This function can take one or two multiple response responses and generate a summary table with them. You can also cut these columns by other categorical columns by specify the cols parameter.
Usage
build_mtable(
x,
mcols,
cols = NULL,
table_title = "",
use_questions = FALSE,
use_NA = FALSE,
wt = NULL,
footnote = ""
)Arguments
- x
a data frame or tidy object.
- mcols
the column(s) that are multiple response questions. See the
Detailsfor more details of how these columns should be structured.- cols
the column(s) that we want to calculate the sum/percentage of and the multiple response question.
- table_title
the title of the table sheet
- use_questions
if the data has column labels (was a imported .sav) file, convert the column label to a footnote with the question.
- use_NA
logical. whether to include
NAvalues in the table. For more complicatedNAprocessing post creation, we recommend using filter.- wt
Specify a weighting variable, if
NULLno weight is applied.- footnote
optional parameter to pass a custom footnote to the question, this parameter overwrites
use_questions.
Value
a xlr_table object. Use write_xlsx to write to an excel file.
See xlr_table for more information.
Details
A multiple response response is a series of columns with a single unique response that stores survey data where a respondent may have chosen multiple options. This function works if this data is stored in a wide format. To have a valid multiple response column all the columns should start with the same text, and each contain a unique value. That is it has the form:
data.frame(multi_col_1 = c(1,NA,1),
multi_col_2 = c(1,1,1),
multi_col_3 = c(NA,NA,1)
)
#> multi_col_1 multi_col_2 multi_col_3
#> 1 1 1 NA
#> 2 NA 1 NA
#> 3 1 1 1This is how popular survey platforms such as Qualtrics output this data type. If your data is long, you will need to pivot the data before hand, we recommend using pivot_wider.
By default this function converts labelled to a xlr_vector
by default (and underlying it is a character() type).
This function and its family (build_table, build_qtable) is designed to
work with data with columns of type haven::labelled,
which is the default format of data read with haven::read_sav/has the format
of .sav. .sav is the default file function type of data from SPSS and
can be exported from popular survey providers such as Qualtrics. When you
read in data with haven::read_sav it imports data with the questions,
labels for the response options etc.
See labelled and read_sav if you would like more details on the importing type.
Examples
library(xlr)
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
# You can use this function to calculate the number of people that have
# responded to the question `What is your favourite colour`
build_mtable(clothes_opinions,
"Q2",
table_title = "What is your favourite colour?")
#>
#> ── What is your favourite colour? ──────────────────────────────────────────────
#> # A xlr_table: 6 x 4
#> Q2 N N_group Percent
#> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 Black 897 995 90%
#> 2 Blue 192 995 19%
#> 3 Green 323 995 32%
#> 4 Grey 494 995 50%
#> 5 Red 792 995 80%
#> 6 Yellow 208 995 21%
# The function also lets you to see the number of NA questions (this is
# where someone doesn't answer any option)
build_mtable(clothes_opinions,
"Q2",
table_title = "What is your favourite colour?",
use_NA = TRUE)
#>
#> ── What is your favourite colour? ──────────────────────────────────────────────
#> # A xlr_table: 7 x 4
#> Q2 N N_group Percent
#> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 Black 897 1,000 90%
#> 2 Blue 192 1,000 19%
#> 3 Green 323 1,000 32%
#> 4 Grey 494 1,000 49%
#> 5 Red 792 1,000 79%
#> 6 Yellow 208 1,000 21%
#> 7 NA 5 1,000 0%
# You can also cut all questions in the multiple response functions by another
# column
build_mtable(clothes_opinions,
"Q2",
gender2,
table_title = "Your favourite colour by gender")
#>
#> ── Your favourite colour by gender ─────────────────────────────────────────────
#> # A xlr_table: 18 x 5
#> gender2 Q2 N N_group Percent
#> <x_vctr> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 male Black 413 460 90%
#> 2 male Blue 82 460 18%
#> 3 male Green 144 460 31%
#> 4 male Grey 225 460 49%
#> 5 male Red 371 460 81%
#> 6 male Yellow 95 460 21%
#> 7 female Black 413 460 90%
#> 8 female Blue 97 460 21%
#> 9 female Green 158 460 34%
#> 10 female Grey 237 460 52%
#> 11 female Red 356 460 77%
#> 12 female Yellow 94 460 20%
#> 13 non-binary Black 71 75 95%
#> 14 non-binary Blue 13 75 17%
#> 15 non-binary Green 21 75 28%
#> 16 non-binary Grey 32 75 43%
#> 17 non-binary Red 65 75 87%
#> 18 non-binary Yellow 19 75 25%
# By setting `use_questions=TRUE` then the footnote will be the questions
# labels. This is useful to see what the question is.
# The function will try to pull out this based on the question label, and
# will manipulate try and get the correct label.
build_mtable(clothes_opinions,
"Q2",
gender2,
table_title = "Your favourite colour by gender",
use_questions = TRUE)
#>
#> ── Your favourite colour by gender ─────────────────────────────────────────────
#> # A xlr_table: 18 x 5
#> gender2 Q2 N N_group Percent
#> <x_vctr> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 male Black 413 460 90%
#> 2 male Blue 82 460 18%
#> 3 male Green 144 460 31%
#> 4 male Grey 225 460 49%
#> 5 male Red 371 460 81%
#> 6 male Yellow 95 460 21%
#> 7 female Black 413 460 90%
#> 8 female Blue 97 460 21%
#> 9 female Green 158 460 34%
#> 10 female Grey 237 460 52%
#> 11 female Red 356 460 77%
#> 12 female Yellow 94 460 20%
#> 13 non-binary Black 71 75 95%
#> 14 non-binary Blue 13 75 17%
#> 15 non-binary Green 21 75 28%
#> 16 non-binary Grey 32 75 43%
#> 17 non-binary Red 65 75 87%
#> 18 non-binary Yellow 19 75 25%
#> Questions
#> The gender of the participant
# It is common for your data to include 'other' responses in a multiple
# response column. You should remove the column before running build_mtable
clothes_opinions |>
select(-Q3_other) |>
build_mtable("Q3")
#> # A xlr_table: 3 x 4
#> Q3 N N_group Percent
#> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 Earrings 290 869 33%
#> 2 Necklaces 201 869 23%
#> 3 Rings 783 869 90%
# You can also specify up to a maxium of two different multiple response
# columns.
clothes_opinions |>
select(-Q3_other) |>
build_mtable(c("Q2", "Q3"))
#> # A xlr_table: 18 x 5
#> Q2 Q3 N N_Q2 Percent
#> <x_vctr> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 Black Earrings 264 897 29%
#> 2 Black Necklaces 180 897 20%
#> 3 Black Rings 693 897 77%
#> 4 Blue Earrings 59 192 31%
#> 5 Blue Necklaces 49 192 26%
#> 6 Blue Rings 145 192 76%
#> 7 Green Earrings 98 323 30%
#> 8 Green Necklaces 73 323 23%
#> 9 Green Rings 247 323 76%
#> 10 Grey Earrings 143 494 29%
#> 11 Grey Necklaces 96 494 19%
#> 12 Grey Rings 391 494 79%
#> 13 Red Earrings 228 792 29%
#> 14 Red Necklaces 160 792 20%
#> 15 Red Rings 620 792 78%
#> 16 Yellow Earrings 52 208 25%
#> 17 Yellow Necklaces 35 208 17%
#> 18 Yellow Rings 161 208 77%
# These cam also be cut by other columns.
clothes_opinions |>
select(-Q3_other) |>
build_mtable(c("Q2", "Q3"),
gender2)
#> # A xlr_table: 54 x 6
#> gender2 Q2 Q3 N N_group Percent
#> <x_vctr> <x_vctr> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 male Black Earrings 127 413 31%
#> 2 male Black Necklaces 78 413 19%
#> 3 male Black Rings 315 413 76%
#> 4 male Blue Earrings 23 82 28%
#> 5 male Blue Necklaces 21 82 26%
#> 6 male Blue Rings 61 82 74%
#> 7 male Green Earrings 43 144 30%
#> 8 male Green Necklaces 31 144 22%
#> 9 male Green Rings 108 144 75%
#> 10 male Grey Earrings 72 225 32%
#> # ℹ 44 more rows
# This function also supports weights and manual footnotes
clothes_opinions |>
select(-Q3_other) |>
build_mtable(c("Q2", "Q3"),
gender2,
wt = weight,
footnote = "This is an example footnote.")
#> # A xlr_table: 54 x 6
#> gender2 Q2 Q3 N N_group Percent
#> <x_vctr> <x_vctr> <x_vctr> <x_dbl> <x_dbl> <x_pct>
#> 1 male Black Earrings 127,605.0 422,100.0 30%
#> 2 male Black Necklaces 73,623.0 422,100.0 17%
#> 3 male Black Rings 326,852.0 422,100.0 77%
#> 4 male Blue Earrings 20,749.0 78,108.0 27%
#> 5 male Blue Necklaces 15,806.0 78,108.0 20%
#> 6 male Blue Rings 54,686.0 78,108.0 70%
#> 7 male Green Earrings 44,326.0 146,440.0 30%
#> 8 male Green Necklaces 29,053.0 146,440.0 20%
#> 9 male Green Rings 110,288.0 146,440.0 75%
#> 10 male Grey Earrings 76,754.0 232,228.0 33%
#> # ℹ 44 more rows
#> This is an example footnote.