AcP = Academic performance
Indexes
ProjectID = read.csv("ProjectIDs.csv")
ProjectID## X ProjectID Course Year Semester AssName LxRx LxSxRx
## 1 1 45 BIOL1040 2013 2 Report 3 L1R3 L1S2R3
## 2 2 44 BIOL1040 2013 2 Report 2 L1R2 L1S2R2
## 3 3 43 BIOL1040 2013 2 Report 1 L1R1 L1S2R1
## 4 4 40 BIOL1040 2013 1 Report 4 L1R4 L1S1R4
## 5 5 39 BIOL1040 2013 1 Report 3 L1R3 L1S1R3
## 6 6 32 BIOL1040 2013 1 Report 2 L1R2 L1S1R2
## 7 7 31 BIOL1040 2013 1 Report 1 L1R1 L1S1R1
## 8 8 34 BIOM2011 2013 1 Report 1 L2R1 L2S1R1
## 9 9 41 BIOM2011 2013 1 Report 2 L2R2 L2S1R2
## 10 10 48 BIOM2011 2013 2 Report 1 L2R1 L2S2R1
## 11 11 50 BIOM2011 2013 2 Report 2 L2R2 L2S2R2
## 12 12 28 BIOM2013 2013 1 Oral L2R1 L2S1R1
## 13 13 42 BIOM2013 2013 1 Report L2R2 L2S1R2
## 14 14 63 BIOM2013 2014 1 Oral L2R1 L2S1R1LxRx = Level and Report eg Level 1 (BIOL1040 = first year) Report 1 (1st Report) Sx = semester
Setting up sequences
source("ProjectNames.R")
project.names()
rm(biol, biol.names, names.all)
str(biom)## int [1:7] 34 41 48 50 28 42 63biom.reports = biom[c(1:4,6)]
biom.reports.names = biom.names[c(1:4,6)]Academic Performance
Ac.performance = function(projects, project.names)
{
file.names <- lapply(projects, function(x){paste0("AcP", x, "DeID.csv")})
AcP.biom <- lapply(file.names, read.csv, header=TRUE, stringsAsFactors=FALSE)
names(AcP.biom) <- project.names
AcP.biom <<- AcP.biom
}
Ac.performance(biom.reports, biom.reports.names)aligning marks
list2env(AcP.biom, environment())## <environment: R_GlobalEnv>biom2011S1 = merge(BIOM2011Sem1Report1, BIOM2011Sem1Report2[2:20], by="StudentID", all.x=T)
biom2011S2 = merge(BIOM2011Sem2Report1, BIOM2011Sem2Report2[2:20], by="StudentID", all.x=T)
biom2011 = rbind(biom2011S1, biom2011S2)
biom2011[,2] = NULL
remove = rev(which(is.na(biom2011$Intro.y)))
if (length(remove) > 0)
{
biom2011 = biom2011[-remove,]
}
which(is.na(biom2011$Disc.InterpFindings.y))## integer(0)which(is.na(biom2011$Disc.IntegrateLit.y))## integer(0)remove = rev(which(is.na(biom2011$Intro.x)))
if (length(remove) > 0)
{
biom2011 = biom2011[-remove,]
}
remove = rev(which(is.na(biom2011$Disc.InterpFindings.x)))
if (length(remove) > 0)
{
biom2011 = biom2011[-remove,]
}
which(is.na(biom2011$Disc.IntegrateLit.x))## integer(0)dim(biom2011)## [1] 320 37names(biom2011)## [1] "StudentID" "SubmissionID.x"
## [3] "MarkerID.x" "Publish.Time.x"
## [5] "Intro.x" "Hypoth.x"
## [7] "Methods.x" "Results.text.x"
## [9] "Fig.Tables.x" "Legend.Title.x"
## [11] "Disc.InterpFindings.x" "Disc.IntegrateLit.x"
## [13] "Refs.x" "Kn.PhysMechs.x"
## [15] "Kn.ExpApproach.x" "W.Structure.x"
## [17] "W.LanguageJargon.x" "W.GrammarSpelling.x"
## [19] "Final.Grade.x" "SubmissionID.y"
## [21] "MarkerID.y" "Publish.Time.y"
## [23] "Intro.y" "Hypoth.y"
## [25] "Methods.y" "Results.text.y"
## [27] "Fig.Tables.y" "Legend.Title.y"
## [29] "Disc.InterpFindings.y" "Disc.IntegrateLit.y"
## [31] "Refs.y" "Kn.PhysMechs.y"
## [33] "Kn.ExpApproach.y" "W.Structure.y"
## [35] "W.LanguageJargon.y" "W.GrammarSpelling.y"
## [37] "Final.Grade.y"average marks
df = biom2011
str(df)## 'data.frame': 320 obs. of 37 variables:
## $ StudentID : chr "S8112187" "S8242993" "S8282157" "S8285971" ...
## $ SubmissionID.x : int 3393 3253 3326 3301 3198 3264 3216 3285 3388 3231 ...
## $ MarkerID.x : chr "T28" "T25" "T26" "T19" ...
## $ Publish.Time.x : chr "29/04/13 14:29" "29/04/13 14:26" "29/04/13 14:31" "29/04/13 14:26" ...
## $ Intro.x : int 65 100 50 50 80 35 65 80 50 100 ...
## $ Hypoth.x : int 100 100 80 80 100 65 100 65 100 100 ...
## $ Methods.x : int 80 100 80 80 65 80 100 65 65 80 ...
## $ Results.text.x : int 65 50 65 80 20 80 80 100 65 80 ...
## $ Fig.Tables.x : int 100 20 100 65 80 100 100 80 80 100 ...
## $ Legend.Title.x : int 100 35 80 80 65 100 100 100 100 100 ...
## $ Disc.InterpFindings.x: int 100 65 35 50 20 35 65 50 65 50 ...
## $ Disc.IntegrateLit.x : int 80 50 0 35 35 50 65 50 65 65 ...
## $ Refs.x : int 80 80 20 50 100 80 65 80 65 100 ...
## $ Kn.PhysMechs.x : int 100 80 20 50 65 50 65 65 50 50 ...
## $ Kn.ExpApproach.x : int 80 65 35 50 35 50 65 80 65 65 ...
## $ W.Structure.x : int 80 80 50 50 80 65 50 80 65 20 ...
## $ W.LanguageJargon.x : int 80 50 50 50 65 65 80 65 50 65 ...
## $ W.GrammarSpelling.x : chr "100" "65" "50" "65" ...
## $ Final.Grade.x : num 86.8 70.8 46 57.5 58.8 ...
## $ SubmissionID.y : int 5036 4865 4970 4907 4996 4859 4854 4889 4981 4848 ...
## $ MarkerID.y : chr "T19" "T25" "T32" "T19" ...
## $ Publish.Time.y : chr "4/06/13 13:47" "4/06/13 13:44" "4/06/13 13:47" "4/06/13 13:44" ...
## $ Intro.y : int 50 80 80 50 35 50 65 0 80 100 ...
## $ Hypoth.y : int 80 50 100 80 100 100 80 80 100 50 ...
## $ Methods.y : int 80 100 80 65 100 50 100 100 80 100 ...
## $ Results.text.y : int 65 80 80 50 80 100 80 100 80 100 ...
## $ Fig.Tables.y : int 80 50 100 80 100 100 100 80 100 65 ...
## $ Legend.Title.y : int 100 65 80 50 80 80 80 80 100 80 ...
## $ Disc.InterpFindings.y: int 50 100 50 50 50 65 100 0 80 80 ...
## $ Disc.IntegrateLit.y : int 35 80 65 35 20 80 80 0 80 80 ...
## $ Refs.y : int 80 80 65 80 65 80 80 0 80 100 ...
## $ Kn.PhysMechs.y : int 50 80 65 35 35 80 80 0 65 80 ...
## $ Kn.ExpApproach.y : int 50 80 65 35 50 80 80 35 80 100 ...
## $ W.Structure.y : int 65 100 100 50 80 80 65 50 65 65 ...
## $ W.LanguageJargon.y : int 65 100 100 65 50 80 80 80 80 80 ...
## $ W.GrammarSpelling.y : int 65 80 80 65 35 80 100 65 80 100 ...
## $ Final.Grade.y : num 61.5 82.2 75.8 53 57.8 ...m = df[,c(5,11,12)]
df$R1arg = round(apply(m, 1, mean), digits=2)
df[1:10,c(5, 11:12, 38)]## Intro.x Disc.InterpFindings.x Disc.IntegrateLit.x R1arg
## 1 65 100 80 81.67
## 2 100 65 50 71.67
## 3 50 35 0 28.33
## 4 50 50 35 45.00
## 5 80 20 35 45.00
## 6 35 35 50 40.00
## 7 65 65 65 65.00
## 8 80 50 50 60.00
## 9 50 65 65 60.00
## 10 100 50 65 71.67m = df[,c(23,29,30)]
df$R2arg = round(apply(m, 1, mean), digits=2)
df[1:10,c(23,29,30,39)]## Intro.y Disc.InterpFindings.y Disc.IntegrateLit.y R2arg
## 1 50 50 35 45.00
## 2 80 100 80 86.67
## 3 80 50 65 65.00
## 4 50 50 35 45.00
## 5 35 50 20 35.00
## 6 50 65 80 65.00
## 7 65 100 80 81.67
## 8 0 0 0 0.00
## 9 80 80 80 80.00
## 10 100 80 80 86.67biom2011 = dfload FbU
Fb.use = function(...)
{
file.names <- lapply(biom, function(x){paste0("FbU", x, "DeID.csv")})
FbU.biom <- lapply(file.names, read.csv, header=TRUE, stringsAsFactors=FALSE)
names(FbU.biom) <- biom.names
FbU.biom <<- FbU.biom
}
Fb.use()
list2env(FbU.biom, environment())## <environment: R_GlobalEnv>FbU = rbind(BIOM2011Sem1Report1, BIOM2011Sem2Report1, BIOM2013Sem1Report)
df = FbU
df.studs = round(tapply(df$msec.B4.scroll, df$StudentID, sum, na.rm=T)/60000)
head(df.studs)## S8112187 S8112239 S8239027 S8241553 S8242993 S8282157
## 192 12 31 5 18 8df.studs = as.data.frame(df.studs)
df.studs$StudentID = rownames(df.studs)
rownames(df.studs) = 1:nrow(df.studs)
names(df.studs) = c("Open.min", "StudentID")
FbU.sum = df.studs
df.studs = tapply(df$Duration, df$StudentID, sum, na.rm=T)
df.studs = as.data.frame(df.studs)
df.studs$StudentID = rownames(df.studs)
rownames(df.studs) = 1:nrow(df.studs)
names(df.studs) = c("Audio.min", "StudentID")
FbU.sum = merge(FbU.sum, df.studs, by="StudentID", all.x=T)
head(FbU.sum)## StudentID Open.min Audio.min
## 1 S8112187 192 181.1
## 2 S8112239 12 275.4
## 3 S8239027 31 170.0
## 4 S8241553 5 114.7
## 5 S8242993 18 0.0
## 6 S8282157 8 0.0merge into biom2011
df = biom2011
df = merge(df, FbU.sum, by="StudentID", all.x=T)
biom2011 = dfused already - list
used = c("S8515233", "S8581667", "S8578821", "S8580595", "S8588339", "S8586153", "S8534409", "S8624445", "S8575723", "S8581099", "S8600971")
used## [1] "S8515233" "S8581667" "S8578821" "S8580595" "S8588339" "S8586153"
## [7] "S8534409" "S8624445" "S8575723" "S8581099" "S8600971"used already - into data
df = biom2011
used[1]## [1] "S8515233"which(df[,1] == used[1])## [1] 53Used = NULL
for (i in 1:length(used))
{
Used[i] = which(df[,1] == used[i])
}
df$used = NA
df[1:5,37:41]## Final.Grade.y R1arg R2arg Open.min Audio.min
## 1 61.50 81.67 45.00 192 181.1
## 2 77.25 35.00 65.00 12 275.4
## 3 83.00 60.00 70.00 31 170.0
## 4 68.25 60.00 60.00 5 114.7
## 5 82.25 71.67 86.67 18 0.0for (i in 1:length(Used))
{
df$used[Used[i]] = "Used"
}
df[1:10,c(1,37:41)]## StudentID Final.Grade.y R1arg R2arg Open.min Audio.min
## 1 S8112187 61.50 81.67 45.00 192 181.06
## 2 S8112239 77.25 35.00 65.00 12 275.36
## 3 S8239027 83.00 60.00 70.00 31 169.99
## 4 S8241553 68.25 60.00 60.00 5 114.66
## 5 S8242993 82.25 71.67 86.67 18 0.00
## 6 S8282157 75.75 28.33 65.00 8 0.00
## 7 S8285971 64.25 45.00 60.00 2 31.18
## 8 S8285971 53.00 45.00 45.00 2 31.18
## 9 S8288655 57.75 45.00 35.00 71 0.00
## 10 S8288857 75.50 40.00 65.00 NA NAstud.used = subset(df, used == "Used")
stud.used[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 53 S8515233 3223 T25 100 100
## 93 S8534409 3300 T19 50 50
## 138 S8575723 8718 T09 80 80
## 167 S8578821 3274 T32 50 80
## 199 S8580595 3277 T30 50 80
## 202 S8581099 8742 T09 50 80
## 208 S8581667 3233 T25 80 100
## 250 S8586153 3299 T31 50 100
## 275 S8588339 3283 T31 80 80
## 295 S8600971 8746 T35 50 50
## 306 S8624445 8715 T35 35 50
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 53 80 100 80
## 93 35 35 35
## 138 100 80 100
## 167 50 80 35
## 199 50 80 35
## 202 50 80 20
## 208 65 100 80
## 250 65 100 80
## 275 80 80 80
## 295 50 50 50
## 306 50 50 50
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 53 80 86.67 93.33 381 318.58 Used
## 93 35 40.00 40.00 386 351.18 Used
## 138 100 93.33 86.67 10 203.33 Used
## 167 100 45.00 86.67 54 949.75 Used
## 199 100 45.00 86.67 75 17.97 Used
## 202 80 40.00 80.00 617 895.75 Used
## 208 100 75.00 100.00 NA NA Used
## 250 65 65.00 88.33 1021 481.72 Used
## 275 100 80.00 86.67 885 220.45 Used
## 295 50 50.00 50.00 958 459.76 Used
## 306 50 45.00 50.00 NA NA Usedstud.used[,c(5, 23, 11, 29, 12, 30, 38:42)]## Intro.x Intro.y Disc.InterpFindings.x Disc.InterpFindings.y
## 53 100 100 80 100
## 93 50 50 35 35
## 138 80 80 100 80
## 167 50 80 50 80
## 199 50 80 50 80
## 202 50 80 50 80
## 208 80 100 65 100
## 250 50 100 65 100
## 275 80 80 80 80
## 295 50 50 50 50
## 306 35 50 50 50
## Disc.IntegrateLit.x Disc.IntegrateLit.y R1arg R2arg Open.min
## 53 80 80 86.67 93.33 381
## 93 35 35 40.00 40.00 386
## 138 100 100 93.33 86.67 10
## 167 35 100 45.00 86.67 54
## 199 35 100 45.00 86.67 75
## 202 20 80 40.00 80.00 617
## 208 80 100 75.00 100.00 NA
## 250 80 65 65.00 88.33 1021
## 275 80 100 80.00 86.67 885
## 295 50 50 50.00 50.00 958
## 306 50 50 45.00 50.00 NA
## Audio.min used
## 53 318.58 Used
## 93 351.18 Used
## 138 203.33 Used
## 167 949.75 Used
## 199 17.97 Used
## 202 895.75 Used
## 208 NA Used
## 250 481.72 Used
## 275 220.45 Used
## 295 459.76 Used
## 306 NA Usedbiom2011 = dfcategorising used students - excellent to excellent
df = stud.used
ee.used = subset(df, Intro.x > 79 & Intro.y > 79 & Disc.InterpFindings.x > 79 & Disc.InterpFindings.y > 79 & Disc.IntegrateLit.x > 79 & Disc.IntegrateLit.y > 79)
ee2.used = ee.used[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
df2 = biom2011
ee = subset(df2, Intro.x > 79 & Intro.y > 79 & Disc.InterpFindings.x > 79 & Disc.InterpFindings.y > 79 & Disc.IntegrateLit.x > 79 & Disc.IntegrateLit.y > 79)
ee2 = ee[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
ee2## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 53 S8515233 3223 T25 100 100
## 138 S8575723 8718 T09 80 80
## 176 S8579423 3236 T28 100 100
## 275 S8588339 3283 T31 80 80
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 53 80 100 80
## 138 100 80 100
## 176 80 100 80
## 275 80 80 80
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 53 80 86.67 93.33 381 318.6 Used
## 138 100 93.33 86.67 10 203.3 Used
## 176 100 86.67 100.00 NA NA <NA>
## 275 100 80.00 86.67 885 220.5 Usedee2.used## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 53 S8515233 3223 T25 100 100
## 138 S8575723 8718 T09 80 80
## 275 S8588339 3283 T31 80 80
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 53 80 100 80
## 138 100 80 100
## 275 80 80 80
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 53 80 86.67 93.33 381 318.6 Used
## 138 100 93.33 86.67 10 203.3 Used
## 275 100 80.00 86.67 885 220.5 Useddim(ee2)## [1] 4 14dim(ee2.used)## [1] 3 14Turns out that S8579423 did not look at their feedback. So only 3 students fit the criteria of looking at their feedback and receiving 80-100% for all 3 criteria (Intro and 2x Disc) for both Reports 1 and 2
categorising used students - poor to poor
df = stud.used
pp.used = subset(df, Intro.x < 51 & Intro.y < 51 & Disc.InterpFindings.x < 51 & Disc.InterpFindings.y < 51 & Disc.IntegrateLit.x < 51 & Disc.IntegrateLit.y < 51)
pp2.used = pp.used[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
df2 = biom2011
pp = subset(df2, Intro.x < 51 & Intro.y < 51 & Disc.InterpFindings.x < 51 & Disc.InterpFindings.y < 51 & Disc.IntegrateLit.x < 51 & Disc.IntegrateLit.y < 51)
pp2 = pp[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
pp2## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 8 S8285971 3301 T19 50 50
## 19 S8401995 3356 T09 20 35
## 24 S8411017 3227 T25 35 50
## 25 S8419713 8728 T37 50 35
## 27 S8452823 8769 T30 50 50
## 28 S8456015 3339 T26 50 50
## 29 S8456363 3200 T09 20 35
## 30 S8458433 3359 T32 35 50
## 32 S8465383 3338 T26 20 50
## 36 S8468457 3390 T09 35 35
## 40 S8471835 8736 T36 35 50
## 41 S8472219 8726 T37 35 50
## 42 S8472709 8781 T29 20 20
## 43 S8473277 3288 T30 50 50
## 46 S8475961 3179 T29 50 35
## 71 S8527413 8753 T37 50 50
## 80 S8529621 3251 T30 35 35
## 84 S8530473 3220 T05 35 35
## 87 S8530961 3282 T19 50 50
## 88 S8531381 3298 T31 35 50
## 90 S8532163 3363 T33 35 50
## 93 S8534409 3300 T19 50 50
## 94 S8534971 3306 T32 35 50
## 96 S8535223 3402 T33 20 0
## 100 S8536879 3337 T30 50 35
## 102 S8538157 3332 T09 50 50
## 105 S8548067 3239 T31 20 50
## 107 S8549911 8714 T29 35 35
## 108 S8549911 3389 T33 20 35
## 115 S8563805 3206 T05 20 50
## 119 S8566849 3305 T19 35 35
## 121 S8566867 8749 T29 20 35
## 122 S8567033 3346 T33 35 50
## 124 S8568505 8689 T29 35 35
## 131 S8574613 3624 T19 50 50
## 136 S8575513 8731 T29 50 35
## 147 S8576411 8732 T37 50 50
## 154 S8577287 3336 T33 20 50
## 173 S8579223 3192 T29 50 50
## 177 S8579545 3361 T26 20 20
## 182 S8579733 3237 T31 35 50
## 183 S8579733 8737 T29 35 35
## 195 S8580303 3315 T32 35 50
## 216 S8582201 3202 T29 35 35
## 217 S8582337 8684 T35 50 50
## 233 S8583875 8700 T36 35 35
## 237 S8584681 3182 T29 35 50
## 249 S8586065 9152 T29 20 50
## 271 S8588015 3175 T29 35 50
## 280 S8589129 3878 T28 20 50
## 290 S8594143 3297 T31 20 50
## 295 S8600971 8746 T35 50 50
## 296 S8602821 3181 T29 35 35
## 298 S8605999 8710 T36 50 50
## 301 S8607681 3169 T08 35 35
## 302 S8607681 3169 T08 35 20
## 306 S8624445 8715 T35 35 50
## 315 S8651257 3335 T33 20 50
## 316 S8658567 8702 T29 35 50
## 320 S8668771 8745 T35 20 35
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 8 50 50 35
## 19 0 20 35
## 24 35 50 0
## 25 50 35 35
## 27 35 35 50
## 28 35 20 50
## 29 20 50 20
## 30 20 35 20
## 32 20 50 35
## 36 35 50 35
## 40 50 0 35
## 41 50 50 35
## 42 0 0 0
## 43 0 0 20
## 46 50 50 0
## 71 50 50 50
## 80 35 20 35
## 84 35 50 20
## 87 50 50 35
## 88 35 50 20
## 90 50 50 35
## 93 35 35 35
## 94 20 35 35
## 96 35 0 35
## 100 50 35 35
## 102 0 50 20
## 105 20 20 20
## 107 35 35 0
## 108 35 35 20
## 115 20 20 20
## 119 35 50 35
## 121 35 35 35
## 122 35 50 35
## 124 20 50 20
## 131 50 35 20
## 136 50 50 50
## 147 35 35 35
## 154 20 50 20
## 173 50 50 35
## 177 20 35 0
## 182 20 35 35
## 183 20 35 35
## 195 35 50 20
## 216 20 50 35
## 217 50 50 50
## 233 35 0 35
## 237 35 50 20
## 249 35 0 20
## 271 35 50 20
## 280 20 35 20
## 290 35 50 50
## 295 50 50 50
## 296 35 35 20
## 298 35 35 50
## 301 50 35 50
## 302 50 0 50
## 306 50 50 50
## 315 35 50 35
## 316 50 35 35
## 320 20 50 20
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 8 35 45.00 45.00 2 31.18 <NA>
## 19 35 18.33 30.00 3 0.00 <NA>
## 24 50 23.33 50.00 NA NA <NA>
## 25 35 45.00 35.00 NA NA <NA>
## 27 35 45.00 40.00 12 31.14 <NA>
## 28 35 45.00 35.00 NA NA <NA>
## 29 20 20.00 35.00 48 1870.88 <NA>
## 30 35 25.00 40.00 NA NA <NA>
## 32 50 25.00 50.00 NA NA <NA>
## 36 35 35.00 40.00 165 4530.56 <NA>
## 40 0 40.00 16.67 2 0.00 <NA>
## 41 35 40.00 45.00 1 0.00 <NA>
## 42 0 6.67 6.67 NA NA <NA>
## 43 50 23.33 33.33 1 0.00 <NA>
## 46 35 33.33 40.00 NA NA <NA>
## 71 50 50.00 50.00 NA NA <NA>
## 80 35 35.00 30.00 2 0.00 <NA>
## 84 35 30.00 40.00 16 282.31 <NA>
## 87 50 45.00 50.00 NA NA <NA>
## 88 20 30.00 40.00 2 0.00 <NA>
## 90 50 40.00 50.00 270 284.98 <NA>
## 93 35 40.00 40.00 386 351.18 Used
## 94 35 30.00 40.00 6 0.00 <NA>
## 96 0 30.00 0.00 NA NA <NA>
## 100 50 45.00 40.00 8 280.84 <NA>
## 102 20 23.33 40.00 NA NA <NA>
## 105 0 20.00 23.33 1 0.00 <NA>
## 107 35 23.33 35.00 4 41.47 <NA>
## 108 35 25.00 35.00 4 41.47 <NA>
## 115 35 20.00 35.00 NA NA <NA>
## 119 50 35.00 45.00 34 733.43 <NA>
## 121 20 30.00 30.00 NA NA <NA>
## 122 50 35.00 50.00 NA NA <NA>
## 124 35 25.00 40.00 NA NA <NA>
## 131 50 40.00 45.00 28 604.87 <NA>
## 136 35 50.00 40.00 NA NA <NA>
## 147 35 40.00 40.00 NA NA <NA>
## 154 50 20.00 50.00 18 0.00 <NA>
## 173 50 45.00 50.00 NA NA <NA>
## 177 35 13.33 30.00 30 182.92 <NA>
## 182 20 30.00 35.00 7 0.00 <NA>
## 183 35 30.00 35.00 7 0.00 <NA>
## 195 50 30.00 50.00 19 24.88 <NA>
## 216 50 30.00 45.00 NA NA <NA>
## 217 50 50.00 50.00 4 0.00 <NA>
## 233 35 35.00 23.33 NA NA <NA>
## 237 50 30.00 50.00 NA NA <NA>
## 249 0 25.00 16.67 NA NA <NA>
## 271 50 30.00 50.00 NA NA <NA>
## 280 35 20.00 40.00 1453 1093.14 <NA>
## 290 50 35.00 50.00 16 401.22 <NA>
## 295 50 50.00 50.00 958 459.76 Used
## 296 35 30.00 35.00 NA NA <NA>
## 298 50 45.00 45.00 2 0.00 <NA>
## 301 35 45.00 35.00 5 59.18 <NA>
## 302 20 45.00 13.33 5 59.18 <NA>
## 306 50 45.00 50.00 NA NA Used
## 315 35 30.00 45.00 1329 345.85 <NA>
## 316 35 40.00 40.00 NA NA <NA>
## 320 50 20.00 45.00 17 0.00 <NA>pp2.used## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 93 S8534409 3300 T19 50 50
## 295 S8600971 8746 T35 50 50
## 306 S8624445 8715 T35 35 50
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 93 35 35 35
## 295 50 50 50
## 306 50 50 50
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 93 35 40 40 386 351.2 Used
## 295 50 50 50 958 459.8 Used
## 306 50 45 50 NA NA Useddim(pp2)## [1] 60 14dim(pp2.used)## [1] 3 1460 students fit the criteria of receiving 50% or less for all 3 criteria (Intro and 2x Disc) for both Reports 1 and 2. Not sure how many of these students looked at their feedback. So far have only used 3 poor to poor students
categorising used students - poor to excellent
df = stud.used
pe.used = subset(df, Intro.x < 51 & Intro.y > 79 & Disc.InterpFindings.x < 51 & Disc.InterpFindings.y > 79 & Disc.IntegrateLit.x < 51 & Disc.IntegrateLit.y > 79 )
pe2.used = pe.used[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
df2 = biom2011
pe = subset(df2, Intro.x < 51 & Intro.y > 79 & Disc.InterpFindings.x < 51 & Disc.InterpFindings.y > 79 & Disc.IntegrateLit.x < 51 & Disc.IntegrateLit.y > 79 )
pe2 = pe[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
pe2## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 50 S8497039 3364 T33 20 80
## 167 S8578821 3274 T32 50 80
## 199 S8580595 3277 T30 50 80
## 202 S8581099 8742 T09 50 80
## 235 S8584217 3266 T05 50 80
## 256 S8586659 3218 T31 50 80
## 260 S8587235 3316 T32 50 80
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 50 35 100 35
## 167 50 80 35
## 199 50 80 35
## 202 50 80 20
## 235 50 100 35
## 256 50 80 50
## 260 50 80 35
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 50 100 30 93.33 50 325.84 <NA>
## 167 100 45 86.67 54 949.75 Used
## 199 100 45 86.67 75 17.97 Used
## 202 80 40 80.00 617 895.75 Used
## 235 100 45 93.33 NA NA <NA>
## 256 80 50 80.00 352 598.61 <NA>
## 260 80 45 80.00 NA NA <NA>pe2.used## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 167 S8578821 3274 T32 50 80
## 199 S8580595 3277 T30 50 80
## 202 S8581099 8742 T09 50 80
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 167 50 80 35
## 199 50 80 35
## 202 50 80 20
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 167 100 45 86.67 54 949.75 Used
## 199 100 45 86.67 75 17.97 Used
## 202 80 40 80.00 617 895.75 Useddim(pe2)## [1] 7 14dim(pe2.used)## [1] 3 14Only 7 students fit the criteria of receiving 50% or less for all 3 criteria (Intro and 2x Disc) for Report 1 and then 80-100% for all 3 criteria (Intro and 2x Disc) for Report 2. Not sure how many of these students looked at their feedback. So far have only used 3 poor to excellent students
categorising used students - excellent to poor
df = stud.used
ep.used = subset(df, Intro.x > 79 & Intro.y < 51 & Disc.InterpFindings.x > 79 & Disc.InterpFindings.y < 51 & Disc.IntegrateLit.x > 79 & Disc.IntegrateLit.y < 51 )
ep2.used = ep.used[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
df2 = biom2011
ep = subset(df2, Intro.x > 79 & Intro.y < 51 & Disc.InterpFindings.x > 79 & Disc.InterpFindings.y < 51 & Disc.IntegrateLit.x > 79 & Disc.IntegrateLit.y < 51 )
ep2 = ep[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
ep2## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 125 S8570207 3387 T26 80 35
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 125 80 50 80
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 125 35 80 40 NA NA <NA>ep2.used## [1] StudentID SubmissionID.x MarkerID.x
## [4] Intro.x Intro.y Disc.InterpFindings.x
## [7] Disc.InterpFindings.y Disc.IntegrateLit.x Disc.IntegrateLit.y
## [10] R1arg R2arg Open.min
## [13] Audio.min used
## <0 rows> (or 0-length row.names)dim(ep2)## [1] 1 14dim(ep2.used)## [1] 0 14Only 1 student fits the criteria of receiveing 80-100% for all 3 criteria (Intro and 2x Disc) for Report 1 and then receiving 50% or less for all 3 criteria (Intro and 2x Disc) for Report 2. Not if they looked at their feedback. So far have not used any excellent to poor students
So out of list of 11 students, who is not excellent to excellent, poor to poor, or poor to excellent
stud.ee = ee2.used[,1]
stud.pp = pp2.used[,1]
stud.pe = pe2.used[,1]
df = stud.used
df$category = NA
d = stud.ee
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "ee"
}
d = stud.pp
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "pp"
}
d = stud.pe
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "pe"
}
dim(df)## [1] 11 43which(is.na(df[,43]))## [1] 7 8df[c(7:8),c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 208 S8581667 3233 T25 80 100
## 250 S8586153 3299 T31 50 100
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 208 65 100 80
## 250 65 100 80
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 208 100 75 100.00 NA NA Used
## 250 65 65 88.33 1021 481.7 Usedd = c(df[7,1], df[8,1])
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "improved"
}
df## StudentID SubmissionID.x MarkerID.x Publish.Time.x Intro.x Hypoth.x
## 53 S8515233 3223 T25 29/04/13 14:26 100 80
## 93 S8534409 3300 T19 29/04/13 14:26 50 80
## 138 S8575723 8718 T09 19/09/13 13:26 80 100
## 167 S8578821 3274 T32 29/04/13 14:26 50 80
## 199 S8580595 3277 T30 29/04/13 14:26 50 80
## 202 S8581099 8742 T09 19/09/13 13:26 50 100
## 208 S8581667 3233 T25 30/04/13 10:38 80 100
## 250 S8586153 3299 T31 29/04/13 14:26 50 80
## 275 S8588339 3283 T31 29/04/13 14:26 80 100
## 295 S8600971 8746 T35 19/09/13 13:26 50 100
## 306 S8624445 8715 T35 19/09/13 13:26 35 80
## Methods.x Results.text.x Fig.Tables.x Legend.Title.x
## 53 80 80 100 100
## 93 50 50 50 80
## 138 100 100 65 100
## 167 100 65 100 100
## 199 50 80 80 65
## 202 80 80 100 100
## 208 100 50 100 100
## 250 65 50 35 65
## 275 80 100 100 100
## 295 65 65 65 50
## 306 65 50 65 35
## Disc.InterpFindings.x Disc.IntegrateLit.x Refs.x Kn.PhysMechs.x
## 53 80 80 100 80
## 93 35 35 50 35
## 138 100 100 100 80
## 167 50 35 80 65
## 199 50 35 35 50
## 202 50 20 65 35
## 208 65 80 100 65
## 250 65 80 65 65
## 275 80 80 80 80
## 295 50 50 80 35
## 306 50 50 80 35
## Kn.ExpApproach.x W.Structure.x W.LanguageJargon.x W.GrammarSpelling.x
## 53 65 65 100 100
## 93 50 65 35 50
## 138 80 80 80 100
## 167 35 100 65 100
## 199 50 50 50 50
## 202 50 65 50 65
## 208 65 80 100 100
## 250 80 65 80 65
## 275 65 80 80 80
## 295 65 65 65 65
## 306 65 50 65 65
## Final.Grade.x SubmissionID.y MarkerID.y Publish.Time.y Intro.y
## 53 85.50 4807 T25 4/06/13 13:44 100
## 93 47.75 4871 T19 4/06/13 13:44 50
## 138 90.25 10629 T09 31/10/13 12:04 80
## 167 69.50 4920 T32 4/06/13 13:47 80
## 199 53.00 4916 T30 4/06/13 13:47 80
## 202 59.75 10667 T35 31/10/13 12:04 80
## 208 82.00 4841 T25 4/06/13 13:44 100
## 250 65.00 4887 T31 4/06/13 13:44 100
## 275 83.25 4910 T31 4/06/13 13:44 80
## 295 58.50 10677 T35 31/10/13 12:04 50
## 306 53.00 10652 T35 31/10/13 12:04 50
## Hypoth.y Methods.y Results.text.y Fig.Tables.y Legend.Title.y
## 53 100 100 80 100 100
## 93 65 65 80 20 80
## 138 100 100 65 100 100
## 167 100 80 100 100 100
## 199 80 80 80 100 80
## 202 100 100 100 100 80
## 208 100 100 80 80 100
## 250 100 100 80 80 100
## 275 100 100 100 100 100
## 295 35 80 80 100 100
## 306 35 50 50 100 80
## Disc.InterpFindings.y Disc.IntegrateLit.y Refs.y Kn.PhysMechs.y
## 53 100 80 65 80
## 93 35 35 50 35
## 138 80 100 100 100
## 167 80 100 65 100
## 199 80 100 100 100
## 202 80 80 100 80
## 208 100 100 100 80
## 250 100 65 100 80
## 275 80 100 100 80
## 295 50 50 65 50
## 306 50 50 65 50
## Kn.ExpApproach.y W.Structure.y W.LanguageJargon.y W.GrammarSpelling.y
## 53 100 100 80 100
## 93 50 50 50 50
## 138 100 100 80 100
## 167 100 100 100 80
## 199 100 65 80 80
## 202 80 100 100 80
## 208 80 100 80 100
## 250 100 100 100 80
## 275 80 80 80 80
## 295 50 65 65 50
## 306 50 80 80 80
## Final.Grade.y R1arg R2arg Open.min Audio.min used category
## 53 91.25 86.67 93.33 381 318.58 Used ee
## 93 48.50 40.00 40.00 386 351.18 Used pp
## 138 92.25 93.33 86.67 10 203.33 Used ee
## 167 91.25 45.00 86.67 54 949.75 Used pe
## 199 87.25 45.00 86.67 75 17.97 Used pe
## 202 88.00 40.00 80.00 617 895.75 Used pe
## 208 93.00 75.00 100.00 NA NA Used improved
## 250 90.50 65.00 88.33 1021 481.72 Used improved
## 275 89.00 80.00 86.67 885 220.45 Used ee
## 295 61.00 50.00 50.00 958 459.76 Used pp
## 306 58.50 45.00 50.00 NA NA Used ppstud.used = dfSo 2 studens were ‘improved’ ie average to excellent
So overall that gives Mai 2 students in the ‘improved’ category (2 improved, 3 pe), the only 3 students available who were excellent to excellent (3 ee), and 3 pp.
There are many more students who were poor to poor who could be added There are a few more students who were poor to excellent who could be added
categorising used students - excellent to average
df = stud.used
ea.used = subset(df, Intro.x > 79 & Intro.y < 66 & Disc.InterpFindings.x > 79 & Disc.InterpFindings.y < 66 & Disc.IntegrateLit.x > 79 & Disc.IntegrateLit.y < 66)
ea2.used = ea.used[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
df2 = biom2011
ea = subset(df2, Intro.x > 79 & Intro.y < 66 & Disc.InterpFindings.x > 79 & Disc.InterpFindings.y < 66 & Disc.IntegrateLit.x > 79 & Disc.IntegrateLit.y < 66)
ea2 = ea[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
ea2## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 125 S8570207 3387 T26 80 35
## 148 S8576659 3333 T26 100 65
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 125 80 50 80
## 148 100 65 100
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 125 35 80 40 NA NA <NA>
## 148 65 100 65 19 340.6 <NA>ea2.used## [1] StudentID SubmissionID.x MarkerID.x
## [4] Intro.x Intro.y Disc.InterpFindings.x
## [7] Disc.InterpFindings.y Disc.IntegrateLit.x Disc.IntegrateLit.y
## [10] R1arg R2arg Open.min
## [13] Audio.min used
## <0 rows> (or 0-length row.names)dim(ea2)## [1] 2 14dim(ea2.used)## [1] 0 14categorising used students - average to poor
df = stud.used
ap.used = subset(df, Intro.x > 64 & Intro.y < 51 & Disc.InterpFindings.x > 64 & Disc.InterpFindings.y < 51 & Disc.IntegrateLit.x > 64 & Disc.IntegrateLit.y < 51)
ap2.used = ap.used[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
df2 = biom2011
ap = subset(df2, Intro.x > 64 & Intro.y < 51 & Disc.InterpFindings.x > 64 & Disc.InterpFindings.y < 51 & Disc.IntegrateLit.x > 64 & Disc.IntegrateLit.y < 51)
ap2 = ap[,c(1:3, 5, 23, 11, 29, 12, 30, 38:42)]
ap2## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 1 S8112187 3393 T28 65 50
## 26 S8434511 3259 T31 65 50
## 125 S8570207 3387 T26 80 35
## 231 S8583795 3345 T26 65 50
## 240 S8584831 8716 T09 80 50
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 1 100 50 80
## 26 80 50 80
## 125 80 50 80
## 231 65 35 65
## 240 65 0 65
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min used
## 1 35 81.67 45.00 192 181.1 <NA>
## 26 50 75.00 50.00 26 601.4 <NA>
## 125 35 80.00 40.00 NA NA <NA>
## 231 50 65.00 45.00 0 0.0 <NA>
## 240 20 70.00 23.33 32 0.0 <NA>ap2.used## [1] StudentID SubmissionID.x MarkerID.x
## [4] Intro.x Intro.y Disc.InterpFindings.x
## [7] Disc.InterpFindings.y Disc.IntegrateLit.x Disc.IntegrateLit.y
## [10] R1arg R2arg Open.min
## [13] Audio.min used
## <0 rows> (or 0-length row.names)dim(ap2)## [1] 5 14dim(ap2.used)## [1] 0 14categorising all students
all.ee = ee2[,1]
all.pp = pp2[,1]
all.pe = pe2[,1]
all.ea = ea2[,1]
all.ap = ap2[,1]
df = biom2011
df$category = NA
d = all.ee
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "ee"
}
d = all.pp
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement length
## Warning: number of items to replace is not a multiple of replacement lengthfor (i in 1:length(e))
{
df$category[e[i]] = "pp"
}
d = all.pe
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "pe"
}
d = all.ea
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "ea"
}
d = all.ap
e = NULL
for (i in 1:length(d))
{
e[i] = which(df[,1] == d[i])
}
for (i in 1:length(e))
{
df$category[e[i]] = "ap"
}
dim(df)## [1] 320 43df[1:5,c(38:42)]## R1arg R2arg Open.min Audio.min used
## 1 81.67 45.00 192 181.1 <NA>
## 2 35.00 65.00 12 275.4 <NA>
## 3 60.00 70.00 31 170.0 <NA>
## 4 60.00 60.00 5 114.7 <NA>
## 5 71.67 86.67 18 0.0 <NA>ifelse(is.na(df[,42]), "unused", df[,42]) -> temp
df$used = temp
biom2011 = df
biom2011$category[208] = "improved"
biom2011$category[250] = "improved"subsets of students to send to Mai
df = biom2011
unused.pp = subset(df, used == "unused" & category == "pp" & Open.min > 3)
unused.pp[,c(1:3, 5, 23, 11, 29, 12, 30, 38:41)]## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 27 S8452823 8769 T30 50 50
## 29 S8456363 3200 T09 20 35
## 36 S8468457 3390 T09 35 35
## 84 S8530473 3220 T05 35 35
## 90 S8532163 3363 T33 35 50
## 94 S8534971 3306 T32 35 50
## 100 S8536879 3337 T30 50 35
## 107 S8549911 8714 T29 35 35
## 119 S8566849 3305 T19 35 35
## 131 S8574613 3624 T19 50 50
## 154 S8577287 3336 T33 20 50
## 177 S8579545 3361 T26 20 20
## 182 S8579733 3237 T31 35 50
## 195 S8580303 3315 T32 35 50
## 217 S8582337 8684 T35 50 50
## 280 S8589129 3878 T28 20 50
## 290 S8594143 3297 T31 20 50
## 301 S8607681 3169 T08 35 35
## 315 S8651257 3335 T33 20 50
## 320 S8668771 8745 T35 20 35
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 27 35 35 50
## 29 20 50 20
## 36 35 50 35
## 84 35 50 20
## 90 50 50 35
## 94 20 35 35
## 100 50 35 35
## 107 35 35 0
## 119 35 50 35
## 131 50 35 20
## 154 20 50 20
## 177 20 35 0
## 182 20 35 35
## 195 35 50 20
## 217 50 50 50
## 280 20 35 20
## 290 35 50 50
## 301 50 35 50
## 315 35 50 35
## 320 20 50 20
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min
## 27 35 45.00 40 12 31.14
## 29 20 20.00 35 48 1870.88
## 36 35 35.00 40 165 4530.56
## 84 35 30.00 40 16 282.31
## 90 50 40.00 50 270 284.98
## 94 35 30.00 40 6 0.00
## 100 50 45.00 40 8 280.84
## 107 35 23.33 35 4 41.47
## 119 50 35.00 45 34 733.43
## 131 50 40.00 45 28 604.87
## 154 50 20.00 50 18 0.00
## 177 35 13.33 30 30 182.92
## 182 20 30.00 35 7 0.00
## 195 50 30.00 50 19 24.88
## 217 50 50.00 50 4 0.00
## 280 35 20.00 40 1453 1093.14
## 290 50 35.00 50 16 401.22
## 301 35 45.00 35 5 59.18
## 315 35 30.00 45 1329 345.85
## 320 50 20.00 45 17 0.00unused.pp.stud = unused.pp[,c(1:3,20:21,38:41,43)]
unused.pe = subset(df, used == "unused" & category == "pe" & Open.min > 3)
unused.pe[,c(1:3, 5, 23, 11, 29, 12, 30, 38:41)]## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 50 S8497039 3364 T33 20 80
## 256 S8586659 3218 T31 50 80
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 50 35 100 35
## 256 50 80 50
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min
## 50 100 30 93.33 50 325.8
## 256 80 50 80.00 352 598.6unused.pe.stud = unused.pe[,c(1:3,20:21,38:41,43)]
unused.ea = subset(df, used == "unused" & category == "ea" & Open.min > 3)
unused.ea[,c(1:3, 5, 23, 11, 29, 12, 30, 38:41)]## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 148 S8576659 3333 T26 100 65
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 148 100 65 100
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min
## 148 65 100 65 19 340.6unused.ea.stud = unused.ea[,c(1:3,20:21,38:41,43)]
unused.ap = subset(df, used == "unused" & category == "ap" & Open.min > 3)
unused.ap[,c(1:3, 5, 23, 11, 29, 12, 30, 38:41)]## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 1 S8112187 3393 T28 65 50
## 26 S8434511 3259 T31 65 50
## 240 S8584831 8716 T09 80 50
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 1 100 50 80
## 26 80 50 80
## 240 65 0 65
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min
## 1 35 81.67 45.00 192 181.1
## 26 50 75.00 50.00 26 601.4
## 240 20 70.00 23.33 32 0.0unused.ap.stud = unused.ap[,c(1:3,20:21,38:41,43)]
get.decline = rbind(unused.ea.stud, unused.ap.stud)
get.pp = unused.pp.studSo, have all the excellent to excellent and, have 5 improved just need 5 declined
dim(get.decline)## [1] 4 10get.decline## StudentID SubmissionID.x MarkerID.x SubmissionID.y MarkerID.y R1arg
## 148 S8576659 3333 T26 4971 T34 100.00
## 1 S8112187 3393 T28 5036 T19 81.67
## 26 S8434511 3259 T31 4802 T31 75.00
## 240 S8584831 8716 T09 10621 T09 70.00
## R2arg Open.min Audio.min category
## 148 65.00 19 340.6 ea
## 1 45.00 192 181.1 ap
## 26 50.00 26 601.4 ap
## 240 23.33 32 0.0 apusing get decline
df = biom2011
which(df[,1] == "S8576659")## [1] 148df[148,42] = "new.used"
df[1,42] = "new.used"
df[26,42] = "dud"
df[240,42] = "new.used"
biom2011 = dflooking for more declined
unused.ap2 = subset(df, used == "unused" & category == "ap")
unused.ap2[,c(1:3, 5, 23, 11, 29, 12, 30, 38:41)]## StudentID SubmissionID.x MarkerID.x Intro.x Intro.y
## 125 S8570207 3387 T26 80 35
## 231 S8583795 3345 T26 65 50
## Disc.InterpFindings.x Disc.InterpFindings.y Disc.IntegrateLit.x
## 125 80 50 80
## 231 65 35 65
## Disc.IntegrateLit.y R1arg R2arg Open.min Audio.min
## 125 35 80 40 NA NA
## 231 50 65 45 0 0unused.ap2.stud = unused.ap2[,c(1:3,38:41,43)]
unused.ea2 = subset(df, used == "unused" & category == "ea")
unused.ea2[,c(1:3, 5, 23, 11, 29, 12, 30, 38:41)]## [1] StudentID SubmissionID.x MarkerID.x
## [4] Intro.x Intro.y Disc.InterpFindings.x
## [7] Disc.InterpFindings.y Disc.IntegrateLit.x Disc.IntegrateLit.y
## [10] R1arg R2arg Open.min
## [13] Audio.min
## <0 rows> (or 0-length row.names)unused.ea2.stud = unused.ea2[,c(1:3,38:41,43)]
ep## StudentID SubmissionID.x MarkerID.x Publish.Time.x Intro.x Hypoth.x
## 125 S8570207 3387 T26 29/04/13 14:29 80 100
## Methods.x Results.text.x Fig.Tables.x Legend.Title.x
## 125 50 65 50 65
## Disc.InterpFindings.x Disc.IntegrateLit.x Refs.x Kn.PhysMechs.x
## 125 80 80 80 80
## Kn.ExpApproach.x W.Structure.x W.LanguageJargon.x W.GrammarSpelling.x
## 125 80 80 80 80
## Final.Grade.x SubmissionID.y MarkerID.y Publish.Time.y Intro.y
## 125 75 4942 T34 4/06/13 13:48 35
## Hypoth.y Methods.y Results.text.y Fig.Tables.y Legend.Title.y
## 125 80 50 35 80 35
## Disc.InterpFindings.y Disc.IntegrateLit.y Refs.y Kn.PhysMechs.y
## 125 50 35 80 50
## Kn.ExpApproach.y W.Structure.y W.LanguageJargon.y W.GrammarSpelling.y
## 125 65 80 65 35
## Final.Grade.y R1arg R2arg Open.min Audio.min used
## 125 52.25 80 40 NA NA <NA>and 2 more poor to poor
dim(get.pp)## [1] 20 10get.pp## StudentID SubmissionID.x MarkerID.x SubmissionID.y MarkerID.y R1arg
## 27 S8452823 8769 T30 10646 T29 45.00
## 29 S8456363 3200 T09 4994 T09 20.00
## 36 S8468457 3390 T09 5381 T09 35.00
## 84 S8530473 3220 T05 4858 T05 30.00
## 90 S8532163 3363 T33 4959 T09 40.00
## 94 S8534971 3306 T32 4872 T32 30.00
## 100 S8536879 3337 T30 4961 T30 45.00
## 107 S8549911 8714 T29 10651 T29 23.33
## 119 S8566849 3305 T19 4878 T19 35.00
## 131 S8574613 3624 T19 4879 T19 40.00
## 154 S8577287 3336 T33 4939 T09 20.00
## 177 S8579545 3361 T26 4965 T34 13.33
## 182 S8579733 3237 T31 4819 T31 30.00
## 195 S8580303 3315 T32 4919 T32 30.00
## 217 S8582337 8684 T35 10665 T35 50.00
## 280 S8589129 3878 T28 5022 T28 20.00
## 290 S8594143 3297 T31 4873 T31 35.00
## 301 S8607681 3169 T08 5001 T08 45.00
## 315 S8651257 3335 T33 4947 T33 30.00
## 320 S8668771 8745 T35 10655 T35 20.00
## R2arg Open.min Audio.min category
## 27 40 12 31.14 pp
## 29 35 48 1870.88 pp
## 36 40 165 4530.56 pp
## 84 40 16 282.31 pp
## 90 50 270 284.98 pp
## 94 40 6 0.00 pp
## 100 40 8 280.84 pp
## 107 35 4 41.47 pp
## 119 45 34 733.43 pp
## 131 45 28 604.87 pp
## 154 50 18 0.00 pp
## 177 30 30 182.92 pp
## 182 35 7 0.00 pp
## 195 50 19 24.88 pp
## 217 50 4 0.00 pp
## 280 40 1453 1093.14 pp
## 290 50 16 401.22 pp
## 301 35 5 59.18 pp
## 315 45 1329 345.85 pp
## 320 45 17 0.00 ppunused.pp## StudentID SubmissionID.x MarkerID.x Publish.Time.x Intro.x Hypoth.x
## 27 S8452823 8769 T30 19/09/13 13:26 50 65
## 29 S8456363 3200 T09 30/04/13 10:44 20 65
## 36 S8468457 3390 T09 29/04/13 14:29 35 100
## 84 S8530473 3220 T05 29/04/13 14:26 35 100
## 90 S8532163 3363 T33 29/04/13 14:29 35 80
## 94 S8534971 3306 T32 29/04/13 14:26 35 50
## 100 S8536879 3337 T30 29/04/13 14:31 50 100
## 107 S8549911 8714 T29 19/09/13 13:26 35 65
## 119 S8566849 3305 T19 29/04/13 14:26 35 35
## 131 S8574613 3624 T19 29/04/13 14:26 50 65
## 154 S8577287 3336 T33 29/04/13 14:31 20 35
## 177 S8579545 3361 T26 29/04/13 14:30 20 100
## 182 S8579733 3237 T31 29/04/13 14:29 35 80
## 195 S8580303 3315 T32 29/04/13 14:26 35 80
## 217 S8582337 8684 T35 19/09/13 13:26 50 50
## 280 S8589129 3878 T28 29/04/13 14:29 20 80
## 290 S8594143 3297 T31 29/04/13 14:26 20 65
## 301 S8607681 3169 T08 29/04/13 14:26 35 80
## 315 S8651257 3335 T33 29/04/13 14:31 20 65
## 320 S8668771 8745 T35 19/09/13 13:26 20 50
## Methods.x Results.text.x Fig.Tables.x Legend.Title.x
## 27 50 65 65 50
## 29 35 0 35 35
## 36 80 20 100 100
## 84 80 80 80 100
## 90 50 80 100 80
## 94 35 35 80 35
## 100 65 65 100 100
## 107 65 65 80 35
## 119 35 35 50 50
## 131 50 65 0 0
## 154 35 20 100 50
## 177 80 0 100 65
## 182 50 20 50 80
## 195 80 50 100 100
## 217 80 50 50 35
## 280 80 50 80 100
## 290 50 35 20 20
## 301 65 65 80 80
## 315 100 35 35 50
## 320 50 35 80 65
## Disc.InterpFindings.x Disc.IntegrateLit.x Refs.x Kn.PhysMechs.x
## 27 35 50 50 35
## 29 20 20 50 0
## 36 35 35 20 35
## 84 35 20 80 35
## 90 50 35 80 50
## 94 20 35 35 20
## 100 50 35 35 35
## 107 35 0 50 35
## 119 35 35 50 50
## 131 50 20 65 35
## 154 20 20 35 0
## 177 20 0 20 20
## 182 20 35 35 20
## 195 35 20 35 50
## 217 50 50 65 35
## 280 20 20 20 35
## 290 35 50 50 35
## 301 50 50 35 20
## 315 35 35 65 35
## 320 20 20 50 20
## Kn.ExpApproach.x W.Structure.x W.LanguageJargon.x W.GrammarSpelling.x
## 27 35 50 50 50
## 29 20 50 20 20
## 36 20 80 50 50
## 84 50 65 80 80
## 90 65 65 65 65
## 94 35 100 50 80
## 100 50 50 50 35
## 107 35 50 65 65
## 119 50 50 35 35
## 131 50 50 50 65
## 154 20 35 20 20
## 177 20 50 65 65
## 182 35 80 20 50
## 195 35 80 65 100
## 217 50 50 50 50
## 280 35 50 65 80
## 290 35 65 50 50
## 301 65 50 50 65
## 315 20 50 35 50
## 320 65 35 65 65
## Final.Grade.x SubmissionID.y MarkerID.y Publish.Time.y Intro.y
## 27 48.50 10646 T29 31/10/13 12:04 50
## 29 24.25 4994 T09 4/06/13 13:47 35
## 36 50.75 5381 T09 4/06/13 13:47 35
## 84 58.00 4858 T05 4/06/13 13:48 35
## 90 58.50 4959 T09 4/06/13 13:47 50
## 94 40.50 4872 T32 4/06/13 13:44 50
## 100 54.50 4961 T30 4/06/13 13:47 35
## 107 44.25 10651 T29 31/10/13 12:04 35
## 119 41.00 4878 T19 4/06/13 13:44 35
## 131 42.75 4879 T19 4/06/13 13:44 50
## 154 26.25 4939 T09 4/06/13 13:47 50
## 177 39.25 4965 T34 4/06/13 13:47 20
## 182 39.50 4819 T31 4/06/13 13:44 50
## 195 56.75 4919 T32 4/06/13 13:47 50
## 217 51.50 10665 T35 31/10/13 12:04 50
## 280 47.25 5022 T28 4/06/13 13:47 50
## 290 40.25 4873 T31 4/06/13 13:44 50
## 301 51.50 5001 T08 12/08/13 10:07 35
## 315 44.50 4947 T33 4/06/13 13:47 50
## 320 39.50 10655 T35 31/10/13 12:04 35
## Hypoth.y Methods.y Results.text.y Fig.Tables.y Legend.Title.y
## 27 100 80 50 80 65
## 29 100 80 65 80 50
## 36 100 100 100 100 100
## 84 100 80 100 100 80
## 90 100 80 65 65 35
## 94 100 80 35 80 80
## 100 100 80 80 100 80
## 107 80 50 50 100 50
## 119 100 80 50 80 65
## 131 65 50 50 80 50
## 154 100 80 35 65 50
## 177 65 65 50 50 0
## 182 65 50 80 80 65
## 195 100 80 100 100 100
## 217 100 65 65 80 50
## 280 100 80 50 80 80
## 290 65 50 50 35 65
## 301 100 80 0 100 100
## 315 65 80 35 50 50
## 320 80 50 35 80 50
## Disc.InterpFindings.y Disc.IntegrateLit.y Refs.y Kn.PhysMechs.y
## 27 35 35 65 35
## 29 50 20 80 35
## 36 50 35 65 35
## 84 50 35 80 35
## 90 50 50 100 35
## 94 35 35 35 35
## 100 35 50 65 50
## 107 35 35 65 35
## 119 50 50 65 50
## 131 35 50 80 50
## 154 50 50 50 20
## 177 35 35 50 50
## 182 35 20 35 50
## 195 50 50 65 50
## 217 50 50 50 50
## 280 35 35 35 35
## 290 50 50 65 50
## 301 35 35 35 35
## 315 50 35 80 35
## 320 50 50 80 50
## Kn.ExpApproach.y W.Structure.y W.LanguageJargon.y W.GrammarSpelling.y
## 27 35 50 35 50
## 29 50 80 35 20
## 36 50 80 50 80
## 84 35 65 65 80
## 90 65 100 65 50
## 94 35 80 50 65
## 100 65 50 50 50
## 107 35 50 50 80
## 119 50 65 35 50
## 131 50 50 65 65
## 154 35 80 20 20
## 177 50 50 35 50
## 182 50 65 50 50
## 195 65 100 50 100
## 217 65 50 80 80
## 280 35 50 65 65
## 290 50 65 50 50
## 301 50 50 65 65
## 315 50 50 50 80
## 320 65 80 80 80
## Final.Grade.y R1arg R2arg Open.min Audio.min used category
## 27 52.50 45.00 40 12 31.14 unused pp
## 29 51.75 20.00 35 48 1870.88 unused pp
## 36 63.50 35.00 40 165 4530.56 unused pp
## 84 60.50 30.00 40 16 282.31 unused pp
## 90 60.50 40.00 50 270 284.98 unused pp
## 94 53.25 30.00 40 6 0.00 unused pp
## 100 59.50 45.00 40 8 280.84 unused pp
## 107 48.75 23.33 35 4 41.47 unused pp
## 119 57.00 35.00 45 34 733.43 unused pp
## 131 53.75 40.00 45 28 604.87 unused pp
## 154 48.75 20.00 50 18 0.00 unused pp
## 177 43.00 13.33 30 30 182.92 unused pp
## 182 50.00 30.00 35 7 0.00 unused pp
## 195 69.50 30.00 50 19 24.88 unused pp
## 217 60.00 50.00 50 4 0.00 unused pp
## 280 53.25 20.00 40 1453 1093.14 unused pp
## 290 52.25 35.00 50 16 401.22 unused pp
## 301 52.00 45.00 35 5 59.18 unused pp
## 315 52.25 30.00 45 1329 345.85 unused pp
## 320 56.75 20.00 45 17 0.00 unused ppunused.pp2 = subset(unused.pp, Open.min >30)
dim(unused.pp2)## [1] 6 43unused.pp2## StudentID SubmissionID.x MarkerID.x Publish.Time.x Intro.x Hypoth.x
## 29 S8456363 3200 T09 30/04/13 10:44 20 65
## 36 S8468457 3390 T09 29/04/13 14:29 35 100
## 90 S8532163 3363 T33 29/04/13 14:29 35 80
## 119 S8566849 3305 T19 29/04/13 14:26 35 35
## 280 S8589129 3878 T28 29/04/13 14:29 20 80
## 315 S8651257 3335 T33 29/04/13 14:31 20 65
## Methods.x Results.text.x Fig.Tables.x Legend.Title.x
## 29 35 0 35 35
## 36 80 20 100 100
## 90 50 80 100 80
## 119 35 35 50 50
## 280 80 50 80 100
## 315 100 35 35 50
## Disc.InterpFindings.x Disc.IntegrateLit.x Refs.x Kn.PhysMechs.x
## 29 20 20 50 0
## 36 35 35 20 35
## 90 50 35 80 50
## 119 35 35 50 50
## 280 20 20 20 35
## 315 35 35 65 35
## Kn.ExpApproach.x W.Structure.x W.LanguageJargon.x W.GrammarSpelling.x
## 29 20 50 20 20
## 36 20 80 50 50
## 90 65 65 65 65
## 119 50 50 35 35
## 280 35 50 65 80
## 315 20 50 35 50
## Final.Grade.x SubmissionID.y MarkerID.y Publish.Time.y Intro.y
## 29 24.25 4994 T09 4/06/13 13:47 35
## 36 50.75 5381 T09 4/06/13 13:47 35
## 90 58.50 4959 T09 4/06/13 13:47 50
## 119 41.00 4878 T19 4/06/13 13:44 35
## 280 47.25 5022 T28 4/06/13 13:47 50
## 315 44.50 4947 T33 4/06/13 13:47 50
## Hypoth.y Methods.y Results.text.y Fig.Tables.y Legend.Title.y
## 29 100 80 65 80 50
## 36 100 100 100 100 100
## 90 100 80 65 65 35
## 119 100 80 50 80 65
## 280 100 80 50 80 80
## 315 65 80 35 50 50
## Disc.InterpFindings.y Disc.IntegrateLit.y Refs.y Kn.PhysMechs.y
## 29 50 20 80 35
## 36 50 35 65 35
## 90 50 50 100 35
## 119 50 50 65 50
## 280 35 35 35 35
## 315 50 35 80 35
## Kn.ExpApproach.y W.Structure.y W.LanguageJargon.y W.GrammarSpelling.y
## 29 50 80 35 20
## 36 50 80 50 80
## 90 65 100 65 50
## 119 50 65 35 50
## 280 35 50 65 65
## 315 50 50 50 80
## Final.Grade.y R1arg R2arg Open.min Audio.min used category
## 29 51.75 20 35 48 1870.9 unused pp
## 36 63.50 35 40 165 4530.6 unused pp
## 90 60.50 40 50 270 285.0 unused pp
## 119 57.00 35 45 34 733.4 unused pp
## 280 53.25 20 40 1453 1093.1 unused pp
## 315 52.25 30 45 1329 345.9 unused ppdf2 = unused.pp2
df2[,c(1:3,20:21,38:41,43)]## StudentID SubmissionID.x MarkerID.x SubmissionID.y MarkerID.y R1arg
## 29 S8456363 3200 T09 4994 T09 20
## 36 S8468457 3390 T09 5381 T09 35
## 90 S8532163 3363 T33 4959 T09 40
## 119 S8566849 3305 T19 4878 T19 35
## 280 S8589129 3878 T28 5022 T28 20
## 315 S8651257 3335 T33 4947 T33 30
## R2arg Open.min Audio.min category
## 29 35 48 1870.9 pp
## 36 40 165 4530.6 pp
## 90 50 270 285.0 pp
## 119 45 34 733.4 pp
## 280 40 1453 1093.1 pp
## 315 45 1329 345.9 ppusing pp
df = biom2011
df[36,42] = "new.used"
df[280,42]= "new.used"
biom2011 = dffor Hyab, setting up AcP and FbU for BIOM2013
list2env(AcP.biom, environment())## <environment: R_GlobalEnv>AcP.2013 = BIOM2013Sem1Report
list2env(FbU.biom, environment())## <environment: R_GlobalEnv>FbU.2013 = BIOM2013Sem1Report
AcP.2013[1:3,]## X SubmissionID StudentID MarkerID Publish.Time Summary Results.text
## 1 1 5391 S8587733 T31 5/06/13 10:25 N/A 80
## 2 2 5390 S8577745 N/A N/A N/A NA
## 3 3 5389 S8538305 T31 5/06/13 10:25 N/A 80
## Methods Hypoth Intro Fig.Tables Legend.Title Disc.InterpFindings
## 1 50 100 65 80 65 50
## 2 NA NA NA NA NA NA
## 3 80 100 80 80 100 80
## Disc.IntegrateLit Refs Kn.PhysMechs Kn.ExpApproach W.Structure
## 1 35 80 50 65 65
## 2 NA NA NA NA NA
## 3 65 80 80 80 100
## W.LanguageJargon W.GrammarSpelling Final.Grade
## 1 50 50 59.25
## 2 NA NA NA
## 3 80 65 80.75dim(AcP.2013)## [1] 65 21AcP.2013 = AcP.2013[,2:21]
FbU.2013[1:3,]## X EventID SubmissionID StudentID Consent MarkerID Audio.Annot Log.Type
## 1 1 384337 5442 S8530999 Yes T40 0 2
## 2 2 384338 5442 S8530999 Yes T40 0 2
## 3 3 384339 5442 S8530999 Yes T40 0 2
## Interaction Date State Pages Start End Page.Size
## 1 Automatic 5/06/13 14:41 opened NA NA NA NA
## 2 Annotations 5/06/13 14:42 NA NA NA NA
## 3 Scroll 5/06/13 14:42 NA 0 0 6
## msec.B4.scroll AnnotID From.time Current.time Duration Filename
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 11861 NA NA NA NAdim(FbU.2013)## [1] 4980 21FbU.2013 = FbU.2013[,2:21]analysing FbU
df = FbU.2013
df.studs = round(tapply(df$msec.B4.scroll, df$StudentID, sum, na.rm=T)/60000)
head(df.studs)## S8473219 S8515099 S8515955 S8521469 S8530999 S8533655
## 4 25 8 46 12 2df.studs = as.data.frame(df.studs)
df.studs$StudentID = rownames(df.studs)
rownames(df.studs) = 1:nrow(df.studs)
names(df.studs) = c("Open.min", "StudentID")
FbU.sum = df.studs
df.studs = tapply(df$Duration, df$StudentID, sum, na.rm=T)
df.studs = as.data.frame(df.studs)
df.studs$StudentID = rownames(df.studs)
rownames(df.studs) = 1:nrow(df.studs)
names(df.studs) = c("Audio.min", "StudentID")
FbU.sum = merge(FbU.sum, df.studs, by="StudentID", all.x=T)
head(FbU.sum)## StudentID Open.min Audio.min
## 1 S8473219 4 0.0
## 2 S8515099 25 126.2
## 3 S8515955 8 0.0
## 4 S8521469 46 0.0
## 5 S8530999 12 0.0
## 6 S8533655 2 0.0merge into AcP.2013
df = AcP.2013
df = merge(df, FbU.sum, by="StudentID", all.x=T)
head(df)## StudentID SubmissionID MarkerID Publish.Time Summary Results.text
## 1 S8473219 5439 T39 5/06/13 10:25 N/A 80
## 2 S8515099 5438 T34 5/06/13 10:25 N/A 35
## 3 S8515955 5421 T39 5/06/13 10:25 N/A 80
## 4 S8521007 5388 T42 5/06/13 10:25 N/A 50
## 5 S8521469 5437 T39 5/06/13 10:25 N/A 80
## 6 S8524971 5397 T39 5/06/13 10:25 N/A 50
## Methods Hypoth Intro Fig.Tables Legend.Title Disc.InterpFindings
## 1 80 100 65 80 50 65
## 2 65 80 50 50 0 35
## 3 80 65 65 100 65 65
## 4 65 65 50 35 50 35
## 5 100 65 100 80 100 100
## 6 100 100 35 80 65 50
## Disc.IntegrateLit Refs Kn.PhysMechs Kn.ExpApproach W.Structure
## 1 50 100 50 50 50
## 2 0 50 35 50 50
## 3 65 100 80 65 50
## 4 35 20 35 50 65
## 5 100 100 100 100 65
## 6 50 100 35 65 65
## W.LanguageJargon W.GrammarSpelling Final.Grade Open.min Audio.min
## 1 50 65 64.75 4 0.0
## 2 35 65 41.00 25 126.2
## 3 50 65 71.50 8 0.0
## 4 65 65 47.00 NA NA
## 5 65 65 91.00 46 0.0
## 6 65 65 61.50 NA NAAcP.2013 = dfcategorising for Hyab
df = AcP.2013
excel = subset(df, Disc.InterpFindings > 79 & Disc.IntegrateLit > 79)
poor = subset(df, Disc.InterpFindings < 51 & Disc.IntegrateLit < 51)
dim(excel)## [1] 16 22dim(poor)## [1] 18 22names(excel)## [1] "StudentID" "SubmissionID" "MarkerID"
## [4] "Publish.Time" "Summary" "Results.text"
## [7] "Methods" "Hypoth" "Intro"
## [10] "Fig.Tables" "Legend.Title" "Disc.InterpFindings"
## [13] "Disc.IntegrateLit" "Refs" "Kn.PhysMechs"
## [16] "Kn.ExpApproach" "W.Structure" "W.LanguageJargon"
## [19] "W.GrammarSpelling" "Final.Grade" "Open.min"
## [22] "Audio.min"excel[,c(1:3, 12:17, 20)]## StudentID SubmissionID MarkerID Disc.InterpFindings Disc.IntegrateLit
## 5 S8521469 5437 T39 100 100
## 11 S8538543 5396 T34 80 80
## 14 S8559631 5394 T41 80 100
## 28 S8577745 5399 T31 100 80
## 29 S8577833 5409 T41 100 100
## 31 S8578165 5426 T40 80 80
## 32 S8578203 5418 T41 80 100
## 33 S8578771 5422 T34 80 80
## 34 S8578975 5511 T39 100 100
## 44 S8582787 5429 T34 80 80
## 45 S8583063 5420 T34 100 80
## 48 S8583699 5428 T39 100 80
## 56 S8585719 5398 T34 100 80
## 57 S8586581 5400 T40 80 80
## 58 S8586823 5416 T40 80 80
## 63 S8606467 5406 T40 80 80
## Refs Kn.PhysMechs Kn.ExpApproach W.Structure Final.Grade
## 5 100 100 100 65 91.00
## 11 80 100 100 100 94.00
## 14 100 0 80 65 67.00
## 28 100 100 100 65 86.00
## 29 100 100 100 100 95.25
## 31 65 80 80 65 74.00
## 32 80 0 100 100 81.00
## 33 80 80 80 65 76.50
## 34 100 80 100 80 90.25
## 44 80 50 80 65 65.75
## 45 100 80 80 80 83.50
## 48 100 80 100 80 85.50
## 56 80 100 80 100 93.00
## 57 50 80 80 80 74.00
## 58 80 65 65 80 72.50
## 63 80 65 80 65 75.75excel.all = subset(df, Disc.InterpFindings > 79 & Disc.IntegrateLit > 79 & Kn.PhysMechs > 79 & Kn.ExpApproach > 79 & W.Structure > 79)
dim(excel.all)## [1] 7 22excel.all[,c(1:3, 12:17, 20)]## StudentID SubmissionID MarkerID Disc.InterpFindings Disc.IntegrateLit
## 11 S8538543 5396 T34 80 80
## 29 S8577833 5409 T41 100 100
## 34 S8578975 5511 T39 100 100
## 45 S8583063 5420 T34 100 80
## 48 S8583699 5428 T39 100 80
## 56 S8585719 5398 T34 100 80
## 57 S8586581 5400 T40 80 80
## Refs Kn.PhysMechs Kn.ExpApproach W.Structure Final.Grade
## 11 80 100 100 100 94.00
## 29 100 100 100 100 95.25
## 34 100 80 100 80 90.25
## 45 100 80 80 80 83.50
## 48 100 80 100 80 85.50
## 56 80 100 80 100 93.00
## 57 50 80 80 80 74.00excel.all[,1]## [1] "S8538543" "S8577833" "S8578975" "S8583063" "S8583699" "S8585719"
## [7] "S8586581"poor.all = subset(df, Disc.InterpFindings < 51 & Disc.IntegrateLit < 51 & Kn.PhysMechs < 51 & Kn.ExpApproach < 51 & W.Structure < 51)
dim(poor.all)## [1] 10 22poor.all[,c(1:3, 12:17, 20)]## StudentID SubmissionID MarkerID Disc.InterpFindings Disc.IntegrateLit
## 2 S8515099 5438 T34 35 0
## 7 S8530999 5442 T40 20 0
## 13 S8547061 5462 T31 50 20
## 35 S8579291 5411 T31 50 20
## 42 S8581257 5413 T40 35 35
## 51 S8584709 5443 T39 35 35
## 52 S8584835 5405 T34 35 0
## 55 S8585533 5423 T31 35 20
## 64 S8636887 5441 T40 50 50
## 65 S8640985 5427 T40 50 50
## Refs Kn.PhysMechs Kn.ExpApproach W.Structure Final.Grade
## 2 50 35 50 50 41.00
## 7 0 20 35 20 26.75
## 13 35 50 35 50 47.00
## 35 20 35 50 35 47.00
## 42 50 35 35 50 41.00
## 51 100 20 20 50 49.75
## 52 0 35 50 35 40.00
## 55 50 35 20 50 42.50
## 64 65 50 35 50 47.75
## 65 20 50 50 50 48.25ave.all = subset(df, Disc.InterpFindings < 66 & Disc.IntegrateLit < 66 & Kn.PhysMechs < 66 & Kn.ExpApproach < 66 & W.Structure < 66 & Disc.InterpFindings > 49 & Disc.IntegrateLit > 49 & Kn.PhysMechs > 49 & Kn.ExpApproach > 49 & W.Structure > 49)
dim(ave.all)## [1] 14 22ave.all[,c(1:3, 12:17, 20)]## StudentID SubmissionID MarkerID Disc.InterpFindings Disc.IntegrateLit
## 1 S8473219 5439 T39 65 50
## 16 S8567069 5419 T39 65 50
## 17 S8569817 5461 T39 65 65
## 18 S8572275 5444 T39 65 65
## 19 S8572551 5401 T39 65 50
## 26 S8576839 5415 T34 65 65
## 30 S8577973 5434 T34 65 65
## 36 S8579295 5436 T40 65 65
## 39 S8579941 5407 T40 65 65
## 41 S8580187 5402 T41 50 65
## 47 S8583679 5446 T40 65 50
## 49 S8583863 5430 T34 65 65
## 59 S8587197 5403 T39 65 65
## 65 S8640985 5427 T40 50 50
## Refs Kn.PhysMechs Kn.ExpApproach W.Structure Final.Grade
## 1 100 50 50 50 64.75
## 16 100 65 65 65 73.75
## 17 100 65 65 50 75.00
## 18 100 50 65 50 65.75
## 19 100 50 50 50 64.00
## 26 65 50 50 50 59.25
## 30 65 65 65 65 57.50
## 36 65 65 65 50 68.25
## 39 65 65 65 65 63.50
## 41 100 50 50 50 59.00
## 47 65 50 65 65 65.00
## 49 65 65 65 50 61.50
## 59 100 65 65 65 73.50
## 65 20 50 50 50 48.25using for Hyab
df = AcP.2013
df$used = "unused"
df[2,23] = "used poor"
df[7,23] = "used poor"
df[35,23] = "used poor"
df[1,23] = "used ave"
df[16,23] = "used ave"
df[18,23] = "used ave"
df[11,23] = "used excel"
df[29,23] = "used excel"
df[45,23] = "used excel"
df[57,23] = "used excel"
AcP.2013 = dfchecking new.used students for Mai
names(biom2011)## [1] "StudentID" "SubmissionID.x"
## [3] "MarkerID.x" "Publish.Time.x"
## [5] "Intro.x" "Hypoth.x"
## [7] "Methods.x" "Results.text.x"
## [9] "Fig.Tables.x" "Legend.Title.x"
## [11] "Disc.InterpFindings.x" "Disc.IntegrateLit.x"
## [13] "Refs.x" "Kn.PhysMechs.x"
## [15] "Kn.ExpApproach.x" "W.Structure.x"
## [17] "W.LanguageJargon.x" "W.GrammarSpelling.x"
## [19] "Final.Grade.x" "SubmissionID.y"
## [21] "MarkerID.y" "Publish.Time.y"
## [23] "Intro.y" "Hypoth.y"
## [25] "Methods.y" "Results.text.y"
## [27] "Fig.Tables.y" "Legend.Title.y"
## [29] "Disc.InterpFindings.y" "Disc.IntegrateLit.y"
## [31] "Refs.y" "Kn.PhysMechs.y"
## [33] "Kn.ExpApproach.y" "W.Structure.y"
## [35] "W.LanguageJargon.y" "W.GrammarSpelling.y"
## [37] "Final.Grade.y" "R1arg"
## [39] "R2arg" "Open.min"
## [41] "Audio.min" "used"
## [43] "category"mai = which(biom2011[,42] == "new.used")
biom2011[mai,]## StudentID SubmissionID.x MarkerID.x Publish.Time.x Intro.x Hypoth.x
## 1 S8112187 3393 T28 29/04/13 14:29 65 100
## 36 S8468457 3390 T09 29/04/13 14:29 35 100
## 148 S8576659 3333 T26 29/04/13 14:31 100 100
## 240 S8584831 8716 T09 19/09/13 13:26 80 100
## 280 S8589129 3878 T28 29/04/13 14:29 20 80
## Methods.x Results.text.x Fig.Tables.x Legend.Title.x
## 1 80 65 100 100
## 36 80 20 100 100
## 148 100 100 100 80
## 240 100 100 65 100
## 280 80 50 80 100
## Disc.InterpFindings.x Disc.IntegrateLit.x Refs.x Kn.PhysMechs.x
## 1 100 80 80 100
## 36 35 35 20 35
## 148 100 100 80 100
## 240 65 65 80 50
## 280 20 20 20 35
## Kn.ExpApproach.x W.Structure.x W.LanguageJargon.x W.GrammarSpelling.x
## 1 80 80 80 100
## 36 20 80 50 50
## 148 100 100 80 100
## 240 65 50 65 80
## 280 35 50 65 80
## Final.Grade.x SubmissionID.y MarkerID.y Publish.Time.y Intro.y
## 1 86.75 5036 T19 4/06/13 13:47 50
## 36 50.75 5381 T09 4/06/13 13:47 35
## 148 97.00 4971 T34 4/06/13 13:47 65
## 240 75.25 10621 T09 31/10/13 12:04 50
## 280 47.25 5022 T28 4/06/13 13:47 50
## Hypoth.y Methods.y Results.text.y Fig.Tables.y Legend.Title.y
## 1 80 80 65 80 100
## 36 100 100 100 100 100
## 148 80 80 65 80 80
## 240 100 100 100 100 35
## 280 100 80 50 80 80
## Disc.InterpFindings.y Disc.IntegrateLit.y Refs.y Kn.PhysMechs.y
## 1 50 35 80 50
## 36 50 35 65 35
## 148 65 65 80 80
## 240 0 20 50 35
## 280 35 35 35 35
## Kn.ExpApproach.y W.Structure.y W.LanguageJargon.y W.GrammarSpelling.y
## 1 50 65 65 65
## 36 50 80 50 80
## 148 65 80 65 65
## 240 65 50 65 65
## 280 35 50 65 65
## Final.Grade.y R1arg R2arg Open.min Audio.min used category
## 1 61.50 81.67 45.00 192 181.1 new.used ap
## 36 63.50 35.00 40.00 165 4530.6 new.used pp
## 148 72.50 100.00 65.00 19 340.6 new.used ea
## 240 54.50 70.00 23.33 32 0.0 new.used ap
## 280 53.25 20.00 40.00 1453 1093.1 new.used ppbiom2011[mai,c(1,42:43)]## StudentID used category
## 1 S8112187 new.used ap
## 36 S8468457 new.used pp
## 148 S8576659 new.used ea
## 240 S8584831 new.used ap
## 280 S8589129 new.used ppget.decline## StudentID SubmissionID.x MarkerID.x SubmissionID.y MarkerID.y R1arg
## 148 S8576659 3333 T26 4971 T34 100.00
## 1 S8112187 3393 T28 5036 T19 81.67
## 26 S8434511 3259 T31 4802 T31 75.00
## 240 S8584831 8716 T09 10621 T09 70.00
## R2arg Open.min Audio.min category
## 148 65.00 19 340.6 ea
## 1 45.00 192 181.1 ap
## 26 50.00 26 601.4 ap
## 240 23.33 32 0.0 apmiss = which(biom2011[,1] == "S8434511")
biom2011[miss,]## StudentID SubmissionID.x MarkerID.x Publish.Time.x Intro.x Hypoth.x
## 26 S8434511 3259 T31 29/04/13 14:26 65 100
## Methods.x Results.text.x Fig.Tables.x Legend.Title.x
## 26 65 65 80 100
## Disc.InterpFindings.x Disc.IntegrateLit.x Refs.x Kn.PhysMechs.x
## 26 80 80 80 80
## Kn.ExpApproach.x W.Structure.x W.LanguageJargon.x W.GrammarSpelling.x
## 26 65 100 80 80
## Final.Grade.x SubmissionID.y MarkerID.y Publish.Time.y Intro.y Hypoth.y
## 26 78.5 4802 T31 4/06/13 13:44 50 80
## Methods.y Results.text.y Fig.Tables.y Legend.Title.y
## 26 100 100 100 65
## Disc.InterpFindings.y Disc.IntegrateLit.y Refs.y Kn.PhysMechs.y
## 26 50 50 65 50
## Kn.ExpApproach.y W.Structure.y W.LanguageJargon.y W.GrammarSpelling.y
## 26 80 80 50 50
## Final.Grade.y R1arg R2arg Open.min Audio.min used category
## 26 66 75 50 26 601.4 dud ap