{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.5.1 - Beginner - Introduction to Data Visualization I\n", "\n", "COMET Team
*Anneke Dresselhuis, Jonathan Graves* \n", "2023-01-12\n", "\n", "## Outline\n", "\n", "### Prerequisites\n", "\n", "- Introduction to Jupyter\n", "- Introduction to R\n", " - Be able to load data and packages in R\n", " - Be able to create variables and objects in R\n", " - Be familiar with the general syntax of R commands\n", "\n", "### Learning Outcomes\n", "\n", "- Identify best practices for data visualization design\n", "- Describe when to use the following kinds of visualizations to answer\n", " specific questions using a data set:\n", " - scatterplots\n", " - line plots\n", " - bar plots\n", " - histograms\n", "- Use the `ggplot2` package in R to create and refine the above\n", " visualizations using\n", " - geometric objects\n", " - aesthetic mappings: `x`, `y`, `fill`, `color`\n", " - labeling: `xlab`, `ylab`, `labs`\n", " - font control and legend positioning: theme\n", "- Describe the difference between vector and raster file outputs\n", "- Use `ggsave` to save visualizations in `.png` and `.svg` format\n", "\n", "### References\n", "\n", "- Timbers, T., Campbell, T., Lee, M. (2022). [*Data Science: A First\n", " Introduction*.](https://datasciencebook.ca/viz.html)\n", "- Metwalli, S. A. (2021, July 15). Data Visualization 101: How to\n", " choose a chart type. Medium. Retrieved June 10, 2022, from\n", " https://towardsdatascience.com/data-visualization-101-how-to-choose-a-chart-type-9b8830e558d6\n", "\n", "## Part 1: Understanding Visualization\n", "\n", "### Introduction\n", "\n", "> **“The purpose of a visualization is to answer a question about a data\n", "> set of interest.”** \n", "> Timbers, T., Campbell, T., Lee, M. (2022). [*Data Science: A First\n", "> Introduction*.](https://datasciencebook.ca/viz.html)\n", "\n", "In econometrics, good data visualizations should always…\n", "\n", "1. Answer a well-thought-out and relevant economic research question.\n", "2. Provide readers with a clear understanding of the research question\n", " and answer\n", "\n", "Questions to keep in mind:\n", "\n", "- *Who is our audience?*\n", "- *What do they know?*\n", "- *What is the question we’re trying to answer?*\n", "\n", "Not only are data visualizations incredibly important as narrative\n", "outputs from data analysis, they can also help us identify patterns or\n", "anomalies as we process our data.\n", "\n", "### Principles of Design: Data Visualization *DOs* and *DONT’s*\n", "\n", "> **DO** use data visualization to tell the story of the data\n", "> *truthfully* \n", "> **DO** remember that a visualization’s accuracy is only as good as the\n", "> data is \n", "> **DO** label your axes in font sizes that are readable and use\n", "> descriptive titles\n", "\n", "> **DON’T** choose colours that are very similar to each other when\n", "> trying to distinguish 2 variables (red & blue \\> red & orange) \n", "> **DON’T** use design features (eg, exaggerated scaling) to manipulate\n", "> readers into believing a particular narrative of the data\n", "\n", "### Types of Visualizations:\n", "\n", "The four following plot types we will be working with can all be found\n", "in the `ggplot2` package:\n", "\n", "Note: There are other plots that can be generated using this package which we’ll explore in *Introduction to Data Visualization II* or check out [R studio’s ggplot2 Cheat Sheet](https://www.rstudio.com/resources/cheatsheets/)\n", "\n", "- **Scatter plot**\n", " - Visualizes the relationship between two quantitative variables\n", " - Good for showing relationships and groupings among variables\n", " from relatively large datasets\n", "- **Line plot**\n", " - Visualizes trends with respect to an independent, ordered\n", " quantity (e.g time)\n", " - Good for when one of our variables is ordinal (time-like) or to\n", " display multiple series on a common timeline\n", "- **Bar plot**\n", " - Visualizes comparisons of amounts\n", " - Good for comparing a few categories as parts of a whole or\n", " across time\n", "- **Histogram**\n", " - Visualizes the distribution of one quantitative variable\n", " - Good for working with a discrete variable and visualizing all\n", " its possible values and how often they occur\n", "\n", "*Definitions adapted from: [Data Science: A First\n", "Introduction](https://datasciencebook.ca/viz.html)*[.](https://datasciencebook.ca/viz.html)\n", "\n", "![Examples of Four Plot\n", "Types](attachment:media/plot_type_examples.png \"Figure 4.1: Examples of scatter, line and bar plots, as well as histograms.\")\n", "\n", "*Figure 1. Examples of scatter, line and bar plots, as well as\n", "histograms. (from [Data Science: A First\n", "Introduction](https://datasciencebook.ca/viz.html))*\n", "\n", "### Loading data\n", "\n", "In this tutorial, we will be working with the Penn World Table 10.0.\n", "This data is via:\n", "\n", "- Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015),\n", " “The Next Generation of the Penn World Table” American Economic\n", " Review, 105(10), 3150-3182, available for download at\n", " https://www.rug.nl/ggdc/productivity/pwt/\n", "\n", "To download the dataset we will be using for this notebook:\n", "\n", "1. Click the link provided above. The PWT page should appear\n", "2. Scroll down until three access options appear\n", "3. Click Stata and a Stata file (`.dta`) should immediately download.\n", " Now move that file to your media directory and we can start the\n", " analysis!\n", "\n", "Let’s start by importing the packages and data into our notebook. if\n", "you’re not sure what a variable represents, check out the documentation\n", "on the link above." ], "attachments": { "media/plot_type_examples.png": { "image/png": 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xEVN8o8OX1U7266+/lj///FMT3PjxAzP1Y0JLRLa0fv16+eCDD6Rly5bSuXNn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\nDCIe96tcubLDeXIxe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j5sC1ULw9FM4UGbB1o5d2zYDJzjgYGB2lk/wfFTZURtOLTE+aOJ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\nRU5ErkJggg==\n" } }, "id": "daaeea5d-c36b-41f2-bccd-c53860341852" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "source(\"beginner_intro_to_data_visualization1_tests.r\")" ], "id": "48f9f1f1-3d87-4d61-a82d-7f51a817a743" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# import packages\n", "library(tidyverse) # contains ggplot2, which is what we'll be using!\n", "library(haven)\n", "\n", "# load the data\n", "pwt_data <- read_dta(\"../datasets_beginner/pwt100.dta\") # make sure that the .dta file has this exact name\n", "\n", "# declare factors\n", "pwt_data <- as_factor(pwt_data)\n", "\n", "pwt_data <- pwt_data %>%\n", " mutate(countrycode = as.factor(countrycode)) %>%\n", " mutate(country = as.factor(country)) %>%\n", " mutate(currency_unit = as.factor(currency_unit))\n", "\n", "# check that it looks OK\n", "# there will be a lot of missing data\n", "glimpse(pwt_data)" ], "id": "a10ce1af-668a-4b5a-bf3b-103c08c5464f" }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, this data set includes 12,810 observations and many\n", "different variables.\n", "\n", "Question time: **How many variables are included in this data set?** \n", "*Hint: variables are stored in columns*" ], "id": "55d6c7e6-69c7-46f5-9586-a86c0398c613" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Fill in the ... below with your answer to the above question\n", "\n", "answer_1 <- ...\n", "\n", "test_1()" ], "id": "e0c119ab-1687-4ea4-bc13-4df18dbb48cd" }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Understanding `ggplot2`\n", "\n", "R uses a “language” for how graphics are created called the **grammar of\n", "graphics**, which is a system of best practices from statistical\n", "visualization theory that centres data in the process.\n", "\n", "> **Note**: the `ggplot2` cheatsheet is an important companion to this\n", "> Notebook. This is a CC-by-SA Material from RStudio’s\n", "> [website](https://www.rstudio.com/resources/cheatsheets/)\n", "\n", "#### Layers with `ggplot`\n", "\n", "In this “grammar” of graphics, we create a series of “layers” which\n", "implement a specific visual output:\n", "\n", "1. Identify a dataset from which we want to create our graph (`data=`)\n", "2. Associate variables in that dataset to **aesthetics** (`aes=`)\n", " - **Aesthetics** represent different properties of a graph (e.g:\n", " “what goes on the $x$-axis”, or “what does the color of the line\n", " represent”). Each type of visualization is associated with a\n", " collection of necessary and optional aesthetic features.\n", "3. Attach a coordinate system and a plot type to the graph using\n", " `geom`, which takes the aesthetics and describes them\n", " - This includes options like `position` which indicates how to\n", " combine elements (e.g. stack the bars in a barchart, or place\n", " them side-by-side)\n", "4. Finally, tweak the visualization by adding labels or changing the\n", " colour scheme\n", "\n", "Let’s see what this looks like in practice.\n", "\n", "#### Interpreting the Data\n", "\n", "A few of the key variables represent the following:\n", "\n", "`rgdpe` = expenditure-side real GDP (millions of USD) \n", "`pop` = population of a given country (millions of people) \n", "`year` = year of data recording (1950-2019) \n", "`country` = country being studied (183 countries are captured in this\n", "data set) \n", "`hc` = an index of human capital per person, which is based on average\n", "years of schooling and the return to education \n", "`emp` = number of persons engaged in employment (millions)\n", "\n", "#### Beginning our Analysis\n", "\n", "Let’s say we are interested in creating a visualization that answers the\n", "following question: \n", "**How has real GDP per Capita changed over time in North American\n", "countries?**" ], "id": "58cbdcf1-33e9-4595-88c4-15a85869554f" }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n", "is_executing": true } }, "outputs": [], "source": [ "# First, filter the dataset to only include data on North American countries\n", "NA_data <- filter(pwt_data, (countrycode == \"CAN\")|(countrycode == \"USA\")|(countrycode == \"MEX\"))\n", "\n", "# We can take a look at our the rgdpe/pop variable by making a quick histogram here\n", "histogram <- ggplot(data = NA_data, aes(x = rgdpe/pop)) + \n", " geom_histogram(colour = \"black\", bins = 20)\n", "histogram" ], "id": "8b522457-4826-4914-b3f3-7e8aeb52a886" }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like a solid number of GDP per capita measurements are under\n", "20,000. Let’s get back to our main chart to find out what might be\n", "driving this!" ], "id": "1f059c41-34ac-4314-ad0b-d8614f15c71a" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Use the ggplot command and specify the data frame that is to be used (NA_data in this case) and the set of plot aesthetics (which variables will be included) \n", "plot <- ggplot(data = NA_data, # this declares the data for the chart; all variable names are in this data\n", " aes(# this is a list of the aesthetic features of the chart\n", " x = year, # for example, the x-axis will be \"year\" (a continuous variable)\n", " y = rgdpe/pop, # the y-axis will be expenditure-based real GDP per capita\n", " fill = country, # this means that the country variable in our dataset will determine the colour of the bars\n", " color = country # country variable will also determine the color of the borders or outline\n", " ),\n", " )\n", "\n", "# Now, input the labels to the aesthetic features added above\n", "plot <- plot + labs( # add human-readable, aesthetic labels\n", " x = \"Year\", # label for the x aesthetic (x-axis title)\n", " y = \"Real GDP per capita (expenditure-based)\", #y-axis title\n", " color = \"Country\", # adds the label \"Country\" to the legend and tells us which colour is used to represent which country\n", " fill = \"Country\", # similarly, tells us about the colours used to fill\n", " title = \"North American Real GDP per Capita over Time\") # and title of plot\n", "\n", "# Because the variable \"country\" is expressed by colours, we are able to change the colours used in the chart using the commands below. Try playing with different palettes. To display other palettes use the command display.brewer.all()\n", "plot <- plot + scale_fill_brewer(palette=\"Accent\") #set the colour palette for fills\n", "plot <- plot + scale_color_brewer(palette=\"Accent\") #set the colour palette for outlines\n", "options(repr.plot.width = 15, repr.plot.height = 9) #adjusts plot size: try playing around with the dimensions, and then return the values to width = 15 and height = 9\n", "\n", "# Finally, input the type of vizualisation of the chart\n", "plot1 <- plot + geom_col( # now we add the visualization geom_col() produces a bar graph)\n", " position = \"dodge\") # this places the visualizations side-by-side\n", " # if you change position to \"stack\" it will be a stacked graph!\n", "plot1" ], "id": "1531c611-4690-45af-9a11-b89428275751" }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wanted to change the visualization and make this a line graph\n", "instead of a bar chart, we could do the following:" ], "id": "b68d990e-d11c-478b-b84a-405791774daa" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# fig.width = 40\n", "plot2 <- plot + geom_line()\n", "\n", "plot2 # show the plot" ], "id": "18cbb164-66be-454d-98c0-3ecf069f2f8f" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s work through a few more examples together. We’ll also learn how to\n", "adjust text size in the next section as well!\n", "\n", "## Part 2: Building a Visualization\n", "\n", "It’s important to note that we should build a visualization\n", "piece-by-piece and making adjustments along the way. Don’t worry about\n", "getting it completely right on the first try!\n", "\n", "Let’s say we are interested in creating a visualization that answers the\n", "following question: \n", "**What is the relationship between GDP per capita and human capital in\n", "the world today?**\n", "\n", "The first thing we want to do is identify what data we need:\n", "\n", "- GDP: `rdgpe` or `rdgpo` (Quiz: what’s the difference?)\n", "- Population: `pop`\n", "- Human capital: variable `hc`\n", "- Data from “today”: `year == 2019`, the most recent data in our\n", " sample\n", "\n", "Let’s start out by `filter`-ing the data to just get 2019 data." ], "id": "3609d488-1258-4704-a8cd-2ea69e915802" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "figure_data <- filter(pwt_data, year == 2019)\n", "\n", "head(figure_data$year)" ], "id": "56ee625e-0913-4423-8f65-e74a8f2810ea" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nice, it looks like we’ve got all the 2019 data! Let’s first consider\n", "what kind of visualization we want.\n", "\n", "We are interested in the **relationship between two quantitative\n", "variables** understanding **how they move together**\n", "\n", "While there are a couple of options, we’ll start with a **scatterplot**.\n", "If we consult our\n", "[cheat-sheet](https://www.rstudio.com/resources/cheatsheets/), we can\n", "see that scatterplots are the `geom_point()` command. This *requires*\n", "the aesthetic properties:\n", "\n", "- `x`, the $x$-axis\n", "- `y`, the $y$-axis\n", "\n", "We then have other optional ones, like\n", "`alpha, color, fill, shape, size, stroke` see [R studio’s ggplot2 Cheat\n", "Sheet](https://www.rstudio.com/resources/cheatsheets/).\n", "\n", "> Note:\n", ">\n", "> 1. You can assign aesthetics on *either* the `ggplot` layer *or* on a\n", "> `geom`. The only difference is that the `ggplot` aesthetics are\n", "> automatically inherited by all other layers.\n", "> 2. Generally, any aesthetic property which can be assigned in `aes()`\n", "> can also be assigned to the `geom` directly; ie: if you wanted to\n", "> make a line dashed or a point red, you could do this by setting\n", "> `geom_point(color = \"red\")`. However, this will apply to all parts\n", "> of the `geom` so use it wisely!\n", "\n", "Let’s start simple, and make `x` represent human capital, and `y`\n", "represent real GDP per capita. We can start our visualization by\n", "creating our `ggplot` object and assigning all these properties:" ], "id": "addf21e8-ad6a-423a-8b48-77a5e915f9c1" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "figure <- ggplot(data = figure_data, # associate the data we chose\n", " aes(\n", " x = hc, # x is human capital\n", " y = rgdpe/pop # we divide rgdpe by pop to get gdp per capita\n", " ))\n", "\n", "figure <- figure + labs(x = \"Human Capital\",\n", " y = \"Real GDP per capita (expenditure-based)\",\n", " title = \"Global GDP per Capita and Human Capital in 2019\") +\n", " theme(\n", " text = element_text(\n", " size = 15)) #increases text size: try playing around with this number!\n", "\n", "# note: you can set aethestics to be simple functions of variables!" ], "id": "e376420f-e51c-49b1-ac66-1cd766bfb64a" }, { "cell_type": "markdown", "metadata": {}, "source": [ "After running the previous cell, nothing was printed in our notebook;\n", "this is because we need to assign our visualization! Right now, it’s\n", "just data and properties. Let’s test it our by adding our `geom_point()`\n", "layer:" ], "id": "5697a567-639a-4ada-bfef-4bc2580de6dc" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "figure + geom_point()" ], "id": "f7714116-7445-4536-b381-3431ced61ed1" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nice! Now let’s make the size of each point relative to the population\n", "so bigger countries would be more prominent on the graph. We can do this\n", "by assigning the aesthetic again:" ], "id": "975d377f-4051-4502-917f-f2b3a71b8d08" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "figure + geom_point(aes(\n", " size = pop,)) # assigns the size of the point to be relative to the population values" ], "id": "e35bf9dc-a157-4d95-95a1-fcd9d4e497e3" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let’s make colour of each point change as the employment (`emp`)\n", "rate changes so that darker colors would represent higher labour force\n", "utilization. Again, we can do this by assigning the aesthetic:" ], "id": "b067e469-afb1-4024-a100-3d0f61a77910" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "figure <- figure + geom_point(aes(\n", " size = pop,\n", " colour = 100*emp/pop))\n", "\n", "figure" ], "id": "def1ec67-2cdf-4e41-95e5-868bd60bbbae" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great work! If we wanted to change the colours, we can set colours in R\n", "is using palettes. The list of all the palette options are:" ], "id": "0313e545-740b-4d84-853a-5735011beb92" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "RColorBrewer::display.brewer.all() " ], "id": "b9524162-8ce6-4a6e-a31a-a151c3c61dc9" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s choose `YlOrRed`. We can apply this using the following (somewhat\n", "cryptic) command:" ], "id": "07bec902-12d8-481c-9cdc-169519483b0e" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "figure <- figure + scale_color_distiller(palette=\"YlOrRd\")\n", "\n", "figure\n", "\n", "options(repr.plot.width = 15, repr.plot.height = 9)" ], "id": "740d244b-7b02-498a-84af-00de16c5109b" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that we used `color_brewer` earlier and `color_distiller` here.\n", "\n", "- `color_brewer` is for visualizations with discrete variables\n", "\n", "- `color_distiller` is for continuous.\n", "\n", "As you see, building visualization requires lots of trial and error!\n", "Always ask yourself: “Is this effective? Is this what I want it to do?”\n", "\n", "### Exporting Visualizations\n", "\n", "Once we’ve decided that our graph can successfully answer our economic\n", "question, we can export it from Jupyter using the `ggplot` package with\n", "the following command:\n", "\n", "- `ggsave`: save a visualization using the following key arguments\n", " - `(\"file_name.file_format\", my_plot, width = #, height = #)`\n", "\n", "> You can check out an expanded list of possible arguments at the [R\n", "> documentation page for\n", "> `ggsave`](https://www.rdocumentation.org/packages/ggplot2/versions/1.0.0/topics/ggsave)\n", "\n", "1. The first part of the argument `\"file_name.file_format\"` is where we\n", " decide on the name and file format to be saved in the Jupyter\n", " workspace.\n", " - You can add `\"folder/file_name.file_format\"`) to save to a\n", " specific folder, (The format depends on the context you plan to\n", " use the visualization in. Images are typically stored in either\n", " **raster** or **vector** formats. See [*Data Science: A First\n", " Introduction*.](https://datasciencebook.ca/viz.html#saving-the-visualization)\n", "\n", "**Raster images** are represented as a 2-D grid of square pixels, each\n", "with its own color. Compressed rastor images are “lossy” if the image\n", "cannot be perfectly re-created but differences are minimal. “Lossless”\n", "formats, on the other hand, allow a perfect display of the original\n", "image.\n", "\n", "Common raster file types:\n", "\n", "- JPEG (.jpg, .jpeg): lossy, usually used for photographs\n", "- PNG (.png): lossless, usually used for plots / line drawings\n", "- BMP (.bmp): lossless, raw image data, no compression (rarely used)\n", "- TIFF (.tif, .tiff): typically lossless, no compression, used mostly\n", " in graphic arts, publishing\n", "- Open-source software: GIMP\n", "\n", "**Vector images** are represented as a collection of mathematical\n", "objects (lines, surfaces, shapes, curves). When the computer displays\n", "the image, it redraws all of the elements using their mathematical\n", "formulas.\n", "\n", "Common vector file types:\n", "\n", "- SVG (.svg): general-purpose use\n", "- EPS (.eps): general-purpose use (rarely used)\n", "- Open-source software: Inkscape\n", "\n", "| | Raster Image | Vector Image |\n", "|------------|------------------------------|------------------------------|\n", "| Pros | Takes the same amount of space and time to load regardless of the image’s content. | High quality image: you can zoom in/scale up without compromising quality. |\n", "| Cons | May look “pixelated” when zoomed in. | May take longer to load depending on complexity of the image is. |\n", "\n", "1. The second part of the argument, `my_plot` specifies which plot in\n", " our analysis we’d like to export\n", "\n", "2. The last key part of the argument `width =` and `height =` specifies\n", " the dimensions of our image. If we haven’t made modifications to the\n", " size, these commands can be left out. Since we adjusted the graph\n", " output size using\n", " `options(repr.plot.width = 15, repr.plot.height = 9)`we will specify\n", " these dimensions as we export.\n", "\n", "Try uncommenting the code section below and saving our “Global GDP per\n", "capita and Human Capital in 2019” graph in the Jupyter directory that\n", "this notebook is stored in." ], "id": "9628217d-03a0-4802-9890-d0410f76e80e" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ggsave(\"gdp_hc_plot.png\", figure, width = 15, height = 9)" ], "id": "e2c6ae76-cb5f-474a-b5b2-dae10d58fb46" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Did you see file appear in the directory? Now try saving the same graph\n", "as an `.svg` in the code cell below." ], "id": "0fd7fab5-ec2a-47b4-a800-9620bd92100f" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ggsave(\"gdp_hc_plot. ...\", figure, width = ..., height = ...)" ], "id": "b36e7a11-cc09-4346-ba7f-1d643ebb9192" }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we have seen, R makes it easy to create high-quality, impactful\n", "graphics. We’ll let you try it on your own now!\n", "\n", "## Part 3: Making Your Own Chart\n", "\n", "For the final section of this notebook, you’ll make your own\n", "visualization using the Penn Data again. Let’s say you want to build a\n", "visualization on **the relationship between the economic development of\n", "China and United States over time.**\n", "\n", "Some variables you might want to consider are:\n", "\n", "- `year`: the year of observation\n", "- `rtfpna`: total factor productivity [(here’s a link, if you’re ECON\n", " 102 is\n", " rusty)](https://en.wikipedia.org/wiki/Total_factor_productivity)\n", "- `rgdpe`: real GDP (expenditure-based)\n", "- `pop`: population\n", "- `ccon`: real consumption of households\n", "- `avh`: average hours worked\n", "\n", "To be clear: you don’t need to use *all* of these variables in your\n", "visualization.\n", "\n", "1. Start by deciding what variables are *essential* and which ones are\n", " *optional*. Choose at least two to include in your visualization.\n", "2. Decide what kind of visualization you want to make. Relate your\n", " choices to the best practices for types of visualizations. See\n", " [cheat-sheet](media/data-visualization.pdf) for more.\n", "3. Finally, decide how you want to present it; what should the final\n", " product look like?\n", "\n", "A good idea is to create it in layers, like we did before - updating as\n", "you go. We’ll start you off with some of the data and code scaffolding:" ], "id": "ceee92a1-498b-421c-95c1-4f9ff3108514" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# my_data <- filter(pwt_data, (countrycode == \"USA\")|(countrycode == \"CHN\"))\n", "\n", "# my_figure # give your plot a descriptive title\n", "# <- ggplot(data = my_data, aes( #add your aesthetics below\n", "# x = ..., \n", "# y = ...,\n", "# color = ...)) + # remember this is optional\n", "# labs(x = \"...\", # what labels do you want to add?\n", "# y = \"...\",\n", "# title = \"...\") +\n", "# theme(text = element_text(size = ...))+\n", "# geom_...() # what geom will you use? Does it need options?\n", "\n", "#my_figure \n", "\n", "# uncomment (delete the leading \"#\" symbol) to use these lines. \n", "# Pro tip, you can uncomment an entire section by highlighting it and selecting \"command + /\"" ], "id": "e6934c26-f733-4e3d-a268-31006eaf5e88" }, { "cell_type": "markdown", "metadata": {}, "source": [ "See if you can piece together a decent graph from what you’ve learned so\n", "far. Depending on the direction you choose, your plot might look\n", "something like this one below. If you’re stuck, try to re-create this\n", "one, before starting on your own.\n", "\n", "![Total Factor Productivity Plot](attachment:media/TFP_plot.png)\n", "\n", "This visualization was made using the following features:\n", "\n", "- `y = year`, `x = rtfpna`\n", "- `geom_line()` function with argument: `size = 3` in between the\n", " parentheses to make the lines a bit more visible\n", "- `color = country` to create two unique lines on the graph for China\n", " and the US\n", "- `labs(color = \"Country\")` to give nice, human readable title to our\n", " color legend" ], "attachments": { "media/TFP_plot.png": { "image/png": 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Xe6Ordm8FA5JZB3bGLzjXgbMboVfxoe7iPMl3chYW6ousEAQc7DFm77nnHn0L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\n4C3KZeGor6OWX11I4THjZKqekJFJmddfZ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4d/08GGu3vlcswfiW1MM7isxIMTCasM5RbJ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QLuqDkjTwuH23roHmJfL65DptZZ130ZrZ0+gHsazzA0AStQhzWzFqnHU+ONf2cJ1yNDRwW53A\nu18RUHoIPZDx7htEpgc1XN6hcRNj8cb7v29z8FFj6n+kgPs0u+uPNpW3O8vN8azTGblmMo3+K+5L\nDMZ3KGRNow+HrH5OURRlAANR9Ljlq+jopSvp2C4uBy9aTq/XN3RbHO0KEAYxKxohJMHGjRvpmWee\nkWns7QmwbQEvzNraWvm7qqpqW7hJTOGHENkeOBdCX8M+O0p6/umnn4rn61dfcR/DuDsxuw3fw+d/\n//vfx7Z8zYgRI+i5556jSZMmbTuGiooKevnll+V1K9tVdiEIIHjie9AF23vEhnl8UlNTI56p8Gyt\nr6+XsmvPihUrZB/QExsbG2Nbt6e6uppef/11evbZZ+mzzz6Lbe0enRqFfvHFF7R8+fLYX1ahHXjg\ngfSd73xHYhJMnz499g7RunXr6OKLL6Y1a9bEtiiK0usgnlcC4QheJi5MWxsMBiifo6umyniuMBqj\nKZKRvi2YcmhMCMREMS1PkiL14jXkfSMeLX7LFngBDenDjPRxuGzCw8qMZbMNNrQhZDad/jNycKea\ncNjEA5vQDjvDfJfvbQcPuhCewvv5JwPSqw3iXYnHTa82NFLBvMV0+qp11MjlTF4+Vwyw+L3Xaxv4\nvUX0PA9C8Vn1Hk2OHB78/2xDrL21G7yjvkWidGxRATXMmkaF/Pl0nzq/HXyO0exsipQMNbe1Hi+5\nVyzTOKMdgXboi08lBIixX2ttIf9Bh4nXaMoQDlF45Ghpv+1AGAXXJiRgGoDjEoT0qa/j89tgrN9W\n0sWddRq9oiiDA+6/nqmuoRcqa+i5qq4tb1RW0xp/gDz9IIxC9IPmBTEOScZHjhxJxx13HB100EE8\n1HOIWBnnk08+oWnTptENN9wgfyMkJaatg3322YfGjx9PlZWV8jfAlPtsHjNhqjn2ie/Co3XLli2x\nT1hAcMXndt99d/H83HHHHcUpsbOJlxJ9Hl6uCDNw/PHHy9/Dhg2j//3vf/IavzVhwgR5DRBGE+c+\na9YsOvbYY+nII4+k4cOHf0MbxDT8oqIiEZahE+bn59OUKVNi75KIxBBqIcZiHyiL3NxciX3aHoTu\nLC4ullnq0CF32203+f2HH3449omu0alRyC677CIXKRlcPAD43e9+R3fffXdsi6IovQ0S00SGwAA1\nCEcieCED8AA0QNqDc0VnYzAgYaRHivsweVCyQNweNlzER1sgbpdvNouWPYCIr4kEdJRdaYJ61ktI\nIg/EGe3AA8/R3EytRxwjXkr4TiIkBEVWFoUnTRXPHVs8XvJ8MrCm04sgygOQD5qaaej8JfSd5aup\nDsKFm+tV24EmXsOg5/WJy1bTwfw5xB/NMdUNRUD5wpthMZ52ty3Pdszmon1m0jiq4rIP2AnzaQ7O\nPBqNUHjceBHI7MAUY/dKJGDSOKMmxPPQl0m+N/7LayuG2HZwHYrkFVBwxg4dx7PtQ+Rh38hRRmFU\nwsNsWCeelQMOj5s8C+Za52bXDnBfCi/g0GTufwz3h6IoykDDiTEkbFG0jV1anOROMLbqTZBjBxxz\nzDEiEGK2NJbXXntNtu+1116yBsi/AyDwAXzu5z//ubyGtyT+jufvgbAKL8kbb7xx2z4hTEK8HDqU\nba42QHCEZymEwPhnb775ZpozZ07sEx1z6qmnyhqhL+08N9uD34BQG3+NJQ4SrwPMJo+/h9xE8KyF\ncAlwzPHvQBDF67YeoRCJ4VUKJ8z4Pq699lp66qmn6JRTTol9imTqP5K933XXXds+Bw9W7B/nhJAA\nXaXTlg3U4mRYuXKlrHsjsKyiKAZ4kB0uTeDJ53SRc0tFr4pqqYA0u1wGxniqaEjdboogZgy/Tikg\nOg4tMwt6uIYVm6yBQW/BZebcashIz+DY+noqvQDhf1iCsgEwwnmQITH2WppjGxODafSB3faUafW2\ncFm7N24gR1X6T/eFqAlB9IuWVpq2eDkdvnQlbQkExRtN7hXTQBPbvR56o66Bcr5aRC/GvEfTuzR6\njxwu4x+u44Em7lNTmTrd9HComer5PkqxVqhHkQRMEyYljDPqWsUDYS4zxQDXI9e6VZLEx9j2c/sV\n2PsAESINNa5/4HY7NGKUud3GuW2Ex+jAu/5IUuiZ94XVvtqBe2PWjuItmlLXTFEURUkIpoNjyncc\neHnOnj1bxL1kYohC0IsDYRJT8eFp2TajPLwnEdcT3HTTTbKGhyl0NoiRJ598smwD5557Lh1++OGx\nvzoGSZb23HNP8eSE1yucGvF9ZKQ3CaURG7sPU9rhEfvCCy/IbPI45513ngjCmOrenvb7QVJ3gJAA\nEydOlNfgyiuvlARRiEsaL6+3335b1meffbasgc/nEw9enFNcjO4KHdo0cBEuLS2VCwPeffddOWBk\n2bJbEGQWrrP777+/fH4IMiwritI3QLCCx6jJM8PpIGddjXgmDGRDXLxF49Po7UQJCKPZOVbMrxQU\nRsPFJXzcfOx2x8bn46yrFxGvN44c+3SE2EgzZdAFOEbxGO1jYZTrd7iMO11MObQrG97maKynptN/\nLlNKkzY02TgNTpttCTOGpFcQRL1ffWY2cFMcDxcGBNF1gSDNWrKC9l+8gpa0tFrT5Tsj9saEvu8u\nX03fWrGW8l1OSdikfE0ml89LtfW0qZnL11S2ThftuX4FzSgpIX9f30d9DfdH4dHjrMbF7r7lMnLW\nVpOzoX5Ax/HtDkh253vnDctr3a6MUK78z7/XPiKQphRot4fDYzRkvP6uzZusdja2aSDAvZHEDXUv\nX2oUfTGNPjB7J17rNHpFUZR0AomJ2hOfXg5vThNxUbCtOIh4n+Cyyy6TdVviYmc8vmc8limm0Lfn\nRz/6UexVcnz44YeSXOnHP/6xCL2vvPKKZKSHUIqp7vDMbEtbMTcOpsfjuCHqQrB96623JPbnl19+\nmXTOoSeeeELWEJbbc9FFF8ka+wV77LGHrHH+8BSNg9+CxyiOp6t0aA1h/j4y0SMQKkBMA5z02rVr\nbRfEFG3rJXrrrbfGXimK0utAsJKp9NzY2hkgsXhXDn/A3rgaKOA8O8pIjwz+Hk/qeWng2PILJGGF\n/TVkYyvoJ2dtjfn8ugP238r7b2iwryOxY+qXMASo38Ul/NsltuI/PET9Bx9B4aHDxGsqEfC5gqmK\nM5Ts9F4vhabPIgoavNq8HvJ+9rGIp+lkvEPGLXG7aFMgRHOWr6JZ85fQguYWa8o86k9X2gF8h8vh\nf7V15P5iIb3e0CSiay/UxrQk2+WiH8FbFKKzCW57Hp/7IdVOmWF+kDVQwH3L/VIkO9vcpnEZuNav\ntYR35RtALEZ4EM/cBA9mgkEK7rgLRTOzpD1LKaS/zaVITp7x+vMbljjaG31af4Fp9PPmWu2AXTuL\nfsflptDk6ZZorCiKovQZdiJfZ+iOANeeeBImxM5ErMz2C0AcTxDPGA9HxPaMHj069ip5MD3+3nvv\nldifKBNM5//+978vyZFwPIip2hF33HGHHCecJ5F/CLE/d9555+1io5qoq6uTdfvzxhKf8h/PZH/n\nnXfSt771LRGTIYbiM9/73vckqVN36XAE8uijj0ohNcBIZqCEY67/v/71L9vlvvvukwVuv/geDrYz\nvP/++/SHP/yBrr76avr3v/+9raCSBXEYcHwIbIvloYceSpjRSlEGEnK38SA8UlBoNkDYAIV3jjFb\n+0CAjStkpDdOe45GYpnfU89TC9cQMcesa2hzfLiGaFurtvaOEYmyq+KODAdi135zmUVKSq1jiG3q\nK+SQgiFq+f4PydHE7TqSkOAaRsL8d5N4S7cceZy8NgGpCkJhE3+vIhSmApdT4mXCAzew617m6fSI\nu1q+iVzo5Huj3HsYyEs4z2q+349YuZamL1hCHzZwubCxLsffyb55O/B9iFhOBx29dBUdt3qdlGPW\nIPcehffsozW1VNnK9chUFC43HbpmGY0qHUrBfriP+ho5Pz7P8JhEcUat6fQaZ3R7ojm5lPnf5xIK\nbPJQ6KDDydHGeyJVwBFjSnk00cM0bktcG9dZbcoAIT6NHg9gbWF7JTx+ojyUSzkxW1EUZYCDqePA\nbnp4e+yyvSfzvWSJ62XQrl588UV6+umnv7FA24p7VcYFXTuNrSeOCaIm9L+5c+fK3/GYqCYQTgDT\n5qERYlo9ji++IG5oZ8A0+fbnjgXH8+1vfzv2Kct7FiEFIMjCgxRls++++9KMGTNin+gaSVt3OYjF\nx5SVlUl2Krjq2i2nn366LJ2JcRAH8QV++tOfSsYrKOd//OMfJRDtRx99FPtEYubPny8ZvhCDAYIq\nlG7EScC0fqjfijIoCMem0yeK54XEOgh6PVCBuIep9CbxlzuOSFEJGyM916n1KLiGQ80JmJDIwV2B\n5Ei9YEQihini0JoMVC67fplGHwPT/EPjJlLjb66g8IjR4imFmLmt+x1EDeddmnAKPRLiNPJxT168\nnEbNW0xTFywh95eL6J9VNYSgL8EZs8WDxzidHobu59wf8TpVwbkXxoLSH71qHY2fv4T+W9tgiSrJ\nCqIYdKHuYYkNwIxgfx43PVNdS76vFtFHTc2D2ns0k+vi2evRvvL9YyprLp/7P3+PWmfsKOLIYADx\nRSECUcjUL7nJvWqFxhltB6bOexbNI++7bxKZkr9xfxEeNUamq3eUbK6/gEdwaPhI47gE/Y0Tmdt7\no0/rB9Bq4pzdy5dI3bYFXr6YRp9CibIURVH6ggjGAhj/YJZWVxb+bnfDECEEJJg3b56s7Yi/1zZ7\nem8Q9/REAiEIgEhY1HaB9hYXBuPJmjCjuz3I9p4MEFAxNT3u+GgHYnuCjhIzIdk6gJBaWFgor+Mk\nm2soLy9P1vvtt992544FHqxts+ADzGo/55xzRCeECAv9EWEB/vSnP8U+0Xk6ZbvgRxFjtDeA4vvx\nxx+LtyjiG9x///1SwMiU1Ta4aiLOPPNMcStG8NXf//73Mo0fsRPgInzJJZckjPegKAMFJF7CVGKj\nqAbhC5np08DrravAU7TjjPTcsXSzU+0tYNyGh5UZjUgRfsu5s+kNcZv3nTgjPR9bacdT1XsTh7+V\nwiVDqPHs86nulj9T/TW3kv+IY3mwFDB63iDGZkskSqPmL6XlzX5LuBLxiuiXq9fRrRVbKZ/rQ2DX\nPXk/BkPV6yHfxx9ImIMO5MJ+IcvhoGI+p6vLt1LeFwvohZq62HnyteyMIMoD1h+WFNIpxTzAwcA1\nGXEUv8McvGgFfW/NOsrl38TxDCay+Z65r6qaahF03+Q563LTsWtXUFl1JTVNm2mV72AgHKLgxMlm\nEYjri2vzBr63/RpnNAbEQniCZt97t4R+sb2H+d7EwyD/oUfwOoXHuBBvR7LhZ+o3+FxdG9ZSFCE+\nBgJuywMa18Z43XiF2NZi5CuKogwSQjweeGDSePr3lAn0QBeXe6ZNooPycqm1o/FpAuKekBdffLGs\n7YAgB84//3xZ9wTQ00DbJEHIcA+gh9mB2djxeJpwAgRtEz/F+ec//xl7lRiImZjyfsYZZ8S2bA/C\nZ4IxY8bIGsSPvS3xmdlxJ8o4iDPa0sJ9oA3OdjbmsceyDcdg5nh7oOu1dZLEbPBPPvkk9tfXoIwA\nHCW7Spes6rvvvpt22WWX2F/f5MQTTxRvUbjSdga4CCO7FmIGtOWaa67hMYVDpuknYuHChWxfhERR\nb088Y1eynqeKktZA9IvHGbUDotpAz0yPc6wxC6MQjyOFqSuMQpyCMIrjtAXidsXmXrmGUZdVP4ye\nOyi7/shI3w4Is87mJknY4mxsELHUJIqiFuS73DRp8XL+Iv8RFwrji8dDF20oF2E1vNteIs7YioFc\nJo76WnKtXbVNCEwF4I8EL823mprJN28RXb+RzwUCA47RzihvD84V15P70EPzc2nl7Kl0/5gR9NDY\nkfTS5AnW+xA0DOW7DfyWz0tPVNZSxleL6cPmFpnOP1jI5Hp11vpNVtmb4IHwQ5+/R+GSUgoPKeVy\n778HDH0KhLFRY612xa4eOZxy37kqBlicyS6CEor6Min773dYU7FNZYKwMNk5FJyxg/mBTiqAB2rD\nR1oP1AxtqyvmWdJBK5MW4Jp5538pswxs22DcD2XDKVJQkLozVxRFUXoBOCmcVFxApxQX0cnFhV1a\nTi8ppnE83gx1NC5NAOJTYgo4ssgjWThEOQh5ECChGWFa9pIlS0SIxPTynsITC68CwQ9AbETycnho\nvvPOO3T77bfL9jhxARNCI8D09OzsbJl2DmfCONDn4omZOgIOg8jk/uSTT9Jxxx0n+YPioifOH9P5\nkYwJ/O1vf5M1iB9726RHc+bMkfUtt9wia4CQmigzaHt2xGOqxkHcUACP0bYeqosXL6bddtuN9tpr\nr9gWoh/84Acyfb79Pn75y1/KOi4yd4VOjz5nzpwpHpxffPFFbMs3qaiokIsE99ZErsltWbZsmUx7\nt5t+jyCucMvtyFMVFw8V207JhrAKl+HupO9XlLSBDRAIo8YpdWxguQa4x6gkX6qOZaVvD9oI/heB\nuz93zikJriHERxiRdvB5IfkSvK96/AxgoIowal8/HGEIowlCNaQgRR437b98NdVBOLA7LxiubJze\nt7WKvOPGUyS/QP62I+rLIN+nH0pcuP4GZwIP0Wau07stXUVHLl1JAdRpTEfujCAaDNGu2Vm0eNZU\nemXiGNlnZSjMS4gOyM2i1h2n0+EF+ZZnk00fux1uF99aUTpk8Qq6bNOWQSGO5nK9+ivXn0iQ7wtT\n2fN1+d7KxZTd3ECtY8eLcJ3EVRoQyHlyXQuNHWds1yAmuVZqnFEAD9HMpx6yYhrHDJHt4HsM8ZRb\nDz+KyzSS2nWJr320uMQSCu3ge8bZ2sL9Wi2/Tu+xCVpIB//nXrSAr52hLnPfHZy9s2SlVxRFGWxA\nHG3mfqE7S0/42t9222104403SrxKCKBZWVkimEKIw7RsZJ5vr0H54TzB2M1EjufGgSYF4l6XSGwU\nJ+6ledRRR4lG9eyzz8rfmCkN0RPeqdgeXzDlHcIhsr7HQcZ3AO0s/jnoc5h9DWpqamSdCIibECKf\neeYZ0dvgxYn94PxxbNDUIHAeeuihsW8Q/fCHP5R1POkRzu+uu+6SbRBb48dyxBFHUHl5OZ177rny\nHrYh2z04+OCD5Xvxz4Lc3FwReqHXISN+/D2EFkAs2Lbng0TvAGUV/xwWlBHK9uijj5b3u4KDTzpp\nu/qqq66SoLAAFw0xQNvz1Vdf0YUXXrgtpmcyu0cA1csvv1wUaajC7TnllFOkwrZVxe1A5UDhtc+e\nhe+vX78+obiKC4FYDe1de9MJnAPiTqCs0vk8lG4Cr7aWZsq74XL76Xe4JwN+qvvdneSoYyMkBu7V\n0tJSiTeCJ2ZoZNISHDeXQd71l9kLRHL+Aaq/7FrLAOf7JuXgY45m51DBr39mvIZIPlR39e8soa8H\nzyGal8+/+1P+/Vz7321ssH6X29q2v4v6gyeYGAygs02V+jOEDdNfbyyn2zZVmAUGwIO0Yo+LKnff\nmRqeeYK8H75D5LOeln4DPucon1r9b38n95nUpz4GJZvD5e/iFxdsrKA/bYKHaKyuJ1PuOGYsoTDN\nzMuhB0YPpx15MFgXClHA5nxcvM8i3v+LdfV01Iq1vIU/g3rX0W9hX8EQvThlPB2Qky0Jr0yg/mAa\nDgZbyGKZTn0YjhTl45y7iF/xOZvKxeuj5mf/TT4eINcf930J22BM9jUAiWZkUMZrL5PvrdfwNDu2\ntQ1BxA+eQE1n/iJhAjUTGAPhoXxVVVVaj4GiGZkSVzT73r9SFEn4THA7G5o0lRp/8kty1ncuUWl/\ngL4M4xIHptah/2gH+rTGn/yKwqPHiXDY18THQLW1tduMti7B4wpXdSXl/PF6iiITf/v98O84+HrV\nX3EDRTOzueKmz0NGxQzqD4x7jH8wg1FROgPqT35+vvRdmHXb330Y+lM4pvW0UxmEw548N5QbvBgx\nduwu8NbEDGS0/wjlaMqXA5EO3qU77rijiHhtQeZ0xPk87LDDeFjuFpsagh8cC+MxTQE8UZ9//nkZ\ns0CnintnAnhCIt8OpqjDk/SEE06IvbM9jzzyiGSpR/zN+KxpeHvi+NvH5EwEtDgIjrD/oSXtueee\nNHny5Ni73+S1114TYRZT7JENPg6SJMHztP0xr1q1ShZ4jxYUFMi2hx9+mDZt2iRl2N4bF3ofpsOj\n7iErPrLb24FwmRCTccy4DvjNeEKtrtIpYTQ+SECjD/fbRCC1Pk76gQceEJfXROCiIh4o1nbBbf/v\n//5PVPuO3INRMBdccIF4oELpR4FCOcdABwmZsG4L1Pu4oo8GAEsniiPlwPEXFRWlRIOq9CO4T7lh\nyLn6Ystbwcb7AoJowy1/tjx3YnUedR+NIRriVBK2Og3XfUdzE+Xc/Fs2OrKs8mgL3yd8ttRw3R+6\nZHz3FTAic661kgnZGpGNDdT0qwspUjZCpkD3CCg7v59yrrlYBNLtQBvJ9anpyhtJMsK3aS9RfzAw\nQTuEp6mpUH8K3S56oqaOTlq2OnYvdHBMgSB9vuMMmtpUT67fX0+UnbP9d/g8YcA3/fw8ivSDAe9z\nOiiHB4H/3lJJP1q7UTzFko4hClD/g0GamJdL94weQfvnZlNDKGwriLYHWefxqWNWraNXK6tkWniH\nv8v7dfJnQjvNpFqupyZpFPUn/qQ+3R7u5fP9eTtfjwvX8fWAQG2H20NnrVpM//jiffJzOTRdxO0T\n7jGD9+RARDLPr1tDWXffzpUpN7a1DbG62fD7u77x0C5Z4oYcjJa0HQO53BIrNPuGK4gy2NAznUc4\nJG1x47W3WkJjEvdvfxPNyqbM+/9O7mWLuD22MbYRK/Vbx1Bg3wOtcCZ9THwMBMM9yPWwq30YHgD4\nXnmRvKYHAHzPI6lWA187ZxfquZKaoP7g4V5cGE3bMbTSL6D+QFiPe8elgjAKESs+dbqnSGVhVFGS\nFkYhluCGhWtse49MO6A6QzH+2c9+JjEPEgHxFK7Mjz/+uLjytgcZpzAtH67EiYBLM7xaoeIjBipu\nPCjJULzhkYrjaQuUdSjk6LwQHwEKuKIMFPzXXkrR+npy2Ihq0fo6yrju90QlyMU9ACnfRK3XXkYO\nGN/tBqdRHrA6i0vIC4/RFCf49z9TePFCctg8sY02NZHvjJ+TY+ftvey7xfo11HrT1eSAp2o7pOyG\nlJL3kqtjW1Kb2lCYCt94x/IUTcZIYYP1hLKh9OT0KRT47cUUaWq0v3/YaHfvtie5T/1xbEvfUcXX\nYJ8v5tMSTNeBCJes8YUp9vzd0txsemnWDNolJyv2Rtd4s66eDvp0Lv8+/4FBbqLjCIboV+NG058m\nfrMPHkg43kTAeC5jUzl4vBR45n4rOR6SeN1kH2B/MND6qzOJMrO4qLYvq2htDWX86R/23tqDhAD3\n3ZHaWnIYDFIM2x0N9eTDw81YJtd0IfLmKxR4+nFy4KFlO6IBbld33p3cp3H9SHPkGvI4y2ETFiLa\n2kKeQ44g19Hb50RQFEUZyKgwqqQySQujeHoBb0Skwo9nfUoEvBbhHpzM5x977DEJ2Prggw9KLIH2\nIGsYvEDfeOON2BZ74KI7adIk8Txty29+8xtxSYYIOmrUqNhWktgHEFMxOMcTGriw4wlJuhJ/uoNp\nQGnrLaH0CJiGnXH3HeRas9IShdrT3ET+k35EoR122RbjCk0B7nF4UqeKx19XQAwz91efk+/R+4my\nsmNb24AEO5OmUesZZ4t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6avlcHabqKorS50iSIXj4mYQsHryLh6BJCEtHcE7VyGhrOKe4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\nF8y8hKwbOgp94ojjaEYfwg4saKtpQz6ni4ul5C+3SXT9Ojvo21C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q9Cu/RoDfr+hqKbnz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7KlOxFeKapSafn4flPlhIpyQlEMH113GxrCtzNd9xu3mW1gkH096Q8gXZ0S3fqieazgx3C0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\nAAAAAAAYPIJRYAjpcElaIfru5zfLy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