Preliminary practice

## Instruction

This document is interactive! You don’t need to start Rstudio to run the commands, intead, you just need to enter your commands in interactive displays such as this one just below. Try any command (e.g. 1+1), press Run code and see what happens.

In this documents, we give you the solution: click on the button “Solution” to reveal (a proposition of) solution. In the next interactive display, compute the square of 2 ($$2^2$$) and pu the result in an object called x.

x <- 2^2

Sometimes, we also give you a hint, and then the solution: click on the button “Hints” to reveal the hint, and on the button “Next hint” to reveal the solution. In the next interactive display, compute the square-root of 2 ($$\sqrt{2}$$) and put the result in a object called y.

?sqrt
y <- sqrt(2)

NB: anytime, you can clear your work using by clicking “Start Over” (in the left panel, below section titles).

## 1. Import and handle data

### Do you remember this ?

We want to import the file BirthWeight.txt (in the folder data) which is tab delimited:

dat1 <- read.table("BirthWeight.txt", header = T)

We then check whether it was correctly imported using the function head() (NB: you might also want to use the function View() in a more interactive fashion).

Tip 1: read.table() may handle various field delimiters such as “;” or “,”. They may be specified as follow: read.table(“my_file.txt”, sep = “,”)

Tip 2: if your files are genuine .csv files, you may import them using read.csv() [when fields are delimited by “,”] or read.csv2() [when fields are delimited by “;”]

Tip 3: you may also directly import .xlsx (or .xls) files by using ad hoc functions such as read.xls() in the package gdata

### R commands

What is the class of dat1?

dat1
?class
class(dat1)

What is the structure of dat1?

dat1
?str
str(dat1)

What are the variable names (i.e. the columns) in dat1?

dat1
?colnames
colnames(dat1)

Call the variable bw with the $ syntax. dat1 dat1$bw

Call the variable bw with the ["NAME_OF_THE_VARIABLE"] syntax.

dat1
dat1[, "bw"]

Select values in bw higher or equal to 2000, and put the result in an object called sel_dat1.

dat1
sel_dat1 <- dat1$bw[dat1$bw >= 2000]

Finally, subset the data frame such that it contains only values of $$bw >= 2000$$ and $$bpd >= 90$$ and exclude the 4th column (ID number), and put the result in an object called sub_dat1.

dat1
sub_dat1 <- dat1[dat1$bw >= 2000 & dat1$bpd >= 90, c("bw", "bpd", "ad")]

## 2. Very basic commands in statistics

### Commands in R

Calculate the quantile of order 0.975 from a Gaussian distribution (mean = 0, standard-deviation = 1)

0.975
?qnorm
qnorm(0.975)

Now, could you calculate quantile of order 0.975 from a Gaussian distribution of mean = 3 and standard-deviation = 5)?

0.975
?qnorm
qnorm(0.975, mean = 3, sd = 5)

Make a histogram of the distribution of bpd (in dataset dat1).

dat1
?hist
hist(dat1$bpd) Compute the average, the median, the standard deviation as well as the 25% and 75% empirical quartiles for the distribution of bpd: dat1$bpd
# average
mean(dat1$bpd) # median median(dat1$bpd)
# standard deviation
sd(dat1$bpd) # average quantile(dat1$bpd, c(0.25, 0.75))

Transform the ad variable, wich is continuous, into two classes: small, $$<= 100$$ and large $$> 100$$ and create a factor object called ad_categories.

dat1$ad ?cut ad_categories <- cut(dat1$ad, c(-Inf, 100, Inf), labels = c("small", "large"))
ad_categories <- cut(dat1$ad, c(-Inf, 100, Inf), labels = c("small", "large")) Add the factor ad_categories to the existing data frame, as a new column (also) called ad_categories. ad_categories "Remember the syntax df$new_column <- new_object"
dat1$ad_categories <- factor(ad_categories) Finally, make a boxplot of the ad as a function of the latter classes (i.e., small and large). ad_categories <- cut(dat1$ad, c(-Inf, 100, Inf), labels = c("small", "large"))
dat1$ad_categories <- ad_categories dat1$bw
?boxplot
boxplot(bw ~ ad_categories, data = dat1)

### To go further

Optional (1): you might want to make a fancier histogram (of the distribution of bpd) using the ggplot2 package (!) Tweak the code below to make it fit our dataset and objects:

ggplot(data = your_data_frame, aes(x = your_variable_of_interest)) + # creates a ggplot object
geom_histogram(binwidth = 5, fill = "steelblue", col = "steelblue4") +  # add the histogram
ggtitle("Distribution of bpd values") # add a title
ggplot(data = dat1, aes(x = bpd)) +
geom_histogram(binwidth = 5, fill = "steelblue", col = "steelblue4") +
ggtitle("Distribution of bpd values")

Optional (2): you might want to make a fancier boxplot (of bw as a function of the classes “small” and “large” ad) using the ggplot2 package (!):

ad_categories <- cut(dat1$ad, c(-Inf, 100, Inf), labels = c("small", "large")) dat1$ad_categories <- ad_categories
ggplot(data = your_data_frame, aes(x = your_categories, y = your_variable_of_interest, fill = your_categories)) + # creates a ggplot object
geom_boxplot(outlier.shape = NA) +  # creates boxplots
geom_jitter(height = 0, width = 0.1) + # add dots on the top of it
ggtitle("Distribution of Birth weight as a function of classes of abdominal diameter") + # add a tittle
xlab("Abdominal diameter") + # add a x-axis label
ylab("Weight at birth") + # add a y-axis label
theme_classic() # use a simple background 
ggplot(data = dat1, aes(x = ad_categories, y = bw, fill = ad_categories)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(height = 0, width = 0.1) +
ggtitle("Distribution of Birth weight as a function of classes of abdominal diameter") +
xlab("Abdominal diameter") +
ylab("Weight at birth") +
theme_classic()