Rstudio

Make a folder on your computer where you want to keep files to use in Rstudio. Do NOT use Danish characters æ, ø, å in the folder name (or anywhere in the path to the folder).
Set the working directory to this folder: Session -> Set Working Directory -> Choose Directory (shortcut: Ctrl+Shift+H).
Make the change permanent by setting the default directory in: Tools -> Global Options -> Choose Directory.

R extensions

The functionality of R can be extended through libraries or packages (much like plugins in browsers etc.). Some are installed by default in R and you just need to load them.
To install a new package in Rstudio use the menu: Tools -> Install Packages
You need to know the name of the package you want to install. You can also do it through a command:

install.packages("mosaic")

When it is installed you can load it through the library command:

library(mosaic)

This loads the mosaic package which has a lot of convenient functions for this course (we will get back to that later). It also prints a lot of info about functions that have been changed by the mosaic package, but you can safely ignore that.

R help

You get help via ?<command>:

?sum

Use tab to make Rstudio guess what you have started typing.
Search for help:

help.search("plot")

You can find a cheat sheet with the R functions we use for this course here.

Data example

We use data about pengiuns from the R package palmerpenguins

pingviner <- palmerpenguins::penguins
pingviner

## # A tibble: 344 x 8
##    species island    bill_length_mm bill_depth_mm flipp… body… sex    year
##    <fctr>  <fctr>             <dbl>         <dbl>  <int> <int> <fct> <int>
##  1 Adelie  Torgersen           39.1          18.7    181  3750 male   2007
##  2 Adelie  Torgersen           39.5          17.4    186  3800 fema…  2007
##  3 Adelie  Torgersen           40.3          18.0    195  3250 fema…  2007
##  4 Adelie  Torgersen           NA            NA       NA    NA <NA>   2007
##  5 Adelie  Torgersen           36.7          19.3    193  3450 fema…  2007
##  6 Adelie  Torgersen           39.3          20.6    190  3650 male   2007
##  7 Adelie  Torgersen           38.9          17.8    181  3625 fema…  2007
##  8 Adelie  Torgersen           39.2          19.6    195  4675 male   2007
##  9 Adelie  Torgersen           34.1          18.1    193  3475 <NA>   2007
## 10 Adelie  Torgersen           42.0          20.2    190  4250 <NA>   2007
## # ... with 334 more rows

What is fundamentally different about the the variables (columns) species and body_mass_g?

Data types

Quantitative variables

The measurements have numerical values.
Quantative data often comes about in one of the following ways:
- Continuous variables: measurements of time, length, size, age, mass, etc.
- Discrete variables: counts of e.g. words in a text, hits on a webpage, number of arrivals to a queue in one hour, etc.
Measurements like this have a well-defined scale and in R they are stored as the type numeric.
It is important to be able to distinguish between discrete count variables and continuous variables, since this often determines how we describe the uncertainty of a measurement.
Are any of the measurements in our data set quantitative?

Categorical/qualitative variables

The measurement is one of a set of given categories, e.g. sex (male/female), social status, satisfaction score (low/medium/high), etc.
The measurement is usually stored (which is also recommended) as a factor in R. The possible categories are called levels. Example: the levels of the factor “sex” is male/female.
Factors have two so-called scales:
- Nominal scale: There is no natural ordering of the factor levels, e.g. sex and hair color.
- Ordinal scale: There is a natural ordering of the factor levels, e.g. social status and satisfaction score. A factor in R can have a so-called attribute assigned, which tells if it is ordinal.
Are any of the measurements in our data set categorical/qualitative?

Graphics for quantitative variables

Scatterplot

To study the relation between two quantitative variables a scatterplot is used:

gf_point(bill_length_mm ~ bill_depth_mm, color = ~ species, data = pingviner)

We could also draw the graph for each species:

gf_point(bill_length_mm ~ bill_depth_mm | species, color = ~ species, data = pingviner)

If we want a regression line along with the points we can do:

gf_point(bill_length_mm ~ bill_depth_mm, color = ~ species, data = pingviner) %>% 
  gf_lm()

A scatterplot is not very good for a single quantitative variable:

gf_point(1 ~ bill_length_mm, color = ~ species, data = pingviner)

gf_point(species ~ bill_length_mm, color = ~ species, data = pingviner)

Histogram

For a single quantitative variable a histogram offers more details:

gf_histogram( ~ bill_length_mm, data = pingviner)

How to make a histogram for some variable x:
- Divide the interval from the minimum value of x to the maximum value of x in an appropriate number of equal sized sub-intervals.
- Draw a box over each sub-interval with the height being proportional to the number of observations in the sub-interval.

Not great for comparing groups:

gf_histogram( ~ bill_length_mm, color = ~species, data = pingviner)

Boxplot

Boxplot can be good for comparing groups (notice we put the values on the y-axis here as it is more conventional for boxplots):

gf_boxplot(bill_length_mm ~ species, color = ~ species, data = pingviner)

To understand the details of the boxplot we need to introduce percentiles/quantiles and in particular quartiles which can be seen here:

Q <- quantile(bill_length_mm ~ species, data = pingviner, na.rm = TRUE)
Q

##     species 0% 25% 50% 75% 100%
## 1    Adelie 32  37  39  41   46
## 2 Chinstrap 41  46  50  51   58
## 3    Gentoo 41  45  47  50   60

50-percentile is the median and it is a measure of the center of data.
0-percentile is the minimum value.
25-percentile is called the lower quartile (Q1). Median of lower 50% of data.
75-percentile is called the upper quartile (Q3). Median of upper 50% of data.
100-percentil is the maximum value.
Interquartile Range (IQR): a measure of variability given by the difference of the upper and lower quartiles:
Details about how to find/calculate percentiles are postponed to later.

How to draw a box plot

Box:
- Calculate the median, lower and upper quartiles.
- Plot a line by the median and draw a box between the upper and lower quartiles.
Whiskers:
- Calculate interquartile range and call it IQR.
- Calculate the following values:
  - L = lower quartile - 1.5*IQR
  - U = upper quartile + 1.5*IQR
- Draw a line from lower quartile to the smallest measurement, which is larger than L.
- Similarly, draw a line from upper quartile to the largest measurement which is smaller than U.
Outliers: Measurements smaller than L or larger than U are drawn as circles.

Note: Whiskers are minimum and maximum of the observations that are not deemed to be outliers.

Selecting columns/variables

To subset columns of data use select() (automatically loaded from dplyr package by mosaic):

# Assigning the data subset to an object
bill_data <- select(pingviner, bill_length_mm, bill_depth_mm,
                    species, sex, island, year)

# This particular subset can be written shorter by (overwriting the object):
bill_data <- select(pingviner, -flipper_length_mm, -body_mass_g)

Special role of first argument gives rise to this “pipe” (%>%) syntax:

bill_data <- pingviner %>% select(-flipper_length_mm, -body_mass_g)

We read this as: first take the dataset pingviner and then select all columns except flipper_length_mm and body_mass_g.
The resulting dataset doesn’t have flipper length and body mass:

names(bill_data)

## [1] "species"        "island"         "bill_length_mm" "bill_depth_mm" 
## [5] "sex"            "year"

Filtering rows/cases

We use filter() to subset rows/cases. E.g. all penguins from Biscoe islands:

pingviner %>% filter(island == "Biscoe")

## # A tibble: 168 x 8
##    species island bill_length_mm bill_depth_mm flipper… body_… sex    year
##    <fctr>  <fctr>          <dbl>         <dbl>    <int>  <int> <fct> <int>
##  1 Adelie  Biscoe           37.8          18.3      174   3400 fema…  2007
##  2 Adelie  Biscoe           37.7          18.7      180   3600 male   2007
##  3 Adelie  Biscoe           35.9          19.2      189   3800 fema…  2007
##  4 Adelie  Biscoe           38.2          18.1      185   3950 male   2007
##  5 Adelie  Biscoe           38.8          17.2      180   3800 male   2007
##  6 Adelie  Biscoe           35.3          18.9      187   3800 fema…  2007
##  7 Adelie  Biscoe           40.6          18.6      183   3550 male   2007
##  8 Adelie  Biscoe           40.5          17.9      187   3200 fema…  2007
##  9 Adelie  Biscoe           37.9          18.6      172   3150 fema…  2007
## 10 Adelie  Biscoe           40.5          18.9      180   3950 male   2007
## # ... with 158 more rows

All male Gentoo penguins with over 220 mm flippers:

pingviner %>% filter(sex == "male") %>% 
  filter(species == "Gentoo") %>% 
  filter(flipper_length_mm>220)

## # A tibble: 34 x 8
##    species island bill_length_mm bill_depth_mm flipper… body_… sex    year
##    <fctr>  <fctr>          <dbl>         <dbl>    <int>  <int> <fct> <int>
##  1 Gentoo  Biscoe           50.0          16.3      230   5700 male   2007
##  2 Gentoo  Biscoe           49.2          15.2      221   6300 male   2007
##  3 Gentoo  Biscoe           48.7          15.1      222   5350 male   2007
##  4 Gentoo  Biscoe           47.3          15.3      222   5250 male   2007
##  5 Gentoo  Biscoe           59.6          17.0      230   6050 male   2007
##  6 Gentoo  Biscoe           49.6          16.0      225   5700 male   2008
##  7 Gentoo  Biscoe           50.5          15.9      222   5550 male   2008
##  8 Gentoo  Biscoe           50.5          15.9      225   5400 male   2008
##  9 Gentoo  Biscoe           50.1          15.0      225   5000 male   2008
## 10 Gentoo  Biscoe           50.4          15.3      224   5550 male   2008
## # ... with 24 more rows

This could also have been done with a single filter() command (output not shown):

pingviner %>% filter(sex == "male" & species == "Gentoo" & flipper_length_mm>220)

All penguins of species Gentoo or Adelie:

pingviner %>% filter(species == "Gentoo" | species == "Adelie")

## # A tibble: 276 x 8
##    species island    bill_length_mm bill_depth_mm flipp… body… sex    year
##    <fctr>  <fctr>             <dbl>         <dbl>  <int> <int> <fct> <int>
##  1 Adelie  Torgersen           39.1          18.7    181  3750 male   2007
##  2 Adelie  Torgersen           39.5          17.4    186  3800 fema…  2007
##  3 Adelie  Torgersen           40.3          18.0    195  3250 fema…  2007
##  4 Adelie  Torgersen           NA            NA       NA    NA <NA>   2007
##  5 Adelie  Torgersen           36.7          19.3    193  3450 fema…  2007
##  6 Adelie  Torgersen           39.3          20.6    190  3650 male   2007
##  7 Adelie  Torgersen           38.9          17.8    181  3625 fema…  2007
##  8 Adelie  Torgersen           39.2          19.6    195  4675 male   2007
##  9 Adelie  Torgersen           34.1          18.1    193  3475 <NA>   2007
## 10 Adelie  Torgersen           42.0          20.2    190  4250 <NA>   2007
## # ... with 266 more rows

This would be the same as penguins which are not Chinstrap (output not shown):

pingviner %>% filter(species != "Chinstrap")

Arranging rows/cases

We use arrange() to arrange the order of the rows/cases:

pingviner %>% filter(sex == "female") %>% arrange(body_mass_g)

## # A tibble: 165 x 8
##    species   island    bill_length_mm bill_de… flipper… body_… sex    year
##    <fctr>    <fctr>             <dbl>    <dbl>    <int>  <int> <fct> <int>
##  1 Chinstrap Dream               46.9     16.6      192   2700 fema…  2008
##  2 Adelie    Biscoe              36.5     16.6      181   2850 fema…  2008
##  3 Adelie    Biscoe              36.4     17.1      184   2850 fema…  2008
##  4 Adelie    Biscoe              34.5     18.1      187   2900 fema…  2008
##  5 Adelie    Dream               33.1     16.1      178   2900 fema…  2008
##  6 Adelie    Torgersen           38.6     17.0      188   2900 fema…  2009
##  7 Chinstrap Dream               43.2     16.6      187   2900 fema…  2007
##  8 Adelie    Biscoe              37.9     18.6      193   2925 fema…  2009
##  9 Adelie    Dream               37.0     16.9      185   3000 fema…  2007
## 10 Adelie    Dream               37.3     16.8      192   3000 fema…  2009
## # ... with 155 more rows

Use arrange(desc()) for descending values:

pingviner %>% filter(sex == "female") %>% arrange(desc(body_mass_g))

## # A tibble: 165 x 8
##    species island bill_length_mm bill_depth_mm flipper… body_… sex    year
##    <fctr>  <fctr>          <dbl>         <dbl>    <int>  <int> <fct> <int>
##  1 Gentoo  Biscoe           46.5          14.8      217   5200 fema…  2008
##  2 Gentoo  Biscoe           45.2          14.8      212   5200 fema…  2009
##  3 Gentoo  Biscoe           49.1          14.8      220   5150 fema…  2008
##  4 Gentoo  Biscoe           44.9          13.3      213   5100 fema…  2008
##  5 Gentoo  Biscoe           45.1          14.5      207   5050 fema…  2007
##  6 Gentoo  Biscoe           45.1          14.5      215   5000 fema…  2007
##  7 Gentoo  Biscoe           42.9          13.1      215   5000 fema…  2007
##  8 Gentoo  Biscoe           50.5          15.2      216   5000 fema…  2009
##  9 Gentoo  Biscoe           47.2          15.5      215   4975 fema…  2009
## 10 Gentoo  Biscoe           42.6          13.7      213   4950 fema…  2008
## # ... with 155 more rows

Intro and descriptive statistics

Software