Remove the space between the ggplot2 strips

Question

This is my code.

ggplot(df, aes(x=timepoint, y=mean, fill=group)) + geom_bar(position=position_dodge(.3), colour="black", stat="identity", width=0.3, , binwidth=0) + geom_errorbar(position=position_dodge(.3), width=.25, aes(ymin=mean, ymax=mean+sem)) + scale_fill_manual(values=c("#FFFFFF", "#000000"), guide=FALSE) + theme_bw() + ylab(ylab) + xlab("") + # xlim("Baseline", "12w") + scale_x_discrete(expand = c(0,0), limits=c("Baseline","12w")) + scale_y_continuous(expand = c(0,0) ) + theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) 

This is MY output, which I DO NOT ALLOW, there is too much space between the "base" and "12w":

How to remove whitespace between columns?

thanks

df looks like this:

 df <- structure(list(group = c("a1.d.ffa.mean Dysglyc", "a1.c.ffa.mean Control", "b1.d.ffa.mean Dysglyc", "b1.c.ffa.mean Control"), timepoint = c("Baseline", "Baseline", "12w", "12w"), mean = c(1.913509, 2.181959, 2.742249, 1.50846), sem = c(0.10663114, 0.08360294, 0.07890374, 0.08348542 ), p.value = c(0.597738161, 1, 0.007885464, 1), p.value.t = c(0.04408, 1, 0.2455049, 1)), .Names = c("group", "timepoint", "mean", "sem", "p.value", "p.value.t"), class = "data.frame", row.names = c(NA, -4L)) df group timepoint mean sem p.value p.value.t 1 a1.d.ffa.mean Dysglyc Baseline 1.913509 0.10663114 0.597738161 0.0440800 2 a1.c.ffa.mean Control Baseline 2.181959 0.08360294 1.000000000 1.0000000 3 b1.d.ffa.mean Dysglyc 12w 2.742249 0.07890374 0.007885464 0.2455049 4 b1.c.ffa.mean Control 12w 1.508460 0.08348542 1.000000000 1.0000000 

Answer 1

Just adjust the width:

 ggplot(df, aes(x=timepoint, y=mean, fill=group)) + geom_bar(position=position_dodge(0.9), colour="black", stat="identity", width=0.9, , binwidth=0) + geom_errorbar(position=position_dodge(0.9), width=0.85, aes(ymin=mean, ymax=mean+sem)) + theme_bw() 

Answer 2

Something like that?

 df <- structure(list(group = structure(c(2L, 1L, 4L, 3L), .Label = c("a1.c.ffa.mean Control", "a1.d.ffa.mean Dysglyc", "b1.c.ffa.mean Control", "b1.d.ffa.mean Dysglyc" ), class = "factor"), timepoint = structure(c(1L, 1L, NA, NA), .Label = c("Baseline", "12W"), class = "factor"), mean = c(1.913509, 2.181959, 2.742249, 1.50846), sem = c(0.10663114, 0.08360294, 0.07890374, 0.08348542 ), p.value = c(0.597738161, 1, 0.007885464, 1), p.value.t = c(0.04408, 1, 0.2455049, 1)), .Names = c("group", "timepoint", "mean", "sem", "p.value", "p.value.t"), row.names = c(NA, -4L), class = "data.frame") 

reordering factor levels using

 df$timepoint <- factor(df$timepoint, levels= c('Baseline', '12W')) 

write it down

 ggplot(df, aes(x=factor(timepoint), y=mean, fill=group)) + geom_bar(position=position_dodge(.3), colour="black", stat="identity", width=0.3, , binwidth=0) + geom_errorbar(position=position_dodge(.3), width=.25, aes(ymin=mean, ymax=mean+sem)) + scale_fill_manual(values=c("#FFFFFF", "#000000","#FFFFFF", "#000000"), guide=FALSE) + theme_bw() + labs(list(x="", y='skeletal muscle FFA g/100g')) + # xlim("Baseline", "12w") + scale_x_discrete(expand = c(0,0)) + scale_y_continuous(expand = c(0,0) ) + theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) 

This is pretty close to your example ...