w33_psychometrics.Rmd

---
title: "w33 Psychometrics"
author: "Federica Gazzelloni"
date: "8/19/2022"
output: html_document
---

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

```{r}
psych_stats <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-08-16/psych_stats.csv")
```


```{r}
psych_stats%>%head
```

```{r}
my_df <- psych_stats%>%
  arrange(rank)%>%
  select(char_name,uni_name,personality,avg_rating,rank,rating_sd) %>%
  filter(uni_name=="Friends")
my_df
```

Use {tidytext} to select the personality variables to be used in the visualization. In this case a list of encoded variables, such as */U000..* were filtered out, to leave just words in the vector.

```{r}
library(tidytext)
my_df1 <- my_df %>% 
  mutate(personality=gsub("[^A-z]","unknown",personality))%>%
  filter(!personality=="unknown") %>%
  #count(personality) %>%
  unnest_tokens(word, personality) %>%
  inner_join(get_sentiments("bing")) %>%
  distinct(char_name,word,sentiment,avg_rating) 
```

Further wrangling activity on the dataset is done to select only the personality words which are in common and with highest avg rating values for all of the protagonists in **Friends** TV show.

```{r}
by_names <- my_df1%>%
  group_by(word)%>%
  summarize(char_name,avg_rating=mean(avg_rating),.groups="drop")%>%
  ungroup()%>%
  pivot_wider(names_from=char_name,values_from=word)%>%
  drop_na()%>%
  pivot_longer(cols=2:7,names_to="names",values_to="values")%>%
  unnest(values)%>%
  arrange(values) %>%
  count(values) %>%
  group_by(values) %>%
  filter(!n<6 & !n>6) %>%
  ungroup() %>%
  left_join(my_df3,by=c("values"="word"))%>%
  select(-n) %>%
  pivot_wider(names_from=char_name,values_from=values) %>%
  pivot_longer(cols=3:8,names_to="names",values_to="word")%>%
  distinct()%>%
  drop_na()%>%
  mutate(word=str_to_title(word)) %>%
  mutate(id_sentiment=ifelse(sentiment=="positive",1,0))
```


```{r}
library(extrafont)
# loadfonts()
```


```{r}
p <-by_names%>%
  ggplot(aes(x=avg_rating,y=fct_reorder(word,-avg_rating)))+
  geom_col(aes(fill=names), position = position_fill(),color="black")+
  ggthemes::scale_fill_tableau()+
  guides(fill=guide_legend(nrow = 1,reverse = T,keywidth = 0))+
  labs(fill="",
       subtitle="\nordered by common high-rating personality",
       caption="DataSource: Open Source Psychometrics | #TidyTuesday 2022 week33\nDataViz: Federica Gazzelloni (@fgazzelloni)",
       title="Friends: positive and negative personality ratings")+
  ggthemes::theme_fivethirtyeight()+
  theme(text=element_text(color="grey90",family="Public Sans Medium"),
        plot.title = element_text(size=22),
        legend.position = "top",
        legend.background = element_rect(fill="black",color="black"),
        legend.text = element_text(size=12),
        strip.background = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(size=12),
        panel.grid = element_line(size=3),
        plot.background = element_rect(fill="black",color="black"),
        panel.background = element_rect(fill="black",color="black"))
```


```{r}
library(cowplot)
ggdraw(p)+
  draw_image("logo.png",scale=0.25,
             x=-0.35,
             y=0.45)
ggsave("w33_psychometrics.png",
       dpi=320,
       height = 7,
       width = 9)
```

Other visualization not to be used.

```{r}
by_names %>%
  ggplot(aes(fct_reorder(word,avg_rating),avg_rating,
             fill=sentiment,color=sentiment))+
  geom_point()+
  geom_text(aes(label=word),size=3)+
  # ggimage::geom_image(x=0.2,y=0.2,image=image)+
  geom_line(aes(group=sentiment))+
  #geom_col()+
  facet_wrap(~fct_reorder(names,-avg_rating),scale="free")+
  ggthemes::scale_color_tableau()
```