Couple of notes before we start. The list below is not exhaustive (best to read package documentation for that). For instance, it doesnt cover lubridate (which covers date/time related functions), forcats (which covers everything you would want to do to factors), broom (which tidies up messy R objects), modelr (which has helper functions for creating models) or ggplot. I also use data frame and tibble interchangeably, although they are obviously different.

Base R commandTidyverse CommandWhat it does and why you should use the tidyverse versionComment
read.csv()read_csv()reads in a csv file, but its much faster, shows progress bar for large files, can automatically parse data typesalso see read_delim(), read_tsv() and readxl::read_xlsx()
sort(), order()arrange()sort column(n) within a data framesee also order_by()
mtcars\(mpg = … </td> <td style="text-align:left;"> mutate() </td> <td style="text-align:left;"> modify a column </td> <td style="text-align:left;"> see also transmute() which drops existing variables </td> </tr> <tr> <td style="text-align:left;"> mtcars[,c(&quot;mpg&quot;, &quot;am&quot;)], subset() </td> <td style="text-align:left;"> select(), rename() </td> <td style="text-align:left;"> select or rename columns </td> <td style="text-align:left;"> see also pull() </td> </tr> <tr> <td style="text-align:left;"> mtcars[mtcars\)am == 1,], subset()filter()select rows based on a criterion
aggregate()summarise(), summarize(), do()reduce grouped values to a single valuesee also varaints like summarize_if()
ifelse()if_else(), case_when()standand vectorized if else, but stricter than base versionsee also near()
unique()distinct()finds unique rows in a data frame, but its much, faster
length(unique())n_distinct()count the number of distinct values in a vector, faster
sample(), sample.int()sample_n(), sample_frac()sample n rows or a fraction of rows from a dataframe
all.equal()all_equal()checks if two vectors are the same
merge()inner_join(), left_join()perform joins, much faster, verbose, and row order is maintainsee also right_join(), full_join(), semi_join(), anti_join()
rbind(), cbind()bind_rows(), bind_cols()concatenate two dataframes along rows or columns, much faster
x >= left & x <= rightbetween()easier to read and faster implementation for larege datasetssee also near()
nrow(), sum()tally(), count(), add_tally(), add_count()count or sum up rows
c()combine()combine into a vector
extends base Rcumall(), cumany(), cummean()extends base R collection of cumsum(), cumprod() etc
mtcars\(mpg[1,] etc </td> <td style="text-align:left;"> first(), last(), n(), top_n() </td> <td style="text-align:left;"> works within groups, allows you to order by another column(s) and provide defaults for missing values </td> <td style="text-align:left;"> </td> </tr> <tr> <td style="text-align:left;"> split(), aggregate() </td> <td style="text-align:left;"> group_by() </td> <td style="text-align:left;"> create a grouped data frame (tibble) to perform operations on groups </td> <td style="text-align:left;"> see also ungroup() </td> </tr> <tr> <td style="text-align:left;"> intersect(), union() </td> <td style="text-align:left;"> intersect(), union() </td> <td style="text-align:left;"> set operations, but dplyr works on data frames as well </td> <td style="text-align:left;"> </td> </tr> <tr> <td style="text-align:left;"> mtcars\)mpg2 = c(NA, mtcars$mpg[1:nrow(mtcars)-1])lead(), lag()No equivalent command in base R, easier to read
ifelse(…, NA)na_if()convert a value to NA
switch()recode()change certain values in your vectorsee also forcats package when dealing with factors
mtcars[3:5,]slice()select rows bases on row numbers
seq_along(), quantile()row_number(), ntile(), min_ran() etcadd rankings in various ways, much richer set of rankings supported than base r
no easy waycomplete(), expand()expands the dataframe so that supplied columns are completely filled outoften used with nesting(), see also full_seq()
expand.grid()crossing()create a data frame of all possible combinations of supplied vectors
ifelse(is.na(…), …)drop_na(), replace_na()drop rows with missing values or convert NAs to supplied valuessee also fill(), coalesce()
some mix of paste/strsplitseparate(), unite()separate two columns based on regex or combine two columns into one
reshape2::dcast()spread()convert long (tidy) data into wide (untidy) format
reshape2::melt()gather()convert wide (untidy) data into long(tidy) format
replicate()rerun()run an expression n number of times
unlist(lapply(x, [[, n))pluck()extract elements out of a list
lapply(), sapply()map(), map2()apply a function to a set of values, working with listssee also map_chr(), map_lgl(), map_int(), map_dbl(), map_df()
paste0()glue()combine two strings together, but much more powerful because it allows for expressions