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Flextable format for pivot_table

pivot_format.Rd

Flextable format for pivot_table

Usage

pivot_format(
  pivot,
  background = "#81A1C1",
  color = "#FFFFFF",
  border = "#FFFFFF",
  font_size = 11,
  font_name = NULL,
  labels = pivot_labels(),
  formatter = pivot_formatter(),
  zebra_style = c("classic", "stats", "none"),
  zebra_color = "#ECEFF4",
  drop_stats = FALSE,
  keep_data = TRUE
)

Arguments

pivot

A pivot_table object.

background

Background color for the header and column(s) containing row(s).

color

Text color for the header and column(s) containing row(s).

border

Border color (applies to all table).

font_size

Font size (applies to all table).

font_name

Font name (applies to all table).

labels

Custom labels for statistics, see pivot_labels.

formatter

Function to format content, see pivot_formatter.

zebra_style

Add zebra theme to table.

zebra_color

Color to use for zebra theme.

drop_stats

Drop the stats column, can be useful if have only one stat to show.

keep_data

Keep data as attribute, this can be useful to retrieve the data from which the table was formatted.

Value

a flextable object.

Examples

library(flexpivot)
library(magrittr)
data("nobel_laureates")

# With two variables
nobel_laureates %>%
  pivot_table("category", "gender") %>%
  pivot_format()

gender
category
Statistic
male
female
<missing>
Total
Chemistry
N
179
5
0
184
%
18.8%
0.5%
0%
19.4%
Col %
20.6%
9.3%
0%
19.4%
Row %
97.3%
2.7%
0%
100%
Economic Sciences
N
82
2
0
84
%
8.6%
0.2%
0%
8.8%
Col %
9.4%
3.7%
0%
8.8%
Row %
97.6%
2.4%
0%
100%
Literature
N
101
15
0
116
%
10.6%
1.6%
0%
12.2%
Col %
11.6%
27.8%
0%
12.2%
Row %
87.1%
12.9%
0%
100%
Peace
N
90
17
27
134
%
9.5%
1.8%
2.8%
14.1%
Col %
10.4%
31.5%
100%
14.1%
Row %
67.2%
12.7%
20.1%
100%
Physics
N
210
3
0
213
%
22.1%
0.3%
0%
22.4%
Col %
24.2%
5.6%
0%
22.4%
Row %
98.6%
1.4%
0%
100%
Physiology or Medicine
N
207
12
0
219
%
21.8%
1.3%
0%
23%
Col %
23.8%
22.2%
0%
23%
Row %
94.5%
5.5%
0%
100%
Total
N
869
54
27
950
%
91.5%
5.7%
2.8%
100%
Col %
100%
100%
100%
100%
Row %
91.5%
5.7%
2.8%
100%

# Only count
nobel_laureates %>%
  pivot_table("category", "gender", stats = "n") %>%
  pivot_format(drop_stats = TRUE)

gender
category
male
female
<missing>
Total
Chemistry
179
5
0
184
Economic Sciences
82
2
0
84
Literature
101
15
0
116
Peace
90
17
27
134
Physics
210
3
0
213
Physiology or Medicine
207
12
0
219
Total
869
54
27
950

# Only percent
nobel_laureates %>%
  pivot_table("category", "gender", stats = "p") %>%
  pivot_format()

gender
category
Statistic
male
female
<missing>
Total
Chemistry
%
18.8%
0.5%
0%
19.4%
Economic Sciences
%
8.6%
0.2%
0%
8.8%
Literature
%
10.6%
1.6%
0%
12.2%
Peace
%
9.5%
1.8%
2.8%
14.1%
Physics
%
22.1%
0.3%
0%
22.4%
Physiology or Medicine
%
21.8%
1.3%
0%
23%
Total
%
91.5%
5.7%
2.8%
100%


# Two variable as rows
nobel_laureates %>%
  pivot_table(c("birth_continent", "gender"), "category", stats = "p") %>%
  pivot_format()

category
birth_continent
gender
Statistic
Chemistry
Economic Sciences
Literature
Peace
Physics
Physiology or Medicine
Total
Europe
male
%
10.6%
3%
7.5%
5%
11.8%
11.5%
49.5%
female
%
0.2%
0.1%
0.9%
0.5%
0.2%
0.5%
2.5%
<missing>
%
0%
0%
0%
0%
0%
0%
0%
Total
%
10.8%
3.2%
8.4%
5.6%
12%
12%
52%
Oceania
male
%
0.3%
0%
0%
0.2%
0.2%
0.7%
1.5%
female
%
0%
0%
0%
0%
0%
0.1%
0.1%
<missing>
%
0%
0%
0%
0%
0%
0%
0%
Total
%
0.3%
0%
0%
0.2%
0.2%
0.8%
1.6%
North America
male
%
6.1%
5.3%
1.4%
2%
7.8%
7.9%
30.4%
female
%
0.1%
0.1%
0.3%
0.4%
0.1%
0.5%
1.6%
<missing>
%
0%
0%
0%
0%
0%
0%
0%
Total
%
6.2%
5.4%
1.7%
2.4%
7.9%
8.4%
32%
Africa
male
%
0.2%
0%
0.5%
1%
0.2%
0.3%
2.3%
female
%
0.1%
0%
0.1%
0.3%
0%
0%
0.5%
<missing>
%
0%
0%
0%
0%
0%
0%
0%
Total
%
0.3%
0%
0.6%
1.4%
0.2%
0.3%
2.8%
Asia
male
%
1.6%
0.3%
0.9%
0.8%
2.1%
0.9%
6.7%
female
%
0.1%
0%
0.1%
0.5%
0%
0.1%
0.8%
<missing>
%
0%
0%
0%
0%
0%
0%
0%
Total
%
1.7%
0.3%
1%
1.4%
2.1%
1%
7.6%
South America
male
%
0%
0%
0.3%
0.3%
0%
0.4%
1%
female
%
0%
0%
0.1%
0%
0%
0%
0.1%
<missing>
%
0%
0%
0%
0%
0%
0%
0%
Total
%
0%
0%
0.4%
0.3%
0%
0.4%
1.2%
<missing>
male
%
0%
0%
0%
0%
0%
0%
0%
female
%
0%
0%
0%
0%
0%
0%
0%
<missing>
%
0%
0%
0%
2.8%
0%
0%
2.8%
Total
%
0%
0%
0%
2.8%
0%
0%
2.8%
Total
male
%
18.8%
8.6%
10.6%
9.5%
22.1%
21.8%
91.5%
female
%
0.5%
0.2%
1.6%
1.8%
0.3%
1.3%
5.7%
<missing>
%
0%
0%
0%
2.8%
0%
0%
2.8%
Total
%
19.4%
8.8%
12.2%
14.1%
22.4%
23%
100%


# Without cols
pivot_format(pivot_table(nobel_laureates, rows = "category"))

category
N
%
Chemistry
184
19.4%
Economic Sciences
84
8.8%
Literature
116
12.2%
Peace
134
14.1%
Physics
213
22.4%
Physiology or Medicine
219
23%
Total
950
100%
pivot_format(pivot_table(nobel_laureates, rows = c("category", "gender")))

category
gender
N
%
Chemistry
male
179
18.8%
female
5
0.5%
<missing>
0
0%
Total
184
19.4%
Economic Sciences
male
82
8.6%
female
2
0.2%
<missing>
0
0%
Total
84
8.8%
Literature
male
101
10.6%
female
15
1.6%
<missing>
0
0%
Total
116
12.2%
Peace
male
90
9.5%
female
17
1.8%
<missing>
27
2.8%
Total
134
14.1%
Physics
male
210
22.1%
female
3
0.3%
<missing>
0
0%
Total
213
22.4%
Physiology or Medicine
male
207
21.8%
female
12
1.3%
<missing>
0
0%
Total
219
23%
Total
male
869
91.5%
female
54
5.7%
<missing>
27
2.8%
Total
950
100%


# Customize
nobel_laureates %>%
  pivot_table("category") %>%
  pivot_format(
    background = "#A3BE8C",
    labels = pivot_labels(n = "Count", p = "Percentage")
  )

category
Count
Percentage
Chemistry
184
19.4%
Economic Sciences
84
8.8%
Literature
116
12.2%
Peace
134
14.1%
Physics
213
22.4%
Physiology or Medicine
219
23%
Total
950
100%