# Introduction to DataExplorer

#### Boxuan Cui

#### 2026-03-07

This document introduces the package DataExplorer, and shows how it can help you with different tasks throughout your data exploration process.

There are 3 main goals for DataExplorer:

1. Exploratory Data Analysis (EDA)
2. Feature Engineering
3. Data Reporting

The remaining of this guide will be organized in accordance with the goals. As the package evolves, more content will be added.

## Data

We will be using the nycflights13 datasets for this document. If you have not installed the package, please do the following:

    install.packages("nycflights13")
    library(nycflights13)

There are 5 datasets in this package:

- airlines
- airports
- flights
- planes
- weather

If you want to quickly visualize the structure of all, you may do the following:

    library(DataExplorer)
    data_list <- list(airlines, airports, flights, planes, weather)
    plot_str(data_list)

You may also try `plot_str(data_list, type = "r")` for a radial network.

Now let’s merge all tables together for a more robust dataset for later sections.

    merge_airlines <- merge(flights, airlines, by = "carrier", all.x = TRUE)
    merge_planes <- merge(merge_airlines, planes, by = "tailnum", all.x = TRUE, suffixes = c("_flights", "_planes"))
    merge_airports_origin <- merge(merge_planes, airports, by.x = "origin", by.y = "faa", all.x = TRUE, suffixes = c("_carrier", "_origin"))
    final_data <- merge(merge_airports_origin, airports, by.x = "dest", by.y = "faa", all.x = TRUE, suffixes = c("_origin", "_dest"))

## Exploratory Data Analysis

Exploratory data analysis is the process to get to know your data, so that you can generate and test your hypothesis. Visualization techniques are usually applied.

To get introduced to your newly created dataset:

    introduce(final_data)

rows | 336,776
columns | 42
discrete_columns | 16
continuous_columns | 26
all_missing_columns | 0
total_missing_values | 809,170
complete_rows | 906
total_observations | 14,144,592
memory_usage | 97,254,656

To visualize the table above (with some light analysis):

    plot_intro(final_data)

You should immediately notice some surprises:

1. 0.3% complete rows: This means only 0.3% of all rows are not completely missing!
2. 5.7% missing observations: Given the 0.3% complete rows, there are only 5.7% total missing observations.

Missing values are definitely creating problems. Let’s take a look at the missing profiles.

### Missing values

Real-world data is messy, and you can simply use `plot_missing` function to visualize missing profile for each feature.

    plot_missing(final_data)

From the chart, speed variable is mostly missing, and probably not informative. Looks like we have found the culprit for the 0.3% complete rows. Let’s drop it:

    final_data <- drop_columns(final_data, "speed")

Note: You may store the missing data profile with `profile_missing(final_data)` for additional analysis.

### Distributions

#### Bar Charts

To visualize frequency distributions for all discrete features:

    plot_bar(final_data)

    ## 5 columns ignored with more than 50 categories.
    ## dest: 105 categories
    ## tailnum: 4044 categories
    ## time_hour: 6936 categories
    ## model: 128 categories
    ## name: 102 categories

Upon closer inspection of manufacturer variable, it is not hard to identify the following duplications:

- AIRBUS and AIRBUS INDUSTRIE
- CANADAIR and CANADAIR LTD
- MCDONNELL DOUGLAS, MCDONNELL DOUGLAS AIRCRAFT CO and MCDONNELL DOUGLAS CORPORATION

Let’s clean it up and look at the manufacturer distribution again:

    final_data[which(final_data$manufacturer == "AIRBUS INDUSTRIE"),]$manufacturer <- "AIRBUS"
    final_data[which(final_data$manufacturer == "CANADAIR LTD"),]$manufacturer <- "CANADAIR"
    final_data[which(final_data$manufacturer %in% c("MCDONNELL DOUGLAS AIRCRAFT CO", "MCDONNELL DOUGLAS CORPORATION")),]$manufacturer <- "MCDONNELL DOUGLAS"

    plot_bar(final_data$manufacturer)

Feature dst_origin, tzone_origin, year_flights and tz_origin contains only 1 value, so we should drop them:

    final_data <- drop_columns(final_data, c("dst_origin", "tzone_origin", "year_flights", "tz_origin"))

Frequently, it is very beneficial to look at bi-variate frequency distribution. For example, to look at discrete features by arr_delay:

    plot_bar(final_data, with = "arr_delay")

    ## 5 columns ignored with more than 50 categories.
    ## dest: 105 categories
    ## tailnum: 4044 categories
    ## time_hour: 6936 categories
    ## model: 128 categories
    ## name: 102 categories

The resulting distribution looks quite different from the regular frequency distribution.

You may choose to break out all frequencies by a discrete variable:

    plot_bar(final_data, by = "origin")

    ## 5 columns ignored with more than 50 categories.
    ## dest: 105 categories
    ## tailnum: 4044 categories
    ## time_hour: 6936 categories
    ## model: 128 categories
    ## name: 102 categories

#### Histograms & Density Estimates

To visualize distributions for all continuous features:

    plot_histogram(final_data)

Immediately, you could observe that there are datetime features to be further treated, e.g., concatenating year, month and day to form date, and/or adding hour and minute to form datetime.

For the purpose of this vignette, I will not go deep into the analytical tasks. However, we should treat the following features based on the output of the histograms.

- Set flight to categorical, since that is the flight number with no mathematical meaning:

    final_data <- update_columns(final_data, "flight", as.factor)

- Remove year_flights and tz_origin since there is only one value:

    final_data <- drop_columns(final_data, c("year_flights", "tz_origin"))

You may also view the histogram and the density estimates for all continuous features broken down by another feature (discrete or continuous).

    plot_density(final_data, by = "origin")

#### QQ Plot

Quantile-Quantile plot is a way to visualize the deviation from a specific probability distribution. After analyzing these plots, it is often beneficial to apply mathematical transformation (such as log) for models like linear regression. To do so, we can use `plot_qq` function. By default, it compares with normal distribution.

Note: The function will take a long time with many observations, so you may choose to specify an appropriate `sampled_rows`:

    qq_data <- final_data[, c("arr_delay", "air_time", "distance", "seats")]

    plot_qq(qq_data, sampled_rows = 1000L)

From the chart, air_time, distance and seats seems skewed on both tails. Let’s apply a simple log transformation and plot them again.

    log_qq_data <- update_columns(qq_data, 2:4, function(x) log(x + 1))

    plot_qq(log_qq_data[, 2:4], sampled_rows = 1000L)

The distribution looks better now! If necessary, you may also view the QQ plot by another feature:

    qq_data <- final_data[, c("name_origin", "arr_delay", "air_time", "distance", "seats")]

    plot_qq(qq_data, by = "name_origin", sampled_rows = 1000L)

### Correlation Analysis

To visualize correlation heatmap for all non-missing features:

    plot_correlation(na.omit(final_data), maxcat = 5L)
    ## 11 features with more than 5 categories ignored!
    ## dest: 100 categories
    ## tailnum: 3246 categories
    ## carrier: 16 categories
    ## flight: 3773 categories
    ## time_hour: 6642 categories
    ## name_carrier: 16 categories
    ## manufacturer: 24 categories
    ## model: 121 categories
    ## engine: 6 categories
    ## name: 100 categories
    ## tzone_dest: 7 categories

You may also choose to visualize only discrete/continuous features with:

    plot_correlation(na.omit(final_data), type = "c")
    plot_correlation(na.omit(final_data), type = "d")

### Principal Component Analysis

While you can always do `plot_prcomp(na.omit(final_data))` directly, but PCA works better with cleaner data. To perform and visualize PCA on some selected features:

    pca_df <- na.omit(final_data[, c("origin", "dep_delay", "arr_delay", "air_time", "year_planes", "seats")])

    plot_prcomp(pca_df, variance_cap = 0.9, nrow = 2L, ncol = 2L)

With `variance_cap = 0.9`, the first 4 principal components are retained, capturing roughly 84% of the total variance. Let’s interpret the first four:

- PC1 (~25% variance) - Flight delay severity. The dominant loadings are `dep_delay` (0.60) and `arr_delay` (0.61), both pointing in the same direction. This is unsurprising since departure and arrival delays are tightly coupled. A high PC1 score indicates a severely delayed flight. The small negative loadings on `air_time` and `seats` hint that longer flights on larger aircraft tend to absorb delays slightly better.
- PC2 (~22% variance) - Long-haul route profile (JFK hub). `origin_JFK` (0.53), `air_time` (0.47), and `seats` (0.36) all load positively, while `origin_LGA` (-0.30) loads negatively. This component separates JFK’s international and long-haul flights, which use larger, wide-body aircraft, from LaGuardia’s shorter domestic routes on smaller planes. In essence, PC2 captures the type of route rather than whether it was delayed.
- PC3 (~19% variance) - LaGuardia vs. Newark contrast. `origin_LGA` (0.68) and `origin_EWR` (-0.64) dominate this component. Since PC2 already isolated the JFK effect, PC3 picks up the remaining airport distinction: LaGuardia vs. Newark. These two airports serve overlapping domestic markets but differ in carrier mix and runway configurations. The secondary loading on `year_planes` (-0.26) suggests that fleet age varies slightly between the two airports.
- PC4 (~18% variance) - Aircraft age vs. size trade-off. `year_planes` (0.51) loads positively while `seats` (-0.47) and `air_time` (-0.38) load negatively. This reflects a trade-off between newer, smaller regional jets and older, larger wide-body aircraft that fly longer routes. Newer planes in the fleet tend to be narrower-body, shorter-range models, whereas older planes (lower `year_planes`) tend to be wide-bodies with more seats covering longer distances.

Together, these four components reveal that the main sources of variation in this flight data are (1) delay behavior, (2) route length and aircraft class, (3) departure airport identity, and (4) fleet age composition, a clean separation of operationally distinct dimensions.

### Slicing & Dicing

Often, slicing and dicing data in different ways could be crucial to your analysis, and yields insights quickly.

#### Boxplots

Suppose you would like to build a model to predict arrival delays, you may visualize the distribution of all continuous features based on arrival delays with a boxplot:

    ## Reduce data size for demo purpose
    arr_delay_df <- final_data[, c("arr_delay", "month", "day", "hour", "minute", "dep_delay", "distance", "year_planes", "seats")]

    ## Call boxplot function
    plot_boxplot(arr_delay_df, by = "arr_delay")

Among all the subtle changes in correlation with arrival delays, you could immediately spot that planes with 300+ seats tend to have much longer delays (16 ~ 21 hours). You may now drill down further to verify or generate more hypotheses.

#### Scatterplots

An alternative visualization is scatterplot. For example:

    arr_delay_df2 <- final_data[, c("arr_delay", "dep_time", "dep_delay", "arr_time", "air_time", "distance", "year_planes", "seats")]

    plot_scatterplot(arr_delay_df2, by = "arr_delay", sampled_rows = 1000L)

#### Interactive Plots

All plotting functions in DataExplorer support an optional `plotly = TRUE` argument that converts static ggplot2 charts into interactive plotly widgets. This is especially useful when you are exploring large, messy datasets where hovering, zooming, and panning can reveal patterns that are hard to see in a static image.

To use this feature, install the plotly package:

    install.packages("plotly")

Basic usage: Simply pass `plotly = TRUE` to any plot function:

    plot_bar(final_data, plotly = TRUE)

This produces an interactive bar chart where you can hover over each bar to see the exact count, zoom into a specific region, or toggle categories on and off via the legend.

Navigating dense histograms: Interactive plots shine when distributions overlap or have long tails:

    plot_histogram(final_data, plotly = TRUE)

You can click-and-drag to zoom into a specific range, then double-click to reset the view. This is invaluable for spotting outliers or skewed distributions buried in noisy data.

Correlation heatmaps: For datasets with many features, the static correlation heatmap can become difficult to read. The interactive version lets you hover over individual cells to see the exact variable pair and correlation coefficient:

    plot_correlation(final_data, plotly = TRUE)

##### Known limitations

The `plotly = TRUE` option relies on `plotly::ggplotly()` to convert ggplot2 objects. While this works well for most chart types, there are known limitations:

- `geom_label` rendering: Functions that use `geom_label` (e.g., `plot_missing`, `plot_intro`, `plot_prcomp`) may render label text incorrectly in plotly. The labels can overlap, lose their background boxes, or display with unexpected formatting. This is an upstream limitation of `ggplotly()`’s translation of `geom_label`.
- Empty facet panels: Some plots that use `facet_wrap` (e.g., `plot_boxplot`) may show blank panels in plotly. This is a known issue in the plotly R package.
- `coord_flip` translation: Charts using `coord_flip` may not render identically in plotly. Hover text and axis labels can be swapped.
- PDF reports: The `plotly` option only applies to HTML output. When generating PDF reports, static ggplot2 plots are always used regardless of the `plotly` argument.

## Feature Engineering

Feature engineering is the process of creating new features from existing ones. Newly engineered features often generate valuable insights.

For functions in this section, it is preferred to use data.table objects as input, and they will be updated by reference. Otherwise, output object will be returned matching the input class.

### Replace missing values

Missing values may have meanings for a feature. Other than imputation methods, we may also set them to some logical values. For example, for discrete features, we may want to group missing values to a new category. For continuous features, we may want to set missing values to a known number based on existing knowledge.

In DataExplorer, this can be done by `set_missing`. The function automatically matches the argument for either discrete or continuous features, i.e., if you specify a number, all missing continuous values will be set to that number. If you specify a string, all missing discrete values will be set to that string. If you supply both, both types will be set.

    ## Return data.frame
    final_df <- set_missing(final_data, list(0L, "unknown"))
    ## Column [dep_time]: Set 8255 missing values to 0
    ## Column [dep_delay]: Set 8255 missing values to 0
    ## Column [arr_time]: Set 8713 missing values to 0
    ## Column [arr_delay]: Set 9430 missing values to 0
    ## Column [air_time]: Set 9430 missing values to 0
    ## Column [year_planes]: Set 57912 missing values to 0
    ## Column [engines]: Set 52606 missing values to 0
    ## Column [seats]: Set 52606 missing values to 0
    ## Column [lat_dest]: Set 7602 missing values to 0
    ## Column [lon_dest]: Set 7602 missing values to 0
    ## Column [alt_dest]: Set 7602 missing values to 0
    ## Column [tz_dest]: Set 7602 missing values to 0
    ## Column [tailnum]: Set 2512 missing values to unknown
    ## Column [type]: Set 52606 missing values to unknown
    ## Column [manufacturer]: Set 52606 missing values to unknown
    ## Column [model]: Set 52606 missing values to unknown
    ## Column [engine]: Set 52606 missing values to unknown
    ## Column [name]: Set 7602 missing values to unknown
    ## Column [dst_dest]: Set 7602 missing values to unknown
    ## Column [tzone_dest]: Set 7602 missing values to unknown
    plot_missing(final_df)

    ## Update data.table by reference
    # library(data.table)
    # final_dt <- data.table(final_data)
    # set_missing(final_dt, list(0L, "unknown"))
    # plot_missing(final_dt)

### Group sparse categories

From the bar charts above, we observed a number of discrete features with sparse categorical distributions. Sometimes, we want to group low-frequency categories to a new bucket, or reduce the number of categories to a reasonable range. `group_category` will do the work.

Take manufacturer feature for example, suppose we want to group the long tail to another category. We could try with bottom 20% (by count) first:

    group_category(data = final_data, feature = "manufacturer", threshold = 0.2)
    ##   manufacturer   cnt       pct   cum_pct
    ## 1       AIRBUS 88193 0.2618744 0.2618744
    ## 2       BOEING 82912 0.2461933 0.5080677
    ## 3      EMBRAER 66068 0.1961779 0.7042456

As we can see, manufacturer will be shrinked down to 4 categories, i.e., AIRBUS, BOEING, EMBRAER, and OTHER. If you like this threshold, you may specify `update = TRUE` to update the original dataset:

    final_df <- group_category(data = final_data, feature = "manufacturer", threshold = 0.2, update = TRUE)
    plot_bar(final_df$manufacturer)

Instead of shrinking categories by frequency, you may also group the categories by another continuous metric. For example, if you want to bucket the carrier with bottom 20% distance traveled, you may do the following:

    group_category(data = final_data, feature = "name_carrier", threshold = 0.2, measure = "distance")
    ##             name_carrier      cnt       pct   cum_pct
    ## 1  United Air Lines Inc. 89705524 0.2561422 0.2561422
    ## 2   Delta Air Lines Inc. 59507317 0.1699153 0.4260575
    ## 3        JetBlue Airways 58384137 0.1667082 0.5927657
    ## 4 American Airlines Inc. 43864584 0.1252495 0.7180152

Similarly, if you like it, you may add `update = TRUE` to update the original dataset.

    final_df <- group_category(data = final_data, feature = "name_carrier", threshold = 0.2, measure = "distance", update = TRUE)
    plot_bar(final_df$name_carrier)

### Dummify data (one hot encoding)

To transform the data into binary format (so that ML algorithms can pick it up), `dummify` will do the job. The function preserves original data structure, so that only eligible discrete features will be turned into binary format.

    plot_str(
      list(
        "original" = final_data,
        "dummified" = dummify(final_data, maxcat = 5L)
      )
    )

    ## 11 features with more than 5 categories ignored!
    ## dest: 105 categories
    ## tailnum: 4044 categories
    ## carrier: 16 categories
    ## flight: 3844 categories
    ## time_hour: 6936 categories
    ## name_carrier: 16 categories
    ## manufacturer: 32 categories
    ## model: 128 categories
    ## engine: 7 categories
    ## name: 102 categories
    ## tzone_dest: 8 categories

Note the `maxcat` argument. If a discrete feature has more categories than `maxcat`, it will not be dummified. As a result, it will be returned untouched.

### Drop features

After viewing the feature distribution, you often want to drop features that are insignificant. For example, features like dst_dest has mostly one value, and it doesn’t provide any valuable information. You can use `drop_columns` to quickly drop features. The function takes either names or column indices.

    identical(
      drop_columns(final_data, c("dst_dest", "tzone_dest")),
      drop_columns(final_data, c(36, 37))
    )
    ## [1] TRUE

### Update features

To quickly update many features with the same treatment, you may use `update_columns`. A very common use case is to set features to a different type, e.g., continuous to discrete. To quickly set all time related features to discrete:

    temporal_features <- c("month", "day", "hour", "minute", "tz_dest")
    final_data <- update_columns(final_data, temporal_features, as.factor)
    str(final_data[, c("month", "day", "hour", "minute", "tz_dest")])
    ## 'data.frame':    336776 obs. of  5 variables:
    ##  $ month  : Factor w/ 12 levels "1","2","3","4",..: 5 10 12 5 12 8 7 7 5 8 ...
    ##  $ day    : Factor w/ 31 levels "1","2","3","4",..: 14 29 19 30 7 25 12 1 15 26 ...
    ##  $ hour   : Factor w/ 20 levels "1","5","6","7",..: 17 17 17 17 17 17 17 17 17 17 ...
    ##  $ minute : Factor w/ 60 levels "0","1","2","3",..: 2 1 2 2 2 8 8 8 2 8 ...
    ##  $ tz_dest: Factor w/ 6 levels "-10","-9","-8",..: 4 4 4 4 4 4 4 4 4 4 ...

You may also use this function to transform selected features with a customized function:

    bin_seat <- function(x) cut(x, breaks = c(0L, 50L, 100L, 150L, 200L, 500L))
    transformed_data <- update_columns(final_data, "seats", bin_seat)

    plot_bar(transformed_data$seats)

## Data Reporting

To organize all the data profiling statistics into a report, you may use the `create_report` function. It will run most of the EDA functions and output a html file.

    create_report(final_data)

To maximize the usage of this function, always supply a response variable (if applicable) to automate various bivariate analyses. For example,

    create_report(final_data, y = "arr_delay")

You may also generate a fully interactive HTML report with `plotly = TRUE`:

    create_report(final_data, y = "arr_delay", plotly = TRUE)

You may also customize each individual section using `configure_report` function. It returns all specified arguments in a list, and then will be passed to `do.call` to be invoked. There are 3 types of arguments in this function:

1. switches (`add_*`): turn on/off a section
2. arguments (`*_args`): customize each section
3. global settings (`global_*`): global theme settings

To turn off a few sections and set a global theme:

    configure_report(
      add_plot_str = FALSE,
      add_plot_qq = FALSE,
      add_plot_prcomp = FALSE,
      add_plot_boxplot = FALSE,
      add_plot_scatterplot = FALSE,
      global_ggtheme = quote(theme_minimal(base_size = 14))
    )
    ## $introduce
    ## list()
    ##
    ## $plot_intro
    ## $plot_intro$ggtheme
    ## theme_minimal(base_size = 14)
    ##
    ## $plot_intro$theme_config
    ## list()
    ##
    ##
    ## $plot_missing
    ## $plot_missing$ggtheme
    ## theme_minimal(base_size = 14)
    ##
    ## $plot_missing$theme_config
    ## list()
    ##
    ##
    ## $plot_histogram
    ## $plot_histogram$ggtheme
    ## theme_minimal(base_size = 14)
    ##
    ## $plot_histogram$theme_config
    ## list()
    ##
    ##
    ## $plot_bar
    ## $plot_bar$ggtheme
    ## theme_minimal(base_size = 14)
    ##
    ## $plot_bar$theme_config
    ## list()
    ##
    ##
    ## $plot_correlation
    ## $plot_correlation$ggtheme
    ## theme_minimal(base_size = 14)
    ##
    ## $plot_correlation$theme_config
    ## list()
    ##
    ## $plot_correlation$cor_args
    ## $plot_correlation$cor_args$use
    ## [1] "pairwise.complete.obs"

The output can be passed directly to `config` argument in `create_repot`, e.g.,

    config <- configure_report(
      add_plot_str = FALSE,
      add_plot_qq = FALSE,
      add_plot_prcomp = FALSE,
      add_plot_boxplot = FALSE,
      add_plot_scatterplot = FALSE,
      global_ggtheme = quote(theme_minimal(base_size = 14))
    )
    create_report(final_data, config = config)

To configure the report without using `configure_report` function, you may edit the template below and pass it directly to `config` argument:

    config <- list(
      "introduce" = list(),
      "plot_intro" = list(),
      "plot_str" = list(
        "type" = "diagonal",
        "fontSize" = 35,
        "width" = 1000,
        "margin" = list("left" = 350, "right" = 250)
      ),
      "plot_missing" = list(),
      "plot_histogram" = list(),
      "plot_density" = list(),
      "plot_qq" = list(sampled_rows = 1000L),
      "plot_bar" = list(),
      "plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
      "plot_prcomp" = list(),
      "plot_boxplot" = list(),
      "plot_scatterplot" = list(sampled_rows = 1000L)
    )
    create_report(final_data, config = config)