PUC-Rio - Data analysis EAT, BSQ (3 countries)
1 Load packages
pacman::p_load(tidyverse, arsenal,janitor, knitr)
2 Load data
This is the processed ds.
load("C:/Users/luisf/Dropbox/Puc-Rio/Projeto - Adicao a internet, imagem e alimentacao/Pesquisa/analise de dados/2020 - Grupo estudos - puc/R base - Dados after processing on December 2020 (PUCRio).RData")3 Check names and reliability of the ds
dados %>% names [1] "id_unique" "id" "data" "pais" "periodo_fac" "idade" "sexo"
[8] "altura" "peso_atual" "peso_desejado" "faz_esporte" "familia_esporte" "cr" "ceri1"
[15] "ceri2" "ceri3" "ceri4" "ceri5" "ceri6" "eat1" "eat2"
[22] "eat3" "eat4" "eat5" "eat6" "eat7" "eat8" "eat9"
[29] "eat10" "eat11" "eat12" "eat13" "eat14" "eat15" "eat16"
[36] "eat17" "eat18" "eat19" "eat20" "eat21" "eat22" "eat23"
[43] "eat24" "eat25" "eat26" "bsq1" "bsq2" "bsq3" "bsq4"
[50] "bsq5" "bsq6" "bsq7" "bsq8" "bsq9" "bsq10" "bsq11"
[57] "bsq12" "bsq13" "bsq14" "bsq15" "bsq16" "bsq17" "bsq18"
[64] "bsq19" "bsq20" "bsq21" "bsq22" "bsq23" "bsq24" "bsq25"
[71] "bsq26" "bsq27" "bsq28" "bsq29" "bsq30" "bsq31" "bsq32"
[78] "bsq33" "bsq34" "imc" "sex_female" "eat_soma" "bsq_soma" "eat1_c"
[85] "eat2_c" "eat3_c" "eat4_c" "eat5_c" "eat6_c" "eat7_c" "eat8_c"
[92] "eat9_c" "eat10_c" "eat11_c" "eat12_c" "eat13_c" "eat14_c" "eat15_c"
[99] "eat16_c" "eat17_c" "eat18_c" "eat19_c" "eat20_c" "eat21_c" "eat22_c"
[106] "eat23_c" "eat24_c" "eat25_c" "eat26_c" "bsq1_c" "bsq2_c" "bsq3_c"
[113] "bsq4_c" "bsq5_c" "bsq6_c" "bsq7_c" "bsq8_c" "bsq9_c" "bsq10_c"
[120] "bsq11_c" "bsq12_c" "bsq13_c" "bsq14_c" "bsq15_c" "bsq16_c" "bsq17_c"
[127] "bsq18_c" "bsq19_c" "bsq20_c" "bsq21_c" "bsq22_c" "bsq23_c" "bsq24_c"
[134] "bsq25_c" "bsq26_c" "bsq27_c" "bsq28_c" "bsq29_c" "bsq30_c" "bsq31_c"
[141] "bsq32_c" "bsq33_c" "bsq34_c" "eat_soma_c" "bsq_soma_c" "delta_peso" "delta_peso_percent"
[148] "country" "stat" "weight_status" 4 EAT
dados %>%
select(starts_with("eat") & ends_with("_c"), -eat_soma_c) %>%
psych::alpha()Number of categories should be increased in order to count frequencies.
Some items were negatively correlated with the total scale and probably
should be reversed.
To do this, run the function again with the 'check.keys=TRUE' optionSome items ( eat13_c ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
Reliability analysis
Call: psych::alpha(x = .)
lower alpha upper 95% confidence boundaries
0.84 0.86 0.87
Reliability if an item is dropped:
Item statistics 4.1 Fatores
dados <- dados %>%
mutate(eat_dieta = rowSums(select(., eat1_c, eat6_c,eat7_c,eat10_c,eat11_c,eat12_c,eat14_c,eat16_c,eat17_c,eat22_c, eat23_c, eat24_c, eat25_c))) %>%
mutate(eat_bulimia = rowSums(select(., eat3_c, eat4_c,eat9_c,eat18_c,eat21_c,eat26_c))) %>%
mutate(eat_oral = rowSums(select(., eat2_c, eat5_c,eat8_c,eat13_c,eat15_c,eat19_c,eat20_c)))4.2 Correlacao entre fatores
dados %>%
select(eat_dieta, eat_bulimia, eat_oral, eat_soma_c) %>%
cor(.) eat_dieta eat_bulimia eat_oral eat_soma_c
eat_dieta 1.0000000 0.7017194 0.3005106 0.9377941
eat_bulimia 0.7017194 1.0000000 0.2369181 0.7877045
eat_oral 0.3005106 0.2369181 1.0000000 0.5702859
eat_soma_c 0.9377941 0.7877045 0.5702859 1.00000004.3 Alfa Dieta
dados %>% select(., eat1_c, eat6_c,eat7_c,eat10_c,eat11_c,eat12_c,eat14_c,eat16_c,eat17_c,eat22_c, eat23_c, eat24_c, eat25_c) %>%
psych::alpha(.)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = .)
lower alpha upper 95% confidence boundaries
0.84 0.86 0.87
Reliability if an item is dropped:
Item statistics 4.4 Alfa Bulimia
dados %>% select(eat3_c, eat4_c,eat9_c,eat18_c,eat21_c,eat26_c) %>%
psych::alpha(.)
Reliability analysis
Call: psych::alpha(x = .)
lower alpha upper 95% confidence boundaries
0.56 0.6 0.64
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
0 0.00676818950930626 0.0303030303030303 0.217687074829932 0.315789473684211 0.406408094435076 0.804920913884007 1 2 3 miss
eat3_c 0.52 0.00 0 0.00 0.00 0 0.04 0.19 0.16 0.09 0
eat4_c 0.81 0.00 0 0.00 0.01 0 0.00 0.08 0.07 0.03 0
eat9_c 0.99 0.01 0 0.00 0.00 0 0.00 0.01 0.00 0.00 0
eat18_c 0.86 0.00 0 0.01 0.00 0 0.00 0.06 0.04 0.02 0
eat21_c 0.76 0.00 0 0.00 0.00 0 0.00 0.11 0.08 0.05 0
eat26_c 0.98 0.00 0 0.00 0.00 0 0.00 0.01 0.00 0.01 04.5 Alfa Controle oral
dados %>% select(eat2_c, eat5_c,eat8_c,eat13_c,eat15_c,eat19_c,eat20_c) %>%
psych::alpha()
Reliability analysis
Call: psych::alpha(x = .)
lower alpha upper 95% confidence boundaries
0.5 0.56 0.61
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
0 0.194207836456559 0.294915254237288 0.306913996627319 0.597643097643098 0.648148148148148 0.738175675675676 1 2 3 miss
eat2_c 0.85 0.01 0.00 0 0 0 0.00 0.09 0.02 0.02 0
eat5_c 0.67 0.00 0.00 0 0 0 0.00 0.14 0.12 0.07 0
eat8_c 0.82 0.00 0.00 0 0 0 0.00 0.09 0.04 0.05 0
eat13_c 0.82 0.00 0.01 0 0 0 0.00 0.09 0.05 0.04 0
eat15_c 0.66 0.00 0.00 0 0 0 0.00 0.13 0.11 0.10 0
eat19_c 0.57 0.00 0.00 0 0 0 0.01 0.19 0.16 0.08 0
eat20_c 0.87 0.00 0.00 0 0 0 0.00 0.08 0.03 0.02 04.6 Plot
5 BSQ-34
5.1 Alpha
dados %>%
select(starts_with("bsq") & ends_with("_c"), -bsq_soma_c) %>%
psych::alpha()Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = .)
lower alpha upper 95% confidence boundaries
0.97 0.97 0.97
Reliability if an item is dropped:
Item statistics 6 H1: Dados epidemiologicos
6.1 BSQ-34
dados %>%
mutate(corte =
if_else(bsq_soma_c >= 110, 1,0)) %>%
{descr::CrossTable(.$country,.$corte,
chisq = T,prop.chisq = F,
expected = T)}
Cell Contents
|-------------------------|
| N |
| Expected N |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
==================================
.$corte
.$country 0 1 Total
----------------------------------
br 164 56 220
181.9 38.1
0.745 0.255 0.370
0.333 0.544
0.276 0.094
----------------------------------
sp 328 47 375
310.1 64.9
0.875 0.125 0.630
0.667 0.456
0.551 0.079
----------------------------------
Total 492 103 595
0.827 0.173
==================================
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 16.17242 d.f. = 1 p = 5.78e-05
Pearson's Chi-squared test with Yates' continuity correction
------------------------------------------------------------
Chi^2 = 15.28234 d.f. = 1 p = 9.26e-05 dados %>%
#filter(sex_female == "female") %>%
mutate(corte =
if_else(bsq_soma_c >= 110, 1,0)) %>%
{epitools::oddsratio(.$country,.$corte, rev = "r")}
$data
Outcome
Predictor 0 1 Total
sp 328 47 375
br 164 56 220
Total 492 103 595
$measure
odds ratio with 95% C.I.
Predictor estimate lower upper
sp 1.000000 NA NA
br 2.377962 1.546147 3.673224
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
sp NA NA NA
br 8.17943e-05 7.985849e-05 5.78299e-05
$correction
[1] FALSE
attr(,"method")
[1] "median-unbiased estimate & mid-p exact CI"6.2 EAT-26
dados %>%
mutate(corte =
if_else(eat_soma_c >= 21, 1,0)) %>%
count(corte) %>%
mutate(freq = n / sum(n))dados %>%
mutate(corte =
if_else(eat_soma_c >= 21, 1,0)) %>%
{descr::CrossTable(.$country,.$corte, prop.chisq = F,
chisq = T,
expected = T)}
Cell Contents
|-------------------------|
| N |
| Expected N |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
==================================
.$corte
.$country 0 1 Total
----------------------------------
br 160 60 220
189.3 30.7
0.727 0.273 0.370
0.312 0.723
0.269 0.101
----------------------------------
sp 352 23 375
322.7 52.3
0.939 0.061 0.630
0.688 0.277
0.592 0.039
----------------------------------
Total 512 83 595
0.861 0.139
==================================
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 51.6188 d.f. = 1 p = 6.74e-13
Pearson's Chi-squared test with Yates' continuity correction
------------------------------------------------------------
Chi^2 = 49.87275 d.f. = 1 p = 1.64e-12 dados %>%
#filter(sex_female == "female") %>%
mutate(corte =
if_else(eat_soma_c >= 21, 1,0)) %>%
{epitools::oddsratio(.$country,.$corte, rev = "r")}$data
Outcome
Predictor 0 1 Total
sp 352 23 375
br 160 60 220
Total 512 83 595
$measure
odds ratio with 95% C.I.
Predictor estimate lower upper
sp 1.00000 NA NA
br 5.69834 3.442265 9.731348
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
sp NA NA NA
br 2.102096e-12 2.098019e-12 6.739306e-13
$correction
[1] FALSE
attr(,"method")
[1] "median-unbiased estimate & mid-p exact CI"(60*352)/(23*160)[1] 5.739137 H2: Respostas diferentes por grupos
Será que a pessoa que está em risco na EAT, tem valor na BSQ alto?
dados %>%
#filter(sex_female == "female") %>%
mutate(corte_eat =
if_else(eat_soma_c >= 21, 1,0)) %>%
{t.test(bsq_soma_c ~ corte_eat, data =.)}
Welch Two Sample t-test
data: bsq_soma_c by corte_eat
t = -13.739, df = 97.745, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-65.54413 -48.99917
sample estimates:
mean in group 0 mean in group 1
65.71439 122.98604 dados %>%
#filter(sex_female == "female") %>%
mutate(corte_bsq =
if_else(bsq_soma_c >= 110, 1,0)) %>%
{t.test(bsq_soma_c ~ corte_bsq, data =.)}
Welch Two Sample t-test
data: bsq_soma_c by corte_bsq
t = -30.262, df = 134.97, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-79.65602 -69.88326
sample estimates:
mean in group 0 mean in group 1
60.76023 135.52987 7.1 Plot
7.2 Plot (Quadrantes)
7.3 Proporção de homens e mulheres
dados%>%
filter(!is.na(sex_female)) %>%
#filter(imc < 24.9) %>%
mutate(corte_bsq =
if_else(bsq_soma_c >= 110, 1,0)) %>%
mutate(corte_eat =
if_else(eat_soma >= 21, 1,0)) %>%
mutate(quadrantes = case_when(
corte_bsq == 1 & corte_eat == 1 ~ "risco",
corte_bsq == 0 & corte_eat == 0 ~ "ok",
corte_bsq == 1 & corte_eat == 0 ~ "risco_bsq",
corte_bsq == 0 & corte_eat == 1 ~ "risco_eat",
)) %>%
group_by(quadrantes, country,sex_female) %>%
summarise(n=n()) %>%
mutate(prop = n/sum(n)) %>%
mutate(prop = round(prop*100,1)) %>%
select(-n) %>%
pivot_wider(names_from = "quadrantes", values_from = "prop")
`summarise()` has grouped output by 'quadrantes', 'country'. You can override using the `.groups` argument.8 H3: (proporções entre homens e mulheres sobre satisfação corporal)
8.1 Gráfico
dados %>%
select(country, sex_female, peso_desejado, peso_atual) %>% #select target variables
rename(Sex = "sex_female") %>% #rename to make easier
mutate(Sex = str_to_sentence(Sex)) %>% #sentene case
na.omit() %>% #don't use missings
mutate(razao = peso_desejado/peso_atual) %>% #fazer razao
group_by(Sex) %>% #agrupar por sexo
mutate(percrank=rank(razao)/length(razao)) %>% #calcular percentil
arrange(Sex,razao) %>%
ggplot(.,
aes(x = razao, y = percrank, colour = Sex, linetype = country)) +
geom_point(size=1) +
geom_line(size = 2, alpha = 0.5) +
geom_vline(xintercept = 1, linetype = "dashed") +
#geom_smooth(level = 0.99) +
labs(x = "Ratio", y = "Percentage") +
theme_bw()8.2 Tabela Brasil
dados %>%
filter(country == "br") %>%
filter(!is.na(sex_female), !is.na(weight_status)) %>%
group_by(sex_female,weight_status) %>%
tabyl(sex_female, weight_status) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns() %>%
kable()8.3 Tabela Espanha
dados %>%
filter(country == "sp") %>%
filter(!is.na(sex_female), !is.na(weight_status)) %>%
group_by(sex_female,weight_status) %>%
tabyl(sex_female, weight_status) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns() %>%
kable()8.4 Qui-quadrado
dados %>%
filter(country == "br") %>%
filter(!is.na(sex_female), !is.na(weight_status)) %>%
{descr::crosstab(.$sex_female, .$weight_status, chisq = T, plot = F)}
Cell Contents
|-------------------------|
| Count |
|-------------------------|
===============================================
.$weight_status
.$sex_female ganhar igual perder Total
-----------------------------------------------
male 25 21 37 83
-----------------------------------------------
female 9 18 94 121
-----------------------------------------------
Total 34 39 131 204
===============================================
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 26.39928 d.f. = 2 p = 1.85e-06
Minimum expected frequency: 13.83333 9 H4: HPossÃvel explicação (Hipótese Erica)
dados %>%
filter(sex_female == "female") %>%
group_by(weight_status) %>%
summarise(mean(imc), mean(peso_atual),mean(altura), n())10 H5: fator protetivo em fazer esporte
10.1 plot
dados %>%
filter(!is.na(faz_esporte)) %>%
ggplot(., aes(x= imc, y = bsq_soma_c, fill = faz_esporte)) +
geom_jitter() +
geom_smooth(method = "lm")
10.2 Regressao múltipla BSQ
dados %>%
lm(bsq_soma_c ~ imc + faz_esporte + country + sex_female, data = .) %>%
apaTables::apa.aov.table()
olsrr::ols_regress(.)10.3 Regressao múltipla EAT
dados %>%
lm(eat_soma_c ~ imc + faz_esporte + country + sex_female, data = .) %>%
apaTables::apa.aov.table()
ANOVA results using eat_soma_c as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared olsrr::ols_regress(.)Error in olsrr::ols_regress(.) : object '.' not found11 H6: Exploratory analisys (Questionnaire’s respose)
11.1 Network plot
#install.packages("NetworkComparisonTest")
library(NetworkComparisonTest)package 㤼㸱NetworkComparisonTest㤼㸲 was built under R version 4.0.5netw<-NCT(data1, data2,make.positive.definite=TRUE, test.edges=FALSE, edges="all")
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dados %>%
na.omit() -> write.csv(., "dados_pesquisa_puc_no_missing.csv", row.names = F)
Error in write.csv(., "dados_pesquisa_puc_no_missing.csv", row.names = F) <- dados %>% :
object '.' not foundplot(Layout)
Error in as.double(y) :
cannot coerce type 'environment' to vector of type 'double'centralityPlot(GGM = list(female_br = graph_female_br, female_sp = graph_female_sp))
Note: z-scores are shown on x-axis rather than raw centrality indices.
11.2 Medias e cohens d
dados %>%
filter(sex_female == "female") %>% #filtrar para apenas mulheres
select(country, eat1_c:eat26_c) %>% #deixar paÃses e escalas
pivot_longer(cols = -country, values_to = 'value1') %>% #mudar base
group_by(country,name) %>% #criar agrupador por paÃses e itens
summarise_at(vars(value1), list(mean=mean, sd=sd)) %>% #criar sumario
pivot_wider(names_from = country, values_from=mean:sd) %>% #apresentar o sumario como tablea
mutate(cohen = (mean_br-mean_sp)/((sd_br+sd_br)/2)) %>%
mutate(cohen_int = factor(case_when(
cohen < abs(0.1) ~ "neg",
cohen < abs(0.3) ~ "small",
cohen < abs(0.5) ~ "med",
TRUE ~ "strong"), levels=c("neg","small","med", "strong"))) %>%
arrange(desc(cohen)) %>%
{length(.$cohen_int[.$cohen_int == "strong"])}
[1] 11dados %>%
filter(sex_female == "female") %>% #filtrar para apenas mulheres
select(country, bsq1_c:bsq34_c) %>% #deixar paÃses e escalas
pivot_longer(cols = -country, values_to = 'value1') %>% #mudar base
group_by(country,name) %>% #criar agrupador por paÃses e itens
summarise_at(vars(value1), list(mean=mean, sd=sd)) %>% #criar sumario
pivot_wider(names_from = country, values_from=mean:sd) %>% #apresentar o sumario como tablea
mutate(cohen = (mean_br-mean_sp)/((sd_br+sd_br)/2)) %>%
mutate(cohen_int = factor(case_when(
cohen < abs(0.1) ~ "neg",
cohen < abs(0.3) ~ "small",
cohen < abs(0.5) ~ "med",
TRUE ~ "strong"), levels=c("neg","small","med", "strong"))) %>% #cohens interpretation
select(-contains("sd")) %>%
pivot_longer(-c(name, cohen, cohen_int), names_to ="pais") %>%
mutate(name = factor(name)) %>%
mutate(name = fct_reorder(name, cohen, .desc = TRUE)) %>% #change X order by cohens d
mutate(pais = if_else(pais == "mean_br","BR","SP")) %>% #change label of pais
ggplot(., aes(x = name, y = value, fill = pais, group=pais)) +
geom_col(stat = "summary", position = "dodge") +
#geom_vline(aes(xintercept=11), linetype="dashed",colour="blue",size=0.7) +
labs(x = "Itens", y = "Resultado", fill = "PaÃs")+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
#facet_grid(. ~ cohen_int) +
facet_wrap(~cohen_int) +
theme(text = element_text(size=15))
Ignoring unknown parameters: stat
dados %>%
filter(sex_female == "female") %>% #filtrar para apenas mulheres
select(country, bsq:eat26_c) %>% #deixar paÃses e escalas
pivot_longer(cols = -country, values_to = 'value1') %>% #mudar base
group_by(country,name) %>% #criar agrupador por paÃses e itens
summarise_at(vars(value1), list(mean=mean, sd=sd)) %>% #criar sumario
pivot_wider(names_from = country, values_from=mean:sd) %>% #apresentar o sumario como tablea
mutate(cohen = (mean_br-mean_sp)/((sd_br+sd_br)/2)) %>%
mutate(cohen_int = factor(case_when(
cohen < abs(0.1) ~ "neg",
cohen < abs(0.3) ~ "small",
cohen < abs(0.5) ~ "med",
TRUE ~ "strong"), levels=c("neg","small","med", "strong"))) %>% #cohens interpretation
select(-contains("sd")) %>%
pivot_longer(-c(name, cohen, cohen_int), names_to ="pais") %>%
mutate(name = factor(name)) %>%
mutate(name = fct_reorder(name, cohen, .desc = TRUE)) %>% #change X order by cohens d
mutate(pais = if_else(pais == "mean_br","BR","SP")) %>% #change label of pais
ggplot(., aes(x = name, y = value, fill = pais, group=pais)) +
geom_col(stat = "summary", position = "dodge") +
#geom_vline(aes(xintercept=11), linetype="dashed",colour="blue",size=0.7) +
labs(x = "Itens", y = "Resultado", fill = "PaÃs")+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
#facet_grid(. ~ cohen_int) +
#facet_wrap(~cohen_int) +
theme(text = element_text(size=15))12 Likert plot
library(likert)Loading required package: xtable
Attaching package: 㤼㸱likert㤼㸲
The following object is masked from 㤼㸱package:dplyr㤼㸲:
recodedados_eat_likert <- dados %>% #get dataset
select(sex_female, eat1_c:eat26_c) %>%
as.data.frame() %>% #get dataset
na.omit() %>%
mutate_at(vars(-sex_female), ~factor(., levels=0:3))
plot(likert(dados_eat_likert[,2:5], grouping=dados_eat_likert$sex_female)) library(patchwork)p1 <- dados %>% #get dataset
filter(country == "br") %>%
select(sex_female, eat1_c:eat26_c) %>%
as.data.frame() %>% #get dataset
na.omit() %>%
mutate_at(vars(-sex_female), ~factor(., levels=0:3)) %>%
{likert(.[,2:10], grouping=.[,1])} %>%
plot() +
ggthemes::theme_few() +
ylab("Br") +
theme(legend.position = "none")
p2 <- dados %>% #get dataset
filter(country == "sp") %>%
select(sex_female, eat1_c:eat26_c) %>%
as.data.frame() %>% #get dataset
na.omit() %>%
mutate_at(vars(-sex_female), ~factor(., levels=0:3)) %>%
{likert(.[,2:10], grouping=.[,1])} %>%
plot() +
ylab("Sp") +
ggthemes::theme_few()
p1+p2
dados %>%
select(country, sex_female, eat1_c:eat26_c) %>%
na.omit() %>%
pivot_longer(cols = -c(country, sex_female),
names_to = "item", values_to = "categoria") %>%
group_by(item, country, sex_female, categoria) %>%
summarise(N = n()) %>%
mutate(Pct = N / sum(N)) %>%
mutate(categoria = factor(round(categoria,0))) %>%
ggplot(aes(x = item, y = Pct, fill = categoria)) +
geom_bar(position="fill", stat="identity")+
scale_y_continuous(labels = scales::percent) +
#geom_text(aes(x=item, y=Pct, group=categoria,label = paste(Pct*100, "%"))) +
coord_flip() +
facet_wrap(~sex_female + country) +
theme_bw() +
theme(legend.position = "bottom")
`summarise()` has grouped output by 'item', 'country', 'sex_female'. You can override using the `.groups` argument.
#Hx: Sex and country differences
12.1 BSQ
apaTables::apa.aov.table(mod_sex_country_bsq)
ANOVA results using bsq_soma_c as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 12.2 Correlacao entre fatores
dados %>%
filter(country == "sp") %>%
select(eat_soma_c, bsq_soma_c) %>%
cor()
eat_soma_c bsq_soma_c
eat_soma_c 1.0000000 0.6778482
bsq_soma_c 0.6778482 1.0000000dados %>%
filter(country == "br") %>%
select(eat_soma_c, bsq_soma_c) %>%
cor()
eat_soma_c bsq_soma_c
eat_soma_c 1.000000 0.694056
bsq_soma_c 0.694056 1.000000
12.3 EAT geral
apaTables::apa.aov.table(mod_sex_country_ead)
ANOVA results using eat_soma_c as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 
apaTables::apa.aov.table(mod_sex_country_eat_dieta)
ANOVA results using eat_dieta as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 12.4 EAT Bulimia
apaTables::apa.aov.table(mod_sex_country_eat_bulimia)
ANOVA results using eat_bulimia as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 12.5 Controle oral
mod_sex_country_eat_controle <- lm(eat_controle ~ sex_female * country, dados)
Error in eval(predvars, data, env) : object 'eat_controle' not foundapaTables::apa.aov.table(mod_sex_country_eat_controle)
ANOVA results using eat_oral as the dependent variable
Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 
##2: Weight differences
12.6 Hipotese erica
---
title: "PUC-Rio - Data analysis EAT, BSQ (3 countries)"
output:
  html_notebook:
    toc: yes
    toc_float: yes
    number_sections: yes
    theme: cerulean 
    highlight: textmate
editor_options: 
  chunk_output_type: inline
---

```{r global options, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, 
                      warning = FALSE, 
                      messages = FALSE, 
                      include = TRUE,
                      results = "hide")
```


# Load packages
```{r}
pacman::p_load(tidyverse, arsenal,janitor, knitr)

```

# Load data 

This is the processed ds.
```{r}
load("C:/Users/luisf/Dropbox/Puc-Rio/Projeto - Adicao a internet, imagem e alimentacao/Pesquisa/analise de dados/2020 - Grupo estudos - puc/R base - Dados after processing on December 2020 (PUCRio).RData")
```

# Check names and reliability of the ds

```{r}
dados %>% names
```

# EAT 

```{r}
dados %>% 
  select(starts_with("eat") & ends_with("_c"), -eat_soma_c) %>% 
  psych::alpha()
```

## Fatores


```{r}
dados <- dados %>% 
  mutate(eat_dieta = rowSums(select(., eat1_c, eat6_c,eat7_c,eat10_c,eat11_c,eat12_c,eat14_c,eat16_c,eat17_c,eat22_c, eat23_c, eat24_c, eat25_c))) %>% 
  mutate(eat_bulimia = rowSums(select(., eat3_c, eat4_c,eat9_c,eat18_c,eat21_c,eat26_c))) %>% 
  mutate(eat_oral = rowSums(select(., eat2_c, eat5_c,eat8_c,eat13_c,eat15_c,eat19_c,eat20_c)))
```



## Correlacao entre fatores

```{r}
dados %>% 
  select(eat_dieta, eat_bulimia, eat_oral, eat_soma_c) %>% 
  cor(.)
```

## Alfa Dieta

```{r}
dados %>% select(., eat1_c, eat6_c,eat7_c,eat10_c,eat11_c,eat12_c,eat14_c,eat16_c,eat17_c,eat22_c, eat23_c, eat24_c, eat25_c) %>% 
  psych::alpha(.)

```


## Alfa Bulimia

```{r}
dados %>% select(eat3_c, eat4_c,eat9_c,eat18_c,eat21_c,eat26_c) %>% 
  psych::alpha(.)
```
## Alfa Controle oral

```{r}
dados %>% select(eat2_c, eat5_c,eat8_c,eat13_c,eat15_c,eat19_c,eat20_c) %>% 
  psych::alpha()
```

## Plot 

```{r}
dados %>% 
  select(eat_dieta, eat_bulimia, eat_oral) %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(fct_reorder(name, value), value)) +
  geom_boxplot()
```


# BSQ-34

## Alpha 
```{r}
dados %>% 
  select(starts_with("bsq") & ends_with("_c"), -bsq_soma_c) %>% 
  psych::alpha()
```



# H1: Dados epidemiologicos

## BSQ-34


```{r}
dados %>% 
  mutate(corte =
           if_else(bsq_soma_c >= 110, 1,0)) %>% 
  count(corte) %>% 
  mutate(freq = n / sum(n))
```


```{r}
dados %>% 
  mutate(corte =
           if_else(bsq_soma_c >= 110, 1,0)) %>% 
  {descr::CrossTable(.$country,.$corte, 
                     chisq = T,prop.chisq = F, 
                     expected = T)}
```

```{r}
dados %>% 
  #filter(sex_female == "female") %>% 
  mutate(corte =
           if_else(bsq_soma_c >= 110, 1,0)) %>% 
  {epitools::oddsratio(.$country,.$corte, rev = "r")}
```


## EAT-26

```{r}
dados %>% 
  mutate(corte =
           if_else(eat_soma_c >= 21, 1,0)) %>% 
  count(corte) %>% 
  mutate(freq = n / sum(n))
```


```{r}
dados %>% 
  mutate(corte =
           if_else(eat_soma_c >= 21, 1,0)) %>% 
  {descr::CrossTable(.$country,.$corte, prop.chisq = F, 
                     chisq = T, 
                     expected = T)}
```

```{r}
dados %>% 
  #filter(sex_female == "female") %>% 
  mutate(corte =
           if_else(eat_soma_c >= 21, 1,0)) %>% 
  {epitools::oddsratio(.$country,.$corte, rev = "r")}

(60*352)/(23*160)
```

# H2: Respostas diferentes por grupos

> Será que a pessoa que está em risco na EAT, tem valor na BSQ alto?

```{r}
dados %>% 
  mutate(corte_eat =
           if_else(eat_soma_c >= 21, 1,0)) %>% 
  {t.test(bsq_soma_c ~ corte_eat, data =.)}
```

```{r}
dados %>% 
  mutate(corte_bsq =
           if_else(bsq_soma_c >= 110, 1,0)) %>% 
  {t.test(bsq_soma_c ~ corte_bsq, data =.)}
```

## Plot 

```{r}
dados%>% 
  filter(!is.na(sex_female)) %>% 
  mutate(corte_bsq =
           if_else(bsq_soma_c >= 110, 1,0)) %>% 
  ggplot(., aes(x= factor(corte_bsq), y = eat_soma_c, fill = sex_female)) +
   geom_bar(stat = "summary", position = "dodge") +
  stat_summary(geom="errorbar", fun.data = mean_se, 
               position = position_dodge(0.95), width = .5) +
      facet_wrap(~country)  +
  geom_hline(yintercept = 21)
```


## Plot (Quadrantes)

```{r}
dados%>% 
  filter(!is.na(sex_female)) %>% 
  #filter(imc < 24.9) %>% 
  mutate(corte_bsq =
           if_else(bsq_soma_c >= 110, 1,0)) %>% 
    mutate(corte_eat =
           if_else(eat_soma >= 21, 1,0)) %>% 
  ggplot(., aes(y=eat_soma_c, x = bsq_soma_c, color = sex_female)) +
  geom_jitter(size = 2) +
  #geom_smooth(method = "lm") +
  facet_wrap(~country) +
  geom_hline(yintercept = 21) +
  geom_vline(xintercept = 110) +
  annotate("rect", xmin=110, xmax=210, ymin=21, ymax=60, alpha=0.2, fill="red") +
  annotate("rect", xmin=0, xmax=110, ymin=0, ymax=21, alpha=0.1, fill="blue") +
  annotate("rect", xmin=0, xmax=110, ymin=0, ymax=21, alpha=0.1, fill="blue") +
  annotate("rect", xmin=0, xmax=110, ymin=0, ymax=21, alpha=0.1, fill="blue") +
    annotate(
    geom = "text", x = 150, y = 60, 
    label = "Risco", hjust = 0, vjust = 1, size = 4
  ) +
  annotate(
    geom = "text", x = 2, y = 2, 
    label = "Ok", hjust = 0, vjust = 1, size = 4
  ) +
  labs(x = "BSQ", y = "EAT", color = "Sexo") +
  theme_classic()
```
## Proporção de homens e mulheres

```{r}
dados%>% 
  filter(!is.na(sex_female)) %>% 
  #filter(imc < 24.9) %>% 
  mutate(corte_bsq =
           if_else(bsq_soma_c >= 110, 1,0)) %>% 
    mutate(corte_eat =
           if_else(eat_soma >= 21, 1,0)) %>% 
  mutate(quadrantes = case_when(
    corte_bsq == 1 &  corte_eat == 1 ~ "risco",
    corte_bsq == 0 &  corte_eat == 0 ~ "ok",
    corte_bsq == 1 &  corte_eat == 0 ~ "risco_bsq",
    corte_bsq == 0 &  corte_eat == 1 ~ "risco_eat",
    
  )) %>% 
  group_by(quadrantes, country,sex_female) %>%
  summarise(n=n()) %>% 
  mutate(prop = n/sum(n)) %>% 
  mutate(prop = round(prop*100,1)) %>%
  select(-n) %>% 
  pivot_wider(names_from = "quadrantes", values_from = "prop")
  
```


# H3: (proporções entre homens e mulheres sobre satisfação corporal)

## Gráfico

```{r}
dados %>% 
  select(sex_female, peso_desejado, peso_atual) %>%  #select target variables
  rename(Sex = "sex_female") %>%  #rename to make easier
  mutate(Sex  = str_to_sentence(Sex)) %>% #sentene case
  na.omit() %>% #don't use missings
  mutate(razao = peso_desejado/peso_atual) %>% #fazer razao 
  group_by(Sex) %>% #agrupar por sexo
  mutate(percrank=rank(razao)/length(razao)) %>% #calcular percentil
  arrange(Sex,razao) %>% 
  ggplot(., 
       aes(x = razao, y = percrank, colour = Sex)) + 
  geom_line(size = 2) +
  geom_vline(xintercept = 1, linetype = "dashed") + 
  geom_smooth(level = 0.99) +
  labs(x = "Ratio",  y = "Percentage") +
  theme_bw()
```


```{r, eval = FALSE }
dados %>% 
  select(country, sex_female, peso_desejado, peso_atual) %>%  #select target variables
  rename(Sex = "sex_female") %>%  #rename to make easier
  mutate(Sex  = str_to_sentence(Sex)) %>% #sentene case
  na.omit() %>% #don't use missings
  mutate(razao = peso_desejado/peso_atual) %>% #fazer razao 
  group_by(Sex) %>% #agrupar por sexo
  mutate(percrank=rank(razao)/length(razao)) %>% #calcular percentil
  arrange(Sex,razao) %>% 
  ggplot(., 
  aes(x = razao, y = percrank, colour = Sex, linetype = country)) +
  geom_point(size=1) +
  geom_line(size = 2, alpha = 0.5) +
  geom_vline(xintercept = 1, linetype = "dashed") + 
  #geom_smooth(level = 0.99) +
  labs(x = "Ratio",  y = "Percentage") +
  theme_bw()
```


## Tabela Brasil


```{r, eval = FALSE }
dados %>% 
  filter(country == "br") %>% 
  filter(!is.na(sex_female), !is.na(weight_status)) %>% 
  group_by(sex_female,weight_status) %>% 
  tabyl(sex_female, weight_status) %>% 
  adorn_totals(c("row", "col")) %>%
  adorn_percentages("row") %>% 
  adorn_pct_formatting(rounding = "half up", digits = 0) %>%
  adorn_ns() %>% 
  kable()
```

## Tabela Espanha

```{r, eval = FALSE }
dados %>% 
  filter(country == "sp") %>% 
  filter(!is.na(sex_female), !is.na(weight_status)) %>% 
  group_by(sex_female,weight_status) %>% 
  tabyl(sex_female, weight_status) %>% 
  adorn_totals(c("row", "col")) %>%
  adorn_percentages("row") %>% 
  adorn_pct_formatting(rounding = "half up", digits = 0) %>%
  adorn_ns() %>% 
  kable()
```
## Qui-quadrado


```{r}
dados %>% 
  filter(country == "br") %>% 
  filter(!is.na(sex_female), !is.na(weight_status)) %>% 
  {descr::crosstab(.$sex_female, .$weight_status, chisq = T, plot = F)}
```

# H4: HPossível explicação (Hipótese Erica)

```{r}
dados %>% 
  filter(sex_female == "female") %>% 
  group_by(weight_status) %>% 
  summarise(mean(imc), mean(peso_atual),mean(altura), n())
```



# H5: fator protetivo em fazer esporte

## plot 

```{r}
dados %>% 
  filter(!is.na(faz_esporte)) %>% 
  ggplot(., aes(x= imc, y = bsq_soma_c, fill = faz_esporte)) +
  geom_jitter() +
  geom_smooth(method = "lm")
```
## Regressao múltipla BSQ

```{r}
dados %>% 
  lm(bsq_soma_c ~ imc + faz_esporte + country + sex_female, data = .) %>% 
  apaTables::apa.aov.table()
  olsrr::ols_regress(.)
```


## Regressao múltipla EAT

```{r}
dados %>% 
  lm(eat_soma_c ~ imc + faz_esporte + country + sex_female, data = .) %>% 
  apaTables::apa.aov.table()
  olsrr::ols_regress(.)
```





# H6: Exploratory analisys (Questionnaire's respose)

## Network plot

```{r}
#install.packages("NetworkComparisonTest")

library(NetworkComparisonTest)

netw<-NCT(data1, data2,make.positive.definite=TRUE, test.edges=FALSE, edges="all")
plot(netw,what="network")
```


```{r}
data1 <- dados %>% 
  #filter(sex_female == "female", country == "br") %>% 
  select(eat1_c:eat26_c) %>% 
  mutate_all(., ~as.numeric(.)) %>% 
  na.omit() %>% 
  data.frame()

data2 <- dados %>% 
  #filter(sex_female == "female", country == "sp") %>% 
  select(eat1_c:eat26_c) %>% 
  mutate_all(., ~as.numeric(.)) %>% 
  na.omit() %>% 
  data.frame()

data3 <- dados %>% 
  filter(sex_female == "male", country == "br") %>% 
  select(eat1_c:eat26_c) %>% 
  mutate_all(., ~as.numeric(.)) %>% 
  na.omit() %>% 
  data.frame()

data4 <- dados %>% 
  filter(sex_female == "male", country == "sp") %>% 
  select(eat1_c:eat26_c) %>% 
  mutate_all(., ~as.numeric(.)) %>% 
  na.omit() %>% 
  data.frame()


```


```{r}
library(qgraph)

CorMat <- cor_auto(data1, detectOrdinal = FALSE)
CorMat2 <- cor_auto(data2, detectOrdinal = FALSE)
CorMat3 <- cor_auto(data3, detectOrdinal = FALSE)
#CorMat4 <- cor_auto(data4, detectOrdinal = FALSE)

# Compute graph with tuning = 0 (BIC):
layout(t(1:2))
graph_female_br <- qgraph(CorMat, graph = "glasso", sampleSize = nrow(dados),
            tuning = 0, layout = "spring", title = "BIC", details = TRUE)


graph_female_sp <- qgraph(CorMat2, graph = "glasso", sampleSize = nrow(dados),
            tuning = 0, layout = "spring", title = "BIC", details = TRUE)

Layout <- averageLayout(graph_female_br, graph_female_sp)

plot(Layout)
```

```{r}
centralityPlot(GGM = list(female_br = graph_female_br, female_sp = graph_female_sp))
```


## Medias e cohens d  


```{r}
dados %>% 
  filter(sex_female == "female") %>% #filtrar para apenas mulheres
  select(country, eat1_c:eat26_c) %>%  #deixar países e escalas
  pivot_longer(cols = -country, values_to = 'value1') %>%  #mudar base
  group_by(country,name) %>% #criar agrupador por países e itens
  summarise_at(vars(value1), list(mean=mean, sd=sd)) %>% #criar sumario
  pivot_wider(names_from = country, values_from=mean:sd) %>% #apresentar o sumario como tablea
  mutate(cohen = (mean_br-mean_sp)/((sd_br+sd_br)/2)) %>% 
  mutate(cohen_int = factor(case_when(
    cohen < abs(0.1) ~ "neg",
    cohen < abs(0.3) ~ "small",
    cohen < abs(0.5) ~ "med",
    TRUE ~ "strong"), levels=c("neg","small","med", "strong"))) %>% 
  arrange(desc(cohen))
```

```{r}
dados %>% 
  filter(sex_female == "female") %>% #filtrar para apenas mulheres
  select(country, eat1_c:eat26_c) %>%  #deixar países e escalas
  pivot_longer(cols = -country, values_to = 'value1') %>%  #mudar base
  group_by(country,name) %>% #criar agrupador por países e itens
  summarise_at(vars(value1), list(mean=mean, sd=sd)) %>% #criar sumario
  pivot_wider(names_from = country, values_from=mean:sd) %>% #apresentar o sumario como tablea
  mutate(cohen = (mean_br-mean_sp)/((sd_br+sd_br)/2)) %>% 
  mutate(cohen_int = factor(case_when(
    cohen < abs(0.1) ~ "neg",
    cohen < abs(0.3) ~ "small",
    cohen < abs(0.5) ~ "med",
    TRUE ~ "strong"), levels=c("neg","small","med", "strong"))) %>% 
  arrange(desc(cohen)) %>% 
  {length(.$cohen_int[.$cohen_int == "strong"])}
```





```{r}
dados %>% 
  filter(sex_female == "female") %>% #filtrar para apenas mulheres
  select(country, bsq1_c:bsq34_c) %>%  #deixar países e escalas
  pivot_longer(cols = -country, values_to = 'value1') %>%  #mudar base
  group_by(country,name) %>% #criar agrupador por países e itens
  summarise_at(vars(value1), list(mean=mean, sd=sd)) %>% #criar sumario
  pivot_wider(names_from = country, values_from=mean:sd) %>% #apresentar o sumario como tablea
  mutate(cohen = (mean_br-mean_sp)/((sd_br+sd_br)/2)) %>% 
  mutate(cohen_int = factor(case_when(
    cohen < abs(0.1) ~ "neg",
    cohen < abs(0.3) ~ "small",
    cohen < abs(0.5) ~ "med",
    TRUE ~ "strong"), levels=c("neg","small","med", "strong"))) %>%   #cohens interpretation
  select(-contains("sd")) %>% 
  pivot_longer(-c(name, cohen, cohen_int), names_to ="pais") %>% 
  mutate(name = factor(name)) %>% 
  mutate(name = fct_reorder(name, cohen, .desc = TRUE)) %>% #change X order by cohens d
  mutate(pais = if_else(pais == "mean_br","BR","SP"))  %>%  #change label of pais
  ggplot(., aes(x = name, y = value, fill = pais, group=pais)) +
  geom_col(stat = "summary", position = "dodge") +
  #geom_vline(aes(xintercept=11), linetype="dashed",colour="blue",size=0.7) +
  labs(x = "Itens", y = "Resultado", fill = "País")+
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  #facet_grid(. ~ cohen_int) +
  #facet_wrap(~cohen_int) +
  theme(text = element_text(size=15))
```




```{r}
dados %>% 
  filter(sex_female == "female") %>% #filtrar para apenas mulheres
  select(country, bsq:eat26_c) %>%  #deixar países e escalas
  pivot_longer(cols = -country, values_to = 'value1') %>%  #mudar base
  group_by(country,name) %>% #criar agrupador por países e itens
  summarise_at(vars(value1), list(mean=mean, sd=sd)) %>% #criar sumario
  pivot_wider(names_from = country, values_from=mean:sd) %>% #apresentar o sumario como tablea
  mutate(cohen = (mean_br-mean_sp)/((sd_br+sd_br)/2)) %>% 
  mutate(cohen_int = factor(case_when(
    cohen < abs(0.1) ~ "neg",
    cohen < abs(0.3) ~ "small",
    cohen < abs(0.5) ~ "med",
    TRUE ~ "strong"), levels=c("neg","small","med", "strong"))) %>%   #cohens interpretation
  select(-contains("sd")) %>% 
  pivot_longer(-c(name, cohen, cohen_int), names_to ="pais") %>% 
  mutate(name = factor(name)) %>% 
  mutate(name = fct_reorder(name, cohen, .desc = TRUE)) %>% #change X order by cohens d
  mutate(pais = if_else(pais == "mean_br","BR","SP"))  %>%  #change label of pais
  ggplot(., aes(x = name, y = value, fill = pais, group=pais)) +
  geom_col(stat = "summary", position = "dodge") +
  #geom_vline(aes(xintercept=11), linetype="dashed",colour="blue",size=0.7) +
  labs(x = "Itens", y = "Resultado", fill = "País")+
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  #facet_grid(. ~ cohen_int) +
  #facet_wrap(~cohen_int) +
  theme(text = element_text(size=15))
```

# Likert plot

```{r}
library(likert)
```
```{r}
dados %>% 
  select(starts_with("eat")) %>% 
  summarytools::descr()
```


```{r}
dados %>% #get dataset
  select(sex_female, eat1_c:eat26_c) %>% 
  as.data.frame() %>% #get dataset 
  mutate_at(vars(-sex_female), ~factor(., levels=0:3)) %>% 
  select(eat1_c:eat26_c) %>% 
  likert() %>%  plot()
  likert(., grouping = dados$sex_female) %>% 
  plot() 
  
```


```{r}
dados$sex_female
```


```{r, eval = FALSE }
dados_eat_likert <- dados %>% #get dataset
  select(sex_female, eat1_c:eat26_c) %>% 
  as.data.frame() %>% #get dataset 
  na.omit() %>% 
  mutate_at(vars(-sex_female), ~factor(., levels=0:3)) 
plot(likert(dados_eat_likert[,2:5], grouping=dados_eat_likert$sex_female)) 

```

```{r}
library(patchwork)
```


```{r}
p1 <- dados %>% #get dataset
  filter(country == "br") %>% 
  select(sex_female, eat1_c:eat26_c) %>% 
  as.data.frame() %>% #get dataset 
  na.omit() %>% 
  mutate_at(vars(-sex_female), ~factor(., levels=0:3)) %>% 
  {likert(.[,2:10], grouping=.[,1])} %>%  
  plot() +  
  ggthemes::theme_few() + 
  ylab("Br") +
  theme(legend.position = "none")
 
p2 <- dados %>% #get dataset
  filter(country == "sp") %>% 
  select(sex_female, eat1_c:eat26_c) %>% 
  as.data.frame() %>% #get dataset 
  na.omit() %>% 
  mutate_at(vars(-sex_female), ~factor(., levels=0:3)) %>% 
  {likert(.[,2:10], grouping=.[,1])} %>%  
  plot() +  
  ylab("Sp") +
  ggthemes::theme_few()

p1+p2
```


```{r}
dados %>% 
  select(country, sex_female, eat1_c:eat26_c) %>%
  na.omit() %>% 
  pivot_longer(cols = -c(country, sex_female),
               names_to = "item", values_to = "categoria") %>% 
  group_by(item, country, sex_female, categoria) %>% 
  summarise(N = n()) %>%
  mutate(Pct = N / sum(N)) %>% 
  mutate(categoria = factor(round(categoria,0))) %>% 
  ggplot(aes(x = item, y = Pct, fill = categoria)) +
  geom_bar(position="fill", stat="identity")+
  scale_y_continuous(labels = scales::percent) +
  #geom_text(aes(x=item, y=Pct, group=categoria,label = paste(Pct*100, "%"))) +
  coord_flip() +
  facet_wrap(~sex_female + country) +
  theme_bw() +
  theme(legend.position = "bottom")
  

```

```{r}
dados %>% 
  filter(sex_female == "male" & country == "br") %>% 
  count(eat1_c)
```


#Hx: Sex and country differences

## BSQ
```{r}
mod_sex_country_bsq <- lm(bsq_soma_c ~ sex_female * country, dados)
```

```{r}
apaTables::apa.aov.table(mod_sex_country_bsq)
```

## Correlacao entre fatores


```{r}
dados %>% 
  filter(country == "sp") %>% 
  select(eat_soma_c, bsq_soma_c) %>% 
  cor()
```

```{r}
dados %>% 
  filter(country == "br") %>% 
  select(eat_soma_c, bsq_soma_c) %>% 
  cor()
```


```{r}
dados %>% 
  select(eat_soma_c, bsq_soma_c, country) %>%
  ggplot(., aes(eat_soma_c, bsq_soma_c, color = country)) +
  geom_jitter() +
  stat_smooth(method = "lm")
  
```


## EAT geral


```{r}
dados %>% 
  select(eat_soma_c, country) %>% 
  group_by(country) %>% 
  summarise(mean(eat_soma_c), sd(eat_soma_c))

```




```{r}
mod_sex_country_ead<- lm(eat_soma_c ~ sex_female * country, dados)

```

```{r}
apaTables::apa.aov.table(mod_sex_country_ead)
```


```{r}
dados %>% 
  filter(!is.na(sex_female)) %>% 
  ggplot(., aes(x = sex_female, y = eat_soma_c, fill = country))+
  geom_bar(stat = "summary", position = "dodge")
```

```{r}
mod_sex_country_eat_dieta<- lm(eat_dieta ~ sex_female * country, dados)

```

```{r}
apaTables::apa.aov.table(mod_sex_country_eat_dieta)
```
## EAT Bulimia
```{r}
mod_sex_country_eat_bulimia <- lm(eat_bulimia ~ sex_female * country, dados)

```

```{r}
apaTables::apa.aov.table(mod_sex_country_eat_bulimia)
```

## Controle oral 
```{r}
mod_sex_country_eat_controle <- lm(eat_oral ~ sex_female * country, dados)

```

```{r}
apaTables::apa.aov.table(mod_sex_country_eat_controle)
```

```{r}
dados %>% 
  filter(!is.na(sex_female)) %>% 
  ggplot(., aes(x = sex_female, y = eat_oral, fill = country))+
  geom_bar(stat = "summary", position = "dodge")
```

##2: Weight differences

```{r}
dados %>%
  select(sex_female,peso_atual, peso_desejado,delta_peso) %>% 
  tableby(sex_female ~., data = .) %>% 
  summary()
```



```{r}
dados %>%
  select(peso_atual, peso_desejado,sex_female) %>% 
  na.omit() %>% 
  ggplot(., aes(x=peso_atual, y = peso_desejado, fill = sex_female)) +
  geom_jitter() +
  geom_smooth() +
  labs(x ="Peso atual", y = "Peso desejado", fill = "Sexo") + 
  theme_bw()
```

```{r}
lm(peso_desejado ~ peso_atual * sex_female, dados) %>% 
  olsrr::ols_regress()
```

## Hipotese erica


```{r}
dados %>%
  select(peso_atual, peso_desejado,sex_female) %>% 
  na.omit() %>% 
  pivot_longer(-sex_female) %>% 
  ggplot(., aes(x=sex_female, y=value, fill=name, group = name)) +
   geom_bar(stat = "summary", position = "dodge") +
  stat_summary(geom="errorbar", fun.data = mean_se, 
               position = position_dodge(0.95), width = .5) +
  theme_bw()
```



```{r}
 
```



```{r}
dados %>% 
  filter(!is.na(stat), !is.na(sex_female)) %>% 
  ggplot(., aes(x=stat, y = eat_soma_c, fill = sex_female)) +
  geom_bar(stat = "summary", position = "dodge") +
    stat_summary(geom="errorbar", fun.data = mean_se, 
               position = position_dodge(0.95), width = .5) +
  facet_wrap(~country) +
  theme_bw()

```


```{r}
dados %>% 
  filter(!is.na(stat), !is.na(sex_female)) %>% 
  ggplot(., aes(x=stat, y = eat_soma_c, colour = sex_female, shape = country, group=interaction(sex_female, country))) +
  stat_summary(geom = "line", fun = "mean") +
  stat_summary(fun.y = "mean", geom = "point")

  geom_bar(stat = "summary", position = "dodge") +
    stat_summary(geom="errorbar", fun.data = mean_se, 
               position = position_dodge(0.95), width = .5)
```



