class7.Rmd

---
title: 'Data Analysis 3: Week 7'
author: "Alexey Bessudnov"
date: "1 March 2019"
output: github_document
---

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

```
Plan for today:

1. Machine learning workshop at the RSS.
2. Exercises on data visualisation.
3. Homework for next week: data structures.

Exercises:

1. Open the data from the youth questionnaire from wave 8.
2. Today we will be working with the variable on BMI (h_ypbmi_dv) and visualise the distribution of BMI by sex, age, and ethnic group.

```{r}
library(tidyverse)
youth8 <- read_tsv("data/UKDA-6614-tab/tab/ukhls_w8/h_youth.tab")

# summary(youth8$h_ypbmi_dv)
youth8 %>% pull(h_ypbmi_dv) %>% summary()

youth8 <- youth8 %>%
  mutate(bmi = recode(h_ypbmi_dv, `-9` = NA_real_))

youth8 %>% pull(bmi) %>% summary()

# simple histogram

youth8 %>%
  ggplot(aes(x = bmi)) +
  geom_histogram(bins = 50) +
  geom_vline(xintercept = 30, colour = "red") +
  xlab("Body mass index") +
  ylab("Number of observations")

# BBC style

library(bbplot)
youth8 %>%
  ggplot(aes(x = bmi)) +
  geom_histogram(bins = 50) +
  geom_vline(xintercept = 30, colour = "red") +
  ylab("Number of observations") +
  bbc_style() +
  xlab("Body mass index")

```

BMI by sex.

Boxplot:

```{r}

youth8 <- youth8 %>%
  mutate(sex = ifelse(h_sex_dv == 2, "female",
                      ifelse(h_sex_dv == 1, "male", NA)))
youth8 %>% count(h_sex_dv, sex)

youth8 %>%
  ggplot(aes(x = sex, y = bmi)) +
  geom_boxplot() +
  # this changes the boxplots from vertical to horizontal
  coord_flip()

```

density by group

```{r}
youth8 %>%
  ggplot(aes(x = bmi, fill = sex)) +
  geom_histogram(position = "dodge")

youth8 %>%
  ggplot(aes(x = bmi, fill = sex)) +
  geom_histogram(bins = 50, position = "identity", alpha = 0.5)

youth8 %>%
  ggplot(aes(x = bmi, colour = sex)) +
  geom_density()

youth8 %>%
  ggplot(aes(x = bmi, fill = sex)) +
  geom_density()

youth8 %>%
  ggplot(aes(x = bmi, fill = sex)) +
  geom_density() +
  # manually setting the colours
  scale_fill_manual(values = c("purple", "yellow"))

```

Barplot with means.

```{r}
youth8 %>%
  group_by(sex) %>%
  summarise(
    meanBMI = mean(bmi, na.rm = TRUE)
  ) %>%
  ggplot(aes(x = sex, y = meanBMI, fill = sex)) +
  geom_bar(stat = "identity")

youth8 %>%
  group_by(sex) %>%
  summarise(
    meanBMI = mean(bmi, na.rm = TRUE)
  ) %>%
  ggplot(aes(x = sex, y = meanBMI)) +
  geom_point() +
  ylim(0, 25) +
  coord_flip()

youth8 %>%
  group_by(h_gor_dv) %>%
  summarise(
    meanBMI = mean(bmi, na.rm = TRUE)
  ) %>%
  ggplot(aes(x = reorder(as.factor(h_gor_dv), meanBMI), y = meanBMI)) +
  geom_point() +
  coord_flip()

```

Faceted chart.

```{r}
youth8 %>%
  ggplot(aes(x = bmi)) +
  geom_histogram(bins = 50) +
  geom_vline(xintercept = 30, colour = "red") +
  xlab("Body mass index") +
  ylab("Number of observations") +
  facet_wrap(~ sex)

```