Edit to categorical data section and the addition of box and whisker plots

.gitignore

@@ -48,3 +48,4 @@ _site/
 !/preview/html
 
 .DS_Store
+.Rhistory

textbook/09/1/Libraries.ipynb

@@ -13,9 +13,13 @@
    "id": "a7571e9f",
    "metadata": {},
    "source": [
-    "To create data visualizations, we will need some additional libraries. In Python, libraries are distributed as *packages*. We will be using the packages `matplotlib` and `seaborn`, which are popular libraries that can be used to visualize data from pandas DataFrames.\n",
+    "To create data visualizations, we will need to import the necessary libraries and packages. We will be using `seaborn` and  `matplotlib` libraries, as well as the `pyplot` package, which are popular tools for visualizing data from `pandas` dataframes. \n",
     "\n",
-    "For our visualizations, we will be using a `seaborn` style with a white grid background. A list of other styles, as well as documentation for `matplotlib` can be found at the end of this section."
+    "The `seaborn` library is multifaceted because it offers many visualization style options. While it can be used to create many types of visualizations, data scientists sometimes use both `seaborn` and `matplotlib` to create graphs and charts that are suited for their visualization needs. Here, we will be using a combination of `matplotlib` and `seaborn` to create our visualizations.\n",
+    "\n",
+    "A link to documentation for the visualization libraries used can be found at the end of this section.\n",
+    "\n",
+    "Let's start by importing our libraries:"
    ]
   },
   {
@@ -330,8 +334,7 @@
    "id": "729aaca8",
    "metadata": {},
    "source": [
-    "The data consist of seven columns or features. (We've set one of these, *Year*, as our row index, leaving six remaining features.)\n",
-    "    \n",
+    "The data consist of an index and six columns. When importing the data, we used the `index_col` argument to set the *Year* column to our index. This will make things easier down the line when we want to extract data for a particular year of interest, rather than thinking of which index corresponds to our year of interest. Information about our data is listed below:\n",
     "\n",
     "**Year**\n",
     ": The year of the collected data\n",
@@ -354,7 +357,7 @@
     "**USA-USD**\n",
     ": Amount of money (in billions, USD) spent on the military in the United States\n",
     "\n",
-    "In the upcoming exercises, we will explore these data via various visualizations. With these visualizations, we can construct a narrative of what the data show and mean."
+    "In the upcoming exercises, we will explore these data using various visualizations. With these visualizations, we can construct a narrative of what the data show and mean."
    ]
   },
   {
@@ -368,6 +371,7 @@
     "\n",
     "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/matplotlib_configuration_api.html\">Matplotlib documentation</a>\n",
     "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/pyplot_summary.html\">Pyplot documentation</a>\n",
+    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org\">Seaborn documentation</a>\n",
     "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\">List of styles for plots in matplotlib</a>\n",
     "- <a target=\"_blank\" href=\"https://data.worldbank.org/indicator/MS.MIL.XPND.CD\">World Bank Data on Military Expenditure (in USD - MS.MIL.XPND.CD)</a>\n",
     "- <a target=\"_blank\" href=\"https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS\">World Bank Data on Military Expenditure (% of GDP - MS.MIL.XPND.GD.ZS)</a>\n",
@@ -393,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,

textbook/09/data-visualization.md

@@ -1,7 +1,9 @@
 # Data Visualization
 
-The distillation of large and complex data sets into more easily digestible forms is an important component of data science. Through effective data visualization, we can concisely communicate the story that our data is telling. We do this by choosing appropriate visual depictions of our data to accurately represent what the data means.
+<i>Evelyn Campbell, Ph.D.</i>
+
+Distilling large and complex sets of data into more easily digestible forms is an important component of data science. Through effective data visualization, we can concisely communicate the story that our data is telling. We do this by choosing appropriate visual depictions of our data to accurately represent what the data means.
 
 The visual that we choose is dependent on the type of data. Two major data types that can be visualized graphically are **numerical data** and **categorical data**.
 
-Numerical data is commonly visualized using *histograms*, *scatter plots* and *line graphs*. Categorical data can be depicted using *bar graphs* and *pie charts*. There are a vast number of other methods to visualize these data types, (*e.g.* box plots, cartograms, heatmaps, *etc.*); but, the aforementioned graphs are the most commonly used among data scientists.
+Numerical data is commonly visualized using <i>histograms</i>, <i>scatter plots</i> and <i>line graphs</i>. Categorical data can be depicted using <i>bar graphs</i>, <i>box and whisker plots</i>, and <i>pie charts</i>. There are a vast number of other methods to visualize these data types, (<i>e.g.</i> cartograms, heatmaps, etc.), but the aforementioned graphs are the most commonly used among data scientists.