Save tour history.

Save a tour path so it can later be displayed in many different ways.

Usage

save_history(
  data,
  tour_path = grand_tour(),
  max_bases = 100,
  start = NULL,
  rescale = FALSE,
  sphere = FALSE,
  step_size = Inf,
  ...
)

Arguments

data

matrix, or data frame containing numeric columns

tour_path

tour path generator

max_bases

maximum number of new bases to generate. Some tour paths (like the guided tour) may generate less than the maximum.

start

starting projection, if you want to specify one

rescale

Default FALSE. If TRUE, rescale all variables to range [0,1]?

sphere

if true, sphere all variables

step_size

distance between each step - defaults to Inf which forces new basis generation at each step.

...

additional arguments passed to tour path

Examples

# You can use a saved history to replay tours with different visualisations

t1 <- save_history(flea[, 1:6], max = 3)
#> Converting input data to the required matrix format.
animate_xy(flea[, 1:6], planned_tour(t1))
#> Converting input data to the required matrix format.
#> Using half_range 4.4


## andrews_history(t1)
## andrews_history(interpolate(t1))

## t1 <- save_history(flea[, 1:6], grand_tour(4), max = 3)
## animate_pcp(flea[, 1:6], planned_tour(t1))
## animate_scatmat(flea[, 1:6], planned_tour(t1))

## t1 <- save_history(flea[, 1:6], grand_tour(1), max = 3)
## animate_dist(flea[, 1:6], planned_tour(t1))

testdata <- matrix(rnorm(100 * 3), ncol = 3)
testdata[1:50, 1] <- testdata[1:50, 1] + 10
testdata <- sphere_data(testdata)
t2 <- save_history(testdata, guided_tour(holes()),
  max = 5
)
#> Target: 0.896, 11.5% better 
#> Target: 0.908, 1.3% better 
#> No better bases found after 25 tries.  Giving up.
#> Final projection: 
#> -0.268   0.963  
#> -0.865  -0.236  
#>  0.423   0.128  
animate_xy(testdata, planned_tour(t2))
#> Using half_range 3.4



# Or you can use saved histories to visualise the path that the tour took.
plot(path_index(interpolate(t2), holes()))


# And you can plot any individual frame using
best_prj <- matrix(t2[,,3], ncol=2)
p <- render_proj(testdata, best_prj)
# which creates a data frame with the elements
# to make the plot, see render_proj() for plotting code
# OR see draw_tour_axes() for similar code in base graphics