# Data wrangling

library(scrutiny)

In general, scrutiny’s techniques for error detection are designed for a focus on the essential points, cutting out time-consuming repetition. There are some bottlenecks, however, such as entering decimal numbers as strings, or splitting strings that look like "7.64 (1.5)".

This vignette shows how to save your time preparing data for error detection. It gives some general tips for these tasks, and then presents scrutiny’s own specialized wrangling functions.

## Trailing zeros

### Motivation

One particular challenge when looking for numeric irregularities using R is that numbers often have to be treated as strings. The reason is that numeric values don’t preserve any trailing zeros. This is a major problem because trailing zeros are as important to, e.g., GRIM or DEBIT as any other trailing digits would be.

The only solution I know of is to work with strings — namely, strings that can be converted to non-NA numeric values. I will discuss two ways to work with them: (1) directly entering or importing numbers as strings, and (2) restoring trailing zeros.

### Enter numbers as strings

#### Automated

Several R packages help to extract tables from PDF. I recommend tabulizer (not currently on CRAN; see installation notes). There are also the pdftables and pdftools packages.

Using tabulizer requires Java to be installed. When it works well, tabulizer is a great tool for importing tables quickly and efficiently. It automatically captures values as strings, so trailing zeros are treated just like other digits.

However, tabulizer might sometimes struggle, especially with older PDF files. That is most likely the fault of the PDF format itself because it has no inbuilt support for tables, so any effort to extract them faces serious ambiguities. (See below, Replace column names by row values, for a solution to one such issue.)

If there are many tables in multiple files formatted in the same way, it can be useful to check if tabulizer reliably and accurately captures them. If it doesn’t, you might have to use copy and paste.

#### With copy and paste

Perhaps not all R users know that RStudio features an option for multiple cursors. These are especially useful in conjunction with tibble::tribble(), which is available via scrutiny. Here’s how to use multiple cursors in the present context:

1. Copy a column of numbers from PDF, pressing and holding Alt on Windows or option on Mac. (This works at least in Adobe Acrobat.)
2. Paste it into a tribble() call as below.
3. Pressing and holding Alt/option, select all the copied numbers.
4. Enter quotation marks and, for tribble()’s syntax, a comma.

You should then get something like this:

flights1 <- tribble(
~x,
"8.97",
"2.61",
"7.26",
"3.64",
"9.26",
"10.46",
"7.39",
)

All that’s missing is the sample size. Add it either via another tribble() column as above or via dplyr::mutate(), which also comes with scrutiny:

flights1 <- flights1 %>%
mutate(n = 28)

flights1
#> # A tibble: 7 × 2
#>   x         n
#>   <chr> <dbl>
#> 1 8.97     28
#> 2 2.61     28
#> 3 7.26     28
#> 4 3.64     28
#> 5 9.26     28
#> 6 10.46    28
#> 7 7.39     28

### Restore trailing zeros

When dealing with numbers that used to have trailing zeros but lost them from being registered as numeric, call restore_zeros() to format them correctly. Suppose all of the following numbers originally had one decimal place, but some no longer do:

vec <- c(4, 6.9, 5, 4.2, 4.8, 7, 4)

vec %>%
decimal_places()
#> [1] 0 1 0 1 1 0 0

Now, get them back with restore_zeros():

vec %>%
restore_zeros()
#> [1] "4.0" "6.9" "5.0" "4.2" "4.8" "7.0" "4.0"

vec %>%
restore_zeros() %>%
decimal_places()
#> [1] 1 1 1 1 1 1 1

This uses the default of going by the longest mantissa and padding the other strings with decimal zeros until they have that many decimal places. However, this is just a heuristic: The longest mantissa might itself have lost decimal places. Specify the width argument to explicitly state the desired mantissa length:

vec %>%
restore_zeros(width = 2)
#> [1] "4.00" "6.90" "5.00" "4.20" "4.80" "7.00" "4.00"

vec %>%
restore_zeros(width = 2) %>%
decimal_places()
#> [1] 2 2 2 2 2 2 2

A convenient way to restore trailing zeros in a data frame is restore_zeros_df(). By default, it operates on all columns that are coercible to numeric (factors don’t count):

iris <- as_tibble(iris)
iris %>%
restore_zeros_df(width = 3)
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>    <chr>        <chr>       <chr>        <chr>       <fct>
#>  1 5.100        3.500       1.400        0.200       setosa
#>  2 4.900        3.000       1.400        0.200       setosa
#>  3 4.700        3.200       1.300        0.200       setosa
#>  4 4.600        3.100       1.500        0.200       setosa
#>  5 5.000        3.600       1.400        0.200       setosa
#>  6 5.400        3.900       1.700        0.400       setosa
#>  7 4.600        3.400       1.400        0.300       setosa
#>  8 5.000        3.400       1.500        0.200       setosa
#>  9 4.400        2.900       1.400        0.200       setosa
#> 10 4.900        3.100       1.500        0.100       setosa
#> # … with 140 more rows

Specify columns mostly like you would in dplyr::select():

iris %>%
restore_zeros_df(starts_with("Sepal"), width = 3)
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>    <chr>        <chr>              <dbl>       <dbl> <fct>
#>  1 5.100        3.500                1.4         0.2 setosa
#>  2 4.900        3.000                1.4         0.2 setosa
#>  3 4.700        3.200                1.3         0.2 setosa
#>  4 4.600        3.100                1.5         0.2 setosa
#>  5 5.000        3.600                1.4         0.2 setosa
#>  6 5.400        3.900                1.7         0.4 setosa
#>  7 4.600        3.400                1.4         0.3 setosa
#>  8 5.000        3.400                1.5         0.2 setosa
#>  9 4.400        2.900                1.4         0.2 setosa
#> 10 4.900        3.100                1.5         0.1 setosa
#> # … with 140 more rows

## Split strings by parentheses

### Basic usage

With summary data copied or extracted from PDF (see above), you might encounter values presented like 5.22 (0.73). Instead of manually teasing them apart, call split_by_parens():

flights2 <- tribble(
~drone,           ~selfpilot,
"0.09 (0.21)",    "0.19 (0.13)",
"0.19 (0.28)",    "0.53 (0.10)",
"0.62 (0.16)",    "0.50 (0.11)",
"0.15 (0.35)",    "0.57 (0.16)",
)

flights2 %>%
split_by_parens()
#> # A tibble: 4 × 4
#>   drone_x drone_sd selfpilot_x selfpilot_sd
#>   <chr>   <chr>    <chr>       <chr>
#> 1 0.09    0.21     0.19        0.13
#> 2 0.19    0.28     0.53        0.10
#> 3 0.62    0.16     0.50        0.11
#> 4 0.15    0.35     0.57        0.16

Optionally, transform these values into a more useful format:

flights2 %>%
split_by_parens(transform = TRUE)
#> # A tibble: 8 × 3
#>   .origin   x     sd
#>   <chr>     <chr> <chr>
#> 1 drone     0.09  0.21
#> 2 drone     0.19  0.28
#> 3 drone     0.62  0.16
#> 4 drone     0.15  0.35
#> 5 selfpilot 0.19  0.13
#> 6 selfpilot 0.53  0.10
#> 7 selfpilot 0.50  0.11
#> 8 selfpilot 0.57  0.16

From here, you can call debit_map() almost right away (supposing you deal with binary distributions’ means and standard deviations):

flights2 %>%
split_by_parens(transform = TRUE) %>%
dplyr::mutate(n = 80) %>%
debit_map()
#> # A tibble: 8 × 12
#>   x     sd        n consistency rounding sd_lo…¹ sd_in…² sd_up…³ sd_in…⁴ x_lower
#>   <chr> <chr> <int> <lgl>       <chr>      <dbl> <lgl>     <dbl> <lgl>     <dbl>
#> 1 0.09  0.21     80 FALSE       up_or_d…   0.205 TRUE      0.215 TRUE      0.085
#> 2 0.19  0.28     80 FALSE       up_or_d…   0.275 TRUE      0.285 TRUE      0.185
#> 3 0.62  0.16     80 FALSE       up_or_d…   0.155 TRUE      0.165 TRUE      0.615
#> 4 0.15  0.35     80 TRUE        up_or_d…   0.345 TRUE      0.355 TRUE      0.145
#> 5 0.19  0.13     80 FALSE       up_or_d…   0.125 TRUE      0.135 TRUE      0.185
#> 6 0.53  0.10     80 FALSE       up_or_d…   0.095 TRUE      0.105 TRUE      0.525
#> 7 0.50  0.11     80 FALSE       up_or_d…   0.105 TRUE      0.115 TRUE      0.495
#> 8 0.57  0.16     80 FALSE       up_or_d…   0.155 TRUE      0.165 TRUE      0.565
#> # … with 2 more variables: x_upper <dbl>, .origin <chr>, and abbreviated
#> #   variable names ¹​sd_lower, ²​sd_incl_lower, ³​sd_upper, ⁴​sd_incl_upper

If your strings look like "2.65 [0.27]", specify the .sep argument as "brackets". Likewise for "2.65 {0.27}" and .sep = "braces". What about other separators, as in "2.65 <0.27>"? Specify .sep as those two substrings, like .sep = c("<", ">"). In all of these cases, the output will be the same as the default would be if the strings were like "2.65 (0.27)".

### Column name suffixes

The defaults for column name suffixes are (1) "x" for the part before the parentheses and (2) "sd" for the part inside of them. However, this won’t fit for all data presented like 5.22 (0.73). Override the defaults by specifying .col1 and/or .col2:

flights2 %>%
split_by_parens(end1 = "beta", end2 = "se")
#> # A tibble: 4 × 4
#>   drone_beta drone_se selfpilot_beta selfpilot_se
#>   <chr>      <chr>    <chr>          <chr>
#> 1 0.09       0.21     0.19           0.13
#> 2 0.19       0.28     0.53           0.10
#> 3 0.62       0.16     0.50           0.11
#> 4 0.15       0.35     0.57           0.16

These suffixes become column names if .transform is set to TRUE:

flights2 %>%
split_by_parens(end1 = "beta", end2 = "se", transform = TRUE)
#> # A tibble: 8 × 3
#>   .origin   beta  se
#>   <chr>     <chr> <chr>
#> 1 drone     0.09  0.21
#> 2 drone     0.19  0.28
#> 3 drone     0.62  0.16
#> 4 drone     0.15  0.35
#> 5 selfpilot 0.19  0.13
#> 6 selfpilot 0.53  0.10
#> 7 selfpilot 0.50  0.11
#> 8 selfpilot 0.57  0.16

### Extract substrings from before_parens() and inside_parens()

There also are specific functions for extracting the parts of the individual string vectors before or inside the parentheses:

flights3 <- flights2 %>%
dplyr::pull(selfpilot)

flights3
#> [1] "0.19 (0.13)" "0.53 (0.10)" "0.50 (0.11)" "0.57 (0.16)"

flights3 %>%
before_parens()
#> [1] "0.19" "0.53" "0.50" "0.57"

flights3 %>%
inside_parens()
#> [1] "0.13" "0.10" "0.11" "0.16"

## Replace column names by row values

When extracting tables from PDF with tabulizer, you might get data frames (converted from matrices) that have wrong, nondescript column names, while the correct column names are stored in one or more rows within the data frame itself.

I will first simulate the problem. x and n should be column names, but instead they are values in the first row:

flights1_with_issues <- flights1 %>%
dplyr::mutate(n = as.character(n)) %>%
tibble::add_row(x = "x", n = "n", .before = 1)

colnames(flights1_with_issues) <- c("Var1", "Var2")

flights1_with_issues
#> # A tibble: 8 × 2
#>   Var1  Var2
#>   <chr> <chr>
#> 1 x     n
#> 2 8.97  28
#> 3 2.61  28
#> 4 7.26  28
#> 5 3.64  28
#> 6 9.26  28
#> 7 10.46 28
#> 8 7.39  28

To remedy the issue, call row_to_colnames() on the data frame. It will replace the column names by the values of one or more rows. The latter are specified by their position numbers as in dplyr::slice(). For these numbers, the default is 1 because the column names will often be stored in the first row, if at all. The specified row or rows are then dropped because they shouldn’t have been rows in the first place.

With the above example:

flights1_with_issues %>%
row_to_colnames()
#> # A tibble: 7 × 2
#>   x     n
#>   <chr> <chr>
#> 1 8.97  28
#> 2 2.61  28
#> 3 7.26  28
#> 4 3.64  28
#> 5 9.26  28
#> 6 10.46 28
#> 7 7.39  28

Note that n is still a string vector, but this is true for all columns in tables extracted with tabulizer.