gghighlight() cannot handle data containing column `x`
rbratslaver opened this issue · comments
Hi - I was hoping you could help me understand why the highlighted line is being moved away from the rest of the data. Thank you.
df <- tibble::tibble(
week = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4,
4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8,
8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11,
12, 12, 12, 12, 12, 12, 13, 13, 13, 14, 14, 15, 15, 15, 16, 16,
16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17),
team_name = c("Broncos", "Cardinals", "Falcons", "Lions", "Panthers", "Raiders",
"Steelers", "Bills", "Dolphins", "Patriots", "Texans", "Bears",
"Bills", "Chargers", "Giants", "Jaguars", "Lions", "Titans",
"Bills", "Chiefs", "Cowboys", "Dolphins", "Giants", "Lions",
"Packers", "Patriots", "Seahawks", "Bills", "Patriots", "Redskins",
"Dolphins", "Jets", "Rams", "Bengals", "Eagles", "Jaguars", "Patriots",
"Ravens", "Saints", "Seahawks", "49ers", "Chiefs", "Cowboys",
"Raiders", "Vikings", "Buccaneers", "Packers", "Raiders", "Titans",
"Colts", "Dolphins", "Colts", "Eagles", "Jaguars", "Jets", "Raiders",
"Seahawks", "Titans", "Vikings", "Bears", "Browns", "Chargers",
"Packers", "Patriots", "Texans", "Broncos", "Browns", "Patriots",
"Browns", "Colts", "Lions", "Panthers", "Saints", "Browns", "Cardinals",
"Colts", "Dolphins", "Eagles", "Jets", "Panthers", "Saints",
"Steelers", "Bills", "Browns", "Giants", "Jaguars", "Texans"),
x = c(76843, 65538, 66410.5, 59523, 62442, 69143, 60147, 74122, 66227,
51557, 88240, 66944, 68174, 77604, 68161, 70893.5, 62472, 62370,
71756, 69238, 61535, 73344, 78213, NaN, 63329, 66113, NaN, 71575,
72220, NaN, 66437, 78161, 57307, 54328, 65523, 66534, 65878,
NaN, 67903, 63593, 83941, 78037, 62068, 52950, 60422, 65589,
66944, 60827.5, 72540, 70020, 73132, 66751, 69144, 54871.5, 78843,
60202, 67616, 68801, 66883, 62148, 51710, 71897, 78488, 78025,
71812, 61990, 68576, 74012.5, 60028, 55603, 77922, 72842, 61758.5,
68798, 72025, 63663.5, 68756, 69596, 78160, 73860, 92570, 60865,
61563.5, 65362, 79110, 71023, 71777)
)
df %>%
ggplot(aes(week,x,color=team_name))+
geom_point()+
geom_line()+
gghighlight(team_name=='Patriots')+
theme(legend.position = 'none')
Hmm, thanks for reporting. It seems there happens some collisions between the aesthetic x and the column name x... Here's a minimal reprex:
library(gghighlight)
#> Loading required package: ggplot2
set.seed(22)
df <- tibble::tibble(
week = rep(1:3, each = 3),
values = runif(9),
x = values,
id = rep(c("a", "b", "c"), times = 3)
)
# works
ggplot(df, aes(week, values, colour = id)) +
geom_line() +
gghighlight(id == "a")
#> Warning: Tried to calculate with group_by(), but the calculation failed.
#> Falling back to ungrouped filter operation...
#> label_key: id
# won't work
ggplot(df, aes(week, x, colour = id)) +
geom_line() +
gghighlight(id == "a")
#> Warning: Tried to calculate with group_by(), but the calculation failed.
#> Falling back to ungrouped filter operation...
#> label_key: id
Created on 2020-04-04 by the reprex package (v0.3.0)
So, an workaround is to rename x to some other name.
I think this might be a more general problem if column names are synonimous with ggplot2-aesthetics? Here's an example for a column named group:
rats <- structure(list(subject = c(8L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 39L, 39L, 39L, 39L, 40L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 12L, 12L, 12L, 12L, 14L, 14L, 14L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 51L, 51L, 51L, 51L, 52L, 52L, 52L, 52L, 52L, 52L, 52L, 53L, 53L, 53L, 53L, 53L, 53L, 53L, 54L, 54L, 54L, 56L, 56L, 56L, 56L, 57L, 57L, 57L, 57L, 57L, 57L, 57L, 59L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 62L, 62L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 3L, 3L, 3L, 3L, 5L, 6L, 6L,
6L, 9L, 9L, 9L, 9L, 9L, 11L, 11L, 11L, 20L, 20L, 20L, 20L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 43L, 43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 47L, 47L, 47L, 47L, 47L, 48L,
48L, 48L, 48L, 48L, 48L, 48L), group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("low dose", "high dose",
"control"), class = "factor"), response = c(71.7008, 78.8162, 71.2811, 78.492, 82.4924, 82.3772, 84.8116, 84.2912, 83.863, 66.6033, 70.6045, 72.0694, 73.8783, 75.8222, 78.2432, 79.6492, 72.7186, 81.5659, 82.134, 83.006, 83.7377, 84.8999, 86.8332, 73.2462, 80.9568, 78.4092, 81.2712, 83.4086, 70.4557, 78.4092, 79.404, 80.8022, 82.662, 82.8794, 85.703, 70.1783, 72.2496, 75.4321, 70.8308, 75.8024, 80, 81.6333, 82.0061, 80.8084, 82.6801, 70.1783, 72.4707, 75.8222, 76.6094, 69.1158, 74.3303, 76.8505, 77.0065, 78.5175, 81.6884, 73.0068, 77.5242, 77.201, 82.801, 81.3204, 83.4086, 75.0733, 78.032, 80.1561, 83.006, 82.2192, 84.0536,
84.864, 69.7711, 74.4043, 76.6094, 77.3886, 82.4197, 80.2808, 82.662, 71.5612, 78.0064, 81.6884, 79.3221, 74.027, 75.4718, 79.511, 80.4487, 70.3449, 76.844, 76.6942, 81.0247, 81.3449, 73.2462, 78.2368, 79.6492, 80.2122, 84.8116, 82.3286, 85.2115, 69.3397, 73.2462, 77.3886, 78.1025, 76.4853, 77.801, 79.0569,
70.825, 75, 76.844, 81.0247, 80.604, 82.801, 83.8153, 74.2765, 77.7817, 79.9062, 84.6463, 71.1196, 74.4043, 75.452, 69.4622, 74.686, 75.743, 78.2432, 77.1557, 77.4661, 80.2808, 73.2462, 74.6525, 76.844, 80.2808, 80.604, 82.3772, 83.4086, 68.884, 72.9178, 76.0592, 79.2023, 71.4213, 76.844, 77.666, 81.8352, 81.4002, 83.4086, 84.7231, 70.4557, 72.1249, 75.452, 78.6003, 77.3369,
79.8812, 80.8022, 67.6018, 70.6612, 74.2024, 69.3109, 73.9256, 75.4718, 77.7817, 72.2011, 76.0066, 77.0779, 80.1561, 81.3941, 83.4865, 82.662, 70.8378, 70.214, 75.4321, 77.4209, 79.0569, 77.2334, 81.0247, 83.5284, 71.7008, 74.8131, 68.4763, 75.4718, 76.4003, 78.2304, 78.5493, 80.2808, 81.3941, 70.8025, 73.4098,
75.6042, 78.6448, 79.0253, 80.604, 81.8841, 75.0067, 78.5684, 79.925, 80.7775, 67.9559, 76.0066, 78.2943, 81.2158, 70.8308, 74.9533, 75.1665, 77.666, 82.7345, 71.8471, 75.1332, 76.2168, 72.8354, 78.5684, 80.604, 82.0731, 70.7248, 74.8131, 75.1665, 77.0065, 81.0247, 78.492, 81.8413, 74.5185, 73.4983, 75.591,
77.9359, 79.196, 81.0247, 82.4621, 86.2844, 69.6419, 74.0945, 78.3901, 74.027, 77.5242, 81.8352, 81.6333, 70.2922, 76.0263, 77.8332, 79.511, 80.2808, 70.8025, 75.2861, 78.3901, 80.9197, 79.6053, 80.6226, 82.662, 66.8431, 73, 74.6525, 77.4661, 77.1038, 70.6045, 74.9533, 78.4092, 78.6003, 80.7217, 82.801, 83.006),
time = c(50L, 60L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 50L, 60L, 70L, 80L,
90L, 100L, 110L, 50L, 50L, 60L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 50L, 60L, 70L, 80L, 90L, 100L, 50L, 60L, 70L, 80L, 90L, 100L, 50L, 60L, 70L, 80L,
90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 50L, 60L, 70L, 80L, 50L, 60L, 70L, 80L, 90L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L,
60L, 70L, 80L, 50L, 60L, 70L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 50L, 60L, 70L, 80L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 50L, 50L, 60L, 70L, 50L, 60L, 70L, 80L, 90L, 50L, 60L, 70L, 50L, 60L, 70L, 80L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L,
70L, 50L, 60L, 70L, 80L, 50L, 60L, 70L, 80L, 90L, 50L, 60L, 70L, 80L, 90L, 100L, 110L, 50L, 60L, 70L, 80L, 90L, 50L, 60L, 70L, 80L, 90L, 100L, 110L), log_time = c(0.405465108108164, 0.916290731874155, 0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843, 0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164, 0.405465108108164, 0.916290731874155, 0.405465108108164,
0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843,
1.87180217690159, 2.01490302054226, 0.405465108108164, 0.916290731874155,
1.25276296849537, 1.50407739677627, 0.405465108108164, 0.916290731874155,
1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 1.87180217690159, 0.405465108108164, 0.916290731874155,
1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159,
2.01490302054226, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164,
0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843,
1.87180217690159, 2.01490302054226, 0.405465108108164, 0.916290731874155,
1.25276296849537, 1.50407739677627, 0.405465108108164, 0.916290731874155,
1.25276296849537, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164,
0.916290731874155, 1.25276296849537, 1.50407739677627, 0.405465108108164,
0.916290731874155, 1.25276296849537, 1.50407739677627, 1.70474809223843,
1.87180217690159, 2.01490302054226, 0.405465108108164, 0.916290731874155,
1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159,
2.01490302054226, 0.405465108108164, 0.916290731874155, 1.25276296849537,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 1.87180217690159, 2.01490302054226, 0.405465108108164,
0.916290731874155, 0.405465108108164, 0.916290731874155,
1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159,
2.01490302054226, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
0.405465108108164, 0.405465108108164, 0.916290731874155,
1.25276296849537, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 0.405465108108164, 0.916290731874155,
1.25276296849537, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226,
0.405465108108164, 0.405465108108164, 0.916290731874155,
1.25276296849537, 1.50407739677627, 1.70474809223843, 1.87180217690159,
2.01490302054226, 0.405465108108164, 0.916290731874155, 1.25276296849537,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226,
0.405465108108164, 0.916290731874155, 1.25276296849537, 1.50407739677627,
1.70474809223843, 0.405465108108164, 0.916290731874155, 1.25276296849537,
1.50407739677627, 1.70474809223843, 1.87180217690159, 2.01490302054226
), fitted_logtime = structure(c(73.0563923887434, 76.8911060161097,
73.6449866461212, 77.4797002734875, 80.0055615673854, 81.8921522512623,
83.3985658192205, 84.6526230652965, 85.7268658786286, 66.7933308005358,
70.6280444279021, 73.1539057218, 75.0404964056769, 76.5469099736351,
77.8009672197111, 78.8752100330432, 74.628987832907, 78.4637014602732,
80.9895627541711, 82.8761534380481, 84.3825670060063, 85.6366242520823,
86.7108670654144, 73.6566398805419, 77.4913535079081, 80.017214801806,
81.903805485683, 83.4102190536411, 72.6601482183849, 76.4948618457512,
79.0207231396491, 80.9073138235261, 82.4137273914842, 83.6677846375602,
84.7420274508923, 70.603070114292, 71.8776085626013, 75.7123221899676,
71.775049494848, 75.6097631222143, 78.1356244161122, 80.0222150999891,
81.5286286679473, 82.7826859140233, 83.8569287273554, 69.3887614180145,
73.2234750453807, 75.7493363392786, 77.6359270231556, 69.8398991586,
73.6746127859662, 76.2004740798642, 78.0870647637411, 79.5934783316993,
80.8475355777753, 72.6115207170072, 76.4462343443735, 78.9720956382714,
80.8586863221483, 82.3650998901065, 83.6191571361825, 73.6190726883147,
77.453786315681, 79.9796476095789, 81.8662382934558, 83.372651861414,
84.62670910749, 85.7009519208221, 70.3942501130725, 74.2289637404388,
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outlier <- "17"library(ggplot2)
library(gghighlight)
p <- ggplot(rats, aes(x = time, y = response, group = subject, col = group)) +
geom_line(alpha = .7) + geom_point(alpha = .5) +
facet_grid(~ group) +
xlab("Age [d]") +
ylab("Distance [pixels]") +
labs(title = "Rats data")
p
# renders
p +
gghighlight(subject %in% outlier, use_direct_label = TRUE,
label_key = subject, calculate_per_facet = TRUE)
#> New names:
#> * group -> group...4
#> * group -> group...5
#> Error: Must group by variables found in `.data`.
#> * Column `group` is not found.
# creates new name for it and then doesn't find the grouping variable anymore...?Created on 2020-07-08 by the reprex package (v0.3.0)
Session info
sessionInfo()
#> R version 4.0.1 (2020-06-06)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Linux Mint 19.1
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
#> [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] gghighlight_0.3.0 ggplot2_3.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] pillar_1.4.4 compiler_4.0.1 highr_0.8 tools_4.0.1
#> [5] digest_0.6.25 evaluate_0.14 lifecycle_0.2.0 tibble_3.0.1
#> [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.6 curl_4.3
#> [13] yaml_2.2.1 xfun_0.15 withr_2.2.0 stringr_1.4.0
#> [17] dplyr_1.0.0 httr_1.4.1 knitr_1.29 xml2_1.3.2
#> [21] generics_0.0.2 vctrs_0.3.1 grid_4.0.1 tidyselect_1.1.0
#> [25] glue_1.4.1 R6_2.4.1 rmarkdown_2.2 purrr_0.3.4
#> [29] farver_2.0.3 magrittr_1.5 scales_1.1.1 ellipsis_0.3.1
#> [33] htmltools_0.5.0 mime_0.9 colorspace_1.4-1 labeling_0.3
#> [37] stringi_1.4.6 munsell_0.5.0 crayon_1.3.4Thanks, it seems so (sorry, I edited your comment because it's too long...).
Thanks, it seems so (sorry, I edited your comment because it's too long...).
well, it's no longer a reproducible example now... the data is here, if you need this for debugging later: https://pastebin.com/2FkCk5CK
For future reference, I really appreciate if you keep in mind providing a minimal reprex :) I think you don't need such a massive amount of lines to show something is also wrong with group column. Anyway, thanks for reporting!
Okay, I found the cause. Since I use dplyr::transmute() here, the later quasures might refer to the prior calculation results instead of the original data. Will fix.
Line 269 in d1e186c