Week 2 - Lecture 2.2
Data
#control
telo.control <- c(0.95, 0.96, 1.07, 1.08, 1.09,
1.1, 1.12, 1.14, 1.18, 1.19,
1.19, 1.2, 1.34, 1.45, 1.49,
1.55)
#Experimental
telo.disturbed <- c(0.83, 0.84, 0.87, 0.93, 0.94,
0.96, 0.99, 1.00, 1.01, 1.01,
1.02, 1.05, 1.07, 1.08, 1.09,
1.09, 1.12, 1.13, 1.16, 1.24,
1.41)
#control Cort
cort.control <-c(6.32, 1.56, 8.61, 4.86, 3.32,
4.31, 1.84, 10.45, 1.47, 4.57,
1.95, 5.93, 8.89, 1.16, NA,
NA)
#experimental cort
cort.disturbed <- c(7.38, 5.39, 5.39, 5.73, 18.35,
0.95, 1.72, 7.26, 1.38, 4.22,
7.52, 12.52, 3.86, 3.12, 1.36,
2.26, 0.85, 4.76, 0.95, 6.92,
2.16)
#W chromosome to determine sex
sex.cntrl <- c("F","M","F","F","F","F","F","M","F","F","M","F","M","M",
"F","F")
#
sex.dist <- c("M", "M", "F", "F", "F", "M", "F", "M", "F", "M", "F",
"F", "M", "F", "M", "M", "F", "M", "F", "M", "M")telo.control## [1] 0.95 0.96 1.07 1.08 1.09 1.10 1.12 1.14 1.18 1.19 1.19 1.20 1.34 1.45 1.49
## [16] 1.55#Tells us what data type this is
is(telo.control)## [1] "numeric" "vector"is.vector(telo.control)## [1] TRUEis.data.frame(telo.control)## [1] FALSElength(telo.control)## [1] 16length(telo.disturbed)## [1] 21##RMarkdown… essentially a word processor Just type into the white and it will just be text and you can just put the r in the grey in between the text
hist(telo.control)
mean(telo.disturbed)## [1] 1.04median(telo.control)## [1] 1.16max(telo.control)## [1] 1.55min(telo.disturbed)## [1] 0.83summary(telo.disturbed)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.83 0.96 1.02 1.04 1.09 1.41hist(telo.control)
boxplot(telo.control)
#Making dataframes #each of these below is a vector and is part ot telo #c operator makes both of these individual operators into a single vector #we then place the combined data into a new thing called telomeres and now that contains the combined vector
telomeres <- c(telo.control , telo.disturbed)
telomeres## [1] 0.95 0.96 1.07 1.08 1.09 1.10 1.12 1.14 1.18 1.19 1.19 1.20 1.34 1.45 1.49
## [16] 1.55 0.83 0.84 0.87 0.93 0.94 0.96 0.99 1.00 1.01 1.01 1.02 1.05 1.07 1.08
## [31] 1.09 1.09 1.12 1.13 1.16 1.24 1.41length(telomeres)## [1] 37is(telomeres)## [1] "numeric" "vector"is.vector(telomeres)## [1] TRUEis.data.frame(telomeres)## [1] FALSE#ls() lists everything in memory
ls()## [1] "cort.control" "cort.disturbed" "sex.cntrl" "sex.dist"
## [5] "telo.control" "telo.disturbed" "telomeres"sex.cntrl ## [1] "F" "M" "F" "F" "F" "F" "F" "M" "F" "F" "M" "F" "M" "M" "F" "F"sex.dist## [1] "M" "M" "F" "F" "F" "M" "F" "M" "F" "M" "F" "F" "M" "F" "M" "M" "F" "M" "F"
## [20] "M" "M"is(sex.cntrl)## [1] "character" "vector" "data.frameRowLabels"
## [4] "SuperClassMethod"is.vector(sex.cntrl)## [1] TRUEsex <- c(sex.cntrl , sex.dist)
sex ## [1] "F" "M" "F" "F" "F" "F" "F" "M" "F" "F" "M" "F" "M" "M" "F" "F" "M" "M" "F"
## [20] "F" "F" "M" "F" "M" "F" "M" "F" "F" "M" "F" "M" "M" "F" "M" "F" "M" "M"length(sex.cntrl) + length(sex.dist)## [1] 37length(sex)## [1] 37length(telomeres) == length(sex)## [1] TRUEtelomeres ## [1] 0.95 0.96 1.07 1.08 1.09 1.10 1.12 1.14 1.18 1.19 1.19 1.20 1.34 1.45 1.49
## [16] 1.55 0.83 0.84 0.87 0.93 0.94 0.96 0.99 1.00 1.01 1.01 1.02 1.05 1.07 1.08
## [31] 1.09 1.09 1.12 1.13 1.16 1.24 1.41sex## [1] "F" "M" "F" "F" "F" "F" "F" "M" "F" "F" "M" "F" "M" "M" "F" "F" "M" "M" "F"
## [20] "F" "F" "M" "F" "M" "F" "M" "F" "F" "M" "F" "M" "M" "F" "M" "F" "M" "M"#must test to make sure that both sides are equal to make aa data frame
df <- data.frame(telomeres,sex)
df## telomeres sex
## 1 0.95 F
## 2 0.96 M
## 3 1.07 F
## 4 1.08 F
## 5 1.09 F
## 6 1.10 F
## 7 1.12 F
## 8 1.14 M
## 9 1.18 F
## 10 1.19 F
## 11 1.19 M
## 12 1.20 F
## 13 1.34 M
## 14 1.45 M
## 15 1.49 F
## 16 1.55 F
## 17 0.83 M
## 18 0.84 M
## 19 0.87 F
## 20 0.93 F
## 21 0.94 F
## 22 0.96 M
## 23 0.99 F
## 24 1.00 M
## 25 1.01 F
## 26 1.01 M
## 27 1.02 F
## 28 1.05 F
## 29 1.07 M
## 30 1.08 F
## 31 1.09 M
## 32 1.09 M
## 33 1.12 F
## 34 1.13 M
## 35 1.16 F
## 36 1.24 M
## 37 1.41 Mis(df)## [1] "data.frame" "list" "oldClass" "vector"is.vector(df)## [1] FALSEis.data.frame(df)## [1] TRUE#dim command gives the dimensions of the "spreadsheet" that we just made
dim(df)## [1] 37 2#length of data frame is # of columns
length(df)## [1] 2#data frames have diensions and length, vectors only have length
#number of rows
nrow(df)## [1] 37#number of columns
ncol(df)## [1] 2#shows head of data frame
head(df)## telomeres sex
## 1 0.95 F
## 2 0.96 M
## 3 1.07 F
## 4 1.08 F
## 5 1.09 F
## 6 1.10 F#Shows bottom of data frame
tail(df)## telomeres sex
## 32 1.09 M
## 33 1.12 F
## 34 1.13 M
## 35 1.16 F
## 36 1.24 M
## 37 1.41 M#compare telomere sets by sex
#put y variable on the left, followed by tilda (~) and x variable on right
#need to have data set up in data frames so boxplot(y ~ x, data = dataframe)
boxplot(telomeres ~ sex, data = df)
length(telo.control)## [1] 16length(telo.disturbed)## [1] 21trt.C <- rep("C", 16)
trt.C## [1] "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C"trt.Cc <- rep("C", length(telo.control))
trt.Cc## [1] "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C" "C"trt.D <- rep("D", 21)
trt.D## [1] "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D"
## [20] "D" "D"trt.Dd <- rep("D", length(telo.disturbed))
trt.Dd## [1] "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D"
## [20] "D" "D"