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Author's title

Author*Unverified author*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationFri, 28 Jul 2017 14:02:28 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jul/28/t150124340496ikhh0cz0jdbnx.htm/, Retrieved Wed, 15 May 2024 04:21:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306776, Retrieved Wed, 15 May 2024 04:21:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [Histogram omzet J...] [2017-07-28 12:02:28] [41db9c2917eeaa94887144dd7479aea5] [Current]
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Dataseries X:
4213144.00
4197453.00
4181541.00
4148612.00
4474366.00
4457128.00
4213144.00
4050930.00
4066621.00
4066621.00
4084080.00
4115462.00
4164303.00
4164303.00
4132921.00
4050930.00
4474366.00
4538898.00
4441437.00
4213144.00
4310826.00
4164303.00
4230382.00
4261985.00
4294914.00
4213144.00
4230382.00
4115462.00
4474366.00
4587739.00
4490278.00
4310826.00
4505969.00
4294914.00
4490278.00
4474366.00
4523207.00
4343755.00
4538898.00
4523207.00
4816032.00
4749953.00
4490278.00
4359446.00
4538898.00
4294914.00
4474366.00
4505969.00
4572048.00
4425746.00
4505969.00
4554810.00
4734262.00
4587739.00
4392596.00
4181541.00
4376905.00
3839875.00
4099771.00
4246073.00
4392596.00
4181541.00
4181541.00
4181541.00
4294914.00
4132921.00
3920319.00
3742414.00
3871478.00
3367598.00
3676335.00
3855787.00
3888716.00
3709264.00
3724955.00
3676335.00
3839875.00
3724955.00
3498430.00
3334669.00
3611582.00
3010241.00
3400748.00
3578653.00
3578653.00
3367598.00
3172455.00
3156764.00
3334669.00
3172455.00
2863939.00
2651337.00
2879630.00
2342821.00
2830789.00
3090464.00
3172455.00
2993003.00
2766257.00
2928471.00
2993003.00
2944162.00
2455973.00
2229448.00
2391441.00
1903473.00
2407353.00
2586805.00
2733107.00
2489123.00
2260830.00
2391441.00
2455973.00
2326909.00
1838941.00
1626339.00
1821482.00
1284673.00
1870323.00
2229448.00




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=306776&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=306776&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306776&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 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
[1000000,1500000[125000010.0083330.0083330
[1500000,2000000[175000050.0416670.050
[2000000,2500000[2250000110.0916670.1416670
[2500000,3000000[2750000110.0916670.2333330
[3000000,3500000[3250000120.10.3333330
[3500000,4000000[3750000150.1250.4583330
[4000000,4500000[4250000500.4166670.8751e-06
[4500000,5000000]4750000150.12510

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[1000000,1500000[ & 1250000 & 1 & 0.008333 & 0.008333 & 0 \tabularnewline
[1500000,2000000[ & 1750000 & 5 & 0.041667 & 0.05 & 0 \tabularnewline
[2000000,2500000[ & 2250000 & 11 & 0.091667 & 0.141667 & 0 \tabularnewline
[2500000,3000000[ & 2750000 & 11 & 0.091667 & 0.233333 & 0 \tabularnewline
[3000000,3500000[ & 3250000 & 12 & 0.1 & 0.333333 & 0 \tabularnewline
[3500000,4000000[ & 3750000 & 15 & 0.125 & 0.458333 & 0 \tabularnewline
[4000000,4500000[ & 4250000 & 50 & 0.416667 & 0.875 & 1e-06 \tabularnewline
[4500000,5000000] & 4750000 & 15 & 0.125 & 1 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306776&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][1000000,1500000[[/C][C]1250000[/C][C]1[/C][C]0.008333[/C][C]0.008333[/C][C]0[/C][/ROW]
[ROW][C][1500000,2000000[[/C][C]1750000[/C][C]5[/C][C]0.041667[/C][C]0.05[/C][C]0[/C][/ROW]
[ROW][C][2000000,2500000[[/C][C]2250000[/C][C]11[/C][C]0.091667[/C][C]0.141667[/C][C]0[/C][/ROW]
[ROW][C][2500000,3000000[[/C][C]2750000[/C][C]11[/C][C]0.091667[/C][C]0.233333[/C][C]0[/C][/ROW]
[ROW][C][3000000,3500000[[/C][C]3250000[/C][C]12[/C][C]0.1[/C][C]0.333333[/C][C]0[/C][/ROW]
[ROW][C][3500000,4000000[[/C][C]3750000[/C][C]15[/C][C]0.125[/C][C]0.458333[/C][C]0[/C][/ROW]
[ROW][C][4000000,4500000[[/C][C]4250000[/C][C]50[/C][C]0.416667[/C][C]0.875[/C][C]1e-06[/C][/ROW]
[ROW][C][4500000,5000000][/C][C]4750000[/C][C]15[/C][C]0.125[/C][C]1[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306776&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306776&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
[1000000,1500000[125000010.0083330.0083330
[1500000,2000000[175000050.0416670.050
[2000000,2500000[2250000110.0916670.1416670
[2500000,3000000[2750000110.0916670.2333330
[3000000,3500000[3250000120.10.3333330
[3500000,4000000[3750000150.1250.4583330
[4000000,4500000[4250000500.4166670.8751e-06
[4500000,5000000]4750000150.12510



Parameters (Session):
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')
}