Formatters for pivot_format
pivot_formatter.RdFormatters for pivot_format
Arguments
- n
Function, applied to n.
- p
Function, applied to p.
- p_col
Function, applied to p_col.
- p_row
Function, applied to p_row.
Value
a list of functions that can be use in pivot_format.
Examples
library(flexpivot)
data("nobel_laureates")
# One variable
pt <- pivot_table(nobel_laureates, rows = "category")
pivot_format(pt)
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(pt, formatter = pivot_formatter(
n = function(x) {
format(round(x * 100), big.mark = " ")
}
))
category
N
%
Chemistry
18 400
19.4%
Economic Sciences
8 400
8.8%
Literature
11 600
12.2%
Peace
13 400
14.1%
Physics
21 300
22.4%
Physiology or Medicine
21 900
23%
Total
95 000
100%
# Two variable as rows
pt <- pivot_table(nobel_laureates, rows = c("category", "gender"))
pivot_format(pt)
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%
pivot_format(pt, formatter = pivot_formatter(
n = function(x) {
format(round(x * 100), big.mark = " ")
}
))
category
gender
N
%
Chemistry
male
17 900
18.8%
female
500
0.5%
<missing>
0
0%
Total
18 400
19.4%
Economic Sciences
male
8 200
8.6%
female
200
0.2%
<missing>
0
0%
Total
8 400
8.8%
Literature
male
10 100
10.6%
female
1 500
1.6%
<missing>
0
0%
Total
11 600
12.2%
Peace
male
9 000
9.5%
female
1 700
1.8%
<missing>
2 700
2.8%
Total
13 400
14.1%
Physics
male
21 000
22.1%
female
300
0.3%
<missing>
0
0%
Total
21 300
22.4%
Physiology or Medicine
male
20 700
21.8%
female
1 200
1.3%
<missing>
0
0%
Total
21 900
23%
Total
male
86 900
91.5%
female
5 400
5.7%
<missing>
2 700
2.8%
Total
95 000
100%
# One row, one column
pt <- pivot_table(nobel_laureates, rows = "category", cols = "gender")
pivot_format(pt)
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%
pivot_format(pt, formatter = pivot_formatter(
n = function(x) {
format(round(x * 100), big.mark = " ")
}
))
gender
category
Statistic
male
female
<missing>
Total
Chemistry
N
17 900
500
0
18 400
%
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
8 200
200
0
8 400
%
8.6%
0.2%
0%
8.8%
Col %
9.4%
3.7%
0%
8.8%
Row %
97.6%
2.4%
0%
100%
Literature
N
10 100
1 500
0
11 600
%
10.6%
1.6%
0%
12.2%
Col %
11.6%
27.8%
0%
12.2%
Row %
87.1%
12.9%
0%
100%
Peace
N
9 000
1 700
2 700
13 400
%
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
21 000
300
0
21 300
%
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
20 700
1 200
0
21 900
%
21.8%
1.3%
0%
23%
Col %
23.8%
22.2%
0%
23%
Row %
94.5%
5.5%
0%
100%
Total
N
86 900
5 400
2 700
95 000
%
91.5%
5.7%
2.8%
100%
Col %
100%
100%
100%
100%
Row %
91.5%
5.7%
2.8%
100%