J. Kavanagh
2023-04-08
This is a series of excercises to get everyone used to the RStudio environment and with some of the code segments and commands we went over in the past few weeks.
My advice is to create a new R Script file for this exercise and copy the following code into it, saving repeatedly as you go.
First load the libaries you’ll be using for these excercises.
Secondly, import the four csv files that make up the SGL Database
read.csv('baptisms.csv',
stringsAsFactors = F,
na.strings= c("NA", " ", "")) -> baptisms
read.csv('burials.csv',
stringsAsFactors = F,
na.strings= c("NA", " ", "")) -> burials
read.csv('marriages.csv',
stringsAsFactors = F,
na.strings= c("NA", " ", "")) -> marriages
read.csv('abjurations.csv',
stringsAsFactors = F,
na.strings= c("NA", " ", "")) -> abjurations
read.csv('signatures.csv',
stringsAsFactors = F,
na.strings= c("NA", " ", "")) -> signaturesOnce the files have been imported they will be imported as data.frames this is the R equivalent of a Excel file. We can get an overview of the data we have just imported using the glimpse() command, which provides an overview of the data.
You can see the different classes of data, in this case everything is a chr or character. Numerical data is often written as int or integer. In the case of the Date of Baptism column we will need to change that to a Date type.
## Rows: 954
## Columns: 69
## $ Child_Name_FR <chr> "Therese Margueritt…
## $ Child_Forename_EN <chr> "Therese Margueritt…
## $ Child_Surname_EN <chr> "Melfort", "Macdonn…
## $ Child_Nationality_Infer <chr> "Irish", "Irish", "…
## $ Gender <chr> "Female", "Male", "…
## $ Child_Religion_Infer <chr> "Roman Catholic", "…
## $ Date_of_Birth <chr> "1689-01-08", "1689…
## $ Date_of_Birth_Partial <chr> NA, NA, NA, NA, NA,…
## $ Date_of_Baptism <chr> "1689-01-08", "1689…
## $ Place_of_Baptism <chr> "St Germain-en-Laye…
## $ Church_of_Baptismal_Ceremony <chr> "Parish church", "P…
## $ Street_of_Residence <chr> NA, NA, NA, NA, NA,…
## $ Parents_Status <chr> "Married", "Married…
## $ Father_Name_FR <chr> "Jean Comte de Melf…
## $ Father_Forename_EN <chr> "John", "Randall", …
## $ Father_Surname_EN <chr> "Melfort", "Macdonn…
## $ Father_Nationality_Stated <chr> NA, NA, NA, NA, NA,…
## $ Father_Nationality_Infer <chr> "Irish", "Irish", "…
## $ Father_Rank <chr> "Count", "Gentleman…
## $ Father_Occupation <chr> "Secretary of the r…
## $ Father_Occupation_Type <chr> "Noble", "Noble", "…
## $ Member_of_Jacobite_Army <chr> "Yes", NA, "Yes", N…
## $ Father_Residence <chr> "St Germain-en-Laye…
## $ Father_Register_Signature <chr> "No", "No", "No", "…
## $ Mother_Name_FR <chr> "Euphenia Vicalace"…
## $ Mother_Forenam_EN <chr> "Euphenia", "Hannah…
## $ Mother_Surname_EN <chr> "Vicalace", "Roche"…
## $ Mother_Nationality_Stated <chr> NA, NA, NA, "Englis…
## $ Mother_Nationality_Infer <chr> NA, "Irish", "Irish…
## $ Mother_Religion_Infer <chr> "Roman Catholic", "…
## $ Mother_Rank <chr> NA, NA, NA, NA, NA,…
## $ Mother_Occupation <chr> NA, NA, NA, NA, NA,…
## $ Mother_Occupation_Type <chr> NA, NA, NA, NA, NA,…
## $ Mother_Residence <chr> "St Germain-en-Laye…
## $ Mother_Register_Signature <chr> "No", "No", "No", "…
## $ Godfather_Name_FR <chr> "Jacques Comte de D…
## $ Godfather_Forename_EN <chr> "Jacque", NA, NA, "…
## $ Godfather_Surname_EN <chr> "Drummond", NA, "St…
## $ Godfather_Nationality_Stated <chr> NA, NA, "English", …
## $ Godfather_Nationality_Infer <chr> "Scottish", NA, "En…
## $ Godfather_Rank <chr> "Count", NA, "Lord"…
## $ Godfather_Occupation <chr> "Count", NA, "Lord"…
## $ Godfather_Occupation_Type <chr> "Noble", NA, "Noble…
## $ Godfather_Residence <chr> "St Germain-en-Laye…
## $ Godfather_familial_relationship_to_father_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godfather_familial_relationship_to_mother_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godfather_Register_Signature <chr> "Yes", "Yes", "Yes"…
## $ Godmother_Name_FR <chr> "Marie Conny", "Ann…
## $ Godmother_Forename_EN <chr> "Marie", "Anne", "H…
## $ Godmother_Surname_EN <chr> "Cormy", "Bagnall",…
## $ Godmother_Nationality_Stated <chr> NA, NA, "English", …
## $ Godmother_Nationality_Infer <chr> "Irish", "Irish", "…
## $ Godmother_Religion_Infer <chr> "Roman Catholic", "…
## $ Godmother_Rank <chr> NA, NA, NA, "Wife o…
## $ Godmother_Occupation <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Occupation_Type <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Father_Name_FR <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Father_Forename_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Father_Surname_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Mother_Name_FR <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Mother_Forename_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Mother_Surname_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Residence <chr> "St Germain-en-Laye…
## $ Godmother_relationship_to_father_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godmother_relationship_to_mother_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Register_Signature <chr> "Yes", "Yes", "Yes"…
## $ Officiating_Priest_Nationality <chr> "French", "French",…
## $ Additional_Notes <chr> NA, NA, NA, NA, NA,…
## $ Archival_Reference <chr> "5MI 1734 [1168921/…We are going to to do two simple tasks here. First is to change the dataframes to table data.frames, its a relatively simple procedure and what is results is a simpler file structure. You don’t have to do this, but it does result in a cleaner visual aesthetic.
baptisms <- as_tibble(baptisms)
burials <- as_tibble(burials)
marriages <- as_tibble(marriages)
abjurations <- as_tibble(abjurations)Second task is to change the date information into a Date type, this will allow us to filter by day, month or years. This needs to occur for any analysis of dates.
You’ll see that the Date_of_Baptism column has been changed to Date from chr
## Rows: 954
## Columns: 69
## $ Child_Name_FR <chr> "Therese Margueritt…
## $ Child_Forename_EN <chr> "Therese Margueritt…
## $ Child_Surname_EN <chr> "Melfort", "Macdonn…
## $ Child_Nationality_Infer <chr> "Irish", "Irish", "…
## $ Gender <chr> "Female", "Male", "…
## $ Child_Religion_Infer <chr> "Roman Catholic", "…
## $ Date_of_Birth <chr> "1689-01-08", "1689…
## $ Date_of_Birth_Partial <chr> NA, NA, NA, NA, NA,…
## $ Date_of_Baptism <date> 1689-01-08, 1689-0…
## $ Place_of_Baptism <chr> "St Germain-en-Laye…
## $ Church_of_Baptismal_Ceremony <chr> "Parish church", "P…
## $ Street_of_Residence <chr> NA, NA, NA, NA, NA,…
## $ Parents_Status <chr> "Married", "Married…
## $ Father_Name_FR <chr> "Jean Comte de Melf…
## $ Father_Forename_EN <chr> "John", "Randall", …
## $ Father_Surname_EN <chr> "Melfort", "Macdonn…
## $ Father_Nationality_Stated <chr> NA, NA, NA, NA, NA,…
## $ Father_Nationality_Infer <chr> "Irish", "Irish", "…
## $ Father_Rank <chr> "Count", "Gentleman…
## $ Father_Occupation <chr> "Secretary of the r…
## $ Father_Occupation_Type <chr> "Noble", "Noble", "…
## $ Member_of_Jacobite_Army <chr> "Yes", NA, "Yes", N…
## $ Father_Residence <chr> "St Germain-en-Laye…
## $ Father_Register_Signature <chr> "No", "No", "No", "…
## $ Mother_Name_FR <chr> "Euphenia Vicalace"…
## $ Mother_Forenam_EN <chr> "Euphenia", "Hannah…
## $ Mother_Surname_EN <chr> "Vicalace", "Roche"…
## $ Mother_Nationality_Stated <chr> NA, NA, NA, "Englis…
## $ Mother_Nationality_Infer <chr> NA, "Irish", "Irish…
## $ Mother_Religion_Infer <chr> "Roman Catholic", "…
## $ Mother_Rank <chr> NA, NA, NA, NA, NA,…
## $ Mother_Occupation <chr> NA, NA, NA, NA, NA,…
## $ Mother_Occupation_Type <chr> NA, NA, NA, NA, NA,…
## $ Mother_Residence <chr> "St Germain-en-Laye…
## $ Mother_Register_Signature <chr> "No", "No", "No", "…
## $ Godfather_Name_FR <chr> "Jacques Comte de D…
## $ Godfather_Forename_EN <chr> "Jacque", NA, NA, "…
## $ Godfather_Surname_EN <chr> "Drummond", NA, "St…
## $ Godfather_Nationality_Stated <chr> NA, NA, "English", …
## $ Godfather_Nationality_Infer <chr> "Scottish", NA, "En…
## $ Godfather_Rank <chr> "Count", NA, "Lord"…
## $ Godfather_Occupation <chr> "Count", NA, "Lord"…
## $ Godfather_Occupation_Type <chr> "Noble", NA, "Noble…
## $ Godfather_Residence <chr> "St Germain-en-Laye…
## $ Godfather_familial_relationship_to_father_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godfather_familial_relationship_to_mother_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godfather_Register_Signature <chr> "Yes", "Yes", "Yes"…
## $ Godmother_Name_FR <chr> "Marie Conny", "Ann…
## $ Godmother_Forename_EN <chr> "Marie", "Anne", "H…
## $ Godmother_Surname_EN <chr> "Cormy", "Bagnall",…
## $ Godmother_Nationality_Stated <chr> NA, NA, "English", …
## $ Godmother_Nationality_Infer <chr> "Irish", "Irish", "…
## $ Godmother_Religion_Infer <chr> "Roman Catholic", "…
## $ Godmother_Rank <chr> NA, NA, NA, "Wife o…
## $ Godmother_Occupation <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Occupation_Type <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Father_Name_FR <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Father_Forename_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Father_Surname_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Mother_Name_FR <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Mother_Forename_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Mother_Surname_EN <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Residence <chr> "St Germain-en-Laye…
## $ Godmother_relationship_to_father_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godmother_relationship_to_mother_of_child <chr> NA, NA, NA, NA, NA,…
## $ Godmother_Register_Signature <chr> "Yes", "Yes", "Yes"…
## $ Officiating_Priest_Nationality <chr> "French", "French",…
## $ Additional_Notes <chr> NA, NA, NA, NA, NA,…
## $ Archival_Reference <chr> "5MI 1734 [1168921/…Using the count() command you can create fast summary statistics for example using the Age_Ranges column within the burials dataset.
## # A tibble: 16 × 2
## Age_Ranges n
## <chr> <int>
## 1 1-5 134
## 2 11-15 15
## 3 16-20 33
## 4 21-25 31
## 5 26-30 34
## 6 31-35 26
## 7 36-40 55
## 8 41-45 37
## 9 46-50 57
## 10 51-55 30
## 11 56-60 54
## 12 6-10 51
## 13 61-65 30
## 14 65 and older 167
## 15 Less than 1 year 146
## 16 <NA> 30As we are using the ‘Tidyverse’ library, the language used is very straightforward. Therefore in order to filter by a specific characteristic, the code is very simple.
# This filters the burials and creates a new subset of Irish only burials
burials %>% filter(Nationality_Infer == "Irish") -> burials_irish
# You can see the difference in size
burials_irish## # A tibble: 897 × 33
## Type.of.Burial Name_FR Foren…¹ Surna…² Natio…³ Gender Relig…⁴ Age_F…⁵ Age_P…⁶
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr>
## 1 Charitable Patric… Patrick MacCor… Irish Male Roman … 2 <NA>
## 2 Charitable Marie … Mary T… Maher Irish Female Roman … NA 20 mon…
## 3 Charitable Alexan… Alexan… Gordon Irish Male Roman … NA 18 mon…
## 4 Charitable George… George… Willia… Irish Male Roman … 2 <NA>
## 5 Charitable Alexis… Alexis MacLau… Irish Male Roman … 2 <NA>
## 6 Charitable Guilla… William O'Brien Irish Male Roman … 2 <NA>
## 7 Charitable Hanain… Hanain… MacDon… Irish Female Roman … 3 <NA>
## 8 Charitable Jacque… Jacque Baggott Irish Male Roman … NA 18 mon…
## 9 Regular Honora… Honora… Jennin… Irish Female Roman … NA 16 mon…
## 10 Regular Jacque… Jacque Carbery Irish Male Roman … 1 <NA>
## # … with 887 more rows, 24 more variables: Age_Ranges <chr>, Occupation <chr>,
## # Occupation_Type <chr>, Marital_Status <chr>, Spouse_Name_FR <chr>,
## # Spouse_Forename_EN <chr>, Spouse_Surname_EN <chr>, Date_of_Burial <date>,
## # Domicile_Inferred <chr>, Place_of_Burial <chr>, Street_Name <chr>,
## # Father_Name_FR <chr>, Father_Forename_EN <chr>, Father_Surname_EN <chr>,
## # Father_Nationality <chr>, Father_Domicile <chr>, Mother_Name_FR <chr>,
## # Mother_Forename_EN <chr>, Mother_Surname_EN <chr>, …These are the main filtering options:
The simple logical operators are for the filter command are:
== (equal to)
=! (not equal to)
& (and)
| (or)
! (not)Exercise 1. Filter the marriages for non-Irish brides and grooms. Create a separate file for each.
To count the number of baptisms, you need to first count all the marriages and create a new table. As R often uses ‘n’ to signify a new variable it is often useful to rename columns to better represent what the data actually refers to.
# Create a new dataframe of baptisms
baptisms_irish %>% count(Date_of_Baptism) -> baptisms_irish_dates
# Check the results, we now have a smaller dataframe showing all the Baptisms per day
baptisms_irish_dates## # A tibble: 694 × 2
## Date_of_Baptism n
## <date> <int>
## 1 1689-03-22 1
## 2 1689-06-14 1
## 3 1689-10-28 1
## 4 1689-12-27 1
## 5 1690-02-04 1
## 6 1690-03-28 1
## 7 1690-04-03 1
## 8 1690-05-11 1
## 9 1690-05-30 1
## 10 1690-08-17 1
## # … with 684 more rowsRenaming the columns prevents confusion later on and makes for more accurate findings.
## # A tibble: 694 × 2
## Date No
## <date> <int>
## 1 1689-03-22 1
## 2 1689-06-14 1
## 3 1689-10-28 1
## 4 1689-12-27 1
## 5 1690-02-04 1
## 6 1690-03-28 1
## 7 1690-04-03 1
## 8 1690-05-11 1
## 9 1690-05-30 1
## 10 1690-08-17 1
## # … with 684 more rows## [1] "Date"Repeat this process for Marriages and Burials
## # A tibble: 190 × 2
## Date_of_Marriage n
## <date> <int>
## 1 1690-08-18 1
## 2 1690-09-04 1
## 3 1690-11-29 1
## 4 1691-05-11 1
## 5 1691-08-08 1
## 6 1692-11-29 1
## 7 1694-04-01 1
## 8 1694-04-06 1
## 9 1694-05-08 1
## 10 1694-06-10 1
## # … with 180 more rows## # A tibble: 190 × 2
## Date No
## <date> <int>
## 1 1690-08-18 1
## 2 1690-09-04 1
## 3 1690-11-29 1
## 4 1691-05-11 1
## 5 1691-08-08 1
## 6 1692-11-29 1
## 7 1694-04-01 1
## 8 1694-04-06 1
## 9 1694-05-08 1
## 10 1694-06-10 1
## # … with 180 more rows## # A tibble: 853 × 2
## Date_of_Burial n
## <date> <int>
## 1 1689-01-06 1
## 2 1689-02-24 1
## 3 1689-04-28 1
## 4 1689-05-29 1
## 5 1689-07-30 1
## 6 1689-09-16 1
## 7 1689-11-06 1
## 8 1690-01-26 1
## 9 1690-03-09 1
## 10 1690-04-09 1
## # … with 843 more rows## # A tibble: 853 × 2
## Date No
## <date> <int>
## 1 1689-01-06 1
## 2 1689-02-24 1
## 3 1689-04-28 1
## 4 1689-05-29 1
## 5 1689-07-30 1
## 6 1689-09-16 1
## 7 1689-11-06 1
## 8 1690-01-26 1
## 9 1690-03-09 1
## 10 1690-04-09 1
## # … with 843 more rowsTo explain the process there are a number of things happening. First we select the dataset we want to analyse baptisms_dates and then we use the group_by() command which allows for the creation of new groups within the Date information.
# We want yearly data so group by year
baptisms_irish_dates %>% group_by(year=floor_date(Date, "year")) %>% summarize(No_of_Baptisms=sum(No)) -> baptisms_irish_yearly
baptisms_irish_yearly## # A tibble: 46 × 2
## year No_of_Baptisms
## <date> <int>
## 1 1689-01-01 4
## 2 1690-01-01 9
## 3 1691-01-01 9
## 4 1692-01-01 27
## 5 1693-01-01 27
## 6 1694-01-01 20
## 7 1695-01-01 18
## 8 1696-01-01 35
## 9 1697-01-01 29
## 10 1698-01-01 37
## # … with 36 more rowsburials_irish_dates %>% group_by(year=floor_date(Date, "year")) %>% summarize(No_of_Burials=sum(No)) -> burials_irish_yearly
burials_irish_yearly## # A tibble: 53 × 2
## year No_of_Burials
## <date> <int>
## 1 1689-01-01 7
## 2 1690-01-01 8
## 3 1691-01-01 8
## 4 1692-01-01 11
## 5 1693-01-01 37
## 6 1694-01-01 12
## 7 1695-01-01 17
## 8 1696-01-01 12
## 9 1697-01-01 15
## 10 1698-01-01 24
## # … with 43 more rowsmarriages_irish_dates %>% group_by(year=floor_date(Date, "year")) %>% summarize(No_of_Marriages=sum(No)) -> marriages_irish_yearly
marriages_irish_yearly## # A tibble: 36 × 2
## year No_of_Marriages
## <date> <int>
## 1 1690-01-01 3
## 2 1691-01-01 2
## 3 1692-01-01 1
## 4 1694-01-01 7
## 5 1695-01-01 7
## 6 1696-01-01 8
## 7 1697-01-01 8
## 8 1698-01-01 16
## 9 1699-01-01 5
## 10 1700-01-01 12
## # … with 26 more rowsGrouped years for each of the three dataframes do not match, this is a feature of historical data. So we need to add new rows for years that are blank.
## Rows: 36
## Columns: 2
## $ year <date> 1690-01-01, 1691-01-01, 1692-01-01, 1694-01-01, 1695-…
## $ No_of_Marriages <int> 3, 2, 1, 7, 7, 8, 8, 16, 5, 12, 11, 10, 11, 6, 8, 7, 8…## Rows: 53
## Columns: 2
## $ year <date> 1689-01-01, 1690-01-01, 1691-01-01, 1692-01-01, 1693-01…
## $ No_of_Burials <int> 7, 8, 8, 11, 37, 12, 17, 12, 15, 24, 27, 31, 27, 19, 24,…## Rows: 46
## Columns: 2
## $ year <date> 1689-01-01, 1690-01-01, 1691-01-01, 1692-01-01, 1693-0…
## $ No_of_Baptisms <int> 4, 9, 9, 27, 27, 20, 18, 35, 29, 37, 51, 32, 35, 30, 33…There should be 52 entries in each, there is an extra NA row in the Burials dataset. Remove that with the na.omit() command
You can view any dataset using the view() command
Since we know that the marriages_irish_yearly totals are missing a number of years we can create add these quickly by creating a sequence of our complete timeline
# This creates a dataframe of all the years in our timeline
all_dates <- data.frame(year=seq(as.Date("1689-01-01"), by='year', length.out=52))
all_dates## year
## 1 1689-01-01
## 2 1690-01-01
## 3 1691-01-01
## 4 1692-01-01
## 5 1693-01-01
## 6 1694-01-01
## 7 1695-01-01
## 8 1696-01-01
## 9 1697-01-01
## 10 1698-01-01
## 11 1699-01-01
## 12 1700-01-01
## 13 1701-01-01
## 14 1702-01-01
## 15 1703-01-01
## 16 1704-01-01
## 17 1705-01-01
## 18 1706-01-01
## 19 1707-01-01
## 20 1708-01-01
## 21 1709-01-01
## 22 1710-01-01
## 23 1711-01-01
## 24 1712-01-01
## 25 1713-01-01
## 26 1714-01-01
## 27 1715-01-01
## 28 1716-01-01
## 29 1717-01-01
## 30 1718-01-01
## 31 1719-01-01
## 32 1720-01-01
## 33 1721-01-01
## 34 1722-01-01
## 35 1723-01-01
## 36 1724-01-01
## 37 1725-01-01
## 38 1726-01-01
## 39 1727-01-01
## 40 1728-01-01
## 41 1729-01-01
## 42 1730-01-01
## 43 1731-01-01
## 44 1732-01-01
## 45 1733-01-01
## 46 1734-01-01
## 47 1735-01-01
## 48 1736-01-01
## 49 1737-01-01
## 50 1738-01-01
## 51 1739-01-01
## 52 1740-01-01Using a command called anti_join() we can determine the missing years from the marriages_irish_yearly dataset
# Use anti_join() to create a dataframe of missing dates
anti_join(all_dates, marriages_irish_yearly, by="year") -> missing_dates
missing_dates## year
## 1 1689-01-01
## 2 1693-01-01
## 3 1719-01-01
## 4 1722-01-01
## 5 1723-01-01
## 6 1724-01-01
## 7 1725-01-01
## 8 1729-01-01
## 9 1730-01-01
## 10 1731-01-01
## 11 1734-01-01
## 12 1735-01-01
## 13 1737-01-01
## 14 1738-01-01
## 15 1739-01-01
## 16 1740-01-01# Now merge the two dataframes together by the year column, remember to include all the values for x and y values
merge(marriages_irish_yearly, missing_dates, by="year", all.y = T, all.x = T) -> marriages_irish_yearly
marriages_irish_yearly## year No_of_Marriages
## 1 1689-01-01 NA
## 2 1690-01-01 3
## 3 1691-01-01 2
## 4 1692-01-01 1
## 5 1693-01-01 NA
## 6 1694-01-01 7
## 7 1695-01-01 7
## 8 1696-01-01 8
## 9 1697-01-01 8
## 10 1698-01-01 16
## 11 1699-01-01 5
## 12 1700-01-01 12
## 13 1701-01-01 11
## 14 1702-01-01 10
## 15 1703-01-01 11
## 16 1704-01-01 6
## 17 1705-01-01 8
## 18 1706-01-01 7
## 19 1707-01-01 8
## 20 1708-01-01 10
## 21 1709-01-01 5
## 22 1710-01-01 14
## 23 1711-01-01 2
## 24 1712-01-01 2
## 25 1713-01-01 7
## 26 1714-01-01 3
## 27 1715-01-01 2
## 28 1716-01-01 4
## 29 1717-01-01 1
## 30 1718-01-01 1
## 31 1719-01-01 NA
## 32 1720-01-01 1
## 33 1721-01-01 2
## 34 1722-01-01 NA
## 35 1723-01-01 NA
## 36 1724-01-01 NA
## 37 1725-01-01 NA
## 38 1726-01-01 2
## 39 1727-01-01 1
## 40 1728-01-01 1
## 41 1729-01-01 NA
## 42 1730-01-01 NA
## 43 1731-01-01 NA
## 44 1732-01-01 2
## 45 1733-01-01 1
## 46 1734-01-01 NA
## 47 1735-01-01 NA
## 48 1736-01-01 1
## 49 1737-01-01 NA
## 50 1738-01-01 NA
## 51 1739-01-01 NA
## 52 1740-01-01 NA## year No_of_Marriages
## 1 1689-01-01 0
## 2 1690-01-01 3
## 3 1691-01-01 2
## 4 1692-01-01 1
## 5 1693-01-01 0
## 6 1694-01-01 7
## 7 1695-01-01 7
## 8 1696-01-01 8
## 9 1697-01-01 8
## 10 1698-01-01 16
## 11 1699-01-01 5
## 12 1700-01-01 12
## 13 1701-01-01 11
## 14 1702-01-01 10
## 15 1703-01-01 11
## 16 1704-01-01 6
## 17 1705-01-01 8
## 18 1706-01-01 7
## 19 1707-01-01 8
## 20 1708-01-01 10
## 21 1709-01-01 5
## 22 1710-01-01 14
## 23 1711-01-01 2
## 24 1712-01-01 2
## 25 1713-01-01 7
## 26 1714-01-01 3
## 27 1715-01-01 2
## 28 1716-01-01 4
## 29 1717-01-01 1
## 30 1718-01-01 1
## 31 1719-01-01 0
## 32 1720-01-01 1
## 33 1721-01-01 2
## 34 1722-01-01 0
## 35 1723-01-01 0
## 36 1724-01-01 0
## 37 1725-01-01 0
## 38 1726-01-01 2
## 39 1727-01-01 1
## 40 1728-01-01 1
## 41 1729-01-01 0
## 42 1730-01-01 0
## 43 1731-01-01 0
## 44 1732-01-01 2
## 45 1733-01-01 1
## 46 1734-01-01 0
## 47 1735-01-01 0
## 48 1736-01-01 1
## 49 1737-01-01 0
## 50 1738-01-01 0
## 51 1739-01-01 0
## 52 1740-01-01 0Exercise 2. Using the all_dates data.frame, repeat the process of adding missing dates for baptisms_irish_yearly.