I want to get a table of the fivenums after stimulating my whole stimulation ten times, by using the code below:
do.call(rbind, lapply(1:10, function(i) mycode()))
Yet all I got was a dataframe like this:
Mean SD Min Q1 Median Q3 Max
1 65.01 0.07 46.37 61.00 65.30 69.65 81.20
2 65.32 0.07 45.60 60.63 65.12 70.25 82.23
3 64.35 0.08 43.97 58.37 64.60 70.56 81.20
4 63.76 0.07 35.23 58.97 63.85 69.18 82.83
5 66.07 0.07 49.47 61.70 66.33 70.68 82.70
6 63.70 0.08 38.53 58.96 64.60 68.76 80.90
7 64.46 0.08 39.63 59.43 64.83 70.23 79.20
8 65.54 0.07 35.70 62.09 66.18 70.72 80.13
9 65.20 0.06 46.53 61.34 65.30 69.36 80.43
10 64.92 0.08 38.23 60.32 65.11 70.10 82.10
I tried to use codes like table(), as.table(), xtab(), but the output looks like this:
Mean 0.07
63.87 0.00
64.41 0.00
64.45 0.00
64.6 0.00
64.87 0.00
65.26 0.00
65.28 0.00
65.52 0.00
66.26 0.00
66.63 0.00
, , Min = 43.37, Q1 = 59.68, Median = 64.67, Q3 = 68.88
SD
Mean 0.07
63.87 0.00
64.41 0.00
64.45 0.00
64.6 0.00
64.87 0.00
65.26 0.00
65.28 0.00
65.52 0.00
66.26 0.00
66.63 0.00
, , Min = 44.73, Q1 = 59.68, Median = 64.67, Q3 = 68.88
Or with extra package flextable, which gives me a table as the image attached. enter image description here
If you are asking how to get a data frame with the quantiles of columns of a numeric data frame then using the built in BOD data frame
data.frame(lapply(BOD, quantile, c(0, .25, .5, .75, 1)))
## Time demand
## 0% 1.00 8.300
## 25% 2.25 11.625
## 50% 3.50 15.800
## 75% 4.75 18.250
## 100% 7.00 19.800
or as a matrix
sapply(BOD, quantile, c(0, .25, .5, .75, 1))
or as the transpose matrix of that
t(sapply(BOD, quantile, c(0, .25, .5, .75, 1)))
or as a table class object
as.table(sapply(BOD, quantile, c(0, .25, .5, .75, 1)))
or if the question is how to get a "table" class object with the same number of rows and columns as a data frame then convert it to a matrix first. We use the input shown in the Note at the end.
tab <- as.table(as.matrix(DF))
class(tab)
## [1] "table"
tab
## Mean SD Min Q1 Median Q3 Max
## 1 65.01 0.07 46.37 61.00 65.30 69.65 81.20
## 2 65.32 0.07 45.60 60.63 65.12 70.25 82.23
## 3 64.35 0.08 43.97 58.37 64.60 70.56 81.20
## 4 63.76 0.07 35.23 58.97 63.85 69.18 82.83
## 5 66.07 0.07 49.47 61.70 66.33 70.68 82.70
## 6 63.70 0.08 38.53 58.96 64.60 68.76 80.90
## 7 64.46 0.08 39.63 59.43 64.83 70.23 79.20
## 8 65.54 0.07 35.70 62.09 66.18 70.72 80.13
## 9 65.20 0.06 46.53 61.34 65.30 69.36 80.43
## 10 64.92 0.08 38.23 60.32 65.11 70.10 82.10
There are also many summarization functions in various packages including skimr::skim, pysch::describe, summarytools::descr and summarytools::dfSummary to name a few.
Lines <- "Mean SD Min Q1 Median Q3 Max
1 65.01 0.07 46.37 61.00 65.30 69.65 81.20
2 65.32 0.07 45.60 60.63 65.12 70.25 82.23
3 64.35 0.08 43.97 58.37 64.60 70.56 81.20
4 63.76 0.07 35.23 58.97 63.85 69.18 82.83
5 66.07 0.07 49.47 61.70 66.33 70.68 82.70
6 63.70 0.08 38.53 58.96 64.60 68.76 80.90
7 64.46 0.08 39.63 59.43 64.83 70.23 79.20
8 65.54 0.07 35.70 62.09 66.18 70.72 80.13
9 65.20 0.06 46.53 61.34 65.30 69.36 80.43
10 64.92 0.08 38.23 60.32 65.11 70.10 82.10"
DF <- read.table(text = Lines)