#PACOTES
library(summarytools)
library(psych)
dados_videos <- read.csv("dados_quali.csv")
describe(dados_videos)
## vars n mean sd median trimmed mad min max range skew
## q1 1 15 4.07 1.03 4 4.15 1.48 2 5 3 -0.48
## q2 2 15 2.20 1.26 2 2.15 1.48 1 4 3 0.44
## q4 3 15 3154.47 1698.33 2292 2959.15 834.70 1729 7119 5390 1.29
## q5 4 15 11.13 15.87 5 8.08 4.45 1 61 60 2.20
## q6 5 15 123.87 100.27 98 116.85 99.33 0 339 339 0.65
## q7 6 15 2.53 2.23 2 2.38 2.97 0 7 7 0.55
## q9 7 15 1.00 0.00 1 1.00 0.00 1 1 0 NaN
## q10 8 15 1.93 0.26 2 2.00 0.00 1 2 1 -3.13
## q11 9 15 1.80 0.41 2 1.85 0.00 1 2 1 -1.35
## q12 10 15 1.73 0.46 2 1.77 0.00 1 2 1 -0.95
## q13 11 15 1.33 0.49 1 1.31 0.00 1 2 1 0.64
## q14 12 15 1.53 0.52 2 1.54 0.00 1 2 1 -0.12
## q15 13 15 1.20 0.41 1 1.15 0.00 1 2 1 1.35
## q16 14 15 1.13 0.35 1 1.08 0.00 1 2 1 1.95
## q17 15 15 1.13 0.35 1 1.08 0.00 1 2 1 1.95
## q18 16 15 1.87 0.35 2 1.92 0.00 1 2 1 -1.95
## q19 17 15 1.40 0.51 1 1.38 0.00 1 2 1 0.37
## q20 18 15 1.73 0.46 2 1.77 0.00 1 2 1 -0.95
## q21 19 15 2.00 0.00 2 2.00 0.00 2 2 0 NaN
## q22 20 15 1.80 0.41 2 1.85 0.00 1 2 1 -1.35
## q23 21 15 1.73 0.46 2 1.77 0.00 1 2 1 -0.95
## q24 22 15 1.67 0.49 2 1.69 0.00 1 2 1 -0.64
## q25 23 15 1.40 0.51 1 1.38 0.00 1 2 1 0.37
## q26 24 15 1.80 0.41 2 1.85 0.00 1 2 1 -1.35
## q27 25 15 2.00 0.00 2 2.00 0.00 2 2 0 NaN
## q28 26 15 1.93 0.26 2 2.00 0.00 1 2 1 -3.13
## q29 27 15 1.87 0.35 2 1.92 0.00 1 2 1 -1.95
## q30 28 15 1.93 0.26 2 2.00 0.00 1 2 1 -3.13
## q31 29 15 1.87 0.35 2 1.92 0.00 1 2 1 -1.95
## q32 30 15 1.67 0.49 2 1.69 0.00 1 2 1 -0.64
## q33 31 15 1.47 0.52 1 1.46 0.00 1 2 1 0.12
## q34 32 15 1.60 0.51 2 1.62 0.00 1 2 1 -0.37
## q35 33 15 1.67 0.49 2 1.69 0.00 1 2 1 -0.64
## q36 34 15 1.87 0.35 2 1.92 0.00 1 2 1 -1.95
## q37 35 15 1.40 0.51 1 1.38 0.00 1 2 1 0.37
## q38 36 15 1.80 0.41 2 1.85 0.00 1 2 1 -1.35
## q39 37 15 1.93 0.26 2 2.00 0.00 1 2 1 -3.13
## q40 38 15 2.00 0.00 2 2.00 0.00 2 2 0 NaN
## q41 39 15 1.80 0.41 2 1.85 0.00 1 2 1 -1.35
## kurtosis se
## q1 -1.32 0.27
## q2 -1.57 0.33
## q4 0.42 438.51
## q5 3.82 4.10
## q6 -0.83 25.89
## q7 -1.04 0.58
## q9 NaN 0.00
## q10 8.39 0.07
## q11 -0.17 0.11
## q12 -1.16 0.12
## q13 -1.69 0.13
## q14 -2.11 0.13
## q15 -0.17 0.11
## q16 1.93 0.09
## q17 1.93 0.09
## q18 1.93 0.09
## q19 -1.98 0.13
## q20 -1.16 0.12
## q21 NaN 0.00
## q22 -0.17 0.11
## q23 -1.16 0.12
## q24 -1.69 0.13
## q25 -1.98 0.13
## q26 -0.17 0.11
## q27 NaN 0.00
## q28 8.39 0.07
## q29 1.93 0.09
## q30 8.39 0.07
## q31 1.93 0.09
## q32 -1.69 0.13
## q33 -2.11 0.13
## q34 -1.98 0.13
## q35 -1.69 0.13
## q36 1.93 0.09
## q37 -1.98 0.13
## q38 -0.17 0.11
## q39 8.39 0.07
## q40 NaN 0.00
## q41 -0.17 0.11
summary(dados_videos)
## q1 q2 q4 q5 q6
## Min. :2.000 Min. :1.0 Min. :1729 Min. : 1.00 Min. : 0.0
## 1st Qu.:3.000 1st Qu.:1.0 1st Qu.:2006 1st Qu.: 3.50 1st Qu.: 52.5
## Median :4.000 Median :2.0 Median :2292 Median : 5.00 Median : 98.0
## Mean :4.067 Mean :2.2 Mean :3154 Mean :11.13 Mean :123.9
## 3rd Qu.:5.000 3rd Qu.:3.5 3rd Qu.:3596 3rd Qu.: 8.50 3rd Qu.:175.5
## Max. :5.000 Max. :4.0 Max. :7119 Max. :61.00 Max. :339.0
## q7 q9 q10 q11 q12
## Min. :0.000 Min. :1 Min. :1.000 Min. :1.0 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1 1st Qu.:2.000 1st Qu.:2.0 1st Qu.:1.500
## Median :2.000 Median :1 Median :2.000 Median :2.0 Median :2.000
## Mean :2.533 Mean :1 Mean :1.933 Mean :1.8 Mean :1.733
## 3rd Qu.:4.000 3rd Qu.:1 3rd Qu.:2.000 3rd Qu.:2.0 3rd Qu.:2.000
## Max. :7.000 Max. :1 Max. :2.000 Max. :2.0 Max. :2.000
## q13 q14 q15 q16 q17
## Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.0 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :2.000 Median :1.0 Median :1.000 Median :1.000
## Mean :1.333 Mean :1.533 Mean :1.2 Mean :1.133 Mean :1.133
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:1.0 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :2.000 Max. :2.000 Max. :2.0 Max. :2.000 Max. :2.000
## q18 q19 q20 q21 q22
## Min. :1.000 Min. :1.0 Min. :1.000 Min. :2 Min. :1.0
## 1st Qu.:2.000 1st Qu.:1.0 1st Qu.:1.500 1st Qu.:2 1st Qu.:2.0
## Median :2.000 Median :1.0 Median :2.000 Median :2 Median :2.0
## Mean :1.867 Mean :1.4 Mean :1.733 Mean :2 Mean :1.8
## 3rd Qu.:2.000 3rd Qu.:2.0 3rd Qu.:2.000 3rd Qu.:2 3rd Qu.:2.0
## Max. :2.000 Max. :2.0 Max. :2.000 Max. :2 Max. :2.0
## q23 q24 q25 q26 q27
## Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.0 Min. :2
## 1st Qu.:1.500 1st Qu.:1.000 1st Qu.:1.0 1st Qu.:2.0 1st Qu.:2
## Median :2.000 Median :2.000 Median :1.0 Median :2.0 Median :2
## Mean :1.733 Mean :1.667 Mean :1.4 Mean :1.8 Mean :2
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.0 3rd Qu.:2.0 3rd Qu.:2
## Max. :2.000 Max. :2.000 Max. :2.0 Max. :2.0 Max. :2
## q28 q29 q30 q31
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.933 Mean :1.867 Mean :1.933 Mean :1.867
## 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 q36
## Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.0 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :2.0 Median :2.000 Median :2.000
## Mean :1.667 Mean :1.467 Mean :1.6 Mean :1.667 Mean :1.867
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.0 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.0 Max. :2.000 Max. :2.000
## q37 q38 q39 q40 q41
## Min. :1.0 Min. :1.0 Min. :1.000 Min. :2 Min. :1.0
## 1st Qu.:1.0 1st Qu.:2.0 1st Qu.:2.000 1st Qu.:2 1st Qu.:2.0
## Median :1.0 Median :2.0 Median :2.000 Median :2 Median :2.0
## Mean :1.4 Mean :1.8 Mean :1.933 Mean :2 Mean :1.8
## 3rd Qu.:2.0 3rd Qu.:2.0 3rd Qu.:2.000 3rd Qu.:2 3rd Qu.:2.0
## Max. :2.0 Max. :2.0 Max. :2.000 Max. :2 Max. :2.0
table<-with(dados_videos,table(q1))
table
## q1
## 2 3 4 5
## 1 4 3 7
prop.table(table)
## q1
## 2 3 4 5
## 0.06666667 0.26666667 0.20000000 0.46666667
table<-with(dados_videos,table(q2))
table
## q2
## 1 2 3 4
## 6 4 1 4
prop.table(table)
## q2
## 1 2 3 4
## 0.40000000 0.26666667 0.06666667 0.26666667
table<-with(dados_videos,table(q9))
table
## q9
## 1
## 15
prop.table(table)
## q9
## 1
## 1
table<-with(dados_videos,table(q10))
table
## q10
## 1 2
## 1 14
prop.table(table)
## q10
## 1 2
## 0.06666667 0.93333333
table<-with(dados_videos,table(q11))
table
## q11
## 1 2
## 3 12
prop.table(table)
## q11
## 1 2
## 0.2 0.8
table<-with(dados_videos,table(q12))
table
## q12
## 1 2
## 4 11
prop.table(table)
## q12
## 1 2
## 0.2666667 0.7333333
table<-with(dados_videos,table(q13))
table
## q13
## 1 2
## 10 5
prop.table(table)
## q13
## 1 2
## 0.6666667 0.3333333
table<-with(dados_videos,table(q14))
table
## q14
## 1 2
## 7 8
prop.table(table)
## q14
## 1 2
## 0.4666667 0.5333333
table<-with(dados_videos,table(q15))
table
## q15
## 1 2
## 12 3
prop.table(table)
## q15
## 1 2
## 0.8 0.2
table<-with(dados_videos,table(q16))
table
## q16
## 1 2
## 13 2
prop.table(table)
## q16
## 1 2
## 0.8666667 0.1333333
#Menciona o glúten como a causa primária de DC
table<-with(dados_videos,table(q17))
table
## q17
## 1 2
## 13 2
prop.table(table)
## q17
## 1 2
## 0.8666667 0.1333333
table<-with(dados_videos,table(q18))
table
## q18
## 1 2
## 2 13
prop.table(table)
## q18
## 1 2
## 0.1333333 0.8666667
#Menciona o risco para o desenvolvimento de outras condições crônicas de saúde
table<-with(dados_videos,table(q19))
table
## q19
## 1 2
## 9 6
prop.table(table)
## q19
## 1 2
## 0.6 0.4
table<-with(dados_videos,table(q20))
table
## q20
## 1 2
## 4 11
prop.table(table)
## q20
## 1 2
## 0.2666667 0.7333333
table<-with(dados_videos,table(q21))
table
## q21
## 2
## 15
prop.table(table)
## q21
## 2
## 1
#Menciona alterações na qualidade de vida
table<-with(dados_videos,table(q22))
table
## q22
## 1 2
## 3 12
prop.table(table)
## q22
## 1 2
## 0.2 0.8
table<-with(dados_videos,table(q23))
table
## q23
## 1 2
## 4 11
prop.table(table)
## q23
## 1 2
## 0.2666667 0.7333333
table<-with(dados_videos,table(q24))
table
## q24
## 1 2
## 5 10
prop.table(table)
## q24
## 1 2
## 0.3333333 0.6666667
table<-with(dados_videos,table(q25))
table
## q25
## 1 2
## 9 6
prop.table(table)
## q25
## 1 2
## 0.6 0.4
table<-with(dados_videos,table(q26))
table
## q26
## 1 2
## 3 12
prop.table(table)
## q26
## 1 2
## 0.2 0.8
table<-with(dados_videos,table(q27))
table
## q27
## 2
## 15
prop.table(table)
## q27
## 2
## 1
table<-with(dados_videos,table(q28))
table
## q28
## 1 2
## 1 14
prop.table(table)
## q28
## 1 2
## 0.06666667 0.93333333
table<-with(dados_videos,table(q29))
table
## q29
## 1 2
## 2 13
prop.table(table)
## q29
## 1 2
## 0.1333333 0.8666667
table<-with(dados_videos,table(q30))
table
## q30
## 1 2
## 1 14
prop.table(table)
## q30
## 1 2
## 0.06666667 0.93333333
table<-with(dados_videos,table(q31))
table
## q31
## 1 2
## 2 13
prop.table(table)
## q31
## 1 2
## 0.1333333 0.8666667
table<-with(dados_videos,table(q32))
table
## q32
## 1 2
## 5 10
prop.table(table)
## q32
## 1 2
## 0.3333333 0.6666667
table<-with(dados_videos,table(q33))
table
## q33
## 1 2
## 8 7
prop.table(table)
## q33
## 1 2
## 0.5333333 0.4666667
table<-with(dados_videos,table(q34))
table
## q34
## 1 2
## 6 9
prop.table(table)
## q34
## 1 2
## 0.4 0.6
table<-with(dados_videos,table(q35))
table
## q35
## 1 2
## 5 10
prop.table(table)
## q35
## 1 2
## 0.3333333 0.6666667
table<-with(dados_videos,table(q36))
table
## q36
## 1 2
## 2 13
prop.table(table)
## q36
## 1 2
## 0.1333333 0.8666667
table<-with(dados_videos,table(q37))
table
## q37
## 1 2
## 9 6
prop.table(table)
## q37
## 1 2
## 0.6 0.4
table<-with(dados_videos,table(q38))
table
## q38
## 1 2
## 3 12
prop.table(table)
## q38
## 1 2
## 0.2 0.8
table<-with(dados_videos,table(q39))
table
## q39
## 1 2
## 1 14
prop.table(table)
## q39
## 1 2
## 0.06666667 0.93333333
table<-with(dados_videos,table(q40))
table
## q40
## 2
## 15
prop.table(table)
## q40
## 2
## 1
table<-with(dados_videos,table(q41))
table
## q41
## 1 2
## 3 12
prop.table(table)
## q41
## 1 2
## 0.2 0.8