This creates an integer vector that will be printed neatly and can easily be
exported to Excel using it's native format.You can
convert a vector back to its base type with as_base_r().
Usage
xlr_integer(x = integer(), style = xlr_format_numeric())
is_xlr_integer(x)
as_xlr_integer(x, style = xlr_format_numeric())Arguments
- x
A numeric vector
For
xlr_integer(): A numeric vectorFor
is_xlr_integer(): An object to testFor
as_xlr_integer(): a vector
- style
Additional styling options for the vector. See xlr_format_numeric for more details.
Details
Internally, xlr_integer uses vec_cast to convert numeric types
to integers. Anything that vec_cast can handle so can xlr_integer. Read
more about casting at vec_cast.
Examples
library(xlr)
# Create a variable to represent an integer
x <- xlr_integer(2)
# This will print nicely
x
#> <xlr_integer[1]>
#> [1] 2
# You can change the styling, which affects how it looks when we save it as an
# excel document
x <- xlr_integer(x, style = xlr_format(font_size = 9, font_colour = "red"))
x
#> <xlr_integer[1]>
#> [1] 2
# We can also define a vector of integers
y <- xlr_integer(c(1,2,3))
y
#> <xlr_integer[3]>
#> [1] 1 2 3
# You can convert existing data to a integer using dplyr verbs
# It formats large numbers nicely
df <- data.frame(col_1 = c(1:100*100))
df |>
dplyr::mutate(col_pct = as_xlr_integer(col_1))
#> col_1 col_pct
#> 1 100 100
#> 2 200 200
#> 3 300 300
#> 4 400 400
#> 5 500 500
#> 6 600 600
#> 7 700 700
#> 8 800 800
#> 9 900 900
#> 10 1000 1,000
#> 11 1100 1,100
#> 12 1200 1,200
#> 13 1300 1,300
#> 14 1400 1,400
#> 15 1500 1,500
#> 16 1600 1,600
#> 17 1700 1,700
#> 18 1800 1,800
#> 19 1900 1,900
#> 20 2000 2,000
#> 21 2100 2,100
#> 22 2200 2,200
#> 23 2300 2,300
#> 24 2400 2,400
#> 25 2500 2,500
#> 26 2600 2,600
#> 27 2700 2,700
#> 28 2800 2,800
#> 29 2900 2,900
#> 30 3000 3,000
#> 31 3100 3,100
#> 32 3200 3,200
#> 33 3300 3,300
#> 34 3400 3,400
#> 35 3500 3,500
#> 36 3600 3,600
#> 37 3700 3,700
#> 38 3800 3,800
#> 39 3900 3,900
#> 40 4000 4,000
#> 41 4100 4,100
#> 42 4200 4,200
#> 43 4300 4,300
#> 44 4400 4,400
#> 45 4500 4,500
#> 46 4600 4,600
#> 47 4700 4,700
#> 48 4800 4,800
#> 49 4900 4,900
#> 50 5000 5,000
#> 51 5100 5,100
#> 52 5200 5,200
#> 53 5300 5,300
#> 54 5400 5,400
#> 55 5500 5,500
#> 56 5600 5,600
#> 57 5700 5,700
#> 58 5800 5,800
#> 59 5900 5,900
#> 60 6000 6,000
#> 61 6100 6,100
#> 62 6200 6,200
#> 63 6300 6,300
#> 64 6400 6,400
#> 65 6500 6,500
#> 66 6600 6,600
#> 67 6700 6,700
#> 68 6800 6,800
#> 69 6900 6,900
#> 70 7000 7,000
#> 71 7100 7,100
#> 72 7200 7,200
#> 73 7300 7,300
#> 74 7400 7,400
#> 75 7500 7,500
#> 76 7600 7,600
#> 77 7700 7,700
#> 78 7800 7,800
#> 79 7900 7,900
#> 80 8000 8,000
#> 81 8100 8,100
#> 82 8200 8,200
#> 83 8300 8,300
#> 84 8400 8,400
#> 85 8500 8,500
#> 86 8600 8,600
#> 87 8700 8,700
#> 88 8800 8,800
#> 89 8900 8,900
#> 90 9000 9,000
#> 91 9100 9,100
#> 92 9200 9,200
#> 93 9300 9,300
#> 94 9400 9,400
#> 95 9500 9,500
#> 96 9600 9,600
#> 97 9700 9,700
#> 98 9800 9,800
#> 99 9900 9,900
#> 100 10000 10,000
# You can use as_xlr_integer to convert a string in a integer
df <- data.frame(col_str = c("12","13","14"))
# now we can convert the string to a integer(), internally it uses the same
# logic as as.integer()
df |>
dplyr::mutate(col_percent = as_xlr_integer(col_str))
#> col_str col_percent
#> 1 12 12
#> 2 13 13
#> 3 14 14