correct_ffostare_visuals.Rmd

---
title: "getting_started"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
library(tidyselect)
library(ggplot2)
library(broom)
library(dplyr)
library(corrplot)
library(kableExtra)
library(knitr)
library(Hmisc)
library(infer)
library(plotly) 
library(skimr)
library(stringr)
library(reshape2)
library(som)
library(readxl)
library(manipulate)
library(lavaan)
library(semPlot)
library(FactoMineR)
library(factoextra)
library(readxl)
library(xtable)
```

```{r}
ffostare_corrected <- read_excel("ffostare_corrected_spssout_full.xlsx")
View(ffostare_corrected)

selected_ffostare <- ffostare_corrected %>%
  select(- Observations, - ResultContainers, - Intervals, - no_sec_behav_ffosta)

znormed_ffostare_v1 <- read_excel("znormed_ffostare_v1.xlsx")
skim(znormed_ffostare_v1)

selected_norms_ffostare_v1 <- znormed_ffostare_v1 %>%
  select(-Observations, - ResultContainers, - Intervals, - Zno_sec_behav_ffosta)
```


```{r}
corr_selected_ffostare_v1 <- cor(selected_ffostare)
melted_corr_selected_ffostare_v1 <- melt(corr_selected_ffostare_v1)
write.csv(corr_selected_ffostare_v1, "corr_selected_ffostare_v1.csv")

ggplot(melted_corr_selected_ffostare_v1, aes(x=Var2, y=Var1, fill=value)) +
  ggtitle("Combined Behavioral Correlations") +
  geom_tile() +
  scale_fill_gradient(low = "dark blue", high = "yellow") + 
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45,
                                   vjust = 1,
                                   size = 9,
                                   hjust = 1)
  )

corrnorm_selected_ffostare_v1 <- cor(selected_norms_ffostare_v1)   
melted_corrnorm_selected_ffostare_v1 <- melt(corrnorm_selected_ffostare_v1)
write.csv(corrnorm_selected_ffostare_v1, "corrnorm_selected_ffostare_v1.csv")
```

storage
selected_norms_ffostare_v1$Zstationary_middle_percent_butters , selected_norms_ffostare_v1$Zlocomotion_percent_butters , selected_norms_ffostare_v1$Zhanging_behaviors_butters , selected_norms_ffostare_v1$Ztactile_exploration_butters , selected_norms_ffostare_v1$Zmo_manip_apple_butters , selected_norms_ffostare_v1$Zstation_fr_ffostare , selected_norms_ffostare_v1$Zstation_mid_ffosta , selected_norms_ffostare_v1$Zlocomote_ffosta , selected_norms_ffostare_v1$Zmovement_ffosta , selected_norms_ffostare_v1$Ztac_expl_ffosta , selected_norms_ffostare_v1$Zstereo_ffosta , selected_norms_ffostare_v1$Zteethgrind_ffosta , selected_norms_ffostare_v1$Zlipsmack_ffosta , selected_norms_ffostare_v1$Ztouch_app_ffosta , selected_norms_ffostare_v1$Zmouth_app_ffosta , selected_norms_ffostare_v1$Zmanip_app_ffosta , selected_norms_ffostare_v1$Zeat_app_ffosta , selected_norms_ffostare_v1$Znonvigi_ffosta , selected_norms_ffostare_v1$Zdir_eye_contact_ffosta


```{r}
selected_norms_ffostare_v1$comp_1 <- selected_norms_ffostare_v1$Zstationary_middle_percent_butters + selected_norms_ffostare_v1$Zlocomotion_percent_butters + selected_norms_ffostare_v1$Zhanging_behaviors_butters + selected_norms_ffostare_v1$Ztactile_exploration_butters + selected_norms_ffostare_v1$Zmo_manip_apple_butters + selected_norms_ffostare_v1$Zstation_fr_ffostare + selected_norms_ffostare_v1$Zstation_mid_ffosta + selected_norms_ffostare_v1$Zlocomote_ffosta + selected_norms_ffostare_v1$Zmovement_ffosta + selected_norms_ffostare_v1$Ztac_expl_ffosta + selected_norms_ffostare_v1$Zstereo_ffosta + selected_norms_ffostare_v1$Zteethgrind_ffosta + selected_norms_ffostare_v1$Zlipsmack_ffosta + selected_norms_ffostare_v1$Ztouch_app_ffosta + selected_norms_ffostare_v1$Zmouth_app_ffosta + selected_norms_ffostare_v1$Zmanip_app_ffosta + selected_norms_ffostare_v1$Zeat_app_ffosta + selected_norms_ffostare_v1$Znonvigi_ffosta + selected_norms_ffostare_v1$Zdir_eye_contact_ffosta

#comp_1 <- selected_norms_ffostare_v1 %>%
  #select(Zstationary_middle_percent_butters , Zlocomotion_percent_butters , Zhanging_behaviors_butters , Ztactile_exploration_butters , Zmo_manip_apple_butters , Zstation_fr_ffostare , Zstation_mid_ffosta , Zlocomote_ffosta , Zmovement_ffosta , Ztac_expl_ffosta , Zstereo_ffosta , Zteethgrind_ffosta , Zlipsmack_ffosta , Ztouch_app_ffosta , Zmouth_app_ffosta , Zmanip_app_ffosta , Zeat_app_ffosta , Znonvigi_ffosta , Zdir_eye_contact_ffosta)

View(comp_1)
  
  
selected_norms_ffostare_v1$comp_2 <- selected_norms_ffostare_v1$ZStationary_back_butters + selected_norms_ffostare_v1$Zstereotypy_butters + selected_norms_ffostare_v1$Zvigilant_percent_butters + selected_norms_ffostare_v1$Zstation_back_ffostare + selected_norms_ffostare_v1$Zself_gro_ffosta + selected_norms_ffostare_v1$Zthreat_stranger_ffosta + selected_norms_ffostare_v1$Zfreeze_back_ffosta 
  
selected_norms_ffostare_v1$comp_3 <- selected_norms_ffostare_v1$Zstationary_front_percent_butters + selected_norms_ffostare_v1$Zmovement_percent_butters + selected_norms_ffostare_v1$Ztouch_butters_percent + selected_norms_ffostare_v1$Zcrouching_butters + selected_norms_ffostare_v1$Znonvigilant_percent_butters + selected_norms_ffostare_v1$Zcrouching_ffosta + selected_norms_ffostare_v1$Zvigi_ffosta + selected_norms_ffostare_v1$Zno_response_ffosta
  
selected_norms_ffostare_v1$comp_4 <- selected_norms_ffostare_v1$Zself_groom_percent_butters + selected_norms_ffostare_v1$Zmouth_butters_percent + selected_norms_ffostare_v1$Zoral_exploration_butters 

View(selected_norms_ffostare_v1)

write.csv(selected_norms_ffostare_v1, "selected_norms_composites_demgraf_v1.csv")
```

```{r}
selected_norms_composites_demgraf_v1 <- read_excel("selected_norms_composites_demgraf_v1.xlsx")
View(selected_norms_composites_demgraf_v1)

composite_scores_withgroups <- selected_norms_composites_demgraf_v1 %>%
  select(Observations, sex, initial_diet, new_diet, comp_1, comp_2, comp_3, comp_4)

composite_scores_withgroups$initial_diet <- as.factor(composite_scores_withgroups$initial_diet)
composite_scores_withgroups$new_diet <- as.factor(composite_scores_withgroups$new_diet)
skim(composite_scores_withgroups)

```

```{r}
#visuals of maternal diet
ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_1, color=initial_diet)) +
  geom_histogram(stat = "identity", binwidth = 30)

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_2, color=initial_diet)) +
  geom_histogram(stat = "identity", binwidth = 30)

comp1_wilcoxtest <- wilcox.test(composite_scores_withgroups$comp_1~composite_scores_withgroups$initial_diet)

comp1_wilcoxtest$p.value

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_3, color=initial_diet)) +
  geom_histogram(stat = "identity", binwidth = 30)

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_4, color=initial_diet)) +
  geom_histogram(stat = "identity", binwidth = 30)

```

```{r}
#matdiet filtered = filtering out all subjects with a new diet value of 2. This eliminates the effects of the new diet

head(composite_scores_withgroups)

matdiet_filtered <- composite_scores_withgroups %>%
  filter(!new_diet == 2)

matdiet_filtered$initial_diet <- as.factor(matdiet_filtered$initial_diet)

skim(matdiet_filtered) #removing new_diet = 2 removes 15 subjects
matdiet_filtered_wiltest_comp1 <- wilcox.test(matdiet_filtered$comp_1~matdiet_filtered$initial_diet)
matdiet_filtered_wiltest_comp2 <- wilcox.test(matdiet_filtered$comp_2~matdiet_filtered$initial_diet)
matdiet_filtered_wiltest_comp3 <- wilcox.test(matdiet_filtered$comp_3~matdiet_filtered$initial_diet)
matdiet_filtered_wiltest_comp4 <- wilcox.test(matdiet_filtered$comp_4~matdiet_filtered$initial_diet)


head(matdiet_filtered)

ggplot(matdiet_filtered, aes(x=initial_diet, y=comp_1)) +
  geom_boxplot()
```


```{r}
#visuals of postnatal diet
ggcomp1 <- ggplot(matdiet_filtered, aes(x=Observations, y=comp_1, color=initial_diet, fill=initial_diet)) +
  geom_bar(stat = "identity", width = 20, position = position_dodge())

ggplot(matdiet_filtered, aes(x=Observations, y=comp_1, color=initial_diet, fill=initial_diet)) +
  geom_bar(stat = "identity", width = 20, position = position_dodge())

ggcompl1_ly <- ggplotly(ggcomp1)

ggcompl1_ly

plot_ly(matdiet_filtered, x = ~Observations, y=~comp_1, type = "bar", name = "Individual Component 1 Scores",colors="")

ggplot(matdiet_filtered, aes(x=Observations, y=comp_2, color=initial_diet, fill=initial_diet)) +
  geom_histogram(stat = "identity")

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_3, color=new_diet)) +
  geom_histogram(stat = "identity", width = 30)

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_4, color=new_diet)) +
  geom_histogram(stat = "identity", width = 30)

#need to do a pca on community 1

```

```{r}
#sex differences
ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_1, color=sex)) +
  geom_histogram(stat = "identity", binwidth = 30)

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_2, color=sex)) +
  geom_histogram(stat = "identity", binwidth = 30)

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_3, color=sex)) +
  geom_histogram(stat = "identity", binwidth = 30)

ggplot(composite_scores_withgroups, aes(x=Observations, y=comp_4, color=sex)) +
  geom_histogram(stat = "identity", binwidth = 30)

matdiet_sex_wiltest_comp1 <- wilcox.test(matdiet_filtered$comp_1~matdiet_filtered$sex)
matdiet_sex_wiltest_comp2 <- wilcox.test(matdiet_filtered$comp_2~matdiet_filtered$sex)
matdiet_sex_wiltest_comp3 <- wilcox.test(matdiet_filtered$comp_3~matdiet_filtered$sex)
matdiet_sex_wiltest_comp4 <- wilcox.test(matdiet_filtered$comp_4~matdiet_filtered$sex)

matdiet_sex_wiltest_comp4$p.value

```


```{r}
#princi_comp1 <- prcomp(comp_1)

#summary(princi_comp1)

#fviz_eig(princi_comp1)
```

```{r}
#creating new composite variables to investigate the groupings

comp1_indi <- selected_norms_ffostare_v1 %>%
  select(Zstationary_middle_percent_butters, Zlocomotion_percent_butters, Zhanging_behaviors_butters, Ztactile_exploration_butters, Zmo_manip_apple_butters, Zstation_fr_ffostare,  Zstation_mid_ffosta, Zlocomote_ffosta, Zmovement_ffosta, Ztac_expl_ffosta,  Zstereo_ffosta, Zteethgrind_ffosta, Zlipsmack_ffosta, Ztouch_app_ffosta, Zmouth_app_ffosta, Zmanip_app_ffosta, Zeat_app_ffosta, Znonvigi_ffosta, Zdir_eye_contact_ffosta)

comp2_indi <- selected_norms_ffostare_v1 %>%
  select(ZStationary_back_butters, Zstereotypy_butters, Zvigilant_percent_butters, Zstation_back_ffostare, Zself_gro_ffosta, Zthreat_stranger_ffosta, Zfreeze_back_ffosta)

comp3_indi <- selected_norms_ffostare_v1 %>%
  select(Zstationary_front_percent_butters, Zmovement_percent_butters, Ztouch_butters_percent, Zcrouching_butters, Znonvigilant_percent_butters, Zcrouching_ffosta, Zvigi_ffosta, Zno_response_ffosta)

comp4_indi <- selected_norms_ffostare_v1 %>%
  select(Zself_groom_percent_butters, Zmouth_butters_percent, Zoral_exploration_butters)


```



```{r}
#here i need to run a PCA for Damien and Lavaan for Elinor

princi_comp1 <- prcomp(comp1_indi)
summary(princi_comp1)
#princi_comp1$sdev
princ1_ploty  <- ggplotly(fviz_eig(princi_comp1, main = "variance explained: components of comp_1"))

xtable(princi_comp1)

princ1_ploty

princi_comp2 <- prcomp(comp2_indi)
summary(princi_comp2)
fviz_eig(princi_comp2, main = 'variance explained: components of comp_2')

princ2_ploty <- ggplotly(fviz_eig(princi_comp2, main = 'variance explained: components of comp_2'))

princi_comp3 <- prcomp(comp3_indi)
summary(princi_comp3)
fviz_eig(princi_comp3, main = "variance explained: components of comp_3")

princ3_ploty <- ggplotly(fviz_eig(princi_comp3, main = 'variance explained: components of comp_3'))

princi_comp4 <- prcomp(comp4_indi)
summary(princi_comp4)
fviz_eig(princi_comp4, main = "variance explained: components of comp_4")

princ4_ploty <- ggplotly(fviz_eig(princi_comp4, main = 'variance explained: components of comp_4'))
```

```{r}
<<<<<<< HEAD
#SEM_comp1 <- 
  
#SEM_comp2 <-
  
#SEM_comp3 <- 
  
#SEM_comp4 <- 
=======
#new behavior variable for removing composites
selected_behaviors_latemods <- selected_norms_ffostare_v1 %>%
  select(- comp_1, - comp_2, - comp_3, - comp_4)

SEM_comp1 <- 'latemod_comp1 =~ Zstationary_middle_percent_butters + Zlocomotion_percent_butters + Zhanging_behaviors_butters + Ztactile_exploration_butters + Zmo_manip_apple_butters + Zstation_fr_ffostare + Zstation_mid_ffosta + Zlocomote_ffosta + Zmovement_ffosta + Ztac_expl_ffosta + Zstereo_ffosta + Zteethgrind_ffosta + Zlipsmack_ffosta + Ztouch_app_ffosta + Zmouth_app_ffosta + Zmanip_app_ffosta + Zeat_app_ffosta + Znonvigi_ffosta + Zdir_eye_contact_ffosta'

SEM_comp1_fit <- cfa(model = SEM_comp1, estimator = "MLR", data = selected_behaviors_latemods)
summary(SEM_comp1_fit, standardize = TRUE, fit.measures = TRUE)  


SEM_comp2 <- 'latemod_comp2 =~ ZStationary_back_butters + Zstereotypy_butters + Zvigilant_percent_butters + Zstation_back_ffostare + Zself_gro_ffosta + Zthreat_stranger_ffosta + Zfreeze_back_ffosta'

SEM_comp2_fit <- cfa(model = SEM_comp2, estimator = "MLR", data = selected_behaviors_latemods)
summary(SEM_comp2_fit, standardize = TRUE, fit.measures = TRUE)
  
SEM_comp3 <- 'latemod_comp3 =~ Zstationary_front_percent_butters + Zmovement_percent_butters + Ztouch_butters_percent + Zcrouching_butters + Znonvigilant_percent_butters + Zcrouching_ffosta + Zvigi_ffosta + Zno_response_ffosta'

SEM_comp3_fit <- cfa(model = SEM_comp3, estimator = "MLR", data = selected_behaviors_latemods)
summary(SEM_comp3_fit, standardize = TRUE, fit.measures = TRUE)
  
SEM_comp4 <- 'latemod_comp4 =~ Zself_groom_percent_butters + Zmouth_butters_percent + Zoral_exploration_butters'

SEM_comp4_fit <- cfa(model = SEM_comp4, estimator = "MLR", data = selected_behaviors_latemods)
summary(SEM_comp4_fit, standardize = TRUE, fit.measures = TRUE)



```