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On March 21, 2020 8:59:58 AM PDT, Ioanna Ioannou <ii54...@msn.com> wrote:
>Hello everyone,
>
>I am having this data.frame. For each row you have 26 values aggregated
>in a cell and separated by a comma. I want to do some calculations for
>all unique names and taxonomy which include the four different damage
>states. I can estimate the results but i am struggling to save them in
>a data.frame and assign next to them the unique combination of the
>name, taxonomy. Any help much appreciated.
>
>
>d1 <- read.csv('test.csv')
>
>D2L <- c(0, 2, 10, 50, 100)
>
>VC_final <- array(NA, length(distinct(d1[,c(65,4,3)])$Name) )
>VC <- matrix(NA,
>length(distinct(d1[,c(65,4,3)])$Name),length(unlist(str_split(as.character(d1[1,]$Y_vals),
>pattern = ","))))
>
># get the rows for the four damage states
>DS1_rows <- d1$Damage_State == unique(d1$Damage_State)[4]
>DS2_rows <- d1$Damage_State == unique(d1$Damage_State)[3]
>DS3_rows <- d1$Damage_State == unique(d1$Damage_State)[2]
>DS4_rows <- d1$Damage_State == unique(d1$Damage_State)[1]
>
># step through all possible values of IM.type and Taxonomy and Name
>#### This is true for this subset not generalibale needs to be checked
>first ##
>
>for(IM in unique(d1$IM_type)) {
> for(Tax in unique(d1$Taxonomy)) {
> for(Name in unique(d1$Name)) {
> # get a logical vector of the rows to be use DS5 in this calculation
> calc_rows <- d1$IM_type == IM & d1$Taxonomy == Tax & d1$Name == Name
>
>
> # check that there are any such rows in the DS5ata frame
> if(sum(calc_rows)) {
> cat(IM,Tax,Name,"\n")
> # if so, fill in the four values for these rows
>VC[calc_rows] <- D2L[1] * (1-
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS1_rows,]$Y_vals), pattern = ","))) ) +
>D2L[2]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS1_rows,]$Y_vals), pattern = ","))) -
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS2_rows,]$Y_vals), pattern = ",")))) +
>D2L[3]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS2_rows,]$Y_vals), pattern = ","))) -
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS3_rows,]$Y_vals), pattern = ",")))) +
>D2L[4] * (as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS3_rows,]$Y_vals), pattern = ","))) -
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS4_rows,]$Y_vals), pattern = ",")))) +
>D2L[5]* as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS4_rows,]$Y_vals), pattern = ",")))
> print(VC[calc_rows] )
> }
> }
> }
>}
>
> for(Tax in unique(d1$Taxonomy)) {
> for(Name in unique(d1$Name)) {
> # get a logical vector of the rows to be use DS5 in this calculation
> calc_rows <- d1$IM_type == IM & d1$Taxonomy == Tax & d1$Name == Name
>
>
> # check that there are any such rows in the DS5ata frame
> if(sum(calc_rows)) {
> cat(IM,Tax,Name,"\n")
> # if so, fill in the four values for these rows
>VC[calc_rows] <- D2L[1] * (1-
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS1_rows,]$Y_vals), pattern = ","))) ) +
>D2L[2]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS1_rows,]$Y_vals), pattern = ","))) -
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS2_rows,]$Y_vals), pattern = ",")))) +
>D2L[3]* (as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS2_rows,]$Y_vals), pattern = ","))) -
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS3_rows,]$Y_vals), pattern = ",")))) +
>D2L[4] * (as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS3_rows,]$Y_vals), pattern = ","))) -
>as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS4_rows,]$Y_vals), pattern = ",")))) +
>D2L[5]* as.numeric(unlist(str_split(as.character(d1[calc_rows &
>DS4_rows,]$Y_vals), pattern = ",")))
> print(unique(VC ))
> }
> }
> }
>
>Vul <- distinct(d1[,c(65,4,3)])
>
>dim(VC) <- c(length(unlist(str_split(as.character(d1[1,]$Y_vals),
>pattern = ","))),length(distinct(d1[,c(65,4,3)])$Name)) ## (rows,
>cols)
>VC
>VC_t <- t(VC)
>Vulnerability <- matrix(apply(VC_t, 1, function(x) paste(x, collapse =
>',')))
>
>Vul$Y_vals <- Vulnerability
>
>
>
>
>Best,
>ioanna
>
>
>
>
>
>
>
>
>
>
>Name Taxonomy Damage_State Y_vals
>Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
>CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1// Slight
>4.61e-149,0.007234459,0.158482316,0.438164341,0.671470035,0.818341464,0.901312438,0.946339742,0.970531767,0.983584997,0.990707537,0.994650876,0.996869188,0.998137671,0.998874868,0.9993101,0.999570978,0.999729626,0.999827443,0.999888548,0.999927197,0.999951931,0.999967938,0.999978407,0.999985325,0.999989939
>Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
>CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//
>Collapse
>0,6.49e-45,1.29e-29,3.35e-22,1.25e-17,1.8e-14,3.81e-12,2.35e-10,6.18e-09,8.78e-08,7.86e-07,4.92e-06,2.32e-05,8.76e-05,0.000274154,0.000736426,0.001740046,0.003688955,0.007130224,0.012730071,0.021221055,0.0333283,0.049687895,0.070771949,0.096832412,0.12787106
>Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
>CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//
>Extensive
>5.02e-182,3.52e-10,8.81e-07,3.62e-05,0.000346166,0.001608096,0.004916965,0.01150426,0.022416772,0.038311015,0.059392175,0.085458446,0.115998702,0.150303282,0.187564259,0.226954808,0.267685669,0.309041053,0.35039806,0.391233913,0.431124831,0.469739614,0.506830242,0.542221151,0.575798268,0.607498531
>Hancilar et. al (2014) - CR/LDUAL school - Case V (Sd)
>CR/LDUAL/HEX:4+HFEX:12.8/YAPP:1990/EDU+EDU2//PLFSQ/IRRE//RSH1//
>Moderate
>0,1.05e-10,4.75e-06,0.000479751,0.006156253,0.02983369,0.084284357,0.171401809,0.281721077,0.401071017,0.516782184,0.620508952,0.708327468,0.779597953,0.835636781,0.87866127,0.911104254,0.935237852,0.95300803,0.965993954,0.97543154,0.982263787,0.987197155,0.990753887,0.993316294,0.99516227
>Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2
>Collapse
>0,4.91e-109,2.88e-47,3.32e-23,1.65e-11,1.78e-05,0.018162775,0.356628282,0.870224163,0.992779045,0.999855873,0.999998652,0.999999993,1,1,1,1,1,1,1,1,1,1,1,1,1
>Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2
>Extensive
>0,1.21e-32,1.78e-05,0.645821244,0.999823159,0.999999999,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
>Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2
>Moderate
>0,0.077161367,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
>Rojas(2010) - CR/LFM/DNO 2storey CR/LFM/DNO/H:2/EDU2 Slight
>0,0.996409276,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
>Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Collapse
>0,1.29e-144,1.99e-71,1.16e-40,3.23e-24,1.59e-14,1.41e-08,6.42e-05,0.00971775,0.153727719,0.562404795,0.889217735,0.985915683,0.998997836,0.999955341,0.999998628,0.999999969,0.999999999,1,1,1,1,1,1,1,1
>Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Extensive
>0,2.12e-51,4.89e-14,0.001339285,0.559153268,0.995244295,0.999997786,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
>Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Moderate
>0,3.22e-07,0.992496021,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
>Rojas(2010) - CR/LFM/DNO 3storey CR/LFM/DNO/H:3 Slight
>0,0.368907496,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
>
>
> [[alternative HTML version deleted]]
>
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--
Sent from my phone. Please excuse my brevity.
______________________________________________
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