# Using Skimr

#### 2020-04-16

## Introduction

`skimr` is designed to provide summary statistics about variables. It is opinionated in its defaults, but easy to modify.

In base R, the most similar functions are `summary()` for vectors and data frames and `fivenum()` for numeric vectors:

    summary(iris)

    ##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width
    ##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100
    ##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300
    ##  Median :5.800   Median :3.000   Median :4.350   Median :1.300
    ##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199
    ##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800
    ##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500
    ##        Species
    ##  setosa    :50
    ##  versicolor:50
    ##  virginica :50
    ##
    ##
    ##

    summary(iris$Sepal.Length)

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##   4.300   5.100   5.800   5.843   6.400   7.900

    fivenum(iris$Sepal.Length)

    ## [1] 4.3 5.1 5.8 6.4 7.9

    summary(iris$Species)

    ##     setosa versicolor  virginica
    ##         50         50         50

## The `skim()` function

The core function of `skimr` is `skim()`, which is designed to work with (grouped) data frames, and will try coerce other objects to data frames if possible. Like `summary()`, `skim()`’s method for data frames presents results for every column; the statistics it provides depend on the class of the variable.

### Skimming data frames

By design, the main focus of `skimr` is on data frames; it is intended to fit well within a data pipeline and relies extensively on tidyverse vocabulary, which focuses on data frames.

Results of `skim()` are printed horizontally, with one section per variable type and one row per variable.

    library(skimr)
    skim(iris)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       iris
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   factor                   1
    ##   numeric                  4
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: factor ───────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate ordered n_unique
    ## 1 Species               0             1 FALSE          3
    ##   top_counts
    ## 1 set: 50, ver: 50, vir: 50
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 Sepal.Length          0             1  5.84 0.828   4.3   5.1  5.8    6.4
    ## 2 Sepal.Width           0             1  3.06 0.436   2     2.8  3      3.3
    ## 3 Petal.Length          0             1  3.76 1.77    1     1.6  4.35   5.1
    ## 4 Petal.Width           0             1  1.20 0.762   0.1   0.3  1.3    1.8
    ##    p100 hist
    ## 1   7.9 ▆▇▇▅▂
    ## 2   4.4 ▁▆▇▂▁
    ## 3   6.9 ▇▁▆▇▂
    ## 4   2.5 ▇▁▇▅▃

The format of the results are a single wide data frame combining the results, with some additional attributes and two metadata columns:

- `skim_variable`: name of the original variable
- `skim_type`: class of the variable

Unlike many other objects within `R`, these columns are intrinsic to the `skim_df` class. Dropping these variables will result in a coercion to a `tibble`. The `is_skim_df()` function is used to assert that an object is a skim_df.

    skim(iris) %>% is_skim_df()

    ## [1] TRUE
    ## attr(,"message")
    ## character(0)

    skim(iris) %>%
      dplyr::select(-skim_type, -skim_variable) %>% is_skim_df()

    ## [1] FALSE
    ## attr(,"message")
    ## [1] "Object is not a `skim_df`: missing column `skim_type`; missing column `skim_variable`"

    skim(iris) %>%
      dplyr::select(-n_missing) %>% is_skim_df()

    ## [1] TRUE
    ## attr(,"message")
    ## character(0)

In order to avoid type coercion, columns for summary statistics for different types are prefixed with the corresponding `skim_type`. This means that the columns of the `skim_df` are somewhat sparse, with quite a few missing values. This is because for some statistics the representations for different types of variables is different. For example, the mean of a Date variable and of a numeric variable are represented differently when printing, but this cannot be supported in a single vector. The exception to this are `n_missing` and `complete_rate` (missing/number of observations) which are the same for all types of variables.

    skim(iris) %>%
      tibble::as_tibble()

    ## # A tibble: 5 x 15
    ##   skim_type skim_variable n_missing complete_rate factor.ordered factor.n_unique
    ##   <chr>     <chr>             <int>         <dbl> <lgl>                    <int>
    ## 1 factor    Species               0             1 FALSE                        3
    ## 2 numeric   Sepal.Length          0             1 NA                          NA
    ## 3 numeric   Sepal.Width           0             1 NA                          NA
    ## 4 numeric   Petal.Length          0             1 NA                          NA
    ## 5 numeric   Petal.Width           0             1 NA                          NA
    ## # … with 9 more variables: factor.top_counts <chr>, numeric.mean <dbl>,
    ## #   numeric.sd <dbl>, numeric.p0 <dbl>, numeric.p25 <dbl>, numeric.p50 <dbl>,
    ## #   numeric.p75 <dbl>, numeric.p100 <dbl>, numeric.hist <chr>

This is in contrast to `summary.data.frame()`, which stores statistics in a `table`. The distinction is important, because the `skim_df` object is pipeable and easy to use for additional manipulation: for example, the user could select all of the variable means, or all summary statistics for a specific variable.

    skim(iris) %>%
      dplyr::filter(skim_variable == "Petal.Length")

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       iris
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   numeric                  1
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 Petal.Length          0             1  3.76  1.77     1   1.6  4.35   5.1
    ##    p100 hist
    ## 1   6.9 ▇▁▆▇▂

Most `dplyr` verbs should work as expected.

    skim(iris) %>%
      dplyr::select(skim_type, skim_variable, n_missing)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       iris
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   factor                   1
    ##   numeric                  4
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: factor ───────────────────────────────────────────────────────
    ##   skim_variable n_missing
    ## 1 Species               0
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing
    ## 1 Sepal.Length          0
    ## 2 Sepal.Width           0
    ## 3 Petal.Length          0
    ## 4 Petal.Width           0

The base skimmers `n_missing` and `complete_rate` are computed for all of the columns in the data. But all other type-based skimmers have a namespace. You need to use a `skim_type` prefix to refer to correct column.

    skim(iris) %>%
      dplyr::select(skim_type, skim_variable, numeric.mean)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       iris
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   factor                   1
    ##   numeric                  4
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: factor ───────────────────────────────────────────────────────
    ##   skim_variable
    ## 1 Species
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable  mean
    ## 1 Sepal.Length   5.84
    ## 2 Sepal.Width    3.06
    ## 3 Petal.Length   3.76
    ## 4 Petal.Width    1.20

`skim()` also supports grouped data created by `dplyr::group_by()`. In this case, one additional column for each grouping variable is added to the `skim_df` object.

    iris %>%
      dplyr::group_by(Species) %>%
      skim()

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       Piped data
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   numeric                  4
    ## ________________________
    ## Group variables            Species
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##    skim_variable Species    n_missing complete_rate  mean    sd    p0   p25
    ##  1 Sepal.Length  setosa             0             1 5.01  0.352   4.3  4.8
    ##  2 Sepal.Length  versicolor         0             1 5.94  0.516   4.9  5.6
    ##  3 Sepal.Length  virginica          0             1 6.59  0.636   4.9  6.22
    ##  4 Sepal.Width   setosa             0             1 3.43  0.379   2.3  3.2
    ##  5 Sepal.Width   versicolor         0             1 2.77  0.314   2    2.52
    ##  6 Sepal.Width   virginica          0             1 2.97  0.322   2.2  2.8
    ##  7 Petal.Length  setosa             0             1 1.46  0.174   1    1.4
    ##  8 Petal.Length  versicolor         0             1 4.26  0.470   3    4
    ##  9 Petal.Length  virginica          0             1 5.55  0.552   4.5  5.1
    ## 10 Petal.Width   setosa             0             1 0.246 0.105   0.1  0.2
    ## 11 Petal.Width   versicolor         0             1 1.33  0.198   1    1.2
    ## 12 Petal.Width   virginica          0             1 2.03  0.275   1.4  1.8
    ##      p50   p75  p100 hist
    ##  1  5     5.2    5.8 ▃▃▇▅▁
    ##  2  5.9   6.3    7   ▂▇▆▃▃
    ##  3  6.5   6.9    7.9 ▁▃▇▃▂
    ##  4  3.4   3.68   4.4 ▁▃▇▅▂
    ##  5  2.8   3      3.4 ▁▅▆▇▂
    ##  6  3     3.18   3.8 ▂▆▇▅▁
    ##  7  1.5   1.58   1.9 ▁▃▇▃▁
    ##  8  4.35  4.6    5.1 ▂▂▇▇▆
    ##  9  5.55  5.88   6.9 ▃▇▇▃▂
    ## 10  0.2   0.3    0.6 ▇▂▂▁▁
    ## 11  1.3   1.5    1.8 ▅▇▃▆▁
    ## 12  2     2.3    2.5 ▂▇▆▅▇

Individual columns from a data frame may be selected using tidyverse-style selectors.

    skim(iris, Sepal.Length, Species)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       iris
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   factor                   1
    ##   numeric                  1
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: factor ───────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate ordered n_unique
    ## 1 Species               0             1 FALSE          3
    ##   top_counts
    ## 1 set: 50, ver: 50, vir: 50
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 Sepal.Length          0             1  5.84 0.828   4.3   5.1   5.8   6.4
    ##    p100 hist
    ## 1   7.9 ▆▇▇▅▂

Or with common `select` helpers.

    skim(iris, starts_with("Sepal"))

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       iris
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   numeric                  2
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 Sepal.Length          0             1  5.84 0.828   4.3   5.1   5.8   6.4
    ## 2 Sepal.Width           0             1  3.06 0.436   2     2.8   3     3.3
    ##    p100 hist
    ## 1   7.9 ▆▇▇▅▂
    ## 2   4.4 ▁▆▇▂▁

If an individual column is of an unsupported class, it is treated as a character variable with a warning.

## Skimming vectors

In `skimr` v2, `skim()` will attempt to coerce non-data frames (such as vectors and matrices) to data frames. In most cases with vectors, the object being evaluated should be equivalent to wrapping the object in `as.data.frame()`.

For example, the `lynx` data set is class `ts`.

    skim(lynx)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       lynx
    ## Number of rows             114
    ## Number of columns          1
    ## _______________________
    ## Column type frequency:
    ##   ts                       1
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: ts ───────────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate start   end frequency deltat  mean    sd
    ## 1 x                     0             1  1821  1934         1      1 1538. 1586.
    ##     min   max median line_graph
    ## 1    39  6991    771 ⡈⢄⡠⢁⣀⠒⣀⠔

Which is the same as coercing to a data frame.

    all.equal(skim(lynx), skim(as.data.frame(lynx)))

    ## [1] "Attributes: < Component \"df_name\": 1 string mismatch >"

## Skimming matrices

`skimr` does not support skimming matrices directly but coerces them to data frames. Columns in the matrix become variables. This behavior is similar to `summary.matrix()`). Three possible ways to handle matrices with `skim()` parallel the three variations of the mean function for matrices.

    m <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), nrow = 4, ncol = 3)
    m

    ##      [,1] [,2] [,3]
    ## [1,]    1    5    9
    ## [2,]    2    6   10
    ## [3,]    3    7   11
    ## [4,]    4    8   12

Skimming the matrix produces similar results to `colMeans()`.

    colMeans(m)

    ## [1]  2.5  6.5 10.5

    skim(m) # Similar to summary.matrix and colMeans()

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       m
    ## Number of rows             4
    ## Number of columns          3
    ## _______________________
    ## Column type frequency:
    ##   numeric                  3
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 V1                    0             1   2.5  1.29     1  1.75   2.5  3.25
    ## 2 V2                    0             1   6.5  1.29     5  5.75   6.5  7.25
    ## 3 V3                    0             1  10.5  1.29     9  9.75  10.5 11.2
    ##    p100 hist
    ## 1     4 ▇▇▁▇▇
    ## 2     8 ▇▇▁▇▇
    ## 3    12 ▇▇▁▇▇

Skimming the transpose of the matrix will give row-wise results.

    rowMeans(m)

    ## [1] 5 6 7 8

    skim(t(m))

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       t(m)
    ## Number of rows             3
    ## Number of columns          4
    ## _______________________
    ## Column type frequency:
    ##   numeric                  4
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 V1                    0             1     5     4     1     3     5     7
    ## 2 V2                    0             1     6     4     2     4     6     8
    ## 3 V3                    0             1     7     4     3     5     7     9
    ## 4 V4                    0             1     8     4     4     6     8    10
    ##    p100 hist
    ## 1     9 ▇▁▇▁▇
    ## 2    10 ▇▁▇▁▇
    ## 3    11 ▇▁▇▁▇
    ## 4    12 ▇▁▇▁▇

And call `c()` on the matrix to get results across all columns.

    skim(c(m))

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       c(m)
    ## Number of rows             12
    ## Number of columns          1
    ## _______________________
    ## Column type frequency:
    ##   numeric                  1
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 data                  0             1   6.5  3.61     1  3.75   6.5  9.25
    ##    p100 hist
    ## 1    12 ▇▅▅▅▇

    mean(m)

    ## [1] 6.5

### Skimming without modification

`skim_tee()` produces the same printed version as `skim()` but returns the original, unmodified data frame. This allows for continued piping of the original data.

    iris_setosa <- iris %>%
      skim_tee() %>%
      dplyr::filter(Species == "setosa")

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       data
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   factor                   1
    ##   numeric                  4
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: factor ───────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate ordered n_unique
    ## 1 Species               0             1 FALSE          3
    ##   top_counts
    ## 1 set: 50, ver: 50, vir: 50
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 Sepal.Length          0             1  5.84 0.828   4.3   5.1  5.8    6.4
    ## 2 Sepal.Width           0             1  3.06 0.436   2     2.8  3      3.3
    ## 3 Petal.Length          0             1  3.76 1.77    1     1.6  4.35   5.1
    ## 4 Petal.Width           0             1  1.20 0.762   0.1   0.3  1.3    1.8
    ##    p100 hist
    ## 1   7.9 ▆▇▇▅▂
    ## 2   4.4 ▁▆▇▂▁
    ## 3   6.9 ▇▁▆▇▂
    ## 4   2.5 ▇▁▇▅▃

    head(iris_setosa)

    ##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    ## 1          5.1         3.5          1.4         0.2  setosa
    ## 2          4.9         3.0          1.4         0.2  setosa
    ## 3          4.7         3.2          1.3         0.2  setosa
    ## 4          4.6         3.1          1.5         0.2  setosa
    ## 5          5.0         3.6          1.4         0.2  setosa
    ## 6          5.4         3.9          1.7         0.4  setosa

Note, that `skim_tee()` is customized differently than `skim` itself. See below for more details.

## Reshaping the results from `skim()`

As noted above, `skim()` returns a wide data frame. This is usually the most sensible format for the majority of operations when investigating data, but the package has some other functions to help with edge cases.

First, `partition()` returns a named list of the wide data frames for each data type. Unlike the original data the partitioned data only has columns corresponding to the skimming functions used for this data type. These data frames are, therefore, not `skim_df` objects.

    iris %>%
      skim() %>%
      partition()

    ## $factor
    ##
    ## ── Variable type: factor ───────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate ordered n_unique
    ## 1 Species               0             1 FALSE          3
    ##   top_counts
    ## 1 set: 50, ver: 50, vir: 50
    ##
    ## $numeric
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 Sepal.Length          0             1  5.84 0.828   4.3   5.1  5.8    6.4
    ## 2 Sepal.Width           0             1  3.06 0.436   2     2.8  3      3.3
    ## 3 Petal.Length          0             1  3.76 1.77    1     1.6  4.35   5.1
    ## 4 Petal.Width           0             1  1.20 0.762   0.1   0.3  1.3    1.8
    ##    p100 hist
    ## 1   7.9 ▆▇▇▅▂
    ## 2   4.4 ▁▆▇▂▁
    ## 3   6.9 ▇▁▆▇▂
    ## 4   2.5 ▇▁▇▅▃

Alternatively, `yank()` selects only the subtable for a specific type. Think of it like `dplyr::select` on column types in the original data. Again, unsuitable columns are dropped.

    iris %>%
      skim() %>%
      yank("numeric")

    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75
    ## 1 Sepal.Length          0             1  5.84 0.828   4.3   5.1  5.8    6.4
    ## 2 Sepal.Width           0             1  3.06 0.436   2     2.8  3      3.3
    ## 3 Petal.Length          0             1  3.76 1.77    1     1.6  4.35   5.1
    ## 4 Petal.Width           0             1  1.20 0.762   0.1   0.3  1.3    1.8
    ##    p100 hist
    ## 1   7.9 ▆▇▇▅▂
    ## 2   4.4 ▁▆▇▂▁
    ## 3   6.9 ▇▁▆▇▂
    ## 4   2.5 ▇▁▇▅▃

`to_long()` returns a single long data frame with columns `variable`, `type`, `statistic` and `formatted`. This is similar but not identical to the `skim_df` object in `skimr` v1.

    iris %>%
      skim() %>%
      to_long() %>%
      head()

    ## # A tibble: 6 x 4
    ##   skim_type skim_variable stat          formatted
    ##   <chr>     <chr>         <chr>         <chr>
    ## 1 factor    Species       n_missing     0
    ## 2 numeric   Sepal.Length  n_missing     0
    ## 3 numeric   Sepal.Width   n_missing     0
    ## 4 numeric   Petal.Length  n_missing     0
    ## 5 numeric   Petal.Width   n_missing     0
    ## 6 factor    Species       complete_rate 1

Since the `skim_variable` and `skim_type` columns are a core component of the `skim_df` class, it’s possible to get unwanted side effects when using `dplyr::select()`. Instead, use `focus()` to select columns of the skimmed results and keep them as a `skim_df`; it always keeps the metadata column.

    iris %>%
      skim() %>%
      focus(n_missing, numeric.mean)

    ## ── Data Summary ────────────────────────
    ##                            Values
    ## Name                       Piped data
    ## Number of rows             150
    ## Number of columns          5
    ## _______________________
    ## Column type frequency:
    ##   factor                   1
    ##   numeric                  4
    ## ________________________
    ## Group variables            None
    ##
    ## ── Variable type: factor ───────────────────────────────────────────────────────
    ##   skim_variable n_missing
    ## 1 Species               0
    ##
    ## ── Variable type: numeric ──────────────────────────────────────────────────────
    ##   skim_variable n_missing  mean
    ## 1 Sepal.Length          0  5.84
    ## 2 Sepal.Width           0  3.06
    ## 3 Petal.Length          0  3.76
    ## 4 Petal.Width           0  1.20

## Rendering the results of `skim()`

The `skim_df` object is a wide data frame. The display is created by default using `print.skim_df()`; users can specify additional options by explicitly calling `print([skim_df object], ...)`.

For documents rendered by `knitr`, the package provides a custom `knit_print` method. To use it, the final line of your code chunk should have a `skim_df` object.

    skim(Orange)

Data summary
Name | Orange
Number of rows | 35
Number of columns | 3
_______________________ |
Column type frequency: |
factor | 1
numeric | 2
________________________ |
Group variables | None

Variable type: factor

skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts
--- | --- | --- | --- | --- | ---
Tree | 0 | 1 | TRUE | 5 | 3: 7, 1: 7, 5: 7, 2: 7

Variable type: numeric

skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
age | 0 | 1 | 922.14 | 491.86 | 118 | 484.0 | 1004 | 1372.0 | 1582 | ▃▇▁▇▇
circumference | 0 | 1 | 115.86 | 57.49 | 30 | 65.5 | 115 | 161.5 | 214 | ▇▃▇▇▅

The same type of rendering is available from reshaped `skim_df` objects, those generated by `partition()` and `yank()` in particular.

    skim(Orange) %>%
      yank("numeric")

Variable type: numeric

skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
age | 0 | 1 | 922.14 | 491.86 | 118 | 484.0 | 1004 | 1372.0 | 1582 | ▃▇▁▇▇
circumference | 0 | 1 | 115.86 | 57.49 | 30 | 65.5 | 115 | 161.5 | 214 | ▇▃▇▇▅

## Customizing print options

Although its not a common use case outside of writing vignettes about `skimr`, you can fall back to default printing methods by adding the chunk option `render = knitr::normal_print`.

You can also disable the `skimr` summary by setting the chunk option `skimr_include_summary = FALSE`.

You can change the number of digits shown in the columns of generated statistics by changing the `skimr_digits` chunk option.

## Modifying `skim()`

`skimr` is opinionated in its choice of defaults, but users can easily add, replace, or remove the statistics for a class. For interactive use, you can create your own skimming function with the `skim_with()` factory. `skimr` also has an API for extensions in other packages. Working with that is covered later.

To add a statistic for a data type, create an `sfl()` (a `skimr` function list) for each class that you want to change:

    my_skim <- skim_with(numeric = sfl(new_mad = mad))
    my_skim(faithful)

Data summary
Name | faithful
Number of rows | 272
Number of columns | 2
_______________________ |
Column type frequency: |
numeric | 2
________________________ |
Group variables | None

Variable type: numeric

skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | new_mad
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
eruptions | 0 | 1 | 3.49 | 1.14 | 1.6 | 2.16 | 4 | 4.45 | 5.1 | ▇▂▂▇▇ | 0.95
waiting | 0 | 1 | 70.90 | 13.59 | 43.0 | 58.00 | 76 | 82.00 | 96.0 | ▃▃▂▇▂ | 11.86

As the previous example suggests, the default is to append new summary statistics to the preexisting set. This behavior isn’t always desirable, especially when you want lots of changes. To stop appending, set `append = FALSE`.

    my_skim <- skim_with(numeric = sfl(new_mad = mad), append = FALSE)
    my_skim(faithful)

Data summary
Name | faithful
Number of rows | 272
Number of columns | 2
_______________________ |
Column type frequency: |
numeric | 2
________________________ |
Group variables | None

Variable type: numeric

skim_variable | n_missing | complete_rate | new_mad
--- | --- | --- | ---
eruptions | 0 | 1 | 0.95
waiting | 0 | 1 | 11.86

You can also use `skim_with()` to remove specific statistics by setting them to `NULL`. This is commonly used to disable the inline histograms and spark graphs.

    no_hist <- skim_with(ts = sfl(line_graph = NULL))
    no_hist(Nile)

Data summary
Name | Nile
Number of rows | 100
Number of columns | 1
_______________________ |
Column type frequency: |
ts | 1
________________________ |
Group variables | None

Variable type: ts

skim_variable | n_missing | complete_rate | start | end | frequency | deltat | mean | sd | min | max | median
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
x | 0 | 1 | 1871 | 1970 | 1 | 1 | 919.35 | 169.23 | 456 | 1370 | 893.5

The same pattern applies to changing skimmers for multiple classes simultaneously. If you want to partially-apply function arguments, use the Tidyverse lambda syntax.

    my_skim <- skim_with(
      numeric = sfl(total = ~ sum(., na.rm = TRUE)),
      factor = sfl(missing = ~ sum(is.na(.))),
      append = FALSE
    )

    my_skim(iris)

Data summary
Name | iris
Number of rows | 150
Number of columns | 5
_______________________ |
Column type frequency: |
factor | 1
numeric | 4
________________________ |
Group variables | None

Variable type: factor

skim_variable | n_missing | complete_rate | missing
--- | --- | --- | ---
Species | 0 | 1 | 0

Variable type: numeric

skim_variable | n_missing | complete_rate | total
--- | --- | --- | ---
Sepal.Length | 0 | 1 | 876.5
Sepal.Width | 0 | 1 | 458.6
Petal.Length | 0 | 1 | 563.7
Petal.Width | 0 | 1 | 179.9

To modify the “base” skimmers, refer to them in a similar manner. Since base skimmers are usually a small group, they must return the same type for all data types in R, `append` doesn’t apply here.

    my_skim <- skim_with(base = sfl(length = length))
    my_skim(faithful)

Data summary
Name | faithful
Number of rows | 272
Number of columns | 2
_______________________ |
Column type frequency: |
numeric | 2
________________________ |
Group variables | None

Variable type: numeric

skim_variable | length | mean | sd | p0 | p25 | p50 | p75 | p100 | hist
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
eruptions | 272 | 3.49 | 1.14 | 1.6 | 2.16 | 4 | 4.45 | 5.1 | ▇▂▂▇▇
waiting | 272 | 70.90 | 13.59 | 43.0 | 58.00 | 76 | 82.00 | 96.0 | ▃▃▂▇▂

## Extending `skimr`

Packages may wish to export their own `skim()` functions. Use `skim_with()` for this. In fact, this is how `skimr` generates its version of `skim()`.

    #' @export
    my_package_skim <- skim_with()

Alternatively, defaults for another data types can be added to `skimr` with the `get_skimmers` generic. The method for your data type should return an `sfl()`. Unlike the `sfl()` used interactively, you also need to set the `skim_type` argument. It should match the method type in the function signature.

    get_skimmers.my_type <- function(column) {
      sfl(
        skim_type = "my_type",
        total = sum
      )
    }

    my_data <- data.frame(
      my_type = structure(1:3, class = c("my_type", "integer"))
    )
    skim(my_data)

Data summary
Name | my_data
Number of rows | 3
Number of columns | 1
_______________________ |
Column type frequency: |
my_type | 1
________________________ |
Group variables | None

Variable type: my_type

skim_variable | n_missing | complete_rate | total
--- | --- | --- | ---
my_type | 0 | 1 | 6

An extended example is available in the vignette Supporting additional objects.

## Solutions to common rendering problems

The details of rendering are dependent on the operating system R is running on, the locale of the installation, and the fonts installed. Rendering may also differ based on whether it occurs in the console or when knitting to specific types of documents such as HTML and PDF.

The most commonly reported problems involve rendering the spark graphs (inline histogram and line chart) on Windows. One common fix is to switch your locale. The function `fix_windows_histograms()` does this for you.

In order to render the sparkgraphs in html or PDF histogram you may need to change fonts to one that supports blocks or Braille (depending on which you need). Please review the separate vignette and associated template for details.