| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
Quarto tables, figures, and stats
Chunks can produce figures and tables
Chunks can produce figures or tables
Figure 1: This is a histogram
Cross-referencing
You can then refer to those with @tbl-one and @fig-hist and the Table and Figure ordering will be correct (and linked)
@fig-hist contains a histogram and @tbl-one a table.gets printed as:
Inline R
Along with just regular text, you can also run R code within the text:
There were `r 3 + 4` participantsbecomes:
There were 7 participants
Inline stats
I often create a list of stats that I want to report in a manuscript:
I can then print these numbers in the text with:
There were `r stats$n` participants with a mean age of `r stats$mean_age`.
which turns into:
There were 1123 participants with a mean age of 43.5.
Inline stats from {gtsummary}
We saw very, very briefly yesterday:
We pulled a statistic from our univariate table
If we’re making a table, we probably want to report numbers from it
If we’re making a table, we probably want to report numbers from it
| Characteristic | Male, N = 6,4031 | Female, N = 6,2831 | Overall, N = 12,6861 |
|---|---|---|---|
| race_eth_cat | |||
| Hispanic | 1,000 (16%) | 1,002 (16%) | 2,002 (16%) |
| Black | 1,613 (25%) | 1,561 (25%) | 3,174 (25%) |
| Non-Black, Non-Hispanic | 3,790 (59%) | 3,720 (59%) | 7,510 (59%) |
| region_cat | |||
| Northeast | 1,296 (21%) | 1,254 (20%) | 2,550 (20%) |
| North Central | 1,488 (24%) | 1,446 (23%) | 2,934 (24%) |
| South | 2,251 (36%) | 2,317 (38%) | 4,568 (37%) |
| West | 1,253 (20%) | 1,142 (19%) | 2,395 (19%) |
| Unknown | 115 | 124 | 239 |
| eyesight_cat | |||
| Excellent | 1,582 (38%) | 1,334 (31%) | 2,916 (35%) |
| Very good | 1,470 (35%) | 1,500 (35%) | 2,970 (35%) |
| Good | 792 (19%) | 1,002 (23%) | 1,794 (21%) |
| Fair | 267 (6.4%) | 365 (8.5%) | 632 (7.5%) |
| Poor | 47 (1.1%) | 85 (2.0%) | 132 (1.6%) |
| Unknown | 2,245 | 1,997 | 4,242 |
| glasses | 1,566 (38%) | 2,328 (54%) | 3,894 (46%) |
| Unknown | 2,241 | 1,995 | 4,236 |
| age_bir | 25 (21, 29) | 22 (19, 27) | 23 (20, 28) |
| Unknown | 3,652 | 3,091 | 6,743 |
| 1 n (%); Median (IQR) | |||
I want to report some stats!
How about the median (IQR) age of the male participants at the birth of their first child?
Or the frequency and percentage of women from the South?
And the overall stats on people who wear glasses?
Better yet…
We can integrate these into the text of our manuscript:
A greater proportion of female (`r inline_text(table1, variable = "glasses", column = "Female")`) than male
(`r inline_text(table1, variable = "glasses", column = "Male")`) participants wore glasses.Which becomes:
A greater proportion of female (2,328 (54%)) than male (1,566 (38%)) participants wore glasses.
Readability
Because this can be hard to read, I’d suggest storing those stats in a chunk before the text:
Exercises
Return to the quarto document with the tables.
- Choose a table to label and caption, and then write a sentence that cross-references it (e.g., Table 1 shows the descriptive statistics)
- From that table, choose at least two statistics to pull out of the table and include in the text using
inline_text(). - Add another statistic to the text that you calculate yourself using the
nlsydata, e.g., the mean number of hours of sleep on weekends.
Quarto tables, figures, and stats