Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationTue, 01 May 2018 00:51:19 +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/2018/May/01/t1525128765t3c1zj0ab2i1s3g.htm/, Retrieved Sat, 04 May 2024 00:06:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315061, Retrieved Sat, 04 May 2024 00:06:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [] [2018-04-30 22:51:19] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
19881.76
19942.16
19899.29
19963.80
19887.38
19855.53
19954.28
19891.00
19885.73
#N/A
19826.77
19804.72
19732.40
19827.25
19799.85
19912.71
20068.51
20100.91
20093.78
19971.13
19864.09
19890.94
19884.91
20071.46
20052.42
20090.29
20054.34
20172.40
20269.37
20412.16
20504.41
20611.86
20619.77
20624.05
#N/A
20743.00
20775.60
20810.32
20821.76
20837.44
20812.24
21115.55
21002.97
21005.71
20954.34
20924.76
20855.73
20858.19
20902.98
20881.48
20837.37
20950.10
20934.55
20914.62
20905.86
20668.01
20661.30
20656.58
20596.72
20550.98
20701.50
20659.32
20728.49
20663.22
20650.21
20689.24
20648.15
20662.95
20656.10
20658.02
20651.30
20591.86
20453.25
#N/A
20636.92
20523.28
20404.49
20578.71
20547.76
20763.89
20996.12
20975.09
20981.33
20940.51
20913.46
20949.89
20957.90
20951.47
21006.94
21012.28
20975.78
20943.11
20919.42
20896.61
20981.94
20979.75
20606.93
20663.02
20804.84
20894.83
20937.91
21012.42
21082.95
21080.28
#N/A
21029.47
21008.65
21144.18
21206.29
21184.04
21136.23
21173.69
21182.53
21271.97
21235.67
21328.47
21374.56
21359.90
21384.28
21528.99
21467.14
21410.03
21397.29
21394.76
21409.55
21310.66
21454.61
21287.03
21349.63
21479.27
#N/A
21478.17
21320.04
21414.34
21408.52
21409.07
21532.14
21553.09
21637.74
21629.72
21574.73
21640.75
21611.78
21580.07
21513.17
21613.43
21711.01
21796.55
21830.31
21891.12
21963.92
22016.24
22026.10
22092.81
22118.42
22085.34
22048.70
21844.01
21858.32
21993.71
21998.99
22024.87
21750.73
21674.51
21703.75
21899.89
21812.09
21783.40
21813.67
21808.40
21865.37
21892.43
21948.10
21987.56
#N/A
21753.31
21807.64
21784.78
21797.79
22057.37
22118.86
22158.18
22203.48
22268.34
22331.35
22370.80
22412.59
22359.23
22349.59
22296.09
22284.32
22340.71
22381.20
22405.09
22557.60
22641.67
22661.64
22775.39
22773.67
22761.07
22830.68
22872.89
22841.01
22871.72
22956.96
22997.44
23157.60
23163.04
23328.63
23273.96
23441.76
23329.46
23400.86
23434.19
23348.74
23377.24
23435.01
23516.26
23539.19
23548.42
23557.23
23563.36
23461.94
23422.21
23439.70
23409.47
23271.28
23458.36
23358.24
23430.33
23590.83
23526.18
#N/A
23557.99
23580.78
23836.71
23940.68
24272.35
24231.59
24290.05
24180.64
24140.91
24211.48
24329.16
24386.03
24504.80
24585.43
24508.66
24651.74
24792.20
24754.75
24726.65
24782.29
24754.06
#N/A
24746.21
24774.30
24837.51
24719.22
#N/A
24824.01
24922.68
25075.13
25295.87
25283.00
25385.80
25369.13
25574.73
25803.19
#N/A
25792.86
26115.65
26017.81
26071.72
26214.60
26210.81
26252.12
26392.79
26616.71
26439.48
26076.89
26149.39
26186.71
25520.96
24345.75
24912.77
24893.35
23860.46
24190.90
24601.27
24640.45
24893.49
25200.37
25219.38
#N/A
24964.75
24797.78
24962.48
25309.99
25709.27
25410.03
25029.20
24608.98
24538.06
24874.76
24884.12
24801.36
24895.21
25335.74
25178.61
25007.03
24758.12
24873.66
24946.51
24610.91
24727.27
24682.31
23957.89
23533.20
24202.60
23857.71
23848.42
24103.11
#N/A
23644.19
24033.36
24264.30
24505.22
23932.76
23979.10
24408.00
24189.45
24483.05
24360.14
24573.04
24786.63
24748.07
24664.89
24462.94
24448.69
24024.13
24083.83
24322.34
24311.19




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=315061&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=315061&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315061&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



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):
par4 <- 'Unknown'
par3 <- 'FALSE'
par2 <- 'grey'
par1 <- ''
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')
}