Introduction to R and RStudio
Figure 1
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Figure 2
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Figure 3
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Visualisation with ggplot2Setting valuesGeometrical objects
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
Controlling the transparency can be a great way to “mute” the visual
effect of certain data, while still keeping it visible. Its a great tool
when you have many data points or if you have several geoms together,
like we will see soon.
Figure 11
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Figure 12
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Figure 13
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Figure 14
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Figure 15
In the graph above, each geom inherited all three mappings: x, y and
colour. If we want only single linear model to be built, we would need
to limit the effect of
colour
aesthetic to only
geom_point()
function, by moving it from the “parent”
function to the layer where we want it to apply. Note, though, that
because we want the colour
to be still mapped to the
island
variable, it needs to be wrapped into
aes()
function and supplied to mapping
argument.
Figure 16
Look at that! The data actually reveals something called the “simpsons
paradox”. It’s when a relationship looks to go in a specific direction,
but when looking into groups within the data the relationship is the
opposite. Here, the overall relationship between bill length and depths
looks negative, but when we take into account that there are different
species, the relationship is actually positive.
Figure 17
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Figure 18
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Figure 19
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Subsetting data with dplyrWrap-up
Figure 1
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Figure 2
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Data sorting and pipes dplyrWrap-up
Data visualisation and scalesPiping into ggplotAdding colourChanging colourChanging the overall lookWrap up
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Figure 11
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Figure 12
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Figure 13
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Figure 14
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Data manipulation with dplyrAdding new variables,Wrap up
Figure 1
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Figure 2
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Figure 3
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Reshaping data with tidyrCreating longer dataWrap up
Figure 1
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Figure 2
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Data summaries with dplyrMotivation
Complex data pipelinesMotivation
Figure 1
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Figure 2
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Figure 3
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Figure 4
The last plot is misleading because the data we have summary data by
species and island. Ignoring the island in the plot, means that the
values for the different measurements cannot be distinguished from
eachother.
Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
ok, so we got what we asked, the year part makes more sense, but its a
very “busy” plot. Its really quite hard to compare everything from
Bisoe, or all the Adelie’s, to each other. How can we make it
easier?
Figure 11
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Figure 12
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Figure 13
facet_grid
is more complex than facet_wrap
as
it will always force the y-axis for rows, and x-axis for columns remain
the same. So wile setting scales to free will help a little, it will
only do so within each row and column, not each subplot. When the
results do not look as you like, swapping what are rows and columns in
the grid can often create better results.
Figure 14
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Figure 15
the classic theme is preferred by many journals, but for facet grid, its
not super nice, since we loose grid information.
Figure 16
Theme light could be a nice option, but the white text of light grey
makes the panel text hard to read.
Figure 17
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Figure 18
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Figure 19
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Figure 20
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Figure 21
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Figure 22
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