Introduction

In this post in the R:case4base series we will look at one of the most common operations on multiple data frames – merge, also known as JOIN in SQL terms.

We will learn how to do the 4 basic types of join – inner, left, right and full join with base R and show how to perform the same with tidyverse’s dplyr and data.table’s methods. A quick benchmark will also be included.

Joins

Contents

1. Merging (joining) two data frames with base R
2. The arguments of merge
3. Merging multiple data frames
4. Alternatives to base R
5. Quick benchmarking
6. TL;DR – Just want the code
7. References

Merging (joining) two data frames with base R

To showcase the merging, we will use a very slightly modified dataset provided by Hadley Wickham’s nycflights13 package, mainly the flights and weather data frames. Let’s get right into it and simply show how to perform the different types of joins with base R.

First, we prepare the data and store the columns we will merge by (join on) into mergeCols:

dataurl <- "https://jozefhajnala.gitlab.io/r/post/data/"
weather <- readRDS(url(paste0(dataurl, "r006/weather.rds")))
flights <- readRDS(url(paste0(dataurl, "r006/flights.rds")))

mergeCols <- c("time_hour", "origin")

head(flights)
##   year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013     1   1      517            515         2      830            819
## 2 2013     1   1      533            529         4      850            830
## 3 2013     1   1      542            540         2      923            850
## 4 2013     1   1      544            545        -1     1004           1022
## 5 2013     1   1      554            600        -6      812            837
## 6 2013     1   1      554            558        -4      740            728
##   arr_delay carrier flight tailnum origin dest air_time distance hour
## 1        11      UA   1545  N14228    EWR  IAH      227     1400    5
## 2        20      UA   1714  N24211    LGA  IAH      227     1416    5
## 3        33      AA   1141  N619AA    JFK  MIA      160     1089    5
## 4       -18      B6    725  N804JB    JFK  BQN      183     1576    5
## 5       -25      DL    461  N668DN    LGA  ATL      116      762    6
## 6        12      UA   1696  N39463    EWR  ORD      150      719    5
##   minute           time_hour
## 1     15 2013-01-01 05:00:00
## 2     29 2013-01-01 05:00:00
## 3     40 2013-01-01 05:00:00
## 4     45 2013-01-01 05:00:00
## 5      0 2013-01-01 06:00:00
## 6     58 2013-01-01 05:00:00
head(weather)
##   origin year month day hour  temp  dewp humid wind_dir wind_speed
## 1    EWR 2013     1   1    1 39.02 26.06 59.37      270   10.35702
## 2    EWR 2013     1   1    2 39.02 26.96 61.63      250    8.05546
## 3    EWR 2013     1   1    3 39.02 28.04 64.43      240   11.50780
## 4    EWR 2013     1   1    4 39.92 28.04 62.21      250   12.65858
## 5    EWR 2013     1   1    5 39.02 28.04 64.43      260   12.65858
## 6    EWR 2013     1   1    6 37.94 28.04 67.21      240   11.50780
##   wind_gust precip pressure visib           time_hour
## 1        NA      0   1012.0    10 2013-01-01 01:00:00
## 2        NA      0   1012.3    10 2013-01-01 02:00:00
## 3        NA      0   1012.5    10 2013-01-01 03:00:00
## 4        NA      0   1012.2    10 2013-01-01 04:00:00
## 5        NA      0   1011.9    10 2013-01-01 05:00:00
## 6        NA      0   1012.4    10 2013-01-01 06:00:00

Now, we show how to perform the 4 merges (joins):

inner <- merge(flights, weather, by = mergeCols)

left  <- merge(flights, weather, by = mergeCols, all.x = TRUE)

right <- merge(flights, weather, by = mergeCols, all.y = TRUE)

full <- merge(flights, weather, by = mergeCols, all = TRUE)

# Cross Join (Cartesian product)
cross <- merge(flights, weather, by = NULL)

# Natural Join
natural <- merge(flights, weather)

The arguments of merge

The key arguments of base merge data.frame method are:

• x, y - the 2 data frames to be merged
• by - names of the columns to merge on. If the column names are different in the two data frames to merge, we can specify by.x and by.y with the names of the columns in the respective data frames. The by argument can also be specified by number, logical vector or left unspecified, in which case it defaults to the intersection of the names of the two data frames. From best practice perspective it is advisable to always specify the argument explicitly, ideally by column names.
• all, all.x, all.y - default to FALSE and can be used specify the type of join we want to perform:
  • all = FALSE (the default) - gives an inner join - combines the rows in the two data frames that match on the by columns
  • all.x = TRUE - gives a left (outer) join - adds rows that are present in x, even though they do not have a matching row in y to the result for all = FALSE
  • all.y = TRUE - gives a right (outer) join - adds rows that are present in y, even though they do not have a matching row in x to the result for all = FALSE
  • all = TRUE - gives a full (outer) join. This is a shorthand for all.x = TRUE and all.y = TRUE

Other arguments include

• sort - if TRUE (default), results are sorted on the by columns
• suffixes - length 2 character vector, specifying the suffixes to be used for making the names of columns in the result which are not used for merging unique
• incomparables - for single-column merging only, a vector of values that cannot be matched. Any value in x matching a value in this vector is assigned the nomatch value (which can be passed using ...)

Merging multiple data frames

For this example, let us have a list of all the data frames included in the nycflights13 package, slightly updated such that they can me merged with the default value for by, purely for this exercise, and store them into a list called flightsList:

flightsList <- readRDS(url(paste0(dataurl, "r006/nycflights13-list.rds")))
lapply(flightsList, function(x) c(toString(dim(x)), toString(names(x))))
## $flights
## [1] "336776, 19"                                                                                                                                                                     
## [2] "year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr_time, arr_delay, carrier, flight, tailnum, origin, dest, air_time, distance, hour, minute, time_hour"
## 
## $weather
## [1] "26115, 15"                                                                                                             
## [2] "origin, year, month, day, hour, temp, dewp, humid, wind_dir, wind_speed, wind_gust, precip, pressure, visib, time_hour"
## 
## $airlines
## [1] "16, 2"         "carrier, name"
## 
## $airports
## [1] "1458, 8"                                           
## [2] "origin, airportname, lat, lon, alt, tz, dst, tzone"
## 
## $planes
## [1] "3322, 9"                                                                            
## [2] "tailnum, yearmanufactured, type, manufacturer, model, engines, seats, speed, engine"

Since merge is designed to work with 2 data frames, merging multiple data frames can of course be achieved by nesting the calls to merge:

multiFull <- merge(merge(merge(merge(
  flightsList[[1L]],
  flightsList[[2L]], all = TRUE),
  flightsList[[3L]], all = TRUE),
  flightsList[[4L]], all = TRUE),
  flightsList[[5L]], all = TRUE)

We can however achieve this same goal much more elegantly, taking advantage of base R’s Reduce function:

# For Inner Join
multi_inner <- Reduce(
  function(x, y, ...) merge(x, y, ...), 
  flightsList
)

# For Full (Outer) Join
multi_full <- Reduce(
  function(x, y, ...) merge(x, y, all = TRUE, ...),
  flightsList
)

Note that this example is oversimplified and the data was updated such that the default values for by give meaningful joins. For example, in the original planes data frame the column year would have been matched onto the year column of the flights data frame, which is nonsensical as the years have different meanings in the two data frames. This is why we renamed the year column in the planes data frame to yearmanufactured for the above example.

Alternatives to base R

library(dplyr)
inner_dplyr <- inner_join(flights, weather, by = mergeCols)
left_dplyr  <- left_join(flights,  weather, by = mergeCols)
right_dplyr <- right_join(flights, weather, by = mergeCols)
full_dplyr  <- full_join(flights,  weather, by = mergeCols)

inner_dplyr <- flights %>% inner_join(weather, by = mergeCols)
left_dplyr  <- flights %>% left_join(weather,  by = mergeCols)
right_dplyr <- flights %>% right_join(weather, by = mergeCols)
full_dplyr  <- flights %>% full_join(weather,  by = mergeCols)

library(data.table)
weather <- as.data.table(weather)
flights <- as.data.table(flights)
setkeyv(weather, mergeCols)
setkeyv(flights, mergeCols)

# Note that this is identical to the code for base 
# The data.table method is called automatically for objects of class data.table
inner_dt <- merge(flights, weather, by = mergeCols)
left_dt  <- merge(flights, weather, by = mergeCols, all.x = TRUE)
right_dt <- merge(flights, weather, by = mergeCols, all.y = TRUE)
full_dt  <- merge(flights, weather, by = mergeCols, all = TRUE)

inner_dt <- flights[weather, on = mergeCols, nomatch = 0]
left_dt  <- weather[flights, on = mergeCols]
right_dt <- flights[weather, on = mergeCols]

Quick benchmarking

For a quick overview, lets look at a basic benchmark without package loading overhead for each of the mentioned packages:

bench_inner <- microbenchmark::microbenchmark(times = 100,
  base        = base::merge.data.frame(flights, weather, by = mergeCols),
  base_nosort = base::merge.data.frame(flights, weather, by = mergeCols, sort = FALSE),
  dt_merge    = merge(flights, weather, by = mergeCols),
  dt_subset   = flights[weather, on = mergeCols, nomatch = 0], 
  dplyr       = inner_join(flights, weather, by = mergeCols),
  dplyr_pipe  = flights %>% inner_join(weather, by = mergeCols)
)

bench_outer <- microbenchmark::microbenchmark(times = 100,
  base        = base::merge.data.frame(flights, weather, by = mergeCols, all = TRUE),
  base_nosort = base::merge.data.frame(flights, weather, by = mergeCols, all = TRUE, sort = FALSE),
  dt_merge    = merge(flights, weather, by = mergeCols, all = TRUE),
  dplyr       = full_join(flights, weather, by = mergeCols),
  dplyr_pipe  = flights %>% full_join(weather, by = mergeCols)
)

TL;DR - Just want the code

No time for reading? Click here to get just the code with commentary

References

• Animated inner join, left join, right join and full join by Garrick Aden-Buie for an easier understanding
• Base merge help
• Join two tbls together with dplyr
• Merge two data.tables
• Joining Data in R with dplyr by Wiliam Surles
• Join (SQL) Wikipedia page
• The nycflights13 package on CRAN

Exactly 100 years ago tomorrow, October 28th, 1918 the independence of Czechoslovakia was proclaimed by the Czechoslovak National Council, resulting in the creation of the first democratic state of Czechs and Slovaks in history.

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