1 R data manipulation
This case study servers as a refresher / tutorial on basic input and manipulation of data.
Input a file that contains ALL (acute lymphoblastic leukemia) patient information
fname <- file.choose() ## "ALLphenoData.tsv"
stopifnot(file.exists(fname))
pdata <- read.delim(fname)Check out the help page ?read.delim for input options, and explore basic properties of the object you’ve created, for instance…
class(pdata)## [1] "data.frame"colnames(pdata)## [1] "id" "diagnosis" "sex" "age" "BT"
## [6] "remission" "CR" "date.cr" "t.4.11." "t.9.22."
## [11] "cyto.normal" "citog" "mol.biol" "fusion.protein" "mdr"
## [16] "kinet" "ccr" "relapse" "transplant" "f.u"
## [21] "date.last.seen"dim(pdata)## [1] 127 21head(pdata)## id diagnosis sex age BT remission CR date.cr t.4.11. t.9.22. cyto.normal citog
## 1 1005 5/21/1997 M 53 B2 CR CR 8/6/1997 FALSE TRUE FALSE t(9;22)
## 2 1010 3/29/2000 M 19 B2 CR CR 6/27/2000 FALSE FALSE FALSE simple alt.
## 3 3002 6/24/1998 F 52 B4 CR CR 8/17/1998 NA NA NA <NA>
## 4 4006 7/17/1997 M 38 B1 CR CR 9/8/1997 TRUE FALSE FALSE t(4;11)
## 5 4007 7/22/1997 M 57 B2 CR CR 9/17/1997 FALSE FALSE FALSE del(6q)
## 6 4008 7/30/1997 M 17 B1 CR CR 9/27/1997 FALSE FALSE FALSE complex alt.
## mol.biol fusion.protein mdr kinet ccr relapse transplant f.u date.last.seen
## 1 BCR/ABL p210 NEG dyploid FALSE FALSE TRUE BMT / DEATH IN CR <NA>
## 2 NEG <NA> POS dyploid FALSE TRUE FALSE REL 8/28/2000
## 3 BCR/ABL p190 NEG dyploid FALSE TRUE FALSE REL 10/15/1999
## 4 ALL1/AF4 <NA> NEG dyploid FALSE TRUE FALSE REL 1/23/1998
## 5 NEG <NA> NEG dyploid FALSE TRUE FALSE REL 11/4/1997
## 6 NEG <NA> NEG hyperd. FALSE TRUE FALSE REL 12/15/1997summary(pdata$sex)## F M NA's
## 42 83 2summary(pdata$cyto.normal)## Mode FALSE TRUE NA's
## logical 69 24 34Remind yourselves about various ways to subset and access columns of a data.frame
pdata[1:5, 3:4]## sex age
## 1 M 53
## 2 M 19
## 3 F 52
## 4 M 38
## 5 M 57pdata[1:5, ]## id diagnosis sex age BT remission CR date.cr t.4.11. t.9.22. cyto.normal citog mol.biol
## 1 1005 5/21/1997 M 53 B2 CR CR 8/6/1997 FALSE TRUE FALSE t(9;22) BCR/ABL
## 2 1010 3/29/2000 M 19 B2 CR CR 6/27/2000 FALSE FALSE FALSE simple alt. NEG
## 3 3002 6/24/1998 F 52 B4 CR CR 8/17/1998 NA NA NA <NA> BCR/ABL
## 4 4006 7/17/1997 M 38 B1 CR CR 9/8/1997 TRUE FALSE FALSE t(4;11) ALL1/AF4
## 5 4007 7/22/1997 M 57 B2 CR CR 9/17/1997 FALSE FALSE FALSE del(6q) NEG
## fusion.protein mdr kinet ccr relapse transplant f.u date.last.seen
## 1 p210 NEG dyploid FALSE FALSE TRUE BMT / DEATH IN CR <NA>
## 2 <NA> POS dyploid FALSE TRUE FALSE REL 8/28/2000
## 3 p190 NEG dyploid FALSE TRUE FALSE REL 10/15/1999
## 4 <NA> NEG dyploid FALSE TRUE FALSE REL 1/23/1998
## 5 <NA> NEG dyploid FALSE TRUE FALSE REL 11/4/1997head(pdata[, 3:5])## sex age BT
## 1 M 53 B2
## 2 M 19 B2
## 3 F 52 B4
## 4 M 38 B1
## 5 M 57 B2
## 6 M 17 B1tail(pdata[, 3:5], 3)## sex age BT
## 125 M 19 T2
## 126 M 30 T3
## 127 M 29 T2head(pdata$age)## [1] 53 19 52 38 57 17head(pdata$sex)## [1] M M F M M M
## Levels: F Mhead(pdata[pdata$age > 21,])## id diagnosis sex age BT remission CR date.cr t.4.11. t.9.22. cyto.normal citog
## 1 1005 5/21/1997 M 53 B2 CR CR 8/6/1997 FALSE TRUE FALSE t(9;22)
## 3 3002 6/24/1998 F 52 B4 CR CR 8/17/1998 NA NA NA <NA>
## 4 4006 7/17/1997 M 38 B1 CR CR 9/8/1997 TRUE FALSE FALSE t(4;11)
## 5 4007 7/22/1997 M 57 B2 CR CR 9/17/1997 FALSE FALSE FALSE del(6q)
## 10 8001 1/15/1997 M 40 B2 CR CR 3/26/1997 FALSE FALSE FALSE del(p15)
## 11 8011 8/21/1998 M 33 B3 CR CR 10/8/1998 FALSE FALSE FALSE del(p15/p16)
## mol.biol fusion.protein mdr kinet ccr relapse transplant f.u date.last.seen
## 1 BCR/ABL p210 NEG dyploid FALSE FALSE TRUE BMT / DEATH IN CR <NA>
## 3 BCR/ABL p190 NEG dyploid FALSE TRUE FALSE REL 10/15/1999
## 4 ALL1/AF4 <NA> NEG dyploid FALSE TRUE FALSE REL 1/23/1998
## 5 NEG <NA> NEG dyploid FALSE TRUE FALSE REL 11/4/1997
## 10 BCR/ABL p190 NEG <NA> FALSE TRUE FALSE REL 7/11/1997
## 11 BCR/ABL p190/p210 NEG dyploid FALSE FALSE TRUE BMT / DEATH IN CR <NA>It seems from below that there are 17 females over 40 in the data set, but when sub-setting pdata to contain just those individuals 19 rows are selected. Why? What can we do to correct this?
idx <- pdata$sex == "F" & pdata$age > 40
table(idx)## idx
## FALSE TRUE
## 108 17dim(pdata[idx,])## [1] 19 21Use the mol.biol column to subset the data to contain just individuals with ‘BCR/ABL’ or ‘NEG’, e.g.,
bcrabl <- pdata[pdata$mol.biol %in% c("BCR/ABL", "NEG"),]The mol.biol column is a factor, and retains all levels even after subsetting. How might you drop the unused factor levels?
bcrabl$mol.biol <- factor(bcrabl$mol.biol)The BT column is a factor describing B- and T-cell subtypes
levels(bcrabl$BT)## [1] "B" "B1" "B2" "B3" "B4" "T" "T1" "T2" "T3" "T4"How might one collapse B1, B2, … to a single type B, and likewise for T1, T2, …, so there are only two subtypes, B and T
table(bcrabl$BT)##
## B B1 B2 B3 B4 T T1 T2 T3 T4
## 4 9 35 22 9 4 1 15 9 2levels(bcrabl$BT) <- substring(levels(bcrabl$BT), 1, 1)
table(bcrabl$BT)##
## B T
## 79 31Use xtabs() (cross-tabulation) to count the number of samples with B- and T-cell types in each of the BCR/ABL and NEG groups
xtabs(~ BT + mol.biol, bcrabl)## mol.biol
## BT BCR/ABL NEG
## B 37 42
## T 0 31Use aggregate() to calculate the average age of males and females in the BCR/ABL and NEG treatment groups.
aggregate(age ~ mol.biol + sex, bcrabl, mean)## mol.biol sex age
## 1 BCR/ABL F 39.93750
## 2 NEG F 30.42105
## 3 BCR/ABL M 40.50000
## 4 NEG M 27.21154Use t.test() to compare the age of individuals in the BCR/ABL versus NEG groups; visualize the results using boxplot(). In both cases, use the formula interface. Consult the help page ?t.test and re-do the test assuming that variance of ages in the two groups is identical. What parts of the test output change?
t.test(age ~ mol.biol, bcrabl)##
## Welch Two Sample t-test
##
## data: age by mol.biol
## t = 4.8172, df = 68.529, p-value = 8.401e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 7.13507 17.22408
## sample estimates:
## mean in group BCR/ABL mean in group NEG
## 40.25000 28.07042boxplot(age ~ mol.biol, bcrabl)
