Glossary

Key Points

Before we Start	Use RStudio to write and run R programs. Use `install.packages()` to install packages (libraries).
Introduction to R	Access individual values by location using `[]`. Access arbitrary sets of data using `[c(...)]`. Use logical operations and logical vectors to access subsets of data.
Starting with Data	Use read_csv to read tabular data in R.
Data Wrangling with dplyr and tidyr	Use the `dplyr` package to manipulate dataframes. Use `select()` to choose variables from a dataframe. Use `filter()` to choose data based on values. Use `group_by()` and `summarize()` to work with subsets of data. Use `mutate()` to create new variables. Use the `tidyr` package to change the layout of dataframes. Use `pivot_wider()` to go from long to wide format. Use `pivot_longer()` to go from wide to long format.
A couple of plots. And making our own functions	Boxplots are useful for visualizing the distribution of a continuous variable. Barplots are useful for visualizing categorical data. Functions allows you to repeat the same set of operations again and again. Loops allows you to apply the same function to lots of data. Logical tests allow you to apply different calculations on different sets of data.
What is the next step?	Practice is important! Working on data that YOU find interesting is a really good idea, The amount of ressources online is immense. KUB Datalab is there for your.

Cheat sheet of functions used in the lessons

download.file() # download files from the internet to your computer
read_csv() # load CSV file into R memory
head() # shows the first 6 rows
view() # invoke a spreadsheet-style data viewer
read_delim() # load a file in table format into R memory
str() # check structure of the object and information about the class, length and content of each column
dim() # check dimension of data frame
nrow() # returns the number of rows
ncol() # returns the number of columns
tail() # shows the last 6 rows
names() # returns the column names (synonym of colnames() for data frame objects)
rownames() # returns the row names
summary() # summary statistics for each column
glimpse # like str() applied to a data frame but tries to show as much data as possible
factor() # create factors
levels() # check levels of a factor
nlevels() # check number of levels of a factor
as.character() # convert an object to a character vector
as.numeric() # convert an object to a numeric vector
as.numeric(as.character(x)) # convert factors where the levels appear as characters to a numeric vector
as.numeric(levels(x))[x] # convert factors where the levels appear as numbers to a numeric vector
plot() # plot an object
addNA() # convert NA into a factor level
data.frame() # create a data.frame object
ymd() # convert a vector representing year, month, and day to a Date vector
paste() # concatenate vectors after converting to character

str() # check structure of the object and information about the class, length and content of each column
view() # invoke a spreadsheet-style data viewer
select() # select columns of a data frame
filter() # allows you to select a subset of rows in a data frame
%>% # pipes to select and filter at the same time
mutate() # create new columns based on the values in existing columns
head() # shows the first 6 rows
group_by() # split the data into groups, apply some analysis to each group, and then combine the results.
summarize() # collapses each group into a single-row summary of that group
mean() # calculate the mean value of a vector
!is.na() # test if there are no missing values
print() # print values to the console
min() # return the minimum value of a vector
arrange() # arrange rows by variables
desc() # transform a vector into a format that will be sorted in descending order
count() # counts the total number of records for each category
pivot_wider() # reshape a data frame by a key-value pair across multiple columns
pivot_longer() # reshape a data frame by collapsing into a key-value pair
replace_na() # Replace NAs with specified values
n_distinct() # get a count of unique values
write_csv() # save to a csv formatted file

read_csv() # load a csv formatted file into R memory
ggplot2(data= , aes(x= , y= )) + geom_point( ) + facet_wrap () + theme_bw() + theme() # skeleton for creating plot layers
aes() # by selecting the variables to be plotted and the variables to define the presentation such as plotting size, shape color, etc.
geom_ # graphical representation of the data in the plot (points, lines, bars). To add a geom to the plot use + operator
facet_wrap() # allows to split one plot into multiple plots based on a factor included in the dataset
labs() # set labels to plot
theme_bw() # set the background to white
theme() # used to locally modify one or more theme elements in a specific ggplot object
+ # arrange ggplots horizontally
/ # arrange ggplots vertically
plot_layout() # set width and height of individual plots in a patchwork of plots
ggsave() # save a ggplot