library(skimr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(knitr)
sum_cut <- diamonds |> group_by(cut) |> skim(carat) |> yank("numeric")

sum_cut_color <- diamonds %>% group_by(cut, color) %>% skim(carat) %>% yank("numeric")

sum_cut #example 1

Variable type: numeric

skim_variable cut n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
carat Fair 0 1 1.05 0.52 0.22 0.70 1.00 1.20 5.01 ▇▂▁▁▁
carat Good 0 1 0.85 0.45 0.23 0.50 0.82 1.01 3.01 ▇▆▂▁▁
carat Very Good 0 1 0.81 0.46 0.20 0.41 0.71 1.02 4.00 ▇▃▁▁▁
carat Premium 0 1 0.89 0.52 0.20 0.41 0.86 1.20 4.01 ▇▆▁▁▁
carat Ideal 0 1 0.70 0.43 0.20 0.35 0.54 1.01 3.50 ▇▂▁▁▁ 
sum_cut_color

Variable type: numeric

skim_variable cut color n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
carat Fair D 0 1 0.92 0.41 0.25 0.70 0.90 1.01 3.40 ▆▇▁▁▁
carat Fair E 0 1 0.86 0.36 0.22 0.55 0.90 1.01 2.04 ▇▇▇▂▁
carat Fair F 0 1 0.90 0.42 0.25 0.60 0.90 1.01 2.58 ▇▇▂▁▁
carat Fair G 0 1 1.02 0.49 0.23 0.70 0.98 1.07 2.60 ▅▇▂▂▁
carat Fair H 0 1 1.22 0.55 0.33 0.90 1.01 1.51 4.13 ▇▃▂▁▁
carat Fair I 0 1 1.20 0.52 0.41 0.88 1.01 1.50 3.02 ▇▇▃▂▁
carat Fair J 0 1 1.34 0.73 0.30 0.90 1.03 1.69 5.01 ▇▃▁▁▁
carat Good D 0 1 0.74 0.36 0.23 0.42 0.70 1.00 2.04 ▇▅▅▁▁
carat Good E 0 1 0.75 0.38 0.23 0.41 0.70 1.00 3.00 ▇▅▁▁▁
carat Good F 0 1 0.78 0.37 0.23 0.49 0.71 1.01 2.67 ▇▆▁▁▁
carat Good G 0 1 0.85 0.43 0.23 0.50 0.90 1.01 2.80 ▇▇▂▁▁
carat Good H 0 1 0.91 0.50 0.25 0.51 0.90 1.09 3.01 ▇▇▂▁▁
carat Good I 0 1 1.06 0.58 0.30 0.70 1.00 1.50 3.01 ▇▆▃▂▁
carat Good J 0 1 1.10 0.54 0.28 0.71 1.02 1.50 3.00 ▇▇▅▂▁
carat Very Good D 0 1 0.70 0.37 0.23 0.40 0.61 1.00 2.58 ▇▅▁▁▁
carat Very Good E 0 1 0.68 0.38 0.20 0.37 0.57 0.94 2.51 ▇▆▁▁▁
carat Very Good F 0 1 0.74 0.39 0.23 0.40 0.70 1.01 2.48 ▇▇▂▁▁
carat Very Good G 0 1 0.77 0.42 0.23 0.40 0.70 1.02 2.52 ▇▆▂▁▁
carat Very Good H 0 1 0.92 0.50 0.23 0.47 0.90 1.20 3.00 ▇▇▂▁▁
carat Very Good I 0 1 1.05 0.55 0.24 0.70 1.00 1.50 4.00 ▇▆▂▁▁
carat Very Good J 0 1 1.13 0.56 0.24 0.71 1.06 1.51 2.74 ▇▇▆▃▁
carat Premium D 0 1 0.72 0.40 0.20 0.40 0.58 1.01 2.57 ▇▅▂▁▁
carat Premium E 0 1 0.72 0.41 0.20 0.38 0.58 1.00 3.05 ▇▃▁▁▁
carat Premium F 0 1 0.83 0.42 0.20 0.43 0.76 1.04 3.01 ▇▆▂▁▁
carat Premium G 0 1 0.84 0.48 0.23 0.40 0.76 1.12 3.01 ▇▆▂▁▁
carat Premium H 0 1 1.02 0.54 0.23 0.51 1.01 1.30 3.24 ▇▇▃▁▁
carat Premium I 0 1 1.14 0.61 0.23 0.59 1.14 1.54 4.01 ▇▇▃▁▁
carat Premium J 0 1 1.29 0.61 0.30 0.81 1.25 1.70 4.01 ▇▇▃▁▁
carat Ideal D 0 1 0.57 0.30 0.20 0.33 0.50 0.71 2.75 ▇▂▁▁▁
carat Ideal E 0 1 0.58 0.31 0.20 0.33 0.50 0.72 2.28 ▇▂▁▁▁
carat Ideal F 0 1 0.66 0.37 0.23 0.35 0.53 0.90 2.45 ▇▃▁▁▁
carat Ideal G 0 1 0.70 0.41 0.23 0.34 0.54 1.03 2.54 ▇▃▂▁▁
carat Ideal H 0 1 0.80 0.49 0.23 0.36 0.70 1.11 3.50 ▇▅▁▁▁
carat Ideal I 0 1 0.91 0.55 0.23 0.41 0.74 1.22 3.22 ▇▃▂▁▁
carat Ideal J 0 1 1.06 0.58 0.23 0.54 1.03 1.41 3.01 ▇▆▃▂▁ 
sum_cut |> print() # example 2
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable cut n_missing complete_rate  mean    sd   p0  p25  p50  p75 p100
## 1 carat         Fa…         0             1 1.05  0.516 0.22 0.7  1    1.2  5.01
## 2 carat         Go…         0             1 0.849 0.454 0.23 0.5  0.82 1.01 3.01
## 3 carat         Ve…         0             1 0.806 0.459 0.2  0.41 0.71 1.02 4   
## 4 carat         Pr…         0             1 0.892 0.515 0.2  0.41 0.86 1.2  4.01
## 5 carat         Id…         0             1 0.703 0.433 0.2  0.35 0.54 1.01 3.5 
## # … with 1 more variable: hist <chr>
sum_cut_color |> print() 
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##    skim_variable cut  color n_missing complete_rate  mean    sd   p0   p25  p50
##  1 carat         Fair D             0             1 0.920 0.405 0.25 0.7   0.9 
##  2 carat         Fair E             0             1 0.857 0.365 0.22 0.552 0.9 
##  3 carat         Fair F             0             1 0.905 0.419 0.25 0.6   0.9 
##  4 carat         Fair G             0             1 1.02  0.493 0.23 0.7   0.98
##  5 carat         Fair H             0             1 1.22  0.548 0.33 0.9   1.01
##  6 carat         Fair I             0             1 1.20  0.522 0.41 0.885 1.01
##  7 carat         Fair J             0             1 1.34  0.734 0.3  0.905 1.03
##  8 carat         Good D             0             1 0.745 0.363 0.23 0.42  0.7 
##  9 carat         Good E             0             1 0.745 0.381 0.23 0.41  0.7 
## 10 carat         Good F             0             1 0.776 0.370 0.23 0.49  0.71
## # … with 25 more rows, and 3 more variables: p75 <dbl>, p100 <dbl>, hist <chr>

Try using knit_print instead of print.

sum_cut |> knit_print(n = 50) # example 2

Variable type: numeric

skim_variable cut n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
carat Fair 0 1 1.05 0.52 0.22 0.70 1.00 1.20 5.01 ▇▂▁▁▁
carat Good 0 1 0.85 0.45 0.23 0.50 0.82 1.01 3.01 ▇▆▂▁▁
carat Very Good 0 1 0.81 0.46 0.20 0.41 0.71 1.02 4.00 ▇▃▁▁▁
carat Premium 0 1 0.89 0.52 0.20 0.41 0.86 1.20 4.01 ▇▆▁▁▁
carat Ideal 0 1 0.70 0.43 0.20 0.35 0.54 1.01 3.50 ▇▂▁▁▁ 
sum_cut_color |> knit_print(n = 50) # example 2

Variable type: numeric

skim_variable cut color n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
carat Fair D 0 1 0.92 0.41 0.25 0.70 0.90 1.01 3.40 ▆▇▁▁▁
carat Fair E 0 1 0.86 0.36 0.22 0.55 0.90 1.01 2.04 ▇▇▇▂▁
carat Fair F 0 1 0.90 0.42 0.25 0.60 0.90 1.01 2.58 ▇▇▂▁▁
carat Fair G 0 1 1.02 0.49 0.23 0.70 0.98 1.07 2.60 ▅▇▂▂▁
carat Fair H 0 1 1.22 0.55 0.33 0.90 1.01 1.51 4.13 ▇▃▂▁▁
carat Fair I 0 1 1.20 0.52 0.41 0.88 1.01 1.50 3.02 ▇▇▃▂▁
carat Fair J 0 1 1.34 0.73 0.30 0.90 1.03 1.69 5.01 ▇▃▁▁▁
carat Good D 0 1 0.74 0.36 0.23 0.42 0.70 1.00 2.04 ▇▅▅▁▁
carat Good E 0 1 0.75 0.38 0.23 0.41 0.70 1.00 3.00 ▇▅▁▁▁
carat Good F 0 1 0.78 0.37 0.23 0.49 0.71 1.01 2.67 ▇▆▁▁▁
carat Good G 0 1 0.85 0.43 0.23 0.50 0.90 1.01 2.80 ▇▇▂▁▁
carat Good H 0 1 0.91 0.50 0.25 0.51 0.90 1.09 3.01 ▇▇▂▁▁
carat Good I 0 1 1.06 0.58 0.30 0.70 1.00 1.50 3.01 ▇▆▃▂▁
carat Good J 0 1 1.10 0.54 0.28 0.71 1.02 1.50 3.00 ▇▇▅▂▁
carat Very Good D 0 1 0.70 0.37 0.23 0.40 0.61 1.00 2.58 ▇▅▁▁▁
carat Very Good E 0 1 0.68 0.38 0.20 0.37 0.57 0.94 2.51 ▇▆▁▁▁
carat Very Good F 0 1 0.74 0.39 0.23 0.40 0.70 1.01 2.48 ▇▇▂▁▁
carat Very Good G 0 1 0.77 0.42 0.23 0.40 0.70 1.02 2.52 ▇▆▂▁▁
carat Very Good H 0 1 0.92 0.50 0.23 0.47 0.90 1.20 3.00 ▇▇▂▁▁
carat Very Good I 0 1 1.05 0.55 0.24 0.70 1.00 1.50 4.00 ▇▆▂▁▁
carat Very Good J 0 1 1.13 0.56 0.24 0.71 1.06 1.51 2.74 ▇▇▆▃▁
carat Premium D 0 1 0.72 0.40 0.20 0.40 0.58 1.01 2.57 ▇▅▂▁▁
carat Premium E 0 1 0.72 0.41 0.20 0.38 0.58 1.00 3.05 ▇▃▁▁▁
carat Premium F 0 1 0.83 0.42 0.20 0.43 0.76 1.04 3.01 ▇▆▂▁▁
carat Premium G 0 1 0.84 0.48 0.23 0.40 0.76 1.12 3.01 ▇▆▂▁▁
carat Premium H 0 1 1.02 0.54 0.23 0.51 1.01 1.30 3.24 ▇▇▃▁▁
carat Premium I 0 1 1.14 0.61 0.23 0.59 1.14 1.54 4.01 ▇▇▃▁▁
carat Premium J 0 1 1.29 0.61 0.30 0.81 1.25 1.70 4.01 ▇▇▃▁▁
carat Ideal D 0 1 0.57 0.30 0.20 0.33 0.50 0.71 2.75 ▇▂▁▁▁
carat Ideal E 0 1 0.58 0.31 0.20 0.33 0.50 0.72 2.28 ▇▂▁▁▁
carat Ideal F 0 1 0.66 0.37 0.23 0.35 0.53 0.90 2.45 ▇▃▁▁▁
carat Ideal G 0 1 0.70 0.41 0.23 0.34 0.54 1.03 2.54 ▇▃▂▁▁
carat Ideal H 0 1 0.80 0.49 0.23 0.36 0.70 1.11 3.50 ▇▅▁▁▁
carat Ideal I 0 1 0.91 0.55 0.23 0.41 0.74 1.22 3.22 ▇▃▂▁▁
carat Ideal J 0 1 1.06 0.58 0.23 0.54 1.03 1.41 3.01 ▇▆▃▂▁