, 2.96… $ trustworthiness_FE_AS NA, NA, NA, NA, NA, NA, NA, 5.71… $ trustworthiness_FE_UN NA, NA, NA, NA, NA, NA, NA, 2.21… $ trustworthiness_MA_AS NA, NA, NA, NA, NA, NA, NA, 5.75… $ trustworthiness_MA_UN NA, NA, NA, NA, NA, NA, NA, 1.92… $ gender_HD_AG NA, NA, NA, NA, NA, NA, NA, 3.38… $ gender_HD_DG NA, NA, NA, NA, NA, NA, NA, 3.25… $ gender_HU_AG NA, NA, NA, NA, NA, NA, NA, 3.00… $ gender_HU_DG NA, NA, NA, NA, NA, NA, NA, 3.21… $ attractiveness_HD_AG NA, NA, NA, NA, NA, NA, NA, 3.96… $ attractiveness_HD_DG NA, NA, NA, NA, NA, NA, NA, 3.92… $ attractiveness_HU_AG NA, NA, NA, NA, NA, NA, NA, 3.75… $ attractiveness_HU_DG NA, NA, NA, NA, NA, NA, NA, 3.75… $ dominance_HD_AG NA, NA, NA, NA, NA, NA, NA, 3.00… $ dominance_HD_DG NA, NA, NA, NA, NA, NA, NA, 3.21… $ dominance_HU_AG NA, NA, NA, NA, NA, NA, NA, 2.96… $ dominance_HU_DG NA, NA, NA, NA, NA, NA, NA, 2.92… $ health_HD_AG NA, NA, NA, NA, NA, NA, NA, 4.00… $ health_HD_DG NA, NA, NA, NA, NA, NA, NA, 3.88… $ health_HU_AG NA, NA, NA, NA, NA, NA, NA, 3.92… $ health_HU_DG NA, NA, NA, NA, NA, NA, NA, 3.92… $ friendliness_HD_AG NA, NA, NA, NA, NA, NA, NA, 4.46… $ friendliness_HD_DG NA, NA, NA, NA, NA, NA, NA, 3.96… $ friendliness_HU_AG NA, NA, NA, NA, NA, NA, NA, 4.50… $ friendliness_HU_DG NA, NA, NA, NA, NA, NA, NA, 4.38… $ trustworthiness_HD_AG NA, NA, NA, NA, NA, NA, NA, 3.92… $ trustworthiness_HD_DG NA, NA, NA, NA, NA, NA, NA, 3.88… $ trustworthiness_HU_AG NA, NA, NA, NA, NA, NA, NA, 3.92… $ trustworthiness_HU_DG NA, NA, NA, NA, NA, NA, NA, 3.88…

master.df$VP

NULL

describe(master.df$CRFstatus)

master.df$CRFstatus n missing distinct 74 3 5

Value complete dropout incomplete not included Frequency 29 3 10 7 Proportion 0.392 0.041 0.135 0.095

Value screening Frequency 25 Proportion 0.338

freq(master.df$CRFstatus, na.rm = TRUE)

var frequency percentage cumulative_perc 1 complete 29 39.19 39.2 2 screening 25 33.78 73.0 3 incomplete 10 13.51 86.5 4 not included 7 9.46 95.9 5 dropout 3 4.05 100.0

ggsave("Synopsis_VP_Status.png")
## Saving 7 x 5 in image

1.2 Summary of the Main SAM-Q Items and demographics

master.df %>%
  select(ID, CRFgroup, CRFstatus, Age, Gender, Education, SAM_A_Main, SAM_M_Main, SAM_S_Main) %>%
  mutate_if(is.numeric, funs(round(., 2))) %>%
  group_by(CRFgroup) %>%
  summarise(n = n(), 
            complete  = sum(CRFstatus %in% "complete"),
            incomplete  = sum(CRFstatus %in% "incomplete"),
            Age     = mean(Age, na.rm = TRUE),
            Female  = sum(Gender == "Female", na.rm = TRUE),
            Male    = sum(Gender == "Male", na.rm = TRUE),
            `Main A Mean` = mean(SAM_A_Main, na.rm = TRUE),
            `Main A SD` = sd(SAM_A_Main, na.rm = TRUE),
            `Main M Mean` = mean(SAM_M_Main, na.rm = TRUE),
            `Main M SD` = sd(SAM_M_Main, na.rm = TRUE),
            `Main S Mean` = mean(SAM_S_Main, na.rm = TRUE),
            `Main S SD` = sd(SAM_S_Main, na.rm = TRUE)) %>%
  kable("html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
CRFgroup n complete incomplete Age Female Male Main A Mean Main A SD Main M Mean Main M SD Main S Mean Main S SD
HC 66 26 9 29.4 10 6 5.26 1.15 5.46 1.27 5.29 1.36
PAT 8 3 1 NaN 0 0 NaN NaN NaN NaN NaN NaN
NA 3 0 0 NaN 0 0 NaN NaN NaN NaN NaN NaN 
#  Data frame for analysis
 df <- master.df %>%
  filter(CRFgroup  == "HC") %>%
  filter(CRFstatus %in% c("complete","incomplete")) # %>%
 # filter(CRF_MRI == "Yes" & CRF_EEG == "Yes")
 
df_status(df)
##                          variable q_zeros p_zeros q_na   p_na q_inf p_inf
## 1                               X       0    0.00    0   0.00     0     0
## 2                              ID       0    0.00    0   0.00     0     0
## 3                           CRFvp       0    0.00    0   0.00     0     0
## 4                        CRFgroup       0    0.00    0   0.00     0     0
## 5                       CRFstatus       0    0.00    0   0.00     0     0
## 6                      CRFdropout       0    0.00    0   0.00     0     0
## 7                        CRFscree       0    0.00    0   0.00     0     0
## 8                    CRFscreeDate       0    0.00    1   2.86     0     0
## 9                    CRFscreeTime       0    0.00    1   2.86     0     0
## 10                     CRFscreeVL       0    0.00    0   0.00     0     0
## 11                     CRFscreeOK       0    0.00    0   0.00     0     0
## 12                    CRFrandList       0    0.00    4  11.43     0     0
## 13                         CRFmri       0    0.00    1   2.86     0     0
## 14                     CRFmriDate       0    0.00    2   5.71     0     0
## 15                     CRFmriTime       0    0.00    2   5.71     0     0
## 16                       CRFmriVL       0    0.00    2   5.71     0     0
## 17                      CRFmriLOK       0    0.00    3   8.57     0     0
## 18                      CRFmriAOK       0    0.00    3   8.57     0     0
## 19                 CRFmriMPedalOK       0    0.00    3   8.57     0     0
## 20                   CRFmriMPlwOK       0    0.00    3   8.57     0     0
## 21                         CRFeeg       0    0.00    3   8.57     0     0
## 22                     CRFeegDate       0    0.00    6  17.14     0     0
## 23                     CRFeegTime       0    0.00    6  17.14     0     0
## 24                       CRFeegVL       0    0.00    6  17.14     0     0
## 25                      CRFeegLOK       0    0.00    6  17.14     0     0
## 26                      CRFeegAOK       0    0.00    6  17.14     0     0
## 27                 CRFeegMPedalOK       0    0.00    6  17.14     0     0
## 28                   CRFeegMPlwOK       0    0.00    6  17.14     0     0
## 29                         SES_VP       0    0.00    8  22.86     0     0
## 30                      SES_Group       0    0.00    8  22.86     0     0
## 31                       SES_Date       0    0.00   20  57.14     0     0
## 32                       SES_Time       0    0.00   20  57.14     0     0
## 33                        SES_Day       0    0.00   20  57.14     0     0
## 34                         SES_VL       0    0.00   20  57.14     0     0
## 35                   SES_1_Gender       0    0.00   20  57.14     0     0
## 36                      SES_2_Age       0    0.00   20  57.14     0     0
## 37            SES_3a_Nationality1       0    0.00   20  57.14     0     0
## 38            SES_3b_Nationality2       0    0.00   31  88.57     0     0
## 39                SES_4_Language1       0    0.00   20  57.14     0     0
## 40                SES_4_Language2       0    0.00   31  88.57     0     0
## 41              SES_5_Immigration       0    0.00   20  57.14     0     0
## 42             SES_6_SozialStatus       0    0.00   20  57.14     0     0
## 43                SES_7_Education       0    0.00   20  57.14     0     0
## 44               SES_8_Workstatus       0    0.00   20  57.14     0     0
## 45                    SES_9a_Job1       0    0.00   23  65.71     0     0
## 46                    SES_9a_Job2       0    0.00   29  82.86     0     0
## 47               SES_9b_JobPensum       0    0.00   28  80.00     0     0
## 48                 SES_9c_LastJob       0    0.00   32  91.43     0     0
## 49           SES_9d_LastJobPesnum       0    0.00   32  91.43     0     0
## 50            SES_10a_PsychFamily       0    0.00   20  57.14     0     0
## 51  SES_10b_PsychFamilyDiagnosis1       0    0.00   31  88.57     0     0
## 52  SES_10b_PsychFamilyDiagnosis2       0    0.00   34  97.14     0     0
## 53  SES_10b_PsychFamilyDiagnosis3       0    0.00   34  97.14     0     0
## 54      SES_10c_PsychFamilyMember       0    0.00   31  88.57     0     0
## 55                         Gender       0    0.00   20  57.14     0     0
## 56                            Age       0    0.00   20  57.14     0     0
## 57                      Education       0    0.00   20  57.14     0     0
## 58                         SAM_VP       0    0.00    1   2.86     0     0
## 59                      SAM_Group       0    0.00    1   2.86     0     0
## 60                       SAM_Date       0    0.00    1   2.86     0     0
## 61                       SAM_Time       0    0.00    2   5.71     0     0
## 62                        SAM_Day       0    0.00    2   5.71     0     0
## 63                     SAM_A_Main       0    0.00    1   2.86     0     0
## 64                   SAM_A_Pos_01       0    0.00    1   2.86     0     0
## 65                   SAM_A_Pos_02       0    0.00    1   2.86     0     0
## 66                   SAM_A_Pos_03       0    0.00    1   2.86     0     0
## 67                   SAM_A_Pos_04       0    0.00    1   2.86     0     0
## 68                   SAM_A_Neg_01       0    0.00    1   2.86     0     0
## 69                   SAM_A_Neg_02       0    0.00    1   2.86     0     0
## 70                   SAM_A_Neg_03       0    0.00    1   2.86     0     0
## 71                   SAM_A_Neg_04       0    0.00    1   2.86     0     0
## 72                     SAM_M_Main       0    0.00    1   2.86     0     0
## 73                   SAM_M_Pos_01       0    0.00    1   2.86     0     0
## 74                   SAM_M_Pos_02       0    0.00    1   2.86     0     0
## 75                   SAM_M_Pos_03       0    0.00    1   2.86     0     0
## 76                   SAM_M_Pos_04       0    0.00    1   2.86     0     0
## 77                   SAM_M_Neg_01       0    0.00    1   2.86     0     0
## 78                   SAM_M_Neg_02       0    0.00    1   2.86     0     0
## 79                   SAM_M_Neg_03       0    0.00    1   2.86     0     0
## 80                   SAM_M_Neg_04       0    0.00    1   2.86     0     0
## 81                     SAM_S_Main       0    0.00    1   2.86     0     0
## 82                   SAM_S_Pos_01       0    0.00    1   2.86     0     0
## 83                   SAM_S_Pos_02       0    0.00    2   5.71     0     0
## 84                   SAM_S_Pos_03       0    0.00    1   2.86     0     0
## 85                   SAM_S_Pos_04       0    0.00    1   2.86     0     0
## 86                   SAM_S_Neg_01       0    0.00    1   2.86     0     0
## 87                   SAM_S_Neg_02       0    0.00    1   2.86     0     0
## 88                   SAM_S_Neg_03       0    0.00    1   2.86     0     0
## 89                   SAM_S_Neg_04       0    0.00    1   2.86     0     0
## 90                         Affect       0    0.00    1   2.86     0     0
## 91                        Motoric       0    0.00    1   2.86     0     0
## 92                       Language       0    0.00   35 100.00     0     0
## 93                   PANAS_MRI_VP       0    0.00   13  37.14     0     0
## 94                PANAS_MRI_Group       0    0.00   13  37.14     0     0
## 95                 PANAS_MRI_Date       0    0.00   20  57.14     0     0
## 96                 PANAS_MRI_Time       0    0.00   20  57.14     0     0
## 97                  PANAS_MRI_Day       0    0.00   20  57.14     0     0
## 98                PANAS_MRI_aktiv       0    0.00   20  57.14     0     0
## 99           PANAS_MRI_bekuemmert       0    0.00   20  57.14     0     0
## 100        PANAS_MRI_interessiert       0    0.00   20  57.14     0     0
## 101             PANAS_MRI_freudig       0    0.00   20  57.14     0     0
## 102          PANAS_MRI_veraergert       0    0.00   20  57.14     0     0
## 103               PANAS_MRI_stark       0    0.00   20  57.14     0     0
## 104            PANAS_MRI_schuldig       0    0.00   20  57.14     0     0
## 105         PANAS_MRI_erschrocken       0    0.00   20  57.14     0     0
## 106          PANAS_MRI_feindselig       0    0.00   20  57.14     0     0
## 107            PANAS_MRI_angeregt       0    0.00   20  57.14     0     0
## 108               PANAS_MRI_stolz       0    0.00   20  57.14     0     0
## 109             PANAS_MRI_gereizt       0    0.00   20  57.14     0     0
## 110          PANAS_MRI_begeistert       0    0.00   20  57.14     0     0
## 111           PANAS_MRI_beschaemt       0    0.00   20  57.14     0     0
## 112                PANAS_MRI_wach       0    0.00   20  57.14     0     0
## 113             PANAS_MRI_nervoes       0    0.00   20  57.14     0     0
## 114        PANAS_MRI_entschlossen       0    0.00   20  57.14     0     0
## 115          PANAS_MRI_aufmerksam       0    0.00   20  57.14     0     0
## 116       PANAS_MRI_durcheinander       0    0.00   20  57.14     0     0
## 117          PANAS_MRI_aengstlich       0    0.00   20  57.14     0     0
## 118                     TEIQue_VP       0    0.00   20  57.14     0     0
## 119                  TEIQue_Group       0    0.00   20  57.14     0     0
## 120                     TEIQue_01       0    0.00   20  57.14     0     0
## 121                     TEIQue_02       0    0.00   20  57.14     0     0
## 122                     TEIQue_03       0    0.00   20  57.14     0     0
## 123                     TEIQue_04       0    0.00   20  57.14     0     0
## 124                     TEIQue_05       0    0.00   20  57.14     0     0
## 125                     TEIQue_06       0    0.00   20  57.14     0     0
## 126                     TEIQue_07       0    0.00   20  57.14     0     0
## 127                     TEIQue_08       0    0.00   20  57.14     0     0
## 128                     TEIQue_09       0    0.00   20  57.14     0     0
## 129                     TEIQue_10       0    0.00   20  57.14     0     0
## 130                     TEIQue_11       0    0.00   20  57.14     0     0
## 131                     TEIQue_12       0    0.00   20  57.14     0     0
## 132                     TEIQue_13       0    0.00   20  57.14     0     0
## 133                     TEIQue_14       0    0.00   20  57.14     0     0
## 134                     TEIQue_15       0    0.00   20  57.14     0     0
## 135                     TEIQue_16       0    0.00   20  57.14     0     0
## 136                     TEIQue_17       0    0.00   20  57.14     0     0
## 137                     TEIQue_18       0    0.00   20  57.14     0     0
## 138                     TEIQue_19       0    0.00   20  57.14     0     0
## 139                     TEIQue_20       0    0.00   20  57.14     0     0
## 140                     TEIQue_21       0    0.00   20  57.14     0     0
## 141                     TEIQue_22       0    0.00   20  57.14     0     0
## 142                     TEIQue_23       0    0.00   20  57.14     0     0
## 143                     TEIQue_24       0    0.00   20  57.14     0     0
## 144                     TEIQue_25       0    0.00   20  57.14     0     0
## 145                     TEIQue_26       0    0.00   20  57.14     0     0
## 146                     TEIQue_27       0    0.00   20  57.14     0     0
## 147                     TEIQue_28       0    0.00   20  57.14     0     0
## 148                     TEIQue_29       0    0.00   20  57.14     0     0
## 149                     TEIQue_30       0    0.00   20  57.14     0     0
## 150                    TEIQue_SUM       0    0.00   20  57.14     0     0
## 151                    Well_Being       0    0.00   20  57.14     0     0
## 152                  Self_Control       0    0.00   20  57.14     0     0
## 153                  Emotionality       0    0.00   20  57.14     0     0
## 154                   Sociability       0    0.00   20  57.14     0     0
## 155                  TEIQue_Total       0    0.00   20  57.14     0     0
## 156                    Row.Number       0    0.00   10  28.57     0     0
## 157              Synopsis.Project       0    0.00   10  28.57     0     0
## 158                          Site       0    0.00   10  28.57     0     0
## 159                     Site.Name       0    0.00   10  28.57     0     0
## 160                  subject.ID.x       0    0.00   10  28.57     0     0
## 161                          Date       0    0.00   10  28.57     0     0
## 162                        Cohort       0    0.00   10  28.57     0     0
## 163              Visit.Identifier       0    0.00   10  28.57     0     0
## 164                    Visit.Name       0    0.00   10  28.57     0     0
## 165                         Rater       0    0.00   10  28.57     0     0
## 166           Visit.Start..Local.       0    0.00   10  28.57     0     0
## 167             Visit.Start..GMT.       0    0.00   10  28.57     0     0
## 168            Visit.Start.Offset       0    0.00   10  28.57     0     0
## 169             Visit.End..Local.       0    0.00   10  28.57     0     0
## 170               Visit.End..GMT.       0    0.00   10  28.57     0     0
## 171              Visit.End.Offset       0    0.00   10  28.57     0     0
## 172               ERT.Long.Status       0    0.00   10  28.57     0     0
## 173                     ERTMDCRTA       0    0.00   10  28.57     0     0
## 174                     ERTMDCRTD       0    0.00   10  28.57     0     0
## 175                     ERTMDCRTF       0    0.00   10  28.57     0     0
## 176                     ERTMDCRTH       0    0.00   10  28.57     0     0
## 177                     ERTMDCRTS       0    0.00   10  28.57     0     0
## 178                    ERTMDCRTSU       0    0.00   10  28.57     0     0
## 179                      ERTMDRTA       0    0.00   10  28.57     0     0
## 180                      ERTMDRTD       0    0.00   10  28.57     0     0
## 181                      ERTMDRTF       0    0.00   10  28.57     0     0
## 182                      ERTMDRTH       0    0.00   10  28.57     0     0
## 183                      ERTMDRTS       0    0.00   10  28.57     0     0
## 184                     ERTMDRTSU       0    0.00   10  28.57     0     0
## 185                     ERTOCRTSD       0    0.00   10  28.57     0     0
## 186                     ERTOMDCRT       0    0.00   10  28.57     0     0
## 187                      ERTOMDRT       0    0.00   10  28.57     0     0
## 188                      ERTORTSD       0    0.00   10  28.57     0     0
## 189                       ERTTFAA       2    5.71   10  28.57     0     0
## 190                       ERTTFAD       2    5.71   10  28.57     0     0
## 191                       ERTTFAF       2    5.71   10  28.57     0     0
## 192                       ERTTFAH       2    5.71   10  28.57     0     0
## 193                       ERTTFAS       2    5.71   10  28.57     0     0
## 194                      ERTTFASU       0    0.00   10  28.57     0     0
## 195                         ERTTH       0    0.00   10  28.57     0     0
## 196                        ERTTHA       0    0.00   10  28.57     0     0
## 197                        ERTTHD       0    0.00   10  28.57     0     0
## 198                        ERTTHF       0    0.00   10  28.57     0     0
## 199                        ERTTHH       0    0.00   10  28.57     0     0
## 200                        ERTTHS       0    0.00   10  28.57     0     0
## 201                       ERTTHSU       0    0.00   10  28.57     0     0
## 202                       ERTUHRA       0    0.00   10  28.57     0     0
## 203                       ERTUHRD       0    0.00   10  28.57     0     0
## 204                       ERTUHRF       0    0.00   10  28.57     0     0
## 205                       ERTUHRH       0    0.00   10  28.57     0     0
## 206                       ERTUHRS       0    0.00   10  28.57     0     0
## 207                      ERTUHRSU       0    0.00   10  28.57     0     0
## 208                          nr.x       0    0.00   10  28.57     0     0
## 209                       group.x       0    0.00   10  28.57     0     0
## 210                       visit.x       0    0.00   10  28.57     0     0
## 211                   ERT_percent       0    0.00   10  28.57     0     0
## 212           AST.Standard.Status       0    0.00   10  28.57     0     0
## 213                       ASTCCMD       0    0.00   10  28.57     0     0
## 214                      ASTCCOST       0    0.00   10  28.57     0     0
## 215                         ASTCE      13   37.14   10  28.57     0     0
## 216                        ASTDBE       7   20.00   10  28.57     0     0
## 217                         ASTDE       3    8.57   10  28.57     0     0
## 218                        ASTICE       3    8.57   10  28.57     0     0
## 219                        ASTLCM       0    0.00   10  28.57     0     0
## 220                       ASTLCMD       0    0.00   10  28.57     0     0
## 221                       ASTLCSD       0    0.00   10  28.57     0     0
## 222                       ASTLDBM       0    0.00   10  28.57     0     0
## 223                      ASTLDBMD       0    0.00   10  28.57     0     0
## 224                      ASTLDBSD       0    0.00   10  28.57     0     0
## 225                        ASTLDM       0    0.00   10  28.57     0     0
## 226                       ASTLDMD       0    0.00   10  28.57     0     0
## 227                       ASTLDSD       0    0.00   10  28.57     0     0
## 228                       ASTLICM       0    0.00   10  28.57     0     0
## 229                      ASTLICMD       0    0.00   10  28.57     0     0
## 230                      ASTLICSD       0    0.00   10  28.57     0     0
## 231                         ASTLM       0    0.00   10  28.57     0     0
## 232                        ASTLMD       0    0.00   10  28.57     0     0
## 233                       ASTLNBM       0    0.00   10  28.57     0     0
## 234                      ASTLNBMD       0    0.00   10  28.57     0     0
## 235                      ASTLNBSD       0    0.00   10  28.57     0     0
## 236                       ASTLSBM       0    0.00   10  28.57     0     0
## 237                      ASTLSBMD       0    0.00   10  28.57     0     0
## 238                      ASTLSBSD       0    0.00   10  28.57     0     0
## 239                        ASTLSD       0    0.00   10  28.57     0     0
## 240                       ASTLSDM       0    0.00   10  28.57     0     0
## 241                      ASTLSDMD       0    0.00   10  28.57     0     0
## 242                      ASTLSDSD       0    0.00   10  28.57     0     0
## 243                       ASTLSWM       0    0.00   10  28.57     0     0
## 244                      ASTLSWMD       0    0.00   10  28.57     0     0
## 245                      ASTLSWSD       0    0.00   10  28.57     0     0
## 246                        ASTNBE       6   17.14   10  28.57     0     0
## 247                        ASTSBE      17   48.57   10  28.57     0     0
## 248                         ASTSE       6   17.14   10  28.57     0     0
## 249                       ASTSWBE       4   11.43   10  28.57     0     0
## 250                       ASTSWCM       0    0.00   10  28.57     0     0
## 251                      ASTSWCMD       0    0.00   10  28.57     0     0
## 252                         ASTTC       0    0.00   10  28.57     0     0
## 253                        ASTTCE      25   71.43   10  28.57     0     0
## 254                        ASTTIC       2    5.71   10  28.57     0     0
## 255                        ASTTOE      15   42.86   10  28.57     0     0
## 256                  subject.ID.y       0    0.00   10  28.57     0     0
## 257                          nr.y       0    0.00   10  28.57     0     0
## 258                       group.y       0    0.00   10  28.57     0     0
## 259                       visit.y       0    0.00   10  28.57     0     0
## 260                  gender_FE_AS       0    0.00    6  17.14     0     0
## 261                  gender_FE_UN       0    0.00    6  17.14     0     0
## 262                  gender_MA_AS       0    0.00    6  17.14     0     0
## 263                  gender_MA_UN       0    0.00    6  17.14     0     0
## 264          attractiveness_FE_AS       0    0.00    6  17.14     0     0
## 265          attractiveness_FE_UN       0    0.00    6  17.14     0     0
## 266          attractiveness_MA_AS       0    0.00    6  17.14     0     0
## 267          attractiveness_MA_UN       0    0.00    6  17.14     0     0
## 268               dominance_FE_AS       0    0.00    6  17.14     0     0
## 269               dominance_FE_UN       0    0.00    6  17.14     0     0
## 270               dominance_MA_AS       0    0.00    6  17.14     0     0
## 271               dominance_MA_UN       0    0.00    6  17.14     0     0
## 272                  health_FE_AS       0    0.00    6  17.14     0     0
## 273                  health_FE_UN       0    0.00    6  17.14     0     0
## 274                  health_MA_AS       0    0.00    6  17.14     0     0
## 275                  health_MA_UN       0    0.00    6  17.14     0     0
## 276            friendliness_FE_AS       0    0.00    6  17.14     0     0
## 277            friendliness_FE_UN       0    0.00    6  17.14     0     0
## 278            friendliness_MA_AS       0    0.00    6  17.14     0     0
## 279            friendliness_MA_UN       0    0.00    6  17.14     0     0
## 280         trustworthiness_FE_AS       0    0.00    6  17.14     0     0
## 281         trustworthiness_FE_UN       0    0.00    6  17.14     0     0
## 282         trustworthiness_MA_AS       0    0.00    6  17.14     0     0
## 283         trustworthiness_MA_UN       0    0.00    6  17.14     0     0
## 284                  gender_HD_AG       0    0.00    6  17.14     0     0
## 285                  gender_HD_DG       0    0.00    6  17.14     0     0
## 286                  gender_HU_AG       0    0.00    6  17.14     0     0
## 287                  gender_HU_DG       0    0.00    6  17.14     0     0
## 288          attractiveness_HD_AG       0    0.00    6  17.14     0     0
## 289          attractiveness_HD_DG       0    0.00    6  17.14     0     0
## 290          attractiveness_HU_AG       0    0.00    6  17.14     0     0
## 291          attractiveness_HU_DG       0    0.00    6  17.14     0     0
## 292               dominance_HD_AG       0    0.00    6  17.14     0     0
## 293               dominance_HD_DG       0    0.00    6  17.14     0     0
## 294               dominance_HU_AG       0    0.00    6  17.14     0     0
## 295               dominance_HU_DG       0    0.00    6  17.14     0     0
## 296                  health_HD_AG       0    0.00    6  17.14     0     0
## 297                  health_HD_DG       0    0.00    6  17.14     0     0
## 298                  health_HU_AG       0    0.00    6  17.14     0     0
## 299                  health_HU_DG       0    0.00    6  17.14     0     0
## 300            friendliness_HD_AG       0    0.00    6  17.14     0     0
## 301            friendliness_HD_DG       0    0.00    6  17.14     0     0
## 302            friendliness_HU_AG       0    0.00    6  17.14     0     0
## 303            friendliness_HU_DG       0    0.00    6  17.14     0     0
## 304         trustworthiness_HD_AG       0    0.00    6  17.14     0     0
## 305         trustworthiness_HD_DG       0    0.00    6  17.14     0     0
## 306         trustworthiness_HU_AG       0    0.00    6  17.14     0     0
## 307         trustworthiness_HU_DG       0    0.00    6  17.14     0     0
##        type unique
## 1   integer     35
## 2   integer     35
## 3   integer     35
## 4    factor      1
## 5    factor      2
## 6    factor      2
## 7    factor      1
## 8    factor     23
## 9    factor     23
## 10   factor      2
## 11   factor      1
## 12  integer     11
## 13   factor      2
## 14  integer     17
## 15  numeric     10
## 16   factor      6
## 17   factor      2
## 18   factor      2
## 19   factor      1
## 20   factor      2
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2 SAM-Q

2.1 Distribution of the Main-Items Ratings

p <- freq(df$SAM_A_Main)

p
##    var frequency percentage cumulative_perc
## 1    6        14      40.00            40.0
## 2    5        10      28.57            68.6
## 3    4         4      11.43            80.0
## 4    7         3       8.57            88.6
## 5    3         2       5.71            94.3
## 6    2         1       2.86            97.1
## 7 <NA>         1       2.86           100.0
# SAM_Q Categories ###########
Category_long <- df %>%
  select(SAM_A_Main, SAM_M_Main, SAM_S_Main) %>%
  mutate(Affect = cut(SAM_A_Main, breaks=c(-Inf, 3, 5, Inf), labels=c("low", "mid", "high"), ordered = TRUE),
         Motoric = cut(SAM_M_Main, breaks=c(-Inf, 3, 5, Inf), labels=c("low", "mid", "high"), ordered  = TRUE),
         Language = cut(SAM_S_Main, breaks=c(-Inf, 3, 5, Inf), labels=c("low", "mid", "high"), ordered = TRUE)) %>%
  gather(Domain, Level, c("Affect", "Motoric", "Language"), factor_key=TRUE) %>% 
  drop_na(Level)

Category_long$Level <- factor(Category_long$Level, levels = c("low", "mid", "high"))
p <- ggplot(Category_long, aes(Level, fill = Level)) + geom_bar() + facet_grid(~ Domain) + 
  theme_minimal() +
  labs(title = "Distribution of SAM-Q Main-Item Rating ", subtitle = "low (1-2), mid (3-5), high (6-7)", 
       x = "Rating", y = "Count", colour = "Rating")
p

ggsave("SAMQ_Distribution1.png")


# SAM_Q Levels #############
p <- df %>%
  select(starts_with("SAM_")) %>%
  mutate(Affect = as.factor(SAM_A_Main),
         Motoric = as.factor(SAM_M_Main),
         Language = as.factor(SAM_S_Main)) %>%  
  gather(Domain, Level, c("Affect", "Motoric", "Language"), factor_key=TRUE) %>%
  drop_na(Level) %>%
  ggplot(., aes(Level, fill = Level)) + geom_bar() + facet_grid(~ Domain) + theme_minimal() +
  labs(title = "Distribution of SAM-Q Main-Item Rating ", x = "Rating", y = "Count", colour = "Rating")
p

ggsave("SAMQ_Distribution2.png")

2.2 Distribution of the Main-Item Combinations

# Parallel Plot SAM-Q ##############
# group by combinations and count
df_grouped <- df %>% group_by(SAM_A_Main, SAM_M_Main, SAM_S_Main) %>% count() %>% drop_na()

# set an id string that denotes the value combination
df_grouped <- df_grouped %>% mutate(id = factor(paste(SAM_A_Main, SAM_M_Main, SAM_S_Main, sep = '-')))
## Warning in mutate_impl(.data, dots): Unequal factor levels: coercing to
## character
## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector

## Warning in mutate_impl(.data, dots): binding character and factor vector,
## coercing into character vector
df_pcp <- melt(df_grouped, id.vars = c('id', 'n'))
df_pcp$value <- factor(df_pcp$value)

y_levels <- levels(factor(1:7))

p <- ggplot(df_pcp, aes(x = variable, y = value, group = id)) +   # group = id is important!
  geom_path(aes(size = n, color = id),
            alpha = 0.5, lineend = 'round', linejoin = 'round') +
  scale_y_discrete(limits = y_levels, expand = c(0.5, 0)) +
  scale_size(breaks = NULL, range = c(1, 7)) +
  theme_minimal() +
  labs(title = "Combinations of SAM-Q Main-Items", x = "Main-Item", y = "Rating", colour = "Combinations")
p

ggsave("SAMQ_Combinations.png")
## Saving 7 x 5 in image

2.3 SAM-Q Main-Item Variance

df_variance <- df %>% select(SAM_A_Main, SAM_M_Main, SAM_S_Main) %>% 
  drop_na() %>%
  rowwise() %>%
  mutate(SAM_Main_Var = var(c(SAM_A_Main, SAM_M_Main, SAM_S_Main)))

ggplot(df_variance, aes(SAM_Main_Var)) + 
  geom_bar(fill = "steelblue") + 
  theme_minimal()  +
  labs(title = "Variance of all three SAM-Q Main-Items", x = "Variance", y = "Count")

ggsave("SAMQ_Variance.png")
## Saving 7 x 5 in image
# pirateplot(data = Category_long, ERT_percent ~ Level + Domain)
# pirateplot(data = Category_long, Affect ~ Level + Domain)
# pirateplot(data = Category_long, TEIQue_Total ~ Level + Domain)
# pirateplot(data = Category_long, dominance_HU_DG ~ Level + Domain)
# pirateplot(data = Category_long, dominance_HD_AG ~ Level + Domain)
# pirateplot(data = Category_long, attractiveness_MA_UN ~ Level + Domain)
# pirateplot(data = Category_long, attractiveness_FE_UN ~ Level + Domain)

3 The Affect Domain

3.1 Emotional Intelligence

  df.Affect df.SAM_A_Main df.SAM_M_Main df.SAM_S_Main df.Emotionality df.Sociability df.Well_Being df.Self_Control df.TEIQue_Total
df.Affect                  
df.SAM_A_Main 0.397
(.143) 		               
df.SAM_M_Main 0.398
(.142) 		0.222
(.427) 		             
df.SAM_S_Main 0.284
(.304) 		0.314
(.254) 		-0.260
(.350) 		           
df.Emotionality -0.208
(.456) 		0.145
(.606) 		-0.645
(.009) 		0.432
(.108) 		         
df.Sociability -0.105
(.710) 		-0.110
(.697) 		-0.442
(.099) 		0.476
(.073) 		0.420
(.119) 		       
df.Well_Being 0.525
(.045) 		0.096
(.733) 		0.430
(.110) 		0.025
(.930) 		-0.182
(.517) 		0.057
(.840) 		     
df.Self_Control 0.297
(.283) 		-0.567
(.028) 		-0.146
(.604) 		0.093
(.743) 		0.071
(.800) 		0.452
(.091) 		0.351
(.200) 		   
df.TEIQue_Total 0.196
(.484) 		-0.178
(.526) 		-0.345
(.208) 		0.474
(.074) 		0.542
(.037) 		0.800
(<.001) 		0.389
(.152) 		0.735
(.002) 		 
Computed correlation used pearson-method with listwise-deletion.

3.2 Emotion Recognition Task (ERT)

## Observations: 1
## Variables: 2
## $ n       <int> 35
## $ meanERT <dbl> NA
  df.Affect df.SAM_A_Main df.ERT_percent df.Age df.Emotionality df.Sociability df.Well_Being df.Self_Control df.TEIQue_Total
df.Affect                  
df.SAM_A_Main 0.397
(.143) 		               
df.ERT_percent 0.240
(.388) 		0.559
(.030) 		             
df.Age 0.095
(.735) 		0.030
(.916) 		-0.438
(.102) 		           
df.Emotionality -0.208
(.456) 		0.145
(.606) 		0.164
(.559) 		-0.424
(.116) 		         
df.Sociability -0.105
(.710) 		-0.110
(.697) 		0.350
(.201) 		-0.516
(.049) 		0.420
(.119) 		       
df.Well_Being 0.525
(.045) 		0.096
(.733) 		0.087
(.757) 		-0.053
(.852) 		-0.182
(.517) 		0.057
(.840) 		     
df.Self_Control 0.297
(.283) 		-0.567
(.028) 		-0.134
(.633) 		-0.112
(.691) 		0.071
(.800) 		0.452
(.091) 		0.351
(.200) 		   
df.TEIQue_Total 0.196
(.484) 		-0.178
(.526) 		0.191
(.496) 		-0.416
(.123) 		0.542
(.037) 		0.800
(<.001) 		0.389
(.152) 		0.735
(.002) 		 
Computed correlation used pearson-method with listwise-deletion. 
## 
## Call:
## lm(formula = ERT_percent ~ SAM_A_Main + ERTOMDCRT + Age + Gender, 
##     data = df)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -13.19  -4.20  -2.57   5.52  10.03 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 51.32571   21.23543    2.42    0.036 *
## SAM_A_Main   4.20364    2.59713    1.62    0.137  
## ERTOMDCRT   -0.00194    0.00681   -0.28    0.781  
## Age         -0.35208    0.16794   -2.10    0.062 .
## GenderMale   0.67360    6.03119    0.11    0.913  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.52 on 10 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.525,  Adjusted R-squared:  0.335 
## F-statistic: 2.76 on 4 and 10 DF,  p-value: 0.0877

4 Face Rating

All subjects rated every presented stimuli after the task on a several 7-point-likert scales

FE = Female MA = Male AS = Aesthetic UN = Unaesthetic HU = Head Up HD = Head Down AG = Averted Gaze DG = Direct Gaze

##    GE_AS          rating bean.num  n  avg inf.lb inf.ub
## 1  FE_AS          gender        1 29 6.38   6.16   6.60
## 2  MA_AS          gender        2 29 1.41   1.19   1.63
## 3  FE_UN          gender        3 29 3.37   2.89   3.76
## 4  MA_UN          gender        4 29 1.35   1.18   1.51
## 5  FE_AS  attractiveness        5 29 5.59   5.27   5.84
## 6  MA_AS  attractiveness        6 29 5.10   4.73   5.43
## 7  FE_UN  attractiveness        7 29 2.22   1.94   2.50
## 8  MA_UN  attractiveness        8 29 2.04   1.72   2.30
## 9  FE_AS       dominance        9 29 4.18   3.94   4.42
## 10 MA_AS       dominance       10 29 4.36   4.05   4.61
## 11 FE_UN       dominance       11 29 3.72   3.29   4.13
## 12 MA_UN       dominance       12 29 3.97   3.52   4.40
## 13 FE_AS          health       13 29 6.18   5.83   6.47
## 14 MA_AS          health       14 29 6.04   5.64   6.34
## 15 FE_UN          health       15 29 2.99   2.60   3.29
## 16 MA_UN          health       16 29 2.86   2.43   3.20
## 17 FE_AS trustworthiness       17 29 5.30   5.04   5.57
## 18 MA_AS trustworthiness       18 29 4.81   4.51   5.09
## 19 FE_UN trustworthiness       19 29 3.26   2.95   3.49
## 20 MA_UN trustworthiness       20 29 2.98   2.65   3.30
## 21 FE_AS    friendliness       21 29 5.46   5.14   5.73
## 22 MA_AS    friendliness       22 29 5.08   4.83   5.36
## 23 FE_UN    friendliness       23 29 3.44   3.13   3.72
## 24 MA_UN    friendliness       24 29 2.99   2.67   3.28

## Correlation Matrix
corrDat1 <- data.frame(df$Affect, df$TEIQue_Total, df$attractiveness_FE_AS,df$attractiveness_FE_UN, df$attractiveness_MA_AS, df$attractiveness_MA_UN)

corrMat1 <- cor(corrDat1, use = "na.or.complete")

# Display
sjt.corr(corrDat1, p.numeric = TRUE, triangle = "lower")
  df.Affect df.TEIQue_Total df.attractiveness_FE_AS df.attractiveness_FE_UN df.attractiveness_MA_AS df.attractiveness_MA_UN
df.Affect            
df.TEIQue_Total 0.196
(.484) 		         
df.attractiveness_FE_AS 0.182
(.517) 		0.129
(.646) 		       
df.attractiveness_FE_UN -0.009
(.975) 		0.064
(.821) 		-0.239
(.390) 		     
df.attractiveness_MA_AS -0.025
(.930) 		0.037
(.897) 		0.695
(.004) 		-0.101
(.721) 		   
df.attractiveness_MA_UN -0.123
(.662) 		0.023
(.935) 		-0.219
(.433) 		0.927
(<.001) 		-0.025
(.929) 		 
Computed correlation used pearson-method with listwise-deletion. 
## Correlation Matrix
corrDat2<- data.frame(df$Affect, df$TEIQue_Total, df$dominance_HD_AG,df$dominance_HD_DG, df$dominance_HU_AG, df$dominance_HU_DG)
corrMat2 <- cor(corrDat2, use = "na.or.complete")

# Display
sjt.corr(corrDat2, p.numeric = TRUE, triangle = "lower")
  df.Affect df.TEIQue_Total df.dominance_HD_AG df.dominance_HD_DG df.dominance_HU_AG df.dominance_HU_DG
df.Affect            
df.TEIQue_Total 0.196
(.484) 		         
df.dominance_HD_AG 0.045
(.874) 		-0.327
(.234) 		       
df.dominance_HD_DG 0.136
(.629) 		-0.082
(.770) 		0.589
(.021) 		     
df.dominance_HU_AG 0.146
(.603) 		-0.142
(.615) 		0.723
(.002) 		0.950
(<.001) 		   
df.dominance_HU_DG 0.135
(.633) 		-0.334
(.224) 		0.765
(.001) 		0.872
(<.001) 		0.928
(<.001) 		 
Computed correlation used pearson-method with listwise-deletion.