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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>R for Archaeologists</title>
<meta charset="utf-8" />
<script src="libs/header-attrs/header-attrs.js"></script>
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
.title[
# R for Archaeologists
]
.subtitle[
## Visualisation
]
---
class: middle
background-image: url("figures/jr-survey.png")
background-size: 67%
.pull-left[
**Joe Roe**
University of Bern
* <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> [joeroe.io](https://joeroe.io)
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* <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> [@joeroe](https://twitter.com/joeroe90)
]
.pull-right[
]
???
* Introducing myself
---
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}
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color: #333333;
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padding-right: 5px;
}
#svxgpepdlq .gt_first_summary_row {
border-top-style: solid;
border-top-color: #D3D3D3;
}
#svxgpepdlq .gt_first_summary_row.thick {
border-top-width: 2px;
}
#svxgpepdlq .gt_last_summary_row {
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border-bottom-color: #D3D3D3;
}
#svxgpepdlq .gt_grand_summary_row {
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}
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padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: double;
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border-top-color: #D3D3D3;
}
#svxgpepdlq .gt_last_grand_summary_row_top {
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border-bottom-style: double;
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border-bottom-color: #D3D3D3;
}
#svxgpepdlq .gt_striped {
background-color: rgba(128, 128, 128, 0.05);
}
#svxgpepdlq .gt_table_body {
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#svxgpepdlq .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
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border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
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border-left-color: #D3D3D3;
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border-right-color: #D3D3D3;
}
#svxgpepdlq .gt_footnote {
margin: 0px;
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
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}
#svxgpepdlq .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
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border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#svxgpepdlq .gt_sourcenote {
font-size: 90%;
padding-top: 4px;
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padding-left: 5px;
padding-right: 5px;
}
#svxgpepdlq .gt_left {
text-align: left;
}
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text-align: center;
}
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text-align: right;
font-variant-numeric: tabular-nums;
}
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#svxgpepdlq .gt_font_italic {
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#svxgpepdlq .gt_super {
font-size: 65%;
}
#svxgpepdlq .gt_footnote_marks {
font-size: 75%;
vertical-align: 0.4em;
position: initial;
}
#svxgpepdlq .gt_asterisk {
font-size: 100%;
vertical-align: 0;
}
#svxgpepdlq .gt_indent_1 {
text-indent: 5px;
}
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text-indent: 10px;
}
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text-indent: 15px;
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#svxgpepdlq .gt_indent_4 {
text-indent: 20px;
}
#svxgpepdlq .gt_indent_5 {
text-indent: 25px;
}
</style>
<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
<thead>
<tr class="gt_col_headings">
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" style="font-weight: bold;" scope="col" id="Day">Day</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" style="font-weight: bold;" scope="col" id="Time">Time</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" style="font-weight: bold;" scope="col" id="Topics">Topics</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" style="font-weight: bold;" scope="col" id="Exercises">Exercises</th>
</tr>
</thead>
<tbody class="gt_table_body">
<tr><td headers="Day" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'><p>Thursday</p>
</div></td>
<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'><p>10–13</p>
</div></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><strong>Introduction to visualisation in R</strong></p>
<ul>
<li>Visualisation in data science</li>
<li>Overview of R graphics packages</li>
<li>ggplot2: The ‘grammar of graphics’</li>
</ul>
</div></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><em>R for Data Science</em>, ch. 3</p>
</div></td></tr>
<tr><td headers="Day" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'></div></td>
<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'><p>14–17</p>
</div></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><strong>Visualisation for Exploratory Data Analysis</strong></p>
<ul>
<li>Applications in archaeology</li>
<li>Visualising ‘tidy data’</li>
<li><code>ggplot2</code> for EDA</li>
</ul>
</div></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><em>R for Data Science</em>, ch. 7 & 11</p>
</div></td></tr>
<tr><td headers="Day" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'><p>Friday</p>
</div></td>
<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'><p>10–13</p>
</div></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><strong>Advanced visualisation exercises</strong></p>
<ul>
<li>Tables: <code>gt</code></li>
<li>Radiocarbon: <code>ggridges</code></li>
<li>Cartography: <code>ggspatial</code></li>
<li>3D: <code>rayshader</code></li>
</ul>
</div></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><em>Groupwork</em></p>
</div></td></tr>
<tr><td headers="Day" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'></div></td>
<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><div class='gt_from_md'><p>14–17</p>
</div></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><strong>Publication-ready visualisation</strong></p>
<ul>
<li>What makes an effective graphic?</li>
<li>Advanced <code>ggplot2</code>: aesthetics and themes</li>
<li>Tips for the real world</li>
</ul>
</div></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><div class='gt_from_md'><p><em>R for Data Science</em>, ch. 28</p>
</div></td></tr>
</tbody>
</table>
</div>
???
* The "Visualisation" module follows on from the introduction to R you've had so far
* We'll start by outlining the through the two major graphics packages in R: "base" and `ggplot2`
*
* Then tomorrow, we'll look at some specific "advanced" visualisation techniques relevant to archaeology:
* First spatial visualisations and mapping, following on from last week's module on spatial analysis
* Then a selection of other specific applications – subject to change!
* And finally, on Wednesday, we'll discuss preparing plots ready for final publication
* I would like this to be as hands-on as possible, so the format for this module is you doing exercises at your own pace, with me dropping in to explain things as needed
---
class: center, middle
View these slides online:
<https://joeroe.io/r4r_visualisation/r4r_visualisation_slides.html>
Source code:
<https://github.com/joeroe/r4r_visualisation>
<small>They're written in RMarkdown!</small>
---
class: inverse, center, middle
# Visualisation with R
## Introduction
???
* Why use R for visualisations? Or: why did we devote a quarter of this course to visualisation?
* Most of you are probably here because, in some way or other, you want to use R to "do statistics"
* As you go up the admittedly steeper learning curve of visualisations in R, you might ask: why not just export the result to a more intuitive programme like Excel, and make the plots there?
* For that I have three answers:
* (or four: the zeroth being because Excel is bad software, don't use it)
---
class: center, middle
.center[<small>Data science workflow. After Wickham & Grolemund, *R for Data Science*</small>]
???
* First—and this is why we are we looking at visualisations now, rather than at the end—is that visualisation is not just the end product of a statistical analysis, something you use to **communicate**, but right at its centre, as something that helps us **understand**.
* In the exploratory data analysis (EDA) or modern "data science" workflow (illustrated very nicely here in a figure from Hadley Wickham's *R for Data Science*), visualisation is one of the pillars of data analysis
* You should use graphics often, to help you understand data as you go through the iterative cycle of interrogating and modelling it
---
class: center, middle
.pull-left[
]
.pull-right[
]
???
* This is an old idea: starting with 'exploratory data analysis', and continued in modern 'data science'
* Computers obviously make it a lot easier to generate plots quickly;
* And R was in fact originally designed to facilitate EDA, so it's very well suited for it: once you've mastered the basic graphics functions, you can generate plots easily and often
---
class: middle
# R: a statistical *programming language*
1. Iterate
2. Scale
3. Reproduce
???
* Second, R is a statisical *programming language*. This has powerful advantages compared to traditional workflows for producing plots:
* Iteration: generate your final plot and realise you need to change something in the source data? No problem!
* Scalability: write code to visualise X about Y; then run it on 1000 (or 10000, or 100000) values of Y
* Reproducibility: everybody can see exactly the steps you took to produce a plot
---
class: center, middle
## `bbplot` <<https://github.com/bbc/bbplot>>
???
* Third, R graphics simply do look very nice. Maybe not always 'out of the box', but the capability is there.
* For example, ... all use R for their statistical graphics
---
# Graphics packages in R
```r
library("archdata")
data("BACups")
```
.pull-left[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-base-1.png" width="100%" />
```r
plot(BACups$H, BACups$RD)
```
]
.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-ggplot-1.png" width="100%" />
```r
ggplot(BACups, aes(x = H, y = RD)) +
geom_point()
```
]
???
* There isn't one way to produce plots in R: you have to pick a **graphics package**
* The main options are "base" (actually `graphics`), `ggplot2`, and `lattice` (now rarely used)
* These packages have major differences in syntax, and by extension flexibility and capabilities
---
.pull-left[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-base-ext-1.png" width="100%" />
```r
par(mfrow = c(1,2))
plot(BACups[BACups$Phase=="Protoapennine",]$H,
BACups[BACups$Phase=="Protoapennine",]$RD,
cex = BACups[BACups$Phase=="Protoapennine",]$ND / 10,
main = "Bronze Age cups",
sub = "Protoapennine",
xlab = "Height",
ylab = "Rim Diameter")
plot(BACups[BACups$Phase=="Subapennine",]$H,
BACups[BACups$Phase=="Subapennine",]$RD,
cex = BACups[BACups$Phase=="Subapennine",]$ND / 10,
main = "(Data from Lukesh and Howe 1978)",
sub = "Subapennine",
xlab = "Height",
ylab = "Rim Diameter")
legend("bottomright",
title = "Neck Diameter",
pch = 1,
legend = c(10, 15, 20, 25),
pt.cex = c(1, 1.5, 2, 2.5))
```
]
.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-ggplot-ext-1.png" width="100%" />
```r
ggplot(BACups, aes(x = H, y = RD, size = ND)) +
geom_point(shape = 1) +
facet_wrap(vars(Phase)) +
labs(x = "Height",
y = "Rim Diameter",
size = "Neck Diameter",
title = "Bronze Age cups",
subtitle = "Data from Lukesh and Howe (1978)")
```
]
???
* These differences are highlighted when you try to do more complex plots
* Don't get hung up on the aesthetic differences – these are defaults, you can easily make one look like the other (or anything else)
* There is some debate over the pros and cons of each – I see ggplot2 as a straightforward upgrade and from this afternoon we will focus on its approach
* But it's helpful to be familiar with base as the 'default' in a lot of cases, e.g. used by many packages to plot complex data structures, so we'll start with that
---
class: inverse, center, middle
# `ggplot2`: the 'grammar of graphics'
???
* `ggplot2` is based on the "grammar of graphics"
* This is a somewhat theoretical concept. Further reading:
* Wickham 2010, *A Layered Grammar of Graphics*
* Wilkinson, Anand & Grossman 2005, *The Grammar of Graphics*
* But it's helpful to have some idea of that theory, to make sense of random prefixes like "aes" or "geom"
---
class: center, middle
# The Grammar of Graphics
???
* The grammer is a structured way of *describing* the relationship between data and graphical elements (declarative) – as opposed to `base` graphics, which is you *telling* R what to plot (imperative)
* Specifically, `ggplot` introduces a *layered* grammer of graphics, allowing you to combine multiple mappings into a single plot
* This makes it much more flexible than base and allows us to maintain readable code for even very complicated plots
* Downsides: data does have to be in a roughly tabular (if not fully "tidy") structure to work well with ggplot. Complex objects from other packages can be a challenge to work with.
* For EDA purposes, it's often better to fall back on `base::plot()` with these objects
* Though packages like `broom` can help
---
# Plotting with `ggplot2`: exercises
**Wickham & Grolemund, *R for data science***
https://r4ds.had.co.nz/
Chapter 3: *Data visualisation*
Chapter 7: *Exploratory Data Analysis* (if you have time)
* Install the `tidyverse` metapackage
* Run Hadley's code examples
* Answer questions as comments (`# Here is my answer...`)
* Do the 'exercises' sections – they're very informative!
---
# Plotting with `ggplot2`: applications?
.pull-left[
<img src="r4r_visualisation_slides_files/figure-html/ggplot-scatter-1.png" width="100%" />
<img src="r4r_visualisation_slides_files/figure-html/ggplot-smooth-1.png" width="100%" />
]
.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/ggplot-bar-1.png" width="100%" />
<img src="r4r_visualisation_slides_files/figure-html/ggplot-boxplot-1.png" width="100%" />
]
???
* We've seen how to do a few fundamental plots with ``ggplot2` this morning, but using "dummy" datasets like `cars` and `diamonds`.
* What are some archaeological applications of these plots?
* Tomorrow we will look at some more niche types of visualisation in archaeology;
* For now, we continue to work on the fundamentals of using visualisation EDA
---
class: middle
# Tidy data
???
* As I mentioned this morning, a big part of producing graphics effectively is having the tools to transform data to the right format
* And `ggplot2` is generally super-charged when used as part of a "tidy data analysis" workflow
* With the example data you've used so far, this has been done for you. With "real world" archaeology data, it won't be!
---
# Plotting with tidy data
.pull-left[
```r
data("emedyd") # From rcarbon
emedyd %>%
filter(SiteName == "Wadi Jilat 13") %>%
arrange(CRA) %>%
mutate(LabID = as_factor(LabID)) %>%
mutate(CalDate = cal(CRA, Error, verbose = FALSE)) %>%
unnest_legacy() %>%
ggplot(aes(calBP, PrDens)) +
facet_wrap(~LabID, ncol = 1,
scales = "free_y",
strip.position = "left") +
geom_area() +
scale_x_reverse(name = "cal BP") +
scale_y_continuous(labels = NULL, name = NULL) +
labs(title = "Wadi Jilat 13",
subtitle = "Radiocarbon chronology")
```
]
.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/tidy-rcarbon-1.png" width="100%" />
]
???
* For example, *pipes* allow us to elegantly and concisely load data, clean and filter, and plot in a single code block
* Half of the code here—the most important half—is *reshaping* the data before even giving it to the plot. Often, it's more than half!
* You can read more about reshaping or "wrangling" data in *R for Data Science*
---
# `ggplot2` for EDA: exercises
**Wickham & Grolemund, *R for data science***
https://r4ds.had.co.nz/
Chapter 3: *Data visualisation* (if you haven't finished)
Chapter 7: *Exploratory Data Analysis*
Chapter 12: *Tidy data* (if you have time)
* Run Hadley's code examples
* Answer questions as comments (`# Here is my answer...`)
* Do the 'exercises' sections – they're very informative!
---
class: inverse, center, middle
# Advanced visualisations
## Plotting archaeological data
---
class: inverse, center, middle
# CartogRaphy
## Mapping with `ggplot2` and friends
---
class: center, middle
# R as a GIS
## (R isn't a GIS)
---
# R spatial ecosystem
.pull-left[
* Spatial data
* `rgdal`, `rgeos`, `rgrass7`, `link2gi`
* `sf`, `sp`
* `raster`, `stars`, `terra`
* Geostatistics
* `spatstat`, `gstat`, `spdep`, `nlme`
* `landsat`, `dismo`
]
.pull-right[
* Base maps + open data
* `maps`, `maptools`, `rnaturalearth`
* `ggmap`, `leaflet`, `mapview`
* `rOpenSci`
* Cartography
* `ggplot2`
* `ggspatial`
* `rasterVis`
* `tmap`
]
---
# Mapping with `ggplot2`: exercises (1/2)
**Wickham, Navarro & Pedersen, *ggplot2: Elegant Graphics for Data Analysis***
https://ggplot2-book.org/
Chapter 6: *Maps*
* Run the code examples
* Reproduce the following using the 'tavo-a27' dataset.
* Hint: In addition to `ggplot2`, you will need the `raster` and `ggspatial` packages.
---
class: center, middle
<img src="r4r_visualisation_slides_files/figure-html/eg-tavo-1.png" width="100%" />
---
# Tables (`gt`)
## The forgotten visualisation
<div id="npzqtpzxze" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>#npzqtpzxze table {
font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
#npzqtpzxze thead, #npzqtpzxze tbody, #npzqtpzxze tfoot, #npzqtpzxze tr, #npzqtpzxze td, #npzqtpzxze th {
border-style: none;
}
#npzqtpzxze p {
margin: 0;
padding: 0;
}
#npzqtpzxze .gt_table {
display: table;
border-collapse: collapse;
line-height: normal;
margin-left: auto;
margin-right: auto;
color: #333333;
font-size: 16px;
font-weight: normal;
font-style: normal;
background-color: #FFFFFF;
width: auto;
border-top-style: solid;
border-top-width: 2px;
border-top-color: #A8A8A8;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #A8A8A8;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
}
#npzqtpzxze .gt_caption {
padding-top: 4px;
padding-bottom: 4px;
}
#npzqtpzxze .gt_title {
color: #333333;
font-size: 125%;
font-weight: initial;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
border-bottom-color: #FFFFFF;
border-bottom-width: 0;
}
#npzqtpzxze .gt_subtitle {
color: #333333;
font-size: 85%;
font-weight: initial;