How to assigning distinct line color for graph?

rexp088 · April 28, 2022, 1:12pm

Hi all, I wanted to have a distinct color of my lines but instead I GOT all the same colored blue.

library(reshape2)
library(wesanderson)

library(rstatix)
library(ggpubr)
library(plyr)
library(datarium)
library(summariser)
library(ggrepel)



#C10

LC10run123 <- read.csv("~/R/Data Analysis/MSc ARTs/Summary/March 2022/lachrymansC10RawPur.csv", stringsAsFactors=TRUE)

mutatedata <- LC10run123 %>% mutate_at(vars("con", "bgrowth", "time", "Purification"), factor)

mutatedata2 <- melt(mutatedata, id.vars=c("con","bgrowth","time", "Purification"),
                    measure.vars="bgrowth")

mutatedata3 <- aggregate(bgrowth ~ con + time + Purification, LC10run123, mean)
# mutatedata3 <- aggregate(bgrowth~ con + time + Purification, LC10run123, sd)

str(mutatedata3)

# fit <- lm(bgrowth~time*con*Purification,data= mutatedata3)
# summary(fit)

ggplot(mutatedata3, 
       aes(x = time, y = bgrowth, group  = con, hue=con))+
      # aes(x = time, y = bgrowth, group  = con))+
  geom_point()+
  # geom_text(data = filter(mutatedata3, time == max(time)),
  #           aes(label = con),
  #           hjust = 0, nudge_x = 3.0) +
  # geom_smooth(formula = y~x+ I(x^2), method=lm , se=FALSE, show.legend = TRUE, aes(color= "red", fill="#69b3a1")) +
  # geom_smooth(formula = y~log(x), method='loess' , se=TRUE, show.legend = TRUE, aes(color= "red", fill="#69b3a1")) +
  geom_smooth(formula = y~x, method=loess , se=TRUE, show.legend = TRUE, span=0.5) +
  # geom_smooth(formula = y~x+I(x^2), method=loess, se=FALSE, show.legend = TRUE, span=0.5) +
  
  stat_smooth()+
  # geom_smooth(formula = y~x+ I(x^2), method=lm , se=FALSE, show.legend = TRUE, aes(color= "red", fill="#69b3a1")) +
  # geom_smooth(formula = y~x+ I(x^2), method=lm , color="red", fill="#69b3a2", se=FALSE, show.legend = TRUE) +
  # annotate("text", label = "y~x", size = 4, x = 96, y = ave(bgrowth))+
  annotate("text",x=72,y=0.5,hjust=0,label="y~x", size=4)+
  
  facet_wrap(~Purification) +
  theme_bw()+
  theme(panel.border = element_blank(),
        panel.background = element_rect(color = "black"),
        strip.background = element_rect(fill = "darkgray"),
        strip.text.x = element_text(face="bold", size=12),
        legend.title=element_text(face="bold"),
        axis.text=element_text(size=12,colour = "black"),
        axis.title.y =element_text(size=12,face="bold", margin = margin(r=7)),
        axis.title.x =element_text(size=12,face="bold", margin = margin(t=7)),
        strip.text = element_text(size=12, face="bold", color="black"))+
  # scale_color_manual(values = c("0" = "black", "5" = "red", "10"= "purple",
  #                               "25"="blue", "50"= "green", "100"= "orange",
  #                               "250"= "skyblue", "500"="maroon", "750"="violet"))+
  scale_y_continuous(breaks = seq(0.1,1, by=0.1)) +
  scale_x_continuous(breaks= seq(0,96, by=24))+
  # scale_color_discrete()+
  
  
  xlab("Time(Hours)") +
  ylab("Bacterial growth (595 nm wavelength)")

Sanjmeh · April 28, 2022, 3:38pm

Instead of using a csv file for the reprex (for which you have to enclose the file plus it takes several steps to execute the code in another's machine), it is usually a better practice to include representative data and paste it in the code using dput. That will ensure the code and data are both available together for anyone to be able to execute in their machine. Note: reprentative data is the bare minimum data to replicate the problem.

rexp088 · April 28, 2022, 9:27pm

Hallo Sanjmeh, thanks for the comment and suggestions.

I really do not know much about how to insert the file data but maybe I can send it to you here like uploading it, is it fit?

time	con	bgrowth	Purification	RhlType
0	0	0.054077777	RAW	C14
0	0	0.0685	RAW	C14
0	0	0.026744443	RAW	C14
0	5	0.053544443	RAW	C14
0	5	0.068288892	RAW	C14
0	5	0.025900001	RAW	C14
0	10	0.050799998	RAW	C14
0	10	0.06631111	RAW	C14
0	10	0.02448889	RAW	C14
0	25	0.053177776	RAW	C14
0	25	0.067511109	RAW	C14
0	25	0.025344443	RAW	C14
0	50	0.054233333	RAW	C14
0	50	0.071433335	RAW	C14
0	50	0.0257	RAW	C14
0	100	0.055088885	RAW	C14
0	100	0.071733333	RAW	C14
0	100	0.026511111	RAW	C14
0	250	0.05564444	RAW	C14
0	250	0.067944443	RAW	C14
0	250	0.026433334	RAW	C14
0	500	0.058188888	RAW	C14
0	500	0.068177779	RAW	C14
0	500	0.025955555	RAW	C14
0	750	0.056088885	RAW	C14
0	750	0.069733334	RAW	C14
0	750	0.026511111	RAW	C14
24	0	0.635544441	RAW	C14
24	0	0.514433345	RAW	C14
24	0	0.490211104	RAW	C14
24	5	0.854355555	RAW	C14
24	5	0.704055555	RAW	C14
24	5	0.699011101	RAW	C14
24	10	0.859155551	RAW	C14
24	10	0.727388886	RAW	C14
24	10	0.717944437	RAW	C14
24	25	0.873033333	RAW	C14
24	25	0.733611114	RAW	C14
24	25	0.741766666	RAW	C14
24	50	0.875222214	RAW	C14
24	50	0.726455557	RAW	C14
24	50	0.742722214	RAW	C14
24	100	0.87636667	RAW	C14
24	100	0.73375555	RAW	C14
24	100	0.753133338	RAW	C14
24	250	0.874999995	RAW	C14
24	250	0.727300002	RAW	C14
24	250	0.749855566	RAW	C14
24	500	0.869066664	RAW	C14
24	500	0.743155553	RAW	C14
24	500	0.755233328	RAW	C14
24	750	0.861655548	RAW	C14
24	750	0.746566667	RAW	C14
24	750	0.740066675	RAW	C14
48	0	0.745566663	RAW	C14
48	0	0.672088886	RAW	C14
48	0	0.539477775	RAW	C14
48	5	0.916099995	RAW	C14
48	5	0.729022223	RAW	C14
48	5	0.726077765	RAW	C14
48	10	0.89887777	RAW	C14
48	10	0.708488893	RAW	C14
48	10	0.688611113	RAW	C14
48	25	0.904488885	RAW	C14
48	25	0.707955564	RAW	C14
48	25	0.746277766	RAW	C14
48	50	0.898722221	RAW	C14
48	50	0.718977787	RAW	C14
48	50	0.695800003	RAW	C14
48	100	0.907077779	RAW	C14
48	100	0.720633333	RAW	C14
48	100	0.740911096	RAW	C14
48	250	0.909433328	RAW	C14
48	250	0.712455556	RAW	C14
48	250	0.722533342	RAW	C14
48	500	0.908500006	RAW	C14
48	500	0.722133343	RAW	C14
48	500	0.733944452	RAW	C14
48	750	0.905811114	RAW	C14
48	750	0.748233337	RAW	C14
48	750	0.735788872	RAW	C14
72	0	0.763200004	RAW	C14
72	0	0.654966658	RAW	C14
72	0	0.506355549	RAW	C14
72	5	0.882088879	RAW	C14
72	5	0.724333325	RAW	C14
72	5	0.578622227	RAW	C14
72	10	0.864133331	RAW	C14
72	10	0.696800006	RAW	C14
72	10	0.573511109	RAW	C14
72	25	0.879533343	RAW	C14
72	25	0.701866659	RAW	C14
72	25	0.591622225	RAW	C14
72	50	0.875988887	RAW	C14
72	50	0.71194444	RAW	C14
72	50	0.591133321	RAW	C14
72	100	0.867644461	RAW	C14
72	100	0.721133337	RAW	C14
72	100	0.596522217	RAW	C14
72	250	0.870155559	RAW	C14
72	250	0.716477784	RAW	C14
72	250	0.590666663	RAW	C14
72	500	0.876722222	RAW	C14
72	500	0.725299993	RAW	C14
72	500	0.589766666	RAW	C14
72	750	0.87406667	RAW	C14
72	750	0.749733321	RAW	C14
72	750	0.598522224	RAW	C14
96	0	0.774755555	RAW	C14
96	0	0.650211121	RAW	C14
96	0	0.489066659	RAW	C14
96	5	0.858644443	RAW	C14
96	5	0.756199994	RAW	C14
96	5	0.555111109	RAW	C14
96	10	0.834744444	RAW	C14
96	10	0.724644447	RAW	C14
96	10	0.54858889	RAW	C14
96	25	0.84894445	RAW	C14
96	25	0.713277775	RAW	C14
96	25	0.571344434	RAW	C14
96	50	0.838288881	RAW	C14
96	50	0.711677781	RAW	C14
96	50	0.567144432	RAW	C14
96	100	0.843344441	RAW	C14
96	100	0.712166665	RAW	C14
96	100	0.574666657	RAW	C14
96	250	0.832666666	RAW	C14
96	250	0.703177782	RAW	C14
96	250	0.57164443	RAW	C14
96	500	0.832288885	RAW	C14
96	500	0.710699994	RAW	C14
96	500	0.569555546	RAW	C14
96	750	0.828622219	RAW	C14
96	750	0.755966662	RAW	C14
96	750	0.570777772	RAW	C14
0	0	0.054077777	PUR	C14
0	0	0.0685	PUR	C14
0	0	0.026744443	PUR	C14
0	5	0.051088887	PUR	C14
0	5	0.071566667	PUR	C14
0	5	0.028622222	PUR	C14
0	10	0.051411111	PUR	C14
0	10	0.070766665	PUR	C14
0	10	0.025388888	PUR	C14
0	25	0.050644443	PUR	C14
0	25	0.067944443	PUR	C14
0	25	0.026144444	PUR	C14
0	50	0.053177775	PUR	C14
0	50	0.068122222	PUR	C14
0	50	0.025433333	PUR	C14
0	100	0.052988886	PUR	C14
0	100	0.066388886	PUR	C14
0	100	0.025122223	PUR	C14
0	250	0.054533332	PUR	C14
0	250	0.065788891	PUR	C14
0	250	0.028088888	PUR	C14
0	500	0.056088887	PUR	C14
0	500	0.066511112	PUR	C14
0	500	0.02512222	PUR	C14
0	750	0.052677778	PUR	C14
0	750	0.070700002	PUR	C14
0	750	0.026266667	PUR	C14
24	0	0.635544441	PUR	C14
24	0	0.514433345	PUR	C14
24	0	0.490211104	PUR	C14
24	5	0.858433341	PUR	C14
24	5	0.72485555	PUR	C14
24	5	0.741488881	PUR	C14
24	10	0.870144448	PUR	C14
24	10	0.751644453	PUR	C14
24	10	0.751744457	PUR	C14
24	25	0.870411106	PUR	C14
24	25	0.737722225	PUR	C14
24	25	0.764088896	PUR	C14
24	50	0.873577782	PUR	C14
24	50	0.716822228	PUR	C14
24	50	0.756466668	PUR	C14
24	100	0.878266667	PUR	C14
24	100	0.728988893	PUR	C14
24	100	0.753377783	PUR	C14
24	250	0.87857777	PUR	C14
24	250	0.719355551	PUR	C14
24	250	0.762000005	PUR	C14
24	500	0.873522217	PUR	C14
24	500	0.715288885	PUR	C14
24	500	0.747466671	PUR	C14
24	750	0.867288883	PUR	C14
24	750	0.749999994	PUR	C14
24	750	0.761455557	PUR	C14
48	0	0.745566663	PUR	C14
48	0	0.672088886	PUR	C14
48	0	0.539477775	PUR	C14
48	5	0.905388882	PUR	C14
48	5	0.728644442	PUR	C14
48	5	0.766877787	PUR	C14
48	10	0.904688885	PUR	C14
48	10	0.719555548	PUR	C14
48	10	0.73775554	PUR	C14
48	25	0.893633329	PUR	C14
48	25	0.711277781	PUR	C14
48	25	0.607800004	PUR	C14
48	50	0.896177785	PUR	C14
48	50	0.697033344	PUR	C14
48	50	0.66645555	PUR	C14
48	100	0.904966669	PUR	C14
48	100	0.703944443	PUR	C14
48	100	0.670399987	PUR	C14
48	250	0.903588894	PUR	C14
48	250	0.681866664	PUR	C14
48	250	0.650077777	PUR	C14
48	500	0.898488883	PUR	C14
48	500	0.678055556	PUR	C14
48	500	0.635422213	PUR	C14
48	750	0.894688888	PUR	C14
48	750	0.725622215	PUR	C14
48	750	0.70873335	PUR	C14
72	0	0.763200004	PUR	C14
72	0	0.654966658	PUR	C14
72	0	0.506355549	PUR	C14
72	5	0.859622219	PUR	C14
72	5	0.705811102	PUR	C14
72	5	0.597322224	PUR	C14
72	10	0.864322224	PUR	C14
72	10	0.697311109	PUR	C14
72	10	0.593388887	PUR	C14
72	25	0.854033343	PUR	C14
72	25	0.693455556	PUR	C14
72	25	0.592122215	PUR	C14
72	50	0.850200003	PUR	C14
72	50	0.683333343	PUR	C14
72	50	0.588788892	PUR	C14
72	100	0.853500001	PUR	C14
72	100	0.692877769	PUR	C14
72	100	0.585011103	PUR	C14
72	250	0.855922215	PUR	C14
72	250	0.682633326	PUR	C14
72	250	0.604422217	PUR	C14
72	500	0.857988887	PUR	C14
72	500	0.680855552	PUR	C14
72	500	0.584433329	PUR	C14
72	750	0.852044442	PUR	C14
72	750	0.729500002	PUR	C14
72	750	0.599611109	PUR	C14
96	0	0.774755555	PUR	C14
96	0	0.650211121	PUR	C14
96	0	0.489066659	PUR	C14
96	5	0.846777774	PUR	C14
96	5	0.708088893	PUR	C14
96	5	0.570877769	PUR	C14
96	10	0.846611107	PUR	C14
96	10	0.693044449	PUR	C14
96	10	0.570133327	PUR	C14
96	25	0.848066665	PUR	C14
96	25	0.683800007	PUR	C14
96	25	0.569522227	PUR	C14
96	50	0.835011109	PUR	C14
96	50	0.673044435	PUR	C14
96	50	0.56244444	PUR	C14
96	100	0.82808889	PUR	C14
96	100	0.679744447	PUR	C14
96	100	0.561466666	PUR	C14
96	250	0.823666663	PUR	C14
96	250	0.666788888	PUR	C14
96	250	0.578866666	PUR	C14
96	500	0.816311099	PUR	C14
96	500	0.675900007	PUR	C14
96	500	0.562899998	PUR	C14
96	750	0.815122211	PUR	C14
96	750	0.732355553	PUR	C14
96	750	0.574244445	PUR	C14

I hope this will solve my problem.

Thank you so much.

All the best, Rex.

Matthias · April 29, 2022, 8:14am

okay, it works with the table but as indicated before it's better with dput()

dput(LC10run123)

LC10run123 = structure(list(time = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 
48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 
48, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 96, 96, 96, 96, 
96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 
96, 96, 96, 96, 96, 96, 96, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
24, 24, 24, 24, 24, 24, 24, 48, 48, 48, 48, 48, 48, 48, 48, 48, 
48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 
48, 48, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 96, 96, 96, 
96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 
96, 96, 96, 96, 96, 96, 96, 96), con = c(0, 0, 0, 5, 5, 5, 10, 
10, 10, 25, 25, 25, 50, 50, 50, 100, 100, 100, 250, 250, 250, 
500, 500, 500, 750, 750, 750, 0, 0, 0, 5, 5, 5, 10, 10, 10, 25, 
25, 25, 50, 50, 50, 100, 100, 100, 250, 250, 250, 500, 500, 500, 
750, 750, 750, 0, 0, 0, 5, 5, 5, 10, 10, 10, 25, 25, 25, 50, 
50, 50, 100, 100, 100, 250, 250, 250, 500, 500, 500, 750, 750, 
750, 0, 0, 0, 5, 5, 5, 10, 10, 10, 25, 25, 25, 50, 50, 50, 100, 
100, 100, 250, 250, 250, 500, 500, 500, 750, 750, 750, 0, 0, 
0, 5, 5, 5, 10, 10, 10, 25, 25, 25, 50, 50, 50, 100, 100, 100, 
250, 250, 250, 500, 500, 500, 750, 750, 750, 0, 0, 0, 5, 5, 5, 
10, 10, 10, 25, 25, 25, 50, 50, 50, 100, 100, 100, 250, 250, 
250, 500, 500, 500, 750, 750, 750, 0, 0, 0, 5, 5, 5, 10, 10, 
10, 25, 25, 25, 50, 50, 50, 100, 100, 100, 250, 250, 250, 500, 
500, 500, 750, 750, 750, 0, 0, 0, 5, 5, 5, 10, 10, 10, 25, 25, 
25, 50, 50, 50, 100, 100, 100, 250, 250, 250, 500, 500, 500, 
750, 750, 750, 0, 0, 0, 5, 5, 5, 10, 10, 10, 25, 25, 25, 50, 
50, 50, 100, 100, 100, 250, 250, 250, 500, 500, 500, 750, 750, 
750, 0, 0, 0, 5, 5, 5, 10, 10, 10, 25, 25, 25, 50, 50, 50, 100, 
100, 100, 250, 250, 250, 500, 500, 500, 750, 750, 750), bgrowth = c(0.054077777, 
0.0685, 0.026744443, 0.053544443, 0.068288892, 0.025900001, 0.050799998, 
0.06631111, 0.02448889, 0.053177776, 0.067511109, 0.025344443, 
0.054233333, 0.071433335, 0.0257, 0.055088885, 0.071733333, 0.026511111, 
0.05564444, 0.067944443, 0.026433334, 0.058188888, 0.068177779, 
0.025955555, 0.056088885, 0.069733334, 0.026511111, 0.635544441, 
0.514433345, 0.490211104, 0.854355555, 0.704055555, 0.699011101, 
0.859155551, 0.727388886, 0.717944437, 0.873033333, 0.733611114, 
0.741766666, 0.875222214, 0.726455557, 0.742722214, 0.87636667, 
0.73375555, 0.753133338, 0.874999995, 0.727300002, 0.749855566, 
0.869066664, 0.743155553, 0.755233328, 0.861655548, 0.746566667, 
0.740066675, 0.745566663, 0.672088886, 0.539477775, 0.916099995, 
0.729022223, 0.726077765, 0.89887777, 0.708488893, 0.688611113, 
0.904488885, 0.707955564, 0.746277766, 0.898722221, 0.718977787, 
0.695800003, 0.907077779, 0.720633333, 0.740911096, 0.909433328, 
0.712455556, 0.722533342, 0.908500006, 0.722133343, 0.733944452, 
0.905811114, 0.748233337, 0.735788872, 0.763200004, 0.654966658, 
0.506355549, 0.882088879, 0.724333325, 0.578622227, 0.864133331, 
0.696800006, 0.573511109, 0.879533343, 0.701866659, 0.591622225, 
0.875988887, 0.71194444, 0.591133321, 0.867644461, 0.721133337, 
0.596522217, 0.870155559, 0.716477784, 0.590666663, 0.876722222, 
0.725299993, 0.589766666, 0.87406667, 0.749733321, 0.598522224, 
0.774755555, 0.650211121, 0.489066659, 0.858644443, 0.756199994, 
0.555111109, 0.834744444, 0.724644447, 0.54858889, 0.84894445, 
0.713277775, 0.571344434, 0.838288881, 0.711677781, 0.567144432, 
0.843344441, 0.712166665, 0.574666657, 0.832666666, 0.703177782, 
0.57164443, 0.832288885, 0.710699994, 0.569555546, 0.828622219, 
0.755966662, 0.570777772, 0.054077777, 0.0685, 0.026744443, 0.051088887, 
0.071566667, 0.028622222, 0.051411111, 0.070766665, 0.025388888, 
0.050644443, 0.067944443, 0.026144444, 0.053177775, 0.068122222, 
0.025433333, 0.052988886, 0.066388886, 0.025122223, 0.054533332, 
0.065788891, 0.028088888, 0.056088887, 0.066511112, 0.02512222, 
0.052677778, 0.070700002, 0.026266667, 0.635544441, 0.514433345, 
0.490211104, 0.858433341, 0.72485555, 0.741488881, 0.870144448, 
0.751644453, 0.751744457, 0.870411106, 0.737722225, 0.764088896, 
0.873577782, 0.716822228, 0.756466668, 0.878266667, 0.728988893, 
0.753377783, 0.87857777, 0.719355551, 0.762000005, 0.873522217, 
0.715288885, 0.747466671, 0.867288883, 0.749999994, 0.761455557, 
0.745566663, 0.672088886, 0.539477775, 0.905388882, 0.728644442, 
0.766877787, 0.904688885, 0.719555548, 0.73775554, 0.893633329, 
0.711277781, 0.607800004, 0.896177785, 0.697033344, 0.66645555, 
0.904966669, 0.703944443, 0.670399987, 0.903588894, 0.681866664, 
0.650077777, 0.898488883, 0.678055556, 0.635422213, 0.894688888, 
0.725622215, 0.70873335, 0.763200004, 0.654966658, 0.506355549, 
0.859622219, 0.705811102, 0.597322224, 0.864322224, 0.697311109, 
0.593388887, 0.854033343, 0.693455556, 0.592122215, 0.850200003, 
0.683333343, 0.588788892, 0.853500001, 0.692877769, 0.585011103, 
0.855922215, 0.682633326, 0.604422217, 0.857988887, 0.680855552, 
0.584433329, 0.852044442, 0.729500002, 0.599611109, 0.774755555, 
0.650211121, 0.489066659, 0.846777774, 0.708088893, 0.570877769, 
0.846611107, 0.693044449, 0.570133327, 0.848066665, 0.683800007, 
0.569522227, 0.835011109, 0.673044435, 0.56244444, 0.82808889, 
0.679744447, 0.561466666, 0.823666663, 0.666788888, 0.578866666, 
0.816311099, 0.675900007, 0.562899998, 0.815122211, 0.732355553, 
0.574244445), Purification = c("RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", "RAW", 
"RAW", "RAW", "RAW", "RAW", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", "PUR", 
"PUR", "PUR", "PUR", "PUR"), RhlType = c("C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", "C14", 
"C14", "C14", "C14", "C14", "C14", "C14")), row.names = c(NA, 
-270L), class = c("tbl_df", "tbl", "data.frame"))

This can be copied directly into R.

Also your mutatedata and mutatedata2 aren't needed for your example so you can leave this out in the question.

You were nearly there, you just needed to define the colours with "colour = as.factor(con)", the "as.factor" is needed because these are numbers and are interpreted as numbers.

ggplot(mutatedata3, 
       aes(x = time, y = bgrowth,
   #        fill = as.factor(con),
           colour = as.factor(con)  )) +
  geom_point() +
  geom_smooth(formula = y~x, method=loess , 
              se= FALSE, 
              show.legend = TRUE, span=0.5) +
  annotate("text",x=72, y=0.5, hjust=0, label="y~x", size=4)+
  facet_wrap(~Purification) +
  theme_bw()+
  theme(panel.border = element_blank(),
        panel.background = element_rect(color = "black"),
        strip.background = element_rect(fill = "darkgray"),
        strip.text.x = element_text(face="bold", size=12),
        legend.title=element_text(face="bold"),
        axis.text=element_text(size=12,colour = "black"),
        axis.title.y =element_text(size=12,face="bold", margin = margin(r=7)),
        axis.title.x =element_text(size=12,face="bold", margin = margin(t=7)),
        strip.text = element_text(size=12, face="bold", color="black"))+
  scale_color_manual(name = "Concentration",
                     values = c("0" = "black", "5" = "red", "10"= "purple",
                               "25"="blue", "50"= "green", "100"= "orange",
                               "250"= "skyblue", "500"="maroon", "750"="violet"),
                     aesthetics = "colour" ) + 
#   or for using with se = TRUE:
#                     aesthetics = c("fill", "colour" ))+
  scale_y_continuous(breaks = seq(0.1,1, by=0.1)) +
  scale_x_continuous(breaks= seq(0,96, by=24))

In the geom_smooth the se can be set to false, with the average value there isn't much to show anyway. If you want to keep it you also need to define the fill values (commented out in the example above).

This however might be more interesting when showing the individual values (that have quite big spreads)

ggplot(LC10run123, 
       aes(x = time, y = bgrowth,
           fill = as.factor(con),
           colour = as.factor(con)  )) +
  geom_point() +
  geom_smooth(formula = y~x, method=loess , 
              se= TRUE, 
              show.legend = TRUE, span=0.5) +
  annotate("text",x=72, y=0.5, hjust=0, label="y~x", size=4)+
  facet_grid(Purification ~ con) +
  theme_bw()+
  theme(legend.position = "none",  # hide the legend here
        panel.border = element_blank(),
        panel.background = element_rect(color = "black"),
        strip.background = element_rect(fill = "darkgray"),
        strip.text.x = element_text(face="bold", size=12),
        legend.title=element_text(face="bold"),
        axis.text=element_text(size=12,colour = "black"),
        axis.title.y =element_text(size=12,face="bold", margin = margin(r=7)),
        axis.title.x =element_text(size=12,face="bold", margin = margin(t=7)),
        strip.text = element_text(size=12, face="bold", color="black"))+
  scale_color_manual(name = "Concentration",
                     values = c("0" = "black", "5" = "red", "10"= "purple",
                                "25"="blue", "50"= "green", "100"= "orange",
                                "250"= "skyblue", "500"="maroon", "750"="violet"),
                       aesthetics = c("fill", "colour" ))+
  scale_y_continuous(breaks = seq(0.1,1, by=0.1)) +
  scale_x_continuous(breaks= seq(0,96, by=24))

rexp088 · April 29, 2022, 9:10am

Thank you so much @Matthias and Sanjmay. I cannot mention a lot because I am a new user here. I am so much grateful for this. Hopefully, I could continue with the statistical analysis after this.

rexp088 · May 3, 2022, 2:35am

Using the same data above, I wanted to make a bar or boxplot just all for the time 96, removing all outliers. Please help me, here is my code:

#CFU FOR MARCH 24 2022

CFU <- read.csv("~/R/Data Analysis/MSc ARTs/Summary/March 2022/CFU.csv", header=TRUE, stringsAsFactors=TRUE)
mutatedata3 <- aggregate(CFU5uL ~ treatment, CFU, mean)

boxplot(CFU$CFU5uL, plot=FALSE)$out

outliers <- boxplot(CFU$CFU5uL, plot=FALSE)$out

CFU %>%

group_by(treatment) %>%

shapiro_test(CFU5uL)

fit <- lm(CFU5uL ~ treatment,data= mutatedata3)
summary(fit)

kruskal.test(CFU5ul~treatment, data = CFU)
pairwise.wilcox.test(CFU5ul, treatment, method=p.adjust.methods, paired = TRUE)

x<-CFU
x<- x[-which(x$CFU5ul %in% outliers),]

outlier<-CFU %>%

group_by('treatment') %>%

identify_outliers('CFU5uL')

eliminated<- subset(CFU, CFU~'CFU5uL' > (Q[1] - 1.5iqr) & CFU~'CFU5uL' < (Q[2]+1.5iqr))

ggplot(CFU,
aes(x = treatment, y =CFU5uL,
# fill = as.factor(treatment),
colour = as.factor(treatment) )) +
geom_point() +

geom_smooth(formula = y~x, method=loess ,

se= FALSE,

show.legend = TRUE, span=0.5) +

annotate("text",x=72, y=0.5, hjust=0, label="y~x", size=4)+

facet_wrap(~Purification) +

geom_jitter()+

theme_prism()+
theme(panel.border = element_blank(),
panel.background = element_rect(color = "black"),
strip.background = element_rect(fill = "darkgray"),
strip.text.x = element_text(face="bold", size=12),
legend.title=element_text(face="bold"),
axis.text=element_text(size=12,colour = "black"),
axis.title.y =element_text(size=12,face="bold", margin = margin(r=7)),
axis.title.x =element_text(size=12,face="bold", margin = margin(t=7)),
strip.text = element_text(size=12, face="bold", color="black"))+

How to assigning distinct line color for graph?

boxplot(CFU$CFU5uL, plot=FALSE)$out

outliers <- boxplot(CFU$CFU5uL, plot=FALSE)$out

CFU %>%

group_by(treatment) %>%

shapiro_test(CFU5uL)

outlier<-CFU %>%

group_by('treatment') %>%

identify_outliers('CFU5uL')

eliminated<- subset(CFU, CFU~'CFU5uL' > (Q[1] - 1.5iqr) & CFU~'CFU5uL' < (Q[2]+1.5iqr))

geom_smooth(formula = y~x, method=loess ,

se= FALSE,

show.legend = TRUE, span=0.5) +

annotate("text",x=72, y=0.5, hjust=0, label="y~x", size=4)+

facet_wrap(~Purification) +

geom_jitter()+

scale_color_manual(name = "Concentration",

values = c("0" = "black", "5" = "red", "10"= "purple",

"25"="blue", "50"= "green", "100"= "orange",

"250"= "skyblue", "500"="maroon", "750"="violet"),

aesthetics = "colour" ) +

or for using with se = TRUE:

aesthetics = c("fill", "colour" ))+

scale_x_continuous(breaks= seq(0,96, by=24))

xlab("treatment") +