All plots examples

1 Odds ratio plots

1.01 Standard odds ratio plot (forest plot)

This is used to visually present the results from a multivariable generalised linear model (usually logistic regression).

Here is the model:

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  finalfit(dependent, explanatory) %>% 
    knitr::kable(row.names=FALSE) # This line only needed for formatting.

Dependent: Mortality 5 year		Alive	Died	OR (univariable)	OR (multivariable)
Age	<40 years	31 (46.3)	36 (53.7)	-	-
	40-59 years	208 (61.4)	131 (38.6)	0.54 (0.32-0.92, p=0.023)	0.57 (0.34-0.98, p=0.041)
	60+ years	272 (53.4)	237 (46.6)	0.75 (0.45-1.25, p=0.270)	0.81 (0.48-1.36, p=0.426)
Sex	Female	243 (55.6)	194 (44.4)	-	-
	Male	268 (56.1)	210 (43.9)	0.98 (0.76-1.27, p=0.889)	0.98 (0.75-1.28, p=0.902)
Obstruction	No	408 (56.7)	312 (43.3)	-	-
	Yes	89 (51.1)	85 (48.9)	1.25 (0.90-1.74, p=0.189)	1.25 (0.90-1.76, p=0.186)
Perforation	No	497 (56.0)	391 (44.0)	-	-
	Yes	14 (51.9)	13 (48.1)	1.18 (0.54-2.55, p=0.672)	1.12 (0.51-2.44, p=0.770)

Simply switch to or_plot() for plot.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory)

# Note this example uses fig.height=3, fig.width=9

Adjust figure width and height to optimise formatting.

1.02 Mixed effects odds ratio plot (forest plot)

This is used to visually present the results from a mixed effects / multilevel / hierarchical generalised linear model (usually logistic regression).

Here is the model:

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
random_effect = "hospital"
colon_s %>%
  finalfit(dependent, explanatory, random_effect = random_effect)%>% 
    knitr::kable(row.names=FALSE) # This line only needed for formatting.

Dependent: Mortality 5 year		Alive	Died	OR (univariable)	OR (multilevel)
Age	<40 years	31 (46.3)	36 (53.7)	-	-
	40-59 years	208 (61.4)	131 (38.6)	0.54 (0.32-0.92, p=0.023)	0.75 (0.39-1.44, p=0.382)
	60+ years	272 (53.4)	237 (46.6)	0.75 (0.45-1.25, p=0.270)	1.03 (0.55-1.96, p=0.916)
Sex	Female	243 (55.6)	194 (44.4)	-	-
	Male	268 (56.1)	210 (43.9)	0.98 (0.76-1.27, p=0.889)	0.80 (0.58-1.11, p=0.180)
Obstruction	No	408 (56.7)	312 (43.3)	-	-
	Yes	89 (51.1)	85 (48.9)	1.25 (0.90-1.74, p=0.189)	1.23 (0.82-1.83, p=0.320)
Perforation	No	497 (56.0)	391 (44.0)	-	-
	Yes	14 (51.9)	13 (48.1)	1.18 (0.54-2.55, p=0.672)	1.03 (0.43-2.51, p=0.940)

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
random_effect = "hospital"
colon_s %>%
  or_plot(dependent, explanatory, random_effect = random_effect)

# Note this example uses fig.height=3, fig.width=9

1.03 Plotting a subset of variables from a full model

You may wish to only present a subset of variables from a full model, say an explanatory variable of interest without covariates to avoid table 2 fallacy.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'

# Run summary_factorlist for variables you wish to include
## Include total_col = TRUE and fit_id = TRUE
factorlist  = colon_s %>% 
    summary_factorlist(dependent, "age.factor", total_col = TRUE, fit_id = TRUE)

# Run full model including factorlist
colon_s %>%
  or_plot(dependent, explanatory, factorlist = factorlist)

# Note this example uses fig.height=2, fig.width=9

1.04 Plotting from model object

When a model takes a long time to run, you do not want to re-run it to plot it. Pass the model object to the plotting function.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
random_effect = "hospital"

fit = colon_s %>% 
    glmmixed(dependent, explanatory, random_effect)

# Equivalent to:
fit = colon_s %>% 
    lme4::glmer(mort_5yr ~ age.factor + sex.factor + obstruct.factor + perfor.factor + (1 | hospital), 
                            family="binomial", data = .)

# Which is incidentally equivalent to:
fit = colon_s %>% 
    lme4::glmer(ff_formula(dependent, explanatory, random_effect),
                            family="binomial", data = .)

# Plot
system.time(colon_s %>%
  or_plot(dependent, explanatory, random_effect = random_effect, glmfit = fit)
)

#>    user  system elapsed 
#>   0.526   0.000   0.533
# Note this example uses fig.height=3, fig.width=9

1.05 Confidence interval type

Change the type of confidence interval. For GLM, this is “profile” by default, but confusingly “default” is also available (Wald) which is quicker but may be biased when the likelihood function is not symmetrical around the maximum likelihood estimate. Translated - best to use “profile” or an alternative approach (like bootstrapping) for final results.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, confint_type = "default")


# Note this example uses fig.height=3, fig.width=9

1.06 Remove reference levels

This will remove reference levels for binary variables.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, remove_ref = TRUE)


# Note this example uses fig.height=3, fig.width=9

1.07 Manually specifiy x-axis breaks

Note, this will not include labels that are outwith the axis range, i.e., if the axis starts at 0.2, adding 0.1 will not result in any change. Use plot_opts to alter axis range.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, breaks = c(0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.8, 2.4))


# Note this example uses fig.height=3, fig.width=9

1.08 Adjust table column spacing

Nudge relative column positions to manage white space. Use in combination with figure width to optimise.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, column_space = c(-0.5, -0.1, 0.5))

# Note this example uses fig.height=3, fig.width=9

1.09 Adjust or remove dependent variable label

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, dependent_label = "Mortality")

# Note this example uses fig.height=3, fig.width=9

1.10 Add dependent variable label prefix

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, prefix = "Figure 1 - ")

# Note this example uses fig.height=3, fig.width=9

1.11 Adjust or remove dependent variable label suffix

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, suffix = "")


# Note this example uses fig.height=3, fig.width=9

1.12 Adjust table text size

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, table_text_size = 3)

# Note this example uses fig.height=4, fig.width=9

1.13 Adjust title text size

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory, title_text_size = 12)

# Note this example uses fig.height=3, fig.width=9

1.14 Add plot options

This adds further ggplot arguments appended in the usual way by “+”.

library(finalfit)
library(ggplot2)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
    or_plot(dependent, explanatory,
                    plot_opts = list(xlim(0.1, 3),
                                                     xlab("OR (95% CI, log)"),
                                                     theme(axis.title = element_text(size=10))
                    )
    )

# Note this example uses fig.height=3, fig.width=9

1.15 Add other options

Pass any options for fit2df() to adjust table, e.g., number of decimal places, confidence interval separator etc.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory,
                digits = c(3,3,3), confint_sep = " to ", column_space = c(-0.5, -0.1, 0.5))

# Note this example uses fig.height=3, fig.width=10

2 Hazard ratio plots

2.01 Standard hazard ratio plot

This is used to visually present the results from a multivariable Cox Proportional Hazards model.

Here is the model:

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
  finalfit(dependent, explanatory) %>% 
    knitr::kable(row.names=FALSE) # This line only needed for formatting.

Dependent: Surv(time, status)		all	HR (univariable)	HR (multivariable)
Age	<40 years	70 (7.5)	-	-
	40-59 years	344 (37.0)	0.76 (0.53-1.09, p=0.132)	0.79 (0.55-1.13, p=0.196)
	60+ years	515 (55.4)	0.93 (0.66-1.31, p=0.668)	0.98 (0.69-1.40, p=0.926)
Sex	Female	445 (47.9)	-	-
	Male	484 (52.1)	1.01 (0.84-1.22, p=0.888)	1.02 (0.85-1.23, p=0.812)
Obstruction	No	732 (80.6)	-	-
	Yes	176 (19.4)	1.29 (1.03-1.62, p=0.028)	1.30 (1.03-1.64, p=0.026)
Perforation	No	902 (97.1)	-	-
	Yes	27 (2.9)	1.17 (0.70-1.95, p=0.556)	1.08 (0.64-1.81, p=0.785)

Simply switch to hr_plot() for plot.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
  hr_plot(dependent, explanatory)

# Note this example uses fig.height=3, fig.width=9

All options list for odds ratio plots above (section 1) apply to hazard ratio plots.

3 Coefficient plots

3.01 Standard coefficient plots

This is used to visually present the results from a multivariable linear regression model.

#’ # Coefficient plot #’ explanatory = c(“age.factor”, “sex.factor”, “obstruct.factor”, “perfor.factor”) #’ dependent = “nodes” #’ colon_s %>% #’ coefficient_plot(dependent, explanatory)

Here is the model:

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
colon_s %>%
  finalfit(dependent, explanatory) %>% 
    knitr::kable(row.names=FALSE) # This line only needed for formatting.

Dependent: nodes		unit	value	Coefficient (univariable)	Coefficient (multivariable)
Age	<40 years	Mean (sd)	4.7 (4.5)	-	-
	40-59 years	Mean (sd)	3.6 (3.3)	-1.14 (-2.08 to -0.21, p=0.016)	-1.21 (-2.16 to -0.26, p=0.012)
	60+ years	Mean (sd)	3.6 (3.6)	-1.19 (-2.10 to -0.28, p=0.010)	-1.25 (-2.18 to -0.33, p=0.008)
Sex	Female	Mean (sd)	3.7 (3.6)	-	-
	Male	Mean (sd)	3.6 (3.6)	-0.14 (-0.60 to 0.33, p=0.565)	-0.07 (-0.54 to 0.40, p=0.779)
Obstruction	No	Mean (sd)	3.7 (3.7)	-	-
	Yes	Mean (sd)	3.5 (3.2)	-0.24 (-0.83 to 0.36, p=0.435)	-0.31 (-0.91 to 0.29, p=0.313)
Perforation	No	Mean (sd)	3.7 (3.6)	-	-
	Yes	Mean (sd)	3.9 (2.8)	0.24 (-1.13 to 1.61, p=0.735)	0.28 (-1.09 to 1.66, p=0.686)

Simply switch to coeffcient_plot() for plot.

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
colon_s %>%
  coefficient_plot(dependent, explanatory)

# Note this example uses fig.height=3, fig.width=9

All options list for odds ratio plots above (section 1) apply to coefficient ratio plots.

4 Wrapper for all plot types

ff_plot() will automatically determine the dependent variable type (binary, survival object, or continuous), and produce the appropriate output (odds ratio, hazard ratio, or coefficient plot).

library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
colon_s %>%
  ff_plot(dependent, explanatory)

# Note this example uses fig.height=3, fig.width=9

5 Kaplan Meier plots

Use finalfit grammar to plot a survival curve with number-at-risk table using survminer::ggsurvplot().

5.1 Standard KM plot

library(finalfit)
explanatory = "perfor.factor"
dependent = "Surv(time, status)"
colon_s %>%
    surv_plot(dependent, explanatory)

5.2 Adjust using options

Pass any of the options from survminer::ggsurvplot().

For example:

library(finalfit)
explanatory = "perfor.factor"
dependent = "Surv(time, status)"
colon_s %>%
    surv_plot(dependent, explanatory, xlab="Time (days)", pval=TRUE, legend="none")

6 Missing data plots

See https://finalfit.org/articles/missing.html

Ewen Harrison

1 Odds ratio plots

1.01 Standard odds ratio plot (forest plot)

1.02 Mixed effects odds ratio plot (forest plot)

1.03 Plotting a subset of variables from a full model

1.04 Plotting from model object

1.05 Confidence interval type

1.06 Remove reference levels

1.07 Manually specifiy x-axis breaks

1.08 Adjust table column spacing

1.09 Adjust or remove dependent variable label

1.10 Add dependent variable label prefix

1.11 Adjust or remove dependent variable label suffix

1.12 Adjust table text size

1.13 Adjust title text size

1.14 Add plot options

1.15 Add other options

2 Hazard ratio plots

2.01 Standard hazard ratio plot

3 Coefficient plots

3.01 Standard coefficient plots

4 Wrapper for all plot types

5 Kaplan Meier plots

5.1 Standard KM plot

5.2 Adjust using options

6 Missing data plots