| Title: | A Collection of Useful Functions by John |
|---|---|
| Description: | A set of general functions that I have used in various projects and other R packages. Miscellaneous operations on data frames, matrices and vectors, ROC and PR statistics. |
| Authors: | John Zobolas [aut, cph, cre] |
| Maintainer: | John Zobolas <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.5.2 |
| Built: | 2024-11-14 02:43:04 UTC |
| Source: | https://github.com/bblodfon/usefun |
data.frame
Use this function on a data.frame object (with values only
in the 3-element set {-1,0,1} ideally - specifying either a positive,
negative or none/absent condition/state/result about something) and add an
extra first or last row vector with zero values, where 1
and -1 will be filled when the column names of the given
data.frame match the values in the values.pos or
values.neg vector parameters respectively.
add_row_to_ternary_df( df, values.pos, values.neg, pos = "first", row.name = NULL )add_row_to_ternary_df( df, values.pos, values.neg, pos = "first", row.name = NULL )
df |
a |
values.pos |
a character vector whose elements are indicators of a
positive state/condition and will be assigned a value of 1.
These elements must be a subset of the column names of the given |
values.neg |
a character vector whose elements are indicators of a
negative state/condition and will be assigned a value of -1.
If empty, no values equal to -1 will be added to the new row.
These elements must be a subset of the column names of the given |
pos |
string. The position where we should put the new row that will be generated. Two possible values: "first" (default) or "last". |
row.name |
string. The name of the new row that we will added. Default value: NULL. |
the df with one extra row, having elements from the {-1,0,1}
set depending on values of values.pos and values.neg vectors.
df = data.frame(c(0,-1,0), c(0,1,-1), c(1,0,0)) colnames(df) = c("A","B","C") df.new = add_row_to_ternary_df(df, values.pos = c("A"), values.neg = c("C"), row.name = "Hello!")df = data.frame(c(0,-1,0), c(0,1,-1), c(1,0,0)) colnames(df) = c("A","B","C") df.new = add_row_to_ternary_df(df, values.pos = c("A"), values.neg = c("C"), row.name = "Hello!")
Given a vector, adds each value and its corresponding name to a data frame of 2 columns as new rows, where the name fills in the 1st column and the value the 2nd column.
add_vector_to_df(df, vec)add_vector_to_df(df, vec)
df |
|
vec |
a vector |
a data.frame with additional rows and each element as a
character.
df = data.frame(c(0,0,1), c(0,0,2)) vec = 1:3 names(vec) = c("a","b","c") add_vector_to_df(df, vec)df = data.frame(c(0,0,1), c(0,0,2)) vec = 1:3 names(vec) = c("a","b","c") add_vector_to_df(df, vec)
Simple function that checks every element of a given matrix (or data.frame) if it surpasses the given threshold either positively or negatively and it outputs 1 for that element, otherwise 0.
binarize_to_thres(mat, thres)binarize_to_thres(mat, thres)
mat |
a matrix or data.frame object |
thres |
a positive numerical value |
a binarized matrix (values either 0 or 1): elements that have 1
correspond to values of mat that they were either larger than the
threshold or smaller than it's negative.
mat = matrix(data = -4:4, nrow = 3, ncol = 3) binarize_to_thres(mat, thres = 0.5) binarize_to_thres(mat, thres = 2.5)mat = matrix(data = -4:4, nrow = 3, ncol = 3) binarize_to_thres(mat, thres = 0.5) binarize_to_thres(mat, thres = 2.5)
100 as-much-as-possible distinct colors!
colors.100colors.100
An object of class character of length 100.
Get the binary representation of any decimal number from 0 to (2^31) - 1. Doesn't work for larger numbers.
dec_to_bin(decimal_num, bits = 32)dec_to_bin(decimal_num, bits = 32)
decimal_num |
decimal number between 0 and (2^31) - 1 |
bits |
number of bits to keep in the result counting from the right. Default value is 32. |
a binary string representation of the given decimal number.
# representing 0 dec_to_bin(0,1) dec_to_bin(0,10) dec_to_bin(0,32) dec_to_bin(0) # representing 24 dec_to_bin(24,6) dec_to_bin(24,21) dec_to_bin(24) dec_to_bin(24,3) # note that this will cut the returned result so be careful!# representing 0 dec_to_bin(0,1) dec_to_bin(0,10) dec_to_bin(0,32) dec_to_bin(0) # representing 24 dec_to_bin(24,6) dec_to_bin(24,21) dec_to_bin(24) dec_to_bin(24,3) # note that this will cut the returned result so be careful!
This function prints and returns the common names of two vectors. The
two vectors don't have to be the same length.
get_common_names(vec1, vec2, vector.names.str = "nodes", with.gt = TRUE)get_common_names(vec1, vec2, vector.names.str = "nodes", with.gt = TRUE)
vec1 |
vector with |
vec2 |
vector with |
vector.names.str |
string. Used for printing, it tell us what are the
|
with.gt |
logical. Determines if the ">" sign will be appended for nice printing in an R notebook (use with the chuck option results = 'asis'). Default value: TRUE. |
the character vector of the common names. If there is only one name
in common, the vector.names.str gets the last character stripped for
readability. If there is no common names, it returns FALSE.
pretty_print_vector_values, pretty_print_string
vec1 = c(1,1,1) vec2 = c(1,2) names(vec1) = c("a","b","c") names(vec2) = c("c","b") common.names = get_common_names(vec1, vec2)vec1 = c(1,1,1) vec2 = c(1,2) names(vec1) = c("a","b","c") names(vec2) = c("c","b") common.names = get_common_names(vec1, vec2)
This function prints and returns the common values of two vectors. The two vectors don't have to be the same length.
get_common_values(vec1, vec2, vector.values.str = "nodes", with.gt = TRUE)get_common_values(vec1, vec2, vector.values.str = "nodes", with.gt = TRUE)
vec1 |
vector |
vec2 |
vector |
vector.values.str |
string. Used for printing, it tell us what are the values of the two vectors (use plural form). Default value: "nodes". |
with.gt |
logical. Determines if the ">" sign will be appended for nice printing in an R notebook (use with the chuck option results = 'asis'). Default value: TRUE. |
the vector of the common values. If there is only one value
in common, the vector.values.str gets the last character stripped for
readability. If there are no common values, it returns NULL.
pretty_print_vector_values, pretty_print_string
vec1 = c(1,2,3) vec2 = c(3,4,1) common.names = get_common_values(vec1, vec2)vec1 = c(1,2,3) vec2 = c(3,4,1) common.names = get_common_values(vec1, vec2)
Use this function to retrieve the parent directory from a string representing the full path of a file or a directory.
get_parent_dir(pathStr)get_parent_dir(pathStr)
pathStr |
string. The name of the directory, can be a full path filename. |
a string representing the parent directory. When a non-file path is used as input (or something along those lines :) then it returns the root ("/") directory.
get_parent_dir("/home/john") get_parent_dir("/home/john/a.txt") get_parent_dir("/home")get_parent_dir("/home/john") get_parent_dir("/home/john/a.txt") get_parent_dir("/home")
Use this function on two numeric vectors with the same names attribute
(columns) and same length, in order to find the percentage of common elements
(value matches between the two vectors). The same names for the two
vectors ensures that their values are logically matched one-to-one.
get_percentage_of_matches(vec1, vec2)get_percentage_of_matches(vec1, vec2)
vec1 |
numeric vector with |
vec2 |
numeric vector with |
the percentage of common values (exact matches) between the two vectors. Can only be a value between 0 (no common elements) and 1 (perfect element match). Note that NaN and NA values are allowed in the input vectors, but they will always count as a mismatch.
vec1 = c(1, 2, 3, 2) vec2 = c(20, 2, 2.5, 8) vec3 = c(1, 2, 333, 222) names.vec = c(seq(1,4)) names(vec1) = names.vec names(vec2) = names.vec names(vec3) = names.vec match.1.2 = get_percentage_of_matches(vec1, vec2) match.1.3 = get_percentage_of_matches(vec1, vec3)vec1 = c(1, 2, 3, 2) vec2 = c(20, 2, 2.5, 8) vec3 = c(1, 2, 333, 222) names.vec = c(seq(1,4)) names(vec1) = names.vec names(vec2) = names.vec names(vec3) = names.vec match.1.2 = get_percentage_of_matches(vec1, vec2) match.1.3 = get_percentage_of_matches(vec1, vec3)
Use this function to generate the most useful statistics related to the generation of a basic ROC (Receiver Operating Characteristic) curve.
get_roc_stats(df, pred_col, label_col, direction = "<")get_roc_stats(df, pred_col, label_col, direction = "<")
df |
a |
pred_col |
string. The name of the column of the |
label_col |
string. The name of the column of the |
direction |
string. Can be either > or < (default value) and indicates the direction/ranking of the prediction values with respect to the positive class labeling (for a specific threshold). If smaller prediction values indicate the positive class/label use < whereas if larger prediction values indicate the positive class/label (e.g. probability of positive class), use >. |
A list with two elements:
roc_stats: a tibble which includes the thresholds for the ROC curve and the confusion matrix stats for each threshold as follows: TP (#True Positives), FN (#False Negatives), TN (#True Negatives), FP (#False Positives), FPR (False Positive Rate - the x-axis values for the ROC curve) and TPR (True Positive Rate - the y-axis values for the ROC curve).
Also included are the dist-from-chance (the vertical distance of
the corresponding (FPR,TPR) point to the chance line or positive diagonal)
and the dist-from-0-1 (the euclidean distance of the corresponding
(FPR,TPR) point from (0,1)).
AUC: a number representing the Area Under the (ROC) Curve.
The returned results provide an easy way to compute two optimal cutpoints (thresholds) that dichotomize the predictions to positive and negative. The first is the Youden index, which is the maximum vertical distance from the ROC curve to the chance line or positive diagonal. The second is the point of the ROC curve closest to the (0,1) - the point of perfect differentiation. See examples below.
# load libraries library(readr) library(dplyr) # load test tibble test_file = system.file("extdata", "test_df.tsv", package = "usefun", mustWork = TRUE) test_df = readr::read_tsv(test_file, col_types = "di") # get ROC stats res = get_roc_stats(df = test_df, pred_col = "score", label_col = "observed") # Plot ROC with a legend showing the AUC value plot(x = res$roc_stats$FPR, y = res$roc_stats$TPR, type = 'l', lwd = 2, col = '#377EB8', main = 'ROC curve', xlab = 'False Positive Rate (FPR)', ylab = 'True Positive Rate (TPR)') legend('bottomright', legend = round(res$AUC, digits = 3), title = 'AUC', col = '#377EB8', pch = 19) grid() abline(a = 0, b = 1, col = '#FF726F', lty = 2) # Get two possible cutoffs youden_index_df = res$roc_stats %>% filter(dist_from_chance == max(dist_from_chance)) min_classification_df = res$roc_stats %>% filter(dist_from_0_1 == min(dist_from_0_1))# load libraries library(readr) library(dplyr) # load test tibble test_file = system.file("extdata", "test_df.tsv", package = "usefun", mustWork = TRUE) test_df = readr::read_tsv(test_file, col_types = "di") # get ROC stats res = get_roc_stats(df = test_df, pred_col = "score", label_col = "observed") # Plot ROC with a legend showing the AUC value plot(x = res$roc_stats$FPR, y = res$roc_stats$TPR, type = 'l', lwd = 2, col = '#377EB8', main = 'ROC curve', xlab = 'False Positive Rate (FPR)', ylab = 'True Positive Rate (TPR)') legend('bottomright', legend = round(res$AUC, digits = 3), title = 'AUC', col = '#377EB8', pch = 19) grid() abline(a = 0, b = 1, col = '#FF726F', lty = 2) # Get two possible cutoffs youden_index_df = res$roc_stats %>% filter(dist_from_chance == max(dist_from_chance)) min_classification_df = res$roc_stats %>% filter(dist_from_0_1 == min(dist_from_0_1))
Use this function on two vectors with same names attribute (column
names), to find for each unique (numeric) value of the first vector, the
average and standard deviation values of the second vector's values (matching
is done by column name)
get_stats_for_unique_values(vec1, vec2)get_stats_for_unique_values(vec1, vec2)
vec1 |
vector with |
vec2 |
vector with |
A data.frame consisting of 3 column vectors. The data.frame
size is nx3, where n is the number of unique values of vec1 (rows).
The columns vectors are:
the first input vector pruned to its unique values
a vector with the average values for each unique value of the first vector (the matching is done by column name)
a vector with the standard deviation values for each unique value of the first vector (the matching is done by column name)
vec1 = c(1, 2, 3, 2) vec2 = c(20, 2, 2.5, 8) names.vec = c(seq(1,4)) names(vec1) = names.vec names(vec2) = names.vec res = get_stats_for_unique_values(vec1, vec2)vec1 = c(1, 2, 3, 2) vec2 = c(20, 2, 2.5, 8) names.vec = c(seq(1,4)) names(vec1) = names.vec names(vec2) = names.vec res = get_stats_for_unique_values(vec1, vec2)
Helper function that checks if a value surpasses the given threshold either positively, negatively or not at all and returns a value indicating in which class (i.e. interval) it belongs.
get_ternary_class_id(value, threshold)get_ternary_class_id(value, threshold)
value |
numeric |
threshold |
numeric |
an integer. There are 3 cases:
: when
: when
: otherwise
This function checks if a given value is inside an interval specified by two boundary values.
is_between(value, low.thres, high.thres, include.high.value = FALSE)is_between(value, low.thres, high.thres, include.high.value = FALSE)
value |
numeric |
low.thres |
numeric. Lower boundary of the interval. |
high.thres |
numeric. Upper boundary of the interval. |
include.high.value |
logical. Whether the upper bound is included in the interval or not. Default value: FALSE. |
a logical specifying if the value is inside the interval
[low.thres,high.thres) (default behaviour) or inside the interval
[low.thres,high.thres] if include.high.value is TRUE.
is_between(3,2,4) is_between(4,2,4) is_between(4,2,4,include.high.value=TRUE)is_between(3,2,4) is_between(4,2,4) is_between(4,2,4,include.high.value=TRUE)
A function to test whether an object is empty. It checks the length
of the object, so it has different behaviour than is.null.
is_empty(obj)is_empty(obj)
obj |
a general object |
a logical specifying if the object is NULL or not.
# TRUE is_empty(NULL) is_empty(c()) # FALSE is_empty("") is_empty(NA) is_empty(NaN)# TRUE is_empty(NULL) is_empty(c()) # FALSE is_empty("") is_empty(NA) is_empty(NaN)
Rearrange a list of data frames by rownames
ldf_arrange_by_rownames(list_df)ldf_arrange_by_rownames(list_df)
list_df |
a (non-empty) list of |
a rearranged list of data frames, where the names of the elements of
the list_df (the 'ids' of the data frames) and the rownames of
the data frames have switched places: the unique row names of the original list's
combined data frames serve as names for the returned list of data
frames, while the data frame 'ids' (names of the original list's
elements) now serve as rownames for the data frames in the new list.
E.g. if in the given list there was a data.frame with id 'A':
a = list_df[["A"]] and rownames(a) = c("row1", "row2"), then
in the rearranged list there would be two data frames with ids
"row1" and "row2", each of them having a row with name "A" where also these
data rows would be the same as before: list_df[["A"]]["row1", ] == returned_list[["row1"]]["A",]
and list_df[["A"]]["row2", ] == returned_list[["row2"]]["A",] respectively.
df.1 = data.frame(matrix(data = 0, nrow = 3, ncol = 3, dimnames = list(c("row1", "row2", "row3"), c("C.1", "C.2", "C.3")))) df.2 = data.frame(matrix(data = 1, nrow = 3, ncol = 3, dimnames = list(c("row1", "row2", "row4"), c("C.1", "C.2", "C.3")))) list_df = list(df.1, df.2) names(list_df) = c("zeros", "ones") res_list_df = ldf_arrange_by_rownames(list_df)df.1 = data.frame(matrix(data = 0, nrow = 3, ncol = 3, dimnames = list(c("row1", "row2", "row3"), c("C.1", "C.2", "C.3")))) df.2 = data.frame(matrix(data = 1, nrow = 3, ncol = 3, dimnames = list(c("row1", "row2", "row4"), c("C.1", "C.2", "C.3")))) list_df = list(df.1, df.2) names(list_df) = c("zeros", "ones") res_list_df = ldf_arrange_by_rownames(list_df)
Use this function when you want to visualize some numbers and their respective color values. Note that more than 42 colors won't be nice to see (too thin bars)!
make_color_bar_plot(color.vector, number.vector, title, x.axis.label = "")make_color_bar_plot(color.vector, number.vector, title, x.axis.label = "")
color.vector |
vector of color values |
number.vector |
vector of numeric values (same length with
|
title |
string. The title of the barplot |
x.axis.label |
string. The x-axis label. Default value: empty string |
color.vector = rainbow(10) number.vector = 1:10 title = "First 10 rainbow() colors" make_color_bar_plot(color.vector, number.vector, title)color.vector = rainbow(10) number.vector = 1:10 title = "First 10 rainbow() colors" make_color_bar_plot(color.vector, number.vector, title)
Combine many density distributions to one common plot.
make_multiple_density_plot( densities, legend.title, title, x.axis.label, legend.size = 1 )make_multiple_density_plot( densities, legend.title, title, x.axis.label, legend.size = 1 )
densities |
a list, each element holding the results from executing
the |
legend.title |
string. The legend title. |
title |
string. The plot title. |
x.axis.label |
string. The x-axis label. |
legend.size |
numeric. Default value: 1. |
mat = matrix(rnorm(60), ncol=20) densities = apply(mat, 1, density) names(densities) = c("1st", "2nd", "3rd") make_multiple_density_plot(densities, legend.title = "Samples", x.axis.label = "", title = "3 Normal Distribution Samples")mat = matrix(rnorm(60), ncol=20) densities = apply(mat, 1, density) names(densities) = c("1st", "2nd", "3rd") make_multiple_density_plot(densities, legend.title = "Samples", x.axis.label = "", title = "3 Normal Distribution Samples")
Check if two matrices are equal. Equality is defined by both of them being matrices in the first place, having the same dimensions as well as the same elements.
mat_equal(x, y)mat_equal(x, y)
x, y
|
matrices |
a logical specifying if the two matrices are equal or not.
Normalize a vector, matrix or data.frame of numeric values in a specified range.
normalize_to_range(x, range = c(0, 1))normalize_to_range(x, range = c(0, 1))
x |
vector, matrix or data.frame with at least two different elements |
range |
vector of two elements specifying the desired normalized range. Default value is c(0,1) |
the normalized data
vec = 1:10 normalize_to_range(vec) normalize_to_range(vec, range = c(-1,1)) mat = matrix(c(0,2,1), ncol = 3, nrow = 4) normalize_to_range(mat, range = c(-5,5))vec = 1:10 normalize_to_range(vec) normalize_to_range(vec, range = c(-1,1)) mat = matrix(c(0,2,1), ncol = 3, nrow = 4) normalize_to_range(mat, range = c(-5,5))
Performs set outersection on two vectors. The opposite operation from
intersect!
outersect(x, y)outersect(x, y)
x, y
|
vectors |
a vector of the non-common elements of x and y.
x = 1:10 y = 2:11 # c(1,11) outersect(x,y)x = 1:10 y = 2:11 # c(1,11) outersect(x,y)
Get partial permutation of a vector
partial_permut(x, exp_sim = 0)partial_permut(x, exp_sim = 0)
x |
a vector with at least 2 elements |
exp_sim |
a value between 0 and 1 indicating the level of expected similarity between the input and output vector. Default value is 0 (random permutation). |
a partially (random) permutated vector. If exp_sim = 0 then
the result is equal to sample(x) (a random permutation). If
exp_sim = 1 then the result is always the same as the input vector.
For exp_sim values between 0 and 1 we randomly sample
a subset of the input vector inversely proportionate to the exp_sim
value (e.g. exp_sim = 0.8 => 20% of the elements) and randomly
permutate these elements only.
set.seed(42) partial_permut(x = LETTERS, exp_sim = 0) partial_permut(x = LETTERS, exp_sim = 0.5) partial_permut(x = LETTERS, exp_sim = 0.9)set.seed(42) partial_permut(x = LETTERS, exp_sim = 0) partial_permut(x = LETTERS, exp_sim = 0.5) partial_permut(x = LETTERS, exp_sim = 0.9)
This function computes the intersection of elements for all possible combinations of the provided sets of IDs. A typical use case is in a cohort of patients with incomplete data across multiple data types. This function helps determine how many patients have complete data for specific combinations of data types, allowing you to find the optimal combinations for analysis.
powerset_icounts(ids)powerset_icounts(ids)
ids |
|
A tibble with columns:
set_combo: name for combo set/vector
num_subsets: number of subsets in the combo set
common_ids: vector of common ids in the combo set
count: number of common ids
library(dplyr) ids = list(a = 1:3, b = 2:5, c = 1:4, d = 3:6, e = 2:6) res = powerset_icounts(ids) res |> filter(num_subsets >= 2, count > 2) |> arrange(desc(count), desc(num_subsets))library(dplyr) ids = list(a = 1:3, b = 2:5, c = 1:4, d = 3:6, e = 2:6) res = powerset_icounts(ids) res |> filter(num_subsets >= 2, count > 2) |> arrange(desc(count), desc(num_subsets))
This functions calculates bootstrap percentile CIs for PR curves using precrec. These can then be used in a plotting function, see example.
pr.boot( labels, preds, boot.n = 10000, boot.stratified = TRUE, alpha = 0.1, ... )pr.boot( labels, preds, boot.n = 10000, boot.stratified = TRUE, alpha = 0.1, ... )
labels |
( |
preds |
( |
boot.n |
( |
boot.stratified |
( |
alpha |
( |
... |
Other parameters to pass on to precrec::evalmod, except |
A tibble with columns:
recall: recall of original data
precision: precision of original data
low_precision: low value of the bootstrap confidence interval
high_precision: high value of the bootstrap confidence interval
Saito, Takaya, Rehmsmeier, Marc (2016). “Precrec: fast and accurate precision-recall and ROC curve calculations in R.” Bioinformatics, 33(1), 145–147. doi:10.1093/bioinformatics/btw570.
set.seed(42) # imbalanced labels labels = sample(c(0,1), 100, replace = TRUE, prob = c(0.8,0.2)) # predictions preds = rnorm(100) # get CIs for PR curve pr_tbl = pr.boot(labels, preds, boot.n = 100, x_bins = 30) # default x_bin is 1000 pr_tbl # draw PR curve + add the bootstrap percentile confidence bands library(ggplot2) pr_tbl |> ggplot(aes(x = recall, y = precision)) + geom_step() + ylim(c(0,1)) + geom_ribbon(aes(ymin = precision_low, ymax = precision_high), alpha = 0.2)set.seed(42) # imbalanced labels labels = sample(c(0,1), 100, replace = TRUE, prob = c(0.8,0.2)) # predictions preds = rnorm(100) # get CIs for PR curve pr_tbl = pr.boot(labels, preds, boot.n = 100, x_bins = 30) # default x_bin is 1000 pr_tbl # draw PR curve + add the bootstrap percentile confidence bands library(ggplot2) pr_tbl |> ggplot(aes(x = recall, y = precision)) + geom_step() + ylim(c(0,1)) + geom_ribbon(aes(ymin = precision_low, ymax = precision_high), alpha = 0.2)
Test the hypothesis that the true difference in PR AUCs is equal to 0.
We implement the same bootstrap method based on the idea from pROC::roc.test().
The PR AUC is calculated using PRROC::pr.curve() with the interpolation
method of Davis (2006).
pr.test( labels, pred1, pred2, boot.n = 10000, boot.stratified = TRUE, alternative = "two.sided" )pr.test( labels, pred1, pred2, boot.n = 10000, boot.stratified = TRUE, alternative = "two.sided" )
labels |
( |
pred1 |
( |
pred2 |
( |
boot.n |
( |
boot.stratified |
( |
alternative |
( |
a list with the AUCs of the two original prediction vectors and the p-value of the bootstrap-based test.
Davis J, Goadrich M (2006). “The relationship between precision-recall and ROC curves.” Proceedings of the 23rd International Conference on Machine Learning, 148(4), 233–240. doi:10.1145/1143844.1143874.
set.seed(42) # imbalanced labels labels = sample(c(0,1), 20, replace = TRUE, prob = c(0.8,0.2)) # predictions pred1 = rnorm(20) pred2 = rnorm(20) pr.test(labels, pred1, pred2, boot.n = 1000, boot.stratified = FALSE) pr.test(labels, pred1, pred2, boot.n = 1000, boot.stratified = TRUE)set.seed(42) # imbalanced labels labels = sample(c(0,1), 20, replace = TRUE, prob = c(0.8,0.2)) # predictions pred1 = rnorm(20) pred2 = rnorm(20) pr.test(labels, pred1, pred2, boot.n = 1000, boot.stratified = FALSE) pr.test(labels, pred1, pred2, boot.n = 1000, boot.stratified = TRUE)
Prints a bold string only when html.output is enabled. Otherwise, it prints
a normal string. The the ">" sign can be appended if nice output in an R
notebook is desired.
pretty_print_bold_string(string, with.gt = TRUE, html.output = TRUE)pretty_print_bold_string(string, with.gt = TRUE, html.output = TRUE)
string |
a string |
with.gt |
logical. Determines if the ">" sign will be appended for nice printing in an R notebook. (use with the chuck option results = 'asis'). Default value: TRUE. |
html.output |
logical. If TRUE, it encapsulates the string with the bold tags for an HTML document. Default value: TRUE. |
Pretty print a name and value
pretty_print_name_and_value(name, value, with.gt = FALSE, with.comma = TRUE)pretty_print_name_and_value(name, value, with.gt = FALSE, with.comma = TRUE)
name |
string |
value |
string |
with.gt |
logical. Determines if the ">" sign will be appended for nice printing in an R notebook (use with the chuck option results = 'asis'). Default value: FALSE. |
with.comma |
logical. Determines if the comma (,) character will be appended to the end of the output. Default value: TRUE. |
pretty_print_name_and_value("aName", "aValue", with.gt = TRUE) pretty_print_name_and_value("aName", "aValue", with.comma = FALSE)pretty_print_name_and_value("aName", "aValue", with.gt = TRUE) pretty_print_name_and_value("aName", "aValue", with.comma = FALSE)
Nice printing of a string in an R notebook (default behaviour). Otherwise, it prints the string to the standard R output.
pretty_print_string(string, with.gt = TRUE)pretty_print_string(string, with.gt = TRUE)
string |
a string |
with.gt |
logical. Determines if the ">" sign will be appended for nice printing in an R notebook (use with the chuck option results = 'asis'). Default value: TRUE. |
Pretty printing of a vector's names attribute
pretty_print_vector_names( vec, vector.names.str = "nodes", sep = ", ", with.gt = TRUE )pretty_print_vector_names( vec, vector.names.str = "nodes", sep = ", ", with.gt = TRUE )
vec |
vector |
vector.names.str |
string. It tell us what are the names of the vector (use plural form) in order to fill the print message. Default value: "nodes". |
sep |
string. The separator character to use to distinguish between the names values. Default value: ", ". |
with.gt |
logical. Determines if the ">" sign will be appended for nice printing in an R notebook (use with the chuck option results = 'asis'). Default value: TRUE. |
It outputs a vector's names and values in this format: name1: value1, name2: value2,.... You can choose how many elements to show in this format. Use with the chuck option results = 'asis' to get a nice printing in an R notebook.
pretty_print_vector_names_and_values(vec, n = -1)pretty_print_vector_names_and_values(vec, n = -1)
vec |
vector with |
n |
the number of elements that you want to print in a nice way. Default value: -1 (pretty print all elements). For any n < 1, all elements are printed. |
Pretty printing of a vector's values
pretty_print_vector_values( vec, vector.values.str = "nodes", sep = ", ", with.gt = TRUE )pretty_print_vector_values( vec, vector.values.str = "nodes", sep = ", ", with.gt = TRUE )
vec |
vector |
vector.values.str |
string. It tell us what are the values of the vector (use plural form) in order to fill the print message. Default value: "nodes". |
sep |
string. The separator character to use to distinguish between the vector values. Default value: ", ". |
with.gt |
logical. Determines if the ">" sign will be appended for nice printing in an R notebook (use with the chuck option results = 'asis'). Default value: TRUE. |
Print an empty line
print_empty_line(html.output = FALSE)print_empty_line(html.output = FALSE)
html.output |
logical. If TRUE, it outputs an empty line for an HTML document, else an empty line for the standard R output. Default value: FALSE. |
Given two vectors, the first one's elements are pruned and reordered according
to the common values of the second vector and the elements' names
(attribute) of the first. If there no common such values, an empty vector is
returned.
prune_and_reorder_vector(vec, filter.vec)prune_and_reorder_vector(vec, filter.vec)
vec |
a vector with |
filter.vec |
a character vector whose values will be used to filter the
|
the pruned and re-arranged vector.
vec = c(1,2,3) names(vec) = c("a","b","c") filter.vec1 = c("a") prune_and_reorder_vector(vec, filter.vec1) filter.vec2 = c("c", "ert", "b") prune_and_reorder_vector(vec, filter.vec2)vec = c(1,2,3) names(vec) = c("a","b","c") filter.vec1 = c("a") prune_and_reorder_vector(vec, filter.vec1) filter.vec2 = c("c", "ert", "b") prune_and_reorder_vector(vec, filter.vec2)
Given a data.frame and an integer value, it checks whether there is a
column vector whose values match the given one. If so, it prunes that
single-valued column from the data.frame
prune_columns_from_df(df, value)prune_columns_from_df(df, value)
df |
|
value |
an integer value |
the column-pruned data.frame
df = data.frame(c(0,0,0), c(0,1,0), c(1,0,0)) prune_columns_from_df(df, value = 0)df = data.frame(c(0,0,0), c(0,1,0), c(1,0,0)) prune_columns_from_df(df, value = 0)
Given a data.frame and an integer value, it checks whether there is a
row vector whose values match the given one. If so, it prunes that
single-valued row from the data.frame
prune_rows_from_df(df, value)prune_rows_from_df(df, value)
df |
|
value |
an integer value |
the row-pruned data.frame
df = data.frame(c(0,0,0), c(0,1,0), c(1,0,0)) prune_rows_from_df(df, value = 0)df = data.frame(c(0,0,0), c(0,1,0), c(1,0,0)) prune_rows_from_df(df, value = 0)
Removes empty or commented lines from a character vector (each element being a line)
remove_commented_and_empty_lines(lines)remove_commented_and_empty_lines(lines)
lines |
a character vector, usually the result from using the
|
a character vector of the pruned lines
Function for saving a data.frame to a specified file.
Column and row names are written by default and the tab is used
as a delimiter.
save_df_to_file(df, file)save_df_to_file(df, file)
df |
data.frame |
file |
string. The name of the file, can be a full path. |
Function for saving a matrix to a specified file. Uses the
save_df_to_file function.
save_mat_to_file(mat, file)save_mat_to_file(mat, file)
mat |
matrix |
file |
string. The name of the file, can be a full path. |
Function for saving a vector with or without its row names to a
specified file.
By default the tab is used as a delimiter.
save_vector_to_file(vector, file, with.row.names = FALSE)save_vector_to_file(vector, file, with.row.names = FALSE)
vector |
vector |
file |
string. The name of the file, can be a full path. |
with.row.names |
logical. If TRUE, then the |
Use this function to transform a given decimal number to the desired precision by choosing the number of digits after the decimal point.
specify_decimal(number, digits.to.keep)specify_decimal(number, digits.to.keep)
number |
numeric |
digits.to.keep |
numeric. Refers to the digits to keep after decimal point '.'. This value should be 15 or less. |
the pruned number in string format
# 0.123 specify_decimal(0.1233213, 3)# 0.123 specify_decimal(0.1233213, 3)