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Stanford University School of Medicine [cph, fnd]| Title: | Plots the CHOIR Body Map |
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| Description: | Collection of utility functions for visualizing body map data collected with the Collaborative Health Outcomes Information Registry. |
| Authors: | Eric Cramer [aut, cre] ,
Stanford University School of Medicine [cph, fnd] |
| Maintainer: | Eric Cramer <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.2.9000 |
| Built: | 2025-02-24 05:34:27 UTC |
| Source: | https://github.com/emcramer/choirbm |
Takes a list of data frames where each data frame is the CBM of a patient and the values column is a binary endorsement of the CBM segment. Then it collapses the information to a single data frame for plotting by adding the 'value' columns of each data frame.
agg_choirbm_list(cbm_list)agg_choirbm_list(cbm_list)
cbm_list |
a list of CBMs to collapse |
map_df a single CBM data frame with the value column summed.
## Not run: data(validation) cbm_list <- lapply(validation[["bodymap_regions_csv"]], string_to_map) agg_df <- agg_choirbm_list(cbm_list) ## End(Not run)## Not run: data(validation) cbm_list <- lapply(validation[["bodymap_regions_csv"]], string_to_map) agg_df <- agg_choirbm_list(cbm_list) ## End(Not run)
Compare CBM segment endorsement across categorical variable using Chi-Square
comp_choirbm_chi(cbm_list, ...)comp_choirbm_chi(cbm_list, ...)
cbm_list |
a named list of CBMs |
... |
additional parameters passed to p.adjust() |
a data frame with the p-values, chi statistic, and degrees of freedom
## Not run: data(validation) # split male and female data male_data <- validation[validation[['gender']] == "Male", ] male_bodymap_list <- lapply( male_data[["bodymap_regions_csv"]] , string_to_map) male_bodymap_df <- agg_choirbm_list(male_bodymap_list) female_data <- validation[validation[['gender']] == "Female", ] female_bodymap_list <- lapply( female_data[["bodymap_regions_csv"]] , string_to_map) female_bodymap_df <- agg_choirbm_list(female_bodymap_list) # compare with chi square test chi_res <- comp_choirbm_chi( list("male" = male_bodymap_df , "female" = female_bodymap_df) , method = 'bonferroni' ) ## End(Not run)## Not run: data(validation) # split male and female data male_data <- validation[validation[['gender']] == "Male", ] male_bodymap_list <- lapply( male_data[["bodymap_regions_csv"]] , string_to_map) male_bodymap_df <- agg_choirbm_list(male_bodymap_list) female_data <- validation[validation[['gender']] == "Female", ] female_bodymap_list <- lapply( female_data[["bodymap_regions_csv"]] , string_to_map) female_bodymap_df <- agg_choirbm_list(female_bodymap_list) # compare with chi square test chi_res <- comp_choirbm_chi( list("male" = male_bodymap_df , "female" = female_bodymap_df) , method = 'bonferroni' ) ## End(Not run)
Examine the effect of a continuous variable on CBM location endorsement
comp_choirbm_glm(in_df, comp_var, method = "bonferroni", ...)comp_choirbm_glm(in_df, comp_var, method = "bonferroni", ...)
in_df |
a data.frame with at least one column for the CBM as a delimited string, and another column as the continuous variable for modeling. |
comp_var |
the name of the variable to model as a string. |
... |
additional parameters passed to glm. |
a data.frame with the following columns: id, term, estimate, std.error, statistic, p.value. Each row is the result of one glm using the continuous variable to predict CBM location endorsement.
## Not run: data(validation) set.seed(123) sampled_data <- validation[sample(1:nrow(validation), 100, replace = FALSE),] model_ouput <- comp_choirbm_glm(sampled_data, "age") ## End(Not run)## Not run: data(validation) set.seed(123) sampled_data <- validation[sample(1:nrow(validation), 100, replace = FALSE),] model_ouput <- comp_choirbm_glm(sampled_data, "age") ## End(Not run)
Compare CBM segment endorsement across categorical variable using z-test(s)
comp_choirbm_ztest(cbm_list, tail = "two", p.method = "bonferroni")comp_choirbm_ztest(cbm_list, tail = "two", p.method = "bonferroni")
cbm_list |
a named list of CBMs |
tail |
whether to do a single or two tailed z test |
... |
additional parameters passed to p.adjust() |
a data frame with the p-values and z statistic
library(CHOIRBM) # isolate and process male data male_data <- validation[validation[["gender"]] == "Male", ] # isolate and process female data female_data <- validation[validation[["gender"]] == "Female", ] comp_choirbm_ztest(list( "male" = male_data, "female" = female_data), tail = "two")library(CHOIRBM) # isolate and process male data male_data <- validation[validation[["gender"]] == "Male", ] # isolate and process female data female_data <- validation[validation[["gender"]] == "Female", ] comp_choirbm_ztest(list( "male" = male_data, "female" = female_data), tail = "two")
Calculates the raw number of times two locations on the CBM are endorsed together in a data set.
comp_cooccurrence(df)comp_cooccurrence(df)
df |
a data.frame with the CBMs as delimited strings in a single column. |
a data.frame with every combination of CBM locations and the number of times those locations occur together (the "co-occurrence").
## Not run: set.seed(123) sampled_data <- validation[sample(1:nrow(validation), 100, replace = FALSE),] colnames(sampled_data)[5] <- "bodymap" con_mat <- comp_cooccurrence(sampled_data) ## End(Not run)## Not run: set.seed(123) sampled_data <- validation[sample(1:nrow(validation), 100, replace = FALSE),] colnames(sampled_data)[5] <- "bodymap" con_mat <- comp_cooccurrence(sampled_data) ## End(Not run)
convert_bodymap Helper function to convert a single bodymap
convert_bodymap(segments)convert_bodymap(segments)
segments |
a character vector containing segment numbers as individual strings in the vector that need to be adjusted/standardized |
a character vector containing standardized segment numbers as individual strings in the vector
exampledata <- data.frame( GENDER = as.character(c("Male", "Female", "Female")), BODYMAP_CSV = as.character(c("112,125","112,113","128,117")) ) convert_bodymap(exampledata[2,2])exampledata <- data.frame( GENDER = as.character(c("Male", "Female", "Female")), BODYMAP_CSV = as.character(c("112,125","112,113","128,117")) ) convert_bodymap(exampledata[2,2])
convert_bodymaps Function to convert multiple bodymaps
convert_bodymaps(f_maps)convert_bodymaps(f_maps)
f_maps |
a character vector where each string is a CHOIR bodymap in csv form |
a character vector of bodymaps using the male CHOIR bodymap numberings as a standard. Each bodymap is in csv form
exampledata <- data.frame( GENDER = as.character(c("Male", "Female", "Female")), BODYMAP_CSV = as.character(c("112,125","112,113","128,117")) ) convert_bodymaps( as.character( exampledata$BODYMAP_CSV[exampledata$GENDER == 'Female'] ) )exampledata <- data.frame( GENDER = as.character(c("Male", "Female", "Female")), BODYMAP_CSV = as.character(c("112,125","112,113","128,117")) ) convert_bodymaps( as.character( exampledata$BODYMAP_CSV[exampledata$GENDER == 'Female'] ) )
Creates a data frame with CHOIR Body Map segment IDs and a randomly associated value. Also adds grouping information for facetting while plotting.
gen_example_data(seed = 123)gen_example_data(seed = 123)
seed |
integer to seed the random number generator |
values data.frame
cbm_df <- gen_example_data() head(cbm_df)cbm_df <- gen_example_data() head(cbm_df)
Counts the number of areas a person endorses/indicates on their CHOIR Body Map.
num_areas(cbm_str, delim = ",")num_areas(cbm_str, delim = ",")
cbm_str |
a delimited string of 3-digit codes indicating CBM areas. |
delim |
the delimiter character, defaults to a comma. |
nareas
cbm_str <- c("101,102,103,104") num_areas(cbm_str, ",")cbm_str <- c("101,102,103,104") num_areas(cbm_str, ",")
Generates a concurrence matrix as a heatmap to show which CBM locations are commonly endorsed together.
plot_cooccurrence(con_mat, ...)plot_cooccurrence(con_mat, ...)
con_mat |
a long form data frame or matrix produced by the plot_concurrence function, with every combination of locations and the number of times each combination occurs. |
... |
additional parameters for plotting |
a ggplot heatmap of the concurrence.
## Not run: set.seed(123) sampled_data <- validation[sample(1:nrow(validation), 100, replace = FALSE),] con_mat <- comp_cooccurrence(sampled_data) plot_cooccurrence(con_mat) ## End(Not run)## Not run: set.seed(123) sampled_data <- validation[sample(1:nrow(validation), 100, replace = FALSE),] con_mat <- comp_cooccurrence(sampled_data) plot_cooccurrence(con_mat) ## End(Not run)
Creates a new plot of the front and back of the female CHOIR body map.
plot_female_choirbm(df, value)plot_female_choirbm(df, value)
df |
data.frame |
value |
string |
ggrob
cbm_df <- gen_example_data() plot_female_choirbm(cbm_df, "value")cbm_df <- gen_example_data() plot_female_choirbm(cbm_df, "value")
Creates a new plot of the male CHOIR body map.
plot_male_choirbm(df, value)plot_male_choirbm(df, value)
df |
data.frame |
value |
string |
ggrob
cbm_df <- gen_example_data() plot_male_choirbm(cbm_df, "value")cbm_df <- gen_example_data() plot_male_choirbm(cbm_df, "value")
This is a wrapper for ggplot2's histogram function that incorporates calculating the number of CBM areas each individual indicates.
plot_nareas_histogram(cbms, ...)plot_nareas_histogram(cbms, ...)
cbms |
a list of delimited CBM strings |
... |
additional arguments passed to geom_histogram |
a histogram of the number of CBM areas endorsed by individuals in the dataset.
## Not run: data(validation) below20 <- validation[ sapply(validation$bodymap_regions_csv, num_areas) < 20 , ] plot_nareas_histogram( below20$bodymap_regions_csv , binwidth = 1 , fill = "grey" , color = "white") ## End(Not run)## Not run: data(validation) below20 <- validation[ sapply(validation$bodymap_regions_csv, num_areas) < 20 , ] plot_nareas_histogram( below20$bodymap_regions_csv , binwidth = 1 , fill = "grey" , color = "white") ## End(Not run)
prep_bodymaps converts a single charcter vector of bodymaps into a list of character vectors, each a bodymap
prep_bodymaps(maps)prep_bodymaps(maps)
maps |
a character vector containing the endorsed bodymap segments of patients in csv form |
a list of character vectors, where each vector contains the patient's endorsed segments
exampledata <- data.frame( GENDER = as.character(c("Male", "Female", "Female")), BODYMAP_CSV = as.character(c("112,125","112,113","128,117")) ) prep_bodymaps(as.character(exampledata$BODYMAP_CSV))exampledata <- data.frame( GENDER = as.character(c("Male", "Female", "Female")), BODYMAP_CSV = as.character(c("112,125","112,113","128,117")) ) prep_bodymaps(as.character(exampledata$BODYMAP_CSV))
Takes a string of IDs that are separated by a comma and converts the information into a data frame with a binary indication of whether or not an ID appeared. Useful for plotting an individual's CHOIR BM or for isolating particular sections to highlight.
string_to_map(map_str = "", delim = ",")string_to_map(map_str = "", delim = ",")
map_str |
The delimited CBM string. |
delim |
The delimiter for the CBM string. |
ret_df data.frame with all of the CHOIR BM segment IDs with a 1 if the segment was present and 0 otherwise.
# from a choir database cbm_str <- "101,102,103,104,201,202" cbm_df <- string_to_map(cbm_str) # plot in a male or female bodymap... plot_male_choirbm(cbm_df, "value") # from a REDCap project cbm_str <- "b07,b18,b19,b23,b24,b28,b33,f01,f03,f08,f17,f27,f29" cbm_df <- string_to_map(cbm_str) # plot in a male or female bodymap... plot_male_choirbm(cbm_df, "value")# from a choir database cbm_str <- "101,102,103,104,201,202" cbm_df <- string_to_map(cbm_str) # plot in a male or female bodymap... plot_male_choirbm(cbm_df, "value") # from a REDCap project cbm_str <- "b07,b18,b19,b23,b24,b28,b33,f01,f03,f08,f17,f27,f29" cbm_df <- string_to_map(cbm_str) # plot in a male or female bodymap... plot_male_choirbm(cbm_df, "value")
A non-identifiable, simulated data set generated by randomly permuting data from the CHOIR Body Map validation study.
data(validation)data(validation)
An object of class "data.frame"
A randomly generated numeric code for each patient.
The patient's gender.
The patient's race.
The patient's age.
The patient's CHOIR Body Map in a comma separated string.
A simulated pain score for demonstration purposes.
This data set was derived from the data collected during the study validating the CHOIR Body Map as an instrument for recording a patient's anatomical pain location. doi:10.1097/pr9.0000000000000880
data(validation) head(validation)data(validation) head(validation)