#PACOTES
library(summarytools)
library(psych)
## Warning: package 'psych' was built under R version 4.0.5
#dados_videos <- read.csv("dados_quali.csv")
dados_videos <- read.csv("banco_aline_02.csv")
describe(dados_videos)
## vars n mean sd median trimmed mad min
## Carimbo.de.data.hora* 1 99 50.00 28.72 50 50.00 37.06 1
## ID* 2 99 49.24 27.85 50 49.41 35.58 1
## q1 3 99 3.96 1.20 4 3.96 1.48 2
## q2 4 99 2.23 0.92 2 2.17 1.48 1
## q3 5 99 2.24 0.77 2 2.30 1.48 1
## q4 6 99 22432.42 70106.90 5326 8695.06 5215.79 1456
## q5 7 99 736.29 806.64 487 566.19 386.96 35
## q6 8 99 1099.96 2927.95 156 377.80 177.91 0
## q7 9 99 27.41 142.10 4 7.10 5.93 0
## q9 10 99 1.07 0.26 1 1.00 0.00 1
## q10 11 99 1.59 0.50 2 1.60 0.00 1
## q11 12 99 1.57 0.50 2 1.58 0.00 1
## q12 13 99 1.72 0.45 2 1.77 0.00 1
## q13 14 99 1.49 0.50 1 1.49 0.00 1
## q14 15 99 1.56 0.50 2 1.57 0.00 1
## q15 16 99 1.27 0.45 1 1.22 0.00 1
## q16 17 99 1.44 0.50 1 1.43 0.00 1
## q17 18 99 1.10 0.30 1 1.01 0.00 1
## q18 19 99 1.67 0.47 2 1.70 0.00 1
## q19 20 99 1.42 0.50 1 1.41 0.00 1
## q20 21 99 1.74 0.44 2 1.79 0.00 1
## q21 22 99 1.64 0.48 2 1.67 0.00 1
## q22 23 99 1.62 0.49 2 1.64 0.00 1
## q23 24 99 1.59 0.50 2 1.60 0.00 1
## q24 25 99 1.57 0.50 2 1.58 0.00 1
## q25 26 99 1.33 0.47 1 1.30 0.00 1
## q26 27 99 1.72 0.45 2 1.77 0.00 1
## q27 28 99 1.90 0.30 2 1.99 0.00 1
## q28 29 99 1.69 0.47 2 1.73 0.00 1
## q29 30 99 1.75 0.44 2 1.80 0.00 1
## q30 31 99 1.84 0.37 2 1.91 0.00 1
## q31 32 99 1.86 0.35 2 1.94 0.00 1
## q32 33 99 1.67 0.47 2 1.70 0.00 1
## q33 34 99 1.43 0.50 1 1.42 0.00 1
## q34 35 99 1.55 0.50 2 1.56 0.00 1
## q35 36 99 1.66 0.48 2 1.69 0.00 1
## q36 37 99 1.81 0.40 2 1.88 0.00 1
## q37 38 99 1.38 0.49 1 1.36 0.00 1
## q38 39 99 1.65 0.48 2 1.68 0.00 1
## q39 40 99 1.92 0.27 2 2.00 0.00 1
## q40 41 99 1.94 0.24 2 2.00 0.00 1
## q41 42 99 1.63 0.49 2 1.65 0.00 1
## max range skew kurtosis se
## Carimbo.de.data.hora* 99 98 0.00 -1.24 2.89
## ID* 96 95 -0.04 -1.24 2.80
## q1 6 4 -0.10 -1.12 0.12
## q2 4 3 0.38 -0.70 0.09
## q3 3 2 -0.44 -1.22 0.08
## q4 613011 611555 6.72 50.41 7046.01
## q5 3717 3682 2.15 4.06 81.07
## q6 22000 22000 4.67 26.28 294.27
## q7 1400 1400 9.12 84.96 14.28
## q9 2 1 3.30 8.97 0.03
## q10 2 1 -0.34 -1.90 0.05
## q11 2 1 -0.26 -1.95 0.05
## q12 2 1 -0.95 -1.11 0.05
## q13 2 1 0.02 -2.02 0.05
## q14 2 1 -0.22 -1.97 0.05
## q15 2 1 1.01 -1.00 0.04
## q16 2 1 0.22 -1.97 0.05
## q17 2 1 2.61 4.85 0.03
## q18 2 1 -0.70 -1.53 0.05
## q19 2 1 0.30 -1.93 0.05
## q20 2 1 -1.06 -0.88 0.04
## q21 2 1 -0.56 -1.71 0.05
## q22 2 1 -0.47 -1.80 0.05
## q23 2 1 -0.34 -1.90 0.05
## q24 2 1 -0.26 -1.95 0.05
## q25 2 1 0.70 -1.53 0.05
## q26 2 1 -0.95 -1.11 0.05
## q27 2 1 -2.61 4.85 0.03
## q28 2 1 -0.79 -1.38 0.05
## q29 2 1 -1.12 -0.75 0.04
## q30 2 1 -1.81 1.29 0.04
## q31 2 1 -2.03 2.13 0.04
## q32 2 1 -0.70 -1.53 0.05
## q33 2 1 0.26 -1.95 0.05
## q34 2 1 -0.18 -1.99 0.05
## q35 2 1 -0.65 -1.59 0.05
## q36 2 1 -1.54 0.38 0.04
## q37 2 1 0.47 -1.80 0.05
## q38 2 1 -0.60 -1.65 0.05
## q39 2 1 -3.03 7.25 0.03
## q40 2 1 -3.63 11.27 0.02
## q41 2 1 -0.51 -1.75 0.05
summary(dados_videos)
## Carimbo.de.data.hora ID q1 q2
## Length:99 Length:99 Min. :2.00 Min. :1.000
## Class :character Class :character 1st Qu.:3.00 1st Qu.:2.000
## Mode :character Mode :character Median :4.00 Median :2.000
## Mean :3.96 Mean :2.232
## 3rd Qu.:5.00 3rd Qu.:3.000
## Max. :6.00 Max. :4.000
## q3 q4 q5 q6
## Min. :1.000 Min. : 1456 Min. : 35.0 Min. : 0.0
## 1st Qu.:2.000 1st Qu.: 2164 1st Qu.: 285.0 1st Qu.: 54.5
## Median :2.000 Median : 5326 Median : 487.0 Median : 156.0
## Mean :2.242 Mean : 22432 Mean : 736.3 Mean : 1100.0
## 3rd Qu.:3.000 3rd Qu.: 13791 3rd Qu.: 797.0 3rd Qu.: 578.5
## Max. :3.000 Max. :613011 Max. :3717.0 Max. :22000.0
## q7 q9 q10 q11
## Min. : 0.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median : 4.00 Median :1.000 Median :2.000 Median :2.000
## Mean : 27.41 Mean :1.071 Mean :1.586 Mean :1.566
## 3rd Qu.: 11.00 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :1400.00 Max. :2.000 Max. :2.000 Max. :2.000
## q12 q13 q14 q15
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :2.000 Median :1.000
## Mean :1.717 Mean :1.495 Mean :1.556 Mean :1.273
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## q16 q17 q18 q19
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :2.000 Median :1.000
## Mean :1.444 Mean :1.101 Mean :1.667 Mean :1.424
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## q20 q21 q22 q23
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.737 Mean :1.636 Mean :1.616 Mean :1.586
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## q24 q25 q26 q27
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :2.000 Median :2.000
## Mean :1.566 Mean :1.333 Mean :1.717 Mean :1.899
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## q28 q29 q30 q31
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.500 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.687 Mean :1.747 Mean :1.838 Mean :1.859
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## q32 q33 q34 q35
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :2.000 Median :2.000
## Mean :1.667 Mean :1.434 Mean :1.545 Mean :1.657
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## q36 q37 q38 q39
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :2.000 Median :2.000
## Mean :1.808 Mean :1.384 Mean :1.646 Mean :1.919
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## q40 q41
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :1.939 Mean :1.626
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000
table<-with(dados_videos,table(q1))
table
## q1
## 2 3 4 5 6
## 13 26 20 32 8
prop.table(table)
## q1
## 2 3 4 5 6
## 0.13131313 0.26262626 0.20202020 0.32323232 0.08080808
table<-with(dados_videos,table(q2))
table
## q2
## 1 2 3 4
## 22 43 23 11
prop.table(table)
## q2
## 1 2 3 4
## 0.2222222 0.4343434 0.2323232 0.1111111
table<-with(dados_videos,table(q9))
table
## q9
## 1 2
## 92 7
prop.table(table)
## q9
## 1 2
## 0.92929293 0.07070707
table<-with(dados_videos,table(q10))
table
## q10
## 1 2
## 41 58
prop.table(table)
## q10
## 1 2
## 0.4141414 0.5858586
table<-with(dados_videos,table(q11))
table
## q11
## 1 2
## 43 56
prop.table(table)
## q11
## 1 2
## 0.4343434 0.5656566
table<-with(dados_videos,table(q12))
table
## q12
## 1 2
## 28 71
prop.table(table)
## q12
## 1 2
## 0.2828283 0.7171717
table<-with(dados_videos,table(q13))
table
## q13
## 1 2
## 50 49
prop.table(table)
## q13
## 1 2
## 0.5050505 0.4949495
table<-with(dados_videos,table(q14))
table
## q14
## 1 2
## 44 55
prop.table(table)
## q14
## 1 2
## 0.4444444 0.5555556
table<-with(dados_videos,table(q15))
table
## q15
## 1 2
## 72 27
prop.table(table)
## q15
## 1 2
## 0.7272727 0.2727273
table<-with(dados_videos,table(q16))
table
## q16
## 1 2
## 55 44
prop.table(table)
## q16
## 1 2
## 0.5555556 0.4444444
table<-with(dados_videos,table(q17))
table
## q17
## 1 2
## 89 10
prop.table(table)
## q17
## 1 2
## 0.8989899 0.1010101
table<-with(dados_videos,table(q18))
table
## q18
## 1 2
## 33 66
prop.table(table)
## q18
## 1 2
## 0.3333333 0.6666667
table<-with(dados_videos,table(q19))
table
## q19
## 1 2
## 57 42
prop.table(table)
## q19
## 1 2
## 0.5757576 0.4242424
table<-with(dados_videos,table(q20))
table
## q20
## 1 2
## 26 73
prop.table(table)
## q20
## 1 2
## 0.2626263 0.7373737
table<-with(dados_videos,table(q21))
table
## q21
## 1 2
## 36 63
prop.table(table)
## q21
## 1 2
## 0.3636364 0.6363636
table<-with(dados_videos,table(q22))
table
## q22
## 1 2
## 38 61
prop.table(table)
## q22
## 1 2
## 0.3838384 0.6161616
table<-with(dados_videos,table(q23))
table
## q23
## 1 2
## 41 58
prop.table(table)
## q23
## 1 2
## 0.4141414 0.5858586
table<-with(dados_videos,table(q24))
table
## q24
## 1 2
## 43 56
prop.table(table)
## q24
## 1 2
## 0.4343434 0.5656566
table<-with(dados_videos,table(q25))
table
## q25
## 1 2
## 66 33
prop.table(table)
## q25
## 1 2
## 0.6666667 0.3333333
table<-with(dados_videos,table(q26))
table
## q26
## 1 2
## 28 71
prop.table(table)
## q26
## 1 2
## 0.2828283 0.7171717
table<-with(dados_videos,table(q27))
table
## q27
## 1 2
## 10 89
prop.table(table)
## q27
## 1 2
## 0.1010101 0.8989899
table<-with(dados_videos,table(q28))
table
## q28
## 1 2
## 31 68
prop.table(table)
## q28
## 1 2
## 0.3131313 0.6868687
table<-with(dados_videos,table(q29))
table
## q29
## 1 2
## 25 74
prop.table(table)
## q29
## 1 2
## 0.2525253 0.7474747
table<-with(dados_videos,table(q30))
table
## q30
## 1 2
## 16 83
prop.table(table)
## q30
## 1 2
## 0.1616162 0.8383838
table<-with(dados_videos,table(q31))
table
## q31
## 1 2
## 14 85
prop.table(table)
## q31
## 1 2
## 0.1414141 0.8585859
table<-with(dados_videos,table(q32))
table
## q32
## 1 2
## 33 66
prop.table(table)
## q32
## 1 2
## 0.3333333 0.6666667
table<-with(dados_videos,table(q33))
table
## q33
## 1 2
## 56 43
prop.table(table)
## q33
## 1 2
## 0.5656566 0.4343434
table<-with(dados_videos,table(q34))
table
## q34
## 1 2
## 45 54
prop.table(table)
## q34
## 1 2
## 0.4545455 0.5454545
table<-with(dados_videos,table(q35))
table
## q35
## 1 2
## 34 65
prop.table(table)
## q35
## 1 2
## 0.3434343 0.6565657
table<-with(dados_videos,table(q36))
table
## q36
## 1 2
## 19 80
prop.table(table)
## q36
## 1 2
## 0.1919192 0.8080808
table<-with(dados_videos,table(q37))
table
## q37
## 1 2
## 61 38
prop.table(table)
## q37
## 1 2
## 0.6161616 0.3838384
table<-with(dados_videos,table(q38))
table
## q38
## 1 2
## 35 64
prop.table(table)
## q38
## 1 2
## 0.3535354 0.6464646
table<-with(dados_videos,table(q39))
table
## q39
## 1 2
## 8 91
prop.table(table)
## q39
## 1 2
## 0.08080808 0.91919192
table<-with(dados_videos,table(q40))
table
## q40
## 1 2
## 6 93
prop.table(table)
## q40
## 1 2
## 0.06060606 0.93939394
table<-with(dados_videos,table(q41))
table
## q41
## 1 2
## 37 62
prop.table(table)
## q41
## 1 2
## 0.3737374 0.6262626