R for Excel Users: Your Entire Excel Workflow, Translated to R

Every VLOOKUP, pivot table, IF formula, filter, sort, and chart you use in Excel has a direct one-line equivalent in R, and this guide maps each one, with runnable code you can edit in place.

You already think in data. Every filter, sort, formula, and pivot you build in Excel is real analysis. R does the same jobs with code instead of clicks, which means the same logic runs again next week, next quarter, and on a dataset ten times the size, without rebuilding anything by hand.

How do you replace Excel's AutoFilter and sorting in R?

AutoFilter and Sort are where Excel users spend their mornings. In R both collapse into one readable line per operation. You don't click a dropdown, tick boxes, and lose the state next time the file opens, you write filter() and arrange() once, and the same logic runs every time on any dataset with the same columns. Here is the exact translation on a small sales dataset we will reuse throughout the guide.

RBuild the sample sales tibble

# Load the three packages we'll use throughout library(dplyr) library(tidyr) library(ggplot2) # Sample sales data, think of this as an Excel sheet sales <- tibble( order_id = 1:10, region = c("East", "West", "East", "North", "South", "East", "West", "North", "South", "East"), product = c("Laptop", "Phone", "Laptop", "Tablet", "Phone", "Monitor", "Laptop", "Phone", "Tablet", "Monitor"), quantity = c(8, 3, 12, 5, 7, 2, 15, 9, 4, 6), unit_price = c(999, 599, 999, 399, 599, 249, 999, 599, 399, 249) ) # Excel: AutoFilter → Region = "East" AND Quantity > 5 east_bulk <- sales |> filter(region == "East" & quantity > 5) east_bulk #> # A tibble: 3 × 5 #> order_id region product quantity unit_price #> <int> <chr> <chr> <dbl> <dbl> #> 1 1 East Laptop 8 999 #> 2 3 East Laptop 12 999 #> 3 10 East Monitor 6 249

Three rows come back, the same three that would stay visible if you clicked Region = East and typed >5 into the Quantity filter. The difference is that this line is re-runnable tomorrow on a million rows, and it will never silently "forget" the filter because someone re-opened the file.

Sorting is just as direct. arrange() takes one or more columns, and desc() flips the order.

RFilter and sort like AutoFilter

# Excel: Sort by Quantity, largest first sorted_sales <- sales |> arrange(desc(quantity)) head(sorted_sales, 4) #> # A tibble: 4 × 5 #> order_id region product quantity unit_price #> <int> <chr> <chr> <dbl> <dbl> #> 1 7 West Laptop 15 999 #> 2 3 East Laptop 12 999 #> 3 8 North Phone 9 599 #> 4 1 East Laptop 8 999

arrange() doesn't touch sales itself, it returns a sorted copy. That's a nice safety net: in Excel you have to remember to click "Sort" again on the original data to undo it. In R there is nothing to undo.

Key Insight

Filter picks rows, arrange reorders them. Two verbs, one grammar, and both are re-runnable, which is what Excel sorts and filters are not.

Try it: Write a pipeline that keeps only West region rows where quantity >= 10, and save it to ex_west_bulk.

RExercise: Filter bulk West laptops

# Your turn: ex_west_bulk <- sales |> filter(TRUE) # replace with your condition ex_west_bulk #> Expected: one row, order_id 7, West, Laptop, quantity 15

Click to reveal solution

RBulk West laptops solution

ex_west_bulk <- sales |> filter(region == "West" & quantity >= 10) ex_west_bulk #> # A tibble: 1 × 5 #> order_id region product quantity unit_price #> <int> <chr> <chr> <dbl> <dbl> #> 1 7 West Laptop 15 999

Explanation: & is the "AND" operator, both conditions must be true. Excel writes this as two separate AutoFilter dropdowns; R writes it as one line.

How do you write Excel formulas as R columns?

In Excel you add a calculated column by typing a formula into cell F2 and dragging it down. In R you add calculated columns with mutate(). The formula is written once, not once per row.

Let's compute a revenue column (quantity * unit_price) and a total with 8% sales tax.

RCalculated columns with mutate

# Excel: =D2*E2 dragged down for revenue sales_rev <- sales |> mutate( revenue = quantity * unit_price, total_with_tax = revenue * 1.08 ) head(sales_rev, 3) #> # A tibble: 3 × 7 #> order_id region product quantity unit_price revenue total_with_tax #> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 1 East Laptop 8 999 7992 8631. #> 2 2 West Phone 3 599 1797 1941. #> 3 3 East Laptop 12 999 11988 12947.

Notice how total_with_tax is computed from revenue in the same mutate() call. Excel can't do that without referencing another cell. R evaluates left-to-right inside one mutate(), so the second line sees the first.

Nested IFs are the other classic Excel formula headache. The R replacement is case_when(), which reads top to bottom and returns the value for the first condition that matches, exactly the same semantics as a chain of IFs, but you can actually read it a week later.

RTier rows with casewhen

# Excel: =IF(revenue>10000,"High",IF(revenue>3000,"Medium","Low")) sales_tiered <- sales_rev |> mutate( tier = case_when( revenue > 10000 ~ "High", revenue > 3000 ~ "Medium", TRUE ~ "Low" ) ) sales_tiered |> select(order_id, region, product, revenue, tier) #> # A tibble: 10 × 5 #> order_id region product revenue tier #> <int> <chr> <chr> <dbl> <chr> #> 1 1 East Laptop 7992 Medium #> 2 2 West Phone 1797 Low #> 3 3 East Laptop 11988 High #> 4 4 North Tablet 1995 Low #> 5 5 South Phone 4193 Medium #> 6 6 East Monitor 498 Low #> 7 7 West Laptop 14985 High #> 8 8 North Phone 5391 Medium #> 9 9 South Tablet 1596 Low #> 10 10 East Monitor 1494 Low

The TRUE ~ "Low" line is the "everything else" catch-all, the same role as the final ELSE in a nested IF. Three clear rows beat one illegible IF(IF(IF(...))).

Tip

case_when reads top to bottom, first match wins. Order your conditions from most specific to most general, or the general rule will eat the specific ones before they get a chance to match.

Try it: Add an ex_order_size column using case_when that labels each row as "Bulk" when quantity > 10 and "Single" otherwise.

RExercise: Tag bulk versus single

# Your turn: ex_tagged <- sales |> mutate( ex_order_size = "Single" # replace with case_when ) ex_tagged |> select(order_id, quantity, ex_order_size) #> Expected: two Bulk rows (orders 3 and 7), eight Single rows

Click to reveal solution

RBulk versus single solution

ex_tagged <- sales |> mutate( ex_order_size = case_when( quantity > 10 ~ "Bulk", TRUE ~ "Single" ) ) ex_tagged |> select(order_id, quantity, ex_order_size) |> head(4) #> # A tibble: 4 × 3 #> order_id quantity ex_order_size #> <int> <dbl> <chr> #> 1 1 8 Single #> 2 2 3 Single #> 3 3 12 Bulk #> 4 4 5 Single

Explanation: A two-branch case_when is the same shape as =IF(quantity>10, "Bulk", "Single"), just written with ~ (read as "then") instead of commas.

How do you replace VLOOKUP with R joins?

VLOOKUP is the most-used Excel function for a reason: it lets you look up a key in another table and pull back a value. In R, that job belongs to left_join(). It's safer than VLOOKUP (joins on column names, not column numbers), handles multi-column keys, and doesn't silently break when someone inserts a column in the lookup sheet.

RReplace VLOOKUP with leftjoin

# A lookup table, think of it as a second Excel sheet product_info <- tibble( product = c("Laptop", "Phone", "Tablet", "Monitor"), category = c("Computing", "Mobile", "Mobile", "Computing"), brand = c("Acme", "Acme", "Zenon", "Zenon") ) # Excel: =VLOOKUP(C2, ProductInfo!A:C, 2, FALSE) → category # Excel: =VLOOKUP(C2, ProductInfo!A:C, 3, FALSE) → brand sales_joined <- sales_tiered |> left_join(product_info, by = "product") sales_joined |> select(order_id, product, category, brand, tier) |> head(4) #> # A tibble: 4 × 5 #> order_id product category brand tier #> <int> <chr> <chr> <chr> <chr> #> 1 1 Laptop Computing Acme Medium #> 2 2 Phone Mobile Acme Low #> 3 3 Laptop Computing Acme High #> 4 4 Tablet Mobile Zenon Low

Two VLOOKUP formulas compressed into one left_join(). Every column from product_info comes across in a single call. And if a product in sales has no match in product_info, the joined row gets NA for the new columns, that's the exact behaviour Excel shows as #N/A.

Warning

Duplicate keys in the lookup silently inflate rows. If product_info had two rows for "Laptop", every "Laptop" row in sales would be duplicated after the join. VLOOKUP hides this (it returns the first match only). left_join() shows you the truth. To mimic VLOOKUP, call distinct(product_info, product, .keep_all = TRUE) before the join.

Try it: Create a small ex_region_manager lookup with columns region and manager for the four regions (pick any names), then left-join it onto sales and save the result to ex_sales_mgr.

RExercise: Join region manager table

# Your turn: ex_region_manager <- tibble( region = c("East", "West", "North", "South"), manager = c("", "", "", "") # fill in names ) ex_sales_mgr <- sales # chain your left_join here ex_sales_mgr |> select(order_id, region) #> Expected: each row has the manager name for its region

Click to reveal solution

RRegion manager solution

ex_region_manager <- tibble( region = c("East", "West", "North", "South"), manager = c("Ava", "Ben", "Cleo", "Dan") ) ex_sales_mgr <- sales |> left_join(ex_region_manager, by = "region") ex_sales_mgr |> select(order_id, region, manager) |> head(3) #> # A tibble: 3 × 3 #> order_id region manager #> <int> <chr> <chr> #> 1 1 East Ava #> 2 2 West Ben #> 3 3 East Ava

Explanation: left_join() keeps every row from sales (the left side) and attaches the matching manager from the lookup. No match? You would get NA, exactly what #N/A means in Excel.

How do you build a pivot table in R?

An Excel pivot table has three drag zones: Rows, Columns, and Values. The R equivalents are group_by() (rows and columns), summarise() (values), and pivot_wider() if you want the "Columns" zone to actually spread across the page.

Start with the simple case, total revenue and order count per region. That's SUMIF and COUNTIF in one pass.

RPivot table with groupby

# Excel: Pivot table with Region in Rows, sum(revenue) and count in Values region_summary <- sales_joined |> group_by(region) |> summarise( total_revenue = sum(revenue), orders = n(), .groups = "drop" ) region_summary #> # A tibble: 4 × 3 #> region total_revenue orders #> <chr> <dbl> <int> #> 1 East 21972 4 #> 2 North 7386 2 #> 3 South 5789 2 #> 4 West 16782 2

One pipeline, four rows back, and the same answer a 30-second pivot table trip would give you in Excel. Need to do this every Monday morning? Save the script. Need to do it on a new quarter's data? Change the input, re-run.

Now the full crosstab, regions as rows, products as columns, revenue in the cells. In Excel this is "drag Product into Columns". In R it's pivot_wider() after the group.

RRegion and product crosstab

# Excel: Pivot with Region in Rows, Product in Columns, sum(revenue) in Values region_product_crosstab <- sales_joined |> group_by(region, product) |> summarise(total = sum(revenue), .groups = "drop") |> pivot_wider(names_from = product, values_from = total, values_fill = 0) region_product_crosstab #> # A tibble: 4 × 5 #> region Laptop Monitor Phone Tablet #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 East 19980 1992 0 0 #> 2 North 0 0 5391 1995 #> 3 South 0 0 4193 1596 #> 4 West 14985 0 1797 0

values_fill = 0 does the same job as ticking "Show zero for empty cells" in Excel's pivot options, the difference is that the setting lives inside the code, so it never gets lost.

Key Insight

Pivot table = group_by + summarise + pivot_wider. The first verb is the Rows box, the second is the Values box, and the third is the Columns box. Three verbs, and your pivot is reproducible forever.

Try it: Count how many orders were placed in each region. Save it to ex_order_counts.

RExercise: Count orders per region

# Your turn: ex_order_counts <- sales # chain group_by + summarise here ex_order_counts #> Expected: 4 rows, East 4, North 2, South 2, West 2

Click to reveal solution

ROrders per region solution

ex_order_counts <- sales |> group_by(region) |> summarise(orders = n(), .groups = "drop") ex_order_counts #> # A tibble: 4 × 2 #> region orders #> <chr> <int> #> 1 East 4 #> 2 North 2 #> 3 South 2 #> 4 West 2

Explanation: n() inside summarise() counts the rows in each group, that's COUNTIF with the "criteria" already split out by group_by.

How do you reshape Excel data without copy-paste?

Reshaping is the Excel operation people dread most. In Excel you either live with a wide layout, or you use Power Query's Unpivot (and hope it still works next week). In R, reshaping is just two functions: pivot_longer() takes wide data to long, pivot_wider() goes the other way. Here's a quarterly sales frame, the shape a finance team would email you, getting turned on its side.

RWide to long with pivotlonger

# Wide quarterly layout, typical Excel shape quarterly_wide <- tibble( region = c("East", "West", "North", "South"), Q1 = c(10000, 8000, 5000, 4000), Q2 = c(12000, 9500, 6200, 4300), Q3 = c(15000, 11000, 7100, 4800), Q4 = c(18000, 12500, 8300, 5200) ) # Excel: Get & Transform → Unpivot other columns quarterly_long <- quarterly_wide |> pivot_longer( cols = Q1:Q4, names_to = "quarter", values_to = "revenue" ) head(quarterly_long, 5) #> # A tibble: 5 × 3 #> region quarter revenue #> <chr> <chr> <dbl> #> 1 East Q1 10000 #> 2 East Q2 12000 #> 3 East Q3 15000 #> 4 East Q4 18000 #> 5 West Q1 8000

Sixteen rows (four regions × four quarters), one row per quarter per region. Once data is in this "long" shape, every dplyr verb you already know works on it, and every ggplot2 chart expects it.

Tip

Long format is the shape dplyr and ggplot2 want. When a pipeline feels awkward, there's a good chance the data is still wide. A single pivot_longer() at the top usually makes the next five lines write themselves.

The sibling problem is Text to Columns: a single cell holds two pieces of information separated by a delimiter, and you need them in their own columns. separate_wider_delim() handles it in one line.

RSplit combined column with separate

# Combined column, "Region-Product", like a sheet someone pasted together combo_tbl <- tibble( combo = c("East-Laptop", "West-Phone", "North-Tablet"), revenue = c(19980, 1797, 1995) ) # Excel: Data → Text to Columns → Delimited → "-" combo_split <- combo_tbl |> separate_wider_delim( cols = combo, delim = "-", names = c("region", "product") ) combo_split #> # A tibble: 3 × 3 #> region product revenue #> <chr> <chr> <dbl> #> 1 East Laptop 19980 #> 2 West Phone 1797 #> 3 North Tablet 1995

One function, one line, exactly the same result, and the next time a colleague sends you a sheet with the same layout, you re-run the same script.

Try it: Given this wide two-month frame, reshape it to long format and save to ex_long.

RExercise: Pivot store sales long

ex_wide <- tibble( store = c("A", "B", "C"), Jan = c(100, 200, 150), Feb = c(110, 210, 160) ) # Your turn: ex_long <- ex_wide # chain pivot_longer here ex_long #> Expected: 6 rows, one per (store, month)

Click to reveal solution

RStore sales long solution

ex_long <- ex_wide |> pivot_longer(cols = Jan:Feb, names_to = "month", values_to = "sales") ex_long #> # A tibble: 6 × 3 #> store month sales #> <chr> <chr> <dbl> #> 1 A Jan 100 #> 2 A Feb 110 #> 3 B Jan 200 #> 4 B Feb 210 #> 5 C Jan 150 #> 6 C Feb 160

Explanation: cols = Jan:Feb says "take these columns", names_to is where their names go, and values_to is where their numbers go.

How do you replace Excel charts and conditional formatting?

Excel charts are tied to the shape of a selection: click a range, pick a chart type, and hope the defaults are reasonable. ggplot2 is the opposite, you describe what you want (which columns, which geometry, which aesthetic) and the chart shape follows. The payoff is that you never have to rebuild a chart when the underlying data changes.

RBar chart with ggplot2

# Excel: Insert → Bar Chart on the pivot table region_chart <- ggplot(region_summary, aes(x = region, y = total_revenue)) + geom_col(fill = "#4C78A8") + labs( title = "Total revenue by region", x = NULL, y = "Revenue ($)" ) + theme_minimal() region_chart

Read the code as a sentence: "Plot region_summary, map region to the x-axis and total_revenue to the y-axis, draw columns, label them." Swap geom_col() for geom_point() and you get a dot plot. Swap x = region for x = product and you get a totally different chart, no rebuild, no resizing, no broken formulas.

Conditional formatting, the colour-by-value heatmap Excel gives you when you highlight a range and pick "Color Scales", is just a fill aesthetic on a geom_tile() in ggplot2. The gt package does the same thing inside a table. Both are one-liners once you see the pattern.

Note

Plots render directly below the code block. Click Run and the chart appears in the output area, no PNG export, no file save, no "update linked chart" dialog.

Try it: Build a bar chart of total_revenue per product (not region) using sales_joined. Hint: group_by(product) |> summarise(total_revenue = sum(revenue)) first, then feed the result into ggplot().

RExercise: Bar chart by product

# Your turn: ex_product_chart <- sales_joined |> group_by(product) |> summarise(total_revenue = sum(revenue), .groups = "drop") # add ggplot here ex_product_chart #> Expected: a bar chart with four bars (Laptop, Monitor, Phone, Tablet)

Click to reveal solution

RProduct bar chart solution

ex_product_chart <- sales_joined |> group_by(product) |> summarise(total_revenue = sum(revenue), .groups = "drop") |> ggplot(aes(x = product, y = total_revenue)) + geom_col(fill = "#E45756") + labs(title = "Revenue by product", x = NULL, y = "Revenue ($)") + theme_minimal() ex_product_chart

Explanation: The pipe carries the summarised tibble straight into ggplot(). The same three-step pattern, group, summarise, plot, handles nearly every chart you'd draw in Excel.

Practice Exercises

Exercise 1: Quarterly tax totals by region

Using sales, keep only rows where quantity > 1, compute a revenue column, left-join the ex_region_tax lookup below, compute tax = revenue * tax_rate, summarise total_tax per region, and save the result to my_tax_by_region.

RExercise: Tax by region pipeline

ex_region_tax <- tibble( region = c("East", "West", "North", "South"), tax_rate = c(0.08, 0.07, 0.06, 0.09) ) # Write your pipeline below: # my_tax_by_region #> Expected: 4 rows, one per region, with a total_tax column

Click to reveal solution

RTax pipeline solution

my_tax_by_region <- sales |> filter(quantity > 1) |> mutate(revenue = quantity * unit_price) |> left_join(ex_region_tax, by = "region") |> mutate(tax = revenue * tax_rate) |> group_by(region) |> summarise(total_tax = sum(tax), .groups = "drop") my_tax_by_region #> # A tibble: 4 × 2 #> region total_tax #> <chr> <dbl> #> 1 East 1757.8 #> 2 North 443.2 #> 3 South 521.0 #> 4 West 1174.7

Explanation: Five verbs in one pipeline, filter, mutate, left_join, mutate, summarise. That's one "morning in Excel" compressed into a paragraph of code.

Exercise 2: Region × tier crosstab

Build a crosstab that shows total revenue per region and tier. Start from sales, add revenue, add a tier column using case_when() (Low < 3000, Medium 3000-10000, High > 10000), group by region and tier, summarise, and pivot wider so tiers become columns. Save the result to my_crosstab.

RExercise: Tier by region crosstab

# Write your pipeline below: # my_crosstab #> Expected: 4 rows (one per region), columns Low / Medium / High

Click to reveal solution

RTier crosstab solution

my_crosstab <- sales |> mutate( revenue = quantity * unit_price, tier = case_when( revenue > 10000 ~ "High", revenue >= 3000 ~ "Medium", TRUE ~ "Low" ) ) |> group_by(region, tier) |> summarise(total = sum(revenue), .groups = "drop") |> pivot_wider(names_from = tier, values_from = total, values_fill = 0) my_crosstab #> # A tibble: 4 × 4 #> region Low Medium High #> <chr> <dbl> <dbl> <dbl> #> 1 East 1992 7992 11988 #> 2 North 1995 5391 0 #> 3 South 1596 4193 0 #> 4 West 1797 0 14985

Explanation: The full Excel pivot table, rows, values, columns, lives in one re-runnable pipeline. Drop in a different input next quarter and the crosstab rebuilds itself.

Complete Example, A Monthly Sales Report in One Pipe

Here is the point of doing all this: the whole report, in one pipeline. Filter, compute, tier, join, group, summarise, spread into a crosstab, and draw a chart. What takes a practised Excel user twenty minutes of clicking, copying, and re-formatting is twelve lines of code that run instantly on ten rows or ten million.

REnd-to-end monthly report

# One pipeline, the whole report monthly_report <- sales |> filter(quantity > 1) |> mutate( revenue = quantity * unit_price, tier = case_when( revenue > 10000 ~ "High", revenue >= 3000 ~ "Medium", TRUE ~ "Low" ) ) |> left_join(product_info, by = "product") |> group_by(region, category) |> summarise(total_revenue = sum(revenue), .groups = "drop") |> pivot_wider( names_from = category, values_from = total_revenue, values_fill = 0 ) monthly_report #> # A tibble: 4 × 3 #> region Computing Mobile #> <chr> <dbl> <dbl> #> 1 East 19980 0 #> 2 North 0 7386 #> 3 South 0 5789 #> 4 West 14985 1797

REnd-to-end report chart

# And the chart, one extra pipe monthly_report |> pivot_longer(-region, names_to = "category", values_to = "revenue") |> ggplot(aes(x = region, y = revenue, fill = category)) + geom_col(position = "dodge") + labs(title = "Revenue by region and category", x = NULL, y = "Revenue ($)") + theme_minimal()

Two things worth noticing. First, the report and the chart share the same pipeline, no copy-paste between sheets, no "link broken" dialogs. Second, everything upstream of monthly_report is reusable: swap in fresh data and the report rebuilds with zero manual work.

Warning

Save your script, not the output. The Excel habit is "save the workbook"; the R habit is "save the script that produces the output". Ship the code, rerun it on tomorrow's data, and the report regenerates itself. Hand-editing cells in the output breaks this the moment you rerun.

Summary

Here is the full Excel-to-R translation table in one place. Bookmark this section, it's the fastest cheat-sheet you'll reach for in the first week.

Excel operation	R equivalent	Package
AutoFilter	`filter()`	dplyr
Sort A-Z / Z-A	`arrange()` / `arrange(desc())`	dplyr
Formula in a new column	`mutate()`	dplyr
`IF()`	`if_else()`	dplyr
Nested `IF()` / `IFS()`	`case_when()`	dplyr
VLOOKUP / INDEX-MATCH	`left_join()`	dplyr
SUMIF / COUNTIF / AVERAGEIF	`group_by()` + `summarise()`	dplyr
Pivot table	`group_by()` + `summarise()` + `pivot_wider()`	dplyr + tidyr
Unpivot (Get & Transform)	`pivot_longer()`	tidyr
Text to Columns	`separate_wider_delim()`	tidyr
Charts	`ggplot()` + `geom_*()`	ggplot2
Conditional formatting	`scale_fill_gradient()`, `gt` tables	ggplot2, gt

Three reasons the trip is worth it, once you have the map:

Reproducibility, the same script runs the same way next week, next quarter, on next year's data. No "the filters got lost when I reopened it."
Scale, dplyr handles millions of rows without freezing; Excel usually gives up around a hundred thousand.
Version control, a script is a text file. Git can tell you exactly what changed, when, and by whom. A spreadsheet can't.

References

dplyr documentation, verbs for data transformation. Link
tidyr documentation, pivot_longer(), pivot_wider(), separate_wider_delim(). Link
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G., R for Data Science (2nd ed.), chapters on data transformation and relational data. Link
Bryan, J. et al., R for Excel Users (RStudio workshop materials). Link
ggplot2 documentation, the grammar of graphics in R. Link
Microsoft, Excel functions (by category) reference. Link

Continue Learning

dplyr filter() and select(), the two verbs that replace Excel's AutoFilter and column-hiding, explained in depth.
dplyr joins, every join type (inner, left, right, full, semi, anti) with the cases where each one beats VLOOKUP.
Is R Worth Learning in 2026?, the bigger case for moving off spreadsheets, with data on where R pays off and where Excel still wins.

R for Excel Users: Your Entire Excel Workflow, Translated to R

How do you replace Excel's AutoFilter and sorting in R?

How do you write Excel formulas as R columns?

How do you replace VLOOKUP with R joins?

How do you build a pivot table in R?

How do you reshape Excel data without copy-paste?

How do you replace Excel charts and conditional formatting?

Practice Exercises

Exercise 1: Quarterly tax totals by region

Exercise 2: Region × tier crosstab

Complete Example, A Monthly Sales Report in One Pipe

Summary

References

Continue Learning

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