epi_stats_dates: Calculate Descriptive Date Statistics

In AntonioJBT/episcout: Quickly Clean, Explore and Visualise Large Epidemiological Datasets

Calculate Descriptive Date Statistics

Description

epi_stats_dates() calculates and returns key descriptive statistics for a vector of dates, including minimum, maximum, median, interquartile range (IQR), and the quartiles. It is compatible with both Date and IDate objects.

Usage

epi_stats_dates(date_vector)

Arguments

date_vector

A vector of dates of class Date or IDate.

Value

A data.frame containing the following statistics:

• Min: The earliest date in the vector.

• Max: The latest date in the vector.

• Median: The median date.

• IQR: The interquartile range of the dates.

• 1st Quartile: The first quartile (25th percentile).

• Median (again): The median (50th percentile), duplicated for clarity.

• 3rd Quartile: The third quartile (75th percentile).

Note

Note that origin date by default in R is "1970-01-01".

Author(s)

Antonio J Berlanga-Taylor <\url{https://github.com/AntonioJBT/episcout}>

See Also

epi_stats_summary, epi_stats_format, epi_stats_numeric.

TO DO:

Examples

sample_dates <- as.Date(c("2020-01-01", "2020-05-15", "2020-12-31", "2021-01-01", "2021-07-15"))
date_stats <- epi_stats_dates(sample_dates)
print(date_stats)



#####
# Load libraries needed:
library(episcout)
library(dplyr)
library(purrr)
library(e1071)
library(tibble)
library(tidyr)
#####

#####
# Generate a data frame:
n <- 1000
df <- data.frame(var_id = rep(1:(n / 2), each = 2),
                 var_to_rep = rep(c("Pre", "Post"), n / 2),
                 x = rnorm(n),
                 y = rbinom(n, 1, 0.50),
                 z = rpois(n, 2)
                )

# Explore first and last rows for first columns:
epi_head_and_tail(df)

# Add character/factor columns:
col_chr <- data.frame('chr1' = rep(c('A', 'B'), length.out = 1000),
                      'chr2' = rep(c('C', 'D'), length.out = 1000)
                      )
dim(col_chr)
df_cont_chr <- tibble::as.tibble(cbind(df, col_chr))
epi_head_and_tail(df_cont_chr)
epi_head_and_tail(df_cont_chr, last_cols = TRUE)

# Check variable types are what you expect:
epi_clean_count_classes(df_cont_chr)
str(df_cont_chr)
dim(df_cont_chr)
# var_id, y and z can be treated as factors or characters.
summary(as.factor(df_cont_chr$y))
summary(as.factor(df_cont_chr$z))
# Here we'll only transform y though:
df_cont_chr$y <- as.factor(df_cont_chr$y)
epi_clean_count_classes(df_cont_chr)
str(df_cont_chr)

# Designate some values as codes to be counted separately:
codes <- c('Pre', 'A', 'C', '1', '3')
#####

#####
# Count when codes are present, pass these as character or factor, specify
#  action is to count codes only:
stat_sum1 <- epi_stats_summary(df = df_cont_chr,
                               codes = codes,
                               class_type = 'chr_fct',
                               action = 'codes_only'
                               )
class(stat_sum1)
stat_sum1
#####

#####
# Add total for percentage calculation and order column to tidy up results:
perc_n <- nrow(df_cont_chr)
order_by <- 'percent'
stat_sum_tidy <- epi_stats_tidy(sum_df = stat_sum1,
                                order_by = order_by,
                                perc_n = perc_n
                                )
stat_sum_tidy
# Format them if needed:
epi_stats_format(stat_sum_tidy, digits = 0)
epi_stats_format(stat_sum_tidy, digits = 2)
#####

#####
# Count integer or numeric codes:
stat_sum2 <- epi_stats_summary(df_cont_chr,
                               codes = codes,
                               class_type = 'int_num',
                               action = 'codes_only'
                               )
stat_sum2
# Tidy and format them:
stat_sum_tidy <- epi_stats_tidy(sum_df = stat_sum2,
                                order_by = order_by,
                                perc_n = perc_n
                                )
stat_sum_tidy
epi_stats_format(stat_sum_tidy, digits = 0)
epi_stats_format(stat_sum_tidy, digits = 2, skip = c(2, 3))
#####

#####
# Get summary stats excluding contingency codes for character and factor columns:
stat_sum3 <- epi_stats_summary(df_cont_chr,
                               codes = codes,
                               class_type = 'chr_fct',
                               action = 'exclude'
                               )
stat_sum3
# Tidy and format:
stat_sum_tidy <- epi_stats_tidy(sum_df = stat_sum3,
                                order_by = order_by,
                                perc_n = perc_n
                                )
stat_sum_tidy
epi_stats_format(stat_sum_tidy, digits = 0)
epi_stats_format(stat_sum_tidy, digits = 1)
#####

#####
# Get summary stats for numeric/integer columns
# while excluding certain codes/values:
stat_sum4 <- epi_stats_summary(df = df_cont_chr,
                               codes = codes,
                               class_type = 'int_num',
                               action = 'exclude'
                               )
class(stat_sum4)
stat_sum4
# Numeric data summary doesn't need tidying but could be formatted:
epi_stats_format(stat_sum4, digits = 2)
#####

#####
# If there are no codes to return the result is an empty data.frame (tibble):
codes <- c('Per', 'X', '55')
stat_sum_zero <- epi_stats_summary(df_cont_chr,
                                   codes = codes,
                                   class_type = 'chr_fct',
                                   action = 'codes_only'
                                   )
stat_sum_zero
#####

AntonioJBT/episcout documentation built on Dec. 1, 2024, 4:07 a.m.