matrix
{base}
Creates a matrix from the given set of values.
as.integer
{base}
Convert an object to integer values
prop.table
{base}
Transform frequency table into table of proportions
table
{base}
Obtain frequency table of a variable/cross-tabulation of two variables
sum
{base}
Get sum of numeric values or a vector
select
{dplyr}
Select columns from a tibble/data frame
mutate
{dplyr}
Create new variables
correlation_matrix
{corrtable}
Create a formatted correlation matrix of variables
datasummary
{modelsummary}
Create customizable summary-statistics tables
data.frame
{base}
Create a data.frame from vectors
mean
{base}
Get mean of a vector
round
{base}
Rounds numbers
factor
{base}
Create a factor
geom_point
{GGPLOT2}
Generates a scatter plot
replace
{base}
Replace elements in a vector
scale_x_continuous
{GGPLOT2}
Customise continuous x axis
element_blank
{GGPLOT2}
This theme element draws nothing, and assigns no space
read_excel
{readxl}
Read an Excel file
library
{base}
Load an R package
labs
{GGPLOT2}
Customise labels in GGPLOT2
summarise
{dplyr}
collapse the dataset to a summary statistic. Usually used with group_by()
scale_y_continuous
{GGPLOT2}
Customise continuous y axis
setwd
{base}
Set Working Directory
sd
{stats}
Get standard deviation of a vector
margin
{GGPLOT2}
Set margins around plot elements (used inside theme elements)
summary
{base}
Obtain summary statistics or detailed regression output
element_text
{GGPLOT2}
Customise text in GGPLOT2
cut
{base}
Convert Numeric to Factor
geom_jitter
{GGPLOT2}
Add randomly displaced points to reduce overplotting
c
{base}
Combine values/vectors into a vector
is.na
{base}
Check if a value is NA/elements of vector are NA
aes
{GGPLOT2}
Construct aesthetic mapping of a ggplot graph
filter
{dplyr}
Filter out rows of a data frame according to logical vector
read_csv
{readr}
Read CSV files
ggplot
{GGPLOT2}
Create a ggplot graph
pwr.r.test
{pwr}
Power analysis for correlation tests
geom_smooth
{GGPLOT2}
Generates a smoothed conditional means curve / line
theme
{GGPLOT2}
Customize ggplot themes
write.csv
{utils}
write a csv file to a data frame
as.character
{base}
Coerce a vector to character
lm
{stats}
Fit linear models using least squares
factor
{base}
Create factor variables
theme_classic
{GGPLOT2}
A minimalistic theme with no gridlines
as.numeric
{base}
Coerce a vector to numeric
recode
{dplyr}
Recode a variable
vdem
{vdemdata}
V-Dem country-year dataset
with
{base}
evaluate expression in the context of a data frame
chisq.test
{stats}
Chi-Squared test (e.g. for cross-tabulations)
complete.cases
{stats}
Find Complete Cases
group_by
{dplyr}
Group tibble/data.frame by a factor variable. All further tidyverse operations are performed group-wise
cor.test
{stats}
Test for Association/Correlation Between Paired Samples
The end!