Title: Quickly Explore Your Data Using 'ggplot2' and 'table1' Summary Tables
Version: 0.3.1
Description: Quickly and easily perform exploratory data analysis by uploading your data as a 'csv' file. Start generating insights using 'ggplot2' plots and 'table1' tables with descriptive stats, all using an easy-to-use point and click 'Shiny' interface.
URL: https://github.com/smouksassi/ggquickeda, https://smouksassi.github.io/ggquickeda/
BugReports: https://github.com/smouksassi/ggquickeda/issues
Depends: R (≥ 4.1.0)
Imports: colourpicker, dplyr, data.table, DT, Formula, GGally (≥ 2.1.0), ggbeeswarm, ggh4x, ggplot2 (≥ 3.4.0), ggpmisc, ggrepel (≥ 0.7.0), ggpubr, ggstance, glue, gridExtra, Hmisc, markdown, methods, plotly, quantreg, rlang, scales, shiny (≥ 1.0.4), shinyjs (≥ 1.1), shinyjqui, stringr, survival, survminer, tidyr, table1 (≥ 1.4.2), zoo, shinyFiles, RPostgres, forcats, ggridges, rms, tibble, patchwork (≥ 1.2.0)
Suggests: knitr, rmarkdown
License: MIT + file LICENSE
SystemRequirements: pandoc with https support
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 7.3.0
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-01-15 09:36:53 UTC; smouksas
Author: Samer Mouksassi ORCID iD [aut, cre], Dean Attali [aut], James Craig [aut] (implemented bookmarking and created pkgdown website), Benjamin Rich [aut] (provided summary stats tables table1 tab code), Michael Sachs [aut] (provided ggkm initial code)
Maintainer: Samer Mouksassi <samermouksassi@gmail.com>
Repository: CRAN
Date/Publication: 2024-01-15 10:20:02 UTC

Add 'ggplot2' layer to a sourceable 'ggplot2'

Description

Overwrite the plus operator so that if a "sourceable" object is used, the source code is kept

Usage

e1 + e2

Geom Proto

Description

Geom Proto

Geom Proto

Geom Proto

Geom Proto

Geom Proto

Geom Proto

Attach dependencies to source code Attach dependencies to the source code (any input variables are automatically attached)

Description

Attach dependencies to source code Attach dependencies to the source code (any input variables are automatically attached)

Usage

attach_source_dep(x, deps)

cloglog transformation utility

Description

cloglog transformation utility

Usage

cloglog_trans()

Cumulative hazard transformation utility

Description

Cumulative hazard transformation utility

Usage

cumhaz_trans()

step function utility

Description

step function utility

Usage

dostep(x, y)

Event transformation utility

Description

Event transformation utility

Usage

event_trans()

Add a Kaplan-Meier survival curve

Description

Add a Kaplan-Meier survival curve

Usage

geom_km(
  mapping = NULL,
  data = NULL,
  stat = "km",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  na.rm = TRUE,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

Aesthetics

geom_km understands the following aesthetics (required aesthetics are in bold):

See Also

The default stat for this geom is stat_km() see that documentation for more options to control the underlying statistical transformation.

Examples

library(ggplot2)
set.seed(123)
sex <- rbinom(250, 1, .5)
df <- data.frame(time = exp(rnorm(250, mean = sex)), status = rbinom(250, 1, .75), sex = sex)
ggplot(df, aes(time = time, status = status, color = factor(sex))) + geom_km()

Add confidence bands to a Kaplan-Meier survival curve

Description

Add confidence bands to a Kaplan-Meier survival curve

Usage

geom_kmband(
  mapping = NULL,
  data = NULL,
  stat = "kmband",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  na.rm = TRUE,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

Aesthetics

geom_kmband understands the following aesthetics (required aesthetics are in bold):

See Also

The default stat for this geom is stat_kmband(). See that documentation for more options to control the underlying statistical transformation.

Examples

library(ggplot2)
sex <- rbinom(250, 1, .5)
df <- data.frame(time = exp(rnorm(250, mean = sex)), status = rbinom(250, 1, .75), sex = sex)
ggplot(df, aes(time = time, status = status, color = factor(sex), fill =factor(sex))) +
 geom_km() + geom_kmband()

Add tick marks to a Kaplan-Meier survival curve

Description

Adds tickmarks at the times when there are censored observations but no events

Usage

geom_kmticks(
  mapping = NULL,
  data = NULL,
  stat = "kmticks",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  na.rm = TRUE,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

Aesthetics

geom_kmticks understands the following aesthetics (required aesthetics are in bold):

See Also

The default stat for this geom is stat_kmticks see that documentation for more options to control the underlying statistical transformation.

Examples

library(ggplot2)
sex <- rbinom(250, 1, .5)
df <- data.frame(time = exp(rnorm(250, mean = sex)), status = rbinom(250, 1, .75), sex = sex)
ggplot(df, aes(time = time, status = status, color = factor(sex), group = factor(sex))) +
 geom_km() + geom_kmticks(col="black")

Retrieve the source code of a "sourceable" 'ggplot2'

Description

Retrieve the source code of a "sourceable" 'ggplot2'

Usage

get_source_code(x)

Create a continuous exposure fit plot

Description

Produces a logistic fit plot with a facettable exposures/quantiles/distributions in ggplot2

Usage

ggcontinuousexpdist(
  data = effICGI,
  response = "response",
  endpoint = "Endpoint",
  DOSE = "DOSE",
  color_fill = "DOSE",
  exposure_metrics = c("AUC", "CMAX"),
  exposure_metric_split = c("median", "tertile", "quartile", "none"),
  exposure_metric_soc_value = -99,
  exposure_metric_plac_value = 0,
  exposure_distribution = c("distributions", "lineranges", "none"),
  dose_plac_value = "Placebo",
  xlab = "Exposure Values",
  ylab = "Probability of Response",
  mean_text_size = 5,
  mean_obs_bydose = TRUE,
  N_text_size = 5,
  binlimits_text_size = 5,
  binlimits_ypos = -Inf,
  binlimits_color = "gray70",
  dist_position_scaler = 0.2,
  dist_offset = 0,
  lineranges_ypos = -1,
  lineranges_dodge = 1,
  yproj = TRUE,
  yproj_xpos = 0,
  yproj_dodge = 0.2,
  yaxis_position = c("left", "right"),
  facet_formula = NULL,
  theme_certara = TRUE
)

Arguments

data

Data to use with multiple endpoints stacked into Endpoint(endpoint name), response 0/1

response

name of the column holding the valuesresponse 0/1

endpoint

name of the column holding the name/key of the endpoint default to Endpoint

DOSE

name of the column holding the DOSE values default to DOSE

color_fill

name of the column to be used for color/fill default to DOSE column

exposure_metrics

name(s) of the column(s) to be stacked into expname exptile and split into exposure_metric_split

exposure_metric_split

one of "median", "tertile", "quartile", "none"

exposure_metric_soc_value

special exposure code for standard of care default -99

exposure_metric_plac_value

special exposure code for placebo default 0

exposure_distribution

one of distributions, lineranges or none

dose_plac_value

string identifying placebo in DOSE column

xlab

text to be used as x axis label

ylab

text to be used as y axis label

mean_text_size

mean text size default to 5

mean_obs_bydose

observed mean by dose TRUE/FALSE

N_text_size

N respondents/Ntotal by exposure bin text size default to 5

binlimits_text_size

5 binlimits text size

binlimits_ypos

binlimits y position default to 0

binlimits_color

binlimits text color default to "gray70"

dist_position_scaler

space occupied by the distribution default to 0.2

dist_offset

offset where the distribution position starts 0

lineranges_ypos

where to put the lineranges -1

lineranges_dodge

lineranges vertical dodge value 1

yproj

project the probabilities on y axis TRUE/FALSE

yproj_xpos

y projection x position 0

yproj_dodge

y projection dodge value 0.2

yaxis_position

where to put y axis "left" or "right"

facet_formula

facet formula to be use otherwise endpoint ~ expname

theme_certara

apply certara colors and format for strips and default colour/fill

Examples

# Example 1
library(ggplot2)
library(patchwork)
effICGI <- logistic_data |>
dplyr::filter(!is.na(ICGI7))|>
dplyr::filter(!is.na(AUC))
effICGI$DOSE <- factor(effICGI$DOSE,
                      levels=c("0", "600", "1200","1800","2400"),
                      labels=c("Placebo", "600 mg", "1200 mg","1800 mg","2400 mg"))
effICGI$STUDY <- factor(effICGI$STUDY)    
effICGI <- tidyr::gather(effICGI,Endpoint,response,ICGI7,BRLS)
a <- ggcontinuousexpdist(data = effICGI |> dplyr::filter(Endpoint =="ICGI7"),
                 response = "response",
                 endpoint = "Endpoint",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 1, dist_offset = -1 ,
                 yproj_xpos =  -20 ,
                 yproj_dodge = 20 ,
                 exposure_distribution ="distributions")

b <- ggcontinuousexpdist(data = effICGI |> dplyr::filter(Endpoint =="BRLS"),
                 response = "response",
                 endpoint = "Endpoint",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 4.2, dist_offset = 5 ,
                 yproj_xpos =  -20 ,
                 yproj_dodge = 20 ,
                 exposure_distribution ="distributions")            
a / b +
plot_layout(guides = "collect") &
 theme(legend.position = "top")

#Example 2
effICGI$SEX <- as.factor(effICGI$SEX)
ggcontinuousexpdist(data = effICGI |>
  dplyr::filter(Endpoint =="ICGI7"),
                 response = "response",
                 endpoint = "Endpoint",
                 color_fill = "SEX",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 1, dist_offset = -1 ,
                 yproj_xpos =  -20 ,
                 yproj_dodge = 20 ,
                 exposure_distribution ="lineranges")
## Not run: 
#Example 5

## End(Not run)

Create a Kaplan-Meier plot with risk table

Description

Produces a km plot with a facettable risk table in ggplot2

Usage

ggkmrisktable(
  data = lung_long,
  time = "time",
  status = "DV",
  endpoint = "Endpoint",
  groupvar1 = "Endpoint",
  groupvar2 = "expname",
  groupvar3 = "none",
  exposure_metrics = c("age", "ph.karno"),
  exposure_metric_split = c("median", "tertile", "quartile", "none"),
  exposure_metric_soc_value = -99,
  exposure_metric_plac_value = 0,
  color_fill = "exptile",
  linetype = "exptile",
  xlab = "Time of follow_up",
  ylab = "Overall survival probability",
  nrisk_table_plot = TRUE,
  nrisk_table_variables = c("n.risk", "pct.risk", "n.event", "cum.n.event", "n.censor"),
  nrisk_table_breaktimeby = NULL,
  nrisk_table_textsize = 4,
  nrisk_position_scaler = 0.2,
  nrisk_position_dodge = 0.2,
  nrisk_offset = 0,
  nrisk_filterout0 = FALSE,
  km_logrank_pvalue = FALSE,
  km_logrank_pvalue_pos = c("left", "right"),
  km_trans = c("identity", "event", "cumhaz", "cloglog"),
  km_ticks = TRUE,
  km_band = TRUE,
  km_conf_int = 0.95,
  km_conf_type = c("log", "plain", "log", "log-log", "logit", "none"),
  km_conf_lower = c("usual", "peto", "modified"),
  km_median = c("none", "median", "medianci", "table"),
  km_median_table_pos = c("left", "right"),
  km_median_table_order = c("default", "reverse"),
  km_yaxis_position = c("left", "right"),
  facet_formula = NULL,
  facet_ncol = NULL,
  facet_strip_position = c("top", "top", "top", "top"),
  theme_certara = TRUE
)

Arguments

data

Data to use with multiple endpoints stacked into time, status, endpoint name

time

name of the column holding the time to event information default to time

status

name of the column holding the event information default to DV

endpoint

name of the column holding the name/key of the endpoint default to Endpoint

groupvar1

name of the column to group by, default Endpoint

groupvar2

name of the column to group by in addition to groupvar1, default expname

groupvar3

name of the column to group by in addition to groupvar1 and groupvar2, default "none"

exposure_metrics

name(s) of the column(s) to be stacked into expname exptile and split into exposure_metric_split

exposure_metric_split

one of "median", "tertile", "quartile", "none"

exposure_metric_soc_value

special exposure code for standard of care default -99

exposure_metric_plac_value

special exposure code for placebo default 0

color_fill

name of the column to be used for color/fill default to exptile

linetype

name of the column to be used for linetype default to exptile

xlab

text to be used as x axis label

ylab

text to be used as y axis label

nrisk_table_plot

TRUE

nrisk_table_variables

one or more from: "n.risk", "pct.risk", "n.event, "cum.n.event, "n.censor"

nrisk_table_breaktimeby

NULL

nrisk_table_textsize

4

nrisk_position_scaler

0.2

nrisk_position_dodge

0.2, negative values will reverse the order

nrisk_offset

0

nrisk_filterout0

FALSE

km_logrank_pvalue

FALSE

km_logrank_pvalue_pos

"left" or "right"

km_trans

one of "identity","event","cumhaz","cloglog"

km_ticks

TRUE

km_band

TRUE

km_conf_int

0.95

km_conf_type

default one of "log", "plain", "log-log", "logit", "none"

km_conf_lower

one of "usual", "peto", "modified"

km_median

add median survival information one of "none", "median", "medianci", "table"

km_median_table_pos

when table is chosen where to put it "left" or "right

km_median_table_order

when table is chosen the order of the entries "default" or "reverse"

km_yaxis_position

where to put y axis on "left" or "right

facet_formula

facet formula to be used otherwise ~ groupvar1 + groupvar2 + groupvar3

facet_ncol

NULL if not specified the automatic waiver will be used

facet_strip_position

position in sequence for the variable used in faceting default to c("top","top","top","top")

theme_certara

apply certara colors and format for strips and default colour/fill

Examples

library(tidyr)
# Example 1
lung_long <-  survival::lung |>
 dplyr::mutate(status = ifelse(status==1,0,1)) |>
 tidyr::gather(Endpoint,DV,status) |>
 dplyr::filter(!is.na(ph.karno))|>
 dplyr::filter(!is.na(pat.karno))|>
 dplyr::filter(!is.na(ph.ecog))
lung_long$ph.ecog <- ifelse(lung_long$ph.ecog>1,2,lung_long$ph.ecog)
lung_long$ph.ecog <- as.factor(lung_long$ph.ecog )
lung_long$ph.ecog <- as.factor(lung_long$ph.ecog )
lung_long$facetdum <- "(all)"

ggkmrisktable(data = lung_long, time= "time", status ="DV",
             exposure_metrics =c("age","ph.karno"),
             exposure_metric_split = "tertile",
             color_fill = "exptile",
             linetype = "expname",
             groupvar1 = "Endpoint",
             groupvar2 = "exptile",
             xlab = "Time of follow_up",
             ylab ="Overall survival probability",
             nrisk_table_variables = c("n.risk","n.event"),
             km_median = "medianci",
             km_band = FALSE,
             nrisk_table_breaktimeby = 200,
             facet_ncol = 3)
#Example 2
ggkmrisktable(data = lung_long, time= "time", status ="DV",
             exposure_metrics =c("age","ph.karno"),
             exposure_metric_split = "quartile",
             color_fill = "exptile",
             linetype = "none",
             groupvar1 = "Endpoint",
             groupvar2 = "exptile",
             xlab = "Time of follow_up",
             ylab ="Overall survival probability",
             nrisk_table_variables = c("cum.n.event","pct.risk","n.censor"),
             km_median = "medianci",
             km_band = TRUE,
             km_trans = "event",
             nrisk_table_breaktimeby = 200,
             facet_ncol = 3,
             facet_formula = ~expname)
## Not run: 
#Example 3
ggkmrisktable(data = lung_long, time = "time", status = "DV",
             exposure_metrics =c("ph.karno","pat.karno"),
             exposure_metric_split = "median",
             color_fill = "exptile",
             linetype = "exptile",
             groupvar1 = "Endpoint",
             groupvar2 = "expname",
             xlab = "Time of follow_up",
             ylab ="Overall survival probability",
             nrisk_table_variables = c("n.event"),
             km_trans = "event",
             km_median = "table",
             km_median_table_pos = "right",
             km_logrank_pvalue = TRUE,
             km_band = TRUE,
             nrisk_table_breaktimeby = 200,
             facet_ncol = 3,
             facet_formula = ~expname)
#Example 4
ggkmrisktable(data=lung_long,
             exposure_metrics = c("ph.karno","age"),
             exposure_metric_split = "median",
             time = "time",
             status ="DV",
             color_fill = "ph.ecog",
             linetype = "ph.ecog",
             groupvar1 = "Endpoint",
             groupvar2 = "expname",
             groupvar3 = "exptile",
             nrisk_filterout0 = FALSE,
             nrisk_table_breaktimeby = 200,
             km_logrank_pvalue = TRUE,
             km_median = "table",
             km_median_table_pos = "left",
             facet_formula = ~expname+exptile)
#Example 5

ggkmrisktable(data=lung_long,
             exposure_metrics = c("ph.karno","age"),
             exposure_metric_split = "none",
              color_fill = "facetdum",
             linetype = "none",
             nrisk_table_variables = c("n.risk", "pct.risk", "n.event", "cum.n.event", "n.censor"),
              km_median = "table",
              nrisk_position_scaler = 0.1
             )             
             

## End(Not run)

Create a logistic fit plot

Description

Produces a logistic fit plot with a facettable exposures/quantiles/distributions in ggplot2

Usage

gglogisticexpdist(
  data = effICGI,
  response = "response",
  endpoint = "Endpoint",
  DOSE = "DOSE",
  color_fill = "DOSE",
  exposure_metrics = c("AUC", "CMAX"),
  exposure_metric_split = c("median", "tertile", "quartile", "none"),
  exposure_metric_soc_value = -99,
  exposure_metric_plac_value = 0,
  exposure_distribution = c("distributions", "lineranges", "none"),
  dose_plac_value = "Placebo",
  xlab = "Exposure Values",
  ylab = "Probability of Response",
  prob_text_size = 5,
  prob_obs_bydose = TRUE,
  N_text_size = 5,
  binlimits_text_size = 5,
  binlimits_ypos = 0,
  binlimits_color = "gray70",
  dist_position_scaler = 0.2,
  dist_offset = 0,
  lineranges_ypos = 0.2,
  lineranges_dodge = 0.15,
  yproj = TRUE,
  yproj_xpos = 0,
  yproj_dodge = 0.2,
  yaxis_position = c("left", "right"),
  facet_formula = NULL,
  theme_certara = TRUE
)

Arguments

data

Data to use with multiple endpoints stacked into Endpoint(endpoint name), response 0/1

response

name of the column holding the values response 0/1

endpoint

name of the column holding the name/key of the endpoint default to Endpoint

DOSE

name of the column holding the DOSE values default to DOSE

color_fill

name of the column to be used for color/fill default to DOSE column

exposure_metrics

name(s) of the column(s) to be stacked into expname exptile and split into exposure_metric_split

exposure_metric_split

one of "median", "tertile", "quartile", "none"

exposure_metric_soc_value

special exposure code for standard of care default -99

exposure_metric_plac_value

special exposure code for placebo default 0

exposure_distribution

one of distributions, lineranges or none

dose_plac_value

string identifying placebo in DOSE column

xlab

text to be used as x axis label

ylab

text to be used as y axis label

prob_text_size

probability text size default to 5

prob_obs_bydose

observed probability by dose TRUE/FALSE

N_text_size

N responders/Ntotal by exposure bin text size default to 5

binlimits_text_size

5 binlimits text size

binlimits_ypos

binlimits y position default to 0

binlimits_color

binlimits text color default to "gray70"

dist_position_scaler

space occupied by the distribution default to 0.2

dist_offset

offset where the distribution position starts 0

lineranges_ypos

where to put the lineranges -1

lineranges_dodge

lineranges vertical dodge value 1

yproj

project the probabilities on y axis TRUE/FALSE

yproj_xpos

y projection x position 0

yproj_dodge

y projection dodge value 0.2

yaxis_position

where to put y axis "left" or "right"

facet_formula

facet formula to be use otherwise endpoint ~ expname

theme_certara

apply certara colors and format for strips and default colour/fill

Examples

# Example 1
library(ggplot2)
effICGI <- logistic_data |>
dplyr::filter(!is.na(ICGI))|>
dplyr::filter(!is.na(AUC))
effICGI$DOSE <- factor(effICGI$DOSE,
                      levels=c("0", "600", "1200","1800","2400"),
                      labels=c("Placebo", "600 mg", "1200 mg","1800 mg","2400 mg"))
effICGI$STUDY <- factor(effICGI$STUDY)    
effICGI$ICGI2 <- effICGI$ICGI
effICGI <- tidyr::gather(effICGI,Endpoint,response,ICGI,ICGI2)
gglogisticexpdist(data = effICGI |>
                 dplyr::filter(Endpoint=="ICGI"),
                 response = "response",
                 endpoint = "Endpoint",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 exposure_distribution ="distributions",
                 yproj_xpos = -15,
                 yproj_dodge = 10,
                 dist_position_scaler = 0.1,
                 dist_offset = -0.1)
                 
# Example 2                
gglogisticexpdist(data = effICGI |>
                 dplyr::filter(Endpoint=="ICGI"),
                 response = "response",
                 endpoint = "Endpoint",
                 exposure_metrics = c("CMAX"),
                 exposure_metric_split = c("tertile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 exposure_distribution ="lineranges",
                 lineranges_ypos = -0.2,
                 lineranges_dodge = 0.4,
                 prob_obs_bydose = TRUE,
                 yproj_xpos = -5,
                 yproj_dodge = 5,
                 dist_position_scaler = 0.1)


## Not run: 
#' # Example 3                
library(ggh4x)
gglogisticexpdist(data = effICGI |>
                 dplyr::filter(Endpoint=="ICGI"), 
                 response = "response",
                 endpoint = "Endpoint",
                 DOSE = "DOSE",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_distribution ="distributions",
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 0.15)+
 facet_grid2(Endpoint~expname+DOSE2,scales="free",
 margins = "DOSE2",strip = strip_nested())
# Example 4  
effICGI$SEX <- as.factor(effICGI$SEX)               
gglogisticexpdist(data = effICGI  |>
                 dplyr::filter(Endpoint=="ICGI"), 
                 response = "response",
                 endpoint = "Endpoint",
                 DOSE = "DOSE",
                 color_fill = "SEX",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_distribution ="distributions",
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 lineranges_ypos = -0.2,
                 yproj_xpos = -10,
                 yproj_dodge = 20,
                 prob_text_size = 6,
                 binlimits_text_size = 6,
                 N_text_size = 4,
                 dist_position_scaler = 0.15)+
                 ggplot2::scale_x_continuous(breaks = seq(0,350,50),
                 expand = ggplot2::expansion(add= c(0,0),mult=c(0,0)))+
                 ggplot2::coord_cartesian(xlim = c(-30,355))+
                 ggplot2::facet_grid(Endpoint~expname+color_fill2, margins ="color_fill2" )

#Example 4b
  effICGI$SEX <- as.factor(effICGI$SEX)
 gglogisticexpdist(data = effICGI |>
  dplyr::filter(Endpoint =="ICGI"),
                 response = "response",
                 endpoint = "Endpoint",
                 color_fill = "SEX",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 1, dist_offset = -1 ,
                 yproj_xpos =  -20 ,
                 yproj_dodge = 20 ,
                 exposure_distribution ="lineranges")
                 
#Example 5
gglogisticexpdist(data = effICGI |> dplyr::filter(Endpoint=="ICGI"), 
                  response = "response",
                  endpoint = "Endpoint",
                  DOSE = "DOSE",
                  exposure_metrics = c("AUC"),
                  exposure_metric_split = c("quartile"),
                  exposure_distribution ="distributions",
                  exposure_metric_soc_value = -99,
                  exposure_metric_plac_value = 0,
                  dist_position_scaler = 0.15)+
                 facet_grid(Endpoint~expname+exptile,scales="free",
                 margins = "exptile")
#Example 6
a <- gglogisticexpdist(data = effICGI, # 
                  response = "response",
                  endpoint = "Endpoint",
                  DOSE = "DOSE",yproj_dodge = 36,
                  exposure_metrics = c("AUC"),
                  exposure_metric_split = c("quartile"),
                  exposure_distribution ="lineranges",
                  exposure_metric_soc_value = -99,
                  exposure_metric_plac_value = 0) +
  facet_grid(Endpoint~expname,switch = "both")
b <-   gglogisticexpdist(data = effICGI, # 
                    response = "response",
                    endpoint = "Endpoint",
                    DOSE = "DOSE",yproj_dodge = 2,
                    exposure_metrics = c("CMAX"),
                    exposure_metric_split = c("quartile"),
                    exposure_distribution ="lineranges",
                    exposure_metric_soc_value = -99,
                    exposure_metric_plac_value = 0,
                    yaxis_position = "right")+
  facet_grid(Endpoint~expname,switch = "x")+
  theme(strip.text.y.right = element_blank(),
        strip.background.y = element_blank())
library(patchwork)
(a | b ) +
  plot_layout(guides = "collect")&
  theme(legend.position = "top")
                  

## End(Not run)

Simulated Exposure Response Data

Description

A dataset containing data suitable for logistic regression

Usage

logistic_data

Format

A data frame with 600 rows and 10 variables

STUDY

Study identifier

ID

Subject Identifier

DOSE

Dose, in mg

GBDS

Dose, in alternative salt

SEX

Sex of the subject

AGE

age of the subject, in years

WT

weight of the subject, in kg

RACE

Race of the subject

CRCL

Creatinine clearance

BRLS

RLS score

PRLS

RLS score

AUC

Area under the curve exposure

CMAX

Maximun concentration exposure

ICGI

response 0/1

ICGI7

response 1 to 7

Source

inspired from a real data submission

Examples

logistic_data

merge step function utility

Description

merge step function utility

Usage

merge_steps(s1, s2)

Run the ggquickeda application

Description

Run the ggquickeda application.

Usage

run_ggquickeda(data = NULL, ...)

Arguments

data

The initial data.frame to load into the application.

...

Additional arguments for bookmarking

Examples

if (interactive()) {
  run_ggquickeda()
}

Simulated Pharmacokinetic Concentration Data

Description

A dataset containing concentration-time data with the given dose and some subject characteristics to help in the app exploration.

Usage

sample_data

Format

A data frame with 600 rows and 10 variables

ID

Subject Identifier, an integer from 1 to 150

Time

Time of dose given or drug sample measured, in hours

Amt

dose given at the corresponding Time, in milligrams

Conc

drug concentrations in the plasma sample, in mg/L

Age

age of the subject, in years

Weight

weight of the subject, in kg

Gender

Sex of the subject, a factor with Female and Male levels

Race

Race of the subject, a factor with Asian, Black, Caucasian, Hispanic and Other levels

Dose

dose group of the subject, in milligrams

AGECAT

age category of the subject, a variable cutting Age into two values 0/1

Source

"sd_oral_richpk" from 'PKPDmisc' R package with an additional AGECAT variable

Examples

sample_data

Make a 'ggplot2' object sourceable

Description

Define a 'ggplot2' object as "sourceable", which means that it knows how to keep track of its source code (parameter must be a 'ggplot2' object)

Usage

sourceable(x)

step function utility from ggalt

Description

step function utility from ggalt

Usage

stairstepn(data, yvars = "y")

Adds a Kaplan Meier Estimate of Survival

Description

Adds a Kaplan Meier Estimate of Survival

Usage

stat_km(
  mapping = NULL,
  data = NULL,
  geom = "km",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  trans = scales::identity_trans(),
  firstx = 0,
  firsty = 1,
  type = "kaplan-meier",
  start.time = 0,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

trans

Transformation to apply to the survival probabilities. Defaults to "identity". Other options include "event", "cumhaz", "cloglog", or define your own using trans_new.

firstx, firsty

the starting point for the survival curves. By default, the plot program obeys tradition by having the plot start at ⁠(0,1)⁠.

type

an older argument that combined stype and ctype, now deprecated. Legal values were "kaplan-meier" which is equivalent to stype=1, ctype=1, "fleming-harrington" which is equivalent to stype=2, ctype=1, and "fh2" which is equivalent to stype=2, ctype=2.

start.time

numeric value specifying a time to start calculating survival information. The resulting curve is the survival conditional on surviving to start.time.

...

Other arguments passed to survfit.formula

Details

This stat is for computing the confidence intervals for the Kaplan-Meier survival estimate for right-censored data. It requires the aesthetic mapping x for the observation times and status which indicates the event status, 0=alive, 1=dead or 1/2 (2=death). Logical status is not supported.

Value

a data.frame with additional columns:

x

x in data

y

Kaplan-Meier Survival Estimate at x

Aesthetics

stat_km understands the following aesthetics (required aesthetics are in bold):

Examples

library(ggplot2)
sex <- rbinom(250, 1, .5)
df <- data.frame(time = exp(rnorm(250, mean = sex)), status = rbinom(250, 1, .75), sex = sex)
ggplot(df, aes(time = time, status = status, color = factor(sex))) +
 stat_km()

## Examples illustrating the options passed to survfit.formula

p1 <- ggplot(df, aes(time = time, status = status))
p1 + stat_km()
p1 + stat_km(trans = "cumhaz")
# for cloglog plots also log transform the time axis
p1 + stat_km(trans = "cloglog") + scale_x_log10()
p1 + stat_km(type = "fleming-harrington")
p1 + stat_km(start.time = 5)

Adds confidence bands to a Kaplan Meier Estimate of Survival

Description

Adds confidence bands to a Kaplan Meier Estimate of Survival

Usage

stat_kmband(
  mapping = NULL,
  data = NULL,
  geom = "kmband",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  trans = "identity",
  firstx = 0,
  firsty = 1,
  type = "kaplan-meier",
  error = "greenwood",
  conf.type = "log",
  conf.lower = "usual",
  start.time = 0,
  conf.int = 0.95,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

trans

Transformation to apply to the survival probabilities. Defaults to "identity". Other options include "event", "cumhaz", "cloglog", or define your own using scales::trans_new().

firstx, firsty

the starting point for the survival curves. By default, the plot program obeys tradition by having the plot start at (0,1).

type

an older argument that combined stype and ctype, now deprecated. Legal values were "kaplan-meier" which is equivalent to stype=1, ctype=1, "fleming-harrington" which is equivalent to stype=2, ctype=1, and "fh2" which is equivalent to stype=2, ctype=2.

error

either the string "greenwood" for the Greenwood formula or "tsiatis" for the Tsiatis formula, (only the first character is necessary). The default is "greenwood".

conf.type

One of "none", "plain", "log" (the default), "log-log" or "logit".

conf.lower

a character string to specify modified lower limits to the curve, the upper limit remains unchanged. Possible values are "usual" (unmodified), "peto", and "modified". The modified lower limit is based on an "effective n" argument. The confidence bands will agree with the usual calculation at each death time, but unlike the usual bands the confidence interval becomes wider at each censored observation. The extra width is obtained by multiplying the usual variance by a factor m/n, where n is the number currently at risk and m is the number at risk at the last death time. (The bands thus agree with the un-modified bands at each death time.) This is especially useful for survival curves with a long flat tail. The Peto lower limit is based on the same "effective n" argument as the modified limit, but also replaces the usual Greenwood variance term with a simple approximation. It is known to be conservative.

start.time

numeric value specifying a time to start calculating survival information. The resulting curve is the survival conditional on surviving to start.time.

conf.int

the level for a two-sided confidence interval on the survival curve(s). Default is 0.95.

...

Other arguments passed to survfit.formula

Details

This stat is for computing the confidence intervals for the Kaplan-Meier survival estimate for right-censored data. It requires the aesthetic mapping x for the observation times and status which indicates the event status, 0=alive, 1=dead or 1/2 (2=death). Logical status is not supported.

Value

a data.frame with additional columns:

x

x in data

ymin

Lower confidence limit of KM curve

ymax

Upper confidence limit of KM curve

Aesthetics

stat_kmband understands the following aesthetics (required aesthetics are in bold):

Examples

library(ggplot2)
sex <- rbinom(250, 1, .5)
df <- data.frame(time = exp(rnorm(250, mean = sex)), status = rbinom(250, 1, .75), sex = sex)
ggplot(df, aes(time = time, status = status, color = factor(sex))) +
 stat_km()

## Examples illustrating the options passed to survfit.formula

p1 <- ggplot(df, aes(time = time, status = status))
p1 + stat_km() + stat_kmband(conf.int = .99)
p1 + stat_kmband(error = "greenwood",fill="red",alpha=0.2) +
 stat_kmband(error = "tsiatis",fill="blue",alpha=0.2)+ stat_km()
p1 + stat_km() + stat_kmband(conf.type = "log-log")+ stat_kmband(conf.type = "log")

Adds tick marks to a Kaplan Meier Estimate of Survival

Description

Adds tick marks to a Kaplan Meier Estimate of Survival

Usage

stat_kmticks(
  mapping = NULL,
  data = NULL,
  geom = "kmticks",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  trans,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

trans

Transformation to apply to the survival probabilities. Defaults to "identity". Other options include "event", "cumhaz", "cloglog", or define your own using trans_new.

...

Other arguments passed to survfit.formula

Details

This stat is for computing the tick marks for a Kaplan-Meier survival estimate for right-censored data. The tick marks will appear at each censoring time which is also not a death time, which is the default for plot.survfit. It requires the aesthetic mapping x for the observation times and status which indicates the event status, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE = death) or 1/2 (2=death).

Value

a data.frame with additional columns:

x

x in data

y

Kaplan-Meier Survival Estimate at x

Aesthetics

stat_kmticks understands the following aesthetics (required aesthetics are in bold):

See Also

stat_km; stat_kmband

Examples

library(ggplot2)
sex <- rbinom(250, 1, .5)
df <- data.frame(time = exp(rnorm(250, mean = sex)), status = rbinom(250, 1, .75), sex = sex)
ggplot(df, aes(time = time, status = status, color = factor(sex))) +
 stat_km() + stat_kmticks()