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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationThu, 08 Feb 2018 10:53:06 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Feb/08/t1518083623gpfc898c7xmmbq0.htm/, Retrieved Wed, 08 May 2024 21:23:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=314973, Retrieved Wed, 08 May 2024 21:23:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [Woodlice] [2018-02-08 09:53:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	8	14.8		1	12	16.15
2	10	11.3		2	11	19.99
3	11	16.9		3	12	20.65
4	8	29.8		4	11	24.8
5	11	19.3		5	12	27.09
6	9	12.7		6	9	13.83
7	8	17.8		7	9	18.12
8	9	13.3		8	10	14.2
9	9	12.7		9	9	11.53
10	8	25.6		10	11	19.35
11	8	21.4		11	8	9.5
12	9	14		12	10	11.3
13	9	18.2		13	8	18.74
14	12	34.9		14	10	14.65
15	10	10.83		15	9	15.18
16	7	64.3		16	8	21.6
17	9	22.5		17	11	15.32
18	12	18.6		18	11	9.3
19	8	18.2		19	8	43.1
20	8	30.6		20	11	12.2
21	12	21.8		21	13	17.3
22	10	29.58		22	7	21.2
23	10	26.47		23	12	35.2
24	11	32		24	11	22.5
25	8	22.74		25	9	22
26	11	20.53		26	8	20.3
27	11	16.26		27	10	22.2
28	12	14.36		28	10	10.4
29	8	18.47		29	10	15.4
30	10	17.3		30	9	12.2
31	8	18.33		31	8	8.9
32	9	9.92		32	8	16.3
33	8	14.66		33	10	13
34	8	12.46		34	8	11.9
35	9	18.67		35	13	13.32
36	8	16.64		36	12	DNF
37	9	12.02		37	10	11.85
38	10	13.2		38	13	69.97
39	8	49.77		39	10	12
40	12	10.91		40	11	18.99
41	11	13.74		41	8	26.08
42	10	12.41		42	8	23.93
43	11	39.16		43	12	32.75
44	10	12.61		44	9	27.07
45	11	11.7		45	9	20.6
46	9	17.29		46	10	9.5
47	11	15.05		47	7	18.02
48	10	14.38		48	11	18.42
49	6	32.71		49	11	17.04
50	11	30.99		50	7	20.45
51	11	10.89		51	9	20.46
52	8	53.24		52	9	18.89
53	8	16.39		53	8	16.95
54	11	20.03		54	10	16.38
55	11	13		55	16	15.85
56	11	15.46		56	15	16.32
57	11	17.24		57	8	33.14
58	8	40.89		58	11	16.3
59	16	22.6		59	13	31.8
60	11	52.11		60	17	15.82
61	10	23.85		61	13	22.04
62	9	18.67		62	15	19.04
63	10	37.13		63	11	31.94
64	15	17.37		64	12	11.8
65	14	21.09		65	10	14.61
66	12	17.93		66	13	16.2
67	10	14.4		67	10	20.2
68	10	22.34		68	11	25.39
69	13	17.65		69	12	16.53
70	13	16.2		70	11	12.08
71	15	50.78		71	9	32.41
72	10	15.26		72	12	13.71
73	11	17.43				
74	10	12.8				
75	9	18.67				
76	14	19

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314973&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=314973&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314973&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par3 == 'TRUE') par3 <- TRUE
if (par3 == 'FALSE') par3 <- FALSE
if (par4 == 'Unknown') par1 <- as.numeric(par1)
if (par4 == 'Interval/Ratio') par1 <- as.numeric(par1)
if (par4 == '3-point Likert') par1 <- c(1:3 - 0.5, 3.5)
if (par4 == '4-point Likert') par1 <- c(1:4 - 0.5, 4.5)
if (par4 == '5-point Likert') par1 <- c(1:5 - 0.5, 5.5)
if (par4 == '6-point Likert') par1 <- c(1:6 - 0.5, 6.5)
if (par4 == '7-point Likert') par1 <- c(1:7 - 0.5, 7.5)
if (par4 == '8-point Likert') par1 <- c(1:8 - 0.5, 8.5)
if (par4 == '9-point Likert') par1 <- c(1:9 - 0.5, 9.5)
if (par4 == '10-point Likert') par1 <- c(1:10 - 0.5, 10.5)
bitmap(file='test1.png')
if(is.numeric(x[1])) {
if (is.na(par1)) {
myhist<-hist(x,col=par2,main=main,xlab=xlab,right=par3)
} else {
if (par1 < 0) par1 <- 3
if (par1 > 50) par1 <- 50
myhist<-hist(x,breaks=par1,col=par2,main=main,xlab=xlab,right=par3)
}
} else {
barplot(mytab <- sort(table(x),T),col=par2,main='Frequency Plot',xlab=xlab,ylab='Absolute Frequency')
}
dev.off()
if(is.numeric(x[1])) {
myhist
n <- length(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Frequency Table (Histogram)',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bins',header=TRUE)
a<-table.element(a,'Midpoint',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.element(a,'Cumul. Rel. Freq.',header=TRUE)
a<-table.element(a,'Density',header=TRUE)
a<-table.row.end(a)
crf <- 0
if (par3 == FALSE) mybracket <- '[' else mybracket <- ']'
mynumrows <- (length(myhist$breaks)-1)
for (i in 1:mynumrows) {
a<-table.row.start(a)
if (i == 1)
dum <- paste('[',myhist$breaks[i],sep='')
else
dum <- paste(mybracket,myhist$breaks[i],sep='')
dum <- paste(dum,myhist$breaks[i+1],sep=',')
if (i==mynumrows)
dum <- paste(dum,']',sep='')
else
dum <- paste(dum,mybracket,sep='')
a<-table.element(a,dum,header=TRUE)
a<-table.element(a,myhist$mids[i])
a<-table.element(a,myhist$counts[i])
rf <- myhist$counts[i]/n
crf <- crf + rf
a<-table.element(a,round(rf,6))
a<-table.element(a,round(crf,6))
a<-table.element(a,round(myhist$density[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
} else {
mytab
reltab <- mytab / sum(mytab)
n <- length(mytab)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Frequency Table (Categorical Data)',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Category',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,labels(mytab)$x[i],header=TRUE)
a<-table.element(a,mytab[i])
a<-table.element(a,round(reltab[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
}