line_graphs.qmd

# Line graphs {#sec-line-graphs}

```{r}
#| label: setup
#| message: false
#| warning: false
#| include: false
library(tidyverse)
library(lubridate)
library(scales)
library(knitr)
library(kableExtra)
library(colorblindr)
library(downlit)
# fonts ----
library(extrafont)
library(sysfonts)
source("_common.R")
# use font
showtext::showtext_auto()
# set theme
ggplot2::theme_set(theme_ggp2g(
    base_size = 14))
```

```{r}
#| label: status
#| results: "asis"
#| echo: false
# status ----
# polish, dev, draft, complete
status("complete")
```

:::: {.callout-note collapse="false" icon=false}

<br>

```{r}
#| label: full_code_display
#| eval: true
#| echo: false
#| warning: false
#| message: false
#| out-height: '60%'
#| out-width: '60%'
#| fig-align: right
library(fivethirtyeight)
library(ggplot2)
library(paletteer)
library(ggthemes)
# remove extra dates
US_births_2004_2014 <- filter(fivethirtyeight::US_births_2000_2014, year > 2003)
avg_birth_ym <- US_births_2004_2014 |>
    dplyr::bind_rows(fivethirtyeight::US_births_1994_2003) |> 
    dplyr::mutate(
        mon_chr = dplyr::if_else(
            condition = month < 10,
            true = paste0("0", month),
            false = as.character(month)),
        month = lubridate::month(date, label = TRUE),
        yr_mnth = paste0(year, "-", mon_chr),
        yr_mnth = lubridate::ym(yr_mnth),
        yr_qtr = paste0(lubridate::year(date),
                              "/0", 
                              quarter(date)),
        yr_qtr = factor(yr_qtr, ordered = TRUE)) |> 
    dplyr::group_by(yr_mnth) |>
    dplyr::summarise(
        avg_births = mean(births, na.rm = TRUE)) |>
    dplyr::ungroup() 
labs_line <- labs(title = "Average US births", 
                subtitle = "1994-2014",
                y = "Average number of US births",
                x = "Year/Month")
ggp2_line <- ggplot(avg_birth_ym,
    aes(y = avg_births, x = yr_mnth)) + 
    geom_line()
ggp2_line + 
    labs_line
```

::: {style="font-size: 1.10em; color: #02577A;"}
**This graph requires:**
:::

::: {style="font-size: 1.05em; color: #282b2d;"}
**`r emo::ji("check")` a numeric (continuous) variable**
:::

::: {style="font-size: 1.05em; color: #282b2d;"}
**`r emo::ji("check")` a numeric (date) variable**
:::

::::

## Description 

Line graphs display changes of a numeric quantitative variable (on the `y` axis) over time (on the `x` axis). 

## Set up 

::: {style="font-size: 1.15em; color: #1e83c8;"}
**PACKAGES:**
:::


Install packages.


::: {style="font-size: 0.85em;"}
```{r}
#| label: pkg_code_line_graph
#| code-fold: show
#| eval: false
#| echo: true
#| warning: false
#| message: false
#| results: hide
install.packages("fivethirtyeight")
library(fivethirtyeight)
library(ggplot2)
```
:::



::: {style="font-size: 1.15em; color: #1e83c8;"}
**DATA:**
:::

::: {.column-margin}
![](www/538.png){fig-align="right" width="40%" height="40%"}
:::


We'll be using the `US_births_1994_2003` and `US_births_2000_2014` datasets from the `fivethirtyeight` package. 

  - Remove duplicate dates from `US_births_2000_2014`
  
  - Binding these together (they have identical columns)  

  - Use `year` and `month`  to create `yr_mnth`  

  - Use `year` and `quarter` to create `yr_qtr` 
  
  - Group by yr_month to create `avg_births`, then un-group
  
  - Save as `avg_birth_ym`

::: {style="font-size: 0.85em;"}
```{r}
#| label: data_line
#| code-fold: show
#| eval: true
#| echo: true
US_births_2004_2014 <-
    filter(fivethirtyeight::US_births_2000_2014, year > 2003)
avg_birth_ym <- US_births_2004_2014 |>
    dplyr::bind_rows(fivethirtyeight::US_births_1994_2003) |>
    dplyr::mutate(
        mon_chr = dplyr::if_else(
            condition = month < 10,
            true = paste0("0", month),
            false = as.character(month)
        ),
        month = lubridate::month(date, label = TRUE),
        yr_mnth = paste0(year, "-", mon_chr),
        yr_mnth = lubridate::ym(yr_mnth),
        yr_qtr = paste0(lubridate::year(date),
            "/0",
            lubridate::quarter(date)),
        yr_qtr = factor(yr_qtr, ordered = TRUE)
    ) |>
    dplyr::group_by(yr_mnth) |>
    dplyr::summarise(avg_births = mean(births, na.rm = TRUE)) |>
    dplyr::ungroup() 
dplyr::glimpse(avg_birth_ym)
```
:::

## Grammar

::: {style="font-size: 1.15em; color: #1e83c8;"}
**CODE:**
:::


  - Create labels with `labs()` 

  - Initialize the graph with `ggplot()` and provide `data`

  - Map `yr_mnth` to `x` and `avg_births` to `y`

  - Add `geom_line()`

::: {style="font-size: 0.85em;"}
```{r}
#| label: code_graph_line
#| code-fold: show
#| eval: false
#| echo: true 
#| warning: false
#| message: false
#| out-height: '100%'
#| out-width: '100%'
labs_line <- labs(title = "Average US births", 
                subtitle = "1994-2014",
                y = "Average number of US births",
                x = "Year/Month")
ggp2_line <- ggplot(data = avg_birth_ym,
    mapping = aes(x = yr_mnth, y = avg_births)) + 
    geom_line()
ggp2_line + 
    labs_line
```
:::



::: {style="font-size: 1.15em; color: #1e83c8;"}
**GRAPH:**
:::

```{r}
#| label: create_graph_line
#| eval: true
#| echo: false
#| warning: false
#| message: false
#| out-height: '100%'
#| out-width: '100%'
#| layout-nrow: 1
#| column: page-inset-right
labs_line <- labs(title = "Average US births", 
                subtitle = "1994-2014",
                y = "Average number of US births",
                x = "Year/Month")
ggp2_line <- ggplot(data = avg_birth_ym,
    mapping = aes(x = yr_mnth, y = avg_births)) + 
    geom_line()
ggp2_line + 
    labs_line
```