Add a constant line to all plots in facet_wrap() - r

I have the following code:
p1 <- ggplot(df_test, aes(x=AA_Number,y=Energy_Profile,col='red')) + geom_line() + facet_wrap(~Model, ncol=3) + geom_hline(yintercept=-0.03, colour='blue') + geom_line(data=df_templates, colour="green")
print(p1)
It produces this output:
I am having trouble merging the data in green into one plot and then plotting it over the other three plots in red.
Essentially the plot in green is my constant and I want to see how my data in red varies from the constant, by overlaying the data in green on top of each of the plots in red.
Anybody have any ideas?
Data:
df_test:
structure(list(Model = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
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"AA", "Energy_Profile"), row.names = c(NA, 614L), class = "data.frame")
df_templates:
structure(list(Model = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
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3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("2kqx_renumberedA",
"2kqx_renumberedB", "3lz8_renumbered"), class = "factor"), AA_Number = c(3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
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598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L, 606L, 607L, 608L,
609L, 610L, 611L), AA = structure(c(6L, 10L, 11L, 4L, 18L, 18L,
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15L, 13L, 4L, 14L, 2L, 11L, 16L, 17L), .Label = c("ALA", "ARG",
"ASN", "ASP", "GLN", "GLU", "GLY", "HIS", "ILE", "LEU", "LYS",
"MET", "PHE", "PRO", "SER", "THR", "TRP", "TYR", "VAL"), class = "factor"),
Energy_Profile = c(-0.018, -0.019, -0.019, -0.02, -0.022,
-0.023, -0.024, -0.025, -0.025, -0.025, -0.025, -0.025, -0.025,
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-0.023, -0.024, -0.025, -0.027, -0.028, -0.029, -0.03, -0.03,
-0.032, -0.032, -0.033, -0.033, -0.033, -0.033, -0.033, -0.032,
-0.031, -0.029, -0.018, -0.019, -0.019, -0.02, -0.022, -0.023,
-0.024, -0.025, -0.025, -0.025, -0.025, -0.025, -0.025, -0.026,
-0.025, -0.026, -0.028, -0.029, -0.029, -0.027, -0.026, -0.026,
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-0.025, -0.025, -0.026, -0.025, -0.024, -0.024, -0.024, -0.023,
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-0.028, -0.027, -0.025, -0.023, -0.022, -0.022, -0.022, -0.023,
-0.024, -0.025, -0.027, -0.028, -0.029, -0.03, -0.03, -0.032,
-0.032, -0.033, -0.033, -0.033, -0.033, -0.033, -0.032, -0.031,
-0.029, -0.015, -0.019, -0.023, -0.026, -0.029, -0.032, -0.037,
-0.04, -0.044, -0.046, -0.047, -0.046, -0.046, -0.044, -0.042,
-0.039, -0.038, -0.037, -0.037, -0.038, -0.038, -0.039, -0.041,
-0.042, -0.043, -0.044, -0.044, -0.045, -0.045, -0.043, -0.035,
-0.024, -0.01, 0.0021, 0.014, 0.027, 0.037, 0.035, 0.026,
0.015, 0.0039, -0.008, -0.021, -0.032, -0.039, -0.042, -0.045,
-0.048, -0.049, -0.05, -0.049, -0.048, -0.046, -0.043, -0.04,
-0.039, -0.037, -0.036, -0.037, -0.037, -0.037, -0.035, -0.032,
-0.028, -0.024, -0.019, -0.012, -0.0097, -0.011, -0.013,
-0.014, -0.017, -0.023, -0.03, -0.035, -0.04, -0.044, -0.049,
-0.053, -0.055, -0.055, -0.053, -0.05, -0.048, -0.045, -0.043,
-0.041, -0.04, -0.041, -0.041, -0.041, -0.041, -0.042, -0.042,
-0.043, -0.044, -0.046, -0.047, -0.048, -0.049, -0.047, -0.042,
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-0.037, -0.027, -0.017, -0.0058, 0.0041, 0.0065, 0.0052,
0.0053, 0.0048, 0.0059, 0.0058, 0.0057, 0.0074, 0.007, 0.0014,
-0.0049, -0.015, -0.023, -0.031, -0.037, -0.04, -0.039, -0.039,
-0.037, -0.036, -0.034, -0.029, -0.023, -0.017, -0.009, -0.0017,
0.007, 0.011, 0.01, 0.0045, -0.0012, -0.0089, -0.017, -0.025,
-0.031, -0.033, -0.034, -0.035, -0.036, -0.038, -0.04, -0.042,
-0.046, -0.049, -0.052, -0.052, -0.051, -0.049, -0.046, -0.042,
-0.035, -0.027, -0.019, -0.013, -0.0065, -6.1e-05, 0.0045,
0.003, -0.0013, -0.0071, -0.014, -0.021, -0.029, -0.036,
-0.041, -0.044, -0.046, -0.046, -0.045, -0.044, -0.043, -0.041,
-0.039, -0.038, -0.038, -0.038, -0.039, -0.039, -0.038, -0.034,
-0.03, -0.025, -0.02, -0.013, -0.0082, -0.008, -0.011, -0.016,
-0.021, -0.028, -0.034, -0.04, -0.042, -0.043, -0.041, -0.04,
-0.038, -0.035, -0.033, -0.032, -0.032, -0.033, -0.034, -0.035,
-0.037, -0.038, -0.038, -0.039, -0.038, -0.038, -0.037, -0.037,
-0.036, -0.037, -0.036, -0.037, -0.039, -0.041, -0.043, -0.045,
-0.046, -0.048, -0.048, -0.047, -0.045, -0.043, -0.04, -0.038,
-0.037, -0.036, -0.037, -0.039, -0.041, -0.043, -0.046, -0.047,
-0.048, -0.048, -0.048, -0.046, -0.042, -0.04, -0.038, -0.036,
-0.033, -0.032, -0.031, -0.032, -0.032, -0.033, -0.034, -0.035,
-0.036, -0.037, -0.038, -0.039)), .Names = c("Model", "AA_Number",
"AA", "Energy_Profile"), class = "data.frame", row.names = c(NA,
-532L))
In my df_test data I provided here, I could only put one plot as I reach the character limit.


Your data frame df_templates is also faceted because it has the same column Model as given in facet_wrap(). If you rename this column, for example, to Model2
colnames(df_templates)<-c("AA_Number","AA","Energy_Profile","Model2")
Then this data frame is not faceted.
ggplot(df_test, aes(x=AA_Number,y=Energy_Profile,col='red')) + geom_line() +
geom_hline(yintercept=-0.03, colour='blue') +
geom_line(data=df_templates,colour="green")+
facet_wrap(~Model,ncol=3)

Related

Setting legend with Plotly

I am working in R with Plotly. Below you can see my bar chart and data.
t_df3<-data.frame(structure(list(deciles = c(0.001, 0.002, 0.003, 0.004, 0.005,
0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004,
0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003,
0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002,
0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.001,
0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01,
0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,
0.01, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008,
0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007,
0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006,
0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005,
0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004,
0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003,
0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01), variable.x = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), .Label = c("Food",
"Alcoholic Beverages", "Clothing", "Housing Water", "Furnishings",
"Health", "Transport", "Communication", "Recreation", "Education",
"Restaurants", "Miscellaneous Goods"), class = "factor"), value.x = c(958.823102803738,
1292.9, 1575.2, 1807, 1911.8,
2041.2, 2376.83, 2723, 3161.9,
4130.448, 120.0, 304, 246.4,
249.8, 285, 382.1, 494.5,
691.6, 787, 948.8, 11.9,
22.9085309734513, 62, 77.3, 201.184778761062,
239.961132743363, 330, 588.19178761062, 766.9,
2117.2, 46, 91.0239292035398, 166.0,
329.5, 525, 772.3, 1060.3112920354,
1297.9, 1680, 3963.0, 106.8,
175.2, 228, 295.961379310345, 300.2,
404.8, 447, 496.528551724138, 731.2,
1916.6995862069, 68, 71.9, 111.1,
154.9, 201, 253.49, 248.205798165138,
247.0, 421, 850.106642201835, 19.2,
14.9, 40, 96.0269734513274, 200.4,
354.0, 457, 745.2, 929.6,
2654.9, 4.9, 12.6, 50.6,
77.0251034482759, 168., 259.1, 364.2,
571.067586206897, 828.672, 1452.7, 6.75,
4.5, 5.41241379310345, 25.2, 46.1,
68.2, 125.2, 104.550620689655, 258.9,
951.36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15.5, 14.1,
44.5, 79.2, 119.3, 216.31,
316.8, 577.3, 901.7, 1897.1,
70.5037168141593, 97.10, 136.4, 189.5,
250.2, 333.9, 439.5, 583.7,
725.7, 1283.5), color.x = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), .Label = c("blue",
"cyan", "darkgreen", "red", "brown", "chartreuse", "green", "purple",
"gold", "tomato", "darkturquoise", "forestgreen"), class = "factor"),
deciles = c(0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007,
0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006,
0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005,
0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004,
0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003,
0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.001, 0.002,
0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.001,
0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01,
0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,
0.01, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008,
0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007,
0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006,
0.007, 0.008, 0.009, 0.01, 0.001, 0.002, 0.003, 0.004, 0.005,
0.006, 0.007, 0.008, 0.009, 0.01), variable = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L), .Label = c("Food", "Alcoholic Beverages",
"Clothing", "Housing Water", "Furnishings", "Health", "Transport",
"Communication", "Recreation", "Education", "Restaurants",
"Miscellaneous Goods"), class = "factor"), value = c(145.111841584158,
195.676277227723, 238.405544554455, 273.52, 289.35,
308.935841584158, 359.719128712871, 412.1, 478.5,
625.117306930693, 96.9022702702703, 245.5, 198.9,
201.624648648649, 230.688432432432, 308.3, 399.0,
558.103135135135, 635.512216216216, 765.6, 11.9,
22.9085309734513, 62.3668672566372, 77.3, 201.1,
239.961132743363, 330.529486725664, 588.1, 766.9,
2117.21543362832, 46.402407079646, 91.0, 166.0,
329.568637168142, 525.05182300885, 772.307681415929, 1060.3112920354,
1297.90619469027, 1680.52311504425, 3963.01847787611, 106.808275862069,
175.229793103448, 228.774620689655, 295.961379310345, 300.238344827586,
404.881655172414, 447.85324137931, 496.528551724138, 731.241931034483,
1916.9, 68.8187889908257, 71.9220550458716, 111.1,
154.5, 201.2, 253.4, 248.2,
247.1, 421.0, 850.106642201835, 19.2,
14.94, 40.8, 96.0269734513274, 200.4,
354.09, 457.8, 745.21614159292, 929.6,
2654.1, 4.97, 12.6554482758621, 50.6,
77.0, 168.119172413793, 259.120551724138, 364.27,
571.07, 828.672, 1452.78786206897, 6.75310344827586,
4.55834482758621, 5.41241379310345, 25.2513103448276, 46.1428965517241,
68.2262068965517, 125.256827586207, 104.550620689655, 258.918620689655,
951.36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15.5453793103448, 14.1020689655172,
44.5, 79.2, 119.3, 216.3,
316.8, 577.3, 901.7, 1897.1,
70.5, 97.10, 136.421309734513, 189.527575221239,
250.2, 333.986336283186, 439.591433628319, 583.765805309735,
725.7, 1283.5), color = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L), .Label = c("blue", "cyan", "darkgreen",
"red", "brown", "chartreuse", "green", "purple", "gold",
"tomato", "darkturquoise", "forestgreen"), class = "factor")), class = "data.frame", row.names = c(NA,
-120L)))
Above is artificial data and below is code for bar chart
library(plotly)
plt <- plot_ly(t_df3) %>%
add_trace(x = ~deciles, y = ~value.x, type = 'bar',name = 'Left-scale',marker = list(color = ~color.x), name = ~variable.x) %>%
add_trace(x = ~deciles, y = ~value, type = 'bar', name = 'Right-scale',marker = list(color = ~color), name = ~variable) %>%
layout(
xaxis = list(title = '',font = t_8),
yaxis = list(title = ''),
legend = list(x = 0.01, y = 0.99,font = t_10),
barmode = 'bar'
)
plt
On the left side, you can see a legend, but I am not satisfied with this legend and I want to change this legend, with a legend with the structure of the data (e.g. Food, Alcoholic Beverages, etc.). The structure is same for left and also right bars.So can anybody help with this ?

Would this be suitable?
If so, then this is how you can make this plot.
First, I melted the data.
t_df4 <- pivot_longer(t_df3, cols = c(value, value.x),
names_to = "group") %>%
mutate(group = ifelse(group == "value", "right_side",
"left_side"))
Then I plotted.
plot_ly(t_df4, x = ~list(deciles, group), y = ~value,
color = ~variable, colors = ~as.character(color), type = "bar") %>%
layout(barmode = "stack",
xaxis = list(title = ''),
yaxis = list(title = ''),
legend = list(x = 0.01, y = 0.99))

Why does strip text color from facet_wrap not correspond to element_text color?

Please, find my data sample p below.
Question: why does the strip text color from facet_wrap() not change as specified in element_text(colour)?
I have produced this plot
I would like the strip text color (UICC Stage I, II, III and IV) to match the color of the geom_point as specified in cols. It currently loads #E1B930 on all text items.
What is wrong with the following script?
cols = c("#E1B930", "#2C77BF","#E38072","#6DBCC3")
ggplot(p, aes(x=n.fjernet,y=os.neck)) + geom_point(aes(color=uiccc),shape=20, size=5,alpha=0.7) +
geom_quantile(quantiles = 0.5,col="black", size=1,linetype=2) + facet_wrap(.~factor(uiccc)) +
scale_fill_manual(values=cols) +
scale_colour_manual(values=cols) +
scale_x_continuous(breaks = seq(0,50, by=10), name="Lymph nodal yield") +
scale_y_continuous(name="Time to death") +
theme(strip.text.x = element_text(size=12,face="bold", colour = cols),
strip.text.y = element_text(size=12, face="bold"),
strip.background = element_rect(fill="white"),
legend.position="none")
My data
p <- structure(list(uiccc = structure(c(4L, 3L, 3L, 2L, 2L, 2L, 2L,
4L, 1L, 1L, 2L, 1L, 4L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 2L,
3L, 2L, 4L, 1L, 1L, 2L, 4L, 4L, 1L, 3L, 3L, 4L, 3L, 1L, 4L, 2L,
3L, 4L, 4L, 4L, 3L, 2L, 4L, 1L, 4L, 2L, 4L, 4L, 2L, 4L, 4L, 1L,
4L, 2L, 3L, 2L, 2L, 3L, 2L, 4L, 4L, 2L, 2L, 3L, 1L, 4L, 4L, 4L,
4L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 4L,
2L, 4L, 1L, 2L, 1L, 1L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 3L,
3L, 4L, 1L, 1L, 3L, 1L, 4L, 2L, 1L, 3L, 1L, 2L, 1L, 1L, 4L, 1L,
1L, 4L, 1L, 1L, 3L, 2L, 2L, 1L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 2L,
2L, 4L, 4L, 2L, 3L, 4L, 2L, 4L, 1L, 1L, 3L, 3L, 1L, 1L, 3L, 4L,
4L, 2L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 2L, 2L, 4L, 3L,
1L, 4L, 3L, 4L, 4L, 3L, 1L, 4L, 4L, 4L, 4L, 2L, 2L, 4L, 4L, 1L,
4L, 4L, 2L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 4L, 4L, 2L, 4L, 4L, 2L,
4L, 4L, 4L, 4L, 1L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 2L, 3L, 1L, 2L, 1L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
3L, 4L, 4L, 1L, 3L, 3L, 4L, 3L), .Label = c("UICC Stage I", "UICC Stage II",
"UICC Stage III", "UICC Stage IV"), class = "factor"), os.neck = c(11.5,
74.38, 17.02, 7.89, 96.03, 40.48, 17.74, 14.65, 62.46, 12.55,
9.92, 26.05, 45.47, 17.38, 39.72, 51.45, 8.61, 76.98, 67.09,
94.79, 72.15, 93.93, 17.05, 12.48, 91.6, 15.87, 11.04, 67.22,
67.02, 8.94, 6.6, 5.09, 10.68, 17.15, 0.07, 5.19, 40.77, 0.2,
170.88, 5.55, 1.61, 38.28, 10.58, 32.99, 110.98, 103.69, 122.32,
14.78, 42.74, 4.04, 8.28, 84.96, 11.7, 49.97, 120.48, 52.6, 71.26,
16.3, 100.14, 55.03, 6.51, 89.89, 51.71, 24.97, 55.66, 21.91,
81.48, 30.92, 1.58, 7.52, 30.75, 3.45, 19.22, 5.42, 17.68, 45.54,
76.22, 125.34, 83.62, 30.82, 90.32, 1.84, 19.98, 20.53, 32.59,
54.77, 2.3, 106.84, 22.28, 45.18, 4.47, 39.66, 32.3, 16.23, 3.88,
2.23, 0.23, 18.73, 0.79, 28.75, 79.54, 14.46, 15.15, 54.97, 48.59,
34.83, 58.42, 35.29, 45.73, 57.53, 63.11, 65.05, 29.54, 77.21,
63.48, 83.35, 34.3, 64.49, 29.54, 62.69, 21.62, 49.35, 99.02,
15.8, 41.89, 12.98, 13.8, 43.6, 57.23, 31.38, 70.74, 39.46, 20.76,
67.22, 127.15, 74.12, 1.97, 7.39, 25.17, 28.22, 14, 36.53, 20.83,
19.55, 40.77, 27.76, 45.31, 34.46, 35.55, 26.94, 9.43, 10.51,
6.8, 8.18, 8.02, 14.29, 6.11, 13.8, 4.9, 4.04, 14.82, 11.66,
73.07, 92.91, 99.98, 10.64, 10.05, 95.8, 7.23, 12.81, 43.99,
13.9, 10.25, 16.36, 18.2, 18.76, 12.32, 8.64, 11.79, 112.04,
70.97, 31.28, 28.85, 21.49, 19.94, 22.14, 29.44, 67.62, 11.01,
45.24, 110.72, 20.24, 14.06, 12.88, 31.51, 8.08, 13.08, 21.45,
24.28, 21.98, 32.89, 23.26, 15.41, 15.41, 13.8, 40.12, 8.02,
15.77, 49.81, 18.17, 24.21, 47.08, 6.6, 37.16, 13.01, 8.38, 14.36,
18.27, 17.28, 73.76, 68.21, 22.83, 2.66, 69.06, 17.05, 8.61,
23.33, 13.34, 12.65, 8.77, 128.92, 16.1, 4.99, 11.73, 22.97,
40.12, 20.37, 2.04, 45.73), mors = 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, 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, 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), n.fjernet = c(18L, 11L,
14L, 15L, 9L, 6L, 3L, 16L, 4L, 6L, 10L, 13L, 33L, 16L, 6L, 9L,
23L, 9L, 8L, 13L, 5L, 30L, 25L, 3L, 9L, 9L, 12L, 7L, 38L, 5L,
7L, 15L, 4L, 6L, 15L, 9L, 8L, 7L, 4L, 6L, 10L, 8L, 4L, 9L, 10L,
14L, 14L, 3L, 4L, 6L, 6L, 20L, 3L, 26L, 13L, 13L, 13L, 13L, 3L,
7L, 6L, 5L, 10L, 15L, 29L, 7L, 6L, 11L, 17L, 14L, 18L, 22L, 9L,
20L, 34L, 9L, 8L, 8L, 11L, 3L, 4L, 4L, 5L, 3L, 2L, 8L, 5L, 18L,
7L, 9L, 13L, 18L, 19L, 14L, 46L, 23L, 11L, 6L, 18L, 20L, 4L,
2L, 7L, 7L, 4L, 11L, 13L, 13L, 9L, 9L, 9L, 12L, 11L, 16L, 6L,
13L, 8L, 17L, 5L, 8L, 22L, 19L, 3L, 15L, 14L, 7L, 18L, 9L, 10L,
18L, 24L, 11L, 15L, 7L, 6L, 4L, 24L, 23L, 8L, 20L, 9L, 22L, 11L,
2L, 24L, 15L, 5L, 8L, 11L, 11L, 11L, 15L, 6L, 16L, 7L, 9L, 16L,
11L, 33L, 27L, 16L, 57L, 5L, 7L, 8L, 11L, 15L, 15L, 12L, 5L,
9L, 49L, 11L, 28L, 19L, 13L, 23L, 11L, 12L, 10L, 4L, 14L, 6L,
12L, 32L, 13L, 12L, 4L, 11L, 17L, 10L, 5L, 15L, 21L, 19L, 11L,
31L, 9L, 20L, 11L, 16L, 12L, 6L, 16L, 27L, 30L, 18L, 18L, 10L,
7L, 23L, 16L, 15L, 4L, 12L, 9L, 10L, 11L, 7L, 8L, 8L, 7L, 6L,
9L, 9L, 13L, 15L, 12L, 35L, 12L, 5L, 19L, 27L, 34L, 10L, 16L,
18L, 6L, 22L)), row.names = c(3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 20L, 22L, 24L, 28L, 29L,
31L, 34L, 35L, 39L, 40L, 42L, 43L, 44L, 47L, 48L, 49L, 50L, 54L,
56L, 57L, 58L, 59L, 60L, 62L, 63L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 80L, 81L, 82L, 83L, 86L, 87L, 88L, 89L,
94L, 97L, 99L, 101L, 102L, 106L, 113L, 115L, 117L, 122L, 129L,
132L, 136L, 142L, 143L, 145L, 146L, 148L, 153L, 154L, 158L, 159L,
163L, 164L, 167L, 169L, 171L, 174L, 175L, 178L, 179L, 185L, 188L,
191L, 197L, 210L, 218L, 220L, 230L, 236L, 238L, 239L, 240L, 241L,
242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 252L, 253L,
254L, 255L, 256L, 257L, 258L, 259L, 261L, 262L, 263L, 264L, 265L,
266L, 270L, 275L, 277L, 278L, 280L, 282L, 284L, 286L, 289L, 293L,
295L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 310L, 312L,
313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L,
325L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L,
338L, 348L, 349L, 351L, 352L, 353L, 354L, 357L, 358L, 359L, 360L,
361L, 362L, 363L, 365L, 366L, 368L, 371L, 372L, 374L, 376L, 378L,
379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L,
390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L,
401L, 402L, 403L, 405L, 407L, 409L, 410L, 411L, 412L, 413L, 414L,
415L, 416L, 417L, 418L, 419L, 421L, 422L, 424L, 425L, 426L, 427L,
428L, 429L, 430L), class = "data.frame")

This is a modification of grob values for strip.text color using the grid package:
library(grid)
library(ggplot2)
g <- ggplot_gtable(ggplot_build(plot))
strip_both <- which(grepl('strip-', g$layout$name))
colors = c("#E38072","#6DBCC3", "#E1B930", "#2C77BF")
k <- 1
for (i in strip_both) {
j <- which(grepl("text", g$grobs[[i]]$grobs[[1]]$childrenOrder))
g$grobs[[i]]$grobs[[1]]$children[[j]]$children[[1]]$gp$col <- colors[k]
k <- k+1
}
grid.draw(g)
Assigning plot object to plot:
cols = c("#E1B930", "#2C77BF","#E38072","#6DBCC3")
plot <- ggplot(p, aes(x=n.fjernet,y=os.neck)) + geom_point(aes(color=uiccc),shape=20, size=5,alpha=0.7) +
geom_quantile(quantiles = 0.5,col="black", size=1,linetype=2) + facet_wrap(.~factor(uiccc)) +
scale_fill_manual(values=cols) +
scale_colour_manual(values=cols) +
scale_x_continuous(breaks = seq(0,50, by=10), name="Lymph nodal yield") +
scale_y_continuous(name="Time to death") +
theme(strip.text.x = element_text(size=12,face="bold"),
strip.text.y = element_text(size=12, face="bold"),
strip.background = element_rect(fill="white"),
legend.position="none")

Alternatively, plot them per group, then combine:
# named colours per group
cols <- setNames(c("#E1B930", "#2C77BF","#E38072","#6DBCC3"),
levels(p$uiccc))
# set pretty limits
Xlim <- round(range(p$n.fjernet), -1)
Ylim <- round(range(p$os.neck), -1)
ggList <- lapply(split(p, p$uiccc), function(i){
title <- i[1, "uiccc"]
ggplot(i, aes(x = n.fjernet, y = os.neck)) +
geom_point(aes(color = uiccc), shape = 20, size = 5, alpha = 0.7, show.legend = FALSE) +
geom_quantile(quantiles = 0.5, col = "black", size = 1, linetype = 2) +
ggtitle(title) +
scale_fill_manual(values = cols) +
scale_colour_manual(values = cols) +
scale_x_continuous(limits = Xlim) +
scale_y_continuous(limits = Ylim) +
theme_classic() +
theme(plot.title = element_text(colour = cols[ title ]))
})
Then use patchwork or cowplot to combine:
patchwork::wrap_plots(ggList)
#or
cowplot::plot_grid(plotlist = ggList)

Alternatively to #Greg's answer (that I think should be the validated answer), you can get the same plot by adding annotation in place of facet labeling:
library(tidyverse)
ggplot(df, aes(x=n.fjernet,y=os.neck)) + geom_point(aes(color=uiccc),shape=20, size=5,alpha=0.7) +
geom_quantile(quantiles = 0.5,col="black", size=1,linetype=2) + facet_wrap(.~factor(uiccc)) +
scale_fill_manual(values=cols) +
scale_colour_manual(values=cols) +
scale_x_continuous(breaks = seq(0,50, by=10), name="Lymph nodal yield") +
scale_y_continuous(name="Time to death") +
theme(#strip.text.x = element_text(size=12,face="bold", colour = cols),
#strip.text.y = element_text(size=12, face="bold"),
#strip.background = element_rect(fill="white"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
legend.position="none",
plot.margin = unit(c(1,3,1,1), "lines")) +
coord_cartesian(clip = "off",ylim = c(0,175))+
geom_text(data = . %>% distinct(uiccc),
aes(label = factor(uiccc), color = uiccc), y = 200, x = 30, hjust = 0.5, fontface = "bold")

Different results in Stata and R with the "same" anova code

I have some Stata code and I want to replicate the results in R. However, even with the same dataset and, I think, the same code, I get different results in R from those in Stata. I think it could be because Stata makes the order of the regression different than keyed in.
Do I need exactly the same order as in Stata to get the same results and how can I do this?
I changed all the variables to factors and tried again but the problem is still there.
I noticed that when I change the order of the explanatory variables I get different results, but I don`t find "the right order" to replicate the Stata results.
Stata code:
. anova testm2 c.testm1 i.hptreat c.cortm1 c.cortm2 i.female if inelig == 0 & anyoutv1 == 0
Number of obs =39 R-squared =0.7048
Root MSE= 16.0144 Adj R-squared =0.6601
Source | Partial SS df MS F Prob>F
---------------------------------------------------------------
Model | 20209.281 5 4041.8563 15.76 0.0000
testm1 | 3516.6527 1 3516.6527 13.71 0.0008
hptreat| 1183.5007 1 1183.5007 4.61 0.0391
cortm1 | 8.5753841 1 8.5753841 0.03 0.8560
cortm2 | 2810.9353 1 2810.9353 10.96 0.0023
female | 2557.3444 1 2557.3444 9.97 0.0034
Residual| 8463.2532 33 256.46222
----------------------------------------------------------------
Total | 28672.535 38 754.54038
R code:
FosseTest<-aov(testm2~testm1+hptreat+cortm1+cortm2+female,data=X2data)
summary(FosseTest)
Df Sum Sq Mean Sq F value Pr(>F)
testm1 1 15121 15121 58.962 7.68e-09 ***
hptreat 1 524 524 2.043 0.16228
cortm1 1 23 23 0.089 0.76715
cortm2 1 1984 1984 7.735 0.00888 **
female 1 2557 2557 9.972 0.00339 **
Residuals 33 8463 256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
You can see that I get totally different values in the replication.
in the X2data Set I already subset the values for if inelig == 0 & anyoutv1 == 0
for the reconstruction of the data:
dput(X2data)
structure(list(id = c(29L, 30L, 31L, 32L, 34L, 35L, 36L, 37L,
39L, 41L, 42L, 43L, 44L, 46L, 47L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 57L, 58L, 59L, 60L, 61L, 62L, 64L, 65L, 66L, 67L, 68L, 69L,
70L, 71L, 72L, 73L, 74L), inelig = 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), .Label = c("Analytic sample (keep)", "Ineligible (drop)"
), class = "factor"), ccydrop = c(0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), cortm1v2 = c(0.003, 0.086, 0.047, 0.106, NA, 0.153, 0.086,
0.005, 0.133, 0.036, 0.03, 0.015, 0.014, 0.111, 0.389, 0.298,
0.4, 0.215, 0.062, 0.021, 0.075, 0.073, 0.033, 0.243, 0.126,
0.147, 0.019, 0.048, 0.28, 0.052, 0.039, 0.105, 0.111, 0.133,
0.065, 0.051, 0.143, 0.127, 0.095), cortm2v2 = c(0.025, 0.167,
0.059, 0.112, 0.171, 0.183, 0.102, 0.018, 0.08, 0.015, 0.027,
0.05, 0.025, 0.046, 0.085, 0.144, 0.155, 0.09, 0.057, 0.023,
0.038, 0.205, 0.035, 0.198, 0.112, 0.211, 0.042, 0.142, 0.328,
0.076, 0.067, 0.094, 0.245, 0.153, 0.115, 0.127, 0.257, 0.125,
0.096), cdiffv2 = c(0.022, 0.081, 0.012, 0.006, NA, 0.03, 0.016,
0.013, -0.053, -0.021, -0.003, 0.035, 0.011, -0.065, -0.304,
-0.154, -0.245, -0.125, -0.005, 0.002, -0.037, 0.132, 0.002,
-0.045, -0.014, 0.064, 0.023, 0.094, 0.048, 0.024, 0.028, -0.011,
0.134, 0.02, 0.05, 0.076, 0.114, -0.002, 0.001), testm1v2 = c(38.72,
32.77, 32.32, 17.99, 73.58, 80.69, 48.56, 21.92, 27.24, 40.93,
31.73, 60.05, 38.04, 30.17, 59.07, 26.92, 25.41, 47.81, 63.02,
34.49, 104.38, 38.08, 30.99, 35.23, 104.81, 49.33, 50.03, 11.65,
143.57, 48.31, 90.37, 48.56, 41.67, 75.23, 60.56, 39.03, 18.16,
37.9, 84.5), testm2v2 = c(62.37, 29.23, 27.51, 28.66, 44.67,
105.48, 42.67, 15.01, 21.33, 10.87, 2.14, 44.53, 35.8, 10.43,
47.54, 48.5, 38.98, 91.32, 52.94, 22.43, 58.68, 81.63, 34.79,
38.57, 94.86, 50.83, 55.75, 45.33, 111.62, 65.15, 81.08, 50.08,
44.86, 58.63, 85.85, 58.69, 16.35, 35.97, 99.08), tdiffv2 = c(23.65,
-3.54, -4.81, 10.67, -28.91, 24.79, -5.89, -6.91, -5.91, -30.06,
-29.59, -15.52, -2.24, -19.74, -11.53, 21.58, 13.57, 43.51, -10.08,
-12.06, -45.7, 43.55, 3.8, 3.34, -9.95, 1.5, 5.72, 33.68, -31.95,
16.84, -9.29000000000001, 1.52, 3.19, -16.6, 25.29, 19.66, -1.81,
-1.93, 14.58), testoutv1 = 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), .Label = c("Not selected", "Selected"), class = "factor"),
cortoutv1 = 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
), .Label = c("Not selected", "Selected"), class = "factor"),
anyoutv1 = 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
), .Label = c("Not selected", "Selected"), class = "factor"),
testoutv2 = 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
), .Label = c("Not selected", "Selected"), class = "factor"),
cortoutv2 = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("Not selected", "Selected"), class = "factor"),
anyoutv2 = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("Not selected", "Selected"), class = "factor"),
pose1rate = c(6L, 7L, 6L, 6L, 7L, 7L, 6L, 7L, 5L, 6L, 7L,
4L, 7L, 7L, 7L, 6L, 7L, 7L, 7L, 7L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), pose2rate = c(6L,
6L, 5L, 7L, 7L, 7L, 7L, 7L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 6L, 7L, 6L, 7L, 7L, 7L,
6L, 7L, 7L, 7L, 7L, 7L, 6L, 6L), poseratem = c(6, 6.5, 5.5,
6.5, 7, 7, 6.5, 7, 5.5, 6.5, 7, 5.5, 7, 7, 7, 6, 6.5, 7,
7, 7, 6.5, 7, 7, 7, 7, 6.5, 7, 6.5, 7, 7, 7, 6.5, 7, 7, 7,
7, 7, 6.5, 6.5), saldiff = c(24.30555556, 20.83333333, 29.16666667,
18.75, 23.61111111, 34.02777778, 18.05555556, 19.44444444,
21.52777778, 15.97222222, 22.91666667, 13.88888889, 22.22222222,
25, 22.22222222, 22.22222222, 18.05555556, 17.36111111, 22.22222222,
27.08333333, 20.83333333, 24.30555556, 22.22222222, 28.47222222,
24.30555556, 25, 27.77777778, 22.22222222, 15.97222222, 24.30555556,
21.52777778, 19.44444444, 15.97222222, 15.27777778, 15.97222222,
24.30555556, 19.44444444, 24.30555556, 15.27777778), sal2manip = c(19.80555556,
16.33333333, 24.66666667, 14.25, 19.11111111, 29.52777778,
13.55555556, 14.94444444, 17.02777778, 11.47222222, 18.41666667,
9.38888889, 17.72222222, 20.5, 17.72222222, 17.72222222,
13.55555556, 12.86111111, 17.72222222, 22.58333333, 16.33333333,
19.80555556, 17.72222222, 23.97222222, 19.80555556, 20.5,
23.27777778, 17.72222222, 11.47222222, 19.80555556, 17.02777778,
14.94444444, 11.47222222, 10.77777778, 11.47222222, 19.80555556,
14.94444444, 19.80555556, 10.77777778), hptreat = structure(c(2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("0", "1"), class = "factor"),
female = structure(c(1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L
), .Label = c("0", "1"), class = "factor"), age = c(19L,
20L, 20L, 18L, 21L, 20L, 18L, 21L, 35L, 20L, 18L, 20L, 20L,
18L, 20L, 25L, 18L, 23L, 21L, 19L, 20L, 20L, 30L, 19L, 22L,
18L, 19L, 22L, 19L, 20L, 28L, 28L, 19L, 19L, 20L, 25L, 20L,
25L, 23L), cort1a1 = c(0.004, 0.085, 0.049, 0.107, 0.486,
0.159, 0.088, 0.004, 0.138, 0.035, 0.03, 0.018, 0.017, 0.111,
0.39, 0.292, 0.396, 0.213, 0.065, 0.022, 0.074, 0.077, 0.035,
0.241, 0.126, 0.154, 0.021, 0.05, 0.296, 0.054, 0.04, 0.109,
0.114, 0.133, 0.063, 0.055, 0.149, 0.134, 0.098), cort1a2 = c(0.001,
0.086, 0.045, 0.105, 0.482, 0.147, 0.085, 0.005, 0.127, 0.037,
0.031, 0.013, 0.011, 0.111, 0.389, 0.304, 0.405, 0.218, 0.059,
0.02, 0.076, 0.069, 0.032, 0.246, 0.126, 0.141, 0.017, 0.046,
0.264, 0.051, 0.038, 0.101, 0.109, 0.133, 0.068, 0.048, 0.137,
0.12, 0.092), cort2a1 = c(0.027, 0.174, 0.056, 0.111, 0.175,
0.179, 0.103, 0.021, 0.079, 0.014, 0.028, 0.051, 0.024, 0.051,
0.083, 0.148, 0.156, 0.086, 0.062, 0.024, 0.038, 0.209, 0.036,
0.199, 0.114, 0.207, 0.041, 0.141, 0.333, 0.078, 0.065, 0.088,
0.238, 0.157, 0.119, 0.132, 0.268, 0.132, 0.099), cort2a2 = c(0.023,
0.161, 0.062, 0.113, 0.166, 0.188, 0.101, 0.016, 0.081, 0.015,
0.026, 0.049, 0.026, 0.041, 0.086, 0.139, 0.154, 0.093, 0.052,
0.022, 0.038, 0.202, 0.034, 0.198, 0.111, 0.215, 0.042, 0.142,
0.324, 0.075, 0.068, 0.101, 0.252, 0.149, 0.111, 0.123, 0.247,
0.118, 0.093), cortm1 = c(0.0024999999, 0.085500002, 0.046999998,
0.106, 0.484, 0.153, 0.086499996, 0.0044999998, 0.13249999,
0.035999998, 0.0305, 0.0155, 0.014, 0.111, 0.38949999, 0.29800001,
0.4005, 0.2155, 0.061999999, 0.021, 0.075000003, 0.072999999,
0.033500001, 0.24349999, 0.126, 0.14749999, 0.018999999,
0.048, 0.28, 0.052499998, 0.039000001, 0.105, 0.1115, 0.133,
0.065499999, 0.0515, 0.14300001, 0.127, 0.094999999), cortm2 = c(0.025,
0.1675, 0.059, 0.112, 0.1705, 0.18350001, 0.102, 0.0185,
0.079999998, 0.0145, 0.027000001, 0.050000001, 0.025, 0.046,
0.0845, 0.1435, 0.155, 0.089500003, 0.057, 0.023, 0.037999999,
0.20550001, 0.035, 0.19850001, 0.1125, 0.211, 0.041499998,
0.1415, 0.3285, 0.076499999, 0.066500001, 0.094499998, 0.245,
0.153, 0.115, 0.1275, 0.25749999, 0.125, 0.096000001), cdiff = c(0.022500001,
0.082000002, 0.012000002, 0.0060000047, -0.31349999, 0.03050001,
0.015500002, 0.014, -0.052499995, -0.021499999, -0.0034999996,
0.034500003, 0.011, -0.064999998, -0.30500001, -0.15450001,
-0.2455, -0.12599999, -0.004999999, 0.0020000003, -0.037000004,
0.13250001, 0.0014999993, -0.044999987, -0.013500005, 0.063500002,
0.022499999, 0.093499996, 0.048500001, 0.024, 0.0275, -0.010499999,
0.13350001, 0.019999996, 0.049500003, 0.075999998, 0.11449999,
-0.0020000041, 0.001000002), test1a1 = c(39.87, 33.22, 32.52,
19.74, 78.85, 83.51, 48.37, 22.31, 28.17, 41.44, 32.92, 61.4,
40.31, 30.36, 59.44, 27.52, 26.14, 46.75, 63.73, 34.03, 98.47,
36.62, 30.26, 37.15, 105.64, 47.99, 50.15, 11.33, 149.12,
48.57, 92.04, 51.22, 42.25, 77.07, 62.75, 38.8, 17.91, 40.28,
88.47), test1a2 = c(37.58, 32.32, 32.12, 16.25, 68.31, 77.88,
48.75, 21.53, 26.32, 40.42, 30.55, 58.7, 35.78, 29.97, 58.7,
26.32, 24.69, 48.87, 62.32, 34.95, 110.29, 39.53, 31.72,
33.32, 103.99, 50.67, 49.9, 11.97, 138.02, 48.05, 88.7, 45.89,
41.08, 73.39, 58.38, 39.25, 18.41, 35.53, 80.54), test2a1 = c(64.22,
29.43, 27.98, 28.17, 46.14, 105.92, 43.68, 16.41, 21.42,
11.35, 1.66, 44.17, 38.58, 11.11, 48.57, 48.31, 39.71, 92.04,
52.73, 22.3, 58.23, 82.01, 35.76, 39.59, 94.06, 50.52, 55.82,
45.91, 115.13, 67.59, 82.97, 49.89, 45.09, 57.86, 86.76,
58.83, 16.53, 36.7, 100.4), test2a2 = c(60.53, 29.04, 27.04,
29.14, 43.2, 105.05, 41.66, 13.62, 21.25, 10.39, 2.63, 44.9,
33.02, 9.75, 46.52, 48.7, 38.25, 90.59, 53.15, 22.57, 59.14,
81.24, 33.81, 37.55, 95.66, 51.14, 55.69, 44.74, 108.1, 62.71,
79.18, 50.27, 44.63, 59.39, 84.94, 58.55, 16.16, 35.24, 97.75
), testm1 = c(38.724998, 32.77, 32.32, 17.995001, 73.580002,
80.695, 48.560001, 21.92, 27.245001, 40.93, 31.735001, 60.049999,
38.044998, 30.165001, 59.07, 26.92, 25.415001, 47.810001,
63.025002, 34.490002, 104.38, 38.075001, 30.99, 35.235001,
104.815, 49.330002, 50.025002, 11.65, 143.57001, 48.310001,
90.370003, 48.555, 41.665001, 75.230003, 60.564999, 39.025002,
18.16, 37.904999, 84.504997), testm2 = c(62.375, 29.235001,
27.51, 28.655001, 44.669998, 105.485, 42.669998, 15.015,
21.334999, 10.87, 2.145, 44.535, 35.799999, 10.43, 47.544998,
48.505001, 38.98, 91.315002, 52.939999, 22.434999, 58.685001,
81.625, 34.785, 38.57, 94.860001, 50.830002, 55.755001, 45.325001,
111.615, 65.150002, 81.074997, 50.080002, 44.860001, 58.625,
85.849998, 58.689999, 16.344999, 35.970001, 99.074997), tdiff = c(23.650002,
-3.5349998, -4.8099995, 10.66, -28.910004, 24.790001, -5.8900032,
-6.9049997, -5.9100018, -30.060001, -29.59, -15.514999, -2.2449989,
-19.735001, -11.525002, 21.585001, 13.564999, 43.505001,
-10.085003, -12.055002, -45.694996, 43.549999, 3.7950001,
3.3349991, -9.9550018, 1.5, 5.7299995, 33.675003, -31.955009,
16.84, -9.2950058, 1.5250015, 3.1949997, -16.605003, 25.285,
19.664997, -1.8150005, -1.9349976, 14.57), feelpower = structure(c(2L,
3L, 1L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 4L, 3L,
4L, 3L, 1L, 3L, 4L, 2L, 2L, 3L), .Label = c("2", "3", "Not at all",
"Very much"), class = "factor"), incharge = structure(c(1L,
1L, 3L, 4L, 1L, 2L, 3L, 3L, 1L, 1L, 3L, 4L, 3L, 2L, 2L, 1L,
3L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 3L, 1L, 1L, 4L, 3L, 1L, 1L), .Label = c("2", "3", "Not at all",
"Very much"), class = "factor"), powm = structure(c(3L, 1L,
1L, 5L, 2L, 4L, 6L, 6L, 1L, 1L, 6L, 7L, 6L, 3L, 4L, 2L, 1L,
4L, 4L, 3L, 2L, 4L, 2L, 2L, 3L, 3L, 3L, 4L, 1L, 5L, 1L, 4L,
6L, 2L, 1L, 7L, 2L, 3L, 1L), .Label = c("1.5", "2", "2.5",
"3", "3.5", "Not at all", "Very much"), class = "factor"),
diceroll = structure(c(2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L
), .Label = c("No", "Yes"), class = "factor")), row.names = c(2L,
3L, 4L, 5L, 7L, 8L, 9L, 10L, 12L, 14L, 15L, 16L, 17L, 19L, 20L,
22L, 23L, 24L, 25L, 26L, 27L, 28L, 30L, 31L, 32L, 33L, 34L, 35L,
37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L), class = "data.frame")

You can get the same results in R using drop1(FosseTest, test = "F"). This will test the effect of leaving one of the variables off the aov.
drop1(FosseTest, test = "F")
#
# Single term deletions
#
# Model:
# testm2 ~ testm1 + hptreat + cortm1 + cortm2 + female
# Df Sum of Sq RSS AIC F value Pr(>F)
# <none> 8463.3 221.82
# testm1 1 3516.7 11979.9 233.37 13.7122 0.0007751 ***
# hptreat 1 1183.5 9646.8 224.92 4.6147 0.0391333 *
# cortm1 1 8.6 8471.8 219.86 0.0334 0.8560279
# cortm2 1 2810.9 11274.2 231.00 10.9604 0.0022605 **
# female 1 2557.3 11020.6 230.11 9.9716 0.0033895 **
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(FosseTest) displays the sequential effect of addeding the variables one after another.
There was a different way how to access this, but at the moment I can't remember...

How to differentiate Bars in geom_bar without color: ggplot

Note: A similar question is present at link, but I posed it a separate question due to: 1) only a hack is provided to the previos question which I thought would make code unnecessary complex 2) I thought after 2013 a fix might have been suggested for this
I am using following code to draw bars/stacks
ggplot(finaldataframe,aes(day,score))+
geom_bar(aes(fill=identify),stat="identity",position = "dodge",width = .7, show.legend = TRUE)+
labs(x= "Day of the Month", y="Anomaly Score") +
scale_fill_discrete(name="Method", labels=c("Mean","Maximum","Cumulative \n sum"))+
theme(axis.text= element_text(color="Black"))+ scale_x_continuous(breaks=seq(1,31,5))
A portion of output is as
The problem with this figure is that once I print this via black and white printer It gets hard to differentiate between different stacks. Is there any way to make the stacks differentiable on a black and white print. I am looking for some what like this:
For reproduction, Here is the dput of dataframe:
> dput(finaldataframe)
finaldataframe = structure(list(day = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L), score = c(0, 0.02, 0.01, 0, 0.02, 0.01, 0.01,
0.02, 0.02, 0.28, 0.24, 0.01, 0.94, 0.22, 0.25, 0.01, 0.31, 0.22,
0.24, 0.83, 0.4, 0.44, 0.06, 0.02, 0.37, 0.07, 0.12, 0.06, 0.1,
0.06, 0.1, 0, 0.05, 0.04, 0.02, 0.05, 0.01, 0.02, 0.03, 0.04,
0.37, 0.36, 0.04, 1, 0.28, 0.34, 0.03, 0.55, 0.35, 0.32, 1, 0.71,
1, 0.13, 0.04, 0.47, 0.12, 0.17, 0.1, 0.18, 0.1, 0.14, 0, 0.02,
0.01, 0, 0.02, 0.01, 0.01, 0.02, 0.02, 0.3, 0.25, 0.01, 1, 0.23,
0.27, 0, 0.33, 0.24, 0.26, 0.89, 0.42, 0.47, 0.06, 0.02, 0.4,
0.07, 0.13, 0.06, 0.11, 0.06, 0.1), identify = 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, 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), .Label = c("Mean",
"Maximum", "Cummulative Sum"), class = "factor")), .Names = c("day",
"score", "identify"), row.names = c(NA, 93L), class = "data.frame")

Changing the order of plotting levels in Latitice

I am trying to get a boxplot with a specific order of the levels that are being plotted.
Using the following data and code I generate the boxplot, but the order in which I need this is 6,12,15,18.
I have tried a number of thing using the with() function but can't make it work.
library(lattice)
rate<-structure(list(Temp = c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L), Rep = c(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, 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, 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, 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), Ind = structure(c(1L, 1L, 1L, 1L, 5L, 5L,
5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L,
6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 5L, 5L,
5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L,
6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L), .Label = c("B", "MBCT",
"MBT", "MSCT", "MST", "S"), class = "factor"), Week = c(1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L,
6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L, 1L, 2L, 6L, 8L), Weight = c(1.756,
1.756, 1.756, 1.756, 0.92, 0.92, 0.92, 0.92, 1.201, 1.201, 1.201,
1.201, 2.601, 2.601, 2.601, 2.601, 2.057, 2.057, 2.057, 2.057,
0.784, 0.784, 0.784, 0.784, 0.663, 0.663, 0.663, 0.663, 1.272,
1.272, 1.272, 1.272, 3.389, 3.389, 3.389, 3.389, 1.433, 1.433,
1.433, 1.433, 3.822, 3.822, 3.822, 3.822, 1.55, 1.55, 1.55, 1.55,
1.198, 1.198, 1.198, 1.198, 1.029, 1.029, 1.029, 1.029, 1.113,
1.113, 1.113, 1.113, 0.261, 0.261, 0.261, 0.261, 0.639, 0.639,
0.639, 0.639, 0.749, 0.749, 0.749, 0.749, 1.083, 1.083, 1.083,
1.083, 1.429, 1.429, 1.429, 1.429, 3.083, 3.083, 3.083, 3.083,
1.061, 1.061, 1.061, 1.061, 1.154, 1.154, 1.154, 1.154, 1.691,
1.691, 1.691, 1.691, 1.185, 1.185, 1.185, 1.185, 0.552, 0.552,
0.552, 0.552, 1.507, 1.507, 1.507, 1.507, 1.175, 1.175, 1.175,
1.175, 1.773, 1.773, 1.773, 1.773, 1.712, 1.712, 1.712, 1.712,
3.784, 3.784, 3.784, 3.784, 0.715, 0.715, 0.715, 0.715, 1.271,
1.271, 1.271, 1.271, 0.788, 0.788, 0.788, 0.788, 1.72, 1.72,
1.72, 1.72, 0.571, 0.571, 0.571, 0.571, 1, 1, 1, 1, 1.037, 1.037,
1.037, 1.037, 1.656, 1.656, 1.656, 1.656, 2.083, 2.083, 2.083,
2.083), Rate = c(0.387, 0.116, -0.141, 0.184, 0.785, 0.151, -0.69,
0.16, 0.477, 0.368, -0.544, 0.49, 0.152, 0.183, -0.137, 0.259,
0.239, 0.292, 0.018, 0.411, 0.322, 0.073, -0.148, 0.287, 0.214,
0.21, -0.579, 0.419, 0.23, 0.271, 0.685, 0.426, 0.248, 0.125,
0.053, 0.176, 0.465, 0.107, 0.02, 0.339, 0.261, 0.327, 0.279,
0.424, 0.308, 0.223, 0.287, 0.383, 0.306, 0.24, 0.258, 0.253,
0.437, 0.315, 0.275, 0.481, 0.372, 0.306, 0.267, 0.449, 0.727,
0.441, 0.624, 1.262, 0.334, 0.447, 0.548, 0.654, 0.846, 0.661,
0.66, 0.734, 0.191, 0.316, 0.551, 0.581, 0.332, 0.403, 0.509,
0.603, 0.411, 0.683, 0.427, 0.516, 0.498, 0.674, 0.371, 0.326,
0.288, 0.435, 0.297, 0.435, 0.165, 0.387, 0.212, 0.345, 0.334,
0.664, 0.526, 0.338, 0.094, 0.066, 0.39, 0.525, 0.215, 0.431,
0.151, 0.361, 0.153, 0.297, 0.127, 0.339, 0.292, 0.434, 0.411,
0.442, 0.25, 0.607, 0.369, 0.567, 0.189, 0.39, 0.372, 0.333,
0.339, 0.327, 0.449, 0.224, 0.086, 0.242, 0.465, 0.374, -0.063,
-0.006, 0.364, 0.308, 0.069, 0.223, 0.397, 0.264, 0.478, 0.345,
0.582, 0.36, 0.426, 0.403, 0.583, 0.544, 0.57, 0.567, 0.388,
0.531, 0.111, 0.125, 0.366, 0.266, 0.26, 0.315, 0.387, 0.549)), .Names = c("Temp",
"Rep", "Ind", "Week", "Weight", "Rate"), class = "data.frame", row.names = c(NA,
-160L))
rate$Temp <- as.character(rate$Temp)
rate$Week <- as.character(rate$Week)
rate$Rep <- as.character(rate$Rep)
rate$Weight<- as.character(rate$Weight)
bwplot(Rate~Temp, rate,
main="Boxplot for data over all weeks by temperature"
)

This can be tackled in the same manner as your question from a month ago. You need to set the order of levels of a factor. I would generally advise you work with factors, unless you have a really good reason to use characters.
rate$Temp <- as.factor(rate$Temp)
levels(rate$Temp) <- c("6", "12", "15", "18")

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