Description Usage Arguments Value Examples

Data jittering reduces overplotting by adding small variances to values. grid_jitter2 removes it entirely by first rounding points to a custom grid and then reallocating individual duplicate points to the nearest vacant cells within a maximum tolerance threshold. If no vacant cells are available the function will abort. This function applies Hungarian algorithm with small area constraints. (By contrast grid_jitter applies Hungarian to match the entire points set to the whole grid.) This function is much faster than grid_jitter with bigger point sets and grids.

1 2 |

`x` |
Numeric vector or 2 column matrix or data.frame of data points to plot |

`y` |
Numeric vector, y coordinates matching x (if a vector) |

`nx` |
Numeric, grid x/y dimensions |

`ny` |
Numeric |

`tol` |
Numeric, the maximum distance overplotted points are allowed to move to the nearest vacant grid cell |

`plotresults` |
Logical, output plots to illustrate point displacements and cell-reallocations |

`file` |
String, if not NULL the result of plotresults will save to filename instead of rendering in R |

`w` |
Numeric, width/height (inches) of plotresults if output to file (PDF) |

`h` |
Numeric |

A 2 column matrix of grid-jittered point coordinates

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
# 1. normal distribution
d1 = data.frame(x = rnorm(500), y = rnorm(500))
d1g = grid_jitter2(d1, nx=75, ny=75, tol=5)
# 2. faithful dataset
d2 = faithful
d2g = grid_jitter2(d2, nx=50, ny=50, tol=3)
# 3. mpg example
d3 = ggplot2::mpg[,c(3,8)]
d3g = grid_jitter2(d3, nx=100, ny=100, tol=3)
# 4. diamonds - carat vs price
d4 = ggplot2::diamonds[sample(1:53940, 1000),c('carat','price')]
d4g = grid_jitter2(d4, nx=500, ny=500, tol=2) # fails
d4g = grid_jitter2(d4, nx=500, ny=500, tol=5, plotresults=FALSE)
# 5. US States
d5 = data.frame(x=state.center$x, y=state.center$y, id=state.abb)
d5j = grid_jitter2(d5[,1:2], nx=10, ny=8, tol=3, plotresults=TRUE)
cols = sample(colorRampPalette(c('darkblue','blue','lightblue'))(50))
par(mai=rep(.6,4), mfrow=c(1,2))
plot(d5[,1:2], pch=15, cex=2.5, col=cols, asp=2.5, xlab=NA, ylab=NA)
text(d5[,1:2], labels=state.abb, col='white', cex=.7)
plot(d5j, pch=15, cex=2.5, col=cols, asp=2.5, xlab=NA, ylab=NA)
text(d5j, labels=d5$id, col='white', cex=.7)
# 6. to illuste cell reallocation
d5 = data.frame(x = c(1,rep(50, 398), 100), y = c(1,rep(50, 398), 100))
d5g = grid_jitter2(d5, nx=30, ny=30, tol=15)
``` |

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