This function helps analyse a block of questions or matrix questions into a single table. It also lets the user cut these questions by other questions in the data. The block of questions mush have the same response options.
Usage
build_qtable(
x,
block_cols,
cols = NULL,
table_title = "",
use_questions = FALSE,
use_NA = FALSE,
wt = NULL,
footnote = ""
)Arguments
- x
a data frame or tidy object
- block_cols
<tidyr_tidy_select> statement. These are the columns that make up the question block, they must have the same response option. Most question block columns start with the same piece of text, so you should use
starts_with('column_text'). See the Examples below.- cols
<tidyr_tidy_select> statement. These are the column(s) that we want to cut the questions in the question block by.
- table_title
a string. The title of the table sheet
- use_questions
a logical. If the data has column labels (was a imported .sav) file, convert the column label to a footnote with the question.
- use_NA
a logical. Whether to include
NAvalues in the table. For more complicatedNAprocessing post creation, we recommend using filter.- wt
a quoted or unquote column name. Specify a weighting variable, if
NULLno weight is applied.- footnote
a character vector. 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
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.
By default this function converts labelled to a xlr_vector
by default (and underlying it is a character() type).
See labelled and read_sav if you would like more details on the importing type.
Examples
library(xlr)
# You can use this function to get a block of questions
build_qtable(
clothes_opinions,
starts_with("Q1"),
table_title = "This is an example table")
#>
#> ── This is an example table ────────────────────────────────────────────────────
#> # A xlr_table: 20 x 4
#> `Question Block` value N Percent
#> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 Pants are good to wear Strongly Disagree 180 18%
#> 2 Pants are good to wear Disagree 194 20%
#> 3 Pants are good to wear Neutral 204 21%
#> 4 Pants are good to wear Agree 205 21%
#> 5 Pants are good to wear Strongly Agree 192 20%
#> 6 Shirts are good to wear Strongly Disagree 192 20%
#> 7 Shirts are good to wear Disagree 173 18%
#> 8 Shirts are good to wear Neutral 227 23%
#> 9 Shirts are good to wear Agree 195 20%
#> 10 Shirts are good to wear Strongly Agree 188 19%
#> 11 Shoes are good to wear Strongly Disagree 210 22%
#> 12 Shoes are good to wear Disagree 191 20%
#> 13 Shoes are good to wear Neutral 188 19%
#> 14 Shoes are good to wear Agree 211 22%
#> 15 Shoes are good to wear Strongly Agree 175 18%
#> 16 Q1_4 Strongly Disagree 201 21%
#> 17 Q1_4 Disagree 190 19%
#> 18 Q1_4 Neutral 183 19%
#> 19 Q1_4 Agree 203 21%
#> 20 Q1_4 Strongly Agree 198 20%
# Another way you could select the same columns
build_qtable(
clothes_opinions,
c(Q1_1,Q1_2,Q1_3,Q1_4),
table_title = "This is an example table")
#>
#> ── This is an example table ────────────────────────────────────────────────────
#> # A xlr_table: 20 x 4
#> `Question Block` value N Percent
#> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 Pants are good to wear Strongly Disagree 180 18%
#> 2 Pants are good to wear Disagree 194 20%
#> 3 Pants are good to wear Neutral 204 21%
#> 4 Pants are good to wear Agree 205 21%
#> 5 Pants are good to wear Strongly Agree 192 20%
#> 6 Shirts are good to wear Strongly Disagree 192 20%
#> 7 Shirts are good to wear Disagree 173 18%
#> 8 Shirts are good to wear Neutral 227 23%
#> 9 Shirts are good to wear Agree 195 20%
#> 10 Shirts are good to wear Strongly Agree 188 19%
#> 11 Shoes are good to wear Strongly Disagree 210 22%
#> 12 Shoes are good to wear Disagree 191 20%
#> 13 Shoes are good to wear Neutral 188 19%
#> 14 Shoes are good to wear Agree 211 22%
#> 15 Shoes are good to wear Strongly Agree 175 18%
#> 16 Q1_4 Strongly Disagree 201 21%
#> 17 Q1_4 Disagree 190 19%
#> 18 Q1_4 Neutral 183 19%
#> 19 Q1_4 Agree 203 21%
#> 20 Q1_4 Strongly Agree 198 20%
# Yet another way to select the same columns
build_qtable(
clothes_opinions,
all_of(c("Q1_1","Q1_2","Q1_3","Q1_4")),
table_title = "This is an example table")
#>
#> ── This is an example table ────────────────────────────────────────────────────
#> # A xlr_table: 20 x 4
#> `Question Block` value N Percent
#> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 Pants are good to wear Strongly Disagree 180 18%
#> 2 Pants are good to wear Disagree 194 20%
#> 3 Pants are good to wear Neutral 204 21%
#> 4 Pants are good to wear Agree 205 21%
#> 5 Pants are good to wear Strongly Agree 192 20%
#> 6 Shirts are good to wear Strongly Disagree 192 20%
#> 7 Shirts are good to wear Disagree 173 18%
#> 8 Shirts are good to wear Neutral 227 23%
#> 9 Shirts are good to wear Agree 195 20%
#> 10 Shirts are good to wear Strongly Agree 188 19%
#> 11 Shoes are good to wear Strongly Disagree 210 22%
#> 12 Shoes are good to wear Disagree 191 20%
#> 13 Shoes are good to wear Neutral 188 19%
#> 14 Shoes are good to wear Agree 211 22%
#> 15 Shoes are good to wear Strongly Agree 175 18%
#> 16 Q1_4 Strongly Disagree 201 21%
#> 17 Q1_4 Disagree 190 19%
#> 18 Q1_4 Neutral 183 19%
#> 19 Q1_4 Agree 203 21%
#> 20 Q1_4 Strongly Agree 198 20%
# You can also cut all questions in the block by a single column
build_qtable(
clothes_opinions,
starts_with("Q1"),
gender2,
table_title = "This is the second example table")
#>
#> ── This is the second example table ────────────────────────────────────────────
#> # A xlr_table: 60 x 5
#> gender2 `Question Block` value N Percent
#> <x_vctr> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 male Pants are good to wear Strongly Disagree 75 17%
#> 2 male Pants are good to wear Disagree 96 21%
#> 3 male Pants are good to wear Neutral 97 21%
#> 4 male Pants are good to wear Agree 89 20%
#> 5 male Pants are good to wear Strongly Agree 95 21%
#> 6 male Shirts are good to wear Strongly Disagree 85 19%
#> 7 male Shirts are good to wear Disagree 84 19%
#> 8 male Shirts are good to wear Neutral 96 21%
#> 9 male Shirts are good to wear Agree 90 20%
#> 10 male Shirts are good to wear Strongly Agree 97 21%
#> # ℹ 50 more rows
# You can also cut all questions in the block by a multiple columns
# By setting `use_questions=TRUE` then the footnote will be the questions
# labels, for the cut questions
build_qtable(
clothes_opinions,
starts_with("Q1"),
c(gender2,age_group),
table_title = "This is the third example table",
use_questions = TRUE)
#>
#> ── This is the third example table ─────────────────────────────────────────────
#> # A xlr_table: 240 x 6
#> gender2 age_group `Question Block` value N Percent
#> <x_vctr> <x_vctr> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 male 18-30 Pants are good to wear Strongly Disagree 16 14%
#> 2 male 18-30 Pants are good to wear Disagree 21 18%
#> 3 male 18-30 Pants are good to wear Neutral 26 23%
#> 4 male 18-30 Pants are good to wear Agree 22 19%
#> 5 male 18-30 Pants are good to wear Strongly Agree 29 25%
#> 6 male 18-30 Shirts are good to wear Strongly Disagree 18 16%
#> 7 male 18-30 Shirts are good to wear Disagree 23 20%
#> 8 male 18-30 Shirts are good to wear Neutral 26 23%
#> 9 male 18-30 Shirts are good to wear Agree 18 16%
#> 10 male 18-30 Shirts are good to wear Strongly Agree 29 25%
#> # ℹ 230 more rows
#> Questions
#> The gender of the participant
# You can also use weights, these weights can be either doubles or integers
# based weights
# You can also set a footnote
build_qtable(
clothes_opinions,
starts_with("Q1"),
age_group,
table_title = "This is the fourth example table",
wt = weight,
footnote = paste0("This is a footnote, you can use it if you want ",
"more detail in your table."))
#>
#> ── This is the fourth example table ────────────────────────────────────────────
#> # A xlr_table: 80 x 5
#> age_group `Question Block` value N Percent
#> <x_vctr> <x_vctr> <x_vctr> <x_dbl> <x_pct>
#> 1 18-30 Pants are good to wear Strongly Disagree 39,736.0 16%
#> 2 18-30 Pants are good to wear Disagree 48,447.0 19%
#> 3 18-30 Pants are good to wear Neutral 60,462.0 24%
#> 4 18-30 Pants are good to wear Agree 47,143.0 19%
#> 5 18-30 Pants are good to wear Strongly Agree 58,756.0 23%
#> 6 18-30 Shirts are good to wear Strongly Disagree 47,604.0 19%
#> 7 18-30 Shirts are good to wear Disagree 48,981.0 19%
#> 8 18-30 Shirts are good to wear Neutral 56,225.0 22%
#> 9 18-30 Shirts are good to wear Agree 48,690.0 19%
#> 10 18-30 Shirts are good to wear Strongly Agree 53,044.0 21%
#> # ℹ 70 more rows
#> This is a footnote, you can use it if you want more detail in your table.