High-Performance Open-Source Archive
merge_by_range() that merges data frames by keys
and range matching. Can be used to merge data frames by time ranges,
such as merging patient visits with exam results.first_mode().screen_data_list() that screens and joins
multi-table clinical data by expression.to_wide() that converts long-format clinical data
in database to wide format for analysis and publication.keep_by_keyword() to keep string segment by regex
keyword position. Could be used to desensitize character vectors.data_overview() to get a quick overview of a data
frame. It includes diagnostic information about common data issues and
gives recommendations for data cleaning.mad_outlier(),
zscore_outlier(), iqr_outlier().merge_by_substring() to merge data frames by
substring matching.recursive argument to
combine_files().classif_model_compare().remove_inequal argument to
value_initial_cleaning().vec2code() to a more robust
format.vec2code(), where
name2code(x) is equivalent to
vec2code(names(x)).time_roc_plot() that plot time-dependent ROC
curve using timeROC::timeROC().baseline_table() now uses invisible return if
save_table is TRUE.time2 argument to functions that support cox
models.cluster argument to functions to utilize robust
Robust Covariance Matrix Estimates in cox models and Generalized
Estimating Equations in glm models.PRAUC results and PR plot to
classif_model_compare().rcs_plot() and added a new
predictor_effect_plot() function which can plot linear and
categorical effects.exclusion_count() which counts the number of
excluded samples at each exclusion criteria.na_min() and na_max(), similar to
base::min() and base::max(), but return
NA if all values are NA and omit
NAs by default.ggplot2 4.0.0 compatibility.combine_multichoice() which combines multichoice
results.common_prefix() to find common prefixes in
character vectors.group argument to cut_by() to cut
data by quantiles with groupings.char_initial_cleaning() to clean character
vectors.indicate_duplicates() to determine duplicate
elements, including their first occurrence.df_view_nonnum()
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