How to convert output from the function summary(data_frame) into a data frame in R

I don't know that I would consider this to be a "presentable" format, but you could always unclass the result of summary if you really insist on a data.frame in a "wide" form.

data.frame(unclass(summary(airquality)))
##         X....Ozone     X...Solar.R       X.....Wind      X.....Temp      X....Month      X.....Day
## 1 Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00   Min.   :5.000   Min.   : 1.0  
## 2 1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00   1st Qu.:6.000   1st Qu.: 8.0  
## 3 Median : 31.50   Median :205.0   Median : 9.700   Median :79.00   Median :7.000   Median :16.0  
## 4 Mean   : 42.13   Mean   :185.9   Mean   : 9.958   Mean   :77.88   Mean   :6.993   Mean   :15.8  
## 5 3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500   3rd Qu.:85.00   3rd Qu.:8.000   3rd Qu.:23.0  
## 6 Max.   :168.00   Max.   :334.0   Max.   :20.700   Max.   :97.00   Max.   :9.000   Max.   :31.0  
## 7     NA's   :37       NA's   :7               <NA>            <NA>            <NA>           <NA>

I find that output to include a lot of redundant information, however.

I suppose you could also consider a function like the following:

summaryDF <- function(indf) {
  require(splitstackshape)
  temp <- data.table(summary(indf))[, c("V2", "N"), with = FALSE]
  dcast.data.table(cSplit(temp, "N", ":")[!is.na(N_1)],
                   N_1 ~ V2, value.var = "N_2")
}


summaryDF(airquality)
##        N_1      Day      Temp      Wind     Month     Ozone    Solar.R
## 1: 1st Qu.      8.0     72.00     7.400     6.000     18.00      115.8
## 2: 3rd Qu.     23.0     85.00    11.500     8.000     63.25      258.8
## 3:    Max.     31.0     97.00    20.700     9.000    168.00      334.0
## 4:    Mean     15.8     77.88     9.958     6.993     42.13      185.9
## 5:  Median     16.0     79.00     9.700     7.000     31.50      205.0
## 6:    Min.      1.0     56.00     1.700     5.000      1.00        7.0
## 7:    NA's       NA        NA        NA        NA     37.00        7.0

Don't expect miracles on datasets with different types of columns though. For example, summaryDF(iris) wouldn't be meaningful.

Also, if you don't have any NA values in your dataset, you may be able to just get away with sapply:

sapply(mtcars, summary)
##           mpg   cyl  disp    hp  drat    wt  qsec     vs     am  gear  carb
## Min.    10.40 4.000  71.1  52.0 2.760 1.513 14.50 0.0000 0.0000 3.000 1.000
## 1st Qu. 15.42 4.000 120.8  96.5 3.080 2.581 16.89 0.0000 0.0000 3.000 2.000
## Median  19.20 6.000 196.3 123.0 3.695 3.325 17.71 0.0000 0.0000 4.000 2.000
## Mean    20.09 6.188 230.7 146.7 3.597 3.217 17.85 0.4375 0.4062 3.688 2.812
## 3rd Qu. 22.80 8.000 326.0 180.0 3.920 3.610 18.90 1.0000 1.0000 4.000 4.000
## Max.    33.90 8.000 472.0 335.0 4.930 5.424 22.90 1.0000 1.0000 5.000 8.000

As simple as as.data.frame(describe(mydata)) using the psych package.