In LUMC/rcourse: R Open Online Course (ROOC)

# library(rooc)
# source("../R/Doc.R")
# source("../R/Globals.R")

pulse <- read_pulse()

Change layout of values in a table

Source: data import cheat sheet : tidyr

Two main functions to manipulate the layout of the table, pivot_longer transforms the table from wide to long format and pivot_wider, which does the opposite, converts the table from long to wide format.

`pivot_longer` : wide to long format

This function allows collapsing 'similar' variables into one variable while guaranteeing the data set's consistency. For example take the variables pulse1 and pulse2 in the following subset of the pulse data set:

pulses <- pulse  %>%  
      select(name,pulse1,pulse2)  %>% 
      head(3) 
pulses

We can transform the table as such that all pulse values are under a single variable, say pulse:

dfLong <- pulses %>% pivot_longer(c(pulse1, pulse2), names_to = "pulse", values_to = "level")
dfLong

This is called the long version of the original (wide) table and contains the same information.

Alternatively you achieve the same result using ! (negation operator):

dfLong <- pulses %>% pivot_longer(!name, names_to = "pulse", values_to = "level")
dfLong

Can you find ohter variables in the pulse data set which can be transformed to long format?

msg <- "Yes, for example smokes and alcohol, both are categorical with values {yes,no}."
qa(msg)

Let's reshape the pulse dataset with variables drug = {smokes,alcohol} and use = {yes,no}.

pulse %>%  pivot_longer(c(smokes,alcohol), names_to = "drug", values_to = "use")

`pivot_wider` : long to wide format

dfWide <- dfLong %>% pivot_wider(names_from = "pulse", values_from = "level")
dfWide

Below, pulses tibble has been transformed into pulses2. What can you say about this transformation?

pulses2 <- pulses %>% 
           pivot_longer(!name, names_to = "pulse", values_to = "level") %>% 
           pivot_wider(names_from = "pulse", values_from = "level")

msg <- "pulses and pulses2 are identical"
qa(msg)

ggplot and reshaping

Often you will need to reshape your data for ggplot visualisations. For example we would like to compare pulse1 and pulse2 variables with a boxplot. You may plot pulse1 and pulse2 in separate plots one after the other:

require(gridExtra)  # Make sure gridExtra is installed with install.packages("gridExtra").

bp1 <- ggplot(pulse %>% drop_na()) +  # remove missing
  aes(y=pulse1) +
  geom_boxplot()
bp2 <- ggplot(pulse %>% drop_na()) +  # remove missing
  aes(y=pulse2) +
  geom_boxplot()
grid.arrange(bp1, bp2, ncol=2)

As you can see the y-scale of each plot is set dynamically and can be misleading. To resolve this, we would need a single plot with aesthetic mapping of x being the categorical variable pulse and y the pulse values as was shown above.

ggplot(pulse %>% 
         drop_na() %>%  # remove missing 
         pivot_longer( c(pulse1,pulse2), names_to = "pulse", values_to = "level")
       ) +  
  aes(x=pulse, y=level) +
  geom_boxplot()