This set of functions can be used to interpolate between single data types, i.e. data not part of data.frames but stored in vectors. All functions come in two flavours: the standard and a *_t version. The standard reads the data as a list of states, each tween matched element-wise from state to state. The *_t version uses the transposed representation where each element is a vector of states. The standard approach can be used when each tween has the same number of states and you want to control the number of point in each state transition. The latter is useful when each tween consists of different numbers of states and/or you want to specify the total number of points for each tween.
tween(data, n, ease = "linear")
tween_t(data, n, ease = "linear")
tween_colour(data, n, ease = "linear")
tween_color(data, n, ease = "linear")
tween_colour_t(data, n, ease = "linear")
tween_color_t(data, n, ease = "linear")
tween_constant(data, n, ease = "linear")
tween_constant_t(data, n, ease = "linear")
tween_date(data, n, ease = "linear")
tween_date_t(data, n, ease = "linear")
tween_datetime(data, n, ease = "linear")
tween_datetime_t(data, n, ease = "linear")
tween_numeric(data, n, ease = "linear")
tween_numeric_t(data, n, ease = "linear")A list of vectors or a single vector. In the standard functions
each element in the list must be of equal length; for the *_t functions
lengths can differ. If a single vector is used it will be eqivalent to using
as.list(data) for the standard functions and list(data) for the
*_t functions.
The number of elements per transition or tween. See details
The easing function to use for each transition or tween. See
details. Defaults to 'linear'
A list with an element for each tween. That means that the length of
the return is equal to the length of the elements in data for the
standard functions and equal to the length of data for the *_t
functions.
tween and tween_t are wrappers around the other functions that tries to guess
the type of input data and choose the appropriate tween function. Unless you
have data that could be understood as a colour but is in fact a character
vector it should be safe to use these wrappers. It is probably safer and more
verbose to use the explicit functions within package code as they circumvent
the type inference and checks whether the input data matches the tween
function.
tween_numeric will provide a linear interpolation between the points based on
the sequence returned by the easing function. tween_date and tween_datetime
converts to numeric, produces the tweening, and converts back again.
tween_colour converts colours into Lab and does the interpolation there,
converting back to sRGB after the tweening is done. tween_constant is a
catchall that converts the input into character and interpolates by switching
between states halfway through the transition.
The meaning of the n and ease arguments differs somewhat
between the standard and *_t versions of the functions. In the standard
function n and ease refers to the length and easing function of
each transition, being recycled if necessary to length(data) - 1. In
the *_t functions n and ease refers to the total length of each
tween and the easing function to be applied to all transition for each tween.
The will both be recycled to length(data).
tween_numeric and approx()tween_numeric (and tween_numeric_t) is superficially equivalent to
stats::approx(), but there are differences.
stats::approx() will create evenly spaced points, at the expense
of not including the actual points in the input, while the reverse is true
for tween_numeric. Apart from that tween_numeric of course supports easing
functions and is vectorized.
tween_numeric(list(1:3, 10:8, c(20, 60, 30)), 10)
#> [[1]]
#> [1] 1.00000 2.00000 3.00000 4.00000 5.00000 6.00000 7.00000 8.00000
#> [9] 9.00000 10.00000 11.11111 12.22222 13.33333 14.44444 15.55556 16.66667
#> [17] 17.77778 18.88889 20.00000
#>
#> [[2]]
#> [1] 2.000000 2.777778 3.555556 4.333333 5.111111 5.888889 6.666667
#> [8] 7.444444 8.222222 9.000000 14.666667 20.333333 26.000000 31.666667
#> [15] 37.333333 43.000000 48.666667 54.333333 60.000000
#>
#> [[3]]
#> [1] 3.000000 3.555556 4.111111 4.666667 5.222222 5.777778 6.333333
#> [8] 6.888889 7.444444 8.000000 10.444444 12.888889 15.333333 17.777778
#> [15] 20.222222 22.666667 25.111111 27.555556 30.000000
#>
tween_colour_t(list(colours()[1:4], colours()[1:2], colours()[25:100]), 100)
#> [[1]]
#> [1] "#FFFFFF" "#FFFFFF" "#FEFFFF" "#FEFEFF" "#FDFEFF" "#FDFEFF" "#FCFEFF"
#> [8] "#FCFEFF" "#FBFDFF" "#FBFDFF" "#FAFDFF" "#FAFDFF" "#FAFCFF" "#F9FCFF"
#> [15] "#F9FCFF" "#F8FCFF" "#F8FCFF" "#F7FBFF" "#F7FBFF" "#F6FBFF" "#F6FBFF"
#> [22] "#F5FBFF" "#F5FAFF" "#F5FAFF" "#F4FAFF" "#F4FAFF" "#F3F9FF" "#F3F9FF"
#> [29] "#F2F9FF" "#F2F9FF" "#F1F9FF" "#F1F8FF" "#F0F8FF" "#F0F8FF" "#F0F8FE"
#> [36] "#F1F7FD" "#F1F7FB" "#F1F6FA" "#F2F6F9" "#F2F6F8" "#F3F5F6" "#F3F5F5"
#> [43] "#F3F4F4" "#F4F4F3" "#F4F4F2" "#F4F3F0" "#F4F3EF" "#F5F2EE" "#F5F2ED"
#> [50] "#F5F2EC" "#F6F1EA" "#F6F1E9" "#F6F0E8" "#F7F0E7" "#F7F0E5" "#F7EFE4"
#> [57] "#F7EFE3" "#F8EFE2" "#F8EEE1" "#F8EEDF" "#F9EDDE" "#F9EDDD" "#F9EDDC"
#> [64] "#F9ECDB" "#FAECD9" "#FAEBD8" "#FAEBD7" "#FAEBD7" "#FAEBD7" "#FAEBD7"
#> [71] "#FBEBD7" "#FBECD8" "#FBECD8" "#FBECD8" "#FBECD8" "#FBECD8" "#FCECD8"
#> [78] "#FCECD8" "#FCECD8" "#FCEDD9" "#FCEDD9" "#FCEDD9" "#FCEDD9" "#FDEDD9"
#> [85] "#FDEDD9" "#FDEDD9" "#FDEDD9" "#FDEEDA" "#FDEEDA" "#FDEEDA" "#FEEEDA"
#> [92] "#FEEEDA" "#FEEEDA" "#FEEEDA" "#FEEEDA" "#FEEFDB" "#FFEFDB" "#FFEFDB"
#> [99] "#FFEFDB" "#FFEFDB"
#>
#> [[2]]
#> [1] "#FFFFFF" "#FFFFFF" "#FFFFFF" "#FFFFFF" "#FEFFFF" "#FEFFFF" "#FEFFFF"
#> [8] "#FEFFFF" "#FEFEFF" "#FEFEFF" "#FDFEFF" "#FDFEFF" "#FDFEFF" "#FDFEFF"
#> [15] "#FDFEFF" "#FDFEFF" "#FDFEFF" "#FCFEFF" "#FCFEFF" "#FCFEFF" "#FCFEFF"
#> [22] "#FCFEFF" "#FCFDFF" "#FCFDFF" "#FBFDFF" "#FBFDFF" "#FBFDFF" "#FBFDFF"
#> [29] "#FBFDFF" "#FBFDFF" "#FAFDFF" "#FAFDFF" "#FAFDFF" "#FAFDFF" "#FAFDFF"
#> [36] "#FAFDFF" "#FAFCFF" "#F9FCFF" "#F9FCFF" "#F9FCFF" "#F9FCFF" "#F9FCFF"
#> [43] "#F9FCFF" "#F9FCFF" "#F8FCFF" "#F8FCFF" "#F8FCFF" "#F8FCFF" "#F8FCFF"
#> [50] "#F8FCFF" "#F7FBFF" "#F7FBFF" "#F7FBFF" "#F7FBFF" "#F7FBFF" "#F7FBFF"
#> [57] "#F7FBFF" "#F6FBFF" "#F6FBFF" "#F6FBFF" "#F6FBFF" "#F6FBFF" "#F6FBFF"
#> [64] "#F5FBFF" "#F5FAFF" "#F5FAFF" "#F5FAFF" "#F5FAFF" "#F5FAFF" "#F5FAFF"
#> [71] "#F4FAFF" "#F4FAFF" "#F4FAFF" "#F4FAFF" "#F4FAFF" "#F4FAFF" "#F4FAFF"
#> [78] "#F3FAFF" "#F3F9FF" "#F3F9FF" "#F3F9FF" "#F3F9FF" "#F3F9FF" "#F2F9FF"
#> [85] "#F2F9FF" "#F2F9FF" "#F2F9FF" "#F2F9FF" "#F2F9FF" "#F2F9FF" "#F1F9FF"
#> [92] "#F1F9FF" "#F1F8FF" "#F1F8FF" "#F1F8FF" "#F1F8FF" "#F0F8FF" "#F0F8FF"
#> [99] "#F0F8FF" "#F0F8FF"
#>
#> [[3]]
#> [1] "#FFEBCD" "#B781EA" "#0000FF" "#0000FF" "#0000FF" "#0000F6" "#0000EE"
#> [8] "#0000DD" "#0000CD" "#0000AB" "#00008B" "#5213B6" "#8A2BE2" "#A72583"
#> [15] "#A52A2A" "#D13535" "#FF4040" "#F63D3D" "#EE3B3B" "#DD3737" "#CD3333"
#> [22] "#AB2B2B" "#8B2323" "#B77052" "#DEB887" "#EEC591" "#FFD39B" "#F6CC96"
#> [29] "#EEC591" "#DDB787" "#CDAA7D" "#AB8E69" "#8B7355" "#7C897A" "#5F9EA0"
#> [36] "#7BC9CE" "#98F5FF" "#93EDF6" "#8EE5EE" "#84D5DD" "#7AC5CD" "#66A5AB"
#> [43] "#53868B" "#73C165" "#7FFF00" "#7FFF00" "#7FFF00" "#7AF600" "#76EE00"
#> [50] "#66CD00" "#458B00" "#D2691E" "#FF7F24" "#EE7621" "#CD661D" "#8B4513"
#> [57] "#FF7F50" "#FF7256" "#EE6A50" "#CD5B45" "#8B3E2F" "#6495ED" "#FFF8DC"
#> [64] "#FFF8DC" "#EEE8CD" "#CDC8B1" "#8B8878" "#00FFFF" "#00FFFF" "#00EEEE"
#> [71] "#00CDCD" "#008B8B" "#00008B" "#008B8B" "#B8860B" "#FFB90F" "#EEAD0E"
#> [78] "#CD950C" "#8B6508" "#A9A9A9" "#006400" "#A9A9A9" "#BDB76B" "#8B008B"
#> [85] "#556B2F" "#CAFF70" "#BCEE68" "#A2CD5A" "#6E8B3D" "#FF8C00" "#FF7F00"
#> [92] "#EE7600" "#CD6600" "#8B4500" "#9932CC" "#BF3EFF" "#B23AEE" "#9A32CD"
#> [99] "#68228B" "#8B0000"
#>