Coding Lab: Visualizing data with ggplot2

Ari Anisfeld

Summer 2020

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How to use ggplot

I How to map data to aesthetics with aes() (and what thatmeans)

I How to visualize the mappings with geomsI How to get more out of your data by using multiple aestheticsI How to use facets to add dimensionality

There are whole books on how to use ggplot. This is a quickintroduction!

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Understanding ggplot()By itself, ggplot() tells R to prepare to make a plot.texas_annual_sales <-texas_housing_data %>%group_by(year) %>%summarize(total_volume = sum(volume, na.rm = TRUE))

ggplot(data = texas_annual_sales)

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Adding a mappingAdding mapping = aes() says how the data will map to“aesthetics”.

I e.g. tell R to make x-axis year and y-axis total_volume.I Each row of the data has (year, total_volume).

I R will map that to the coordinate pair (x,y) .I Look at the data before moving on!

ggplot(data = texas_annual_sales,mapping = aes(x = year, y = total_volume))

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Visualizing the mapping with a geomgeom_<name> tells R what type of visualization to produce.

Here we see points.

I Each row of the data has (year, total_volume).I R will map that to the coordinate pair (x,y).

ggplot(data = texas_annual_sales,mapping = aes(x = year, y = total_volume)) +

geom_point()

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Visualizing the mapping with a geomHere we see bars.

I Each row of the data has (year, total_volume).I R will map that to the coordinate pair (x,y)

ggplot(data = texas_annual_sales,mapping = aes(x = year, y = total_volume)) +

geom_col()

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Visualizing the mapping with a geom

Here we see a line connecting each (x,y) pair.ggplot(data = texas_annual_sales,

mapping = aes(x = year, y = total_volume)) +geom_line()

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Visualizing the mapping with a geomHere we see a smooth line. R does a statistical transformation!

I Now R doesn’t visualize the mapping (year, total_volume) to each (x,y)pair

I Instead it fits a model to the (x,y) and then plots the “smooth” line

ggplot(data = texas_annual_sales,mapping = aes(x = year, y = total_volume)) +

geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

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Visualizing the mapping with a geom

We can overlay several geom.ggplot(data = texas_annual_sales,

mapping = aes(x = year, y = total_volume)) +geom_smooth() +geom_point()

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Visualizing the mapping with a geom

I We saw that we can visualize a relationship between twovariables mapping data to x and y

I The data can be visualized with different geoms that can becomposed (+) together.

I We can even calculate new variables with statistics and plotthose on the fly.

Next: Now we’ll look at aesthetics that go beyond x and y axes.

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Using aesthetics to explore data.We’ll use midwest data and start with only mapping to x and y

midwest %>%ggplot(aes(x = percollege,

y = percbelowpoverty)) +geom_point()

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Using aesthetics to explore data.I color maps data to the color of points or lines.

I Each state is assigned a color.I This works with discrete data and continuous data.

midwest %>%ggplot(aes(x = percollege,

y = percbelowpoverty,color = state)) +

geom_point()

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Using aesthetics to explore data.I shape maps data to the shape of points.

I Each state is assigned a shape.I This works with discrete data only.

midwest %>%ggplot(aes(x = percollege,

y = percbelowpoverty,shape = state)) +

geom_point()

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Using aesthetics to explore data.I alpha maps data to the transparency of points.

I Here we map the percentage of people within a known povertystatus to alpha

midwest %>%ggplot(aes(x = percollege,

y = percbelowpoverty,alpha = poptotal)) +

geom_point()

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Using aesthetics to explore data.I size maps data to the size of points and width of lines.

I Here we map the percentage of people within a known povertystatus to size

midwest %>%ggplot(aes(x = percollege,

y = percbelowpoverty,size = poptotal)) +

geom_point()

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Using aesthetics to explore data.

We can combine any and all aesthetics, and even map the same variable tomultiple aestheticsmidwest %>%

ggplot(aes(x = percollege,y = percbelowpoverty,alpha = percpovertyknown,size = poptotal,color = state))+

geom_point()

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Using aesthetics to explore data.

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Using aesthetics to explore data

Different geoms have specific aesthetics that go with them.

I use ? to see which aesthetics a geom accepts (e.g?geom_point)

I the bold aesthetics are required.I the ggplot cheatsheet shows all the geoms with their associated

aesthetics

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FacetsFacets provide an additional tool to explore multidimensional datamidwest %>%

ggplot(aes(x = log(poptotal),y = percbelowpoverty)) +

geom_point() +facet_wrap(vars(state))

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discrete vs continuous dataaes discrete continuous

limited number of classes unlimited number of classesusually chr or lgl numeric

x, y yes yescolor, fill yes yesshape yes (6 or fewer categories) nosize, alpha not advised yesfacet yes not advised

Here, discrete and continuous have different meaning than in math

I For ggplot meaning is more fluid.I If you do group_by with the var and there are fewer than 6 to

10 groups, discrete visualizations can workI If your “discrete” data is numeric, as.character() or

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color can be continuousmidwest %>%

ggplot(aes(x = percollege,y = percbelowpoverty,color = percpovertyknown)) +

geom_point()

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shape does not play well with many categoriesI Will only map to 6 categories, the rest become NA.I We can override this behavior and get up to 25 distinct shapes

midwest %>%ggplot(aes(x = percollege,

y = percbelowpoverty,shape = county)) +

geom_point() +# legend off, otherwise it overwhelmstheme(legend.position = "none")

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alpha and size can be misleading with discrete datamidwest %>%

ggplot(aes(x = percollege,y = percbelowpoverty,alpha = state)) +

geom_point()

## Warning: Using alpha for a discrete variable is not advised.

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Adding vertical linestexas_annual_sales %>%

ggplot(aes(x = year, y = total_volume)) +geom_point() +geom_vline(aes(xintercept = 2007),

linetype = "dotted")

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I add horizontal lines with geom_hline()I add any linear fit using geom_abline() by providing a slope

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Key take aways

I ggplot starts by mapping data to “aesthetics”.I e.g. What data shows up on x and y axes and how color,

size and shape appear on the plot.I We need to be aware of ‘continuous’ vs. ‘discrete’ variables.

I Then, we use geoms to create a visualization based on themapping.

I Again we need to be aware of ‘continuous’ vs. ‘discrete’variables.

I Making quick plots helps us understand data and makes usaware of data issues

Resources: R for Data Science chap. 3 (r4ds.had.co.nz); RStudio’sggplot cheatsheet.

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Appendix: Some graphs you made along the way

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lab 0: a map

geom_path is like geom_line, but connects (x, y) pairs in theorder they appear in the data set.storms %>%

group_by(name, year) %>%filter(max(category) == 5) %>%

ggplot(aes(x = long, y = lat, color = name)) +geom_path() +borders("world") +coord_quickmap(xlim = c(-130, -60), ylim = c(20, 50))

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lab 0: a map

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Dean

Emily

Felix

Gilbert

Hugo

Isabel

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lab 1: a line plot

french_data <-wid_data %>%filter(type == "Net personal wealth",

country == "France") %>%mutate(perc_national_wealth = value * 100)

french_data %>%ggplot(aes(y = perc_national_wealth,

x = year,color = percentile)) +

geom_line()

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lab 1: a line plot

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lab 2: distributions

I geom_density() only requires an x asthetic and it calculatesthe distribution to plot.

I We can set the aesthetics manually, independent of data fornicer graphs.chi_sq_samples <-tibble(x = c(rchisq(100000, 2),

rchisq(100000, 3),rchisq(100000, 4)),

df = rep(c("2", "3", "4"), each = 1e5))

chi_sq_samples %>%ggplot(aes(x = x, fill = df)) +geom_density( alpha = .5) +labs(fill = "df", x = "sample")

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lab 2: distributions

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lab 4: grouped bar graphs

I position = "dodge2" tells R to put bars next to each other,rather than stacked on top of each other.

I Notice we use fill and not color because we’re “filling” anarea.mean_share_per_country %>%

ggplot(aes(y = country,x = mean_share,fill = percentile)) +

geom_col(position = "dodge2") +labs(x = "Mean share of national wealth",

y = "",fill = "Wealth\npercentile")

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lab 4: grouped bar graphs

ChinaFrance

IndiaKorea

RussiaS Africa

UKUSA

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Wealthpercentile

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lab 4: faceted bar graph

I Notice that we manipulate our data to the right specificationbefore making this graph

I Using facet_wrap we get a distinct graph for each timeperiod.

mean_share_per_country_with_time %>%ggplot(aes(x = country,

y = mean_share,fill = percentile)) +

geom_col(position = "dodge2") +facet_wrap(vars(time_period))

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lab 4: faceted bar graph

1980 to 1999 2000 to present

1959 and earlier 1960 to 1979

ChinaIndiaUSA ChinaIndiaUSA

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