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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# UK unemployment data uncertainty 2013-2018"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Examples of fan charts and their variations explored by the Winton Centre for Risk and Evidence Communication.\n",
"\n",
"In this example we'll be using UK unemployment data provided by the [Office for National Statistics](https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment) between 2013 and 2018 to represent statistical uncertainty in data collection."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1 - Cleaning up the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generating data to find confidence intervals of 30%, 60%, and 95% to represent in visual form.\n",
"\n",
"Read in the data and generate confidence intervals of 30%, 60%, 95%."
]
},
{
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"execution_count": 1,
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"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import norm\n",
"\n",
"\n",
"def generate_interval_point(p, center, std):\n",
" point = [p]\n",
" boundary_point = norm.ppf(point, loc=center, scale=std)\n",
" return boundary_point[0]\n",
"\n",
"# Takes csv with CI and generate data.\n",
"def create_data():\n",
" # Read the data\n",
" df = pd.read_csv('uk_unemployment_2013-2018.csv')\n",
"\n",
" # Finds std from given 95% CI. Assumes it follows normal curve\n",
" df['std'] = df['95%CI'] / 1000 / 1.96\n",
"\n",
" # print(list(df.dtypes.index))\n",
"\n",
" std = list(df['std'])\n",
"\n",
" # Scale to be in thousands\n",
" y = list(df['Number of unemployed people']/1000)\n",
"\n",
" y_n_95 = []\n",
" y_p_95 = []\n",
"\n",
" y_n_60 = []\n",
" y_p_60 = []\n",
"\n",
" y_n_30 = []\n",
" y_p_30 = []\n",
"\n",
" y_median = []\n",
"\n",
" for i in range(len(y)):\n",
" y_n_95.append(generate_interval_point(0.025, y[i], std[i]))\n",
" y_p_95.append(generate_interval_point(0.975, y[i], std[i]))\n",
"\n",
" y_n_60.append(generate_interval_point(0.2, y[i], std[i]))\n",
" y_p_60.append(generate_interval_point(0.8, y[i], std[i]))\n",
"\n",
" y_n_30.append(generate_interval_point(0.35, y[i], std[i]))\n",
" y_p_30.append(generate_interval_point(0.65, y[i], std[i]))\n",
"\n",
" y_median.append(generate_interval_point(0.5, y[i], std[i]))\n",
"\n",
" # Improving x-axis label for this chart.\n",
" df['DateLabel'] = df['DateLabel'].replace(np.nan, '', regex=True)\n",
"\n",
" x = list(df['Date'])\n",
" x_label = list((df['DateLabel']))\n",
"\n",
" return x, x_label, y_median, y_p_95, y_n_95, y_p_30, y_n_30, y_p_60, y_n_60"
]
},
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"['Aug-Oct 2013', 'Sep-Nov 2013', 'Oct-Dec 2013', 'Nov-Jan 2014', 'Dec-Feb 2014', 'Jan-Mar 2014', 'Feb-Apr 2014', 'Mar-May 2014', 'Apr-Jun 2014', 'May-Jul 2014', 'Jun-Aug 2014', 'Jul-Sep 2014', 'Aug-Oct 2014', 'Sep-Nov 2014', 'Oct-Dec 2014', 'Nov-Jan 2015', 'Dec-Feb 2015', 'Jan-Mar 2015', 'Feb-Apr 2015', 'Mar-May 2015', 'Apr-Jun 2015', 'May-Jul 2015', 'Jun-Aug 2015', 'Jul-Sep 2015', 'Aug-Oct 2015', 'Sep-Nov 2015', 'Oct-Dec 2015', 'Nov-Jan 2016', 'Dec-Feb 2016', 'Jan-Mar 2016', 'Feb-Apr 2016', 'Mar-May 2016', 'Apr-Jun 2016', 'May-Jul 2016', 'Jun-Aug 2016', 'Jul-Sep 2016', 'Aug-Oct 2016', 'Sep-Nov 2016', 'Oct-Dec 2016', 'Nov-Jan 2017', 'Dec-Feb 2017', 'Jan-Mar 2017', 'Feb-Apr 2017', 'Mar-May 2017', 'Apr-Jun 2017', 'May-Jul 2017', 'Jun-Aug 2017', 'Jul-Sep 2017', 'Aug-Oct 2017', 'Sep-Nov 2017', 'Oct-Dec 2017', 'Nov-Jan 2018', 'Dec-Feb 2018', 'Jan-Mar 2018', 'Feb-Apr 2018', 'Mar-May 2018', 'Apr-Jun 2018', 'May-Jul 2018', 'Jun-Aug 2018', 'Jul-Sep 2018', 'Aug-Oct 2018']\n",
"\n",
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]
}
],
"source": [
"# Lets take quick look at x, y values typically produced without uncertainty.\n",
"x, x_label, y_median, y_p_95, y_n_95, y_p_30, y_n_30, y_p_60, y_n_60 = create_data()\n",
"\n",
"\n",
"print(x) # dates\n",
"print()\n",
"print(y_median) # y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 2 - Using the library"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 2.1 - A solid fan chart\n",
"This is an example of a fan chart as used by the Bank of England and Office for National Statistics to represent uncertainty.\n",
"\n",
"Below we'll create a basic fan chart and customise the chart.\n",
"\n",
"Click and drag on the charts to enlarge a section. Double click to zoom back out."
]
},
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""
],
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""
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],
"source": [
"from fuzzy.core import FanPlotly\n",
"\n",
"solid_ci = FanPlotly(\n",
" x, y_median,\n",
" ci95p=y_p_95, ci95n=y_n_95,\n",
" ci60p=y_p_60, ci60n=y_n_60,\n",
" ci30p=y_p_30, ci30n=y_n_30,\n",
")\n",
"solid_ci.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The scaling and labeling defaults are not optimal. \n",
"\n",
"For example, the y-axis needs to be labelled (2,500 really means 2,500,000). To change the defaults, we can pass in a plotly [layout](https://plot.ly/python/user-guide/#layout) object.\n",
"\n",
"Refer to [plotly documation](https://plot.ly/python/reference/#layout) for all the options.\n",
"\n",
"We will be customizing\n",
"- Labels\n",
"- x-axis\n",
"- y-axis\n",
"- Font\n",
"- Ticks\n",
"- Margin"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# for ticks, change step size. so it only plots ticks on those parts.\n",
"x_new = x[0::2]\n",
"x_label_new = x_label[0::2]\n",
"# Add in new line between month and year\n",
"x_label_new = [x_label.replace('-', '
20') for x_label in x_label_new]\n",
"\n",
"\n",
"layout = {\n",
" 'showlegend': False,\n",
" 'title': 'UK Migration figures (2013-2018)',\n",
" 'xaxis': {\n",
" 'title': 'Date',\n",
" 'titlefont': {\n",
" 'family': 'Arial, sans-serif',\n",
" 'size': 18,\n",
" 'color': 'black',\n",
" },\n",
" 'ticktext':x_label_new,\n",
" 'tickvals':x_new,\n",
" 'showgrid':False,\n",
" 'showline': True,\n",
"\n",
" 'tickmode':'array',\n",
" 'ticks': 'outside',\n",
" 'tickangle': 0,\n",
" 'showticklabels': True,\n",
" 'tickwidth': 2,\n",
" 'tickcolor': '#000',\n",
" },\n",
" 'yaxis': {\n",
" 'title': 'Unemployment (in thousands)',\n",
" 'titlefont': {\n",
" 'family': 'Arial, sans-serif',\n",
" 'size': 18,\n",
" 'color': 'black',\n",
" },\n",
" 'showgrid':False,\n",
" 'range': [1000000/1000, 2600000/1000],\n",
" 'showline': True,\n",
" 'tickmode': 'array',\n",
" 'ticks': 'outside',\n",
" 'mirror': False,\n",
" 'tickwidth': 2,\n",
"\n",
" },\n",
" 'margin': {\n",
" 'pad':14,\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 2.2 - Improved solid fan chart\n",
"\n",
"Lets pass in a layout object to improve things."
]
},
{
"cell_type": "code",
"execution_count": 5,
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"\n",
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"\n",
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" x, y_median,\n",
" ci95p=y_p_95, ci95n=y_n_95,\n",
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" color='#fc8f8f',\n",
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"### Plot 2.4 - Density chart\n",
"This example uses a normal distribution of colour opacity to represent the likely true position of the line.\n",
"\n",
"We can pass in hex value to change the color and width of central line. Default value is 1."
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""
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"source": [
"from fuzzy.core import DensPlotly\n",
"\n",
"dens_chart = DensPlotly(\n",
" x=x, y=y_median,\n",
" ci95p=y_p_95, ci95n=y_n_95,\n",
" color_levels=30,\n",
" layout=layout,\n",
" median_line_color='#8b4513',\n",
" median_line_width=0.5,\n",
")\n",
"\n",
"dens_chart.plot()"
]
},
{
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"metadata": {},
"source": [
"### Plot 2.5 - Fuzzy fan chart\n",
"\n",
"This is a fan chart where the boundaries between the bands has been blurred to emphasise that the uncertainty. The original bands are stll distinguishable in magnification.\n",
"\n",
"You may perceive more bands than are actually drawn when using this representation. Your perception of these visual artefacts will vary with parameters such as colour, fuzz_size (The width of the blurring), color_levels (The number of colour levels used to implement the blur).\n",
"\n",
"Note: Depending on your machine, a chart using a color_levels value above 70 will take a long time to display."
]
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"from fuzzy.core import StandardErrorPlot\n",
"\n",
"# Creating my own plotly figure.\n",
"my_plot_red = {\n",
" 'marker': {'color': 'red', 'size': 10, 'symbol': 104},\n",
" 'mode': 'lines',\n",
" 'name': 'My custom plot',\n",
" 'text': ['one', 'two', 'three'],\n",
" 'type': 'scatter',\n",
" 'x': ['Aug-Oct 2013', 'Sep-Nov 2013', 'Oct-Dec 2013', 'Nov-Jan 2014', 'Dec-Feb 2014'],\n",
" 'y': [1200, 1800, 1400, 1500, 2400]\n",
" }\n",
"\n",
"# Creating Standard Error plot.\n",
"standard_error = StandardErrorPlot(\n",
" x, y_median,\n",
" ci95p=y_p_95, ci95n=y_n_95,\n",
" layout=layout,\n",
" figs=[my_plot_red]\n",
")\n",
"\n",
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},
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"source": [
"### 3.2 - Exporting the plotly data structure\n",
"\n",
"The plotly data structure is a serialised form of the plot which may be useful in a larger system.\n",
"\n",
"Here we export a StandardErrorPlot and then compose it with other line plots."
]
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"source": [
"from fuzzy.core import StandardErrorPlot\n",
"\n",
"# Creating Standard Error plot.\n",
"standard_error = StandardErrorPlot(\n",
" x, y_median,\n",
" ci95p=y_p_95, ci95n=y_n_95,\n",
" layout=layout,\n",
")\n",
"\n",
"# Extract\n",
"standard_error_export = standard_error.export()\n",
"\n",
"# Creating my own plotly figure.\n",
"my_plot_green = {\n",
" 'marker': {'color': 'green', 'size': 10, 'symbol': 104},\n",
" 'mode': 'lines',\n",
" 'name': 'My custom plot green',\n",
" 'text': ['one', 'two', 'three'],\n",
" 'type': 'scatter',\n",
" 'x': ['Aug-Oct 2013', 'Sep-Nov 2013', 'Oct-Dec 2013', 'Nov-Jan 2014', 'Dec-Feb 2014'],\n",
" 'y': [2421, 1251, 1861, 1500, 1765]\n",
" }\n",
"\n",
"# Append addictional plot(s) to the list. \n",
"all_data = standard_error_export + [my_plot_green]\n",
"\n",
"# Plot using plotly.\n",
"import plotly.plotly as py\n",
"\n",
"py.iplot(all_data)"
]
},
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"cell_type": "code",
"execution_count": 11,
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"source": [
"# Add both custom plots\n",
"all_data_two = standard_error_export + [my_plot_red, my_plot_green]\n",
"\n",
"py.iplot(all_data_two)"
]
}
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