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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationThu, 17 Nov 2016 10:55:14 +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/2016/Nov/17/t1479376520j1ntclys4og8yjp.htm/, Retrieved Sun, 05 May 2024 09:15:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=296798, Retrieved Sun, 05 May 2024 09:15:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [] [2016-11-17 09:55:14] [219800a2f11ddd28e3280d87dbde8c8d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
945
18
46
47
665
872
743
194
8
246
862
437
615
470
635
413
91
169
36
675
130
110
63
3384
168
169
79
107
936
331
837
861
641
177
645
594
34
153
81
994
42
354
813
995
35
219
571
642
417
63
646
525
4529
799
287
212
942
750
201
284
124
39
540
310
958
124
275
835
38
485
704
720
837
553
999
754
344
314
909
947
944
36
43
860
469
494
788
239
692
626
344
423
159
807
418
86
200
999
534
299
732
24
5265
871
354
15
337
427
127
496
864
88
1
429
386
610
486
936
566
5
491
117
2495
698
202
886
486
528
301
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

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=296798&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=296798&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296798&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






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[0,1000[5001250.8680560.8680560.000969
[1000,2000[1500000.8680560
[2000,3000[250010.0069440.8758e-06
[3000,4000[350010.0069440.8819448e-06
[4000,5000[450010.0069440.8888898e-06
[5000,6000]550010.0069440.8958338e-06

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[0,1000[ & 500 & 125 & 0.868056 & 0.868056 & 0.000969 \tabularnewline
[1000,2000[ & 1500 & 0 & 0 & 0.868056 & 0 \tabularnewline
[2000,3000[ & 2500 & 1 & 0.006944 & 0.875 & 8e-06 \tabularnewline
[3000,4000[ & 3500 & 1 & 0.006944 & 0.881944 & 8e-06 \tabularnewline
[4000,5000[ & 4500 & 1 & 0.006944 & 0.888889 & 8e-06 \tabularnewline
[5000,6000] & 5500 & 1 & 0.006944 & 0.895833 & 8e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296798&T=1

[TABLE]
[ROW][C]Frequency Table (Histogram)[/C][/ROW]
[ROW][C]Bins[/C][C]Midpoint[/C][C]Abs. Frequency[/C][C]Rel. Frequency[/C][C]Cumul. Rel. Freq.[/C][C]Density[/C][/ROW]
[ROW][C][0,1000[[/C][C]500[/C][C]125[/C][C]0.868056[/C][C]0.868056[/C][C]0.000969[/C][/ROW]
[ROW][C][1000,2000[[/C][C]1500[/C][C]0[/C][C]0[/C][C]0.868056[/C][C]0[/C][/ROW]
[ROW][C][2000,3000[[/C][C]2500[/C][C]1[/C][C]0.006944[/C][C]0.875[/C][C]8e-06[/C][/ROW]
[ROW][C][3000,4000[[/C][C]3500[/C][C]1[/C][C]0.006944[/C][C]0.881944[/C][C]8e-06[/C][/ROW]
[ROW][C][4000,5000[[/C][C]4500[/C][C]1[/C][C]0.006944[/C][C]0.888889[/C][C]8e-06[/C][/ROW]
[ROW][C][5000,6000][/C][C]5500[/C][C]1[/C][C]0.006944[/C][C]0.895833[/C][C]8e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=296798&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296798&T=1

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


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

Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[0,1000[5001250.8680560.8680560.000969
[1000,2000[1500000.8680560
[2000,3000[250010.0069440.8758e-06
[3000,4000[350010.0069440.8819448e-06
[4000,5000[450010.0069440.8888898e-06
[5000,6000]550010.0069440.8958338e-06


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = 6 ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
R code (references can be found in the software module):
par4 <- 'Unknown'
par3 <- 'FALSE'
par2 <- 'grey'
par1 <- '3'
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')
}