on 17-Nov-2019 (Sun)

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For PCA/SVD, the scale/*** of the data matters

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For PCA/SVD, the scale/units of the data matters

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For PCA/SVD, the ***/units of the data matters

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For PCA/SVD, the scale/units of the data matters

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PC’s/SV’s may *** real patterns

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PC’s/SV’s may mix real patterns

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SVD can be *** intensive for very large matrices

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SVD can be computationally intensive for very large matrices

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To quote from Hadley Wickham’s book on ggplot2, we want to “shorten the distance from mind to ***”.

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To quote from Hadley Wickham’s book on ggplot2, we want to “shorten the distance from mind to page”.

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”…the grammar tells us that a statistical graphic is a *** from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

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”…the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

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”…the *** tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

Answer

”…the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

Question

”…the grammar tells us that a statistical graphic is a mapping from *** to *** attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

Answer

”…the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

Question

”…the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of *** objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

Answer

”…the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

Question

”…the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from *** book

Answer

”…the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system” – from ggplot2 book

Question

The grammar allows for a more compact summary of the base *** of a language

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The grammar allows for a more compact summary of the base components of a language

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in ggplot2 in R, qplot() plots are made up of *** (size, shape, color) and *** (points, lines).

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in ggplot2 in R, qplot() plots are made up of aesthetics (size, shape, color) and geoms (points, lines).

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in ggplot2 in R, qplot() plots are made up of aesthetics (***, ***, ***) and geoms (points, lines).

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in ggplot2 in R, qplot() plots are made up of aesthetics (size, shape, color) and geoms (points, lines).

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in ggplot2 in R, qplot() plots are made up of aesthetics (size, shape, color) and geoms (***, ***).

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in ggplot2 in R, qplot() plots are made up of aesthetics (size, shape, color) and geoms (points, lines).

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***() is very flexible for doing things qplot() cannot do.

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ggplot() is very flexible for doing things qplot() cannot do.

Question

your data should have appropriate [...] so that you can quickly look at a dataset and know

• what the variables are

• what the values of each variable mean

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your data should have appropriate metadata so that you can quickly look at a dataset and know

• what the variables are

• what the values of each variable mean

Question

your data should have appropriate metadata so that you can quickly look at a dataset and know

• what the [...] are

• what the [...] of each variable mean

Answer

your data should have appropriate metadata so that you can quickly look at a dataset and know

• what the variables are

• what the values of each variable mean

Question

in ggplot2 for R, non-numeric or categorical variables should be coded as *** variables and have meaningful labels for each level of the factor.

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in ggplot2 for R, non-numeric or categorical variables should be coded as factor variables and have meaningful labels for each level of the factor.

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in ggplot2 for R, non-numeric or categorical variables should be coded as factor variables and have meaningful *** for each level of the factor.

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in ggplot2 for R, non-numeric or categorical variables should be coded as factor variables and have meaningful labels for each level of the factor.

Question

in ggplot2 for R, non-numeric or categorical variables should be coded as factor variables and have meaningful labels for each level of the ***.

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in ggplot2 for R, non-numeric or categorical variables should be coded as factor variables and have meaningful labels for each level of the factor.

Question

for ggplot2 in R, non-numeric or categorical variables should be coded as factor variables and have meaningful labels for each level of the factor. If a variable represents temperature categories, it might be better to use “cold”, “mild”, and “hot” rather than “ [...] ”, “ [...] ”, and “ [...] ”

Answer

for ggplot2 in R, non-numeric or categorical variables should be coded as factor variables and have meaningful labels for each level of the factor. If a variable represents temperature categories, it might be better to use “cold”, “mild”, and “hot” rather than “1”, “2”, and “3”

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including the proper *** can make your exploratory plots essentially self-documenting

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including the proper metadata can make your exploratory plots essentially self-documenting

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including the proper metadata can make your exploratory plots essentially self-***

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including the proper metadata can make your exploratory plots essentially self-documenting

Question

for ggplot2 in R, in the call to qplot() you must specify the '***' argument so that qplot() knows where to look up the variables.

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for ggplot2 in R, in the call to qplot() you must specify the 'data' argument so that qplot() knows where to look up the variables.

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in ggplot2 for R, color is an *** and the color of each point can be mapped to a ***.

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in ggplot2 for R, color is an aesthetic and the color of each point can be mapped to a variable.

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in ggplot2 for R, a smooth is a “***” that you can add along with your data points.

qplot(displ, hwy, data = mpg, *** = c("point", "smooth"))

Answer

in ggplot2 for R, a smooth is a “geom” that you can add along with your data points.

qplot(displ, hwy, data = mpg, geom = c("point", "smooth"))

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in ggplot2 for R, specify geom = "***" if you want the smoother overlayed with the points.

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in ggplot2 for R, specify geom = "point" if you want the smoother overlayed with the points.

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a way to create multiple panels of plots based on the levels of categorical variable in ggplot2 for R is ***.

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a way to create multiple panels of plots based on the levels of categorical variable in ggplot2 for R is facets.

Question

An alternative to histograms is a density smoother, which sometimes can be easier to visualize when there are multiple groups.

Here is a density smooth of an entire study population.

qplot(log(eno), data = maacs, geom = "***")

Answer

An alternative to histograms is a density smoother, which sometimes can be easier to visualize when there are multiple groups.

Here is a density smooth of an entire study population.

qplot(log(eno), data = maacs, geom = "density")

Question

An alternative to histograms is a density ***, which sometimes can be easier to visualize when there are multiple groups.

Here is a density *** of an entire study population.

qplot(log(eno), data = maacs, geom = "density")

Answer

An alternative to histograms is a density smoother, which sometimes can be easier to visualize when there are multiple groups.

Here is a density smooth of an entire study population.

qplot(log(eno), data = maacs, geom = "density")

Question

aesthetic mappings: describe how data are mapped to ***, ***, ***, ***

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aesthetic mappings: describe how data are mapped to color, size, shape, location

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these mappings describe how data are mapped to color, size, shape, location : *** mappings

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these mappings describe how data are mapped to color, size, shape, location : aesthetic mappings

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in ggplot2 for R, *** are geometric objects like points, lines, shapes

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in ggplot2 for R, geoms are geometric objects like points, lines, shapes

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in ggplot2 for R, geoms are *** objects like ***, ***, ***

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in ggplot2 for R, geoms are geometric objects like points, lines, shapes

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in ggplot2 for R, *** describe how conditional/panel plots should be constructed

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in ggplot2 for R, facets describe how conditional/panel plots should be constructed

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in ggplot2 for R, facets describe how ***/*** plots should be constructed

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in ggplot2 for R, facets describe how conditional/panel plots should be constructed

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in ggplot2 for R, *** are statistical transformations like binning, quantiles, smoothing.

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in ggplot2 for R, stats are statistical transformations like binning, quantiles, smoothing.

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in ggplot2 for R, stats are *** transformations like ***, ***, ***.

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in ggplot2 for R, stats are statistical transformations like binning, quantiles, smoothing.

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in ggplot2 for R, stats are statistical *** like binning, quantiles, smoothing.

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in ggplot2 for R, stats are statistical transformations like binning, quantiles, smoothing.

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in ggplot2 for R, *** are what scale an aesthetic map uses (example: male = red, female = blue).

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in ggplot2 for R, scales are what scale an aesthetic map uses (example: male = red, female = blue).

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in ggplot2 for R, scales are what *** an aesthetic map uses (example: male = red, female = blue).

Answer

in ggplot2 for R, scales are what scale an aesthetic map uses (example: male = red, female = blue).

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in ggplot2 for R, scales are what scale an *** uses (example: male = red, female = blue).

Answer

in ggplot2 for R, scales are what scale an aesthetic map uses (example: male = red, female = blue).

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in ggplot2 for R *** describes the system in which the locations of the geoms will be drawn

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in ggplot2 for R coordinate system describes the system in which the locations of the geoms will be drawn

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in ggplot2 for R coordinate system describes the system in which the locations of the *** will be drawn

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in ggplot2 for R coordinate system describes the system in which the locations of the geoms will be drawn

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in ggplot2 for R coordinate system describes the system in which the *** of the geoms will be ***

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in ggplot2 for R coordinate system describes the system in which the locations of the geoms will be drawn