{ggplot2}
create smoothed lines to plot
{base}
return number of elements in a vector
{ggplot2}
create frequency polygon
{base}
calculate absolute value
{lubridate}
return or set year component of a date-time
{base}
compute maxima of input values
{base}
calculate mean of elements of vector
{ggplot2}
create bar chart based on counts in data
{stringr}
detect presence of pattern in string
{base}
extract file information
{dplyr}
group data by levels of column
{base}
sum elements of vector
{base}
create vector of numbers, characters, etc.
{base}
determine whether elements of vector are missing
{ggplot2}
create histogram of counts as bars
{base}
compute base 10 logarithm
{base}
create repetitions of numbers, characters, etc.
{dplyr}
select only unique/distinct rows from data frame
{dplyr}
create or modify data columns
{base}
load R packages
{ggplot2}
create aesthetic mappings between data and plot
{base}
compute minima of input values
{base}
coerce object to numeric
{dplyr}
conditionally set values across multiple conditions
{ggplot2}
evaluate aesthetic mapping after statistical transformation
{ggplot2}
add layer of points to plot
{base}
divide range of vector into intervals
{ggplot2}
modify axis, legend, and plot labels
{lubridate}
return or set month component of a date-time
{stats}
generate random sample from normal distribution
{base}
sort a vector or factor into ascending or descending order (base R)
{dplyr}
vectorized if statement that checks conditionals and returns different outputs based on answer
{tidyr}
pivot data frame to be wider
{stringr}
compare literal bytes in a string
{utils}
load specific data set
{dplyr}
count unique values of variables
{stats}
generate random number from uniform distribution
{ggplot2}
create a plotting area
{stringr}
remove all instances of a pattern in a string
{stringr}
returns number of characters in a string
{forcats}
reverse order of factor levels
{base}
return vector of minimum and maximum
{ggplot2}
create subplots from one variable
{stringr}
change text to upper case
{tidyr}
fill in missing values
{dplyr}
keep rows based on values of columns
{stats}
compute sample median
{dplyr}
return current group size
{lubridate}
get days component of date-time
{dplyr}
summarize data usually by grouping variable
{stringr}
extract parts of a string
{stringr}
flatten a string
{dplyr}
change order of rows based on values of columns
{forcats}
reorder factor levels in the order of most to least frequent
{dplyr}
keep specified columns
{stats}
compute standard deviation
{tibble}
create tibble
The end!