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Getting started with xlr
xlr is designed to help with the analysis of survey data and conduct
modelling, and then export this information to Excel in an
easy and consistent way.
It is designed to help people with little R experience analyse survey
data quickly and easily. This includes functions to quickly analyse
multiple-response questions and block questions.
This package also provides a simple and easy to use interface with
Excel, building on the excellent work of the [openxlsx]
package. It is designed with ease of use in mind, at the expense of some
functionality of openxlsx.
This package relies extensively on the tidyverse and so we use many of
the same terms and concepts here.
There are three aspects to this package:
Functions to help with the analysis of survey complex survey
data.
Functions to help export this data to
Excel.
New R types to support consistent formatting in
Excel.
Data: clothes_opinions
To explore how to analyse complex data we’ll use the dataset
clothes_opinions. This dataset contains fake survey data
with peoples opinions on clothes as well as a series of demographic
characteristics. It is documented in ?clothes_opinions.
dim(clothes_opinions)
#> [1] 1000 20
clothes_opinions
#> # A tibble: 1,000 × 20
#> weight group gender gender2 age age_group Q1_1 Q1_2 Q1_3 Q1_4
#> <int> <chr> <chr> <dbl+lbl> <int> <chr> <int+l> <int+l> <int+l> <int+l>
#> 1 1072 a female 2 [female] 25 18-30 1 [Str… 5 [Str… 5 [Str… 2 [Dis…
#> 2 219 a female 2 [female] 64 51-65 3 [Neu… 1 [Str… 2 [Dis… 2 [Dis…
#> 3 1187 a male 1 [male] 35 31-40 4 [Agr… 3 [Neu… 5 [Str… 1 [Str…
#> 4 1860 a male 1 [male] 55 51-65 4 [Agr… 1 [Str… 2 [Dis… 2 [Dis…
#> # ℹ 996 more rows
#> # ℹ 10 more variables: Q2_1 <chr>, Q2_2 <chr>, Q2_3 <chr>, Q2_4 <chr>,
#> # Q2_5 <chr>, Q2_6 <chr>, Q3_1 <dbl+lbl>, Q3_2 <dbl+lbl>, Q3_3 <dbl+lbl>,
#> # Q3_other <chr>
clothes_opinions is a tibble a adapted type
of data.frame. You can learn more about tibbles at https://tibble.tidyverse.org.
It was designed to mimic data initially saved as a .sav
file and imported with haven::import_spss(). Data with this
form can easily be exported from major survey platforms such as Qualtrics, and includes useful
information such as question labels which xlr functions can utilise
automatically.
Analysing survey data
There are three main functions to analyse survey data:
build_table() which creates 1, 2, 3, … -way
tables.
build_mtable() which creates 1, 2, 3, … -way tables
for multiple response questions.
build_qtable() which creates 1, 2, 3, … -way tables
for a block of questions.
We will start by introducing xlr_table() and go through
all of its functionality. This functionality is similar for
build_mtable() and build_qtable().
build_table()
This function provides roughly the same functionality as base R’s
table() except it provides a lot more support for easy use.
Like all functions in this package, we follow the convention of the
first argument is the data.frame (or tibble) that you want to work on.
The second argument are the columns we want to build a table with, and
the remainder are options for build_table().
Let’s first calculate the number of people of each gender in
clothes_opinions:
clothes_opinions |>
build_table(gender2)
#> # A xlr_table: 3 x 3
#> gender2 N Percent
#> <x_vctr> <x_int> <x_pct>
#> 1 male 461 46%
#> 2 female 464 46%
#> 3 non-binary 75 8%
You can see that we have outputted a table which shows the number of
people and the percentage of each gender in our data set.
You can easily create two or three way tables by passing additional
columns to the cols argument:
clothes_opinions |>
build_table(c(age_group, gender2),
table_title = "Gender by age make up of clothing opinion data")
#>
#> ── Gender by age make up of clothing opinion data ──────────────────────────────
#> # A xlr_table: 12 x 4
#> age_group gender2 N Percent
#> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 18-30 male 116 46%
#> 2 18-30 female 118 47%
#> 3 18-30 non-binary 18 7%
#> 4 31-40 male 96 48%
#> # ℹ 8 more rows
clothes_opinions |>
build_table(c(age_group, gender2, Q1_1),
table_title = "Responses to Q1_1 by age and gender")
#>
#> ── Responses to Q1_1 by age and gender ─────────────────────────────────────────
#> # A xlr_table: 60 x 5
#> age_group gender2 Q1_1 N Percent
#> <x_vctr> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 18-30 male Strongly Disagree 18 16%
#> 2 18-30 male Disagree 21 18%
#> 3 18-30 male Neutral 26 22%
#> 4 18-30 male Agree 22 19%
#> # ℹ 56 more rows
The cols argument uses tidy selections
to select the columns we want to make a table with. See <tidy-select>
for more details on the selectors. This should be familiar for people
who use dplyr verbs.
The data returns has a long format by default.
Currently wide table table is unsupported but may be included in a
future update.
Introduction to xlr_table()
You can also see that it has a special type xlr_table().
This is a S3 class defined by xlr to help output the data to
Excel. It contains the table data, a table title and a
footnote, as well as hidden data to help format the table in
Excel. You can learn more about xlr_table() in
the vignette to do.
You can directly pass a title and a footnote to
build_table() by passing them as arguments to
build_table():
clothes_opinions |>
build_table(gender2,
table_title = "Gender make up of clothing opinion data",
footnote = "This shows that the data has a representative sample.")
#>
#> ── Gender make up of clothing opinion data ─────────────────────────────────────
#> # A xlr_table: 3 x 3
#> gender2 N Percent
#> <x_vctr> <x_int> <x_pct>
#> 1 male 461 46%
#> 2 female 464 46%
#> 3 non-binary 75 8%
#> This shows that the data has a representative sample.
Labels in xlr
Now if you were paying close attention, you may have noticed that
clothes_opinions$gender2 went from being
haven_labelled to a xlr_vector(). A
xlr_vector is a wrapper around other types (see below for
more details), and the base type is character(). All
build_* functions will convert this data automatically, as
well as pull information on the labels. See haven’s
documentation for more details about the labelled type: https://haven.tidyverse.org/reference/labelled.html.
Additionally, if your data is labelled, you can pull out the question
label metadata automatically and include it in the footnote by
specifying use_questions = TRUE. Columns that don’t have a
label are ignored.
clothes_opinions |>
build_table(c(age_group, gender2),
table_title = "Gender by age make up of clothing opinion data",
use_question = TRUE)
#>
#> ── Gender by age make up of clothing opinion data ──────────────────────────────
#> # A xlr_table: 12 x 4
#> age_group gender2 N Percent
#> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 18-30 male 116 46%
#> 2 18-30 female 118 47%
#> 3 18-30 non-binary 18 7%
#> 4 31-40 male 96 48%
#> # ℹ 8 more rows
#> Questions
#> The gender of the participant
Weights
Weights are supported, you only need to pass the column name for the
weights to the wt argument:
clothes_opinions |>
build_table(c(age_group, gender2),
table_title = "Gender by age make up of clothing opinion data (weighted)",
wt = weight)
#>
#> ── Gender by age make up of clothing opinion data (weighted) ───────────────────
#> # A xlr_table: 12 x 4
#> age_group gender2 N Percent
#> <x_vctr> <x_vctr> <x_dbl> <x_pct>
#> 1 18-30 male 122,277.0 47%
#> 2 18-30 female 118,532.0 46%
#> 3 18-30 non-binary 19,378.0 7%
#> 4 31-40 male 94,487.0 47%
#> # ℹ 8 more rows
Missing data
build_table() uses only complete cases by default, that
is for one of the columns we are if a row includes a single
NA it is removed from the table calculation. To include
NA values set use_NA = TRUE. This will mean
that ALL NA’s will be included in the
calculation of the table:
clothes_opinions |>
build_table(c(group, age_group),
table_title = "Survey group by age make up of clothing opinion data",
use_NA = TRUE)
#>
#> ── Survey group by age make up of clothing opinion data ────────────────────────
#> # A xlr_table: 15 x 4
#> group age_group N Percent
#> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 a 18-30 125 27%
#> 2 a 31-40 82 17%
#> 3 a 41-50 106 23%
#> 4 a 51-65 135 29%
#> # ℹ 11 more rows
If you would like to include the NA values of only one
or some of the columns we recommend using dplyr::filter()
as demonstrated below.
clothes_opinions |>
# remove all the rows where group is missing
dplyr::filter(!is.na(group)) |>
# by setting use_NA to true we keep the NA's from the age_group column
build_table(c(group, age_group),
table_title = "Survey group by age make up of clothing opinion data",
use_NA = TRUE)
#>
#> ── Survey group by age make up of clothing opinion data ────────────────────────
#> # A xlr_table: 10 x 4
#> group age_group N Percent
#> <x_vctr> <x_vctr> <x_int> <x_pct>
#> 1 a 18-30 125 27%
#> 2 a 31-40 82 17%
#> 3 a 41-50 106 23%
#> 4 a 51-65 135 29%
#> # ℹ 6 more rows
build_mtable()
This function is design to analyse multiple response questions in
survey data. To do so the data must be wide data with
one response per column (and the remaining responses NA). This
is how Qualtrics exports this data by default. The format is shown
below:
clothes_opinions |>
dplyr::select(starts_with("Q2"))
#> # A tibble: 1,000 × 6
#> Q2_1 Q2_2 Q2_3 Q2_4 Q2_5 Q2_6
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Red <NA> <NA> Blue Black Grey
#> 2 Red <NA> <NA> <NA> Black <NA>
#> 3 Red <NA> Green <NA> Black Grey
#> 4 Red <NA> <NA> <NA> Black <NA>
#> # ℹ 996 more rows
To create a table using the mcol argument to specify the
mutliple response columns you would like to create a table with:
clothes_opinions |>
build_mtable("Q2")
#> # 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%
#> # ℹ 2 more rows
The output N count is the number of people that
responded to each multiple response option, the N_group is
the total number of people that responded to this question. The
Percent is the percentage of people that responded to each
of the options. As you can respond to multiple options this percentage
will likely not add up to 100.
Note, internally build_mtable() uses
tidyselect::starts_with() to select the columns that you
would like to build a table with, because of this you need to pass a
string to mcol.
You can cut your multiple response column by other data by passing
columns to the cols argument. As with
build_table() it uses <tidy-eval> to
select the columns.
clothes_opinions |>
build_mtable(mcol = "Q2",
cols = age_group)
#> # A xlr_table: 24 x 5
#> age_group Q2 N N_group Percent
#> <x_vctr> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 18-30 Black 228 250 91%
#> 2 18-30 Blue 38 250 15%
#> 3 18-30 Green 83 250 33%
#> 4 18-30 Grey 113 250 45%
#> # ℹ 20 more rows
The N_group for this question is the number of people in
each age group that responded to the multiple response question.
It is common that a mulitple response column will include an
other response in a survey. This column needs to be removed
before you analyse the data with build_mtable().
We recommend using dplyr::select as demonstrated below:
clothes_opinions |>
dplyr::select(-Q3_other) |>
build_mtable(mcol = "Q3",
cols = age_group)
#> # A xlr_table: 12 x 5
#> age_group Q3 N N_group Percent
#> <x_vctr> <x_vctr> <x_int> <x_int> <x_pct>
#> 1 18-30 Earrings 77 216 36%
#> 2 18-30 Necklaces 54 216 25%
#> 3 18-30 Rings 193 216 89%
#> 4 31-40 Earrings 55 176 31%
#> # ℹ 8 more rows
As above you can see the build_mtable() automatically
converts haven labelled data to a xlr_vector().
Multiple multiple response columns
You can pass up to two multiple response columns to
build_mtable(), any more and we recommend filtering before
hand.
clothes_opinions |>
dplyr::select(-Q3_other) |>
build_mtable(mcol = 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%
#> # ℹ 14 more rows
The N_group is the number of people in each age_group
that responded to one of the options in age_group. N is the
number of people that responded to the multiple response question in
each group.
build_qtable()
This function is designed to help analyse a block of questions. A
block of questions is where all the responses use the same scale (it is
usually a matrix question). The data should have the form:
clothes_opinions |>
dplyr::select(starts_with("Q1"))
#> # A tibble: 1,000 × 4
#> Q1_1 Q1_2 Q1_3 Q1_4
#> <int+lbl> <int+lbl> <int+lbl> <int+lbl>
#> 1 1 [Strongly Disagree] 5 [Strongly Agree] 5 [Strongly Agree] 2 [Disagree]
#> 2 3 [Neutral] 1 [Strongly Disagree] 2 [Disagree] 2 [Disagree]
#> 3 4 [Agree] 3 [Neutral] 5 [Strongly Agree] 1 [Strongly Di…
#> 4 4 [Agree] 1 [Strongly Disagree] 2 [Disagree] 2 [Disagree]
#> # ℹ 996 more rows
To analyse a question block, you need to pass a
<tidy-eval> selection to the function to select all
columns in the question block. See we pass the same statement we made in
the select statement above.
clothes_opinions |>
build_qtable(starts_with("Q1"))
#> # 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%
#> # ℹ 16 more rows
# You can also select the columns directly
clothes_opinions |>
build_qtable(c(Q1_1,Q1_2,Q1_3,Q1_4))
#> # 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%
#> # ℹ 16 more rows
You can cut all the columns in the question block by another
column(s) by specifying the cols argument. As with
build_table() it uses <tidy-eval> to
select the columns.
clothes_opinions |>
build_qtable(starts_with("Q1"),
gender2)
#> # 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%
#> # ℹ 56 more rows
xlr_table(), xlr types and writing to Excel
A key part of the xlr is to export data to Excel in an
easy, and user friendly way. The workhorse function in this scenario is
xlr_table(), and if you like the default options is meant
to be the function that you will mostly use.
A xlr_table() is a tibble::tibble() with an
optional title, and footnote, as well as a number of formatting
options.
clothes_opinions |>
xlr_table("This is a title",
"this is a footnote with extra information")
#>
#> ── This is a title ─────────────────────────────────────────────────────────────
#> # A xlr_table: 1000 x 20
#> weight group gender gender2 age age_group Q1_1 Q1_2 Q1_3 Q1_4
#> <x_int> <x_vctr> <x_vctr> <x_dbl> <x_int> <x_vctr> <x_int> <x_in> <x_i> <x_i>
#> 1 1,072 a female 2.00 25 18-30 1 5 5 2
#> 2 219 a female 2.00 64 51-65 3 1 2 2
#> 3 1,187 a male 1.00 35 31-40 4 3 5 1
#> 4 1,860 a male 1.00 55 51-65 4 1 2 2
#> # ℹ 996 more rows
#> # ℹ 10 more variables: Q2_1 <x_vctr>, Q2_2 <x_vctr>, Q2_3 <x_vctr>,
#> # Q2_4 <x_vctr>, Q2_5 <x_vctr>, Q2_6 <x_vctr>, Q3_1 <x_dbl>, Q3_2 <x_dbl>,
#> # Q3_3 <x_dbl>, Q3_other <x_vctr>
#> this is a footnote with extra information
When you call xlr_table() it converts the elements of
your table to different xlr types.
Types
xlr types are defined to help with the formatting of columns when
they are output to Excel. There are four difference types
defined in xlr:
xlr_numeric() to format doubles neatly.
xlr_integer() to format integer data
neatly.
xlr_percent() to format numeric data as a
percentage.
xlr_vector() a general type to format data nicely
without specific rules.
All of the above variables contain the argument
xlr_format which only takes a xlr_format()
object. xlr_format() allows you to control different
formatting options when the data is outputted to Excel, it
currently does not change how the data looks in console. You can change
the font size, colour, text position etc. with
xlr_format(). See ?xlr_format for the full
range of options.
For xlr_numeric() and xlr_percent() you can
also set the number of decimal places through setting
dp=x.
dplyr verbs
The xlr_table() type is implemented to work with most
dplyr verbs to make working with the data as seamless as
possible. To find out more see ?xlr_and_dplyr.
Writing data to Excel
xlr makes writing data to Excel easy using the
write_xlsx() function. This function takes either a
xlr_table, tibble or data.frame.
Note when you output a single object you need to specify
write_xlsx(mtcars,
file = "example.xlsx",
sheet_name = "example_sheet")
The output looks like this in Excel:
Example of exporting a data.frame with
write_xlsx, it shows a simple table in Excel.
When you output a xlr_table with this function
additional formatting will be applied to the data as well as the title
and a footnote.
write_xlsx(table,
file = "example.xlsx",
sheet_name = "example_sheet")
#> ℹ Appending file: 'example.xlsx'
The output looks like this in Excel:
Example of using a xlr_table with write_xlsx,
the table now has a table, footnote and formatting!
To update this formatting you either need to update the styles of the
columns using the above, or if you want to modify the style of the table
use update_theme. You can modify the the format of the
title, footnote, column heading or table body. The below example shows
how to update title colour to be red and the underlined:
table <- update_theme(table,
title_format = xlr_format(font_colour = "red",
text_style = "underline"))
write_xlsx(table,
file = "example.xlsx",
sheet_name = "example_sheet")
#> ℹ Appending file: 'example.xlsx'
The output looks like this:
Example of update theme in use, the title is red
and underlined
See ?update_theme for more details.
write_xlsx() and list()
Like openxlsx::write.xlsx() you can also pass a
named list to write_xlsx() and these will
be automatically created as sheets in the Excel file. If
you have a list() of tables, set TOC = TRUE in
order to generate a table of contents for the Excel file.
This is particularly useful when you have a large number of tables.
Putting it all together
Example of how you can use xlr to analyse a survey is below:
output_list <- list()
output_list[["gender"]] <- build_table(clothes_opinions,
gender2,
"Gender in clothes opinions survey")
output_list[["gender age"]] <- build_table(clothes_opinions,
c(gender2, age_group),
"Gender by age in clothes opinions survey")
output_list[["gender age"]] <- build_table(clothes_opinions,
c(gender2, age_group),
"Gender by age in clothes opinions survey")
output_list[["opinions"]] <- build_qtable(clothes_opinions,
starts_with("Q1"),
table_title = "Opinions on different clothing items")
# Sometimes it is neater to use the pipe operator on the data
# This also allows auto completion in RStudio for variable names
output_list[["opinions gender"]] <-
clothes_opinions |>
build_qtable(starts_with("Q1"),
gender2,
table_title = "Opinions on different clothing items by gender2",
use_questions = TRUE)
# now output the data, we turn on the option to generate a table of contents
write_xlsx(output_list,
file = "example2.xlsx",
TOC = TRUE)
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They may not be fully stable and should be used with caution. We make no claims about them.
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