1 Packages
2 import dataset
2.1 backup
2.2 Boxplot
BDI
BAI
CERI
CERM
cybvic
cybagress
2.3 Missing
BDI
BAI
CERI
CERM
cybvic
cybagress
3 Composite scores
3.1 BDI
3.1.1 Add total scores
If the participant left blank all items, do not use
3.1.2 Add classification
3.2 BAI
3.2.1 Add total scores
If the participant left blank all items, do not use
3.2.2 Add classification
3.3 CERI
3.3.1 Add total scores
If the participant left blank all items, do not use
3.4 CERM
3.4.1 Add total scores
If the participant left blank all items, do not use
3.5 cybvic
3.5.1 Add total scores
If the participant left blank all items, do not use
3.6 cybagress
3.6.1 Add total scores
3.6.1.1 If the participant left blank all items, do not use
4 table 2 - médias e dp em função do país
| sum_bai.BRAZIL (N=315) | sum_bdi.BRAZIL (N=315) | sum_ceri.BRAZIL (N=315) | sum_cerm.BRAZIL (N=315) | sum_cybagress.BRAZIL (N=315) | sum_cybvic.BRAZIL (N=315) | sum_bai.PORTUGAL (N=426) | sum_bdi.PORTUGAL (N=426) | sum_ceri.PORTUGAL (N=426) | sum_cerm.PORTUGAL (N=426) | sum_cybagress.PORTUGAL (N=426) | sum_cybvic.PORTUGAL (N=426) | sum_bai.SPAIN (N=1216) | sum_bdi.SPAIN (N=1216) | sum_ceri.SPAIN (N=1216) | sum_cerm.SPAIN (N=1216) | sum_cybagress.SPAIN (N=1216) | sum_cybvic.SPAIN (N=1216) | Total (N=11742) | p value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| value | < 0.001 | |||||||||||||||||||
| N-Miss | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 1 | 0 | 0 | 14 | |
| Mean (SD) | 9.013 (8.403) | 10.895 (8.294) | 19.663 (4.697) | 16.511 (4.142) | 12.610 (3.247) | 13.283 (3.215) | 7.915 (8.042) | 9.054 (7.727) | 17.800 (4.433) | 15.918 (3.569) | 11.704 (3.317) | 12.462 (2.710) | 8.547 (8.057) | 8.859 (7.537) | 17.548 (4.272) | 16.049 (3.561) | 11.488 (2.735) | 12.329 (2.674) | 12.667 (6.391) | |
| Range | 0.000 - 46.000 | 0.000 - 41.000 | 10.000 - 37.000 | 10.000 - 30.000 | 10.000 - 27.000 | 10.000 - 27.000 | 0.000 - 45.000 | 0.000 - 53.000 | 10.000 - 37.000 | 9.000 - 33.000 | 10.000 - 45.000 | 9.000 - 28.000 | 0.000 - 48.000 | 0.000 - 57.000 | 0.000 - 37.000 | 9.000 - 37.000 | 9.000 - 45.000 | 9.000 - 30.000 | 0.000 - 57.000 |
NA4.1 table 3 - médias e dp em função do sexo
| sum_bai.F (N=1214) | sum_bdi.F (N=1214) | sum_ceri.F (N=1214) | sum_cerm.F (N=1214) | sum_cybagress.F (N=1214) | sum_cybvic.F (N=1214) | sum_bai.M (N=736) | sum_bdi.M (N=736) | sum_ceri.M (N=736) | sum_cerm.M (N=736) | sum_cybagress.M (N=736) | sum_cybvic.M (N=736) | Total (N=11700) | p value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| value | < 0.001 | |||||||||||||
| N-Miss | 0 | 4 | 0 | 0 | 0 | 0 | 5 | 4 | 0 | 1 | 0 | 0 | 14 | |
| Mean (SD) | 9.470 (8.446) | 9.588 (7.725) | 17.672 (4.372) | 16.377 (3.621) | 11.345 (2.314) | 12.392 (2.795) | 6.813 (7.142) | 8.676 (7.737) | 18.413 (4.522) | 15.634 (3.696) | 12.327 (3.767) | 12.702 (2.790) | 12.669 (6.387) | |
| Range | 0.000 - 48.000 | 0.000 - 53.000 | 10.000 - 37.000 | 9.000 - 31.000 | 9.000 - 28.000 | 9.000 - 30.000 | 0.000 - 44.000 | 0.000 - 57.000 | 0.000 - 37.000 | 10.000 - 37.000 | 10.000 - 45.000 | 9.000 - 27.000 | 0.000 - 57.000 |
NA4.2 Regression analysis
4.2.1 Regress the results of depression (BDI) on country.
ANOVA results using sum_bdi as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 4.2.2 Regress the results of BAI on country.
ANOVA results using sum_bai as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 4.2.3 Regress the results of CERI on country.
ANOVA results using sum_ceri as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 4.2.4 Regress the results of CERM on country.
ANOVA results using sum_cerm as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 4.2.5 Regress the results of cybvic on country.
ANOVA results using sum_cybvic as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 4.2.6 Regress the results of cybagress on country.
ANOVA results using sum_cybagress as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared ---
title: ""
output:
  html_notebook:
    toc: yes
    toc_float: yes
    number_sections: yes
    theme: united
    highlight: textmate
editor_options: 
  chunk_output_type: inline
---



```{r global options, include = FALSE}
knitr::opts_chunk$set(echo = FALSE, 
                      warning = FALSE, 
                      messages = FALSE, 
                      include = TRUE,
                      results = "show")
```

# Packages

```{r}
library(tidyverse) #environment
library(psych) #psychometric analysis
library(summarytools) #descriptive
library(tableone)
library(arsenal)
```


# import dataset
```{r, eval=FALSE}
ds <- read.csv("C:/Users/lucas/Google Drive/ANOVA/Pesquisa espanhol/dataset_original_mapfre.csv", sep=";")
```

## backup

```{r}
backup <- ds
```

```{r, eval = FALSE }
ds <- janitor::clean_names(ds)

ds <- ds %>% 
  mutate(id = row_number())
```

## Boxplot

> BDI

```{r}
ds %>% 
  select(starts_with("bdi")) %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(x=name, y = value)) +
  geom_boxplot() 
```

> BAI

```{r}
ds %>% 
  select(starts_with("bai")) %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(x=name, y = value)) +
  geom_boxplot()
```

> CERI

```{r}
 ds %>% 
  select(starts_with("ceri")) %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(x=name, y = value)) +
  geom_boxplot()
```

> CERM

```{r}
 ds %>% 
  select(starts_with("cerm")) %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(x=name, y = value)) +
  geom_boxplot()
```

> cybvic

```{r, eval = FALSE }
 ds %>% 
  select(starts_with("cybvic")) %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(x=name, y = value)) +
  geom_boxplot()
```

> cybagress

```{r}
 ds %>% 
  select(starts_with("cybagress")) %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(x=name, y = value)) +
  geom_boxplot()
```

## Missing

> BDI

```{r}
ds %>% 
  select(starts_with("bdi")) %>% 
  DataExplorer::plot_missing()
```

> BAI

```{r}
ds %>% 
  select(starts_with("bai")) %>% 
  DataExplorer::plot_missing()
```

> CERI

```{r}
ds %>% 
  select(starts_with("ceri")) %>% 
  DataExplorer::plot_missing()
```

> CERM

```{r}
ds %>% 
  select(starts_with("cerm")) %>% 
  DataExplorer::plot_missing()
```

> cybvic

```{r}
ds %>% 
  select(starts_with("cybvic")) %>% 
  DataExplorer::plot_missing()
```

> cybagress

```{r}
ds %>% 
  select(starts_with("cybagress")) %>% 
  DataExplorer::plot_missing()
```

# Composite scores

## BDI

### Add total scores

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(sum_bdi = rowSums(select(., starts_with("bdi")), na.rm=T)) #adjust for all missing)
```

If the participant left blank all items, do not use

```{r, eval = FALSE }
#ds  
ds <- ds %>% #get the dataset
  mutate(number_na_bdi = rowSums(is.na(select(.,starts_with("bdi"))))) %>% #create a variable to count missing
  mutate(sum_bdi = if_else(number_na_bdi == 21, NA_real_,sum_bdi)) #if all is missing
```

### Add classification

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(class_bdi = case_when(
    sum_bdi <= 13 ~ "minima",
    sum_bdi < 20 ~ "leve",
    sum_bdi < 29 ~ "moderada",
    sum_bdi >= 29 ~ "grave"))

ds <- ds %>% 
  mutate(class_bdi = factor(class_bdi,
         levels=c("minima","leve","moderada","grave")))
```

## BAI

### Add total scores

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(sum_bai = rowSums(select(., starts_with("bai")), na.rm=T)) #adjust for all missing)
```

If the participant left blank all items, do not use

```{r, eval = FALSE }
#Dataset  
ds <- ds %>% #get the dataset
  mutate(number_na_bai = rowSums(is.na(select(.,starts_with("bai"))))) %>% #create a variable to count missing
  mutate(sum_bai = if_else(number_na_bai == 21, NA_real_,sum_bai)) #if all is missing
```

### Add classification

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(class_bai = case_when(
    sum_bai <= 10 ~ "minima",
    sum_bai < 20 ~ "leve",
    sum_bai < 31 ~ "moderada",
    sum_bai >= 31 ~ "grave"))

ds <- ds %>% 
  mutate(class_bai = factor(class_bai,
         levels=c("minima","leve","moderada","grave")))
```

## CERI

### Add total scores

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(sum_ceri = rowSums(select(., starts_with("ceri")), na.rm=T)) #adjust for all missing)
```

If the participant left blank all items, do not use

```{r, eval = FALSE }
#Dataset  
ds <- ds %>% #get the dataset
  mutate(number_na_ceri = rowSums(is.na(select(.,starts_with("ceri"))))) %>% #create a variable to count missing
  mutate(sum_ceri = if_else(number_na_ceri == 6, NA_real_,sum_ceri)) #if all is missing
```

## CERM

### Add total scores

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(sum_cerm = rowSums(select(., starts_with("cerm")), na.rm=T)) #adjust for all missing)
```

If the participant left blank all items, do not use

```{r, eval = FALSE }
#Dataset  
ds <- ds %>% #get the dataset
  mutate(number_na_cerm = rowSums(is.na(select(.,starts_with("cerm"))))) %>% #create a variable to count missing
  mutate(sum_cerm = if_else(number_na_cerm == 10, NA_real_,sum_cerm)) #if all is missing
```

## cybvic

### Add total scores

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(sum_cybvic = rowSums(select(., starts_with("cybvic")), na.rm=T)) #adjust for all missing)
```

If the participant left blank all items, do not use

```{r, eval = FALSE }
#Dataset  
ds <- ds %>% #get the dataset
  mutate(number_na_cybvic = rowSums(is.na(select(.,starts_with("cybvic"))))) %>% #create a variable to count missing
  mutate(sum_cybvic = if_else(number_na_cybvic == 10, NA_real_,sum_cybvic)) #if all is missing
```

## cybagress

### Add total scores

```{r, eval = FALSE }
ds <- ds %>% 
  mutate(sum_cybagress = rowSums(select(., starts_with("cybagress")), na.rm=T)) #adjust for all missing)
```

#### If the participant left blank all items, do not use

```{r, eval = FALSE }
#Dataset  
ds <- ds %>% #get the dataset
  mutate(number_na_cybagress = rowSums(is.na(select(.,starts_with("cybagress"))))) %>% #create a variable to count missing
  mutate(sum_cybagress = if_else(number_na_cybagress == 10, NA_real_,sum_cybagress)) #if all is missing
```

# table 2 - médias e dp em função do país

```{r}
ds %>%
select(country, sum_bdi, sum_bai, sum_ceri, sum_cerm, sum_cybvic, sum_cybagress) %>% 
  pivot_longer(-c(country)) %>% 
  arsenal::tableby(interaction(name, country) ~ value, data = .) %>% 
  summary() 
```

## table 3 - médias e dp em função do sexo

```{r}
ds %>%
select(sex, sum_bdi, sum_bai, sum_ceri, sum_cerm, sum_cybvic, sum_cybagress) %>% 
  pivot_longer(-c(sex)) %>% 
  arsenal::tableby(interaction(name, sex) ~ value, data = .) %>% 
  summary() 
```

## Regression analysis

### Regress the results of depression (BDI) on country.

```{r}
reg_bdi <- lm(sum_bdi ~ country, ds)

apaTables::apa.aov.table(reg_bdi)
```

### Regress the results of  BAI on country.

```{r}
reg_bai <- lm(sum_bai ~ country, ds)

apaTables::apa.aov.table(reg_bai)
```

### Regress the results of  CERI on country.

```{r}
reg_ceri <- lm(sum_ceri ~ country, ds)

apaTables::apa.aov.table(reg_ceri)
```

### Regress the results of  CERM on country.

```{r}
reg_cerm <- lm(sum_cerm ~ country, ds)

apaTables::apa.aov.table(reg_cerm)
```
### Regress the results of  cybvic on country.

```{r}
reg_cybvic <- lm(sum_cybvic ~ country, ds)

apaTables::apa.aov.table(reg_cybvic)
```


### Regress the results of  cybagress on country.

```{r}
reg_cybagress <- lm(sum_cybagress ~ country, ds)

apaTables::apa.aov.table(reg_cybagress)
```






