diff --git a/mplaltair/__init__.py b/mplaltair/__init__.py
index 1f3d019..4d9fcfd 100644
--- a/mplaltair/__init__.py
+++ b/mplaltair/__init__.py
@@ -1,6 +1,10 @@
 import matplotlib
 import altair
+import matplotlib.pyplot as plt
 from ._convert import _convert
+from ._data import _normalize_data
+from ._axis import convert_axis
+from ._marks import _handle_line
 
 
 def convert(chart):
@@ -14,10 +18,24 @@ def convert(chart):
 
     Returns
     -------
-    mapping : dict
-        Mapping from parts of the encoding to the Matplotlib artists.  This is
-        for later customization.
+    fig : matplotlib.figure
 
+    ax : matplotlib.axes
 
     """
-    return _convert(chart)
+
+    fig, ax = plt.subplots()
+    _normalize_data(chart)
+
+    if chart.mark in ['point', 'circle', 'square']:  # scatter
+        mapping = _convert(chart)
+        ax.scatter(**mapping)
+    elif chart.mark == 'line':  # line
+        _handle_line(chart, ax)
+    else:
+        raise NotImplementedError
+
+    convert_axis(ax, chart)
+    fig.tight_layout()
+
+    return fig, ax
diff --git a/mplaltair/_axis.py b/mplaltair/_axis.py
index 14c7d54..b501dcd 100644
--- a/mplaltair/_axis.py
+++ b/mplaltair/_axis.py
@@ -33,13 +33,19 @@ def _set_limits(channel, scale):
         elif 'type' in scale and scale['type'] != 'linear':
             lims = _set_scale_type(channel, scale)
         else:
-            # Check that a positive minimum is zero if zero is True:
-            if ('zero' not in scale or scale['zero'] == True) and min(channel['data']) > 0:
-                lims[_axis_kwargs[channel['axis']].get('min')] = 0  # quantitative sets min to be 0 by default
+            # Include zero on the axis (or not).
+            # In Altair, scale.zero defaults to False unless the data is unbinned quantitative.
+            if channel['mark'] == 'line' and channel['axis'] == 'x':
+                # Contrary to documentation, Altair defaults to scale.zero=False for the x-axis on line graphs.
+                pass
+            else:
+                # Check that a positive minimum is zero if scale.zero is True:
+                if ('zero' not in scale or scale['zero'] == True) and min(channel['data']) > 0:
+                    lims[_axis_kwargs[channel['axis']].get('min')] = 0  # quantitative sets min to be 0 by default
 
-            # Check that a negative maximum is zero if zero is True:
-            if ('zero' not in scale or scale['zero'] == True) and max(channel['data']) < 0:
-                lims[_axis_kwargs[channel['axis']].get('max')] = 0
+                # Check that a negative maximum is zero if scale.zero is True:
+                if ('zero' not in scale or scale['zero'] == True) and max(channel['data']) < 0:
+                    lims[_axis_kwargs[channel['axis']].get('max')] = 0
 
     elif channel['dtype'] == 'temporal':
         # determine limits
@@ -221,9 +227,8 @@ def convert_axis(ax, chart):
         if channel in ['x', 'y']:
             chart_info = {'ax': ax, 'axis': channel,
                           'data': _locate_channel_data(chart, channel),
-                          'dtype': _locate_channel_dtype(chart, channel)}
-            if chart_info['dtype'] == 'temporal':
-                chart_info['data'] = _convert_to_mpl_date(chart_info['data'])
+                          'dtype': _locate_channel_dtype(chart, channel),
+                          'mark': chart.mark}
 
             scale_info = _locate_channel_scale(chart, channel)
             axis_info = _locate_channel_axis(chart, channel)
diff --git a/mplaltair/_convert.py b/mplaltair/_convert.py
index b74d99d..a63da30 100644
--- a/mplaltair/_convert.py
+++ b/mplaltair/_convert.py
@@ -1,6 +1,4 @@
-import matplotlib.dates as mdates
-import numpy as np
-from ._data import _locate_channel_data, _locate_channel_dtype, _convert_to_mpl_date
+from ._data import _locate_channel_data, _locate_channel_dtype
 
 def _allowed_ranged_marks(enc_channel, mark):
     """TODO: DOCS
@@ -80,6 +78,7 @@ def _process_stroke(dtype, data):
     """
     raise NotImplementedError
 
+
 _mappings = {
     'x': _process_x,
     'y': _process_y,
@@ -120,9 +119,7 @@ def _convert(chart):
     for channel in chart.to_dict()['encoding']:
         data = _locate_channel_data(chart, channel)
         dtype = _locate_channel_dtype(chart, channel)
-        if dtype == 'temporal':
-            data = _convert_to_mpl_date(data)
 
         mapping[_mappings[channel](dtype, data)[0]] = _mappings[channel](dtype, data)[1]
-    
+
     return mapping
diff --git a/mplaltair/_data.py b/mplaltair/_data.py
index 2a29e49..cdd4019 100644
--- a/mplaltair/_data.py
+++ b/mplaltair/_data.py
@@ -1,4 +1,6 @@
+import pandas as pd
 from ._exceptions import ValidationError
+from ._utils import _fetch
 import matplotlib.dates as mdates
 import matplotlib.cbook as cbook
 from datetime import datetime
@@ -51,13 +53,19 @@ def _locate_channel_data(chart, channel):
 
     channel_val = chart.to_dict()['encoding'][channel]
     if channel_val.get('value'):
-        return channel_val.get('value')
+        data = channel_val.get('value')
     elif channel_val.get('aggregate'):
-        return _aggregate_channel()
+        data = _aggregate_channel()
     elif channel_val.get('timeUnit'):
-        return _handle_timeUnit()
+        data = _handle_timeUnit()
     else:  # field is required if the above are not present.
-        return chart.data[channel_val.get('field')].values
+        data = chart.data[channel_val.get('field')].values
+
+    # Take care of temporal conversion immediately
+    if _locate_channel_dtype(chart, channel) == 'temporal':
+        return _convert_to_mpl_date(data)
+    else:
+        return data
 
 
 def _aggregate_channel():
@@ -109,6 +117,35 @@ def _locate_channel_axis(chart, channel):
     else:
         return {}
 
+
+def _locate_channel_field(chart, channel):
+    return chart.to_dict()['encoding'][channel]['field']
+
+
+# FROM ENCODINGS=======================================================================================================
+def _normalize_data(chart):
+    """Converts the data to a Pandas dataframe. Originally Nabarun's code (PR #5).
+
+    Parameters
+    ----------
+    chart : altair.Chart
+        The Altair chart object
+    """
+    spec = chart.to_dict()
+    if not spec['data']:
+        raise ValidationError('Please specify a data source.')
+
+    if spec['data'].get('url'):
+        df = pd.DataFrame(_fetch(spec['data']['url']))
+    elif spec['data'].get('values'):
+        return
+    else:
+        raise NotImplementedError('Given data specification is unsupported at the moment.')
+
+    chart.data = df
+# END STUFF FROM ENCODINGS=============================================================================================
+
+
 def _convert_to_mpl_date(data):
     """Converts datetime, datetime64, strings, and Altair DateTime objects to Matplotlib dates.
 
@@ -127,7 +164,7 @@ def _convert_to_mpl_date(data):
         if len(data) == 0:
             return []
         else:
-            return [_convert_to_mpl_date(i) for i in data]
+            return np.asarray([_convert_to_mpl_date(i) for i in data])
     else:
         if isinstance(data, str):  # string format for dates
             data = mdates.datestr2num(data)
@@ -153,9 +190,8 @@ def _altair_DateTime_to_datetime(dt):
     A datetime object
     """
     MONTHS = {'Jan': 1, 'January': 1, 'Feb': 2, 'February': 2, 'Mar': 3, 'March': 3, 'Apr': 4, 'April': 4,
-              'May': 5, 'May': 5, 'Jun': 6, 'June': 6, 'Jul': 7, 'July': 7, 'Aug': 8, 'August': 8,
-              'Sep': 9, 'Sept': 9, 'September': 9, 'Oct': 10, 'October': 10, 'Nov': 11, 'November': 11,
-              'Dec': 12, 'December': 12}
+              'May': 5, 'Jun': 6, 'June': 6, 'Jul': 7, 'July': 7, 'Aug': 8, 'August': 8, 'Sep': 9, 'Sept': 9,
+              'September': 9, 'Oct': 10, 'October': 10, 'Nov': 11, 'November': 11, 'Dec': 12, 'December': 12}
 
     alt_to_datetime_kw_mapping = {'date': 'day', 'hours': 'hour', 'milliseconds': 'microsecond', 'minutes': 'minute',
                        'month': 'month', 'seconds': 'second', 'year': 'year'}
diff --git a/mplaltair/_marks.py b/mplaltair/_marks.py
new file mode 100644
index 0000000..05631c1
--- /dev/null
+++ b/mplaltair/_marks.py
@@ -0,0 +1,79 @@
+import matplotlib
+import numpy as np
+from ._data import _locate_channel_field, _locate_channel_data, _locate_channel_dtype, _convert_to_mpl_date
+
+
+def _handle_line(chart, ax):
+    """Convert encodings, manipulate data if needed, and plot the line chart on an axes.
+
+    Parameters
+    ----------
+    chart : altair.Chart
+        The Altair chart object
+
+    ax : matplotlib.axes
+        The Matplotlib axes object
+
+    Notes
+    -----
+    Fill isn't necessary until mpl-altair can handle multiple plot types in one plot.
+    Size is unsupported by both Matplotlib and Altair.
+    When both Color and Stroke are provided, color is ignored and stroke is used.
+    Shape is unsupported in line graphs unless another plot type is plotted at the same time.
+    """
+
+    groupbys = []
+    kwargs = {}
+
+    if 'opacity' in chart.to_dict()['encoding']:
+        groupbys.append('opacity')
+
+    if 'stroke' in chart.to_dict()['encoding']:
+        groupbys.append('stroke')
+    elif 'color' in chart.to_dict()['encoding']:
+        groupbys.append('color')
+
+    list_fields = lambda c, g: [_locate_channel_field(c, i) for i in g]
+    if len(groupbys) > 0:
+        for label, subset in chart.data.groupby(list_fields(chart, groupbys)):
+            if 'opacity' in groupbys:
+                kwargs['alpha'] = _opacity_norm(chart, _locate_channel_dtype(chart, 'opacity'),
+                                                subset[_locate_channel_field(chart, 'opacity')].iloc[0])
+
+                if 'color' not in groupbys and 'stroke' not in groupbys:
+                    kwargs['color'] = matplotlib.rcParams['lines.color']
+            ax.plot(subset[_locate_channel_field(chart, 'x')], subset[_locate_channel_field(chart, 'y')], **kwargs)
+    else:
+        ax.plot(_locate_channel_data(chart, 'x'), _locate_channel_data(chart, 'y'))
+
+
+def _opacity_norm(chart, dtype, val):
+    """
+    Normalize the values of a column to be between 0.15 and 1, which is a visible range for opacity.
+
+    Parameters
+    ----------
+    chart : altair.Chart
+        The Altair chart object
+    dtype : str
+        The data type of the column ('quantitative', 'nominal', 'ordinal', or 'temporal')
+    val
+        The specific value to be normalized.
+
+    Returns
+    -------
+    The normalized value (between 0.15 and 1)
+    """
+    arr = _locate_channel_data(chart, 'opacity')
+    if dtype in ['ordinal', 'nominal', 'temporal']:
+        # map categoricals to numbers
+        unique, indices = np.unique(arr, return_inverse=True)
+        arr = indices
+        if dtype == 'temporal':
+            val = unique.tolist().index(_convert_to_mpl_date(val))
+        else:
+            val = unique.tolist().index(val)
+    data_min = arr.min()
+    data_max = arr.max()
+    desired_min, desired_max = (0.15, 1)  # Chosen so that the minimum value is visible
+    return ((val - data_min) / (data_max - data_min)) * (desired_max - desired_min) + desired_min
\ No newline at end of file
diff --git a/mplaltair/_utils.py b/mplaltair/_utils.py
new file mode 100644
index 0000000..831e226
--- /dev/null
+++ b/mplaltair/_utils.py
@@ -0,0 +1,44 @@
+# FROM ENCODINGS=======================================================================================================
+from urllib.request import urlopen
+from urllib.error import HTTPError
+
+import pandas as pd
+
+_PD_READERS = {
+    'json': pd.read_json,
+    'csv': pd.read_csv
+}
+
+def _get_format(url):
+    """Gives back the format of the file from url
+
+    WARNING: It might break. Trying to find a better way.
+    """
+    return url.split('.')[-1]
+
+def _fetch(url):
+    """Downloads the file from the given url as a Pandas DataFrame
+
+    Parameters
+    ----------
+    url : string
+    URL of the file to be downloaded
+
+    Returns
+    -------
+    pd.DataFrame
+    Data in the format of a DataFrame
+
+    Raises
+    ------
+    NotImplementedError
+    Raises when an unsupported file format is given as an URL
+    """
+    try:
+        ext = _get_format(url)
+        reader = _PD_READERS[ext]
+        df = reader(urlopen(url).read())
+    except KeyError:
+        raise NotImplementedError('File format not implemented')
+    return df
+# END OF STUFF FROM ENCODINGS==========================================================================================
diff --git a/mplaltair/tests/baseline_images/test_convert/test_line.png b/mplaltair/tests/baseline_images/test_convert/test_line.png
new file mode 100644
index 0000000..b49cdcd
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diff --git a/mplaltair/tests/baseline_images/test_convert/test_line_color.png b/mplaltair/tests/baseline_images/test_convert/test_line_color.png
new file mode 100644
index 0000000..203b297
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diff --git a/mplaltair/tests/baseline_images/test_convert/test_line_opacity_c:O.png b/mplaltair/tests/baseline_images/test_convert/test_line_opacity_c:O.png
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diff --git a/mplaltair/tests/baseline_images/test_convert/test_line_stroke.png b/mplaltair/tests/baseline_images/test_convert/test_line_stroke.png
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diff --git a/mplaltair/tests/baseline_images/test_convert/test_quantitative_scatter_chart0.png b/mplaltair/tests/baseline_images/test_convert/test_quantitative_scatter_chart0.png
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index 1f3073c..3024b93 100644
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diff --git a/mplaltair/tests/baseline_images/test_convert/test_scatter_temporal_channel0.png b/mplaltair/tests/baseline_images/test_convert/test_scatter_temporal_channel0.png
index 6ec5288..a8f76b8 100644
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diff --git a/mplaltair/tests/baseline_images/test_convert/test_scatter_temporal_channel1.png b/mplaltair/tests/baseline_images/test_convert/test_scatter_temporal_channel1.png
index 6ec5288..a8f76b8 100644
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diff --git a/mplaltair/tests/test_axis.py b/mplaltair/tests/test_axis.py
index dfe23cb..8f65b70 100644
--- a/mplaltair/tests/test_axis.py
+++ b/mplaltair/tests/test_axis.py
@@ -3,7 +3,6 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 from mplaltair import convert
-from .._axis import convert_axis
 import pytest
 
 df_quant = pd.DataFrame({
@@ -26,18 +25,13 @@
 @pytest.mark.xfail(raises=TypeError)
 def test_invalid_temporal():
     chart = alt.Chart(df_temp).mark_point().encode(alt.X('a:T'))
-    fig, ax = plt.subplots()
-    convert_axis(ax, chart)
+    convert(chart)
 
 
 @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_axis')
 def test_axis_more_than_x_and_y():
     chart = alt.Chart(df_quant).mark_point().encode(alt.X('a'), alt.Y('b'), color=alt.Color('c'))
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -46,11 +40,7 @@ def test_axis_more_than_x_and_y():
                                     (df_temp, 'months', 'years'), (df_temp, 'years', 'months'), (df_temp, 'months', 'combination')])
 def test_axis(df, x, y):
     chart = alt.Chart(df).mark_point().encode(alt.X(x), alt.Y(y))
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -71,11 +61,7 @@ def test_axis_zero_quantitative(x, y, zero):
         alt.X(x, scale=alt.Scale(zero=zero)),
         alt.Y(y, scale=alt.Scale(zero=zero))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -92,11 +78,7 @@ def test_axis_domain(df, x, y, x_dom, y_dom):
         alt.X(x, scale=alt.Scale(domain=x_dom)),
         alt.Y(y, scale=alt.Scale(domain=y_dom))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -106,11 +88,7 @@ def test_axis_unaggregated_quantitative():
         alt.X('a', scale=alt.Scale(domain="unaggregated")),
         alt.Y('c', scale=alt.Scale(domain="unaggregated"))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    plt.close()
-    convert_axis(ax, chart)
+    convert(chart)
 
 
 @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_axis')
@@ -124,11 +102,7 @@ def test_axis_values(df, y, vals):
         alt.X('a', axis=alt.Axis(values=[-1, 1, 1.5, 2.125, 3])),
         alt.Y(y, axis=alt.Axis(values=vals))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -141,11 +115,7 @@ def test_axis_tickCount(df, x, tickCount):
     chart = alt.Chart(df).mark_point().encode(
         alt.X(x, axis=alt.Axis(tickCount=tickCount)), alt.Y('a', axis=alt.Axis(tickCount=tickCount))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -156,11 +126,7 @@ def test_axis_scale_basic(df, column, scale_type):
         alt.X(column, scale=alt.Scale(type=scale_type)),
         alt.Y('a')
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -171,11 +137,7 @@ def test_axis_scale_type_x_quantitative(column, type, base, exponent):
         alt.X(column, scale=alt.Scale(type=type, base=base, exponent=exponent)),
         alt.Y('a')
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -186,11 +148,8 @@ def test_axis_scale_type_y_quantitative(column, type, base, exponent):
         alt.X('a'),
         alt.Y(column, scale=alt.Scale(type=type, base=base, exponent=exponent))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
+    plt.show()
     return fig
 
 
@@ -202,10 +161,7 @@ def test_axis_scale_NotImplemented_quantitative(df, x, type):
         alt.X(x, scale=alt.Scale(type=type)),
         alt.Y('a')
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
+    convert(chart)
 
 
 @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_axis')
@@ -217,11 +173,7 @@ def test_axis_formatter(df, x, y, format_x, format_y):
         alt.X(x, axis=alt.Axis(format=format_x)),
         alt.Y(y, axis=alt.Axis(format=format_y))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -233,11 +185,7 @@ def test_axis_formatter_temporal():
         alt.X('months:T', axis=alt.Axis(format='%b %Y')),
         alt.Y('hrs:T', axis=alt.Axis(format='%H:%M:%S'))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
-    fig.tight_layout()
+    fig, ax = convert(chart)
     return fig
 
 
@@ -247,7 +195,4 @@ def test_axis_formatter_fail():
         alt.X('c', axis=alt.Axis(format='-$.2g')),
         alt.Y('b', axis=alt.Axis(format='+.3r'))
     )
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
-    convert_axis(ax, chart)
+    convert(chart)
diff --git a/mplaltair/tests/test_convert.py b/mplaltair/tests/test_convert.py
index 3fe070a..9360303 100644
--- a/mplaltair/tests/test_convert.py
+++ b/mplaltair/tests/test_convert.py
@@ -2,10 +2,10 @@
 
 import altair as alt
 import pandas as pd
-import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
-
+import matplotlib.pyplot as plt
 from mplaltair import convert
+from mplaltair._convert import _convert
 
 
 df = pd.DataFrame({
@@ -25,172 +25,168 @@
 def test_encoding_not_provided():
     chart_spec = alt.Chart(df).mark_point()
     with pytest.raises(ValueError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 def test_invalid_encodings():
     chart_spec = alt.Chart(df).encode(x2='quant').mark_point()
     with pytest.raises(ValueError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.xfail(raises=TypeError)
 def test_invalid_temporal():
     chart = alt.Chart(df).mark_point().encode(alt.X('quant:T'))
-    convert(chart)
+    _convert(chart)
 
 @pytest.mark.parametrize('channel', ['quant', 'ord', 'nom'])
 def test_convert_x_success(channel):
     chart_spec = alt.Chart(df).encode(x=channel).mark_point()
-    mapping = convert(chart_spec)
+    mapping = _convert(chart_spec)
     assert list(mapping['x']) == list(df[channel].values)
 
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_x_success_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.X(column))
-    mapping = convert(chart)
+    mapping = _convert(chart)
     assert list(mapping['x']) == list(mdates.date2num(df[column].values))
-    # assert list(mapping['x']) == list(df[column].values)
 
 def test_convert_x_fail():
     chart_spec = alt.Chart(df).encode(x='b:N').mark_point()
     with pytest.raises(KeyError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.parametrize('channel', ['quant', 'ord', 'nom'])
 def test_convert_y_success(channel):
     chart_spec = alt.Chart(df).encode(y=channel).mark_point()
-    mapping = convert(chart_spec)
+    mapping = _convert(chart_spec)
     assert list(mapping['y']) == list(df[channel].values)
 
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_y_success_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Y(column))
-    mapping = convert(chart)
+    mapping = _convert(chart)
     assert list(mapping['y']) == list(mdates.date2num(df[column].values))
-    # assert list(mapping['y']) == list(df[column].values)
 
 def test_convert_y_fail():
     chart_spec = alt.Chart(df).encode(y='b:N').mark_point()
     with pytest.raises(KeyError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.xfail(raises=ValueError, reason="It doesn't make sense to have x2 and y2 on scatter plots")
 def test_quantitative_x2_y2():
     chart = alt.Chart(df_quant).mark_point().encode(alt.X('a'), alt.Y('b'), alt.X2('c'), alt.Y2('alpha'))
-    convert(chart)
+    _convert(chart)
 
 @pytest.mark.xfail(raises=ValueError)
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_x2_y2_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.X2(column), alt.Y2(column))
-    convert(chart)
+    _convert(chart)
 
 @pytest.mark.parametrize('channel,dtype', [('quant','quantitative'), ('ord','ordinal')])
 def test_convert_color_success(channel, dtype):
     chart_spec = alt.Chart(df).encode(color=alt.Color(field=channel, type=dtype)).mark_point()
-    mapping = convert(chart_spec)
+    mapping = _convert(chart_spec)
     assert list(mapping['c']) == list(df[channel].values)
 
 def test_convert_color_success_nominal():
     chart_spec = alt.Chart(df).encode(color='nom').mark_point()
     with pytest.raises(NotImplementedError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_color_success_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Color(column))
-    mapping = convert(chart)
+    mapping = _convert(chart)
     assert list(mapping['c']) == list(mdates.date2num(df[column].values))
-    # assert list(mapping['c']) == list(df[column].values)
 
 def test_convert_color_fail():
     chart_spec = alt.Chart(df).encode(color='b:N').mark_point()
     with pytest.raises(KeyError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.parametrize('channel,type', [('quant', 'Q'), ('ord', 'O')])
 def test_convert_fill(channel, type):
     chart_spec = alt.Chart(df).encode(fill='{}:{}'.format(channel, type)).mark_point()
-    mapping = convert(chart_spec)
+    mapping = _convert(chart_spec)
     assert list(mapping['c']) == list(df[channel].values)
 
 def test_convert_fill_success_nominal():
     chart_spec = alt.Chart(df).encode(fill='nom').mark_point()
     with pytest.raises(NotImplementedError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_fill_success_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Fill(column))
-    mapping = convert(chart)
+    mapping = _convert(chart)
     assert list(mapping['c']) == list(mdates.date2num(df[column].values))
-    # assert list(mapping['c']) == list(df[column].values)
 
 
 def test_convert_fill_fail():
     chart_spec = alt.Chart(df).encode(fill='b:N').mark_point()
     with pytest.raises(KeyError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The marker argument in scatter() cannot take arrays")
 def test_quantitative_shape():
     chart = alt.Chart(df_quant).mark_point().encode(alt.Shape('shape'))
-    mapping = convert(chart)
+    mapping = _convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The marker argument in scatter() cannot take arrays")
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_shape_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Shape(column))
-    mapping = convert(chart)
+    mapping = _convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="Merge: the dtype for opacity isn't assumed to be quantitative")
 def test_quantitative_opacity_value():
     chart = alt.Chart(df_quant).mark_point().encode(opacity=alt.value(.5))
-    mapping = convert(chart)
+    mapping = _convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The alpha argument in scatter() cannot take arrays")
 def test_quantitative_opacity_array():
     chart = alt.Chart(df_quant).mark_point().encode(alt.Opacity('alpha'))
-    convert(chart)
+    _convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The alpha argument in scatter() cannot take arrays")
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_opacity_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Opacity(column))
-    convert(chart)
+    _convert(chart)
 
 @pytest.mark.parametrize('channel,type', [('quant', 'Q'), ('ord', 'O')])
 def test_convert_size_success(channel, type):
     chart_spec = alt.Chart(df).encode(size='{}:{}'.format(channel, type)).mark_point()
-    mapping = convert(chart_spec)
+    mapping = _convert(chart_spec)
     assert list(mapping['s']) == list(df[channel].values)
 
 def test_convert_size_success_nominal():
     chart_spec = alt.Chart(df).encode(size='nom').mark_point()
     with pytest.raises(NotImplementedError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 def test_convert_size_fail():
     chart_spec = alt.Chart(df).encode(size='b:N').mark_point()
     with pytest.raises(KeyError):
-        convert(chart_spec)
+        _convert(chart_spec)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="Dates would need to be normalized for the size.")
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_size_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Size(column))
-    convert(chart)
+    _convert(chart)
 
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="Stroke is not well supported in Altair")
 def test_quantitative_stroke():
     chart = alt.Chart(df_quant).mark_point().encode(alt.Stroke('fill'))
-    convert(chart)
+    _convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="Stroke is not well defined in Altair")
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_stroke_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Stroke(column))
-    convert(chart)
+    _convert(chart)
 
 
 # Aggregations
@@ -199,12 +195,12 @@ def test_convert_stroke_fail_temporal(column):
 def test_quantitative_x_count_y():
     df_count = pd.DataFrame({"a": [1, 1, 2, 3, 5], "b": [1.4, 1.4, 2.9, 3.18, 5.3]})
     chart = alt.Chart(df_count).mark_point().encode(alt.X('a'), alt.Y('count()'))
-    mapping = convert(chart)
+    mapping = _convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="specifying timeUnit is not supported yet")
 def test_timeUnit():
     chart = alt.Chart(df).mark_point().encode(alt.X('date(combination)'))
-    convert(chart)
+    _convert(chart)
 
 # Plots
 
@@ -218,17 +214,95 @@ def test_timeUnit():
 @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
 @pytest.mark.parametrize("chart", [chart_quant, chart_fill_quant])
 def test_quantitative_scatter(chart):
-    mapping = convert(chart)
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
+    fig, ax = convert(chart)
     return fig
 
 @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
 @pytest.mark.parametrize("channel", [alt.Color("years"), alt.Fill("years")])
 def test_scatter_temporal(channel):
-    chart = alt.Chart(df).mark_point().encode(alt.X("years"), channel)
-    mapping = convert(chart)
-    mapping['y'] = df['quantitative'].values
-    fig, ax = plt.subplots()
-    ax.scatter(**mapping)
+    chart = alt.Chart(df).mark_point().encode(
+        alt.X("years"),
+        alt.Y("quantitative"),
+        channel
+    )
+    fig, ax = convert(chart)
     return fig
+
+
+# Line plots
+df_line = pd.DataFrame({
+        'a': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'b': [3, 2, 1, 7, 8, 9, 4, 5, 6],
+        'c': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'],
+        'd': [1, 1, 1, 2, 2, 2, 3, 3, 3],
+        'dates': ['1968-08-01', '1968-08-01', '1968-08-01', '2010-08-08', '2010-08-08', '2010-08-08', '2015-03-14', '2015-03-14', '2015-03-14']
+    })
+
+
+class TestLines(object):
+    @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
+    def test_line(self):
+        chart = alt.Chart(df_line).mark_line().encode(
+            alt.X('a'),
+            alt.Y('b'),
+        )
+        fig, _ = convert(chart)
+        return fig
+
+    @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
+    @pytest.mark.parametrize('x,y,s', [
+        ('a:Q', 'b:Q', 'd:Q'),
+        pytest.param('a:N', 'b:N', 'c:N', marks=pytest.mark.xfail(raises=NotImplementedError)),
+        pytest.param('a:O', 'b:O', 'c:O', marks=pytest.mark.xfail(raises=NotImplementedError))
+    ])
+    def test_line_stroke(self, x, y, s):
+        chart = alt.Chart(df_line).mark_line().encode(
+            alt.X(x),
+            alt.Y(y),
+            alt.Stroke(s)
+        )
+        fig, _ = convert(chart)
+        return fig
+
+    @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
+    def test_line_color(self):
+        chart = alt.Chart(df_line).mark_line().encode(
+            alt.X('a'),
+            alt.Y('b'),
+            alt.Color('d')
+        )
+        fig, _ = convert(chart)
+        return fig
+
+    @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
+    @pytest.mark.parametrize('o', ['d:Q', 'c:O', 'dates:T'])
+    def test_line_opacity(self, o):
+        chart = alt.Chart(df_line).mark_line().encode(
+            alt.X('a'),
+            alt.Y('b'),
+            alt.Opacity(o)
+        )
+        fig, ax = convert(chart)
+        return fig
+
+    @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
+    @pytest.mark.parametrize('c,o', [('d:Q', 'd:Q'), ('c:N', 'c:O'), ('dates:T', 'dates:T')])
+    def test_line_opacity_color(self, c, o):
+        chart = alt.Chart(df_line).mark_line().encode(
+            alt.X('a'),
+            alt.Y('b'),
+            alt.Color(c),
+            alt.Opacity(o)
+        )
+        fig, ax = convert(chart)
+        return fig
+
+
+class TestBars(object):
+    @pytest.mark.xfail(raises=NotImplementedError)
+    def test_bar_fail(self):
+        chart = alt.Chart(df_line).mark_bar().encode(
+            alt.X('a'),
+            alt.Y('b'),
+        )
+        convert(chart)
diff --git a/mplaltair/tests/test_data.py b/mplaltair/tests/test_data.py
index bdd4154..d6b0335 100644
--- a/mplaltair/tests/test_data.py
+++ b/mplaltair/tests/test_data.py
@@ -23,10 +23,13 @@ def test_data_field_quantitative(column, dtype):
     chart = alt.Chart(df).mark_point().encode(alt.X(field=column, type=dtype))
     for channel in chart.to_dict()['encoding']:
         data = _data._locate_channel_data(chart, channel)
-    assert list(data) == list(df[column].values)
+    if dtype == 'temporal':
+        assert list(data) == list(_data._convert_to_mpl_date(df[column].values))
+    else:
+        assert list(data) == list(df[column].values)
 
 
-@pytest.mark.parametrize("column", ['a', 'b', 'c', 'combination'])
+@pytest.mark.parametrize("column", ['a', 'b', 'c'])
 def test_data_shorthand_quantitative(column):
     chart = alt.Chart(df).mark_point().encode(alt.X(column))
     for channel in chart.to_dict()['encoding']:
@@ -34,11 +37,11 @@ def test_data_shorthand_quantitative(column):
     assert list(data) == list(df[column].values)
 
 
-def test_data_value_quantitative():
-    chart = alt.Chart(df).mark_point().encode(opacity=alt.value(0.5))
+def test_data_shorthand_temporal():
+    chart = alt.Chart(df).mark_point().encode(alt.X('combination'))
     for channel in chart.to_dict()['encoding']:
         data = _data._locate_channel_data(chart, channel)
-    assert data == 0.5
+    assert list(data) == list(_data._convert_to_mpl_date(df['combination'].values))
 
 
 @pytest.mark.parametrize("column", ['a', 'b', 'c'])