Quarto tables, figures, and stats
Chunks can produce figures and tables
```{r}
#| label: tbl-one
#| tbl-cap: "This is a great table"
knitr::kable(mtcars)
```
Table 1: This is a great table
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 |
Chunks can produce figures or tables
```{r}
#| label: fig-hist
#| fig-cap: "This is a histogram"
hist(rnorm(100))
```
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:
Figure 1 contains a histogram and Table 1 a table.
Inline R
Along with just regular text, you can also run R code within the text:
There were `r 3 + 4` participants
becomes:
There were 7 participants
Inline stats
I often create a list of stats that I want to report in a manuscript:
stats <- list(n = nrow(data),
mean_age = mean(data$age))
I can then print these numbers in the text with:
There were `r stats$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:
inline_text(income_table, variable = "age_bir")
[1] "595 (95% CI 538, 652; p<0.001)"
We pulled a statistic from our univariate table
If we’re making a table, we probably want to report numbers from it
```{r}
#| label: tbl-descr
#| tbl-cap: "Descriptive statistics"
#| output-location: slide
table1 <- tbl_summary(
nlsy,
by = sex_cat,
include = c(sex_cat, race_eth_cat, region_cat,
eyesight_cat, glasses, age_bir)) |>
add_overall(last = TRUE)
table1
```
If we’re making a table, we probably want to report numbers from it
Table 2: Descriptive statistics
Characteristic |
Male, N = 6,403 |
Female, N = 6,283 |
Overall, N = 12,686 |
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 |
I want to report some stats!
How about the median (IQR) age of the male participants at the birth of their first child?
inline_text(table1, variable = "age_bir", column = "Male")
Or the frequency and percentage of women from the South?
inline_text(table1, variable = "region_cat", level = "South", column = "Female")
And the overall stats on people who wear glasses?
inline_text(table1, variable = "glasses", column = "stat_0",
pattern = "{n}/{N} ({p}%)")
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:
```{r}
glasses_f <- inline_text(table1, variable = "glasses",
column = "Female")
glasses_m <- inline_text(table1, variable = "glasses",
column = "Male")
```
A greater proportion of female (`r glasses_f`) than male (`r glasses_m`) participants wore glasses.
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
nlsy
data, e.g., the mean number of hours of sleep on weekends.