Fit_4 <- lm(taste ~ Acetic + H2S + Lactic, data = cheddar)

{broom} R package

tidy() summarizes information about model components such as coefficients of a regression.
glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC.
augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

library(broom)
tidy(Fit_4)  %>%
  kable( format = "markdown",digits=4)

term	estimate	std.error	statistic	p.value
(Intercept)	-28.8768	19.7354	-1.4632	0.1554
Acetic	0.3277	4.4598	0.0735	0.9420
H2S	3.9118	1.2484	3.1334	0.0042
Lactic	19.6705	8.6291	2.2796	0.0311

glance(Fit_4) %>%
  kable( format = "markdown",digits=4)

r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.6518	0.6116	10.1307	16.2214	0	3	-109.8888	229.7775	236.7835	2668.411	26	30

augment(Fit_4) %>%
  head() %>%
  kable( format = "markdown",digits=4)

taste	Acetic	H2S	Lactic	.fitted	.resid	.std.resid	.hat	.sigma	.cooksd
12.3	4.543	3.135	0.86	1.7924	10.5076	1.1421	0.1753	10.0688	0.0693
20.9	5.159	5.043	1.53	22.6374	-1.7374	-0.1784	0.0759	10.3250	0.0007
39.0	5.366	5.438	1.57	25.0372	13.9628	1.4215	0.0599	9.9217	0.0322
47.9	5.759	7.496	1.81	37.9375	9.9625	1.0299	0.0883	10.1184	0.0257
5.6	4.663	3.807	0.99	7.0177	-1.4177	-0.1499	0.1288	10.3269	0.0008
25.9	5.697	7.601	1.09	24.1652	1.7348	0.1952	0.2304	10.3238	0.0029

The {broom} R Package