analysis.qmd

---
title: "Analysis"
---

We will use R version 4.3.0 with some libraries:

-   babynames
-   knitr
-   dplyr
-   ggplot2

```{r}
library(babynames)
library(knitr)
library(dplyr)
library(ggplot2)
```

## A glimpse of the data to be used:

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

## Let's go

```{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(most_freq)
}

plot_top <- function(x, babynames, select_sex, top = 10, from = 1950) {
  topx <- x$name[1:top]
  
  p <- babynames |>
    filter(name %in% topx, sex == select_sex, year > from) |>
    ggplot(aes(x = year, y = prop, color = name)) +
    geom_line() +
    scale_color_brewer(palette = "Paired") +
    theme_classic()
  
  return(p)
}
```

```{r}
#| label: "fig-girl"
#| fig-cap: "girls"
get_most_frequent(babynames, select_sex = "F") |>
  plot_top(babynames, select_sex = "F", top = 10)

```

You can see girl names in @fig-girl just above.

```{r}
#| label: "fig-boy"
#| fig-cap: "boys"
get_most_frequent(babynames, select_sex = "M") |>
  plot_top(babynames, select_sex = "M", top = 10)

```

Same for boy names in @fig-boy just above.