Scatter plots IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G - - PowerPoint PPT Presentation

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Scatter plots IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G - - PowerPoint PPT Presentation

Jan 04, 2024 157 likes •674 views

Scatter plots IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT 2 Rick Sca v e a Fo u nder , Sca v e a Academ y 48 geometries geom _* abline conto u r dotplot ji er pointrange ribbon spoke area co u nt errorbar label

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Scatter plots

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

Rick Scavea

Founder, Scavea Academy

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

48 geometries

geom_* abline contour dotplot jier pointrange ribbon spoke area count errorbar label polygon rug step bar crossbar errorbarh line qq segment text bin2d curve freqpoly linerange qq_line sf tile blank density hex map quantile sf_label violin boxplot density2d histogram path raster sf_text vline col density_2d hline point rect smooth

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Common plot types

Plot type Possible Geoms Scaer plots points, jier, abline, smooth, count

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Scatter plots

Each geom can accept specic aesthetic mappings, e.g. geom_point(): Essential x,y

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point()

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Scatter plots

Each geom can accept specic aesthetic mappings, e.g. geom_point(): Essential Optional x,y alpha, color, ll, shape, size, stroke

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point()

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Geom-specific aesthetic mappings

# These result in the same plot! ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point() ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point(aes(col = Species))

Control aesthetic mappings of each layer independently:

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

head(iris, 3) # Raw data Species Sepal.Length Sepal.Width Petal.Length Petal.Width 1 setosa 5.1 3.5 1.4 0.2 2 setosa 4.9 3.0 1.4 0.2 3 setosa 4.7 3.2 1.3 0.2 iris %>% group_by(Species) %>% summarise_all(mean) -> iris.summary iris.summary # Summary statistics # A tibble: 3 x 5 Species Sepal.Length Sepal.Width Petal.Length Petal.Width <fct> <dbl> <dbl> <dbl> <dbl> 1 setosa 5.01 3.43 1.46 0.246 2 versicolor 5.94 2.77 4.26 1.33 3 virginica 6.59 2.97 5.55 2.03

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + # Inherits both data and aes from ggplot() geom_point() + # Different data, but inherited aes geom_point(data = iris.summary, shape = 15, size = 5)

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Shape attribute values

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Example

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point() + geom_point(data = iris.summary, shape = 21, size = 5, fill = "black", stroke = 2)

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

On-the-fly stats by ggplot2

See the second course for the stats layer. Note: Avoid ploing only the mean without a measure of spread, e.g. the standard deviation.

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position = "jitter"

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point(position = "jitter")

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

geom_jitter()

A short-cut to geom_point(position = "jitter")

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_jitter()

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Don't forget to adjust alpha

Combine jiering with alpha-blending if necessary

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_jitter(alpha = 0.6)

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Hollow circles also help

shape = 1 is a. hollow circle.

Not necessary to also use alpha-blending.

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_jitter(shape = 1)

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

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Histograms

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

Rick Scavea

Founder, Scavea Academy

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Common plot types

Plot type Possible Geoms Scaer plots points, jier, abline, smooth, count Bar plots histogram, bar, col, errorbar Line plots line, path

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Histograms

ggplot(iris, aes(x = Sepal.Width)) + geom_histogram()

A plot of binned values i.e. a statistical function

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Default of 30 even bins

ggplot(iris, aes(x = Sepal.Width)) + geom_histogram()

A plot of binned values i.e. a statistical function

# Default bin width: diff(range(iris$Sepal.Width))/30 [1] 0.08

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Intuitive and meaningful bin widths

ggplot(iris, aes(x = Sepal.Width)) + geom_histogram(binwidth = 0.1)

Always set a meaningful bin widths for your data. No spaces between bars.

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Re-position tick marks

ggplot(iris, aes(x = Sepal.Width)) + geom_histogram(binwidth = 0.1, center = 0.05)

Always set a meaningful bin widths for your data. No spaces between bars. X axis labels are between bars.

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Different Species

ggplot(iris, aes(x = Sepal.Width, fill = Species)) + geom_histogram(binwidth = .1, center = 0.05)

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Default position is "stack"

ggplot(iris, aes(x = Sepal.Width, fill = Species)) + geom_histogram(binwidth = .1, center = 0.05, position = "stack")

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

position = "dodge"

ggplot(iris, aes(x = Sepal.Width, fill = Species)) + geom_histogram(binwidth = .1, center = 0.05, position = "dodge")

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position = "fill"

ggplot(iris, aes(x = Sepal.Width, fill = Species)) + geom_histogram(binwidth = .1, center = 0.05, position = "fill")

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Final Slide

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

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Bar plots

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

Rick Scavea

Founder, Scavea Academy

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Bar Plots, with a categorical X-axis

Use geom_bar() or geom_col() Geom Stat Action

geom_bar()

"count" Counts the number of cases at each x position

geom_col()

"identity" Plot actual values All positions from before are available Two types Absolute counts Distributions

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Bar Plots, with a categorical X-axis

Use geom_bar() or geom_col() Geom Stat Action

geom_bar()

"count" Counts the number of cases at each x position

geom_col()

"identity" Plot actual values

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Bar Plots, with a categorical X-axis

Use geom_bar() or geom_col() Geom Stat Action

geom_bar()

"count" Counts the number of cases at each x position

geom_col()

"identity" Plot actual values All positions from before are available Two types Absolute counts Distributions

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Habits of mammals

str(sleep) 'data.frame': 76 obs. of 3 variables: $ vore : Factor w/ 4 levels "carni","herbi",..: 1 4 2 4 2 2 1 1 2 2 ... $ total: num 12.1 17 14.4 14.9 4 14.4 8.7 10.1 3 5.3 ... $ rem : num NA 1.8 2.4 2.3 0.7 2.2 1.4 2.9 NA 0.6 ...

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Bar plot

ggplot(sleep, aes(vore)) + geom_bar()

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Plotting distributions instead of absolute counts

# Calculate Descriptive Statistics: iris %>% select(Species, Sepal.Width) %>% gather(key, value, -Species) %>% group_by(Species) %>% summarise(avg = mean(value), stdev = sd(value))

  • > iris_summ_long

iris_summ_long

Species avg stdev setosa 3.43 0.38 versicolor 2.77 0.31 virginica 2.97 0.32

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Plotting distributions

ggplot(iris_summ_long, aes(x = Species, y = avg)) + geom_col() + geom_errorbar(aes(ymin = avg - stdev, ymax = avg + stdev), width = 0.1)

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

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Line plots

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

Rick Scavea

Founder, Scavea Academy

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Common plot types

Plot type Possible Geoms Scaer plots points, jier, abline, smooth, count Bar plots histogram, bar, col, errorbar Line plots line, path

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Beaver

str(beaver) 'data.frame': 101 obs. of 3 variables: $ time : POSIXct, format: "2000-01-01 09:30:00" "2000-01-01 09:40:00" "2000-01-01 09:50:00" ... $ temp : num 36.6 36.7 36.9 37.1 37.2 ... $ active: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...

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Beaver

ggplot(beaver, aes(x = time, y = temp)) + geom_line()

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Beaver

ggplot(beaver, aes(x = time, y = temp, color = factor(active)) ) + geom_line()

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The fish catch dataset

str(fish) 'data.frame': 427 obs. of 3 variables: $ Species: Factor w/ 7 levels "Pink","Chum",..: 1 2 3 4 5 6 7 1 2 3 ... $ Year : int 1950 1950 1950 1950 1950 1950 1950 1951 1951 1951 ... $ Capture: int 100600 139300 64100 30500 0 23200 10800 259000 155900 51200 ...

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Linetype aesthetic

ggplot(fish, aes(x = Year, y = Capture, linetype = Species)) + geom_line()

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Size aesthetic

ggplot(fish, aes(x = Year, y = Capture, size = Species)) + geom_line()

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Color aesthetic

ggplot(fish, aes(x = Year, y = Capture, color = Species)) + geom_line()

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Aesthetics for categorical variables

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Fill aesthetic with geom_area()

ggplot(fish, aes(x = Year, y = Capture, fill = Species)) + geom_area()

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Using position = "fill"

ggplot(fish, aes(x = Year, y = Capture, fill = Species)) + geom_area(position = "fill")

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geom_ribbon()

ggplot(fish, aes(x = Year, y = Capture, fill = Species)) + geom_ribbon(aes(ymax = Capture, ymin = 0), alpha = 0.3)

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2