Fine tuning vjust ggplot2 - r

I am trying to make a plot similar to this one:
Right now, I am working on the IL-b1 plot (since it comes first in the dataset) and will apply the settings across all plots.
I am currently working on the x-axis labels, but am running into problems with vjust no matter how small I make it. Here is the image of my current code:
Any advice on how to make the labels closer to the x-axis? No matter how small i make vjust is doesn't get any closer...
Here is my current code:
il1b <- ggplot(data = mouse) +
geom_violin(aes(x = Treatment, y = `IL-1b_fold`)) +
geom_jitter(aes(x = Treatment, y = `IL-1b_fold`)) +
theme_classic() +
ggtitle(label = "IL-1\u03b2") +
ylab("fold mock control") +
theme(plot.title = element_text(face = "bold"),
axis.title.x = element_text(face = "bold", size = 12),
axis.title.y = element_text(face = "bold", size = 12),
axis.text.x = element_text(face = "bold", angle = 45, size = 11, color = "black", vjust = -0.00001),
axis.text.y = element_text(size = 11, color = "black"))
setwd(output)
ggsave("il1b.png", il1b, width = 5, height = 5, units = "in")
output is a stored file path to the output directory. This is all within an R Markdown code chunk.
Reprex of first 10 obs in dataset:
wrapr::build_frame(\n \"GBM#\" , \"Treatment\", \"IL-1b\", \"IL-6\" , \"TNF-a\", \"IP-10\" , \"IL-29\", \"IFN-a2\", \"IFN-b\", \"IL-10\", \"IFN-y\", \"IL-1b_fold\", \"IL-6_fold\", \"TNF-a_fold\", \"IP-10_fold\", \"IL-29_fold\", \"IFN-a2_fold\", \"IFN-b_fold\", \"IL-10_fold\", \"IFN-y_fold\" |\n 1 , \"Mock\" , 484.4 , \"2290.62\" , 2055 , 951.4 , 433.4 , 567.8 , 400.4 , 2595 , 60.64 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 |\n 1 , \"PVSRIPO\" , 383.6 , \"23233.55\", 1555 , 9865 , 298.8 , 546.6 , 240.9 , 4816 , 136.4 , 0.792 , 10.14 , 0.7564 , 10.37 , 0.6894 , 0.9628 , 0.6017 , 1.856 , 2.249 |\n 1 , \"Poly(I:C)\", 849.5 , \"37969.47\", 3451 , 1377 , 265 , 457.2 , 255.3 , 8435 , 53.69 , 1.754 , 16.58 , 1.679 , 1.448 , 0.6115 , 0.8052 , 0.6376 , 3.251 , 0.8854 |\n 1 , \"cGAMP\" , 472.2 , \"12495.37\", 4002 , 3.078e+04, 304.4 , 741.4 , 324.7 , 3411 , 42.48 , 0.9748 , 5.455 , 1.947 , 32.35 , 0.7024 , 1.306 , 0.8109 , 1.315 , 0.7005 |\n 2 , \"Mock\" , 23 , \"2851.31\" , 5.25 , 35.06 , 4.54 , 8.69 , 21.84 , 4.11 , 8.55 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 |\n 2 , \"PVSRIPO\" , 11.57 , \"142.84\" , 16.69 , 6116 , 13.57 , 23.46 , 21.84 , 5.63 , 5.14 , 0.503 , 0.0501 , 3.179 , 174.5 , 2.989 , 2.7 , 1 , 1.37 , 0.6012 |\n 2 , \"Poly(I:C)\", 28.41 , \"158.04\" , 38.85 , 822.7 , 7.18 , 3.11 , 6.61 , 5.48 , 4.47 , 1.235 , 0.05543 , 7.4 , 23.47 , 1.581 , 0.3579 , 0.3027 , 1.333 , 0.5228 |\n 2 , \"cGAMP\" , 71.07 , \"1166.8\" , 162.7 , 1794 , 8.08 , 7.72 , 22.55 , 25.62 , 9.94 , 3.09 , 0.4092 , 30.98 , 51.18 , 1.78 , 0.8884 , 1.033 , 6.234 , 1.163 |\n 4 , \"Mock\" , 8.67 , \"1148.81\" , 203.6 , 1.86 , 1.88 , 35.52 , 39.1 , 8.21 , 74.05 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 |\n 4 , \"PVSRIPO\" , 88.52 , \"1997.02\" , 551 , 211.4 , 1.88 , 35.52 , 39.1 , 4.71 , 74.05 , 10.21 , 1.738 , 2.706 , 113.6 , 1 , 1 , 1 , 0.5737 , 1 )\n

Thanks so much to #Daniel Molitor and #teunbrand! I had a general misunderstanding of how vjust functions. The following setting got the desired output: vjust = 0.65
Here is the code for the revised output, which is attached:
il1b <- ggplot(data = mouse) +
geom_violin(aes(x = Treatment, y = `IL-1b_fold`)) +
geom_jitter(aes(x = Treatment, y = `IL-1b_fold`)) +
theme_classic() +
ggtitle(label = "IL-1\u03b2") +
ylab("fold mock control") +
theme(plot.title = element_text(face = "bold"),
axis.title.x = element_text(face = "bold", size = 12),
axis.title.y = element_text(face = "bold", size = 12),
axis.text.x = element_text(face = "bold", angle = 45, size = 11, color = "black", vjust = 0.65),
axis.text.y = element_text(size = 11, color = "black"))
setwd(output)
ggsave("il1b.png", il1b, width = 5, height = 5, units = "in")
Revised output:

Related

Colour dots based on conditions in ggplot

I have this dataset
a <- data.frame(PatientID = c("0002" ,"0004", "0005", "0006" ,"0009" ,"0010" ,"0018", "0019" ,"0020" ,"0027", "0039" ,"0041" ,"0042", "0043" ,"0044" ,"0045", "0046", "0047" ,"0048" ,"0049", "0055"),
volume = c( 200 , 100 , 243 , 99 , 275, 675 ,345 , 234 , 333 ,444, 123 , 274 , 442 , 456 ,666 , 567 , 355 , 623 , 105 , 677 ,876),
Status= c("New" , "Old" , "New" , "New" , "Old", "New" ,"Old" , "New" , "Old" , "New" , "New" ,"New" ,"Old" , "New" ,"New" ,"Old" , "New" , "Old" , "New" , "Old" ,"Old"),
sex = c( 1 , 1 , 1 , 1 , 0, 0 ,0 , 0 , 0 ,1 , 1 , 1 , 0 , 0 ,1 , 1 , 1 , 1 , 1 , 1 ,1), stringsAsFactors = F)
and this code
color <- c("#00B7EB","#EE2A7B")
ggplot(a, aes(y = a$volume, x = a$Status, fill = a$Status)) +
geom_boxplot() +
geom_point(alpha=0.4) +
scale_fill_manual(values=color) +
labs(x='', y='Volume') +
theme_classic() +
theme( text = element_text( size = 15))
This, produces the following plot
THE QUESTION:
What can I do to colour the dots in this ggplot based on the following condition?:
If volume is >100 in women (sex==1) red, otherwise black
If volume is >200 in men (sex==0) red, otherwise black
Thank you so much!
One way to do this is by setting the colour aesthetic of geom_point to your condition:
geom_point(alpha=0.4, aes(colour = (sex == 1 & volume > 100) | (sex == 0 & volume > 200))) +
Then use scale_colour_manual to set the colours to red and black:
scale_colour_manual(values = c("black", "red")) +

The scale_fill_gradientn() successfully fills map but fails showing all colors in the legend

I am trying to create a map plot using ggplot2, I want to show the counties with 0 acreages with the color blue and higher acreage with a gradient from red to green color. I am able to achieve this in the map's fill-in colors, but the legend does not show the blue for the 0.0 acreage value. I pre-divided the color and values and used scale_fill_gradientn(), however the legend is not to success. I am very unsure of what is going wrong, help is highly appreciated. Please refer to the reproducible code below.
library(tidyverse)
library(BBmisc)
d <- data.frame(fips = c(10001 , 10003 , 10005 , 21001 , 21003 , 21005 , 21007 , 21009 , 21011 , 21015 , 21017 , 21021 , 21023 , 21027 , 21029 , 21031 , 21033 , 21035 , 21037 , 21039 , 21041 , 21043 , 21045 , 21047 , 21049 , 21053 , 21055 , 21057 , 21059 , 21061 , 21065 , 21067 , 21069 , 21073 , 21075 , 21077 , 21079 , 21081 , 21083 , 21085 , 21087 , 21089 , 21091 , 21093 , 21097 , 21099 , 21101 , 21103 , 21105 , 21107 , 21109 , 21111 , 21113 , 21117 , 21121 , 21123 , 21125 , 21135 , 21137 , 21139 , 21141 , 21143 , 21145 , 21147 , 21149 , 21151 , 21155 , 21157 , 21159 , 21161 , 21163 , 21165 , 21167 , 21169 , 21171 , 21173 , 21175 , 21177 , 21179 , 21181 , 21183 , 21185 , 21187 , 21191 , 21197 , 21199 , 21201 , 21203 , 21205 , 21207 , 21209 , 21211 , 21213 , 21215 , 21217 , 21219 , 21221 , 21223 , 21225 , 21227 , 21229 , 21231 , 21233 , 21235 , 21239 , 24001 , 24003 , 24005 , 24009 , 24011 , 24013 , 24015 , 24017 , 24019 , 24021 , 24023 , 24025 , 24027 , 24029 , 24031 , 24033 , 24035 , 24037 , 24039 , 24041 , 24043 , 24045 , 24047 , 37001 , 37003 , 37005 , 37007 , 37009 , 37011 , 37013 , 37015 , 37017 , 37019 , 37021 , 37023 , 37025 , 37027 , 37029 , 37031 , 37033 , 37035 , 37037 , 37039 , 37041 , 37043 , 37045 , 37047 , 37049 , 37051 , 37053 , 37055 , 37057 , 37059 , 37061 , 37063 , 37065 , 37067 , 37069 , 37071 , 37073 , 37077 , 37079 , 37081 , 37083 , 37085 , 37087 , 37089 , 37091 , 37093 , 37095 , 37097 , 37099 , 37101 , 37103 , 37105 , 37107 , 37109 , 37111 , 37113 , 37115 , 37117 , 37119 , 37123 , 37125 , 37127 , 37129 , 37131 , 37133 , 37135 , 37137 , 37139 , 37141 , 37143 , 37145 , 37147 , 37149 , 37151 , 37153 , 37155 , 37157 , 37159 , 37161 , 37163 , 37165 , 37167 , 37169 , 37171 , 37173 , 37175 , 37177 , 37179 , 37181 , 37183 , 37185 , 37187 , 37189 , 37191 , 37193 , 37195 , 37197 , 37199 , 42009 , 42011 , 42013 , 42017 , 42025 , 42027 , 42029 , 42037 , 42041 , 42043 , 42045 , 42055 , 42057 , 42061 , 42067 , 42071 , 42075 , 42077 , 42087 , 42089 , 42091 , 42093 , 42095 , 42097 , 42099 , 42101 , 42107 , 42109 , 42119 , 42133 , 51001 , 51003 , 51005 , 51007 , 51009 , 51011 , 51015 , 51017 , 51019 , 51021 , 51023 , 51025 , 51029 , 51031 , 51033 , 51035 , 51036 , 51037 , 51041 , 51043 , 51045 , 51047 , 51049 , 51051 , 51053 , 51057 , 51059 , 51061 , 51063 , 51065 , 51067 , 51069 , 51071 , 51073 , 51075 , 51077 , 51079 , 51081 , 51083 , 51085 , 51087 , 51089 , 51091 , 51093 , 51095 , 51097 , 51099 , 51101 , 51103 , 51105 , 51107 , 51109 , 51111 , 51113 , 51115 , 51117 , 51119 , 51121 , 51125 , 51127 , 51131 , 51133 , 51135 , 51137 , 51139 , 51141 , 51143 , 51145 , 51147 , 51149 , 51153 , 51155 , 51157 , 51159 , 51161 , 51163 , 51165 , 51167 , 51169 , 51171 , 51173 , 51175 , 51177 , 51179 , 51181 , 51183 , 51185 , 51187 , 51191 , 51193 , 51195 , 51197 , 51199 , 51515 , 51520 , 51530 , 51550 , 51590 , 51595 , 51620 , 51630 , 51640 , 51650 , 51660 , 51678 , 51680 , 51683 , 51700 , 51730 , 51740 , 51750 , 51770 , 51775 , 51790 , 51800 , 51810 , 51820 , 51840),
avg_area_acres = c(274826347 , 111810520 , 356958995 , 12225467 , 9850285 , 0 , 161517982 , 36513962 , 10627321 , 8500976 , 11011159 , 5127695 , 0 , 67608594 , 16920827 , 57891895 , 90677813 , 174713438 , 0 , 111737090 , 9175389 , 0 , 9109121 , 256162406 , 4402135 , 2327886 , 52759747 , 5463256 , 319309902 , 15382455 , 3036180 , 10039222 , 16030896 , 3197991 , 212521576 , 4046856 , 0 , 0 , 264745451 , 38377025 , 14006839 , 3338657 , 49178736 , 109703257 , 8824662 , 11173159 , 320523707 , 16273015 , 186374168 , 154012358 , 0 , 6070285 , 3642904 , 0 , 0 , 78336064 , 0 , 13759312 , 17568827 , 61018542 , 236490220 , 27424132 , 96956005 , 0 , 206767773 , 0 , 31173369 , 63229941 , 0 , 15128662 , 61045205 , 0 , 4586437 , 4128988 , 2226557 , 3643585 , 0 , 65578941 , 59265028 , 2428114 , 126995190 , 13925398 , 0 , 4209674 , 8296056 , 28642983 , 0 , 3710771 , 3339207 , 14816021 , 4856228 , 76817157 , 155771635 , 20563941 , 28336357 , 178198387 , 72704615 , 12853799 , 238138534 , 107598640 , 13661127 , 22993123 , 155120806 , 0 , 6206647 , 0 , 18314776 , 43712808 , 11738189 , 196002816 , 82823831 , 73270421 , 46040458 , 189830024 , 112293371 , 0 , 43395064 , 13963462 , 160870422 , 58778462 , 16594705 , 238589996 , 64963441 , 69212640 , 169997441 , 36023074 , 96909783 , 141439602 , 14451553 , 5627142 , 0 , 45419938 , 0 , 0 , 250418385 , 85662981 , 82094768 , 36026453 , 0 , 3060241 , 28376260 , 3218257 , 121200604 , 100712697 , 8906102 , 26029460 , 10120473 , 910542.7 , 29137366 , 890308.4 , 41413487 , 209038639 , 90114900 , 79827082 , 63393639 , 4586437 , 31454134 , 26271517 , 195279264 , 3895807 , 137398970 , 12913433 , 74078849 , 4301357 , 61049669 , 20523472 , 109219080 , 36594962 , 98945220 , 108001879 , 0 , 0 , 55219178 , 46797190 , 108652520 , 50683743 , 0 , 226125804 , 53359636 , 26675951 , 116590206 , 21697878 , 0 , 0 , 0 , 62506851 , 4857862 , 2175578 , 21050066 , 122578107 , 2495561 , 109878020 , 55702537 , 13561244 , 78614157 , 186377501 , 58814313 , 128087113 , 49549681 , 227994714 , 1315228 , 40116230 , 7958818 , 399060448 , 18095735 , 66551696 , 4047642 , 229363918 , 40847556 , 75376798 , 6880835 , 35299653 , 0 , 0 , 139744197 , 212121480 , 29671505 , 70598678 , 31292071 , 133027044 , 0 , 229286061 , 1854809 , 139092802 , 58819874 , 0 , 12750034 , 100796778 , 13602342 , 36732059 , 3508323 , 35866276 , 60717516 , 44689240 , 57780664 , 46795932 , 0 , 61018149 , 3238271 , 13053705 , 24693872 , 108004520 , 80801534 , 54323399 , 17708926 , 7993956 , 14673155 , 24285225 , 55048171 , 80636580 , 30279791 , 0 , 31268017 , 32222596 , 40641504 , 164917845 , 136580985 , 6476463 , 0 , 23802746 , 0 , 4654828 , 14087839 , 0 , 0 , 0 , 2023428 , 29851146 , 2428114 , 6315359 , 73594672 , 0 , 33032534 , 7083806 , 6962731 , 6396171 , 0 , 28336357 , 2226714 , 0 , 60721708 , 86062133 , 0 , 19673255 , 0 , 2428114 , 3374162 , 3136942 , 0 , 32708722 , 13196839 , 0 , 0 , 47560780 , 14006336 , 77238415 , 20642661 , 0 , 0 , 79749226 , 7014551 , 57969280 , 21351097 , 61288393 , 29446382 , 0 , 14370898 , 9958599 , 11536134 , 15545147 , 7589192 , 37162842 , 25584243 , 0 , 2430000 , 24936054 , 88654133 , 67926935 , 4453240 , 15464398 , 2529678 , 0 , 13035090 , 5505674 , 0 , 43800252 , 6638856 , 0 , 0 , 53434851 , 0 , 0 , 20806626 , 0 , 0 , 11820782 , 0 , 131809529 , 9067913 , 5424297 , 67685193 , 105413589 , 0 , 0 , 0 , 66551193 , 0 , 0 , 0 , 0 , 0 , 0 , 116260925 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 85624964 , 55463781 , 0 , 0))
st.id = unique(sapply(as.character(d$fip), function(x) substr(x, 1,2)))
us.map <- tigris::counties(state=st.id, cb = TRUE, year = 2016)
# Projuce counties map
county_map <- fortify(us.map, region="GEOID")
# Obtian state map, to build the state boundaries
#states_map <- map_data("state")
state_dat = tigris::states(cb = TRUE, year = 2016)
state_dat = state_dat[state_dat$STATEFP %in% c(st.id), ]
# Remove Alaska(2), Hawaii(15), Puerto Rico (72), Guam (66), Virgin Islands (78), American Samoa (60), Mariana Islands (69), Micronesia (64), Marshall Islands (68), Palau (70), Minor Islands (74)
state_dat <- state_dat[!state_dat$STATEFP %in% c("02", "15", "72", "66", "78", "60", "69", "64", "68", "70", "74"),]
# Make sure other outling islands are removed.
state_dat <- state_dat[!state_dat$STATEFP %in% c("81", "84", "86", "87", "89", "71", "76", "95", "79"),]
# Projuce state map
state_dat=fortify(state_dat, region = "STUSPS")
str_fipsselect <- 'all'
#str_fipsselect <- 'zero acres'
#str_fipsselect <- 'non-zero acres'
if(str_fipsselect == 'zero acres'){
d <- d %>% filter(avg_area_acres == 0)
} else if(str_fipsselect == 'non-zero acres'){
d <- d %>% filter(avg_area_acres > 0)
}
d$norm_avg_area_acres = normalize(d$avg_area_acres, method = "range")
if (min(d$avg_area_acres) <= 0){
c_cols = c("blue","red","green")
v_cols = c(as.numeric(d[which(d$avg_area_acres==min(d$avg_area_acres))[1],"norm_avg_area_acres"]),
as.numeric(d[which(d$avg_area_acres == min(d$avg_area_acres[d$avg_area_acres!=min(d$avg_area_acres)]))[1], "norm_avg_area_acres"][1]),
as.numeric(d[which(d$avg_area_acres==max(d$avg_area_acres))[1],"norm_avg_area_acres"]))
} else {
c_cols = c("green", "red")
v_cols = c(as.numeric(d[which(d$avg_area_acres==min(d$avg_area_acres))[1],"norm_avg_area_acres"]),
as.numeric(d[which(d$avg_area_acres==max(d$avg_area_acres))[1],"norm_avg_area_acres"]))
}
ggplot() +
geom_polygon(data=state_dat,
aes(x=long, y=lat, group=group), fill="gray94", color=NA, show.legend = FALSE) + # this is for shade within state boundaries
geom_polygon(data=state_dat,
aes(x=long, y=lat, group=group), fill=NA, color="gray25", size=0.1, show.legend = FALSE) + # this is for state boundaries
geom_map(aes(fill=avg_area_acres, map_id = fips),
data = d, map=county_map, color="palevioletred3", size=0.05, show.legend = TRUE) + # this is to show the FIPS which are under study
scale_fill_gradientn(colors = adjustcolor(c_cols, alpha.f = 0.7),
values = v_cols,
labels = function(x) format(x, digits = 2, scientific = TRUE),
name = "Average acres") +
coord_map()
enter image description here
It seems that the issue is more to do with visualizing such a small block of color in the color_bar. The color_bar guide can help you tweak this.Two potential options:
Lengthen the color bar
You can lengthen the color bar to make the blue more visible. The example below is a bit ridiculous but you can play around with adjusting the color_bar height and the size of the overall plot.
ggplot() +
geom_polygon(data=state_dat,
aes(x=long, y=lat, group=group), fill="gray94", color=NA, show.legend = FALSE) + # this is for shade within state boundaries
geom_polygon(data=state_dat,
aes(x=long, y=lat, group=group), fill=NA, color="gray25", size=0.1, show.legend = FALSE) + # this is for state boundaries
geom_map(aes(fill=avg_area_acres, map_id = fips),
data = d, map=county_map, color="palevioletred3", size=0.05, show.legend = TRUE) + # this is to show the FIPS which are under study
scale_fill_gradientn(colors = adjustcolor(c_cols, alpha.f = 0.7),
values = v_cols,
labels = function(x) format(x, digits = 2, scientific = TRUE),
name = "Average acres",
guide = guide_colourbar(barheight = 40)) +
coord_map()
Adjust the bins in the colorbar
This will give more weight to the blue portion by reducing the number of nbin. It is kind of a misrepresentation of your data though, since you have a sharp break between 0 and those values above 0. Using something like color_step or a binned fill scale may be another way to look into this, depending on the distribution of your data.
ggplot() +
geom_polygon(data=state_dat,
aes(x=long, y=lat, group=group), fill="gray94", color=NA, show.legend = FALSE) + # this is for shade within state boundaries
geom_polygon(data=state_dat,
aes(x=long, y=lat, group=group), fill=NA, color="gray25", size=0.1, show.legend = FALSE) + # this is for state boundaries
geom_map(aes(fill=avg_area_acres, map_id = fips),
data = d, map=county_map, color="palevioletred3", size=0.05, show.legend = TRUE) + # this is to show the FIPS which are under study
scale_fill_gradientn(colors = adjustcolor(c_cols, alpha.f = 0.7),
values = v_cols,
labels = function(x) format(x, digits = 2, scientific = TRUE),
name = "Average acres",
guide = guide_colourbar(barheight = 10, nbin = 15)) +
coord_map()

What is wrong with my custom colour palette in this plot?

Using ggsurvplot to draw some Kaplan-Meier curves.
5 curves should be plotted and I want control over their colours.
Here is the output of the survfit being plotted:
> elective_30Decadesurv
Call: survfit(formula = elective30Surv ~ electives$Decade)
n events median 0.95LCL 0.95UCL
electives$Decade=50 14 0 NA NA NA
electives$Decade=60 173 2 NA NA NA
electives$Decade=70 442 5 NA NA NA
electives$Decade=80 168 4 NA NA NA
electives$Decade=90 2 0 NA NA NA
Here is a working plot using the default colour palette, "hue":
> ggsurvplot(elective_30Decadesurv,
data = electives,
palette = "hue",
title = "30 day survival after elective EVAR",
legend = "none",
legend.title = "Decade",
legend.labs = c("5th",
"6th",
"7th",
"8th",
"9th"
),
censor.shape = 124,
ggtheme = survPlotTheme,
risk.table = "nrisk_cumevents",
risk.table.y.text.col = TRUE,
risk.table.fontsize = 3,
risk.table.height = 0.3,
break.time.by = 5,
ylim = c(0.95,
1
),
pval = TRUE,
pval.size = 3,
pval.coord = c(1,
0.96
)
)
See plot in section 3.1.4 of this webpage for the output of the above
The Decade group has 5 entries, so I'm trying to provide five colours to palette.
However, both:
> ggsurvplot(elective_30Decadesurv,
data = electives,
palette = c("#440154",
"#3B528B",
"#21908C",
"#5DC863",
"#5DC863"
),
title = "30 day survival after elective EVAR",
legend = "none",
legend.title = "Decade",
legend.labs = c("5th",
"6th",
"7th",
"8th",
"9th"
),
censor.shape = 124,
ggtheme = survPlotTheme,
risk.table = "nrisk_cumevents",
risk.table.y.text.col = TRUE,
risk.table.fontsize = 3,
risk.table.height = 0.3,
break.time.by = 5,
ylim = c(0.95,
1
),
pval = TRUE,
pval.size = 3,
pval.coord = c(1,
0.96
)
)
And:
> fiveColours <- c("#440154",
"#3B528B",
"#21908C",
"#5DC863",
"#5DC863"
)
> ggsurvplot(elective_30Decadesurv,
data = electives,
palette = fiveColours,
title = "30 day survival after elective EVAR",
legend = "none",
legend.title = "Decade",
legend.labs = c("5th",
"6th",
"7th",
"8th",
"9th"
),
censor.shape = 124,
ggtheme = survPlotTheme,
risk.table = "nrisk_cumevents",
risk.table.y.text.col = TRUE,
risk.table.fontsize = 3,
risk.table.height = 0.3,
break.time.by = 5,
ylim = c(0.95,
1
),
pval = TRUE,
pval.size = 3,
pval.coord = c(1,
0.96
)
)
Give the same error:
Error in names(.cols) <- grp.levels :
'names' attribute [5] must be the same length as the vector [4]
What vector is length [4]?
Is 'names' attribute my colour vector?
If I take one of the colours out of the custom palette, eg fiveColours <- c("#440154","#3B528B","#21908C","#5DC863") I get this error:
Error: Insufficient values in manual scale. 5 needed but only 4 provided.
Which implies the number of colours provided is correct but something else is causing the issue.
I've troubleshot to the limits of my own ability. Help please!
FYI:
> electives %>% select(Decade) %>% group_by(Decade) %>% summarise(n())
# A tibble: 5 x 2
Decade `n()`
<fct> <int>
1 50 14
2 60 173
3 70 442
4 80 168
5 90 2
Should prove the length of the Decade variable and here is how the survival object and survfit were generated:
> elective5Surv <- Surv(electives$surv5Y, electives$dead5Y)
> elective_5Decadesurv <- survfit(elective5Surv ~ electives$Decade)
Ok, I have sorted my own mistake by proof-reading!
Of the five hex colours I’d provided, two were identical (not on purpose.)
I changed the fifth colour to a different hex value (what it was meant to be in the first place) and it works now.
Thanks, Rui, for your response earlier, it helped me down the path!

Scaling radar plot for large value in R(with the data)

I have a data set for which I want to create a radar plot
str(trial4)
'data.frame': 4 obs. of 6 variables:
$ Var1 : Factor w/ 4 levels "2009-04-01","2010-04-01",..: 1 2 3 4
$ Arsenic : int 66 8 8 5
$ Fluoride: int 11775 10788 10724 7130
$ Iron : int 103 60 54 46
$ Nitrate : int 927 856 1106 624
$ Salinity: int 24787 23168 20258 18924
My radar plot code executes fine and I get the plot like this
radarchart(trial4,seg=6)
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
radarchart( trial4 , axistype=1 ,
#custom polygon
pcol=colors_border , pfcol=colors_in , plwd=2 , plty=1,
#custom the grid
cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8,
#custom labels
vlcex=0.8
)
legend(x=0.7, y=1, legend = rownames(trial4[-c(1,2),]), bty = "n", pch=20 , col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)
As one can see, the problem is the scaling I cannot fit in the plot. I searched online, but it's not clear how I can make the polygon fit inside the grid of the radar plot. I tried adding xlim and ylim:
radarchart(trial4,seg=6)
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
radarchart( trial4 , axistype=1 ,
#custom polygon
pcol=colors_border , pfcol=colors_in , plwd=2 , plty=1,
#custom the grid
cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8,
#custom labels
vlcex=0.8,xlim=c(0,2),ylim=c(0,10)
)
legend(x=0.7, y=1, legend = rownames(trial4[-c(1,2),]), bty = "n", pch=20 , col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)
but still I am not able to scale this properly.
Is there any parameter I am missing?.
so here is my data , and the package i am using is fmsb
var1<-c("2009-04-01","2010-04-01","2011-04-01","2012-04-01")
Arsenic<-c(66,8,8,5)
Fluoride<-c(11775,10788,10724,7130)
Iron<-c(103,60,54,46)
Nitrate<-c(927,856,1106,624)
Salinity<-c(24787,23168,20258,18924)
trail4<-as.data.frame(var1,Arsenic,Fluoride,Iron,Nitrate,Salinity)

Superimpose posterior distribution on mean like cat's eye visualization from cumming

I personally like the cat's eye visualization of Cumming that superimposes a sampling distribution over a point estimate:
I would also like to do this with the posterior distribution that is obtained by the Scripts of Kruschke (2015):
plotMCMC( mcmcCoda , data=myData ,
compValMu=100.0 , ropeMu=c(99.0,101.0) ,
compValSigma=15.0 , ropeSigma=c(14,16) ,
compValEff=0.0 , ropeEff=c(-0.1,0.1) ,
pairsPlot=TRUE , showCurve=FALSE ,
saveName=fileNameRoot , saveType=graphFileType )
# Set up window and layout:
openGraph(width=6.0,height=8.0*3/5)
layout( matrix( c(2,3,5, 1,4,6) , nrow=3, byrow=FALSE ) )
par( mar=c(3.5,3.5,2.5,0.5) , mgp=c(2.25,0.7,0) )
# Select thinned steps in chain for plotting of posterior predictive curves:
nCurvesToPlot = 20
stepIdxVec = seq( 1 , chainLength , floor(chainLength/nCurvesToPlot) )
# Compute limits for plots of data with posterior pred. distributions
y = data
xLim = c( min(y)-0.1*(max(y)-min(y)) , max(y)+0.1*(max(y)-min(y)) )
xBreaks = seq( xLim[1] , xLim[2] ,
length=ceiling((xLim[2]-xLim[1])/(sd(y)/4)) )
histInfo = hist(y,breaks=xBreaks,plot=FALSE)
yMax = 1.2 * max( histInfo$density )
xVec = seq( xLim[1] , xLim[2] , length=501 )
#-----------------------------------------------------------------------------
# Plot data y and smattering of posterior predictive curves:
histInfo = hist( y , prob=TRUE , xlim=xLim , ylim=c(0,yMax) , breaks=xBreaks,
col="red2" , border="white" , xlab="y" , ylab="" ,
yaxt="n" , cex.lab=1.5 , main="Data w. Post. Pred." )
for ( stepIdx in 1:length(stepIdxVec) ) {
lines(xVec, dt( (xVec-mu[stepIdxVec[stepIdx]])/sigma[stepIdxVec[stepIdx]],
df=nu[stepIdxVec[stepIdx]] )/sigma[stepIdxVec[stepIdx]] ,
type="l" , col="skyblue" , lwd=1 )
}
text( max(xVec) , yMax , bquote(N==.(length(y))) , adj=c(1.1,1.1) )
#-----------------------------------------------------------------------------
histInfo = plotPost( mu , cex.lab = 1.75 , showCurve=showCurve ,
compVal=compValMu , ROPE=ropeMu ,
xlab=bquote(mu) , main=paste("Mean") ,
col="skyblue" )
#-----------------------------------------------------------------------------
histInfo = plotPost( sigma , cex.lab=1.75 , showCurve=showCurve ,
compVal=compValSigma , ROPE=ropeSigma , cenTend="mode" ,
xlab=bquote(sigma) , main=paste("Scale") ,
col="skyblue" )
#-----------------------------------------------------------------------------
effectSize = ( mu - compValMu ) / sigma
histInfo = plotPost( effectSize , compVal=compValEff , ROPE=ropeEff ,
showCurve=showCurve , cenTend="mode" ,
xlab=bquote( ( mu - .(compValMu) ) / sigma ),
cex.lab=1.75 , main="Effect Size" ,
col="skyblue" )
#-----------------------------------------------------------------------------
postInfo = plotPost( log10(nu) , col="skyblue" , # breaks=30 ,
showCurve=showCurve ,
xlab=bquote("log10("*nu*")") , cex.lab = 1.75 ,
cenTend="mode" ,
main="Normality" ) # (<0.7 suggests kurtosis)
Which looks in the end like this for the mean
Is it possible to superimpose the histogram on the mean like Cumming does it?
I've created a function called plotViolin for another application that should do what you want, or at least be easily modifiable to the specifics of your application. You can find the function about half way down the R script linked here: https://osf.io/wt4vf/

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