In UBC-MDS/nurser: Make EDA process

knitr::opts_chunk$set(
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Introduction

The functions in nurser were developed to provide useful and informative front-end machine learning metrics that are applicable to a wide array of datasets. Currently nurser contains three functions, each of which are independent of one another. This vignette outlines how to use these functions on some real data.

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library(nurser)
library(magrittr)

eda

The eda() function return a list that contains histogram and summary statistics for a given column. Let's see it in action!

To view a histogram of a feature:

result <- eda(mtcars)

result <- eda(mtcars)

hist_mpg <- result$histograms[[1]]
hist_mpg

Now let's see the summary statistics of this feature:

stats_mpg = result$stats$mpg
stats_mpg

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impute_summary

Let's import some continuous data to work with,

iris_data <- iris[1:4]

and add some missing values,

iris_missing <- 
  as.data.frame(lapply(iris_data, 
                       function(x) x[sample(c(TRUE, NA),
                                            size = length(x), 
                                            replace = TRUE,
                                            prob = c(0.75, 0.25))]))

Now, let's take a look at the data to in fact see if the missing values were generated and where they are:

iris_missing %>% head(10)

Great, we have some missing values to compute - let's call impute_summary to get some summary statistics and outputs from different methods.

iris_imputed <- impute_summary(iris_missing)

iris_imputed <- impute_summary(iris_missing)

impute_summary() provides some useful summary statistics and also several imputed dataframes that can be accessed by the impute_summary object attributes. The imputed data frames provided include:

• mean,
• median,
• max,
• min,
• random,
• multiple imputation,
• pmm, and
• random forest

Let's first take a look at the summaries, which can be accessed using $nan_counts (NA counts for each feature) and $nan_rowindex (rows that contain NA values):

iris_imputed$nan_counts

iris_imputed$nan_rowindex %>% head(5)

Now, let's take a look at two of the imputed data frames, mean and multiple imputation:

iris_imputed$hmisc_mean %>% head(10)

iris_imputed$mi_multimp %>% head(10)

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preproc

The preproc() function returns a tibble with preprocessed features. Simply call preproc on your data!

Let's first view our data before preprocessing:

head(iris)

and now after calling preproc:

results = preproc(iris)
head(results)

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UBC-MDS/nurser documentation built on April 3, 2020, 4:22 a.m.