epi_stats_summary: Get summary statistics from a data frame with multiple...

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

Get summary statistics from a data frame with multiple columns

Description

epi_stats_summary() provides summary descriptive statistics for columns belonging to either character and factor (class_type = 'chr_fct') or integer and numeric (class_type = 'int_num') while discarding values provided (codes). This is useful if data frame has contingency codes. Columns are ordered according to order in contingency codes option. Rows are then ordered in decreasing order according to column provided.

Usage

epi_stats_summary(
  df = NULL,
  codes = NULL,
  class_type = "chr_fct",
  action = "exclude"
)

Arguments

df	Data frame
codes	Specify codes to summarise or exclude as string. Default is NULL.
class_type	Class of variables to summarise, 'chr_fct' or 'int_num'. Default is character and factor.
action	Values to summarise, 'codes_only' or 'exclude'. Default is 'exclude'.

Value

A data.frame as tibble with summaries.

Note

Desgined with data frames that require pre-processing and likely have contingency and database codes. Action 'exclude' excludes the string values provided from the summary. Useful to quickly assess what a data.frame contains, types of values in each column and summary statistics if excluding codes.

Author(s)

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

See Also

epi_stats_numeric, epi_stats_format, epi_stats_tidy, epi_clean_cond_chr_fct, epi_clean_cond_numeric.

Examples

#####
# 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 June 8, 2024, 7:47 a.m.