Importing & Tidying the Data

To import the data, please run the following code on the three csv files from the 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

This imports the datasets as data.frames, however, as these can be somewhat limited, we will convert these to table data.frames.

baptisms <- as_tibble(baptisms)
burials <- as_tibble(burials)
marriages <- as_tibble(marriages)
abjurations <- as_tibble(abjurations)

As date ranges are available per dataset, you can covert these to specific date fields.

baptisms$Date_of_Baptism <- as.Date(baptisms$Date_of_Baptism)
burials$Date_of_Burial <- as.Date(burials$Date_of_Burial)
marriages$Date_of_Marriage <- as.Date(marriages$Date_of_Marriage)
abjurations$Date <- as.Date(abjurations$Date)

The majority of data analyses used the %>% function, this is akin to grammar in a sentence, for example, to create a gender breakdown of burials you would use it as follows:

burials %>% count(Gender)
## # A tibble: 2 × 2
##   Gender     n
##   <chr>  <int>
## 1 Female   173
## 2 Male     214

If you wanted to save this information as a separate table you can create a new burials_gender table

burials %>% count(Gender) -> burials_gender
baptisms %>% count(Gender) -> baptisms_gender
marriages %>% count()
## # A tibble: 1 × 1
##       n
##   <int>
## 1   175

The first research question concerns the numbers of Irish people who are present across the entire database. To start with you need to separate out the Irish people from the overall data and then create yearly totals for each. The filter() function will create Irish specific datasets.

# For burials this is quite straightforward as there is only person involved
burials %>% filter(Nationality_Infer == "Irish") -> burials_irish

# For baptisms, you can filter to make sure that child and the parents are all listed as having Irish nationality 
baptisms %>% filter(Child_Nationality_Infer == "Irish" & Father_Nationality_infer == "Irish" & Mother_Nationality_infer == "Irish") -> baptisms_irish

# For marriages, the same principal applies
marriages %>% filter(Groom_Nationality_infer == "Irish" & Bride_Nationality_infer == "Irish") -> marriages_irish

To count the number of marriages, 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.

marriages_irish %>% count(Date_of_Marriage) -> marriages_irish_dates
colnames(marriages_irish_dates) <- c("Date", "No")

Group marriages into years using the floor_date() command from the ‘lubridate’ package. Its fairly intelligent and can reorganise dates into days, months, years etc.

marriages_irish_dates %>% group_by(year=floor_date(Date, "year")) %>% summarize(No_of_Marriages=sum(No)) -> marriages_irish_yearly

This process is then repeated for burials and baptisms

burials_irish %>% count(Date_of_Burial) -> burials_irish_dates
colnames(burials_irish_dates) <- c("Date", "No")

burials_irish_dates %>% group_by(year=floor_date(Date, "year")) %>% summarize(No_of_Burials=sum(No)) -> burials_irish_yearly

baptisms_irish %>% count(Date_of_Baptism) -> baptisms_irish_dates 
colnames(baptisms_irish_dates) <- c("Date", "No")

baptisms_irish_dates %>% group_by(year=floor_date(Date, "year")) %>% summarize(No_of_Baptisms=sum(No)) -> baptisms_irish_yearly

The baptisms and burials yearly totals matched and were merged easily into a new grouped table

inner_join(baptisms_irish_yearly, burials_irish_yearly, by="year") -> births_deaths_yearly

As there are no recorded marriages of Irish grooms & brides for a number of single years, it was necessary to create new empty row values for the marriages table

marriages_irish_yearly %>% add_row(year = c(as.Date("1689-01-01"), 
                                            as.Date("1693-01-01"),
                                            as.Date("1709-01-01"))) -> marriages_irish_yearly

As this introduces NAs, the following command replaces this with 0

marriages_irish_yearly[is.na(marriages_irish_yearly)] <- 0

This was then added to the grouped birth_deaths table

inner_join(births_deaths_yearly, marriages_irish_yearly, by="year") -> irish_yearly_births_deaths_marriages

Tidy the column names

colnames(irish_yearly_births_deaths_marriages) <- c("Year", "Baptisms", "Burials", "Marriages")
# This data is in a 'wide' format, for effective charts it needs to be re-adjusted to 'long' data. There is a package called 'reshape2' which does this automatically. 

irish_yearly <- melt(irish_yearly_births_deaths_marriages, id.vars = "Year")

irish_yearly
##          Year  variable value
## 1  1689-01-01  Baptisms     4
## 2  1690-01-01  Baptisms     9
## 3  1691-01-01  Baptisms     8
## 4  1692-01-01  Baptisms    26
## 5  1693-01-01  Baptisms    27
## 6  1694-01-01  Baptisms    20
## 7  1695-01-01  Baptisms    18
## 8  1696-01-01  Baptisms    35
## 9  1697-01-01  Baptisms    29
## 10 1698-01-01  Baptisms    37
## 11 1699-01-01  Baptisms    51
## 12 1700-01-01  Baptisms    32
## 13 1701-01-01  Baptisms    36
## 14 1702-01-01  Baptisms    30
## 15 1703-01-01  Baptisms    33
## 16 1704-01-01  Baptisms    27
## 17 1705-01-01  Baptisms    20
## 18 1706-01-01  Baptisms    24
## 19 1707-01-01  Baptisms    30
## 20 1708-01-01  Baptisms    35
## 21 1709-01-01  Baptisms     3
## 22 1689-01-01   Burials     8
## 23 1690-01-01   Burials     8
## 24 1691-01-01   Burials     6
## 25 1692-01-01   Burials    10
## 26 1693-01-01   Burials    37
## 27 1694-01-01   Burials    12
## 28 1695-01-01   Burials    17
## 29 1696-01-01   Burials    12
## 30 1697-01-01   Burials    15
## 31 1698-01-01   Burials    24
## 32 1699-01-01   Burials    27
## 33 1700-01-01   Burials    31
## 34 1701-01-01   Burials    27
## 35 1702-01-01   Burials    19
## 36 1703-01-01   Burials    24
## 37 1704-01-01   Burials    27
## 38 1705-01-01   Burials     9
## 39 1706-01-01   Burials    12
## 40 1707-01-01   Burials    17
## 41 1708-01-01   Burials    41
## 42 1709-01-01   Burials     3
## 43 1689-01-01 Marriages     0
## 44 1690-01-01 Marriages     3
## 45 1691-01-01 Marriages     2
## 46 1692-01-01 Marriages     1
## 47 1693-01-01 Marriages     0
## 48 1694-01-01 Marriages     7
## 49 1695-01-01 Marriages     7
## 50 1696-01-01 Marriages     8
## 51 1697-01-01 Marriages     8
## 52 1698-01-01 Marriages    16
## 53 1699-01-01 Marriages     5
## 54 1700-01-01 Marriages    12
## 55 1701-01-01 Marriages    11
## 56 1702-01-01 Marriages    10
## 57 1703-01-01 Marriages    11
## 58 1704-01-01 Marriages     6
## 59 1705-01-01 Marriages     8
## 60 1706-01-01 Marriages     7
## 61 1707-01-01 Marriages     8
## 62 1708-01-01 Marriages     8
## 63 1709-01-01 Marriages     0

Overview of the Registers

# Now here is an example of a line graph showing the comparative rates of deaths, burials and baptisms from the database. 

irish_yearly %>% ggplot(aes(x=Year, y=value, colour=variable, group=variable)) + geom_line() 

# This can be adjusted using a variety of themes and other adjustments 

irish_yearly %>% ggplot(aes(x=Year, y=value,colour=variable, group=variable)) + geom_line() + theme_fivethirtyeight()

Figure 1. Births, Marriages and Deaths, 1689-1708

irish_yearly %>% ggplot(aes(x=Year, y=value, colour=variable, group=variable)) + 
                 geom_line(size=0.8) + 
                 labs(title = "Births, Marriages and Deaths, 1689-1708",
                      subtitle = "(St. Germain-en-Laye)",
                      tag = "Figure 1", 
                      caption = "Database of St. Germain-en-Laye Registers",
                      x = "Year",
                      y = "No.",
                      color = "") +
                 scale_color_colorblind() + 
                 scale_x_date(date_breaks = "7 years", date_labels = "%Y") +
                 theme_classic() +
                 theme(axis.text.x = element_text(colour = "darkslategrey", size = 14), 
                       axis.text.y = element_text(colour = "darkslategrey", size = 14),
                       legend.background = element_rect(fill = "white", size = 4, colour = "white"),
                       legend.justification = c(0, 1),
                       legend.position = c(0.9, 1),
                       text = element_text(family = "Baskerville"),
                       plot.title = element_text(size = 18, margin = margin(b = 10)),
                       plot.subtitle = element_text(size = 12, color = "darkslategrey", margin = margin(b = 25)),
                       plot.caption = element_text(size = 8, margin = margin(t = 10), color = "grey70", hjust = 0)) 

# Creating Charts of Gender Breakdown 

# Marriages has no gender column so one must be created by counting the size of the dataframe

marriages %>% count() -> marriages_gender

marriages_gender$Gender <- c("Female")

marriages_gender$n <- NULL

marriages_gender$Marriages <- c(175)

marriages_gender %>% add_row(Gender = c("Male")) -> marriages_gender

marriages_gender[is.na(marriages_gender)] <- 175

marriages_gender
## # A tibble: 2 × 2
##   Gender Marriages
##   <chr>      <dbl>
## 1 Female       175
## 2 Male         175
# Create _Gender totals for the other three datasets
burials %>% count(Gender) -> burials_gender
colnames(burials_gender) <- c("Gender", "Deaths")

baptisms %>% count(Gender) -> baptisms_gender
colnames(baptisms_gender) <- c("Gender", "Baptisms")

abjurations %>% count(Gender) -> abjurations_gender
colnames(abjurations_gender) <- c("Gender", "Abjurations")
# Merge these into a single dataframe in stages

inner_join(burials_gender, baptisms_gender, by="Gender") -> burials_baptisms_gender

inner_join(marriages_gender, abjurations_gender, by="Gender") -> marriages_abjurations_gender

inner_join(burials_baptisms_gender, marriages_abjurations_gender, by="Gender") -> registers_gender
# Change the data to long format using melt()

melt(registers_gender) -> registers_gender_long
## Using Gender as id variables

Figure 2. Breakdown of gender within the Registers

# Breakdown of Gender within the Registers

registers_gender_long %>% ggplot(aes(x=Gender, y=value)) + 
        geom_bar(aes(fill=variable), position = "dodge", stat = "identity", width = 0.5) +
        labs(title = "Gender Breakdown of Registers, 1689-1708",
             subtitle = "(St. Germain-en-Laye)",
             tag = "Figure 2", 
             caption = "Database of St. Germain-en-Laye Registers",
             x = "Gender",
             y = "No.",
             fill = "Registers") +
        scale_fill_colorblind()+ 
        theme_classic()+
        theme(axis.text.x = element_text(colour = "darkslategrey", size = 16), 
              axis.text.y = element_text(colour = "darkslategrey", size = 16),          
              legend.background = element_rect(fill = "white", size = 4, colour = "white"),
              legend.justification = c(0, 1),
              legend.position = c(0.9, 1),
              text = element_text(family = "Georgia"),
              plot.title = element_text(size = 18, margin = margin(b = 10)),
              plot.subtitle = element_text(size = 12, color = "darkslategrey", margin = margin(b = 25)),
              plot.caption = element_text(size = 8, margin = margin(t = 10), color = "grey70", hjust = 0)) 

### Age and Gender Breakdowns

burials_irish %>% select(Gender, Age_ranges) -> burials_irish_gender_age_range

burials_irish_gender_age_range %>% filter(Gender == "Male") -> burials_irish_male_age_range
burials_irish_gender_age_range %>% filter(Gender == "Female") -> burials_irish_female_age_range

burials_irish_male_age_range %>% count(Age_ranges) -> burials_irish_male_age_range_count
burials_irish_female_age_range %>% count(Age_ranges) -> burials_irish_female_age_range_count

colnames(burials_irish_male_age_range_count) <- c("Age_ranges", "No_of_Deaths_Male")
colnames(burials_irish_female_age_range_count) <- c("Age_ranges", "No_of_Deaths_Female")
# For ease of using Age Ranges create a character list 

namelist <- c("Less than 1 year", "1-5", "6-10", "11-15",
              "16-20", "21-25", "26-30", "31-35", "36-40", 
              "41-45", "46-50", "51-55", "56-60", "61-65",
              "65-100", "NA")

# Then re-order each burials dataframe according to the namelist, so that it begins with less than 1, 1-5 etc etc

burials_irish_female_age_range_count %>% arrange(factor(Age_ranges, levels = namelist)) ->
  burials_irish_female_age_range_count

burials_irish_male_age_range_count %>% arrange(factor(Age_ranges, levels = namelist)) ->
  burials_irish_male_age_range_count
# Next merge the two datasets into a single dataframe

inner_join(burials_irish_female_age_range_count, burials_irish_male_age_range_count, by="Age_ranges") -> burials_age_ranges_count

# Tidy the column names for ease of use 

colnames(burials_age_ranges_count) <- c("Ages","Female","Male")

# Melt the data into a 'long' as opposed to wide format

melt(burials_age_ranges_count) -> burials_total_age_ranges_long
## Using Ages as id variables

Figure 3. Age and gender breakdown of Irish deaths, 1689-1708

# Figure 3 Graph of Burials by age & gender 
  
burials_total_age_ranges_long %>% ggplot(aes(x=factor(Ages, levels = namelist), y=value)) + 
  geom_bar(aes(fill=variable), position = "dodge", stat = "identity", width = 0.5) + 
  labs(title = "Age and Gender breakdown of Irish deaths, 1689-1708 ",
       subtitle = "(St. Germain-en-Laye)",
       tag = "Figure 3", 
       caption = "Database of St. Germain-en-Laye Registers",
       x = "Age Ranges",
       y = "No. of Deaths",
       fill = "Gender") +
  scale_fill_colorblind() + theme_classic(base_family = "Baskerville") + 
  theme(axis.text.x = element_text(colour = "darkslategrey", size = 10), 
        axis.text.y = element_text(colour = "darkslategrey", size = 14),          
        legend.background = element_rect(fill = "white", size = 4, colour = "white"),
        legend.justification = c(0, 1),
        legend.position = c(0.9, 1),
        axis.ticks = element_line(colour = "grey70", size = 0.3),
        text = element_text(family = "Baskerville"),
        plot.title = element_text(face = "bold", size = 18, margin = margin(b = 10)),
        plot.subtitle = element_text(size = 12, color = "darkslategrey", margin = margin(b = 25)),
        plot.caption = element_text(size = 10, margin = margin(t = 10), color = "grey70", hjust = 0)) 

# Non Irish Births, Marriages & Deaths 

# This finds all the death records that have an nationality that is not listed as 'Irish' 

burials %>% filter(Nationality_Infer != "Irish") -> burials_non_irish

burials_non_irish
## # A tibble: 1 × 30
##   Type.of.Burial Name_FR       Forename Surname Nationality_Inf… Gender Religion
##   <chr>          <chr>         <chr>    <chr>   <chr>            <chr>  <chr>   
## 1 Regular        Georges Uuak… George   Watkins English          Male   Roman C…
## # … with 23 more variables: Age..d.m.y. <int>, Age..d.m. <chr>,
## #   Age_ranges <chr>, Occupation <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>,
## #   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>, …
# There are also the parents of the deceased who may be Irish but have a non-Irish parent, either a mother or father using the | symbol in the filter command to signify an either/or 

burials %>% filter(Mother_Nationality != "Irish" | Father_Nationality != "Irish") -> burials_non_irish_parents

# Check your findings using the following commands 

burials %>% count(Mother_Nationality)
## # A tibble: 4 × 2
##   Mother_Nationality     n
##   <chr>              <int>
## 1 English                3
## 2 French                 4
## 3 Irish                176
## 4 <NA>                 204
burials %>% count(Father_Nationality)
## # A tibble: 4 × 2
##   Father_Nationality     n
##   <chr>              <int>
## 1 English                3
## 2 French                 1
## 3 Irish                200
## 4 <NA>                 183
burials_non_irish_parents %>% view()
# Use the rbind() command to join these two burials dataframes together, as they are both from the same parent data the number of columns is the same.

rbind(burials_non_irish, burials_non_irish_parents) -> burials_non_irish
# Next move onto the marriages dataset 

marriages %>% filter(Bride_Nationality_infer != "Irish" | Groom_Nationality_infer != "Irish") -> marriages_non_irish
# For the baptisms dataset filter each individually as there are too many options between the two parents and the child

baptisms %>% filter(Mother_Nationality_infer != "Irish") -> baptisms_non_irish_mother
baptisms %>% filter(Child_Nationality_Infer != "Irish") -> baptisms_non_irish_child
baptisms %>% filter(Father_Nationality_infer != "Irish") -> baptisms_non_irish_father

rbind(baptisms_non_irish_child, baptisms_non_irish_father, baptisms_non_irish_mother) -> baptisms_non_irish
# Check for non-Irish populations in the abjurations dataset
abjurations %>% count(Nationality)
## # A tibble: 1 × 2
##   Nationality     n
##   <chr>       <int>
## 1 Irish          21
# None exist, so this dataset is not part of the next analysis
# Non Irish Marriages 

marriages_non_irish %>% count(Bride_Nationality_infer) %>% filter(Bride_Nationality_infer != "Irish") -> marriages_non_irish_brides

colnames(marriages_non_irish_brides) <- c("Nationality", "Brides")

marriages_non_irish %>% count(Groom_Nationality_infer) %>% filter(Groom_Nationality_infer != "Irish") -> marriages_non_irish_grooms

colnames(marriages_non_irish_grooms) <- c("Nationality", "Grooms")

inner_join(marriages_non_irish_brides, marriages_non_irish_grooms, by="Nationality") -> marriages_non_irish_brides_grooms

Table 1. Breakdown of non-Irish spouses within the Marriage Registers

# The kable package creates very simple & clean tables which display tabular data in a clear format

marriages_non_irish_brides_grooms %>% kbl() %>% kable_minimal() %>% add_header_above(c("", "Non-Irish Spouses" = 2))
Non-Irish Spouses
Nationality Brides Grooms
English 17 7
French 10 2 
# Use the following code to create a table breakdown of the nationality of Witnesses to Marriages

marriages %>% count(Witness_1_Nationality_infer) -> marriages_witness_1_nationality
colnames(marriages_witness_1_nationality) <- c("Nationality", "Witness_1")

marriages %>% count(Witness_2_Nationality_infer) -> marriages_witness_2_nationality
colnames(marriages_witness_2_nationality) <- c("Nationality", "Witness_2")

marriages %>% count(Witness_3_Nationality_infer) -> marriages_witness_3_nationality
colnames(marriages_witness_3_nationality) <- c("Nationality", "Witness_3")

marriages %>% count(Witness_4_Nationality_infer) -> marriages_witness_4_nationality
colnames(marriages_witness_4_nationality) <- c("Nationality", "Witness_4")

marriages %>% count(Witness_5_Nationality_infer) -> marriages_witness_5_nationality
colnames(marriages_witness_5_nationality) <- c("Nationality", "Witness_5")
# In order to merge all these together, you need to add a blank row for Scottish for the dataframes for Witnesses 3-5 

marriages_witness_3_nationality %>% add_row(Nationality = "Scottish") -> marriages_witness_3_nationality

marriages_witness_4_nationality %>% add_row(Nationality = "Scottish") -> marriages_witness_4_nationality

marriages_witness_5_nationality %>% add_row(Nationality = "Scottish") -> marriages_witness_5_nationality
# As this introduces NAs, the following command replaces this with 0

marriages_witness_3_nationality$Witness_3[is.na(marriages_witness_3_nationality$Witness_3)] <- 0

marriages_witness_4_nationality$Witness_4[is.na(marriages_witness_4_nationality$Witness_4)] <- 0

marriages_witness_5_nationality$Witness_5[is.na(marriages_witness_5_nationality$Witness_5)] <- 0
# Now merge all these into a single dataframe 

marriages_witnesses_nationality <- Reduce(inner_join, list(marriages_witness_1_nationality, marriages_witness_2_nationality, marriages_witness_3_nationality, marriages_witness_4_nationality, marriages_witness_5_nationality))
## Joining, by = "Nationality"
## Joining, by = "Nationality"
## Joining, by = "Nationality"
## Joining, by = "Nationality"

Table 2 Breakdown of Witnesses to Marriages

marriages_witnesses_nationality %>% kbl() %>% kable_minimal() %>% add_header_above(c("", "Breakdown of the Nationality of the Witnesses to Marriages" = 5))
Breakdown of the Nationality of the Witnesses to Marriages
Nationality Witness_1 Witness_2 Witness_3 Witness_4 Witness_5
English 9 7 6 3 1
French 10 18 14 8 1
Irish 140 125 98 58 18
Scottish 2 1 0 0 0
NA 14 24 57 106 155 
# Non-Irish Burials

burials_non_irish %>% count(Father_Nationality) %>% filter(Father_Nationality != "Irish") -> burials_non_irish_fathers

colnames(burials_non_irish_fathers) <- c("Nationality", "Fathers")

burials_non_irish %>% count(Mother_Nationality) %>% filter(Mother_Nationality != "Irish") -> burials_non_irish_mothers

colnames(burials_non_irish_mothers) <- c("Nationality", "Mothers")

burials_non_irish %>% count(Nationality_Infer) %>% filter(Nationality_Infer != "Irish") -> burials_non_irish_deceased

colnames(burials_non_irish_deceased) <- c("Nationality", "Deceased")
# Add the missing French row to the deceased row and change the NA to numerical value 
burials_non_irish_deceased %>% add_row(Nationality = "French") -> burials_non_irish_deceased

burials_non_irish_deceased$Deceased[is.na(burials_non_irish_deceased$Deceased)] <- 0

burials_non_irish_parents_deceased <- Reduce(inner_join, 
                                                list(burials_non_irish_deceased, 
                                                     burials_non_irish_mothers, 
                                                     burials_non_irish_fathers))
## Joining, by = "Nationality"
## Joining, by = "Nationality"

Table 3. Non-Irish Burials and non-Irish parents of deceased

# Table Breakdown of non-Irish deaths and non-Irish parents of deceased
burials_non_irish_parents_deceased %>% kbl(full_width = F, font = "Baskerville") %>% kable_minimal() %>% add_header_above(c("", "Non-Irish deceased & non-Irish parents of deceased" = 3))
Non-Irish deceased & non-Irish parents of deceased
Nationality Deceased Mothers Fathers
English 1 3 3
French 0 4 1 
# The following command uses the floor_date() command to group the burials_dates by 'month'  
burials_irish_dates %>% group_by(Month=floor_date(Date, "month")) %>% summarize(No_of_Burials=sum(No)) -> burials_irish_dates_monthly
# The package 'xts' is used to create time series data which is required for dygraphs
xts(x=burials_irish_dates_monthly[,-1], order.by = burials_irish_dates_monthly$Month) -> burials_irish_dates_monthly_xts

Dygraph of Burials by month

# This next section is an adapation of the rainfall analysis undertaken by Chris Brunsdon

burials_irish_dates_monthly_xts %>% dygraph()
burials_irish_dates_monthly_xts %>% dygraph(width=800,height=300) %>% dyRangeSelector %>% dyRoller(rollPeriod = 600)
# Monthly Deaths - Table 

# First add more time series columns
tk_augment_timeseries_signature(burials_irish_dates_monthly) -> burials_irish_dates_monthly_ts
## tk_augment_timeseries_signature(): Using the following .date_var variable: Month
# Select the Month, Year and keep the No_of_Burials

burials_irish_dates_monthly_ts %>% select(year, month.lbl, No_of_Burials) -> burials_irish_dates_monthly_tbl

colnames(burials_irish_dates_monthly_tbl) <- c("Year", "Month", "Burials")
# This create an interactive grouped table of the monthly burials results

burials_irish_dates_monthly_tbl %>% reactable(groupBy = "Year", striped = T, filterable = T)