quick_charts.Rmd

---
title: "Producing charts available on Fingertips"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Producing charts available on Fingertips}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```r
knitr::opts_chunk$set(fig.path = "charts-")
```

## Introduction

This vignette demonstrates how to create some of the charts available in this package. All of the names of the functions match the name for the chart on Fingertips.

### A note on ordering within the charts

The charts produced, generally, order the data alphabetically. In the [static map](#static) section and the [compare areas](#compareareas), there are worked examples of reordering the factor levels so they are presented in the required order in the legends.

## Libraries


```r
library(fingertipscharts)
library(dplyr)
library(tidyr)
# library(webshot)
```

## Overview {#overview}

This function plots the **Overview** view (also known as the Tartan Rug). All of the charts in this vignette, starting with this one, use a function called `create_test_data()` from this packages to generate a dummy dataset. This data has the same structure as data exported from the `fingertips_data()` function in the `fingertipsR` package.


```r
region <- "PAC19"
top_names <- c("C001", region)
dfdom <- create_test_data() %>%
        filter((AreaCode %in% top_names |
                                ParentAreaCode == region)) %>%
        mutate(Value = round(Value, 1))
```

This is how the `overview()` function works:


```r
p <- overview(data = dfdom,
              area = AreaCode,
              indicator = IndicatorName,
              value = Value,
              fill = Significance,
              timeperiod = Timeperiod,
              top_areas = top_names, wrap_length = 40,
              value_label_size = 0.7)
p
```

<img src="charts-overview-1.png" title="plot of chunk overview" alt="plot of chunk overview" width="100%" style="display: block; margin: auto;" />

## Compare indicators

Here is a function that helps produce a scatter plot as displayed on Fingertips. First the data needs to be changed to the correct format. The two variables that will be plotted need to be in separate fields. The `pivot_wider()` function from the `tidyr` package demonstrates how to do this below:


```r
df <- create_test_data() %>%
  filter(IndicatorName %in% c("Indicator 1", "Indicator 2")) %>%
  select(IndicatorName, AreaCode, Value) %>%
  mutate(IndicatorName = gsub(" ", "", IndicatorName)) %>%
  pivot_wider(names_from = IndicatorName,
              values_from = Value)
```

This is how the `compare_indicators()` function works:


```r
p <- compare_indicators(data = df,
                        x = Indicator1,
                        y = Indicator2,
                        xlab = "This is indicator 1",
                        ylab = "This is indicator 2",
                        highlight = c("C001", "AC101"),
                        area = AreaCode,
                        add_R2 = TRUE)
p
```

<img src="charts-compare-indicators-1.png" title="plot of chunk compare-indicators" alt="plot of chunk compare-indicators" width="80%" style="display: block; margin: auto;" />

## Maps

Here the package provides both interactive and static maps.

The shape files need to be sources from the ONS Geography Portal. To do this, click on the API button [here](http://geoportal.statistics.gov.uk/datasets/counties-and-unitary-authorities-december-2016-ultra-generalised-clipped-boundaries-in-england-and-wales), using Counties and Unitary Authorities as an example, and then use the GeoJSON address as the string input to the `ons_api` parameter.


```r
ons_api <- "https://opendata.arcgis.com/datasets/687f346f5023410ba86615655ff33ca9_4.geojson"
```


### Static {#static}

**Note,** before plotting the chart, the levels of the field that is determining the fill of the polygons need to be reordered, as if they aren't, they will be ordered alphabetically in the legend:


```r
ordered_levels <- c("Better",
                    "Similar",
                    "Worse",
                    "Not compared")
df <- mapdata %>%
        mutate(Significance =
                       factor(Significance,
                              levels = ordered_levels))
```

The default map type is static:


```r
p <- fingertipscharts::map(data = df,
                    ons_api = ons_api,
                    area_code = AreaCode,
                    fill = Significance,
                    title = "Map title",
                    subtitle = "Map subtitle",
                    copyright_size = 3)
p
```

<img src="charts-map-static-1.png" title="plot of chunk map-static" alt="plot of chunk map-static" width="80%" style="display: block; margin: auto;" />

### Interactive

It is also possible to have interactive maps. Note, for this to work the value parameter must be included and subtitle doesn't work.


```r
p <- map(df,
         ons_api = ons_api,
         area_code = AreaCode,
         fill = Significance,
         type = "interactive",
         value = Value,
         name_for_label = AreaName,
         title = "Map title<br>with a line break")
p
```

<img src="charts-map-interactive-1.png" title="plot of chunk map-interactive" alt="plot of chunk map-interactive" width="80%" style="display: block; margin: auto;" />

## Trends

This function allows users to plot the trends graph. The dummy dataset is slightly manipulated here to extend over a few years. A significance is then prescribed based on whether the confidence intervals overlap the country value.


```r
df <- create_test_data() %>%
        arrange(IndicatorName) %>%
        mutate(Timeperiod = rep(c("2011", "2012", "2013", "2014", "2015", "2016"),
                                each = 111))
country_val <- df %>%
  filter(AreaCode == "C001") %>%
  select(Timeperiod, Country_val = Value)

df <- df %>%
  left_join(country_val, by = "Timeperiod") %>%
  mutate(Significance = case_when(
    LCI > Country_val ~ "Higher",
    UCI < Country_val ~ "Lower",
    TRUE ~ "Similar"
  ))
```

Here is an example of how to use the `trends()` function:


```r
p <- trends(df,
            timeperiod = Timeperiod,
            value = Value,
            area = AreaCode,
            comparator = "C001",
            area_name = "AC103",
            fill = Significance,
            lowerci = LCI,
            upperci = UCI,
            title = "Title of graph",
            subtitle = "AC103 compared to C001",
            xlab = "Year",
            ylab = "Unit of measurement")
p
```

<img src="charts-trends-1.png" title="plot of chunk trends" alt="plot of chunk trends" width="70%" style="display: block; margin: auto;" />

## Compare areas {#compareareas}

This code filters the dummy dataset for a single region and its parent, and all the child areas within that region.


```r
region <- "PAC10"
top_names <- c("C001", region)
df <- create_test_data() %>%
  filter(IndicatorName == "Indicator 3",
         (ParentAreaCode == region |
            AreaCode %in% top_names))
```

**Note,** before plotting the chart, the levels of the field that is determining the fill of the bars need to be reordered, as if they aren't, they will be ordered alphabetically in the legend:


```r
ordered_levels <- c("Better",
                    "Similar",
                    "Worse",
                    "Not compared")
df <- df %>%
        mutate(Significance =
                       factor(Significance,
                              levels = ordered_levels))
```

The `compare_areas` function makes plotting the compare areas chart straight forward.


```r
p <- compare_areas(df,
                   AreaCode,
                   Value,
                      fill = Significance,
                      lowerci = LCI,
                      upperci = UCI,
                      order = "desc",
                      top_areas = top_names,
                      title = unique(df$IndicatorName))
p
```

<img src="charts-compare-areas-1.png" title="plot of chunk compare-areas" alt="plot of chunk compare-areas" width="80%" style="display: block; margin: auto;" />

## Area profiles

The area profiles image is created using the `area_profiles()` function.


```r
dfspine <- create_test_data()
```



```r
p <- area_profiles(dfspine,
                   value = Value,
                   count = Count,
                   area_code = AreaCode,
                   local_area_code = "AC110",
                   indicator = IndicatorName,
                   timeperiod = Timeperiod,
                   trend = Trend,
                   polarity = Polarity,
                   significance = Significance,
                   area_type = AreaType,
                   cols = "fingertips",
                   median_line_area_code = "C001",
                   comparator_area_code = "PAC11",
                   datatable = TRUE,
                   header_positions = c(-1.2, -0.9, -0.63,
                                        -0.48, -0.36, -0.21,
                                        -0.05, 1.08),
                   relative_domain_text_size = 0.75,
                   relative_text_size = 1.2,
                   bar_width = 0.68,
                   horizontal_arrow_multiplier = 0.7)
p
```

<img src="charts-area-profiles-1.png" title="plot of chunk area-profiles" alt="plot of chunk area-profiles" width="100%" />

## Population

This function provides the ability to draw the traditional population pyramid. Note that the ordering of the age bands are defined prior to the data being provided to the `population()` function:


```r
set.seed(1234)
agelevels <- c("0-4", "5-9","10-14","15-19",
               "20-24","25-29","30-34",
               "35-39","40-44","45-49",
               "50-54","55-59","60-64",
               "65-69","70-74","75-79",
               "80-84","85-89","90+")
local_males <- dnorm(seq(1, 19), mean = 5, sd = 5) * 1e5
local_females <- dnorm(seq(1, 19), mean = 4, sd = 5) * 1e5
region_males <- dnorm(seq(1, 19), mean = 5, sd = 7) * 1e5
region_females <- dnorm(seq(1, 19), mean = 4.5, sd = 6.5) * 1e5
country_males <- dnorm(seq(1, 19), mean = 4, sd = 3) * 1e5
country_females <- dnorm(seq(1, 19), mean = 5, sd = 3) * 1e5

pops <- data.frame(Value = c(local_males,
                             local_females,
                             region_males,
                             region_females,
                             country_males,
                             country_females),
                   AreaName = c(rep("Local", 38),
                                rep("Region", 38),
                                rep("Country", 38)),
                   Sex = rep(c(rep("Male", 19),
                              rep("Female", 19)),
                             times = 3),
                   stringsAsFactors = FALSE) %>%
  mutate(Age = factor(rep(agelevels, 6),
                          levels = agelevels))
```

This is how the function works:


```r
p <- population(pops,
                value = Value,
                sex = Sex,
                age = Age,
                area = AreaName,
                area_name = "Local",
                comparator_1 = "Country",
                comparator_2 = "Region",
                title = "Age Profile",
                subtitle = "Area population for this time period",
                xlab = "% of total population")
p
```

<img src="charts-population-1.png" title="plot of chunk population" alt="plot of chunk population" width="70%" style="display: block; margin: auto;" />

## Box plots

Box plots can also be drawn.


```r
df <- create_test_data() %>%
        arrange(IndicatorName) %>%
        mutate(Timeperiod = rep(c("2011", "2012", "2013", "2014", "2015", "2016"),
                                each = 111))
```

This is how the function works. Note, the levels of the factor for the `timeperiod` parameter may need to be reordered, as this determines the order along the x-axis:


```r
p <- box_plots(df,
               timeperiod = Timeperiod,
               value = Value,
               title = "Box plot for indicator",
               subtitle = "Add a subtitle here",
               ylab = "Unit of measurement")
p
```

<img src="charts-boxplots-1.png" title="plot of chunk boxplots" alt="plot of chunk boxplots" width="80%" style="display: block; margin: auto;" />