analysis.qmd

---
title: "Data Analysis"
---

Load the required R packages:

```{r}
#| output: false
library(babynames)
library(knitr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(pheatmap)
```

# Writing in Quarto part

Let's have a look at the first couple of rows in the data:

```{r}
head(babynames) |> kable()
```

Let's create functions to visualise some of the data according to sex:
```{r}
#| code-fold: true
get_most_frequent <- function(babynames, select_sex, from = 1950) {
  most_freq <- babynames |>
    filter(sex == select_sex, year > from) |>
    group_by(name) |>
    summarise(average = mean(prop)) |>
    arrange(desc(average))
    
  return(list(
    babynames = babynames,
    most_frequent = most_freq,
    sex = select_sex,
    from = from))
}

plot_top <- function(x, top = 10) {
  topx <- x$most_frequent$name[1:top]
  
  p <- x$babynames |>
    filter(name %in% topx, sex == x$sex, year > x$from) |>
    ggplot(aes(x = year, y = prop, color = name)) +
    geom_line() +
    scale_color_brewer(palette = "Paired") +
    theme_classic()
  
  return(p)
}
```

We are going to look at the distribution of baby names over time. In @fig-girls we can see the ten most frequent names for girls. Likewise in @fig-boys, we can see the same of boys.

```{r}
#| label: fig-girls
#| echo: false
#| fig-cap: Distribution of the top ten female names over time.
get_most_frequent(babynames, select_sex = "F") |>
  plot_top()
```

```{r}
#| label: fig-boys
#| echo: false
#| fig-cap: Distribution of the top ten male names over time.
get_most_frequent(babynames, select_sex = "M") |>
  plot_top()
```

# git and github part

We want to plot multiple panels in a figure - an example for the most liked girl's name can be seen in @fig-mult-girls.


```{r}
#| label: fig-mult-girls
#| layout: [[50,50], [100]]
#| fig-cap: "Most favourite girl names - a closer look!"
#| fig-subcap: 
#|   - "Top 5 girl's names over the years"
#|   - "Top 10 girl's names over the years"
#|   - "Heatmap of top 30 girl's names versus years"
# get most frequent girl names from 2010 onwards
from_year <- 2010
most_freq_girls <- get_most_frequent(babynames, select_sex = "F",
                                     from = from_year)

# plot top 5 girl names
most_freq_girls |>
  plot_top(top = 5)

# plot top 10 girl names
most_freq_girls |>
  plot_top(top = 10)

# get top 30 girl names in a matrix
# with names in rows and years in columns
prop_df <- babynames |> 
  filter(name %in% most_freq_girls$most_frequent$name[1:30] & sex == "F") |>
  filter(year >= from_year) |> 
  select(year, name, prop) |>
  pivot_wider(names_from = year,
              values_from = prop)

prop_mat <- as.matrix(prop_df[, 2:ncol(prop_df)])
rownames(prop_mat) <- prop_df$name

# create heatmap
pheatmap(prop_mat, cluster_cols = FALSE, scale = "row")
```