The function auto_merge() allows easy merging of data
tables. This function is optimised to work with the most common country
data formats. Some of the advantages over other merging functions are
that: 1) it allows merging of multiple tables at the
same time, 2) it can automatically detect columns to
merge, 3) it automatically handles different country
naming conventions and date formats, 4) it
automatically pivots country names or years in table headers.
In this vignette we will go through some examples showcasing these functionalities.
Let’s first start by creating some example data to merge. All the
tables created below contain data relating to countries, but their
format and keys are different. tab1 is a classic
cross-sectional data, providing one data point per country.
tab2 and tab3 are typical panel (or
longitudinal) dataset, providing data for countries over different time
periods. The difference between the two is the structure of the table
(tab2 is in a long format, while tab3 is in a
wide format) and the time frequency of observations (annual vs monthly).
Finally, tab4 and tab5 provide sectoral data
for different countries.
# FIFA female world cups won
tab1 <- data.frame(country = c("UNITED STATES", "GERMANY", "NORWAY", "JAPAN"),
FIFA_cups = c(4, 2, 1, 1))
# nominal yearly GDP estimates from IMF (World economic outlook)
tab2 <- data.frame(nation = c("DEU", "JPN", "USA", "DEU", "JPN", "USA"),
year = c(rep(1980,3), rep(2019,3)),
GDP = c(854, 1128, 2857, 3888, 5118, 21380),
unit = "billion current USD")
# fictitious monthly time series
tab3 <- data.frame(Date = c("01.01.2019", "01.02.2019", "01.03.2019"),
Japan = 1:3,
Norway = 2:4,
Germany = 3:5,
US = 4:6)
# fictitious sectoral data
tab4 <- data.frame(year = 2019,
country = "US",
industry = c("Agriculture", "Mining", "Manifacture", "Energy"),
freq = runif(1:4))
# more fictitious sectoral data
tab5 <- data.frame(country = c("Estados Unidos", "Japon", "Estados Unidos", "Japon"),
sector = c("Agriculture", "Agriculture","Energy", "Energy"),
x = c(T, F, T, F))Using auto_merge() is really easy. All the users needs
to do is list the tables to merge. In most cases, the function will be
able take care of everything else.
auto_merge(tab1, tab2, tab3, tab4, tab5)
#> Identifying columns to merge
#> Table 3 - countries detected in column names, pivoting columns: Japan, Norway, Germany, US
#> Data type for variable time differ across tables - converting to character
#> Converting country names
#> Checking time columns
#> The following columns are being merged:
#>
#> ======= ======================= ==== ========
#> \ country time industry
#> ======= ======================= ==== ========
#> Table 1 country
#> Table 2 nation year
#> Table 3 Table3_pivoted_colnames Date
#> Table 4 country year industry
#> Table 5 country sector
#> ======= ======================= ==== ========
#>
Performing merge: 1/4
Performing merge: 2/4
Performing merge: 3/4
Performing merge: 4/4
Merge complete
#> (Set merging_info to TRUE to save merging details)
#> country FIFA_cups time GDP unit Table3_pivoted_values
#> 1 USA 4 1980-01-01 2857 billion current USD NA
#> 2 USA 4 2019-01-01 21380 billion current USD 4
#> 3 USA 4 2019-01-01 21380 billion current USD 4
#> 4 USA 4 2019-01-01 21380 billion current USD 4
#> 5 USA 4 2019-01-01 21380 billion current USD 4
#> 6 DEU 2 1980-01-01 854 billion current USD NA
#> 7 DEU 2 2019-01-01 3888 billion current USD 3
#> 8 NOR 1 <NA> NA <NA> NA
#> 9 JPN 1 1980-01-01 1128 billion current USD NA
#> 10 JPN 1 2019-01-01 5118 billion current USD 1
#> 11 NOR NA 2019-01-01 NA <NA> 2
#> 12 JPN NA 2019-02-01 NA <NA> 2
#> 13 NOR NA 2019-02-01 NA <NA> 3
#> 14 DEU NA 2019-02-01 NA <NA> 4
#> 15 USA NA 2019-02-01 NA <NA> 5
#> 16 JPN NA 2019-03-01 NA <NA> 3
#> 17 NOR NA 2019-03-01 NA <NA> 4
#> 18 DEU NA 2019-03-01 NA <NA> 5
#> 19 USA NA 2019-03-01 NA <NA> 6
#> 20 JPN NA <NA> NA <NA> NA
#> 21 JPN NA <NA> NA <NA> NA
#> industry freq x
#> 1 <NA> NA NA
#> 2 Agriculture 0.34227582 TRUE
#> 3 Mining 0.39830670 NA
#> 4 Manifacture 0.73758041 NA
#> 5 Energy 0.07772621 TRUE
#> 6 <NA> NA NA
#> 7 <NA> NA NA
#> 8 <NA> NA NA
#> 9 <NA> NA NA
#> 10 <NA> NA NA
#> 11 <NA> NA NA
#> 12 <NA> NA NA
#> 13 <NA> NA NA
#> 14 <NA> NA NA
#> 15 <NA> NA NA
#> 16 <NA> NA NA
#> 17 <NA> NA NA
#> 18 <NA> NA NA
#> 19 <NA> NA NA
#> 20 Agriculture NA FALSE
#> 21 Energy NA FALSEThe function will print to the console some information on the merger status. The most important piece of information is the table summarising all the columns that are being merged together (see reproduction below). Each row in this table corresponds to one of the input data tables, each column to a key that is used for merging. The names in this table are the column names from the original data tables. The names in the header of the summary table is the name of the merged columns in the final output table. For instance, the summary table is telling us that the columns “year”, “Date” and “year”, respectively from the third, fourth and fifth input table were merged together into a column called “time”.
| country | time | industry | |
|---|---|---|---|
| Table 1 | country | NA | NA |
| Table 2 | nation | year | NA |
| Table 3 | Table3_pivoted_colnames | Date | NA |
| Table 4 | country | year | industry |
| Table 5 | country | NA | sector |
It is always a good idea to inspect the summary table and make sure that the correct columns were merged. In case the function did not merge the right columns, the user can manually provide a merging order. For more information, you can may refer to the section in this article Manual merging orders.
The function also accept a lists of tables as inputs. This could be useful when passing a dynamic set of tables to the function.
data <- list(tab1, tab2, tab3, tab4, tab5)
# the following two commands are equivalent
auto_merge(tab1, tab2, tab3, tab4, tab5)
auto_merge(data)auto_merge() will automatically recognise country names
and convert them to the same nomenclature for merging. For example, the
country names in tab1 and tab2 have a
different format, but with auto_merge() there is no need of
converting the names beforehand. The function will perform this
automatically. This feature is based on the function
country_name(), which is described in this
article.
It is possible to change the destination nomenclature with the
argument country_to. By default, all country names will be
converted to ISO 3166-1 alpha-3 standard. Any of the nomenclatures
supported by country_name() can be requested.
# changing the country names to UN spanish official name
auto_merge(tab1, tab2, country_to = "UN_es", verbose = FALSE)
#> country FIFA_cups time GDP unit
#> 1 Estados Unidos de América 4 1980 2857 billion current USD
#> 2 Estados Unidos de América 4 2019 21380 billion current USD
#> 3 Alemania 2 1980 854 billion current USD
#> 4 Alemania 2 2019 3888 billion current USD
#> 5 Noruega 1 NA NA <NA>
#> 6 Japón 1 1980 1128 billion current USD
#> 7 Japón 1 2019 5118 billion current USDWhen country names or years are found in the column names, the
function will automatically transform the table from a wide to a long
format by pivoting the country/year columns. At least 3 country
names or years need to be present in the table header to trigger the
automatic pivoting. Pivoting can be turned off by setting
auto_melt = FALSE. For additional information, refer to the
documentation of the function auto_melt().
In the first example below, the auto_melt option is
turned on (this is the default behaviour). The function detects country
names in the header of the table and proceeds to pivoting the
corresponding columns. Pivoted columns will have the name
"Table?_pivoted_colnames" and the data from these columns
will be stored in a column "Table?_pivoted_data" (here ?
stands for the number of the table in the provided input). A message is
printed on the console to inform the user that columns have been
pivoted.
Notice that in the second example no pivoting is performed. As a result, no shared key was found for merging and the two tables were just stacked in the final output.
# with auto_melt
auto_merge(tab1, tab3)
#> Identifying columns to merge
#> Table 2 - countries detected in column names, pivoting columns: Japan, Norway, Germany, US
#> Converting country names
#> Checking time columns
#> The following columns are being merged:
#>
#> ======= ======================= ====
#> \ country time
#> ======= ======================= ====
#> Table 1 country
#> Table 2 Table2_pivoted_colnames Date
#> ======= ======================= ====
#>
Performing merge: 1/1
Merge complete
#> (Set merging_info to TRUE to save merging details)
#> country FIFA_cups time Table2_pivoted_values
#> 1 USA 4 2019-01-01 4
#> 2 USA 4 2019-02-01 5
#> 3 USA 4 2019-03-01 6
#> 4 DEU 2 2019-01-01 3
#> 5 DEU 2 2019-02-01 4
#> 6 DEU 2 2019-03-01 5
#> 7 NOR 1 2019-01-01 2
#> 8 NOR 1 2019-02-01 3
#> 9 NOR 1 2019-03-01 4
#> 10 JPN 1 2019-01-01 1
#> 11 JPN 1 2019-02-01 2
#> 12 JPN 1 2019-03-01 3
# without auto_melt
auto_merge(tab1, tab3, auto_melt = FALSE)
#> Identifying columns to merge
#> Converting country names
#> Checking time columns
#> The following columns are being merged:
#>
#> ======= ======= ====
#> \ country time
#> ======= ======= ====
#> Table 1 country
#> Table 2 Date
#> ======= ======= ====
#>
Performing merge: 1/1
Merge complete
#> (Set merging_info to TRUE to save merging details)
#> country FIFA_cups time Japan Norway Germany US
#> 1 USA 4 <NA> NA NA NA NA
#> 2 DEU 2 <NA> NA NA NA NA
#> 3 NOR 1 <NA> NA NA NA NA
#> 4 JPN 1 <NA> NA NA NA NA
#> 5 <NA> NA 2019-01-01 1 2 3 4
#> 6 <NA> NA 2019-02-01 2 3 4 5
#> 7 <NA> NA 2019-03-01 3 4 5 6By default, the function will return a full join of
all the tables. This means that all the table-key combinations are
conserved in the output table. If no information is available for a
variable, NA will be used to fill the output table. By
contrast, an inner join only keeps the combination of
keys that are available across all tables. Click here for more
information on join types.
An inner join can be requested with the argument
inner_join.
auto_merge(tab1, tab2, tab3, tab4, tab5, inner_join = TRUE)
#> Identifying columns to merge
#> Table 3 - countries detected in column names, pivoting columns: Japan, Norway, Germany, US
#> Data type for variable time differ across tables - converting to character
#> Converting country names
#> Checking time columns
#> The following columns are being merged:
#>
#> ======= ======================= ==== ========
#> \ country time industry
#> ======= ======================= ==== ========
#> Table 1 country
#> Table 2 nation year
#> Table 3 Table3_pivoted_colnames Date
#> Table 4 country year industry
#> Table 5 country sector
#> ======= ======================= ==== ========
#>
Performing merge: 1/4
Performing merge: 2/4
Performing merge: 3/4
Performing merge: 4/4
Merge complete
#> (Set merging_info to TRUE to save merging details)
#> country FIFA_cups time GDP unit Table3_pivoted_values
#> 1 USA 4 2019-01-01 21380 billion current USD 4
#> 2 USA 4 2019-01-01 21380 billion current USD 4
#> industry freq x
#> 1 Agriculture 0.34227582 TRUE
#> 2 Energy 0.07772621 TRUEThe function’s messages tend to be very wordy. If you wish to
suppress messages printed to the console you can just turn them off with
the option verbose = FALSE.
auto_merge(tab1, tab2, tab3, tab4, tab5, verbose = F)If the argument merging_info is set to
TRUE, the function will return a list object containing
additional information on the merger. The following objects are saved in
the output:
merged_table: This is the merged data table (i.e. the
table you get when merging_info = FALSE).info_merged_columns: This is the summary table that is
printed on the console. It gives an overview of all the columns that are
merged together. It is always a good idea to inspect this table and make
sure that the correct columns were merged.info_country_names: If any country column is found,
here the user can find a table summarising the conversion of all the
country names to the destination nomenclature. This can be used to check
if countries were correctly identified.info_time_formats: similar to the above, this table
summarises any conversion that was made to date columns. It can be used
to check how time columns were handled by the function.pivotted_columns: When country names or years are found
in the header of the table, auto_merge() automatically
“pivots” the table. Pivoting transforms the table into a long
format. The name of all columns that have been pivotted is saved in
a list format here.call: This is a list recapitulating the user’s
call.The automatic merging process starts by first identifying the key of
each table, i.e. a set of variables identifying the entries in the
table. This process is optimised for common formats of country data. The
function will then try to match key columns across tables based on their
values. Columns containing country names and time information are
identified and are processed to take into account different
nomenclatures and time formats. This automatic process works for the
most common dataset structures, but it is not foolproof. Therefore, we
always advise to check the columns that are being merged by setting
verbose = TRUE and reading the printout. Moreover, this
automatic detection process can increase the overall merging time
considerably. This can be especially long for tables containing many
columns or when a large number of tables is being merged.
The user can pass a merge order with argument by to save
time during the merger, or if the detection process did not succeed.
Inputs need to be provided either 1) as a list of column names, or 2) a
vector of regular expressions. Here we will go in more details on the
format requirements.
A) List of column names
In case a list is passed, there are 3 key requirements regarding the format.
NA can be inserted for tables that do not contain
the variable, and names should be ordered in the same order of the input
tables (i.e. the first column name should be present in the first table
being merged).In the example below, we are requesting to merge the columns
"country" and "nation" from tab1
and tab2, and the columns "year" from
tab2 and tab4. Notice that we are inserting
NA whenever the key is not present in the table. In
addition, we are also changing the name in the final table to
"COUNTRIES" and "YEARS" by naming the elemnts
in the list.
# asking to merge country and year columns manually with a list of column names
auto_merge(tab1, tab2, tab4,
by = list("COUNTRIES" = c("country", "nation", NA),
"YEARS" = c(NA, "year", "year")))
#> Identifying columns to merge
#> Converting country names
#> Checking time columns
#> The following columns are being merged:
#>
#> ======= ========= =====
#> \ COUNTRIES YEARS
#> ======= ========= =====
#> Table 1 country
#> Table 2 nation year
#> Table 3 year
#> ======= ========= =====
#>
Performing merge: 1/2
Performing merge: 2/2
Merge complete
#> (Set merging_info to TRUE to save merging details)
#> COUNTRIES FIFA_cups YEARS GDP unit country industry
#> 1 USA 4 1980 2857 billion current USD <NA> <NA>
#> 2 USA 4 2019 21380 billion current USD US Agriculture
#> 3 USA 4 2019 21380 billion current USD US Mining
#> 4 USA 4 2019 21380 billion current USD US Manifacture
#> 5 USA 4 2019 21380 billion current USD US Energy
#> 6 DEU 2 1980 854 billion current USD <NA> <NA>
#> 7 DEU 2 2019 3888 billion current USD US Agriculture
#> 8 DEU 2 2019 3888 billion current USD US Mining
#> 9 DEU 2 2019 3888 billion current USD US Manifacture
#> 10 DEU 2 2019 3888 billion current USD US Energy
#> 11 NOR 1 NA NA <NA> <NA> <NA>
#> 12 JPN 1 1980 1128 billion current USD <NA> <NA>
#> 13 JPN 1 2019 5118 billion current USD US Agriculture
#> 14 JPN 1 2019 5118 billion current USD US Mining
#> 15 JPN 1 2019 5118 billion current USD US Manifacture
#> 16 JPN 1 2019 5118 billion current USD US Energy
#> freq
#> 1 NA
#> 2 0.34227582
#> 3 0.39830670
#> 4 0.73758041
#> 5 0.07772621
#> 6 NA
#> 7 0.34227582
#> 8 0.39830670
#> 9 0.73758041
#> 10 0.07772621
#> 11 NA
#> 12 NA
#> 13 0.34227582
#> 14 0.39830670
#> 15 0.73758041
#> 16 0.07772621B) Vector of regular expressions
In case a vector is passed, each element in the vector is interpreted as a regular expression to be used for matching the columns to be merged. This means that we need to provide one regex for each variable we want to join. In our example, we would need one regex for identifying country columns and one for identifying year columns. The function will scan through the column names and pick the first column matching with the regex’s pattern. A name can be provided for the variables in the final table by naming the elements in the vector.
For example, we can achieve exactly the same merger simply with the
following order:
by = c("COUNTRIES" = "country|nation", "YEARS" = "year").
Since the function will pick the first matching column in each table,
the user must be careful that the regex does not match with any other
undesired column in the tables.
# asking to merge country and year columns manually with a vector of regular expressions
auto_merge(tab1, tab2, tab4,
by = list("COUNTRIES" = c("country", "nation", NA),
"YEARS" = c(NA, "year", "year")))
#> Identifying columns to merge
#> Converting country names
#> Checking time columns
#> The following columns are being merged:
#>
#> ======= ========= =====
#> \ COUNTRIES YEARS
#> ======= ========= =====
#> Table 1 country
#> Table 2 nation year
#> Table 3 year
#> ======= ========= =====
#>
Performing merge: 1/2
Performing merge: 2/2
Merge complete
#> (Set merging_info to TRUE to save merging details)
#> COUNTRIES FIFA_cups YEARS GDP unit country industry
#> 1 USA 4 1980 2857 billion current USD <NA> <NA>
#> 2 USA 4 2019 21380 billion current USD US Agriculture
#> 3 USA 4 2019 21380 billion current USD US Mining
#> 4 USA 4 2019 21380 billion current USD US Manifacture
#> 5 USA 4 2019 21380 billion current USD US Energy
#> 6 DEU 2 1980 854 billion current USD <NA> <NA>
#> 7 DEU 2 2019 3888 billion current USD US Agriculture
#> 8 DEU 2 2019 3888 billion current USD US Mining
#> 9 DEU 2 2019 3888 billion current USD US Manifacture
#> 10 DEU 2 2019 3888 billion current USD US Energy
#> 11 NOR 1 NA NA <NA> <NA> <NA>
#> 12 JPN 1 1980 1128 billion current USD <NA> <NA>
#> 13 JPN 1 2019 5118 billion current USD US Agriculture
#> 14 JPN 1 2019 5118 billion current USD US Mining
#> 15 JPN 1 2019 5118 billion current USD US Manifacture
#> 16 JPN 1 2019 5118 billion current USD US Energy
#> freq
#> 1 NA
#> 2 0.34227582
#> 3 0.39830670
#> 4 0.73758041
#> 5 0.07772621
#> 6 NA
#> 7 0.34227582
#> 8 0.39830670
#> 9 0.73758041
#> 10 0.07772621
#> 11 NA
#> 12 NA
#> 13 0.34227582
#> 14 0.39830670
#> 15 0.73758041
#> 16 0.07772621