Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationFri, 02 Oct 2015 21:06:53 +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/2015/Oct/02/t1443816438kn1b4dp2sm9f4jv.htm/, Retrieved Tue, 14 May 2024 18:17:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=281198, Retrieved Tue, 14 May 2024 18:17:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [Frequentietabel-I...] [2015-10-02 20:06:53] [bcb0da8ff6be95621a49a67fe6a7b572] [Current]
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Dataseries X:
2 754 542 000
2 899 512 000
2 928 886 000
3 011 252 000
2 932 895 000
3 069 307 000
2 863 923 000
2 585 491 000
2 993 900 000
3 023 542 000
2 491 370 000
2 341 705 000
2 126 472 000
2 196 705 000
2 368 313 000
2 285 174 000
2 163 877 000
2 299 241 000
2 275 643 000
2 163 091 000
2 416 149 000
2 434 553 000
2 281 937 000
2 440 464 000
2 255 745 000
2 389 872 000
2 863 148 000
2 623 516 000
2 558 136 000
2 898 129 000
2 537 720 000
2 543 469 000
2 779 739 000
2 884 779 000
2 711 624 000
2 817 771 000
2 884 477 000
3 058 996 000
3 285 298 000
2 879 617 000
3 220 416 000
3 144 280 000
2 940 811 000
2 986 507 000
3 153 720 000
2 995 806 000
2 990 242 000
2 879 837 000
2 848 699 000
3 138 385 000
3 532 447 000
3 121 872 000
3 309 250 000
3 215 022 000
2 966 778 000
3 010 284 000
3 083 824 000
3 257 727 000
3 180 374 000
3 036 414 000
2 966 714 000
3 067 677 000
3 339 789 000
3 299 861 000
3 193 328 000
3 181 266 000
3 193 356 000
2 898 282 000
2 929 524 000
3 217 311 000
3 126 249 000
3 131 083 000
3 008 058 000
2 868 318 000
3 207 495 000
3 109 336 000
3 070 725 000
2 989 963 000
3 287 552 000
2 835 238 000
3 368 961 000
3 291 689 000
3 008 536 000
2 974 109 000




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=281198&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=281198&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=281198&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[2e+09,2.2e+09[2.1e+0940.0476190.0476190
[2.2e+09,2.4e+09[2.3e+0980.0952380.1428570
[2.4e+09,2.6e+09[2.5e+0980.0952380.2380950
[2.6e+09,2.8e+09[2.7e+0940.0476190.2857140
[2.8e+09,3e+09[2.9e+09250.2976190.5833330
[3e+09,3.2e+09[3.1e+09220.2619050.8452380
[3.2e+09,3.4e+09[3.3e+09120.1428570.9880950
[3.4e+09,3.6e+09]3.5e+0910.01190510

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[2e+09,2.2e+09[ & 2.1e+09 & 4 & 0.047619 & 0.047619 & 0 \tabularnewline
[2.2e+09,2.4e+09[ & 2.3e+09 & 8 & 0.095238 & 0.142857 & 0 \tabularnewline
[2.4e+09,2.6e+09[ & 2.5e+09 & 8 & 0.095238 & 0.238095 & 0 \tabularnewline
[2.6e+09,2.8e+09[ & 2.7e+09 & 4 & 0.047619 & 0.285714 & 0 \tabularnewline
[2.8e+09,3e+09[ & 2.9e+09 & 25 & 0.297619 & 0.583333 & 0 \tabularnewline
[3e+09,3.2e+09[ & 3.1e+09 & 22 & 0.261905 & 0.845238 & 0 \tabularnewline
[3.2e+09,3.4e+09[ & 3.3e+09 & 12 & 0.142857 & 0.988095 & 0 \tabularnewline
[3.4e+09,3.6e+09] & 3.5e+09 & 1 & 0.011905 & 1 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=281198&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][2e+09,2.2e+09[[/C][C]2.1e+09[/C][C]4[/C][C]0.047619[/C][C]0.047619[/C][C]0[/C][/ROW]
[ROW][C][2.2e+09,2.4e+09[[/C][C]2.3e+09[/C][C]8[/C][C]0.095238[/C][C]0.142857[/C][C]0[/C][/ROW]
[ROW][C][2.4e+09,2.6e+09[[/C][C]2.5e+09[/C][C]8[/C][C]0.095238[/C][C]0.238095[/C][C]0[/C][/ROW]
[ROW][C][2.6e+09,2.8e+09[[/C][C]2.7e+09[/C][C]4[/C][C]0.047619[/C][C]0.285714[/C][C]0[/C][/ROW]
[ROW][C][2.8e+09,3e+09[[/C][C]2.9e+09[/C][C]25[/C][C]0.297619[/C][C]0.583333[/C][C]0[/C][/ROW]
[ROW][C][3e+09,3.2e+09[[/C][C]3.1e+09[/C][C]22[/C][C]0.261905[/C][C]0.845238[/C][C]0[/C][/ROW]
[ROW][C][3.2e+09,3.4e+09[[/C][C]3.3e+09[/C][C]12[/C][C]0.142857[/C][C]0.988095[/C][C]0[/C][/ROW]
[ROW][C][3.4e+09,3.6e+09][/C][C]3.5e+09[/C][C]1[/C][C]0.011905[/C][C]1[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=281198&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=281198&T=1

As an alternative you can also use a QR Code:  

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

Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[2e+09,2.2e+09[2.1e+0940.0476190.0476190
[2.2e+09,2.4e+09[2.3e+0980.0952380.1428570
[2.4e+09,2.6e+09[2.5e+0980.0952380.2380950
[2.6e+09,2.8e+09[2.7e+0940.0476190.2857140
[2.8e+09,3e+09[2.9e+09250.2976190.5833330
[3e+09,3.2e+09[3.1e+09220.2619050.8452380
[3.2e+09,3.4e+09[3.3e+09120.1428570.9880950
[3.4e+09,3.6e+09]3.5e+0910.01190510



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,hyperlink('histogram.htm','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')
}