Tips

Here’s a few general tips. In addition, we strongly recommend using popular cheatsheets, which give a quick and easy reference for common packages and functions, and from Data to Viz, which guides you through choosing a visualisation.

Hotkeys

Code	Hotkey	Description
	Ctrl+Enter	Run current line (when in Script)
<-	Alt+Enter	Assignment
%>%	Ctrl+Shift+M	Pipe
	Esc	Cancel current operation (when in Console)
	F1	Help documentation for selected function

Data manipulation

Importing and exporting data

In case you’ve forgotten, use the read.csv() function to import data:

dataset <- read.csv("data/dataset.csv")

If you’d like to export any files from R to “.csv”, use write.csv()

write.csv(dataset, "data/output_name.csv")

Initial exploration

You’ll want to explore the data to start with - below are a few functions to get started.

Function	Example	Description
names()	names(dataset)	Returns the variable names
str()	str(dataset)	Returns the structure of the dataset (variable names, types and first entries)
$	dataset$variable	Returns a specific variable
unique()	unique(dataset$variable)	Returns the unique values of a variable
summary()	summary(dataset$variable)	Returns a statistical summary of a variable

Removing NAs

We can use the dplyr package to remove rows which have NA:

library(dplyr)

dataset <- dataset %>%
  filter(!is.na(variable_to_check_for_NAs))

We use the exclamation mark ! to negate the result, because is.na returns all the rows that are NA.

Time series data

If you’ve picked a dataset with time-series data (e.g. a “date” variable), you should transform that variable so that it visualises better:

dataset$variable <- as.Date(dataset$variable)

Categorical and ordered data

If you’re dealing with categorical data, it can be helpful to tell R that it has levels:

dataset$variable <- factor(dataset$variable)

To manually specify the order to R, send in an ordered list of the levels joined with c():

dataset$variable <- factor(dataset$variable, levels = c("first_val", "second_val", ... ))

This is particularly useful for the Coffee survey dataset.

Alternatively, if you only need to specify the first (reference) level, use

dataset$variable <- factor(dataset$variable)
dataset$variable <- relevel(dataset$variable, ref = "reference_level")

Renaming variables

Some datasets have cumbersome names for their variables. We can change variable names with

df <- df %>% 
  rename(new_name = old_name)

This is particularly useful for the World population dataset.

Visualisation

We use the ggplot() function with geometries to create visualisations

library(ggplot2)

ggplot(data = dataset,
       mapping = aes(x = ..., y = ..., colour = ..., ...)) +
  geom_first_layer() + 
  geom_second_layer() + 
  ...

Take a look at the ggplot2 documentation for more information.

Plotly workaround

If you’re having issues using ggplotly (it’s producing a blank plot), you can use this workaround to view it in your browser.

plot <- ggplotly(saved_ggplot_image)
htmlwidgets::saveWidget(as_widget(plot), "plots/name_of_plot.html")

Opening that file will show you the image.