| ? and help() |
help you to get more info on a specific function |
| View() |
command to open and manually inspect data in the viewer |
| across() |
apply the same function to several variables |
| add_index() |
build mean/sum index from variables |
| aes(x, y, fill, color, etc.) |
maps data to visual elements |
| arrange() |
arrange data in descending or ascending order |
| bow() |
introduce your scraping agent |
| case_when() |
transform existing variables based on multiple conditions |
| chrome_print() |
take automated screenshots |
| class() |
check what type of data an object contains |
| correlate() |
calculates the correlation between two metric variables |
| crosstab() |
tests the relationship between two categorical variables |
| describe() |
gives us specific descriptive statistics (e.g., mean, standard deviation) |
| describe_cat() |
gives us specific descriptive statistics (e.g., mode) |
| facet_wrap() |
splits a graph into separate panels |
| filter() |
filter specific observations, often based on conditions |
| geom_bar() |
creates a bar chart for counts or values |
| geom_boxplot() |
creates a box plot showing a distribution |
| geom_density() |
creates a smoothed curve of a numeric distribution |
| geom_line() |
creates a line graph connecting observations across x-values |
| geom_point() |
creates a scatter plot using points |
| get_reliability() |
get internal consistency for an index |
| ggplot() |
creating a graph |
| ggsave() |
saves graphs as image files |
| glimpse() |
inspect the structure of a dataset |
| group_by() |
groups a dataset based on a variable for subsequent analyses |
| head() |
show first rows of a dataset |
| html_attr() |
extract the value from a html attribute |
| html_element(), html_elements() |
extract the first (or all) matching HTML nodes from a scraped website |
| html_text() |
extract text from a html node |
| if_else() |
transform existing variables based on two conditions |
| install.packages() |
installs packages on your machine - only has to be run once |
| is.na() |
for every value in a variable, checks whether it contains missing data (TRUE) or not (FALSE) |
| kable() |
creates a table based on input data |
| labs() |
adds titles and labels to a graph |
| library() |
loads existing packages - has to be run everytime you reopen R Studio |
| mutate() |
create new variables |
| na_if() |
setting specific values to missing (NA) |
| ncol() |
get the number of columns for a dataset |
| nrow() |
get the number of rows for a dataset |
| read_csv(), read_csv2() |
read in tabular data (e.g., .csv file) |
| rename() |
rename variables |
| report() |
creates a report of descriptive statistics |
| save.image() |
saves your working directory |
| scrape() |
scrape a website |
| select() |
select specific variables |
| sum() |
creates sums for a variable |
| summarise() |
creates summary statistics for a dataset |
| t_test() |
calculates a t-test for differences in the mean across groups |
| tab_frequencies() |
gives us specific descriptive statistics (e.g., counts, percentages) |
| taskscheduler_create() |
schedule automated scraping runs with R |
| theme_minimal(), theme_classic(), theme_light() |
changes the design of your graph |
| |> |
the pipe, pushes data (left) through the pipe for transformation (right) |