mci.Rmd

---
title: "Study 2 - Masculine Crime Inventory"
author: "Timothy J. Luke & Pär Stern"
date: "12 december 2018"
output: html_document
---

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

library(tidyverse)

mci <- read_csv("./data/MCI_raw.csv")

mci_long <- mci %>% 
  gather(key = "crime", value = "manliness", names(mci[6:33]))

```

## Sample information

```{r}
mci %>%
  summarise(
    Male = sum(gender == "Male"),
    Female = sum(gender == "Female"),
    "Mean Age" = round(mean(age, na.rm = TRUE), 2),
    "SD Age" = round(sd(age, na.rm = TRUE), 2),
    "Median Age" = round(median(age, na.rm = TRUE), 2)
  ) %>% 
  knitr::kable()
```

```{r}
race_table <- table(mci$ethnicity) %>% 
  broom::tidy() %>% 
  rename(
    Ethnicity = Var1,
    Frequency = n
  ) %>% 
  arrange(desc(Frequency))

knitr::kable(race_table)
```

## Descriptives

```{r}

manly_table <- mci_long %>% 
  group_by(crime) %>% 
  summarise(
    mean = mean(manliness, na.rm = TRUE),
    sd = sd(manliness, na.rm = TRUE),
    median = median(manliness, na.rm = TRUE)
  )

knitr::kable(manly_table)

```

```{r}

manly_desc <- manly_table %>% 
  summarise(
    minimum = min(mean),
    maximum = max(mean),
    mean_of_means = mean(mean),
    sd = sd(mean)
  )

knitr::kable(manly_desc)

```

```{r}

ggplot(mci_long,
       aes(
         x = manliness
       )) +
  facet_wrap(~crime,
             nrow = 8) +
  geom_histogram(
    binwidth = 1
  ) +
  theme_classic()

```