1. Purpose

The purpose of this noteboook is to illustrate how the ggvis package can be used to create dynamic html graphs.

2. Load relevant libraries and view practice datasets.

library(ggvis)
library(dplyr)
library(stringr)
library(RColorBrewer)
iris <- as_tibble(iris)
iris
## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # ... with 140 more rows
mtcars
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

3. Create a scatter plot.

ggvis(iris, x = ~Sepal.Width, y = ~Sepal.Length) %>% 
  layer_points(fill = ~Species, stroke = ~Species, size := 50) %>% 
  group_by(Species) %>% 
  layer_model_predictions(stroke = ~Species, fill = ~Species, model = "loess", se = T)

4. Create a boxplot.

ggvis(iris, x = ~Species, y = ~Sepal.Length) %>% 
  layer_boxplots(fill = ~Species, width=0.975, size=1) %>% 
  add_axis("x", grid=F)

5. Create a bar graph.

iris %>% 
  group_by(Species) %>% 
  summarise(Sepal.Width=mean(Sepal.Width), Sepal.Length=mean(Sepal.Length)) %>%
  ggvis(x = ~Species, y = ~Sepal.Length) %>% 
  layer_bars(fill = ~Species)

6. Create a stacked bar graph.

Make a stacked bar graph.

mtcars %>% 
  group_by(cyl=as.factor(cyl), gear=as.factor(gear)) %>% 
  summarise(mpg=mean(mpg)) %>% 
  ggvis(x = ~cyl, y = ~mpg) %>% 
  layer_bars(fill = ~gear)

7. Create a scatterplot with specified colours, renamed titles, updated font families and sizes.

ggvis(iris, ~Sepal.Width, ~Sepal.Length) %>% 
  layer_points(fill = ~Species, stroke = ~Species) %>% 
  scale_nominal(property="fill", range=c("#4575b4", "#A8A8A8", "#d73027")) %>%
  scale_nominal(property="stroke", range=c("#4575b4", "#A8A8A8", "#d73027")) %>%  
  add_axis("x", title="Sepal width", 
           properties=axis_props(
              labels=list(font="Arial", fontSize=12),
              title=list(font="Arial", fontSize=14)
          ))  %>%  
  add_axis("y", title="Sepal length", 
           properties=axis_props(
              labels=list(font="Arial", fontSize=12),
              title=list(font="Arial", fontSize=14)
          )) %>% 
  add_legend(title="Species", scales=c("fill", "stroke"),
           properties=legend_props(
              labels=list(font="Arial", fontSize=12),
              title=list(font="Arial", fontSize=14)
          ))

8. Create interactivity hover-over tooltip effects. Use the id #ggvis-tooltip to change font with css.

ggvis(iris, ~Sepal.Width, ~Sepal.Length) %>% 
  layer_points(fill = ~Species, stroke = ~Species, strokeWidth := 1, strokeWidth.hover := 2, stroke.hover := "black") %>% 
  add_tooltip(function(df) {
    if(is.null(df)) return(NULL)
    paste(
      paste("X-axis:", df$Sepal.Width),
      paste("Y-axis:", df$Sepal.Length),
      paste("Fill:", df$Species),
      sep="<br/>"
    )
  })
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

9. Create bar graph with hover-over tooltip effects.

#The all_values function can be used to identify the names of the variables available. Calculate bar and stacked bar values in the same way by subtracting the upper by the lower.

all_values <- function(df) {
    if(is.null(df)) return(NULL)
    paste0(names(df), ": ", format(df), collapse = "<br />")
  }

iris %>% 
  group_by(Species) %>% 
  summarise(Sepal.Width=mean(Sepal.Width), Sepal.Length=mean(Sepal.Length)) %>%
  ggvis(x = ~Species, y = ~Sepal.Length) %>% 
  layer_bars(fill = ~Species, strokeWidth.hover := 3, strokeWidth := 1) %>% 
  add_tooltip(function(df) {
    if(is.null(df)) return(NULL)
    paste(
      paste("X-axis:", df$x_),
      paste("Y-axis:", df$stack_upr - df$stack_lwr),
      paste("Fill:", df$Species),
      sep="<br/>"
    )
  })
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

10. Create boxplot with hover-over tooltip effects.

all_values <- function(df) {
    if(is.null(df)) return(NULL)
    paste0(names(df), ": ", format(df), collapse = "<br />")
}

ggvis(iris, x = ~Species, y = ~Sepal.Length) %>% 
  layer_boxplots(fill = ~Species, width=0.975, size=1, strokeWidth.hover := 3, strokeWidth := 1) %>% 
  add_axis("x", grid=F) %>% 
  add_tooltip(all_values)
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.
ggvis(iris, x = ~Species, y = ~Sepal.Length) %>% 
  layer_boxplots(fill = ~Species, width=0.975, size=1, strokeWidth.hover := 3, strokeWidth := 1) %>% 
  add_axis("x", grid=F) %>% 
  add_tooltip(function(df) {
    if(is.null(df)) return(NULL)
    paste(
      paste("Whisker minimum", df$min_),
      paste("Lower quartile:", df$lower_),
      paste("Median", df$median_),
      paste("Upper quartile", df$upper_),
      paste("Whisker maximum", df$max_),
      sep="<br/>"
    )
  })
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.