As an addition to the answer above I'd recommend to start using the broom package when you want to access/use various elements of a model object.

First, by using the str command you don't convert the summary into a string, but you just see the structure of your summary, which is a list. So, str means "structure".

The broom package enables you to save the info of your model object as a data frame, which is easier to manipulate. Check my simple example:

library(broom)

fit <- aov(mpg ~ vs, data = mtcars)

# check the summary of the ANOVA (not possible to access info/elements)
fit2 = summary(fit)

fit2

#             Df Sum Sq Mean Sq F value   Pr(>F)    
# vs           1  496.5   496.5   23.66 3.42e-05 ***
# Residuals   30  629.5    21.0 


# create a data frame of the ANOVA
fit3 = tidy(fit)

fit3

#        term df    sumsq    meansq statistic      p.value
# 1        vs  1 496.5279 496.52790  23.66224 3.415937e-05
# 2 Residuals 30 629.5193  20.98398        NA           NA


# get F value (or any other values)
fit3$statistic[1]

#[1] 23.66224

I think for the specific example you provided you don't really need to use the broom method, but if it happens to deal with more complicated model objects it will be really useful to try it.

dplyr package could be nice alternative to this problem:

library(dplyr)

df %>% 
  group_by(group) %>% 
  summarize(mean = mean(dt),
            sum = sum(dt))

To get 1st quadrant and 3rd quadrant

df %>% 
  group_by(group) %>% 
  summarize(q1 = quantile(dt, 0.25),
            q3 = quantile(dt, 0.75))