Add column of ranks with mean/average tied ranks - r

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))

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(.)))

geom_tile tiles displaced when using ggplotly

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!

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,
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-6.77702465958718, -3.39742166553716, 0, 3.47205562528221,
6.85474217379226, 10.1614379102301, 13.3672140455251, 16.4889729851691,
19.4302175391865, 22.3371677435973, 25.1341600948813, -9.04277570278022,
-5.84479490959099, -2.43139589994524, 0.999999964530837,
4.50677619306401, 7.92328961474518, 11.2630522704558, 14.5008862385618,
17.65386274452, 20.6245196833994, 23.5605394277767, 26.3855016695576,
-7.69192585558588, -4.44645028701369, -0.982357198383821,
2.49999997960674, 6.05885704111126, 9.52611073025193, 12.9154738543,
16.20139443748, 19.4011973551469, 22.4159729565176, 25.3955969661306,
28.26251406118, -5.44050941298024, -2.11587588301946, 1.43270724943368,
4.99999997975781, 8.64565844485543, 12.1974793060474, 15.6695098080378,
19.0355747810953, 22.3134216501983, 25.4017284137289, 28.4540261536177,
31.390868095154, -3.18909298608498, 0.214698500430437, 3.84777171900399,
7.49999998232584, 11.2324597893836, 14.8688478588816, 18.4235457714435,
21.8697551319615, 25.2256459476667, 28.3874838685232, 31.5124553108925,
34.5192221013328, -0.93767655435575, 2.54527287783788, 6.26283620549316,
9.99999994622222, 13.8192612112551, 17.5402164225922, 21.177581692552,
24.703935510623, 28.1378702668879, 31.3732392810203, 34.5708845346342,
37.6475760821333, 1.31373989066688, 4.87584724074345, 8.67790062914087,
12.4999999475818, 16.4060626137908, 20.211584962133, 23.9316176184945,
27.5381158626977, 31.0500945945686, 34.3589946886835, 37.6293137257467,
40.775930095563, 3.56515632723005, 7.20642159881506, 11.0929651059621,
14.9999999743196, 18.9928639969906, 22.8829535185927, 26.685653610904,
30.3722961736837, 33.9623188847861, 37.3447501386438, 40.6877428842301,
43.9042840860314, 5.81657278071209, 9.53699603423, 13.5080295429032,
17.4999999732622, 21.5796653306424, 25.5543220714269, 29.439689530804,
33.2064765318009, 36.8745432620148, 40.3305056695729, 43.7461721043463,
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?

Errors when exporting plots to Plot.ly

I have this data (sample of the first 20 rows):
Codering variable value
1 Z1 Week.0 0
2 Z2 Week.0 0
3 Z3 Week.0 0
4 Z4 Week.0 0
5 Z5 Week.0 0
6 Z6 Week.0 0
7 Z7 Week.0 0
8 Z8 Week.0 0
9 Z9 Week.0 0
10 Z101 Week.0 NA
11 Z102 Week.0 NA
12 Z1 Week.1 0
13 Z2 Week.1 0
14 Z3 Week.1 0
15 Z4 Week.1 0
16 Z5 Week.1 0
17 Z6 Week.1 0
18 Z7 Week.1 0
19 Z8 Week.1 0
and I plot it using:
pZ <- ggplot(zmeltdata,aes(x=variable,y=value,color=Codering,group=Codering)) +
geom_line()+
geom_point()+
theme_few()+
theme(legend.position="right")+
scale_color_hue(name = "Treatment group:")+
scale_y_continuous(labels = percent)+
ylab("Germination percentage")+
xlab("Week number")+
labs(title = "Z. monophyllum germination data")
pZ
The graph displays just fine:
Yet when I want to export this to Plot.ly I get the following errors:
> py <- plotly()
> response<-py$ggplotly(pZ)
Error in if (all(xcomp) && all(ycomp)) { :
missing value where TRUE/FALSE needed
In addition: Warning message:
In trace.list[[lind[1]]]$y == trace.list[[lind[2]]]$y :
longer object length is not a multiple of shorter object length
And I have searched for these errors, yet the explanation thoroughly confuses me. "The missing value where TRUE/FALSE needed." is supposed to occur if you use logical termms as IF/ELSE/TRUE/FALSE and such in your process, which I don't at all! Even when checking for any NA's in the value of the graph I get:
> is.na(pZ)
data layers scales mapping theme coordinates facet plot_env labels
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
and the 'longer object length is not multiple of shorter object length' is supposed to pop up when you have objects of different lengths, but I'm only using 1 object with 3 rows that have exactly the same length.. The value of the graph does give me a NULL when I ask for those rows, but that is supposed to happen..
> nrow(zmeltdata)
[1] 143
> nrow(test)
NULL
All in all, I'm very confused and don't know how to correctly handle these errors, could someone elaborate?
Thanks for your time.
EDIT: I have tried to export a different graph to Plot.ly using a random sample of 1:100 and that worked just fine, I'm pretty sure the error is in my data, I just can't figure out how to fix it.
EDIT2: In response to #Gregor:
> dput(head(zmeltdata, 20))
structure(list(Codering = structure(c(16L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 26L, 17L, 18L, 16L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L), .Label = c("B1", "C2", "C3", "C8", "M1", "M101", "M102",
"M2", "M3", "M4", "M5", "M6", "M7", "M8", "M9", "Z1", "Z101",
"Z102", "Z2", "Z3", "Z4", "Z5", "Z6", "Z7", "Z8", "Z9"), class = "factor"),
variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Week.0",
"Week.1", "Week.2", "Week.3", "Week.4", "Week.5", "Week.6",
"Week.7", "Week.8", "Week.9", "Week.10", "Week.11", "Week.12"
), class = "factor"), value = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Codering",
"variable", "value"), row.names = c(NA, 20L), class = "data.frame")
And the tail:
> dput(tail(zmeltdata, 43))
structure(list(Codering = structure(c(19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 17L, 18L, 16L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 17L, 18L, 16L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 17L,
18L, 16L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 17L, 18L), .Label = c("B1",
"C2", "C3", "C8", "M1", "M101", "M102", "M2", "M3", "M4", "M5",
"M6", "M7", "M8", "M9", "Z1", "Z101", "Z102", "Z2", "Z3", "Z4",
"Z5", "Z6", "Z7", "Z8", "Z9"), class = "factor"), variable = structure(c(10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L), .Label = c("Week.0", "Week.1", "Week.2", "Week.3",
"Week.4", "Week.5", "Week.6", "Week.7", "Week.8", "Week.9", "Week.10",
"Week.11", "Week.12"), class = "factor"), value = c(0.1, 0.06,
0.05, 0.09, 0.04, 0.08, 0.05, 0.08, 0, 0, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("Codering",
"variable", "value"), row.names = 101:143, class = "data.frame")
I am not at all surprised by these, there are quite some NA's in the dataset but they shouldn't prove to be an issue, since I have used a similar (bigger) dataset before.
And I also have the .csv file for you to use if you wish: https://www.mediafire.com/?jij1vlp14a29ntt

The issue is about handling NA's... I got https://plot.ly/~marianne2/417/z-monophyllum-germination-data/ by running the following code:
pZ <- ggplot(na.omit(zmeltdata), aes(x=variable, y=value, color=Codering,
group=Codering)) +
geom_line() +
geom_point() +
# theme_few() +
theme(legend.position="right") +
scale_color_hue(name="Treatment group:") +
# scale_y_continuous(labels = percent) +
ylab("Germination percentage") +
xlab("Week number") +
labs(title="Z. monophyllum germination data")
py$ggplotly(pZ, kwargs=list(fileopt="overwrite", filename="test_zdata"))
Note that I had to comment out theme_few() and scale_y_continuous(labels = percent) because from loading only "ggplot2", I would get the following errors:
Error: could not find function "theme_few"
and
Error in structure(list(call = match.call(), aesthetics = aesthetics, :
object 'percent' not found
respectively. I guess these are dependency issues (maybe you're using a version of "ggthemes"?).
I don't know what kind of magic theme_few() does, but if I don't use na.omit() on zmeltdata, my pZ plot looks like this:
Eww, "Week.10" comes after "Week.1" instead of after "Week.9"... So you wouldn't want to send this to plotly anyway! So I cannot exactly reproduce your ggplot example. But I wonder if you really want to keep these NA's (the CSV itself reads "NA", I was expecting blank "cells"). Don't you want to pre-process these anyway?
Note that I get the following warning message when I don't use na.omit() on zmeltdata:
Warning messages:
1: Removed 20 rows containing missing values (geom_path).
2: Removed 47 rows containing missing values (geom_point).
Again, beyond pure displaying/plotting considerations, since this looks like scientific data, wouldn't you want to number weeks with actual numbers, or pad the digits if you really want a string? ("Week.01", "Week.02", etc.)
And it looks like the missing data is all trailing... There's just no data (yet) for weeks 10+, right?
Thanks for reporting,
Marianne

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