rmatrixsummary
Dividing Summary table Matrix into a few table Matrix in R
So I have a matrix which dim is 17 cols and 1000 rows (all of it is numeric), and then I summary the matrix, summary(matrix) then I got these:
My Question is: Is there anyway to split these summary table into a few table? like these
V1 V2 V3 V4 V5 V6
Min
1st Qu
Median
Mean
3rd Qu
Max
V7 V8 V9 V10 V11 V12
Min
1st Qu
Median
Mean
3rd Qu
Max
V13 V14 V15 V16 V17
Min
1st Qu
Median
Mean
3rd Qu
Max
I need to maintain space in my R shiny app for these matrix to be displayed without make it display collide each other like these
Note: sorry if all i can state is a picture
Solution
1) read.dcf/unnest The elements of the matrix are of DCF form so we can use read.dcf and then unnest that:
library(tidyr)
s <- summary(mtcars)
DF <- read.dcf(textConnection(s), all = TRUE)
res <- setNames(data.frame(t(unnest(DF)), check.names = FALSE), trimws(colnames(s)))
giving:
> res
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. 15.43 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
2) subset columns For reduced width this could be broken up into res[1:6] and res[7:11] or more generally if there are n columns and we want k columns per group except possibly for the last group:
n <- ncol(res)
k <- 6
g <- droplevels(gl(n, k, n)) # grouping vector
lapply(split(as.list(res), g), data.frame)
giving:
$`1`
mpg cyl disp hp drat wt
Min. 10.40 4.000 71.1 52.0 2.760 1.513
1st Qu. 15.43 4.000 120.8 96.5 3.080 2.581
Median 19.20 6.000 196.3 123.0 3.695 3.325
Mean 20.09 6.188 230.7 146.7 3.597 3.217
3rd Qu. 22.80 8.000 326.0 180.0 3.920 3.610
Max. 33.90 8.000 472.0 335.0 4.930 5.424
$`2`
qsec vs am gear carb
Min. 14.50 0.0000 0.0000 3.000 1.000
1st Qu. 16.89 0.0000 0.0000 3.000 2.000
Median 17.71 0.0000 0.0000 4.000 2.000
Mean 17.85 0.4375 0.4062 3.688 2.812
3rd Qu. 18.90 1.0000 1.0000 4.000 4.000
Max. 22.90 1.0000 1.0000 5.000 8.000
3) no transpose Another alternative for reduced width is to just not transpose it:
data.frame(unnest(DF), row.names = trimws(colnames(s)), check.names = FALSE)
giving:
Min. 1st Qu. Median Mean 3rd Qu. Max.
mpg 10.40 15.43 19.20 20.09 22.80 33.90
cyl 4.000 4.000 6.000 6.188 8.000 8.000
disp 71.1 120.8 196.3 230.7 326.0 472.0
hp 52.0 96.5 123.0 146.7 180.0 335.0
drat 2.760 3.080 3.695 3.597 3.920 4.930
wt 1.513 2.581 3.325 3.217 3.610 5.424
qsec 14.50 16.89 17.71 17.85 18.90 22.90
vs 0.0000 0.0000 0.0000 0.4375 1.0000 1.0000
am 0.0000 0.0000 0.0000 0.4062 1.0000 1.0000
gear 3.000 3.000 4.000 3.688 4.000 5.000
carb 1.000 2.000 2.000 2.812 4.000 8.000
4) psych::describe A simple alternative is to use psynh::describe
library(psych)
describe(mtcars)
giving:
vars n mean sd median trimmed mad min max range skew kurtosis se
mpg 1 32 20.09 6.03 19.20 19.70 5.41 10.40 33.90 23.50 0.61 -0.37 1.07
cyl 2 32 6.19 1.79 6.00 6.23 2.97 4.00 8.00 4.00 -0.17 -1.76 0.32
disp 3 32 230.72 123.94 196.30 222.52 140.48 71.10 472.00 400.90 0.38 -1.21 21.91
hp 4 32 146.69 68.56 123.00 141.19 77.10 52.00 335.00 283.00 0.73 -0.14 12.12
drat 5 32 3.60 0.53 3.70 3.58 0.70 2.76 4.93 2.17 0.27 -0.71 0.09
wt 6 32 3.22 0.98 3.33 3.15 0.77 1.51 5.42 3.91 0.42 -0.02 0.17
qsec 7 32 17.85 1.79 17.71 17.83 1.42 14.50 22.90 8.40 0.37 0.34 0.32
vs 8 32 0.44 0.50 0.00 0.42 0.00 0.00 1.00 1.00 0.24 -2.00 0.09
am 9 32 0.41 0.50 0.00 0.38 0.00 0.00 1.00 1.00 0.36 -1.92 0.09
gear 10 32 3.69 0.74 4.00 3.62 1.48 3.00 5.00 2.00 0.53 -1.07 0.13
carb 11 32 2.81 1.62 2.00 2.65 1.48 1.00 8.00 7.00 1.05 1.26 0.29
5) Hmisc::describe Hmisc also has a describe function:
library(Hmisc)
describe(mtcars)
giving:
mtcars
11 Variables 32 Observations
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
mpg
n missing distinct Info Mean Gmd .05 .10 .25 .50 .75 .90 .95
32 0 25 0.999 20.09 6.796 12.00 14.34 15.43 19.20 22.80 30.09 31.30
lowest : 10.4 13.3 14.3 14.7 15.0, highest: 26.0 27.3 30.4 32.4 33.9
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
cyl
n missing distinct Info Mean Gmd
32 0 3 0.866 6.188 1.948
Value 4 6 8
Frequency 11 7 14
Proportion 0.344 0.219 0.438
...etc...
6) skimr::skim This is a new package. It can produce spark graphics as part of the summary output; however, that depends on font support which may be tricky so we have disabled that part below. Note that skim requires a data frame as input so if your input is a matrix use skim(as.data.frame(input)).
library(skimr)
skim_with(numeric = list(hist = NULL)) # omit spark histogram
skim(mtcars)
giving:
Skim summary statistics
n obs: 32
n variables: 11
Variable type: numeric
variable missing complete n mean sd min p25 median p75 max
1 am 0 32 32 0.41 0.5 0 0 0 1 1
2 carb 0 32 32 2.81 1.62 1 2 2 4 8
3 cyl 0 32 32 6.19 1.79 4 4 6 8 8
4 disp 0 32 32 230.72 123.94 71.1 120.83 196.3 326 472
5 drat 0 32 32 3.6 0.53 2.76 3.08 3.7 3.92 4.93
6 gear 0 32 32 3.69 0.74 3 3 4 4 5
7 hp 0 32 32 146.69 68.56 52 96.5 123 180 335
8 mpg 0 32 32 20.09 6.03 10.4 15.43 19.2 22.8 33.9
9 qsec 0 32 32 17.85 1.79 14.5 16.89 17.71 18.9 22.9
10 vs 0 32 32 0.44 0.5 0 0 0 1 1
11 wt 0 32 32 3.22 0.98 1.51 2.58 3.33 3.61 5.42
If you want to try the spark graphics see: Skimr - cant seem to produce the histograms
7) pastecs::stat.desc The pastecs package also has a function that could be used:
stat.desc(mtcars)
giving:
mpg cyl disp hp drat wt qsec vs am gear carb
nbr.val 32.0000000 32.0000000 3.200000e+01 32.0000000 32.00000000 32.0000000 32.0000000 32.00000000 32.00000000 32.0000000 32.0000000
nbr.null 0.0000000 0.0000000 0.000000e+00 0.0000000 0.00000000 0.0000000 0.0000000 18.00000000 19.00000000 0.0000000 0.0000000
nbr.na 0.0000000 0.0000000 0.000000e+00 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000
min 10.4000000 4.0000000 7.110000e+01 52.0000000 2.76000000 1.5130000 14.5000000 0.00000000 0.00000000 3.0000000 1.0000000
max 33.9000000 8.0000000 4.720000e+02 335.0000000 4.93000000 5.4240000 22.9000000 1.00000000 1.00000000 5.0000000 8.0000000
range 23.5000000 4.0000000 4.009000e+02 283.0000000 2.17000000 3.9110000 8.4000000 1.00000000 1.00000000 2.0000000 7.0000000
sum 642.9000000 198.0000000 7.383100e+03 4694.0000000 115.09000000 102.9520000 571.1600000 14.00000000 13.00000000 118.0000000 90.0000000
median 19.2000000 6.0000000 1.963000e+02 123.0000000 3.69500000 3.3250000 17.7100000 0.00000000 0.00000000 4.0000000 2.0000000
mean 20.0906250 6.1875000 2.307219e+02 146.6875000 3.59656250 3.2172500 17.8487500 0.43750000 0.40625000 3.6875000 2.8125000
SE.mean 1.0654240 0.3157093 2.190947e+01 12.1203173 0.09451874 0.1729685 0.3158899 0.08909831 0.08820997 0.1304266 0.2855297
CI.mean.0.95 2.1729465 0.6438934 4.468466e+01 24.7195501 0.19277224 0.3527715 0.6442617 0.18171719 0.17990541 0.2660067 0.5823417
var 36.3241028 3.1895161 1.536080e+04 4700.8669355 0.28588135 0.9573790 3.1931661 0.25403226 0.24899194 0.5443548 2.6088710
std.dev 6.0269481 1.7859216 1.239387e+02 68.5628685 0.53467874 0.9784574 1.7869432 0.50401613 0.49899092 0.7378041 1.6152000
coef.var 0.2999881 0.2886338 5.371779e-01 0.4674077 0.14866382 0.3041285 0.1001159 1.15203687 1.22828533 0.2000825 0.5742933
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