Jovan
Reputation: 815
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
Upvotes: 2
Views: 503
Answers (2)
G. Grothendieck
Reputation: 269852
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
Upvotes: 3
Uwe
Reputation: 42564
Another possibility would be to create summary() piecewise:
library(data.table)
for (x in split(i <- seq_along(mtcars), i %/% 4))
as.data.table(mtcars)[, print(summary(.SD)), .SDcols = x]
mpg cyl disp Min. :10.40 Min. :4.000 Min. : 71.1 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 Median :19.20 Median :6.000 Median :196.3 Mean :20.09 Mean :6.188 Mean :230.7 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 Max. :33.90 Max. :8.000 Max. :472.0 hp drat wt qsec Min. : 52.0 Min. :2.760 Min. :1.513 Min. :14.50 1st Qu.: 96.5 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 Median :123.0 Median :3.695 Median :3.325 Median :17.71 Mean :146.7 Mean :3.597 Mean :3.217 Mean :17.85 3rd Qu.:180.0 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 Max. :335.0 Max. :4.930 Max. :5.424 Max. :22.90 vs am gear carb Min. :0.0000 Min. :0.0000 Min. :3.000 Min. :1.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000 Median :0.0000 Median :0.0000 Median :4.000 Median :2.000 Mean :0.4375 Mean :0.4062 Mean :3.688 Mean :2.812 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000 Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :8.000
or simulating OP's matrix:
# create dummy data
mat <- matrix(1:17000, ncol = 17)
# set column names
colnames(mat) <- 1:17
# print summary piecewise
for (x in split(i <- seq_along(dt), i %/% 6))
print(summary(mat[, x]))
1 2 3 4 5 Min. : 1.0 Min. :1001 Min. :2001 Min. :3001 Min. :4001 1st Qu.: 250.8 1st Qu.:1251 1st Qu.:2251 1st Qu.:3251 1st Qu.:4251 Median : 500.5 Median :1500 Median :2500 Median :3500 Median :4500 Mean : 500.5 Mean :1500 Mean :2500 Mean :3500 Mean :4500 3rd Qu.: 750.2 3rd Qu.:1750 3rd Qu.:2750 3rd Qu.:3750 3rd Qu.:4750 Max. :1000.0 Max. :2000 Max. :3000 Max. :4000 Max. :5000 6 7 8 9 10 11 Min. :5001 Min. :6001 Min. :7001 Min. :8001 Min. : 9001 Min. :10001 1st Qu.:5251 1st Qu.:6251 1st Qu.:7251 1st Qu.:8251 1st Qu.: 9251 1st Qu.:10251 Median :5500 Median :6500 Median :7500 Median :8500 Median : 9500 Median :10500 Mean :5500 Mean :6500 Mean :7500 Mean :8500 Mean : 9500 Mean :10500 3rd Qu.:5750 3rd Qu.:6750 3rd Qu.:7750 3rd Qu.:8750 3rd Qu.: 9750 3rd Qu.:10750 Max. :6000 Max. :7000 Max. :8000 Max. :9000 Max. :10000 Max. :11000 12 13 14 15 16 17 Min. :11001 Min. :12001 Min. :13001 Min. :14001 Min. :15001 Min. :16001 1st Qu.:11251 1st Qu.:12251 1st Qu.:13251 1st Qu.:14251 1st Qu.:15251 1st Qu.:16251 Median :11500 Median :12500 Median :13500 Median :14500 Median :15500 Median :16500 Mean :11500 Mean :12500 Mean :13500 Mean :14500 Mean :15500 Mean :16500 3rd Qu.:11750 3rd Qu.:12750 3rd Qu.:13750 3rd Qu.:14750 3rd Qu.:15750 3rd Qu.:16750 Max. :12000 Max. :13000 Max. :14000 Max. :15000 Max. :16000 Max. :17000
Note that in the matrix case it is recommended / required to have column names explicitly set. If the respective matrix attribute is not set, summary() uses default column names which always start at V1.
Upvotes: 0
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