Skills Lab 04 - Data Summaries

Author

You can access the Skills Lab document for Week 4 on Posit Cloud.

Check the Analysing Data Panopto page for recordings or the main Posit Cloud page for other materials.

Join the Google Doc during the live session here: https://rebrand.ly/and_skills_lab_04

Data

smarvus_tib <- readr::read_csv("data/smarvus_data.csv")

Codebook

Run this code chunk to open the Codebook in the Viewer tab.

ricomisc::rstudio_viewer("smarvus_codebook.html", "data")

Research Question

Vote on Variables

Which variable(s) should we focus on in our analysis today? Choose TWO demographic variables and ONE scale variable.

In the following solutions, I’ll choose some variables from the dataset to use so the examples will run - but this may be different than what we do in the Skills Lab live!

Which Measure(s)?

Once you’ve voted, answer in the Google Doc: What do each of the following measures tell us? Why is it useful to calculate and report them?

Number of observations
Mean, SD, and CIs
Range (min and max)
Median

Tip

See Tutorial 04 for an explanation!

Generic Summaries

First, we can easily get some overall information about our numeric variables with some useful functions.

summary(smarvus_tib)

datawizard::describe_distribution(smarvus_tib)

Consider This:

What is useful about the output from these summary functions? What can we use them for?

What can we NOT (easily) use them for?

Solution

This is a great way for you, the analyst, to get a quick look at the data. Without having to do any extra coding, you have useful overall information about most or all of the variables in your dataset. This lets you easily spot problems and get a sense of your data.

However, there are two main issues. First, we can’t very easily see or control what is included in this output. Both summary() and describe_distribution() have some arguments we can change (see the help documentation), but not everything, and it isn’t obvious how to do this.

Second, this is not a great way to present this information. This output isn’t nicely formatted; it would not be a good way to include this summary info in a report.

So, we should make our own summaries instead, that include the information we want, and that look nice in our reports (or take-away papers ).

Summarising A Variable

First, let’s get a look at our continuous variable using dplyr::summarise().

Our variable of choice is: ✨ Enter here! ✨

For the purposes of solutions, I’ll use the Brief Fear of Negative Evaluation scale (bnfe).

smarvus_tib |> 
  dplyr::summarise(
    n = dplyr::n(), 
    min = min(bfne, na.rm = TRUE), 
    max = max(bfne, na.rm = TRUE), 
    mean = mean(bfne, na.rm = TRUE), 
    median = median(bfne, na.rm = TRUE), 
    sd = sd(bfne, na.rm = TRUE), 
    ci_lower = ggplot2::mean_cl_normal(bfne)$ymin,
    ci_upper = ggplot2::mean_cl_normal(bfne)$ymax
  )

# A tibble: 1 × 8
      n   min   max  mean median    sd ci_lower ci_upper
  <int> <dbl> <dbl> <dbl>  <dbl> <dbl>    <dbl>    <dbl>
1  2776     1     5  3.24   3.38  1.12     3.20     3.28

Consider This: CIs vs SDs

Why is it that this variable has a relatively large SD (compared to the scale it’s measured on), but an extremely narrow CI?

Solution

Remember that CIs are calculated based on the square root of the sample size - and that’s quite big here!

Consider This: Returning NAs

Why is it that functions like mean and median return NA if they have even one missing value?

Tip

See Tutorial 04 for an explanation!

Summarising by Groups

Next, let’s get a more fine-grained look by splitting up our summary by another variable.

Our categorical variables are: ✨ Enter here! ✨

For the purposes of solutions, I’ll use gender identity (gender) and SpLD diagnosis (spld).

First, what happens when we group_by() a variable?

smarvus_tib |>
  dplyr::group_by(spld)

# A tibble: 2,776 × 34
# Groups:   spld [3]
   unique_id country   language university degree_major degree_year age   gender
   <chr>     <chr>     <chr>    <chr>      <chr>        <chr>       <chr> <chr> 
 1 X8V0T6    Netherla… English  Universit… Psychology   1st Year    18-21 Femal…
 2 J3W3Y7    England   English  Universit… Psychology   1st Year    18-21 Femal…
 3 S7C2L2    England   English  Universit… Psychology   1st Year    22-25 Femal…
 4 Y4Z6A6    Scotland  English  Universit… Psychology   1st Year    26+   Femal…
 5 L2O9Z1    Australia English  Macquarie… Psychology   1st Year    18-21 Femal…
 6 B5I6O0    Austria   German   Universit… Psychology   1st Year    18-21 Femal…
 7 N8H9D1    England   English  Loughboro… Psychology   1st Year    18-21 Male/…
 8 F2J7V4    England   English  Bournemou… Psychology   1st Year    18-21 Femal…
 9 N9M3V8    Germany   German   Universit… Psychology   1st Year    18-21 Femal…
10 O3F8F8    Australia English  Macquarie… Psychology   1st Year    18-21 Femal…
# ℹ 2,766 more rows
# ℹ 26 more variables: spld <chr>, in_person_lectures <chr>,
#   in_person_practicals <chr>, atms_per <dbl>, belief <dbl>, bfne <dbl>,
#   cas_cre <dbl>, cas_non <dbl>, crt <dbl>, ius_sf_inh <dbl>,
#   ius_sf_pro <dbl>, lsas_sr_per <dbl>, lsas_sr_soc <dbl>, ngse <dbl>,
#   r_mars_course <dbl>, r_mars_num <dbl>, r_mars_test <dbl>, r_tas_bod <dbl>,
#   r_tas_ten <dbl>, r_tas_tes <dbl>, r_tas_worry <dbl>, stars_ask <dbl>, …

Notice the Groups: spld [3] at the top of our tibble. This means that our tibble is grouped by the values of our spld variable, so any subsequent calculations will take place inside those groups. Let’s see what that might look like.

## SAME code as above, just with the new group_by line!

smarvus_tib |> 
  dplyr::group_by(spld) |> 
  dplyr::summarise(
    n = dplyr::n(), 
    min = min(bfne, na.rm = TRUE), 
    max = max(bfne, na.rm = TRUE), 
    mean = mean(bfne, na.rm = TRUE), 
    median = median(bfne, na.rm = TRUE), 
    sd = sd(bfne, na.rm = TRUE), 
    ci_lower = ggplot2::mean_cl_normal(bfne)$ymin,
    ci_upper = ggplot2::mean_cl_normal(bfne)$ymax
  )

# A tibble: 3 × 9
  spld      n   min   max  mean median    sd ci_lower ci_upper
  <chr> <int> <dbl> <dbl> <dbl>  <dbl> <dbl>    <dbl>    <dbl>
1 No     2348     1     5  3.23   3.25  1.12     3.18     3.27
2 Yes     274     1     5  3.26   3.38  1.12     3.13     3.39
3 <NA>    154     1     5  3.38   3.56  1.10     3.20     3.56

So, we get the same information as we did before, but now it’s split up by the groups in the spld variable.

Summarising by Multiple Groups

What do you think will happen when we add in a second categorical variable?

## SAME code as above, now with two categorical variables in group_by

smarvus_tib |> 
  dplyr::group_by(gender, spld) |> 
  dplyr::summarise(
    n = dplyr::n(), 
    min = min(bfne, na.rm = TRUE), 
    max = max(bfne, na.rm = TRUE), 
    mean = mean(bfne, na.rm = TRUE), 
    median = median(bfne, na.rm = TRUE), 
    sd = sd(bfne, na.rm = TRUE), 
    ci_lower = ggplot2::mean_cl_normal(bfne)$ymin,
    ci_upper = ggplot2::mean_cl_normal(bfne)$ymax
  )

# A tibble: 11 × 10
# Groups:   gender [4]
   gender         spld      n   min   max  mean median     sd ci_lower ci_upper
   <chr>          <chr> <int> <dbl> <dbl> <dbl>  <dbl>  <dbl>    <dbl>    <dbl>
 1 Another Gender No       18  2.38  5     4.05   3.94  0.688     3.71     4.39
 2 Another Gender Yes       5  3.5   4.88  4.03   3.88  0.596     3.29     4.76
 3 Another Gender <NA>      4  3.75  5     4.5    4.62  0.568     3.60     5.40
 4 Female/Woman   No     1992  1     5     3.30   3.38  1.10      3.25     3.35
 5 Female/Woman   Yes     218  1     5     3.35   3.5   1.09      3.21     3.50
 6 Female/Woman   <NA>    122  1     5     3.41   3.56  1.07      3.22     3.60
 7 Male/Man       No      331  1     5     2.72   2.75  1.09      2.60     2.84
 8 Male/Man       Yes      51  1     5     2.78   2.75  1.15      2.46     3.10
 9 Male/Man       <NA>     27  1.12  5     3.08   3.12  1.23      2.60     3.57
10 <NA>           No        7  1.38  5     3.64   4.5   1.52      2.23     5.05
11 <NA>           <NA>      1  3     3     3      3    NA        NA       NA

Making Pretty HTML Tables

Recall some of the issues we identified previously with summary functions (like summary())

We can’t very easily see or control what is included in this output.
This output isn’t nicely formatted and it would not be a good way to include this summary info in a report.

We’ve resolved (1) by choosing what appears in the output, but this is still just a tibble and pretty ugly! The knitr::kable() function is the basic tool to turn tibbles into nicely formatted, report-worthy HTML tables. Let’s have a look:

smarvus_tib |> 
  dplyr::group_by(gender, spld) |> 
  dplyr::summarise(
    n = dplyr::n(), 
    min = min(bfne, na.rm = TRUE), 
    max = max(bfne, na.rm = TRUE), 
    mean = mean(bfne, na.rm = TRUE), 
    median = median(bfne, na.rm = TRUE), 
    sd = sd(bfne, na.rm = TRUE), 
    ci_lower = ggplot2::mean_cl_normal(bfne)$ymin,
    ci_upper = ggplot2::mean_cl_normal(bfne)$ymax
  ) |> 
  ## Same code as above up to here
  knitr::kable(
    ## Give a list of names in c() to rename the columns
    ## Use nicely formatted real words, NOT variable names!
    col.names = c("Gender", "SpLD", "N", "Min", "Max", "Mean", "Median", "SD", "CI~upper~", "CI~lower~"),
    ## Round number of decimal places
    digits = 2,
    ## Add a caption
    caption = "Descriptive statistics of BFNE scale by gender and SPLD diagnosis"
  ) |> 
  kableExtra::kable_styling()

Descriptive statistics of BFNE scale by gender and SPLD diagnosis
Gender	SpLD	N	Min	Max	Mean	Median	SD	CI~upper~	CI~lower~
Another Gender	No	18	2.38	5.00	4.05	3.94	0.69	3.71	4.39
Another Gender	Yes	5	3.50	4.88	4.03	3.88	0.60	3.29	4.76
Another Gender	NA	4	3.75	5.00	4.50	4.62	0.57	3.60	5.40
Female/Woman	No	1992	1.00	5.00	3.30	3.38	1.10	3.25	3.35
Female/Woman	Yes	218	1.00	5.00	3.35	3.50	1.09	3.21	3.50
Female/Woman	NA	122	1.00	5.00	3.41	3.56	1.07	3.22	3.60
Male/Man	No	331	1.00	5.00	2.72	2.75	1.09	2.60	2.84
Male/Man	Yes	51	1.00	5.00	2.78	2.75	1.15	2.46	3.10
Male/Man	NA	27	1.12	5.00	3.08	3.12	1.23	2.60	3.57
NA	No	7	1.38	5.00	3.64	4.50	1.52	2.23	5.05
NA	NA	1	3.00	3.00	3.00	3.00	NA	NA	NA

Looking good!

New Functions: knitr::kable() and kableExtra::kable_styling()

The kable() + kable_styling() tag team has a lot of options to make your tables look very pretty in HTML format (which is what we typically render to, including on the TAP!). You can put any tibble into kable() and use it to add nice formatting to the output, so rendered HTML documents - like take-away papers! - present your results in a professional way.

Today we’ve looked at three main arguments in kable() to get you started:

col.names will take a vector of names that it will use for the column names in your table. Be careful to check that the names you put in match with your data!
digits takes a single number, and will round any numbers to that number of decimal places.
caption takes a string, and outputs a nicely formatted caption.

kable_styling() can be customised further, but it does a lot of the heavy lifting without any extra input.

Tip

Want more kable()? Check out the indispensable Create Awesome HTML Tables documentation if you really want to jazz up your tables.

Render

Let’s try and render the document… 🤞

Data

Codebook

Research Question

Vote on Variables

Which Measure(s)?

Generic Summaries

Summarising A Variable

Summarising by Groups

Summarising by Multiple Groups

Making Pretty HTML Tables

Render

Kahoot time!