mean
{base}
Get mean of a vector
read.csv
{utils}
Read a csv file to data frame. Specify stringsAsFactors = FALSE to keep all string columns as characters
summary
{base}
Obtain summary statistics or detailed regression output
dnorm
{stats}
Density distribution for the normal distribution
as.data.frame
{base}
Functions to check if an object is a data frame, or coerce it if possible.
var
{stats}
Calculate variance
range
{base}
Return range of values
min
{base}
Get minimum of a vector
plot
{graphics}
Generic function from base R to produce a plot
glm
{base}
Fits generalized linear models
length
{base}
Returns number of elements in an object
read_excel
{readxl}
Read an Excel file
setwd
{base}
Set Working Directory
max
{base}
Get maximum of a vector
sd
{stats}
Get standard deviation of a vector
which
{base}
return indexes of TRUE entries of a logical vector
median
{stats}
Get median of a vector
expression
{base}
Used in plots to add symbols to axes
subset
{base}
Return subsets of vectors, matrices or data frames which meet conditions.
binomial
{base}
Family argument in glm()
par
{graphics}
Set parameters of the plotting device
ifelse
{base}
Return a or b depending on the value of test
seq
{base}
Create a sequence
as.character
{base}
Coerce a vector to character
c
{base}
Combine values/vectors into a vector
library
{base}
Load an R package
log
{base}
log (default base = e)
filter
{dplyr}
Filter out rows of a data frame according to logical vector
lm
{base}
Fit a linear model
mutate
{dplyr}
Modify/create a column in a data frame
bar
{base}
Used for annotations, such as “expression(paste(”-“,bar(y)))”
rnorm
{base}
Generate random numbers from a normal distribution
list
{base}
Create a list object
sample
{base}
Takes a sample of the specified size
str
{utils}
Get the structure of an R object
data.frame
{base}
Create a data.frame from vectors
pnorm
{base}
Returns the cumulative distribution function (CDF) of the normal distribution
names
{base}
Retrieve names of a list/vector
head
{utils}
Show first 5 rows of a data frame
stargazer
{stargazer}
Create a regression output table
predict
{stats}
Predict Probability
modelsummary
{modelsummary}
Creates regression and data tables
cut
{base}
Convert Numeric to Factor
ordered
{dplyr}
Create an ordered factor
group_tt
{tinytable}
Grouping in tinytable
nobs
{gdata}
Compute the number of non-missing observations
The end!