geom_tile tiles displaced when using ggplotly - r

Issue:
When using ggplotly in combination with geom_tile, in the following reprex (see below), tiles are displaced.
ggplot output:
ggplotly output:
As you can see, the y-values are partially off-placed by one and the x-values are partially off-placed by a lot.
plotly raises the following warning:
Versions: I have the same issue using R version 3.5.3 and 4.0.1., using ggplot2 version 3.1.1/3.3.3 and plotly version 3.9.0/4.9.3 respectively.
Question: How can I fix this issue such that the ggplotly graph has the same, correct tile placement as the original ggplot2 graph?
Reprex:
library(ggplot2)
library(plotly)
library(data.table)
dt <- structure(list(x = c(5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 7, 8, 9, 11, 12, 13, 14, 15,
16, 17, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17), y = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L), Color = c(0.105263157894737,
0.0736842105263158, -0.136842105263158, -0.136842105263158, -0.221052631578947,
0.105263157894737, 0.0421052631578947, -0.031578947368421, -0.136842105263158,
-0.178947368421053, -0.210526315789474, -0.242105263157895, -0.263157894736842,
NA, NA, 0.761904761904762, 0.619047619047619, 0.523809523809524,
0.476190476190476, 0.380952380952381, 1.19047619047619, 1.0952380952381,
1.04761904761905, 0.952380952380952, 0.904761904761905, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.436923076923077,
0.313846153846154, 0.704615384615385, 1.56615384615385, 1.44307692307692,
1.19692307692308, 1.13230769230769, 1.04, 0.947692307692308,
0.916923076923077, 0.898461538461538, 0.876923076923077, 1.76923076923077,
0.569105691056911, 0.447154471544715, 0.894308943089431, 0.853658536585366,
0.731707317073171, 0.634146341463415, 0.58130081300813, 0.33739837398374,
0.313008130081301, 0.272357723577236, 0.252032520325203, 0.235772357723577,
1.4390243902439, NA, 1.09195402298851, 1.07471264367816, 1.47701149425287,
1.4367816091954, 1.83908045977012, 2.35632183908046, 2.18390804597701,
1.95402298850575, 1.94252873563218, 1.92528735632184, 1.91379310344828,
1.89080459770115, 3.59770114942529, NA, 0.457227138643068, 0.752212389380531,
0.604719764011799, 0.471976401179941, 0.678466076696165, 0.575221238938053,
0.457227138643068, 0.56047197640118, 0.530973451327434, 0.497050147492625,
0.463126843657817, 0.548672566371681, NA, 0.273665320771646,
0.165993719156572, 0.122625990728279, 0.272169881860326, 0.182443547181098,
0.0373859727830118, -0.091969493046209, -0.153282488410348, -0.108419321070734,
-0.129355465829221, -0.145057574398086, -0.157918349035442, -0.0354419021982952,
NA, 0.336239103362391, 0.62266500622665, 0.460772104607721, 0.361145703611457,
0.24906600249066, 0.161892901618929, 0.386052303860523, 0.311332503113325,
0.261519302615193, 0.23412204234122, 0.207970112079701, 0.183063511830635,
NA, 0.39348710990502, 0.251017639077341, 0.33921302578019, 0.617367706919946,
0.597014925373134, 0.556309362279512, 0.461329715061058, 0.345997286295794,
0.440976933514247, 0.400271370420624, 0.37449118046133, 0.347354138398915,
0.831750339213026, 0.820189274447949, 0.725552050473186, 0.630914826498423,
0.599369085173502, 0.378548895899054, 0.157728706624606, 0.126182965299685,
0.0630914826498423, 0.517350157728707, 0.495268138801262, 0.476340694006309,
0.451104100946372, 0.892744479495268, 0.632743362831858, 0.411504424778761,
0.942477876106195, 0.853982300884956, 0.809734513274336, 0.721238938053097,
0.676991150442478, 0.455752212389381, 0.438053097345133, 0.411504424778761,
0.389380530973451, 0.36283185840708, 0.327433628318584, 0.746268656716418,
0.54726368159204, 0.462686567164179, 0.412935323383085, 0.114427860696517,
0.796019900497512, 0.746268656716418, 0.681592039800995, 0.6318407960199,
0.592039800995025, 0.552238805970149, 0.527363184079602, 0.492537313432836,
NA, 0.402097902097902, 0.340909090909091, 0.236013986013986,
0.131118881118881, 0.236013986013986, 0.0262237762237762, -0.0174825174825175,
-0.0699300699300699, 0.13986013986014, 0.106643356643357, 0.0769230769230769,
0.0629370629370629, 0.048951048951049, NA, 0.476495726495726,
0.391025641025641, 0.252136752136752, 0.273504273504273, 0.412393162393162,
0.391025641025641, 0.358974358974359, 0.273504273504273, 0.145299145299145,
0.123931623931624, 0.0737179487179487, 0.0416666666666667, 0.111111111111111,
NA, 0.777385159010601, 0.600706713780919, 0.353356890459364,
0.49469964664311, 0.459363957597173, 0.653710247349823, 0.618374558303887,
0.547703180212014, 0.512367491166078, 0.484098939929329, 0.45583038869258,
0.431095406360424, 0.696113074204947, NA, 1.06481481481481, 0.925925925925926,
0.694444444444445, 1.01851851851852, 0.694444444444445, 0.648148148148148,
0.601851851851852, 0.569444444444445, 0.541666666666667, 0.523148148148148,
0.50462962962963, 0.490740740740741, 0.851851851851852, NA, 0.574712643678161,
0.498084291187739, 0.383141762452107, 0.651340996168582, 0.53639846743295,
0.762452107279693, 0.67816091954023, 0.448275862068965, 0.409961685823755,
0.333333333333333, 0.283524904214559, 0.245210727969349, 0.532567049808429,
1.04166666666667, 0.925925925925926, 0.740740740740741, 0.717592592592593,
0.625, 0.671296296296296, 0.625, 0.578703703703704, 0.532407407407407,
0.643518518518518, 0.578703703703704, 0.518518518518518, 0.851851851851852,
NA, 0.775193798449612, 0.682170542635659, 0.578811369509044,
0.527131782945736, 1.25064599483204, 1.19896640826873, 0.96640826873385,
0.837209302325581, 0.75968992248062, 0.689922480620155, 0.674418604651163,
0.664082687338501, 0.86046511627907, 0.783132530120482, 0.642570281124498,
0.582329317269076, 0.522088353413655, 0.642570281124498, 0.522088353413655,
0.421686746987952, 0.281124497991968, 0.200803212851406, 0.180722891566265,
0.112449799196787, 0.0562248995983936, 0.305220883534137, 1.89473684210526,
1.78947368421053, 1.73684210526316, 1.47368421052632, 1.47368421052632,
1.36842105263158, 1.26315789473684, 1.21052631578947, 1.10526315789474,
1.10526315789474, NA, NA, NA, NA, NA, NA, NA, NA, 2.25, 2, 2,
2, 1.53153153153153, 1.35135135135135, 1.21621621621622, 1.08108108108108,
0.990990990990991, 0.986486486486486, 0.981981981981982, 0.711711711711712,
0.621621621621622, 0.576576576576577, 0.576576576576577, 0.576576576576577,
1.02702702702703, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 0.129740518962076, 0.0449101796407186, -0.0998003992015968,
-0.229540918163673, -0.384231536926148, -0.484031936127745, -0.62874251497006,
-0.738522954091816, -0.828343313373254, -0.838323353293413, -0.839321357285429,
-0.839820359281437, -0.800399201596807, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA)), row.names = c(NA, -355L), class = c("data.table",
"data.frame"))
p <- ggplot(dt, aes(x, y, fill = Color)) + geom_tile()
p
g <- ggplotly(p)
g


The problem is that you have the same coordinates repeated more than once with different values for the Color variable. Indeed if you check how many unique combinations for x and y you have there are only 346 not 355 as the number of rows in your data.table.
nrow(unique(dt[, .(x, y)]))
# [1] 346
If you try changing this line of your example:
p <- ggplot(dt, aes(x, y, fill = Color)) + geom_tile()
to
p <- ggplot(unique(dt, by = c('x', 'y')), aes(x, y, fill = Color)) + geom_tile()
you will get the same plot in both ggplot2 and plotly.
Best!

Related

Adjust column order after joining two data frames

I would like to generate a new data frame that joins both datasets (df1 and df2) below and is in the following column order: n, M1, M1_with_normalization, M2, M2_with_normalization, M3, M3_with_normalization, M4 and M4_with_normalization. How can I do this?
df1<- structure(list(n = c(7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35), M1 = c(1L, 29L, 28L, 27L, 25L, 26L, 24L,
20L, 21L, 22L, 23L, 15L, 12L, 17L, 18L, 19L, 16L, 13L, 14L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 4L, 2L, 3L), M2 = c(1, 29, 28, 27,
26, 25, 24, 23, 22, 21, 20, 15, 12, 19, 18, 17, 16, 14, 13, 11,
10, 9, 8, 7, 6, 5, 4, 3, 2), M3 = c(1L, 29L, 28L, 27L, 25L,
26L, 24L, 20L, 21L, 22L, 23L, 15L, 12L, 17L, 18L, 19L, 16L, 13L,
14L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 4L, 2L, 3L), M4 = c(1L,
29L, 28L, 27L, 25L, 26L, 24L, 20L, 21L, 22L, 23L, 15L, 12L, 17L,
18L, 19L, 16L, 13L, 14L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 4L, 2L,
3L)), class = "data.frame", row.names = c(NA, -29L))
df2<-structure(list(n= c(7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35), M1_with_normalization = c(29L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 15L, 18L, 11L, 12L, 13L, 14L, 16L, 17L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L), M2_with_normalization = c(1L,
29L, 28L, 27L, 25L, 26L, 24L, 20L, 21L, 22L, 23L, 15L, 12L, 17L,
18L, 19L, 16L, 13L, 14L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 4L, 2L,
3L), M3_with_normalization = c(1L, 29L, 28L, 27L, 25L, 26L, 24L, 20L, 21L,
22L, 23L, 15L, 12L, 17L, 18L, 19L, 16L, 13L, 14L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 4L, 2L, 3L), M4_with_normalization = c(1L, 29L, 28L, 27L,
25L, 26L, 24L, 20L, 21L, 22L, 23L, 15L, 12L, 17L, 18L, 19L, 16L,
13L, 14L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 4L, 2L, 3L)), class = "data.frame", row.names = c(NA,
29L))

If both the datasets are in the same row order with same dimensions, cbind both the datasets and rearrange the column names
out <- cbind(df1, df2[-1])[c(rbind(names(df1), names(df2)))[-2]]
-output
> head(out, 3)
n M1 M1_with_normalization M2 M2_with_normalization M3 M3_with_normalization M4 M4_with_normalization
1 7 1 29 1 1 1 1 1 1
2 8 29 1 29 29 29 29 29 29
3 9 28 2 28 28 28 28 28 28
Or with dplyr
library(dplyr)
df1 %>%
bind_cols(df2 %>%
select(-n)) %>%
relocate(n, sort(names(.)))

2d contour color map in ggplot2

I have a huge data, please bear with me.
df <- structure(list(W = c(5216400.4123, 5399804.7349, 5595563.3087,
5792353.9932, 5993467.7466, 6189404.9279, 6380940.454, 6566630.3544,
6747453.6816, 6917820.9796, 7086201.8275, 7248213.5225, 5402700.4252,
5592654.9057, 5795404.8549, 5999223.7818, 6207520.1695, 6410455.1037,
6608831.1825, 6801152.8706, 6988434.1695, 7164886.0132, 7339280.46,
7507078.2886, 5589000.4397, 5785505.0748, 5995246.3993, 6206093.5662,
6421572.589, 6631505.2817, 6836721.9131, 7035675.3828, 7229414.6578,
7411951.0459, 7592359.0997, 7765943.0584, 5775300.4552, 5978355.2455,
6195087.9484, 6412963.3504, 6635625.0103, 6852555.4544, 7064612.6447,
7270197.897, 7470395.1463, 7659016.0859, 7845437.7356, 8024807.8293,
5961600.4694, 6171205.4145, 6394929.4952, 6619833.1358, 6849677.4283,
7073605.633, 7292503.372, 7504720.4062, 7711375.6354, 7906081.1161,
8098516.3724, 8283672.5987, 6147900.4816, 6364055.583, 6594771.0412,
6826702.9212, 7063729.8478, 7294655.8114, 7520394.1023, 7739242.9195,
7952356.1253, 8153146.1541, 8351595.0092, 8542537.3639, 6334200.5005,
6556905.7553, 6794612.5881, 7033572.7089, 7277782.2661, 7515705.9882,
7748284.8337, 7973765.4322, 8193336.6123, 8400211.1873, 8604673.6477,
8801402.1321, 6520500.5136, 6749755.9226, 6994454.1327, 7240442.494,
7491834.6868, 7736756.1617, 7976175.5621, 8208287.9459, 8434317.1037,
8647276.2228, 8857752.2827, 9060266.9029, 6706800.528, 6942606.0899,
7194295.6809, 7447312.2777, 7705887.1079, 7957806.3409, 8204066.2961,
8442810.4584, 8675297.5886, 8894341.2573, 9110830.9206, 9319131.6718,
6893100.5418, 7135456.2602, 7394137.2288, 7654182.0653, 7919939.5232,
8178856.5136, 8431957.0234, 8677332.969, 8916278.0804, 9141406.2918,
9363909.5543, 9577996.4404, 7079400.5588, 7328306.4296, 7593978.7729,
7861051.8503, 8133991.9462, 8399906.6912, 8659847.7579, 8911855.4821,
9157258.5684, 9388471.3277, 9616988.1903, 9836861.209, 7265700.5699,
7521156.5994, 7793820.3185, 8067921.639, 8348044.3652, 8620956.868,
8887738.4844, 9146377.9962, 9398239.0552, 9635536.362, 9870066.8286,
10095725.9764, 7377480.5806, 7636866.7018, 7913725.2471, 8192043.5082,
8476475.8166, 8753586.9712, 9024472.9255, 9287091.5011, 9542827.3494,
9783775.3823, 10021914.0099, 10251044.8357, 7452000.5846, 7714006.7682,
7993661.8669, 8274791.4235, 8562096.7846, 8842007.041, 9115629.2162,
9380900.5049, 9639219.5459, 9882601.398, 10123145.4642, 10354590.7474,
7526520.5952, 7791146.8356, 8073598.4841, 8357539.3348, 8647717.7534,
8930427.113, 9206785.5068, 9474709.5143, 9735611.7438, 9981427.4094,
10224376.9194, 10458136.6527, 7638300.6025, 7906856.9365, 8193503.4143,
8481661.2074, 8776149.2063, 9063057.2172, 9343519.9463, 9615423.0209,
9880200.0383, 10129666.4312, 10376224.1032, 10613455.5131, 7824600.6172,
8099707.1074, 8393344.9601, 8688530.993, 8990201.627, 9284107.395,
9571410.6772, 9849945.5329, 10121180.5245, 10376731.4677, 10629302.7393,
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10028208.7979, 10391760.4269, 10757228.8467, 11130725.8198, 11494609.1532,
11850317.9787, 12195170.6632, 12530985.4111, 12847381.8161, 13160089.1051,
13460967.9711), t = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
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12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
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6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
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12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
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9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L), .Label = c("t_m75_tc", "t_m5_tc", "t_m25_tc", "t_p0_tc",
"t_p25_tc", "t_p5_tc", "t_p75_tc", "t_p10_tc", "t_p125_tc", "t_p15_tc",
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"h_1", "h0", "h1", "h2.5", "h5", "h7.5", "h10", "h12.5", "h15",
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47.0326380825422, 8.06798923781962, 11.8675703910931, 15.9230939931377,
19.999999945618, 24.16646674768, 28.2256906109677, 32.1937255147541,
36.0406569032114, 39.7867675812361, 43.3162610409814, 46.8046013208372,
50.1609920983889, 10.3194056876762, 14.1981447805855, 18.3381584482061,
22.4999999638964, 26.7532681695515, 30.8970591589679, 34.9477614660749,
38.8748372419928, 42.6989918944149, 46.302016461938, 49.8630305167836,
53.2893460973168, 12.5708221339073, 16.5287191664524, 20.7532229334868,
24.9999999567965, 29.3400695249561, 33.5684276997172, 37.7017973666391,
41.709017592859, 45.6112162148446, 49.2877719082728, 52.9214596849349,
56.4177000962446, 14.8222385342158, 18.859293574072, 23.1682873813043,
27.499999967824, 31.9268709444106, 36.2397962537599, 40.4558333385042,
44.5431979582271, 48.5234405437337, 52.273527375152, 55.9798888965917,
59.5460540879215, 17.0736549768214, 21.1898679333522, 25.5833518339557,
29.9999999534732, 34.5136723106916, 38.9111648247215, 43.2098692547788,
47.3773783417225, 51.4356648980012, 55.2592827852321, 59.0383180864958,
62.6744081170616)), class = "data.frame", .Names = c("W",
"t", "p", "tt", "hh", "pChange"), row.names = c(NA, -324L))
I am trying to plot a colored contour map with the columns tt, hh and W, W being the z axis.
This is the code I am using::
ggplot(df, aes(x = tt, y = hh, z = W)) +
stat_contour(geom = "polygon", aes(fill = ..level..) ) +
geom_tile(aes(fill = W)) +
stat_contour(bins = 10) +
xlab("% change in temperature") +
ylab("% change in ppt") +
guides(fill = guide_colorbar(title = "W"))
This is the result I got:
What I want is the color to be continuous, as seen here I am following this example for my work.
What is going wrong over here?

The grid is not evenly spaced. One way to make an evenly spaced grid is to use interpolate using loess on an evenly spaced grid:
model <- loess(W ~ tt + hh, data = df)
create an evenly spaced grid using expand.grid:
new.data <- expand.grid(tt = seq(from = min(df$tt), to = max(df$tt), length.out = 500),
hh = seq(from = min(df$hh), to = max(df$hh), length.out = 500))
predict on new data using the model:
gg <- predict(model, newdata = new.data)
combine prediction and new data:
new.data = data.frame(W = as.vector(gg),
new.data)
and now the plot looks like:
ggplot(new.data, aes(x = tt, y = hh, z = W)) +
stat_contour(geom = "polygon", aes(fill = ..level..) ) +
geom_tile(aes(fill = W)) +
stat_contour(bins = 10) +
xlab("% change in temperature") +
ylab("% change in ppt") +
guides(fill = guide_colorbar(title = "W"))
You might also want to check some goodness of fit metric for loess
caret::RMSE(model$fitted, df$W)
#output
7498.393
using a narrower span could provide a better fit, especially if the data is not smooth:
model2 <- loess(W ~ tt + hh, data = df, span = 0.1)
caret::RMSE(model2$fitted, df$W)
#output
964.7582
ggplot(new.data2, aes(x = tt, y = hh, z = W)) +
stat_contour(geom = "polygon", aes(fill = ..level..) ) +
geom_tile(aes(fill = W)) +
stat_contour(bins = 10) +
xlab("% change in temperature") +
ylab("% change in ppt") +
guides(fill = guide_colorbar(title = "W"))
The difference is ever so slight
ggplot(new.data, aes(x = tt, y = hh, z = W)) +
geom_tile(aes(fill = W)) +
geom_contour(aes(x = tt, y = hh, z = W),
color = "red")+
geom_contour(data = new.data2,
aes(x = tt, y = hh, z = W),
color = "white", inherit.aes = FALSE)
EDIT: also check the great post by #Henrik which is linked by him in the comment. Especially the ?akima::interp function.
EDIT2: answer to the questions in comments:
To specify a different fill one can use
scale_fill_gradient
scale_fill_gradient2
scale_fill_gradientn
Here is an example of using scale_fill_gradientn with 5 colors based on quantiles:
v <- ggplot(new.data2, aes(x = tt, y = hh, z = floor(W))) +
geom_tile(aes(fill = W), show.legend = FALSE) +
stat_contour(bins = 10, aes(colour = ..level..)) +
xlab("% change in temperature") +
ylab("% change in ppt") +
guides(fill = guide_colorbar(title = "W")) +
scale_fill_gradientn(values = scales::rescale(quantile(new.data2$W)),
colors = rainbow(5))
I removed the polygon thing since it was below the geom_tile layer and was not visible.
To add direct labels:
library(directlabels)
direct.label(v, list("far.from.others.borders", "calc.boxes", "enlarge.box",
box.color = NA, fill = "transparent", "draw.rects"))

Sorting bars by count in ggplot

This code makes a barplot in ggplot:
ggplot(res, aes(x=TOPIC,y=count), labs(x=NULL)) +
scale_y_continuous(limits=c(0,130),expand=c(0,0)) +
scale_x_discrete("",labels=c("ANA"="Anatomy","BEH"="Behavior","BOUND"="Pop boundaries",
"CC"="Climate change","DIS"="Disease","EVO"="Evolution",
"POPSTAT"="Pop status","POPABU"="Pop abundance",
"POPTR"="Pop trend","HARV"="Harvest","HAB"="Habitat",
"HABP"="Habitat protection","POLL"="Pollution",
"ZOO"="Captivity","SHIP"="Shipping","TOUR"="Tourism",
"REPEC"="Reprod ecology","PHYS"="Physiology","TEK"="TEK",
"HWC"="HWC","PRED"="Predator-prey","METH"="Methods",
"POPGEN"="Pop genetics","RESIMP"="Research impact",
"ISSUE"="Other","PROT"="Protection","PA"="Protected areas",
"PEFF"="Protection efficiency","MINOR"="Minor","REV"="Review")) +
theme_bw(base_size = 14) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.3)) +
geom_bar(stat='identity') +
ggtitle("Peer-reviewed papers per topic")
Here's the data frame:
structure(list(TOPIC = structure(c(20L, 18L, 21L, 3L, 9L, 4L,
7L, 8L, 17L, 27L, 29L, 2L, 24L, 30L, 16L, 28L, 10L, 22L, 12L,
5L, 1L, 19L, 6L, 25L, 11L, 23L, 14L, 15L, 13L, 26L), .Label = c("ANA",
"BEH", "BOUND", "CC", "DIS", "EVO", "HAB", "HABP", "HARV", "HWC",
"ISSUE", "METH", "MINOR", "PA", "PEFF", "PHYS", "POLL", "POPABU",
"POPGEN", "POPSTAT", "POPTR", "PRED", "PROT", "REPEC", "RESIMP",
"REV", "SHIP", "TEK", "TOUR", "ZOO"), class = "factor"), count = c(9,
7, 13, 5, 23, 35, 27, 5, 118, 0, 9, 22, 29, 46, 27, 12, 9, 44,
70, 40, 24, 19, 26, 2, 4, 17, 4, 10, 86, 31)), .Names = c("TOPIC",
"count"), row.names = c(NA, -30L), class = "data.frame")
How do I get the bars sorted by count, decreasing or increasing?

Group by adjoining dates in r

I have the following data frame
df<-structure(list(DATE = c("30/06/15", "23/06/15", "22/06/15", "21/06/15",
"18/06/15", "12/06/15", "09/06/15", "08/06/15", "02/06/15", "08/04/15",
"06/04/15", "05/04/15", "07/03/15", "06/03/15", "04/03/15", "03/03/15",
"02/03/15", "26/01/15", "25/01/15", "20/01/15", "19/01/15", "18/01/15",
"17/01/15", "16/01/15", "15/01/15", "14/01/15", "13/01/15", "12/01/15",
"11/01/15", "10/01/15", "09/01/15", "08/01/15", "07/01/15", "06/01/15",
"05/01/15", "04/01/15", "03/01/15", "02/01/15", "01/01/15", "31/12/14",
"30/12/14", "29/12/14", "28/12/14", "27/12/14", "26/12/14", "25/12/14",
"27/08/14", "26/08/14", "25/08/14"), TICKETS = c(17L, 15L, 22L,
16L, 15L, 10L, 18L, 12L, 20L, 20L, 19L, 12L, 16L, 9L, 20L, 18L,
15L, 19L, 13L, 18L, 21L, 27L, 17L, 17L, 18L, 18L, 21L, 18L, 22L,
20L, 16L, 15L, 23L, 15L, 17L, 12L, 20L, 16L, 9L, 13L, 21L, 16L,
16L, 14L, 10L, 7L, 15L, 12L, 14L), COLUMN = c(17L, 15L, 22L,
16L, 15L, 10L, 18L, 12L, 20L, 20L, 19L, 12L, 16L, 9L, 20L, 18L,
15L, 19L, 13L, 18L, 21L, 27L, 17L, 17L, 18L, 18L, 21L, 18L, 22L,
20L, 16L, 15L, 23L, 15L, 17L, 12L, 20L, 16L, 9L, 13L, 21L, 16L,
16L, 14L, 10L, 7L, 15L, 12L, 14L), FLAG = c(1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1), MA = c(0.318405243036592, 0.163298743855817, 0.706171490988531,
0.240851993446204, 0.163298743855817, 0.153604587657018, 0.168145821955216,
0.158451665756417, 0.153604587657018, 0.163298743855817, 0.182687056253413,
0.172992900054615, 0.172992900054615, 0.158451665756417, 0.182687056253413,
0.177839978154014, 0.187534134352813, 0.153604587657018, 0.172992900054615,
0.168145821955216, 0.20207536865101, 0.226310759148007, 0.187534134352813,
0.221463681048607, 0.240851993446204, 0.303864008738394, 0.318405243036592,
0.33294647733479, 0.391111414527581, 0.39595849262698, 0.410499726925178,
0.420193883123976, 0.463817586018569, 0.478358820316767, 0.468664664117968,
0.439582195521573, 0.434735117422174, 0.381417258328782, 0.342640633533588,
0.323252321135991, 0.337793555434189, 0.328099399235391, 0.303864008738394,
0.284475696340797, 0.226310759148007, 0.163298743855817, 0.158451665756417,
0.177839978154014, 0.168145821955216)), .Names = c("DATE", "TICKETS",
"COLUMN", "FLAG", "MA"), row.names = c(412L, 316L, 302L, 288L,
246L, 162L, 120L, 106L, 22L, 102L, 74L, 60L, 87L, 73L, 45L, 31L,
17L, 351L, 337L, 267L, 253L, 239L, 225L, 211L, 197L, 183L, 169L,
155L, 141L, 127L, 113L, 99L, 85L, 71L, 57L, 43L, 29L, 15L, 1L,
426L, 418L, 405L, 392L, 378L, 364L, 350L, 374L, 360L, 346L), class = "data.frame")
Now, as can be seen, there are some dates where there is clearly a tendency to "group" together, for example december and january (date format is: dd/mm/yy).
Plainly, what I want is to find these groups of adjoining dates (i.e. 25- dec -2014 to 20 - jan - 2015 and june 21st to june 23rd 2015).
As long as there are two days adjoining each other I would count it as a "group". Once "groups" are named, I could filter the longest one, for example.
Idealy I would get something like:
structure(list(DATE = structure(c(47L, 36L, 35L, 34L, 31L, 24L,
20L, 18L, 4L, 17L, 13L, 10L, 15L, 12L, 8L, 6L, 3L, 40L, 37L,
33L, 32L, 30L, 29L, 28L, 27L, 26L, 25L, 23L, 22L, 21L, 19L, 16L,
14L, 11L, 9L, 7L, 5L, 2L, 1L, 49L, 48L, 46L, 45L, 44L, 42L, 39L,
43L, 41L, 38L), .Label = c("01/01/15", "02/01/15", "02/03/15",
"02/06/15", "03/01/15", "03/03/15", "04/01/15", "04/03/15", "05/01/15",
"05/04/15", "06/01/15", "06/03/15", "06/04/15", "07/01/15", "07/03/15",
"08/01/15", "08/04/15", "08/06/15", "09/01/15", "09/06/15", "10/01/15",
"11/01/15", "12/01/15", "12/06/15", "13/01/15", "14/01/15", "15/01/15",
"16/01/15", "17/01/15", "18/01/15", "18/06/15", "19/01/15", "20/01/15",
"21/06/15", "22/06/15", "23/06/15", "25/01/15", "25/08/14", "25/12/14",
"26/01/15", "26/08/14", "26/12/14", "27/08/14", "27/12/14", "28/12/14",
"29/12/14", "30/06/15", "30/12/14", "31/12/14"), class = "factor"),
TICKETS = c(17, 15, 22, 16, 15, 10, 18, 12, 20, 20, 19, 12,
16, 9, 20, 18, 15, 19, 13, 18, 21, 27, 17, 17, 18, 18, 21,
18, 22, 20, 16, 15, 23, 15, 17, 12, 20, 16, 9, 13, 21, 16,
16, 14, 10, 7, 15, 12, 14), COLUMN = c(17, 15, 22, 16, 15,
10, 18, 12, 20, 20, 19, 12, 16, 9, 20, 18, 15, 19, 13, 18,
21, 27, 17, 17, 18, 18, 21, 18, 22, 20, 16, 15, 23, 15, 17,
12, 20, 16, 9, 13, 21, 16, 16, 14, 10, 7, 15, 12, 14), FLAG = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1), MA = c(0.318405243036592,
0.163298743855817, 0.706171490988531, 0.240851993446204,
0.163298743855817, 0.153604587657018, 0.168145821955216,
0.158451665756417, 0.153604587657018, 0.163298743855817,
0.182687056253413, 0.172992900054615, 0.172992900054615,
0.158451665756417, 0.182687056253413, 0.177839978154014,
0.187534134352813, 0.153604587657018, 0.172992900054615,
0.168145821955216, 0.20207536865101, 0.226310759148007, 0.187534134352813,
0.221463681048607, 0.240851993446204, 0.303864008738394,
0.318405243036592, 0.33294647733479, 0.391111414527581, 0.39595849262698,
0.410499726925178, 0.420193883123976, 0.463817586018569,
0.478358820316767, 0.468664664117968, 0.439582195521573,
0.434735117422174, 0.381417258328782, 0.342640633533588,
0.323252321135991, 0.337793555434189, 0.328099399235391,
0.303864008738394, 0.284475696340797, 0.226310759148007,
0.163298743855817, 0.158451665756417, 0.177839978154014,
0.168145821955216), GROUP = c(0, 1, 1, 1, 0, 0, 2, 2, 0,
0, 3, 3, 4, 4, 5, 5, 5, 6, 6, 0, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8,
8, 8)), .Names = c("DATE", "TICKETS", "COLUMN", "FLAG", "MA",
"GROUP"), row.names = c(NA, -49L), class = "data.frame")
Any ideas?

You can use diff and cumsum to generate a group number for any chosen length of gap that would separate a group. (Since the diff will be negative as your proceed backward in time as in this case, you need to use a negative threshold. Here I used -2, which then only allows gaps less than two days to be in same group, but you could use any (lower) number.
df$dt <- as.Date(df$DATE, format="%d/%m/%y")
df$grp <- cumsum( c(0, diff(as.numeric(df$dt)) < -2) )
> df$grp
[1] 0 1 1 1 2 3 4 4 5 6 6 6 7 7 7 7 7 8 8 9
[21] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[41] 9 9 9 9 9 9 10 10 10

Add column of ranks with mean/average tied ranks

I know that this question is very similar to this one:
Add a column of ranks
Considering we have data like this:
test <- data.frame(A=c("aaabbb",
"aaaabb",
"aaaabb",
"aaaaab",
"bbbaaa",
"bbbbaa"),
B=c("10.00",
"00.04",
"00.04",
"00.00",
"20.00",
"00.06"
))
I need the tied ranks to be averaged though, so that I have something like this:
> test
A B C
1 aaabbb 10.00 1
2 aaaabb 00.04 2.5
3 aaaabb 00.04 2.5
4 aaaaab 00.00 3
5 bbbaaa 20.00 4
6 bbbbaa 00.06 5
EDIT:
> dput(qual_orderedadj_ranks)
structure(list(words = structure(c(29L, 7L, 28L, 6L, 19L, 21L,
9L, 11L, 30L, 1L, 8L, 10L, 13L, 12L, 5L, 26L, 27L, 32L, 33L,
3L, 22L, 18L, 16L, 24L, 25L, 31L, 23L, 2L, 17L), .Label = c("average","yellow", "emerald",
"sense","slate", "turcquoise", "green", "orange", "fair", "chestnut", "sand", "good",
"silver", "sense", "sense", "gray", "lousy", "wine", "smalt", "sense", "taupe", "poor",
"blue", "red", "black", "gold", "white", "teal", "terracotta", "purple", "violett",
"olive", "khaki"), class = "factor"), enzo = c(9.57973168019844, 2.68331227860491,
1.85920971038049, 1.28384868054554, 0.885031778228944, 0.740942048756444,
0.415649187810432, 0.0418303446590026, 0.0836608598897025, 0.680367202534345,
1.53377945661345, 1.70660459871111, 39.2413924890553,
239.081124461913, 0, 0, 0, 0, 0, 86.5734538416169, 24.2262630473592,
0.669305983927372, 0.5093534157301, 0.25098462655732, 0.0836608598897025,
0.0418303446590026, 0.276963945905033, 0.839118699701029, 1.00634089909635),
ranks = c(1, 2, 3, 4, 5, 6, 7, 17, 17, 10, 11, 12, 13, 14,
17, 17, 17, 17, 17, 20, 21, 22, 23, 24, 17, 17, 27, 28, 29)), row.names =
c(1L, 2L, 3L, 4L, 6L, 8L, 9L, 28L, 27L, 22L, 21L, 20L, 18L, 16L, 11L,
12L, 13L, 14L, 15L, 17L, 19L, 23L, 24L, 25L, 26L, 29L, 10L, 7L,
5L), .Names = c("words", "enzo", "ranks"), class = "data.frame")

Try this:
within(test, B <- rank(A))
Or, if you want to use the original order in A:
within(test, B <- ave(seq_along(A), by=A))

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