Packages:
library(ggstats)
library(likert)
library(ggplot2)
library(patchwork)The main plot - uses the ggstats package and the Likert_CFB_formed dataset.
gglikert(Likert_CFB_formed,
Arm1:Arm3,
facet_rows = vars(Visit),
labels_hide_below = 0.05,
cutoff = 2.5,
labels_size = 3.3,
totals_include_center = FALSE) +
scale_fill_manual(values=c("#006699", "#CCECFF", "#92D050", "#F0A89A","#FF1F1F")) +
geom_vline(xintercept = 0) +
theme_bw() +
guides(fill=guide_legend(ncol=2, title="", byrow = TRUE)) +
labs(title = "Likert plot of the Achilles reflex over time", subtitle="Overall value for both sides",
caption = "* Labels <5% are hidden\nIndividual percentages may not add exactly to side totals due to rounding issue") +
facet_wrap(~Visit, strip.position="left",ncol = 1) +
theme(panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
legend.position = "top",
plot.title = element_text(size=15, margin = margin(0,0,0,0)),
strip.text = element_text(size=9, margin = margin(1,1,1,1, "mm")),
axis.text = element_text(size=10),
axis.title = element_text(size=11),
legend.text = element_text(size=9, margin = margin(l=1, t=0,b=0,r=0, "mm")),
legend.title = element_text(size=8),
legend.key.size = unit(.5, "cm"),
legend.key.spacing.x = unit(2, 'mm'),
legend.key.spacing.y = unit(0, 'mm'),
legend.box.spacing = unit(0, "pt"),
plot.subtitle = element_text(margin = unit(c(2,0,0,0), "mm"))) +
xlab("Percentage of patients") -> p1
Plot of the missing data fraction - uses the likert package and the Likert_CFB_formed_miss dataset.
You may skip this one. I use it for transparency. You have to prepare the data in a format required by the likert() function.
likert.histogram.plot(likert(Likert_CFB_formed_miss[,2:9], grouping = Likert_CFB_formed_miss[, 1]),
type="bar", center=2,
low.color="#a00000", high.color="#92D050",
plot.percents=FALSE,
text.size = 3.2,
include.center=TRUE) +
theme_bw() +
ylab("# obs.")+
scale_x_discrete(limits = rev(levels(Likert_CFB_formed_miss$Arm))) +
guides(fill=guide_legend(nrow = 2, title="", byrow = TRUE)) +
theme(panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
legend.position = "top",
plot.title = element_text(size=15),
strip.text = element_text(size=9, margin = margin(0.08,0,0.08,0, "cm")),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_text(size=11),
legend.text = element_text(size=9, margin = margin(l=1, t=0,b=0,r=0, "mm")),
legend.title = element_text(size=8),
legend.key.size = unit(.5, "cm"),
legend.key.spacing.y = unit(0, 'mm'),
legend.key.spacing.x = unit(1, 'mm'),
legend.box.spacing = unit(0, "pt"),
strip.background = element_blank(),
strip.text.x = element_blank(),
plot.subtitle = element_text( margin = margin(0,0,-10,0)),
plot.margin = unit(c(0,2,1,-3), "mm")) -> p2
Both plots combined together:
p1 + p2 + patchwork::plot_layout(ncol=2, widths = c(7, 1.2))
You should obtain this result:
The data necessary to recreate the plots:
# for the main plot
Likert_CFB_formed <- structure(list(Visit = structure(c(1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 7L,
8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L,
8L, 5L, 6L, 3L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 5L,
6L, 3L, 4L, 1L, 2L, 5L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 6L, 3L, 4L, 1L, 2L,
7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L,
7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L,
7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L,
7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L,
7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 3L, 1L, 2L,
7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 7L, 8L, 5L, 6L, 4L, 1L, 2L, 7L,
8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 5L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 6L, 4L, 1L, 2L, 7L,
8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L,
8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L,
8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L,
5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 6L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 8L, 5L, 6L, 3L, 4L, 1L,
2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 4L, 1L,
2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L,
2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L,
2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 7L, 8L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L,
3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L,
1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 5L, 6L, 3L, 4L, 1L, 2L,
5L, 6L, 3L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L,
6L, 3L, 4L, 1L, 2L, 7L, 8L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L,
4L, 1L, 2L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L,
2L, 7L, 8L, 5L, 6L, 3L, 4L, 1L, 2L, 7L, 8L, 5L, 6L, 3L, 4L), levels = c("Baseline",
"Discharge", "Week 1", "Week 2", "Month 1", "Month 3", "Month 6",
"Month 12"), class = c("ordered", "factor")), Arm1 = structure(c(NA,
3L, 4L, 4L, 3L, 3L, 3L, 4L, NA, 3L, 2L, 3L, 3L, 3L, 3L, NA, 3L,
NA, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 3L, 2L, 1L, 3L, 3L, 3L, 2L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 3L,
3L, 2L, 2L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 2L, 3L, NA, 1L, 3L, 3L, 3L, 2L, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA,
NA, 2L, 3L, 3L, 4L, 3L, 2L, 1L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 3L,
4L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 4L, 3L, 3L, 4L, 3L,
3L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 1L, 3L, 3L, 1L, 3L,
2L, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 2L, 1L, 3L, 4L, 3L, 4L,
4L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 2L, 3L, 3L,
2L, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, NA, 2L, 1L, 2L, 2L,
2L, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 3L, 4L, 3L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 2L, 2L, 1L, 3L, 3L, NA, 3L, 3L, NA, NA, NA, NA, NA,
NA, NA, NA, 3L, 3L, 3L, 4L, 4L, 3L, 3L, NA, NA, NA, NA, NA, NA,
NA, NA, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 2L, 3L, 2L, 3L, 2L, 5L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 3L,
1L, NA, 2L, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 2L, 3L, 4L, 3L,
3L, 3L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 2L,
3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 3L, 3L, 4L, 3L,
4L, 4L, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 2L, 3L, 3L, 1L, 2L,
2L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
4L, 4L, 2L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 2L,
3L, 1L, 3L, 3L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, NA,
NA, NA, NA, NA, NA, NA, NA, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 3L, 3L,
1L, 1L, 1L, 1L, 3L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, NA, NA,
NA, NA, NA, NA, NA, NA, 1L, 3L, 2L, 1L, 1L, NA, 1L, 1L, NA, NA,
NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 4L, 3L,
3L, 2L, 4L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, NA, NA,
NA, NA, NA, NA, 2L, 2L, 2L, 3L, 2L, NA, NA, NA, NA, NA, NA, NA,
NA, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA,
NA, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA,
NA, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L,
3L, 4L, 4L, 4L, 4L, 3L, 4L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 2L, 4L, 3L, 3L, 3L, 3L,
3L, 1L, 3L, 2L, 2L, 2L, 2L, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L,
1L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L,
4L, 3L, 3L, NA, 1L), levels = c("0 Absent", "1+ Diminished (hyporeflexive)",
"2+ Normal", "3+ Brisker than normal (hyperreflexive)", "4+ Hyperactive (clonus)"
), class = "factor"), Arm2 = structure(c(NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L,
NA, 3L, 3L, 3L, 4L, 2L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 3L, NA, 4L, 3L, 3L, 3L, 3L, 3L, NA, NA,
NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L,
2L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 4L, 3L, NA, 4L, 4L, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 4L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, NA, 2L, 2L, 2L, 3L, 2L,
NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 2L, 3L, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L, 3L, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L, 4L,
4L, 4L, 4L, 4L, 3L, 3L, 1L, 3L, 3L, NA, 3L, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 4L, 4L, 3L, 3L, 3L, NA, 3L, 3L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 3L, 2L, 1L, 1L, 2L, NA, 2L, 3L, 3L, 1L,
2L, 2L, 3L, 2L, 2L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 2L, 2L, 3L,
2L, 1L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA,
NA, NA, 3L, 4L, 4L, 3L, 3L, 3L, 4L, 3L, NA, NA, NA, NA, NA, 3L,
4L, 3L, 3L, 3L, 4L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L,
1L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 4L, 3L, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 3L, NA, 2L, NA, 2L, 3L, 1L, 2L, NA, NA, NA, NA, NA, NA, NA,
NA, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 1L, 3L,
1L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 5L, 2L, NA, 1L, 1L, 2L, NA, 5L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 2L, 2L,
2L, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 4L, 3L, 3L,
3L, 3L, 4L, 4L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L,
2L, 2L, 3L, 2L, 4L, 3L, 3L, 4L, NA, 3L, 3L, 4L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 4L, 2L, 3L, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L, 3L, 4L, 3L, 3L,
3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 3L, 3L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
2L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 2L, 2L, 2L, 1L, NA,
2L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 4L, 4L, 4L, 4L,
NA, NA, NA, NA, NA, 4L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA,
NA, NA, NA, NA, NA, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, NA, NA, NA,
NA, NA, NA, NA, NA, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA,
NA, NA, NA, NA, NA, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 4L, 3L, 3L, 3L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, NA, NA, NA,
NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, NA, NA, NA, NA, NA,
NA, NA, NA, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L,
3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 3L, 3L, 3L, 2L, 4L, 3L, NA, NA, NA, NA, NA, NA, NA,
NA, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA,
NA), levels = c("0 Absent", "1+ Diminished (hyporeflexive)",
"2+ Normal", "3+ Brisker than normal (hyperreflexive)", "4+ Hyperactive (clonus)"
), class = "factor"), Arm3 = structure(c(NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 3L, NA, 2L, 3L, 2L, 2L, 2L, 3L, 3L,
NA, 3L, 4L, 2L, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, NA, NA, NA, NA, NA, NA,
2L, 3L, 4L, NA, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA,
2L, 2L, 2L, 3L, 3L, 4L, 3L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
3L, 3L, NA, 3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 2L, 3L, 3L, 3L, 1L, 3L, 4L, 2L, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
1L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, NA, NA, NA, NA, NA, NA, 2L, 2L,
4L, 1L, 2L, 3L, 2L, 4L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, NA,
1L, 2L, 3L, 1L, 4L, 4L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 4L, 3L,
3L, 3L, 4L, 4L, 3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 3L, 3L,
NA, 3L, 3L, NA, NA, NA, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 1L, 3L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 3L, 2L, 3L, 4L, 4L, 4L, 3L, 3L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 3L, 3L, NA,
3L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA), levels = c("0 Absent", "1+ Diminished (hyporeflexive)",
"2+ Normal", "3+ Brisker than normal (hyperreflexive)", "4+ Hyperactive (clonus)"
), class = "factor")), row.names = c(NA, -1062L), class = "data.frame")
# for the missing data part - can be safely skipped. It's just the same data as above, but in a different format, since the missing data plot is drawn by a different package.
Likert_CFB_formed_miss <-
structure(list(Arm = structure(c(1L, 1L, 1L, 2L, 3L, 3L, 2L,
1L, 2L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 3L, 2L, 1L,
3L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 3L, 1L, 3L, 2L, 3L, 3L,
3L, 1L, 2L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 1L,
1L, 3L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 1L), levels = c("Arm1", "Arm2", "Arm3"
), class = "factor"), Baseline = structure(c(NA, NA, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L,
3L, 3L, 4L, 2L, 2L, 4L, 3L, 3L, 2L, 3L, 2L, 2L, 4L, 1L, 3L, 2L,
1L, 3L, 1L, 4L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 3L, 3L, 3L,
3L, 3L, 4L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 1L, 3L, 1L, 2L,
3L, 1L, 3L, 3L, 2L, 3L, 4L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 1L, 4L,
5L, 3L, 2L, 3L, 3L, 3L, 3L, NA, 3L, 3L, 3L, 4L, 3L, 3L, 4L, 3L,
3L, 3L, 4L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 4L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 4L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor"), Discharge = structure(c(3L,
3L, NA, NA, NA, NA, NA, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 2L, 3L, 3L, 2L, 3L,
2L, 2L, 2L, 1L, NA, 2L, 3L, NA, 4L, 3L, 3L, 4L, 3L, 3L, 2L, 4L,
3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 4L, 3L, 4L, 2L, 2L,
2L, 1L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, NA, 1L, 3L, 2L,
1L, 2L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor"), `Week 1` = structure(c(3L,
3L, 3L, 2L, 2L, NA, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 4L, 3L, 2L, 3L,
2L, 4L, 3L, 3L, 2L, 3L, 4L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 4L, 3L,
2L, 2L, 2L, 1L, 4L, 2L, 3L, 2L, 4L, 3L, 3L, 3L, NA, NA, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 4L, NA, 3L, 3L, 3L,
3L, 3L, 4L, 3L, 2L, 3L, 1L, 3L, 4L, NA, 2L, 3L, 1L, 1L, 3L, 3L,
NA, 2L, 3L, NA, 1L, 2L, 3L, 1L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 1L,
2L, 1L, 3L, 3L, 3L, 3L, NA, 3L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L,
2L, 3L, 1L, 2L, 3L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 3L, NA), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor"), `Week 2` = structure(c(4L,
NA, NA, 3L, 3L, NA, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
4L, 4L, 2L, 3L, 3L, 3L, 2L, 3L, 4L, 1L, 3L, 3L, 2L, 3L, 4L, NA,
2L, 2L, 4L, 2L, 4L, 1L, 3L, 2L, 4L, NA, 3L, NA, NA, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 4L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L,
1L, 3L, 3L, 5L, 2L, 2L, NA, 1L, 3L, 3L, 2L, 4L, 3L, 2L, 4L, 2L,
3L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 4L, NA, 3L, 3L, 3L, 3L, 3L, 1L, 4L, 3L, 3L,
2L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor"), `Month 1` = structure(c(3L,
3L, NA, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, NA, 3L, 2L, 3L,
2L, 4L, 2L, 3L, NA, 3L, 1L, 3L, 4L, 3L, 3L, 1L, 3L, 3L, 4L, NA,
2L, 3L, 2L, 2L, 3L, 1L, 4L, 2L, 4L, 3L, 3L, NA, NA, 1L, 3L, 3L,
4L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, NA, 3L, 1L, 3L, 3L, 1L,
2L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 2L, NA, 2L, 1L, 2L, 1L, NA, 3L,
3L, 2L, 3L, 1L, 1L, 2L, NA, 2L, 3L, 3L, 2L, 3L, 3L, 2L, NA, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 4L, 2L, 3L, 1L, 1L, 3L, 2L, 4L,
3L, 3L, 3L, 2L, 3L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L,
3L, NA, NA, 1L, 4L, 3L, 3L, 4L, 2L, 3L, 3L, 3L, 3L), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor"), `Month 3` = structure(c(3L,
3L, NA, 4L, 2L, 2L, 3L, 3L, NA, 3L, 2L, 2L, 3L, 4L, 4L, 2L, 3L,
3L, 4L, 2L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 4L, 1L, 1L, 3L, 3L, NA,
3L, 3L, 3L, 2L, 1L, 1L, 4L, 2L, 4L, NA, NA, NA, NA, 1L, NA, NA,
4L, 4L, 3L, NA, 2L, 2L, 3L, 3L, 1L, 3L, NA, 3L, NA, 4L, 3L, 1L,
3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, NA, 3L,
NA, 2L, 3L, 2L, 2L, 3L, NA, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L,
4L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 2L, NA, NA, 3L, 3L, 3L,
2L, 3L, 3L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor"), `Month 6` = structure(c(4L,
2L, NA, 3L, 2L, 3L, 4L, 1L, NA, 3L, 1L, NA, 4L, 4L, 2L, 1L, NA,
2L, 3L, NA, NA, NA, 3L, 3L, 3L, 4L, 3L, 4L, 1L, 1L, 3L, 1L, NA,
3L, NA, 4L, 2L, 1L, 2L, 3L, 2L, NA, 1L, 3L, NA, NA, 1L, 1L, 3L,
NA, 3L, 3L, 1L, 2L, 3L, NA, 4L, 2L, 4L, 3L, 4L, NA, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, NA, 3L, 2L, 3L, NA, 2L, 1L, NA, 3L,
NA, 2L, 3L, NA, 2L, 2L, NA, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, NA,
2L, 3L, 3L, 3L, 3L, 4L, 2L, 1L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, NA, NA, 3L, 4L, 3L, 3L, 4L, 1L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 4L, 3L, 4L, 3L, 1L, NA, 3L, 2L, 3L), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor"), `Month 12` = structure(c(4L,
3L, NA, 3L, 3L, 4L, 3L, NA, NA, 1L, 1L, NA, NA, 3L, 3L, 1L, 1L,
2L, 4L, 2L, 3L, NA, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, NA,
3L, NA, 1L, 1L, 2L, 1L, 3L, 1L, NA, 3L, 3L, NA, NA, 1L, 3L, 3L,
3L, 4L, 3L, 1L, 2L, 3L, NA, 3L, 3L, 3L, NA, 3L, NA, 3L, 4L, 2L,
3L, 3L, NA, 3L, 3L, 3L, 1L, NA, 2L, 2L, 2L, 3L, NA, 1L, NA, 3L,
NA, 3L, 3L, 1L, 2L, 3L, NA, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 4L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, NA, NA, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 1L, 3L,
3L, 3L, 3L, 2L, 4L, 3L, 3L, 2L, 3L, NA, 1L, 3L, 4L), levels = c("0 Absent",
"1+ Diminished (hyporeflexive)", "2+ Normal", "3+ Brisker than normal (hyperreflexive)",
"4+ Hyperactive (clonus)"), class = "factor")), row.names = c(NA,
-142L), class = "data.frame")
Likert plot - version 2, some options are "bad", some are "good", both counted to %, yet there is no the neutral one.
The main plot
colors <- rev(RColorBrewer::brewer.pal(6, "RdYlGn"))
colors[3] <- "#FFFF99"
colors[4] <- "#FFCA50"
colors[5] <- "red"
colors[6] <- "#600000"
gglikert(Likert_CFB_formed,
Arm1:Arm3,
facet_rows = vars(Visit),
labels_hide_below = 0.05,
labels_size = 3.2,
totals_include_center = TRUE,
cutoff = 2) +
scale_fill_manual(values=colors) +
geom_vline(xintercept = 0) +
theme_bw() +
guides(fill=guide_legend(nrow = 2, title="", byrow = TRUE)) +
xlab("Percentage of ODI Pain scores (Patients)") +
labs(title = "Likert plot of observed ODI Pain scores",
caption = "* Labels <5% are hidden\nIndividual percentages may not add exactly to side totals due to rounding issue") +
facet_wrap(~Visit, strip.position="left",ncol = 1) +
theme(panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
legend.position = "top",
plot.title = element_text(size=15, margin = margin(0,0,0,0)),
strip.text = element_text(size=9, margin = margin(1,1,1,1, "mm")),
axis.text = element_text(size=10),
axis.title = element_text(size=11),
legend.text = element_text(size=8),
legend.title = element_text(size=8),
legend.key.size = unit(.5, "cm"),
legend.key.spacing.y = unit(0, 'mm'),
legend.box.spacing = unit(0, "pt"),
plot.margin = unit(c(1,2,1,-3), "mm")) -> p1
Plot of the missing data fraction - uses the likert package and the Likert_CFB_formed_miss dataset. You may skip this one.
likert.histogram.plot(likert(Likert_CFB_formed_miss[,2:8], grouping = Likert_CFB_formed_miss[, 1]), type="bar",
center=2,
low.color="#a00000", high.color="#92D050", plot.percents=FALSE,
include.center=TRUE) +
theme_bw() +
ylab("# obs.")+
scale_x_discrete(limits = rev(levels(Likert_CFB_formed_miss$Arm))) +
guides(fill=guide_legend(nrow = 2, title="", byrow = TRUE)) +
theme(panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
legend.position = "top",
plot.title = element_text(size=15),
strip.text = element_text(size=9, margin = margin(0.08,0,0.08,0, "cm")),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_text(size=11),
legend.text = element_text(size=8),
legend.title = element_text(size=8),
legend.key.size = unit(.5, "cm"),
legend.key.spacing.y = unit(0, 'mm'),
legend.key.spacing.x = unit(1, 'mm'),
legend.box.spacing = unit(0, "pt"),
strip.background = element_blank(),
strip.text.x = element_blank(),
plot.subtitle = element_text( margin = margin(0,0,-10,0)),
plot.margin = unit(c(0,2,1,-3), "mm")) -> p2
Both plots combined together:
p1 + p2 + patchwork::plot_layout(ncol=2, widths = c(7, 1.2))
You should obtain this result:
The data necessary to recreate the plots:
# for the main plot
Likert_CFB_formed <- structure(list(Visit = structure(c(2L, 2L, 1L, 2L, 3L, 1L, 2L,
1L, 2L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 4L, 4L, 2L, 2L, 2L,
4L, 1L, 1L, 4L, 2L, 3L, 3L, 3L, 4L, 2L, 2L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 5L, 5L, 1L,
5L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 2L, 3L, 1L, 5L, 2L, 2L, 2L,
1L, 3L, 3L, 1L, 2L, 4L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 6L, 4L,
6L, 2L, 1L, 6L, 6L, 1L, 1L, 4L, 3L, 3L, 4L, 3L, 2L, 4L, 1L, 3L,
4L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 4L, 1L, 3L, 4L, 1L, 4L,
2L, 3L, 1L, 4L, 1L, 3L, 4L, 6L, 4L, 4L, 1L, 1L, 2L, 4L, 5L, 6L,
6L, 5L, 2L, 4L, 1L, 1L, 5L, 5L, 2L, 1L, 6L, 5L, 4L, 2L, 5L, 2L,
3L, 2L, 3L, 1L, 5L, 2L, 1L, 3L, 6L, 2L, 1L, 1L, 4L, 3L, 1L, 5L,
3L, 4L, 5L, 2L, 5L, 6L, 3L, 5L, 1L, 3L, 2L, 2L, 5L, 1L, 1L, 2L,
1L, 1L, 4L, 1L, 5L, 4L, 3L, 4L, 1L, 3L, 1L, 5L, 5L, 3L, 3L, 2L,
2L, 2L, 3L, 2L, 1L, 1L, 2L, 4L, 2L, 4L, 1L, 2L, 5L, 2L, 3L, 3L,
3L, 2L, 4L, 3L, 3L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 5L, 3L, 5L, 5L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 2L, 4L, 4L, 4L, 5L, 5L, 4L, 5L,
5L, 4L, 6L, 4L, 4L, 6L, 4L, 6L, 5L, 5L, 5L, 6L, 5L, 6L, 5L, 7L,
7L, 6L, 5L, 6L, 6L, 7L, 6L, 5L, 5L, 6L, 6L, 1L, 5L, 6L, 6L, 5L,
1L, 1L, 1L, 6L, 7L, 5L, 6L, 1L, 5L, 2L, 5L, 6L, 1L, 5L, 5L, 1L,
1L, 2L, 2L, 6L, 2L, 3L, 5L, 2L, 3L, 2L, 1L, 1L, 6L, 7L, 3L, 3L,
7L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 6L, 6L, 1L, 1L, 1L, 6L, 4L,
2L, 6L, 3L, 7L, 4L, 3L, 6L, 1L, 5L, 4L, 2L, 3L, 3L, 1L, 2L, 4L,
1L, 4L, 2L, 2L, 1L, 1L, 3L, 3L, 7L, 1L, 4L, 2L, 2L, 6L, 3L, 2L,
3L, 7L, 1L, 1L, 4L, 4L, 1L, 4L, 6L, 2L, 6L, 1L, 1L, 4L, 4L, 6L,
2L, 3L, 3L, 1L, 1L, 2L, 3L, 3L, 1L, 2L, 6L, 3L, 1L, 5L, 2L, 6L,
4L, 2L, 7L, 6L, 1L, 6L, 6L, 6L, 4L, 5L, 4L, 3L, 2L, 2L, 2L, 1L,
2L, 3L, 3L, 4L, 2L, 3L, 1L, 7L, 4L, 5L, 2L, 1L, 3L, 5L, 1L, 3L,
2L, 1L, 3L, 1L, 4L, 4L, 7L, 6L, 2L, 2L, 3L, 3L, 3L, 7L, 1L, 3L,
3L, 3L, 7L, 5L, 1L, 1L, 4L, 2L, 3L, 4L, 4L, 2L, 4L, 1L, 7L, 7L,
5L, 1L, 4L, 2L, 3L, 4L, 7L, 4L, 4L, 6L, 5L, 2L, 2L, 5L, 6L, 5L,
2L, 2L, 7L, 7L, 3L, 5L, 4L, 1L, 4L, 3L, 1L, 4L, 1L, 4L, 1L, 5L,
3L, 5L, 7L, 1L, 7L, 1L, 3L, 5L, 1L, 2L, 5L, 3L, 5L, 3L, 1L, 5L,
5L, 3L, 2L, 2L, 2L, 2L, 1L, 7L, 2L, 2L, 2L, 4L, 1L, 4L, 1L, 4L,
5L, 3L, 1L, 2L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 5L, 4L, 1L,
3L, 1L, 3L, 7L, 7L, 1L, 2L, 3L, 4L, 2L, 4L, 1L, 2L, 2L, 2L, 4L,
7L, 3L, 1L, 6L, 4L, 5L, 5L, 5L, 1L, 4L, 1L, 5L, 7L, 7L, 2L, 2L,
7L, 2L, 4L, 5L, 1L, 4L, 3L, 5L, 3L, 5L, 2L, 3L, 1L, 5L, 3L, 3L,
1L, 6L, 7L, 1L, 3L, 5L, 2L, 4L, 4L, 1L, 3L, 7L, 7L, 2L, 4L, 4L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 3L, 2L, 6L, 2L, 1L, 4L, 5L, 2L, 1L,
4L, 2L, 7L, 6L, 7L, 4L, 3L, 2L, 7L, 3L, 2L, 1L, 2L, 1L, 7L, 3L,
1L, 5L, 4L, 3L, 3L, 3L, 4L, 4L, 2L, 6L, 5L, 7L, 4L, 3L, 5L, 4L,
3L, 4L, 3L, 5L, 6L, 2L, 2L, 4L, 5L, 5L, 7L, 3L, 2L, 4L, 7L, 7L,
4L, 6L, 5L, 3L, 4L, 7L, 6L, 7L, 2L, 3L, 5L, 7L, 3L, 4L, 5L, 3L,
4L, 4L, 5L, 6L, 3L, 4L, 4L, 5L, 4L, 7L, 5L, 4L, 6L, 6L, 5L, 5L,
4L, 4L, 5L, 4L, 5L, 4L, 5L, 5L, 4L, 6L, 6L, 4L, 5L, 5L, 5L, 6L,
5L, 5L, 6L, 5L, 5L, 6L, 6L, 5L, 6L, 6L, 5L, 5L, 5L, 6L, 6L, 5L,
6L, 6L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 5L,
6L, 6L, 5L, 6L, 5L, 5L, 5L, 7L, 5L, 5L, 6L, 7L, 6L, 7L, 7L, 6L,
6L, 7L, 6L, 6L, 7L, 7L, 6L, 6L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 6L, 6L, 7L, 6L, 7L, 7L, 6L,
6L, 6L, 6L, 7L, 6L, 7L, 6L, 7L, 6L, 7L, 7L, 6L, 6L, 7L, 6L, 7L,
7L, 6L, 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, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), levels = c("Screening",
"Week 1", "Week 2", "Month 1", "Month 3", "Month 6", "Month 12"
), class = "factor"), Arm1 = structure(c(NA, NA, NA, 2L, NA,
NA, NA, NA, NA, NA, NA, 4L, 2L, NA, NA, NA, 3L, NA, NA, NA, NA,
NA, 3L, NA, NA, NA, NA, NA, NA, NA, 4L, 3L, NA, NA, NA, NA, NA,
4L, NA, NA, NA, NA, NA, NA, NA, NA, 2L, NA, NA, 4L, NA, 5L, NA,
NA, NA, NA, NA, NA, 4L, NA, NA, NA, 2L, NA, NA, NA, 3L, NA, NA,
NA, NA, NA, NA, NA, 5L, 1L, NA, NA, NA, NA, NA, 4L, 3L, 3L, 2L,
NA, 3L, NA, NA, NA, NA, 4L, 4L, NA, NA, 5L, NA, NA, NA, 3L, NA,
2L, NA, NA, 2L, NA, NA, 2L, 2L, 3L, 3L, 2L, NA, 2L, NA, NA, NA,
NA, NA, NA, 3L, NA, 6L, NA, NA, 3L, NA, 1L, NA, 2L, NA, 1L, NA,
NA, NA, NA, NA, NA, 1L, NA, 4L, NA, NA, NA, NA, NA, 4L, 4L, NA,
2L, NA, NA, NA, NA, NA, NA, 4L, NA, NA, NA, NA, NA, 3L, NA, NA,
NA, NA, 1L, 1L, 2L, NA, NA, NA, NA, 6L, 4L, 2L, 4L, NA, NA, NA,
3L, NA, NA, 5L, NA, 6L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 4L,
NA, 3L, NA, 2L, NA, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 2L, NA,
3L, 1L, NA, NA, NA, 2L, NA, 5L, NA, 5L, NA, NA, NA, NA, 5L, 5L,
NA, 1L, NA, 2L, NA, NA, 2L, 3L, NA, NA, NA, 3L, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 5L, NA, NA, NA, NA, NA, NA, NA, 2L, 3L, 4L,
3L, NA, NA, NA, NA, 2L, 4L, NA, 6L, NA, 3L, NA, NA, NA, NA, NA,
NA, NA, 5L, NA, 6L, 1L, NA, 5L, NA, 5L, NA, NA, NA, 3L, 3L, NA,
NA, NA, 4L, NA, 5L, 5L, 2L, 5L, 1L, NA, NA, 4L, 3L, NA, NA, NA,
NA, 2L, NA, 2L, NA, NA, 4L, 2L, NA, NA, NA, 3L, NA, 5L, 3L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, NA, 4L, 3L, NA, 3L, NA,
3L, 4L, 6L, 2L, 3L, NA, NA, NA, NA, 4L, NA, 4L, NA, NA, 1L, NA,
3L, 3L, 2L, NA, 3L, 2L, NA, 2L, NA, 3L, NA, NA, NA, NA, NA, NA,
NA, NA, 3L, 2L, NA, 3L, NA, NA, NA, 4L, NA, 2L, NA, NA, NA, NA,
NA, NA, NA, NA, 4L, 1L, NA, NA, NA, NA, 3L, NA, 3L, 3L, 5L, NA,
3L, 3L, NA, NA, NA, 1L, NA, 4L, 5L, NA, NA, NA, 3L, 4L, NA, 2L,
4L, 1L, NA, NA, NA, NA, NA, 4L, NA, NA, NA, 2L, NA, NA, NA, NA,
3L, NA, 4L, 2L, NA, 2L, 3L, NA, NA, 3L, 3L, NA, NA, 4L, 4L, NA,
4L, 1L, NA, 5L, NA, NA, 3L, NA, NA, 3L, 1L, NA, NA, NA, 3L, 1L,
2L, 1L, NA, 4L, NA, 2L, 2L, 3L, NA, 3L, NA, NA, NA, 5L, 3L, NA,
NA, 1L, 1L, 5L, NA, 4L, 6L, 5L, NA, NA, 5L, NA, 3L, 3L, NA, NA,
4L, 5L, NA, 2L, NA, NA, 3L, 3L, 4L, 3L, 3L, 4L, 3L, NA, 4L, 3L,
NA, 3L, NA, NA, NA, 4L, NA, NA, 6L, 3L, 5L, 5L, 3L, NA, NA, NA,
1L, NA, 4L, 3L, 2L, NA, 1L, NA, 4L, 3L, 1L, 1L, 3L, NA, NA, 3L,
NA, NA, NA, 4L, NA, NA, 2L, NA, NA, 1L, 2L, NA, NA, 4L, NA, NA,
NA, NA, 4L, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, NA, NA, 4L, 4L,
1L, NA, NA, NA, NA, 3L, NA, NA, 3L, 4L, 3L, NA, 3L, NA, 1L, 3L,
2L, 3L, NA, 5L, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, 2L, NA,
2L, 5L, 3L, NA, NA, 5L, NA, 3L, NA, NA, NA, NA, NA, NA, 3L, 5L,
NA, NA, 5L, NA, 3L, 2L, NA, NA, NA, NA, 4L, 1L, 1L, NA, 1L, 3L,
1L, 3L, NA, 2L, 2L, 1L, 5L, 4L, 1L, NA, 2L, 3L, NA, NA, NA, NA,
NA, 2L, NA, 3L, NA, 4L, 2L, NA, NA, 2L, NA, NA, NA, NA, 3L, 2L,
NA, NA, NA, NA, 3L, 1L, 3L, NA, 2L, NA, 3L, NA, 3L, NA, NA, NA,
3L, NA, NA, NA, NA, 1L, 3L, 5L, NA, 3L, 3L, NA, NA, NA, NA, 3L,
2L, NA, 3L, NA, NA, 3L, 2L, NA, 5L, 2L, NA, 4L, NA, 1L, NA, 4L,
NA, NA, NA, 3L, 1L, NA, NA, 4L, NA, NA, NA, 6L, NA, 1L, NA, NA,
1L, NA, NA, 5L, 2L, 1L, 4L, 3L, 4L, NA, 2L, NA, 2L, NA, 3L, NA,
6L, NA, 2L, 4L, 2L, NA, 4L, NA, 1L, NA, NA, NA, 3L, 4L, NA, 3L,
NA, 1L, 3L, NA, 5L, NA, NA, 1L, 1L, NA, 2L, NA, 3L, NA, 3L, NA,
NA, NA, NA, NA, 5L, NA, NA, NA, NA, 4L, 2L, NA, NA, NA, 3L, 5L,
NA, 2L, 5L, 3L, 4L, NA, 1L, NA, 4L, 2L, NA, NA, 1L, 1L, 3L, 3L,
6L, NA, 4L, 2L, 3L, 3L, NA, NA, NA, 5L, NA, NA, 1L, NA, 3L, 3L,
NA, NA, 4L, 1L, 1L, NA, NA, 4L, 4L, 3L, NA, NA, 4L, NA, NA, NA,
1L, NA, NA, 3L, NA, NA, 1L, 3L, 3L, 3L, NA), levels = c("[0] No pain",
"[1] Very mild pain", "[2] Moderate pain", "[3] Fairly severe pain",
"[4] Very severe pain", "[5] Worst imaginable pain"), class = "factor"),
Arm2 = structure(c(NA, NA, NA, NA, NA, 4L, 3L, 3L, 2L, 3L,
NA, NA, NA, 3L, 3L, NA, NA, NA, 2L, NA, 1L, NA, NA, NA, 4L,
NA, 2L, NA, 3L, NA, NA, NA, 3L, NA, NA, 3L, NA, NA, NA, 2L,
NA, NA, 3L, 3L, NA, NA, NA, 1L, 3L, NA, NA, NA, NA, NA, 3L,
NA, 3L, NA, NA, 5L, NA, 5L, NA, 3L, 3L, NA, NA, 3L, 1L, 5L,
NA, NA, 3L, NA, NA, NA, NA, 2L, 5L, 4L, NA, NA, NA, NA, NA,
2L, NA, NA, 1L, 3L, NA, NA, NA, NA, 3L, NA, 3L, 3L, NA, NA,
NA, NA, 2L, 2L, NA, NA, 2L, NA, NA, NA, NA, NA, 4L, NA, NA,
NA, NA, NA, 1L, NA, NA, 3L, NA, NA, NA, NA, 1L, NA, 3L, NA,
NA, NA, NA, 2L, 1L, NA, NA, NA, NA, NA, NA, NA, 2L, NA, 5L,
NA, NA, NA, NA, NA, NA, 1L, 1L, NA, NA, 3L, NA, 4L, NA, NA,
4L, NA, NA, NA, NA, 5L, 5L, NA, NA, NA, NA, NA, 2L, NA, NA,
NA, NA, NA, 2L, 2L, NA, NA, 1L, 5L, NA, NA, NA, 4L, NA, NA,
2L, NA, 4L, 1L, NA, NA, NA, NA, NA, NA, NA, 3L, NA, 3L, 3L,
2L, NA, 3L, NA, NA, NA, NA, 4L, NA, NA, 3L, 4L, NA, NA, NA,
NA, 5L, NA, NA, 3L, 1L, 3L, NA, NA, NA, NA, 1L, NA, 3L, NA,
NA, NA, NA, NA, NA, NA, 2L, NA, 3L, NA, 3L, NA, NA, 2L, NA,
NA, 3L, 1L, NA, NA, NA, 1L, 2L, NA, NA, NA, NA, 2L, 3L, NA,
NA, NA, NA, NA, NA, 6L, NA, 3L, 3L, 4L, 3L, 2L, 4L, NA, NA,
4L, NA, NA, NA, NA, NA, NA, NA, 2L, NA, NA, NA, 1L, 3L, 6L,
NA, 4L, NA, NA, NA, NA, NA, 6L, 1L, NA, NA, 3L, NA, NA, 4L,
NA, NA, NA, 4L, 5L, NA, NA, 4L, 4L, 5L, NA, 2L, NA, NA, 5L,
NA, 1L, 2L, NA, 2L, NA, 3L, 4L, NA, NA, 2L, NA, NA, 4L, NA,
3L, NA, NA, NA, NA, NA, 4L, 2L, 5L, 3L, NA, 1L, NA, 4L, 3L,
NA, 3L, NA, NA, NA, NA, NA, NA, 1L, NA, 3L, NA, NA, 4L, NA,
5L, 4L, 4L, 3L, 3L, NA, NA, 4L, NA, 2L, 2L, 3L, NA, 4L, NA,
NA, 3L, 5L, 3L, 2L, 1L, 6L, 5L, NA, NA, 3L, 1L, NA, 4L, NA,
1L, NA, NA, NA, 3L, NA, NA, 2L, 3L, 2L, NA, 3L, NA, NA, NA,
3L, 5L, NA, NA, 5L, NA, NA, NA, 2L, 5L, 3L, 3L, 3L, NA, 4L,
NA, 2L, NA, 2L, 5L, 2L, NA, NA, 3L, NA, NA, 2L, NA, NA, 3L,
4L, NA, NA, 2L, 5L, NA, NA, 4L, NA, NA, 3L, NA, 3L, 3L, NA,
2L, 3L, NA, NA, NA, 5L, 2L, NA, NA, NA, NA, 4L, NA, 3L, NA,
NA, NA, 3L, NA, 2L, 3L, 3L, NA, NA, 3L, 3L, NA, NA, NA, 3L,
NA, NA, NA, 4L, 3L, NA, 2L, NA, NA, 5L, 3L, NA, NA, 3L, NA,
2L, 2L, NA, NA, NA, NA, NA, NA, NA, 2L, NA, NA, 5L, NA, 3L,
1L, 3L, NA, 3L, 3L, NA, NA, NA, NA, NA, 1L, 1L, 1L, NA, 3L,
NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, NA, 3L, 3L, NA, 3L,
3L, NA, NA, 3L, 1L, NA, 1L, 3L, NA, NA, 3L, 4L, NA, 1L, NA,
2L, 4L, NA, 1L, 4L, 4L, 2L, 2L, 2L, 4L, NA, NA, 4L, 3L, NA,
NA, NA, 2L, 3L, 2L, 2L, NA, 2L, 2L, NA, NA, NA, 5L, NA, NA,
NA, NA, NA, NA, 1L, NA, 4L, 3L, 3L, 1L, 5L, NA, 1L, 3L, 3L,
3L, NA, 3L, NA, NA, NA, 4L, NA, NA, 2L, NA, 1L, 4L, 1L, 4L,
3L, 5L, NA, NA, NA, 4L, NA, 3L, NA, NA, 1L, 3L, 2L, 3L, NA,
NA, NA, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, NA, NA, 1L,
NA, NA, 2L, 3L, 3L, 1L, NA, NA, 2L, NA, 3L, NA, NA, NA, 3L,
NA, 4L, 3L, 4L, 1L, NA, NA, 3L, 3L, 2L, 2L, NA, NA, NA, 3L,
NA, 3L, NA, 2L, NA, 3L, 5L, 3L, NA, 2L, 3L, 4L, 1L, NA, NA,
NA, 3L, NA, NA, 4L, 3L, 4L, 3L, NA, NA, 4L, NA, 2L, 5L, NA,
NA, 3L, NA, NA, 2L, NA, 2L, NA, 2L, NA, 2L, 1L, 4L, NA, NA,
4L, 1L, NA, 2L, 5L, 2L, NA, 2L, NA, 3L, 3L, NA, 4L, 4L, NA,
NA, NA, NA, NA, NA, 4L, NA, 3L, NA, 5L, NA, 2L, NA, 2L, NA,
NA, NA, 1L, NA, 3L, NA, 3L, 1L, 3L, NA, NA, 3L, NA, 3L, NA,
NA, 3L, NA, 6L, 5L, NA, NA, 1L, NA, 1L, NA, 3L, NA, 3L, 4L,
2L, 3L, 5L, NA, 6L, 3L, 3L, 2L, NA, NA, 1L, 5L, 1L, NA, NA,
1L, NA, NA, NA, NA, 1L, NA, 3L, NA, NA, 3L, 3L, NA, NA, NA,
NA, NA, 3L, NA, NA, NA, NA, NA, 3L, 4L, NA, 2L, 4L, NA, 1L,
NA, NA, 3L, 3L, NA, NA, NA, 1L, 3L, NA, NA, NA, 1L, 1L, NA,
4L, 2L, 3L, NA, 2L, 2L, NA, 2L, 2L, NA, NA, NA, NA, 1L), levels = c("[0] No pain",
"[1] Very mild pain", "[2] Moderate pain", "[3] Fairly severe pain",
"[4] Very severe pain", "[5] Worst imaginable pain"), class = "factor"),
Arm3 = structure(c(5L, 4L, 4L, NA, 4L, NA, NA, NA, NA, NA,
3L, NA, NA, NA, NA, 4L, NA, 4L, NA, 4L, NA, 3L, NA, 1L, NA,
4L, NA, 4L, NA, 3L, NA, NA, NA, 2L, 3L, NA, 3L, NA, 3L, NA,
3L, 2L, NA, NA, 3L, 1L, NA, NA, NA, NA, 6L, NA, 3L, 2L, NA,
5L, NA, 3L, NA, NA, 5L, NA, NA, NA, NA, 3L, NA, NA, NA, NA,
5L, 5L, NA, 3L, NA, NA, 2L, NA, NA, NA, 1L, NA, NA, NA, NA,
NA, NA, 2L, NA, NA, 3L, NA, NA, 5L, NA, NA, NA, NA, 1L, NA,
1L, NA, NA, NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, NA, 3L,
3L, 3L, 3L, NA, 2L, NA, NA, NA, 4L, 4L, NA, NA, NA, NA, NA,
5L, NA, 3L, NA, NA, 3L, 2L, 2L, NA, 4L, NA, 1L, NA, 2L, NA,
2L, NA, NA, 2L, NA, 1L, NA, NA, 4L, 5L, NA, NA, NA, 1L, 3L,
NA, 5L, NA, 4L, 3L, NA, NA, NA, NA, NA, 3L, 3L, NA, 3L, NA,
NA, NA, NA, NA, NA, 5L, NA, NA, NA, NA, 3L, NA, NA, 3L, 2L,
NA, 3L, NA, NA, 3L, NA, NA, 4L, NA, 1L, NA, NA, NA, NA, NA,
NA, 3L, NA, 1L, 4L, 3L, NA, NA, NA, NA, NA, NA, 3L, NA, 4L,
NA, NA, NA, 1L, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, 3L,
NA, NA, 4L, 1L, 2L, NA, NA, 4L, NA, 3L, NA, 1L, 3L, NA, 2L,
NA, NA, NA, 1L, 1L, 3L, NA, NA, NA, NA, NA, NA, NA, NA, 2L,
4L, NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L, NA,
NA, NA, NA, 4L, NA, 2L, NA, 4L, NA, 2L, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 4L, NA,
NA, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
4L, NA, NA, 1L, NA, 2L, NA, NA, 3L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 2L, NA, NA, NA, NA, NA, NA, 3L, NA, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
5L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
1L, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 3L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, NA, NA, 5L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), levels = c("[0] No pain",
"[1] Very mild pain", "[2] Moderate pain", "[3] Fairly severe pain",
"[4] Very severe pain", "[5] Worst imaginable pain"), class = "factor")), row.names = c(NA,
-864L), class = "data.frame")
# for the missing data part - can be safely skipped. It's just the same data as above, but in a different format, since the missing data plot is drawn by a different package.
Likert_CFB_formed_miss <-
structure(list(Arm = structure(c(3L, 3L, 1L, 2L, 2L, 2L, 3L,
1L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 1L,
1L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 2L,
3L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L), levels = c("Arm1",
"Arm2", "Arm3"), class = "factor"), Screening = structure(c(3L,
4L, 4L, 4L, 3L, 3L, 4L, 3L, 4L, 4L, 4L, 6L, 5L, 3L, 3L, 5L, 5L,
5L, 3L, 5L, 5L, 4L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 2L,
5L, 4L, 4L, 5L, 5L, 4L, 5L, 3L, 5L, 4L, 5L, 3L, 5L, 5L, 6L, 3L,
1L, 3L, 3L, 4L, 4L, 5L, 4L, 6L, 5L, 3L, 6L, 4L, 3L, 3L, 4L, 5L,
4L, 5L, 3L, 5L, 3L, 3L, 6L, 2L, 5L, 4L, 3L, 3L, 2L, 3L, 5L, 4L,
3L, 2L, 4L, 5L, 3L, 3L, 5L, 4L, 4L, 5L, 3L, 3L, 3L, 4L, 5L, 3L,
3L, 3L, 3L, 4L, 5L, 5L, 3L, 4L, 4L, 4L, 5L, 3L, 3L, 6L, 5L, 3L,
3L, 3L, 3L, 3L, 2L, 4L, 2L, 4L, 3L, 3L, 5L, 4L, 3L, 5L, 5L, 5L,
5L), levels = c("[0] No pain", "[1] Very mild pain", "[2] Moderate pain",
"[3] Fairly severe pain", "[4] Very severe pain", "[5] Worst imaginable pain"
), class = "factor"), `Week 1` = structure(c(5L, 4L, 2L, 3L,
2L, 1L, 3L, 3L, 4L, 3L, 2L, 3L, 4L, 3L, 1L, 3L, 5L, 5L, 1L, 1L,
4L, 1L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 4L,
4L, 3L, 2L, NA, 4L, 1L, 3L, 1L, 2L, 2L, 3L, 5L, 3L, 3L, 4L, 4L,
2L, 2L, NA, 4L, 5L, 2L, 3L, 6L, 4L, 2L, 2L, 4L, 4L, NA, 3L, 3L,
NA, 1L, 4L, 3L, 4L, 5L, 3L, 3L, 5L, 2L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 2L, 4L, 3L, NA, 3L, 2L, 4L, 4L, 3L, 2L, 3L, 2L,
4L, 3L, 3L, 2L, 4L, 3L, 4L, 4L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 4L,
3L, 3L, 3L, 1L, 2L, 4L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 1L), levels = c("[0] No pain",
"[1] Very mild pain", "[2] Moderate pain", "[3] Fairly severe pain",
"[4] Very severe pain", "[5] Worst imaginable pain"), class = "factor"),
`Week 2` = structure(c(NA, 4L, 2L, 3L, 3L, 3L, 3L, 4L, 3L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 5L, 2L, 1L, 5L, 2L, 2L, 3L,
4L, 2L, 3L, 3L, 4L, 4L, 1L, 3L, 1L, 1L, 3L, 2L, 4L, 2L, NA,
4L, 4L, 3L, 3L, 1L, 1L, 3L, 2L, 5L, 4L, 1L, 3L, 3L, 1L, 1L,
5L, 4L, NA, 2L, NA, 5L, 2L, 3L, 2L, 4L, 4L, 4L, 2L, 3L, 3L,
2L, 4L, 4L, 3L, 5L, 3L, 3L, 4L, 1L, 2L, 3L, 3L, 4L, 2L, 5L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 2L, 4L, 3L, 3L, 5L, 1L, 3L,
1L, 4L, 2L, 4L, 1L, 3L, 3L, NA, 3L, 2L, 2L, 1L, 3L, 2L, 3L,
1L, 3L, 4L, 2L, 4L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 4L, 3L, 3L,
3L), levels = c("[0] No pain", "[1] Very mild pain", "[2] Moderate pain",
"[3] Fairly severe pain", "[4] Very severe pain", "[5] Worst imaginable pain"
), class = "factor"), `Month 1` = structure(c(1L, 4L, 3L,
2L, 2L, 2L, 3L, 4L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, NA,
2L, 3L, 6L, 1L, 1L, 3L, 3L, 1L, 3L, 4L, 4L, 3L, 2L, 3L, 1L,
3L, 1L, 5L, 5L, 3L, 3L, 4L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 5L,
4L, 3L, 3L, 2L, 1L, 1L, NA, 3L, 4L, 2L, 4L, 4L, 2L, 3L, 1L,
3L, 4L, NA, 3L, 3L, 6L, 1L, 3L, 4L, 4L, 5L, 3L, 3L, 4L, 1L,
2L, 3L, 2L, 5L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L,
4L, 3L, 3L, 4L, 1L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, NA, NA, 3L, 3L, 2L,
3L, 3L, 4L, 3L, 4L, 5L, 1L), levels = c("[0] No pain", "[1] Very mild pain",
"[2] Moderate pain", "[3] Fairly severe pain", "[4] Very severe pain",
"[5] Worst imaginable pain"), class = "factor"), `Month 3` = structure(c(1L,
3L, 2L, 1L, 3L, 3L, 3L, 4L, 5L, 3L, 2L, 2L, 4L, 2L, 3L, 2L,
1L, 5L, 2L, 3L, 6L, 4L, 2L, 3L, 2L, 2L, 3L, 5L, 3L, 3L, 3L,
4L, 1L, 3L, 1L, 5L, 6L, 3L, 2L, 4L, NA, 1L, 1L, 2L, 3L, 3L,
3L, 3L, 4L, 2L, 3L, 1L, NA, 1L, NA, 3L, NA, 1L, 2L, 5L, 5L,
2L, 3L, 5L, 5L, 1L, 3L, 3L, 3L, 1L, 3L, 5L, 4L, 5L, 5L, 3L,
4L, 1L, 1L, 3L, 1L, 4L, 3L, 3L, 4L, 2L, 3L, 1L, 3L, 1L, 4L,
1L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 5L, 1L, 1L,
3L, 4L, 2L, 4L, 2L, 4L, 3L, 2L, 2L), levels = c("[0] No pain",
"[1] Very mild pain", "[2] Moderate pain", "[3] Fairly severe pain",
"[4] Very severe pain", "[5] Worst imaginable pain"), class = "factor"),
`Month 6` = structure(c(2L, 3L, 4L, 1L, 2L, 1L, 3L, NA, 3L,
2L, 2L, 2L, 4L, 3L, 3L, 1L, 1L, NA, 2L, 2L, 6L, 4L, 2L, 5L,
2L, 1L, 2L, 4L, 3L, 4L, 2L, 3L, NA, 3L, 1L, 3L, NA, 5L, 3L,
3L, 3L, 1L, NA, 1L, 1L, 1L, 2L, NA, 3L, 2L, 3L, 4L, 1L, 1L,
NA, 3L, 5L, 1L, 5L, 5L, NA, 3L, 3L, 3L, NA, 4L, 3L, NA, 3L,
1L, 3L, 4L, 5L, 4L, 4L, 4L, 5L, 1L, 2L, 4L, 2L, 6L, 3L, 4L,
4L, NA, 5L, 1L, 2L, 1L, 6L, 1L, 1L, 3L, 3L, 3L, 4L, 1L, 3L,
2L, 3L, 3L, 2L, 3L, 2L, 1L, NA, NA, 1L, 5L, 5L, 3L, 2L, 2L,
3L, 1L, 2L, 2L, 5L, 1L, 3L, 3L, 5L, 1L, NA, 3L, 3L, 3L, 1L,
3L), levels = c("[0] No pain", "[1] Very mild pain", "[2] Moderate pain",
"[3] Fairly severe pain", "[4] Very severe pain", "[5] Worst imaginable pain"
), class = "factor"), `Month 12` = structure(c(2L, 4L, NA,
3L, 2L, 1L, 1L, NA, 4L, 3L, 2L, 2L, 4L, 2L, 4L, 2L, 1L, NA,
1L, 3L, 6L, 3L, 2L, 3L, 3L, 1L, 2L, 4L, 3L, 3L, 1L, 3L, NA,
2L, 1L, 2L, NA, 5L, 3L, 2L, 4L, 1L, 4L, 3L, 1L, 1L, 2L, NA,
3L, 2L, 2L, 2L, NA, 1L, NA, 4L, 6L, 1L, 4L, 5L, NA, 4L, 2L,
3L, NA, 4L, 4L, 1L, 3L, 2L, 3L, 5L, 6L, 3L, 4L, 4L, 5L, 1L,
3L, 3L, 1L, 6L, 1L, 3L, 3L, 5L, 3L, 3L, 2L, 2L, 4L, 1L, 1L,
2L, 4L, 3L, 4L, 1L, 5L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, NA, NA,
1L, 3L, 4L, 3L, 2L, 4L, 1L, 2L, 2L, 1L, 4L, 1L, 1L, 3L, 3L,
2L, NA, 2L, 3L, 1L, 3L, 1L), levels = c("[0] No pain", "[1] Very mild pain",
"[2] Moderate pain", "[3] Fairly severe pain", "[4] Very severe pain",
"[5] Worst imaginable pain"), class = "factor")), row.names = c(NA,
-130L), class = "data.frame")