Computational Biology & Data Science - Lecture 2
Yasin Kaymaz
2022-11-06
Creating sequences
s4 <- seq(length=10, from=0, by=1)
s4## [1] 0 1 2 3 4 5 6 7 8 9s4 <- seq(length=51, from=-5, by=.2)
s4## [1] -5.0 -4.8 -4.6 -4.4 -4.2 -4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2
## [16] -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
## [31] 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8
## [46] 4.0 4.2 4.4 4.6 4.8 5.0Repeating items
rep(3, time=2)## [1] 3 3x <- c('a', '10')
s5 <- rep(x, times=5)
s5## [1] "a" "10" "a" "10" "a" "10" "a" "10" "a" "10"s6 <- rep(x, each=5)
s6## [1] "a" "a" "a" "a" "a" "10" "10" "10" "10" "10"Handling missing data
1st kind of missing data: ‘Not Available’ or ‘missing value’
z <- c(1:3, NA)
z## [1] 1 2 3 NAind <- is.na(z)
ind## [1] FALSE FALSE FALSE TRUE2nd kind of missing data is produced by numerical computation: Not a Number, NaN
0/0## [1] NaNInf - Inf## [1] NaNIn summary, is.na(xx) is TRUE both for NA and NaN values. To differentiate these, is.nan(xx) is only TRUE for NaNs.
Subsetting data frames
Working with a built-in dataset, mtcars***
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2mtcars[10, 4]## [1] 123mtcars['Merc 280', 'hp']## [1] 123mtcars["Merc 280", ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4mtcars[["hp"]]## [1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
## [20] 65 97 150 150 245 175 66 91 113 264 175 335 109mtcars$hp## [1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
## [20] 65 97 150 150 245 175 66 91 113 264 175 335 109nrow(mtcars)## [1] 32ncol(mtcars)## [1] 11mtcars[, c("mpg", "hp")]## mpg hp
## Mazda RX4 21.0 110
## Mazda RX4 Wag 21.0 110
## Datsun 710 22.8 93
## Hornet 4 Drive 21.4 110
## Hornet Sportabout 18.7 175
## Valiant 18.1 105
## Duster 360 14.3 245
## Merc 240D 24.4 62
## Merc 230 22.8 95
## Merc 280 19.2 123
## Merc 280C 17.8 123
## Merc 450SE 16.4 180
## Merc 450SL 17.3 180
## Merc 450SLC 15.2 180
## Cadillac Fleetwood 10.4 205
## Lincoln Continental 10.4 215
## Chrysler Imperial 14.7 230
## Fiat 128 32.4 66
## Honda Civic 30.4 52
## Toyota Corolla 33.9 65
## Toyota Corona 21.5 97
## Dodge Challenger 15.5 150
## AMC Javelin 15.2 150
## Camaro Z28 13.3 245
## Pontiac Firebird 19.2 175
## Fiat X1-9 27.3 66
## Porsche 914-2 26.0 91
## Lotus Europa 30.4 113
## Ford Pantera L 15.8 264
## Ferrari Dino 19.7 175
## Maserati Bora 15.0 335
## Volvo 142E 21.4 109Using which() function to subset data frame. The which()
function in R returns the position or the index of the value which
satisfies the given condition.
#subset data frame based on a value threshold of certain column
#only print cars that are higher than 20 mpg
mtcars[which(mtcars$mpg > 20), ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Another dataset ToothGrowth
The tooth growth data set is the length of the odontoblasts (teeth) in each of 10 guinea pigs at three Vitamin C dosage levels (0.5, 1, and 2 mg) with two delivery methods (orange juice or ascorbic acid).
data(ToothGrowth)
#To view top 6 rows of the data
head(ToothGrowth)## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5The data contains 60 observations of 3 variables
- len : Tooth length
- supp : Supplement type (VC or OJ)
- dose : Dose in milligrams
#To simplify the variable name
tg <- ToothGrowthMore basic R functions
#dimensions of the data
dim(tg) # returns number of rows and colums## [1] 60 3nrow(tg)# returns number of rows## [1] 60ncol(tg)# returns number of colums## [1] 3#Summary statistics
summary(tg)## len supp dose
## Min. : 4.20 OJ:30 Min. :0.500
## 1st Qu.:13.07 VC:30 1st Qu.:0.500
## Median :19.25 Median :1.000
## Mean :18.81 Mean :1.167
## 3rd Qu.:25.27 3rd Qu.:2.000
## Max. :33.90 Max. :2.000Basic statistical functions
#Average of all given values
mean(tg$len)## [1] 18.81333#Standard deviation of all given values
sd(tg$len)## [1] 7.649315Basic plotting to observe the trend
plot(tg[ ,"dose"], tg[, "len"])
#or plot(tg$dose, tg$len)Welcome to tidyverse
For more practises: https://m-clark.github.io/data-processing-and-visualization/tidyverse.html
Load tidyverse library
library(tidyverse)tg %>% head()## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5tg %>% dim()## [1] 60 3tg %>% select(dose) %>% head()## dose
## 1 0.5
## 2 0.5
## 3 0.5
## 4 0.5
## 5 0.5
## 6 0.5tg %>% select(len) %>% head()## len
## 1 4.2
## 2 11.5
## 3 7.3
## 4 5.8
## 5 6.4
## 6 10.0tg %>% filter(supp == "VC")## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5
## 7 11.2 VC 0.5
## 8 11.2 VC 0.5
## 9 5.2 VC 0.5
## 10 7.0 VC 0.5
## 11 16.5 VC 1.0
## 12 16.5 VC 1.0
## 13 15.2 VC 1.0
## 14 17.3 VC 1.0
## 15 22.5 VC 1.0
## 16 17.3 VC 1.0
## 17 13.6 VC 1.0
## 18 14.5 VC 1.0
## 19 18.8 VC 1.0
## 20 15.5 VC 1.0
## 21 23.6 VC 2.0
## 22 18.5 VC 2.0
## 23 33.9 VC 2.0
## 24 25.5 VC 2.0
## 25 26.4 VC 2.0
## 26 32.5 VC 2.0
## 27 26.7 VC 2.0
## 28 21.5 VC 2.0
## 29 23.3 VC 2.0
## 30 29.5 VC 2.0tg %>% filter(supp == "VC") %>% filter(dose == 2.0)## len supp dose
## 1 23.6 VC 2
## 2 18.5 VC 2
## 3 33.9 VC 2
## 4 25.5 VC 2
## 5 26.4 VC 2
## 6 32.5 VC 2
## 7 26.7 VC 2
## 8 21.5 VC 2
## 9 23.3 VC 2
## 10 29.5 VC 2# Summarize by dose and supp, the mean length of growth.
tg %>%
group_by(supp, dose) %>%
summarize(lenmean=mean(len), lensd=sd(len), count = n())## `summarise()` has grouped output by 'supp'. You can override using the
## `.groups` argument.## # A tibble: 6 × 5
## # Groups: supp [2]
## supp dose lenmean lensd count
## <fct> <dbl> <dbl> <dbl> <int>
## 1 OJ 0.5 13.2 4.46 10
## 2 OJ 1 22.7 3.91 10
## 3 OJ 2 26.1 2.66 10
## 4 VC 0.5 7.98 2.75 10
## 5 VC 1 16.8 2.52 10
## 6 VC 2 26.1 4.80 10# Summarize by supp only.
tg %>%
group_by(supp) %>%
summarize(lenmean=mean(len), lensd=sd(len), count = n())## # A tibble: 2 × 4
## supp lenmean lensd count
## <fct> <dbl> <dbl> <int>
## 1 OJ 20.7 6.61 30
## 2 VC 17.0 8.27 30# Summarize by dose only.
tg %>%
group_by(dose) %>%
summarize(lenmean=mean(len), lensd=sd(len), count = n())## # A tibble: 3 × 4
## dose lenmean lensd count
## <dbl> <dbl> <dbl> <int>
## 1 0.5 10.6 4.50 20
## 2 1 19.7 4.42 20
## 3 2 26.1 3.77 20Visualization with ggplot
Best practices with ggplot: http://www.sthda.com/english/wiki/be-awesome-in-ggplot2-a-practical-guide-to-be-highly-effective-r-software-and-data-visualization
# Plot the length (y) by the dosage (x)
g <- ggplot(tg, aes(x= dose, y= len)) +
geom_point(aes(color=supp))
print(g)
More built-in datasets for future practises
/ ***
DNase Elisa assay of DNase
Orange Growth of Orange Trees
PlantGrowth Results from an Experiment on Plant Growth
Puromycin Reaction Velocity of an Enzymatic Reaction
Theoph Pharmacokinetics of Theophylline
ToothGrowth The Effect of Vitamin C on Tooth Growth in Guinea Pigs
esoph Smoking, Alcohol and (O)esophageal Cancer
fdeaths (UKLungDeaths) Monthly Deaths from Lung Diseases in the UK
iris Edgar Anderson’s Iris Data
sleep Student’s Sleep Data
trees Diameter, Height and Volume for Black Cherry Trees