Prologue

Prepare and read data

For this experiment, we will rely on the complete dataset containing the 1060 periodicals listed in the two directories. In the preprocessing step, we re-factorize certain variables to enable multiple correspondence analysis. We lump together and recode the variables with rare values. In addition, we transform the continuous variables (e.g. circulation) into categorical ones. Finally, we attribute a unique id for each observation (periodical) and read the first column as row names. Since we will perform one MCA for each directory, we will split the original dataset into two samples, one for each directory.

Load original dataset containing 1060 periodicals and 47 variables:

library(readr)
crowdata <- read_delim("crowdata.csv", ";", 
                       escape_double = FALSE, trim_ws = TRUE)

Select relevant variables for MCA:

crowdata <- crowdata %>% mutate(Title = paste(Title_eng, Title_zh, sep = " ")) %>% 
  mutate(Title = str_remove_all(Title,", The NA")) %>% 
  mutate(Title = str_remove_all(Title,", The")) %>% 
  mutate(Title = str_remove_all(Title," NA")) %>%  
  mutate(Title = str_remove_all(Title,", L'")) 

crow_mca <- crowdata %>% select(Title, Year, 
                                City_zh, Established, 
                                Periodicity, Language, 
                                Page_nbr_avrg, Page_surface, Col_nbr_avrg, 
                                Circulation_avrg, Audited,
                                Publisher_type, Nationality)

names(crow_mca)
##  [1] "Title"            "Year"             "City_zh"          "Established"     
##  [5] "Periodicity"      "Language"         "Page_nbr_avrg"    "Page_surface"    
##  [9] "Col_nbr_avrg"     "Circulation_avrg" "Audited"          "Publisher_type"  
## [13] "Nationality"


We re-factorize the place of publication (city) according to the profile of the city and its geographical situation in China (the classification is described in detail in this document:

# load and prepare annotated data for joining
library(readr)
city_code <- read_delim("city_code.csv",
";", escape_double = FALSE, trim_ws = TRUE)
code <- city_code %>% select(City_zh, Profile, Location) 
code <- code %>% mutate(code = paste0(Location, Profile))
place <- code %>% select(City_zh, Location, Profile)
# join 
crow_mca <- right_join(crow_mca, place)


We re-factorize the year of establishment into four main periods (the periodization is described in the first essay):

crow_mca <- crow_mca %>% mutate(Founded=cut(Established, 
                                                   breaks=c(0, 1903, 1916, 1927, 1935), 
                                                   include.lowest = TRUE))

crow_mca <- crow_mca %>% mutate(Founded = fct_recode(Founded, 
                                                     "1829-1903" = "[0,1.9e+03]",
                                                     "1904-1916" = "(1.9e+03,1.92e+03]",
                                                     "1917-1927" = "(1.92e+03,1.93e+03]",
                                                     "1928-1935" = "(1.93e+03,1.94e+03]"))


We re-factorize the periodicity:

crow_mca <- crow_mca %>%
  mutate(Periodicity_fct = fct_collapse(Periodicity,
                                        AQSM = c("Annual", "Quarterly", "Semi-monthly"),
                                        BiWM = c("Biweekly", "Semi-weekly", "Bimonthly"),
                                        Daily = c("Daily", "Tabloid"),
                                        Weekly = c("Weekly"), 
                                        Monthly = c("Monthly")))


We re-factorize the language of publication:

crow_mca <- crow_mca %>%
  mutate(Language_fct = fct_collapse(Language,
                                     Other = c("German", "French", "Russian", "Bilingual"),
                                     English = c("English"),
                                     Japanese = c("Japanese"), 
                                     Chinese = c("Chinese")))


We transform the circulation figures into a categorical variable with 6 thresholds:

crow_mca <- crow_mca %>% mutate(Circulation_thres=cut(Circulation_avrg, 
                                 breaks=c(0, 500, 1000, 2500, 5000, 10000, 50000, 150000), 
                                 include.lowest = TRUE))


We re-factorize the publisher’s profile and nationality (the classification is described in this document):

crow_mca <- crow_mca %>%
  mutate(Publisher_fct = fct_collapse(Publisher_type,
                                      Other = c("Army", "Church", "Hospital"),
                                      Newsgroup = c("Newsgroup"),
                                      Newspaper = c("Newspaper"),
                                      "Publishing house" = c("Publishing house"),
                                      Organization = c("Association", "Business organization"), 
                                      Private = c("Company", "Person"), 
                                      Official = c("Administration", "Political party"), 
                                      Academic = c("University")))

crow_mca <- crow_mca %>%
  mutate(Nationality_fct = fct_collapse(Nationality,
                                      Other = c("Foreign", "Japanese", "German", "Russian", "Irish"),
                                      American = c("American", "Chinese-American"), 
                                      British = c("British"), 
                                      Chinese = c("Chinese")))


We transform the continuous variables related to the format and layout of periodicals into categorical ones (these variables are described in detail this document):

# number of pages 
crow_mca <- crow_mca %>% mutate(Page_nbr_thres=cut(Page_nbr_avrg, 
                                                         breaks=c(0, 10, 25, 50, 100, 500, 1200), 
                                                         include.lowest = TRUE))

# Page size

crow_mca <- crow_mca %>% 
  mutate(PageSize=cut(Page_surface, breaks=c(0, 100, 200, 400), 
                    labels=c("Octavo","Compact","Broadsheet")))

# Layout 

crow_mca <- crow_mca %>% mutate(Col_nbr_thres=cut(Col_nbr_avrg, 
                                                        breaks=c(0, 3, 5, 8, 10, 13), 
                                                        include.lowest = TRUE))


Finally, we re-select and rename the relevant variables to create a clean dataset:

crow_mca <- crow_mca %>% select(Title, Year, Periodicity_fct, Language_fct, Location, Profile, Founded, Publisher_fct, Nationality_fct, Audited, Circulation_thres, Page_nbr_thres, PageSize, Col_nbr_thres)


crow_mca <- crow_mca %>% rename(Periodicity = Periodicity_fct, Language = Language_fct, 
                                Province = Location, City = Profile, 
                                Publisher = Publisher_fct, Nationality = Nationality_fct, 
                                Circulation = Circulation_thres, 
                                PageNbr = Page_nbr_thres, 
                                ColNbr = Col_nbr_thres)


We now have the complete dataset with all periodicals including those with missing data.

We create a unique id for each observation (periodical):

crow_mca_id <- rowid_to_column(crow_mca)

crow_mca_id  <- crow_mca_id  %>% mutate(id = paste(rowid, Title, sep = "."))

crow_mca_id <- crow_mca_id %>% select(id, Year, Periodicity, Language, Province, City, Founded, Publisher, Nationality, Audited, Circulation, PageNbr, PageSize, ColNbr)

We create one subset for each directory:

crow_mca1931 <- crow_mca %>% filter(Year== "1931")
crow_mca1935 <- crow_mca %>% filter(Year== "1935")
crow_mca_id1931 <- crow_mca_id %>% filter(Year== "1931") # 360
crow_mca_id1935 <- crow_mca_id %>% filter(Year== "1935") # 703


We read the first column as row names:

#1931
crow_mca_id1931_tbl <- column_to_rownames(crow_mca_id1931, var = "id") 
crowmca31 <- crow_mca_id1931_tbl %>% select(Periodicity, Language, Province, 
         Founded, Publisher, Nationality, Audited, Circulation, PageNbr, PageSize, ColNbr)

#1935
crow_mca_id1935_tbl <- column_to_rownames(crow_mca_id1935, var = "id") 
crowmca35 <- crow_mca_id1935_tbl %>% select(Periodicity, Language, Province, 
                                            Founded, Publisher, Nationality, Audited, Circulation, PageNbr, PageSize, ColNbr)


We’re all set!

All titles

Load packages

library(FactoMineR)
library(Factoshiny)
library(explor)

1931

We first perform a MCA on the dataset from the 1931 directory. The dataset contains 360 periodicals and 11 variables. We transform all variables into factor variables as required by MCA. We consider all variables as active variables, except for the place of publication (Province) and the period of founding (Founded), which we set as supplementary variables.

crowmca31fct <- crowmca31 %>% mutate_all(factor) 
res_acm31 <- MCA(crowmca31fct, quali.sup = 3:4)

res_acm31
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 360 individuals, described by 11 variables
## *The results are available in the following objects:
## 
##    name                description                                          
## 1  "$eig"              "eigenvalues"                                        
## 2  "$var"              "results for the variables"                          
## 3  "$var$coord"        "coord. of the categories"                           
## 4  "$var$cos2"         "cos2 for the categories"                            
## 5  "$var$contrib"      "contributions of the categories"                    
## 6  "$var$v.test"       "v-test for the categories"                          
## 7  "$ind"              "results for the individuals"                        
## 8  "$ind$coord"        "coord. for the individuals"                         
## 9  "$ind$cos2"         "cos2 for the individuals"                           
## 10 "$ind$contrib"      "contributions of the individuals"                   
## 11 "$quali.sup"        "results for the supplementary categorical variables"
## 12 "$quali.sup$coord"  "coord. for the supplementary categories"            
## 13 "$quali.sup$cos2"   "cos2 for the supplementary categories"              
## 14 "$quali.sup$v.test" "v-test for the supplementary categories"            
## 15 "$call"             "intermediate results"                               
## 16 "$call$marge.col"   "weights of columns"                                 
## 17 "$call$marge.li"    "weights of rows"


Dimensions description

variances31 <- as.data.frame(res_acm31$eig) %>%
  rownames_to_column() %>% # retrieve row names (dim 1, dim 2, etc) in a distinct column
  slice(1:10) %>% # retain information on the 10 first dimensions only 
  mutate(Axis = str_replace_all(rowname, "dim", "Axis")) %>% # create a new variable from rowname, with values "Axis 1, Axis 2, etc. instead of "dim 1, dim 2.." etc) 
  select(-rowname) %>% # remove this column since we don't need it anymore
  mutate(Axis = fct_relevel(Axis, paste("Axis", 1:10))) %>% # to ensure the dimensions are ordered correctly on the graphs 
  select(Axis, `eigenvalue`, `percentage of variance`, `cumulative percentage of variance`)

variances31 %>% 
  flextable() %>% 
  colformat_double(decimal.mark = ",", digits = 2) %>% 
  autofit() %>% 
  set_caption("Eigenvalue & variances along axes")
# pour exporter ce tableau, plusieurs solutions possibles, notamment : 
write_csv2(variances31, "variances31.csv")


Variance

ggplot(variances31, aes(x = Axis)) + 
  geom_bar(aes(y = `percentage of variance`),
           stat = "identity", 
           fill = "red") + 
  xlab("") + 
  ylab("% of variance") + 
  theme_minimal()

Cumulative variance

ggplot(variances31, aes(x = Axis)) +
  geom_bar(aes(y = `cumulative percentage of variance`), 
           stat = "identity", 
           fill = "blue") + 
  xlab("") +
  ylab("cumulative percentage of variance") +
  theme_minimal()

Contribution threshold

threshold31 <- 100 / nrow(res_acm31$var$coord)

Statistics

Variables frequency

frequences31 <- crowmca31fct %>% 
  pivot_longer(everything(),
               names_to = "variables", 
               values_to = "categories") %>%  # count all occurrences of variable/category 
  count(variables, categories) %>% # count only unique pairs of variable/category (i.e. number of unique individuals for each category)
  group_by(variables) %>% 
  mutate(percentage = round(100 * n / nrow(crowmca31fct), 1)) %>% # compute percentage for each group of variables
  ungroup() %>% 
  select(variables, categories, n, percentage)  # re-order variables 

frequences31 %>% 
  flextable() %>% 
  colformat_double(decimal.mark = ",", digits = 1) %>% 
  autofit() %>% 
  set_caption("Variables Frequency on Crow 1931 dataset")
write_csv2(frequences31, "frequences31.csv")

** Active variables**

# Coordinates

coordinates31 <- as_tibble(res_acm31$var$coord,
                         rownames = "categories") %>%  # retrieve coordinates
  mutate_if(is.numeric, round, digits = 2) %>%  # retain just 2 digits
  rename_all(tolower) %>% # remove upper case characters
  rename_all(~ str_replace(., " ", "")) %>% # remove white spaces 
  rename_if(is.numeric, ~ str_c(., "coord", sep = "_")) # add suffix _coord to variable name 

# Contributions

contributions31 <- as_tibble(res_acm31$var$contrib,
                           rownames = "categories") %>%  # retrieve contributions
  mutate_if(is.numeric, round, digits = 2) %>%  # retain just 2 digits
  rename_all(tolower) %>% # remove upper case characters
  rename_all(~ str_replace(., " ", "")) %>% # remove white spaces 
  rename_if(is.numeric, ~ str_c(., "contrib", sep = "_")) # add suffix _contrib to variable name 

# Square cosine 

cos2_31 <- as_tibble(res_acm31$var$cos2,
                  rownames = "categories") %>%  # retrieve cos2
  mutate_if(is.numeric, round, digits = 2) %>%   # retain just 2 digits
  rename_all(tolower) %>% # remove upper case 
  rename_all(~ str_replace(., " ", "")) %>% # remove white spaces  
  rename_if(is.numeric, ~ str_c(., "cos2", sep = "_")) # add suffix _cos2


# vtest 
vtest31 <- as_tibble(res_acm31$var$v.test,
                   rownames = "categories") %>%  # retrieve v.test values
  mutate_if(is.numeric, round, digits = 2) %>%  # retain just 2 digits
  rename_all(tolower) %>%  # remove upper case 
  rename_all(~ str_replace(., " ", "")) %>% # remove white spaces 
  rename_if(is.numeric, ~ str_c(., "vtest", sep = "_")) # add suffix _vtest

# Re-assemble results

results_active31 <- frequences31 %>% 
  right_join(coordinates31) %>% 
  right_join(contributions31) %>% 
  right_join(cos2_31) %>% 
  right_join(vtest31) %>% # merge data using "categories"
  mutate(type = "Active") %>% # add column to specify the type of variable (active)
  select(type, variables, categories, n, percentage,
         contains("dim1"), contains("dim2"),
         contains("dim3"), contains("dim4")) # reorder variables according to their relevance for each axis
  


results_active31 %>% 
  flextable() %>% 
  colformat_double(decimal.mark = ",", digits = 1) %>% 
  autofit() %>% 
  set_caption("Active Variables: Statistical Results")
write_csv2(results_active31, "results_active31.csv")

Supplementary variables

# Coordinates

coord_sup31 <- as_tibble(res_acm31$quali.sup$coord,
                             rownames = "categories") %>%  
  mutate_if(is.numeric, round, digits = 2) %>%  
  rename_all(tolower) %>%
  rename_all(~ str_replace(., " ", "")) %>%
  rename_if(is.numeric, ~ str_c(., "coord", sep = "_"))

# Cosinus carrés 
cos2_sup31 <- as_tibble(res_acm31$quali.sup$cos2,
                      rownames = "categories") %>%  
  mutate_if(is.numeric, round, digits = 2) %>% 
  rename_all(tolower) %>% 
  rename_all(~ str_replace(., " ", "")) %>%
  rename_if(is.numeric, ~ str_c(., "cos2", sep = "_")) 

# vtest
vtest_sup31 <- as_tibble(res_acm31$quali.sup$v.test,
                       rownames = "categories") %>% 
  mutate_if(is.numeric, round, digits = 2) %>% 
  rename_all(tolower) %>%
  rename_all(~ str_replace(., " ", "")) %>%
  rename_if(is.numeric, ~ str_c(., "vtest", sep = "_")) 

# Reassemble

results_sup31 <- frequences31 %>% 
  right_join(coord_sup31) %>% 
  right_join(cos2_sup31) %>% 
  right_join(vtest_sup31) %>% 
  mutate(type = "Supplementary") %>% 
  select(type, variables, categories, n, percentage,
         contains("dim1"), contains("dim2"),
         contains("dim3"), contains("dim4")) 

results_sup31 %>% 
  flextable() %>% 
  colformat_double(decimal.mark = ",", digits = 1) %>% 
  autofit() %>% 
  set_caption("Supplementary Variables: Statistical Results")
write_csv2(results_sup31, "results_sup31.csv")

Assemble active and supplementary variables

results_all31 <- bind_rows(results_active31, results_sup31) 
write_csv2(results_all31, "results_all31.csv")

Visualizations

Point cloud of active variables (size proportionate to importance of variable)

results_active31 %>% 
  filter(dim1_contrib > threshold31 |
           dim2_contrib > threshold31) %>% 
  
  ggplot(aes(x = dim1_coord, y = dim2_coord, 
             label = categories,
             shape = variables, 
             size = n)) + 
  
  geom_point() +
  coord_fixed() + 
  geom_text_repel(size = 3, segment.alpha = 0.5) +
  
  geom_hline(yintercept = 0, colour = "darkgrey", linetype="longdash") +
  geom_vline(xintercept = 0, colour = "darkgrey", linetype="longdash") +
  
  xlab(paste0("Axe 1 (", round(variances31[1, 3], 1), " %)")) +
  ylab(paste0("Axe 2 (", round(variances31[2, 3], 1), " %)")) +
  
  scale_shape_manual(name = "", values = 0:20) +
  guides(shape=guide_legend(title = ""), size = FALSE) +
  
  theme_minimal() +
  theme(legend.position="bottom")

Point cloud of all variables (size proportionate to importance of variable)

results_all31 %>% 
  filter(dim1_contrib > threshold31 |
           dim2_contrib > threshold31 |
           is.na(dim2_contrib) & dim1_coord > 0.29 |
           is.na(dim2_contrib) & dim1_coord < -0.31) %>% 
  ggplot(aes(x = dim1_coord, y = dim2_coord, 
             label = categories,
             shape = variables,
             colour = type, 
             size = n)) + 
  
  geom_point() +
  coord_fixed() + #
  geom_text_repel(size = 3, segment.alpha = 0.5) +
  
  geom_hline(yintercept = 0, colour = "darkgrey", linetype="longdash") +
  geom_vline(xintercept = 0, colour = "darkgrey", linetype="longdash") +
  
  xlab(paste0("Axe 1 (", round(variances31[1, 2], 1), " %)")) +
  ylab(paste0("Axe 2 (", round(variances31[2, 2], 1), " %)")) +
  
  scale_shape_manual(name="", values = 0:20) +
  scale_color_manual(values = c("black", "darkgrey")) + 
  # scale_color_brewer(palette = "Set1") +
  # scale_color_grey() +
  # scale_color_brewer(palette = "Accent")
  
  guides(shape = guide_legend(title="Variable Name (Active & Supplementary)", 
                              title.position = "top"), 
         colour = guide_legend(title = "Variable Type", 
                               title.position = "top",
                               nrow = 2),
         size = FALSE) + 
  
  theme_minimal() +
  theme(legend.position="bottom")


Point cloud of individuals (periodicals) (colored by language, with confidence ellipses)

indiv12 <- as_tibble(res_acm31$ind$coord[,1:2])

indiv12_lang <- crowmca31fct %>% 
  select(Language) %>% 
  bind_cols(indiv12) 

ggplot(indiv12_lang, aes(x = `Dim 1`, y = `Dim 2`, 
                          colour = Language)) + 
  
  geom_point(alpha = 0.6) +
  coord_fixed() + 
  
  stat_ellipse() + 
  
  geom_hline(yintercept = 0, colour = "darkgrey", linetype="longdash") +
  geom_vline(xintercept = 0, colour = "darkgrey", linetype="longdash") +
  
  xlab(paste0("Axe 1 (", round(variances31[1, 3], 1), " %)")) +
  ylab(paste0("Axe 2 (", round(variances31[2, 3], 1), " %)")) +
  
  scale_color_brewer(palette = "Set2") +
  
  guides(colour = guide_legend(title="Language")) +
  
  theme_minimal()


Point cloud of individuals (periodicals) (colored by periodicity, with confidence ellipses)

indiv12_periodicity <- crowmca31fct %>% 
  select(Periodicity) %>% 
  bind_cols(indiv12)

ggplot(indiv12_periodicity, aes(x = `Dim 1`, y = `Dim 2`, 
                          colour = Periodicity)) + 
  
  geom_point(alpha = 0.6) +
  coord_fixed() + 
  
  stat_ellipse() + 
  
  geom_hline(yintercept = 0, colour = "darkgrey", linetype="longdash") +
  geom_vline(xintercept = 0, colour = "darkgrey", linetype="longdash") +
  
  xlab(paste0("Axe 1 (", round(variances31[1, 3], 1), " %)")) +
  ylab(paste0("Axe 2 (", round(variances31[2, 3], 1), " %)")) +
  
  scale_color_brewer(palette = "Set2") +
  
  guides(colour = guide_legend(title="Periodicity")) +
  
  theme_minimal()

Alternative approach with factoextra

Point cloud of individuals with confidence ellipse (language)

grp <- as.factor(crowmca31fct[, "Language"])
fviz_mca_ind(res_acm31,  habillage = grp, label = FALSE, 
             addEllipses = TRUE, repel = TRUE, title = "Graph of Periodicals: Language (1931)")

Point cloud of individuals with confidence ellipse (periodicity)

grp <- as.factor(crowmca31fct[, "Periodicity"])
fviz_mca_ind(res_acm31,  habillage = grp, label = FALSE, 
             addEllipses = TRUE, repel = TRUE, title = "Graph of Periodicals: Periodicity (1931)")

Alternative approaches

res.MCA1<-MCA(crowmca31,graph=FALSE)


Altogether, the two first dimensions capture almost 17% of information (10.67% on the first dimension, 6.35% on the second), which is not so bad given the high number of observations and variables in the dataset. 12 dimensions are necessary to capture at least 50% of information, 22 dimensions to retain 75% of information, and 48 for 100%.

Visualize variables contributions on axis 1:

fviz_contrib(res.MCA1, choice ="var", axes = 1)


Visualize variables contributions on axis 2:

fviz_contrib(res.MCA1, choice ="var", axes = 2)


Visualize individual contributions on first axis (top 20):

fviz_contrib(res.MCA1, choice ="ind", axes = 1, top = 20)


Plot the results:

plot.MCA(res.MCA1, choix='var',title="Newspaper directory (1931): Graph of variables",col.var=c(1,2,3,4,5,6,7,8,9,10,11))

plot.MCA(res.MCA1,invisible= 'ind',selectMod= 'cos2 0',col.var=c(1,1,1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,5,5,5,5,6,6,6,6,6,7,7,7,8,8,8,8,8,8,8,8,9,9,9,9,9,9,9,10,10,10,10,11,11,11,11,11,11),title="Newspaper directory (1931): ACM graph",label =c('var'))


Improve the visualization with Factoextra:

# Graph of variable categories:

fviz_mca_var(res.MCA1, repel = TRUE)

# Biplot of individuals and variables:

fviz_mca_biplot(res.MCA1, repel = TRUE)

The graph of variables delineates four main groups:

  • variables that are equally projected on the two dimensions : number of pages, publisher’s nationality (over 0.5), language (0.25-0.5).
  • variables that are clearly better projected on the first dimension than on the 2nd (publisher’s profile, periodicity, variables related to the format and layout)
  • Variables that are clearly better projected on the second dimension (circulation/audited)
  • Variables that are poorly projected on the two dimensions (founded, province).

One the second graph (ACM), the first dimension clearly opposes Chinese-language daily broadsheets (on the left) and foreign-language (English), small-size (octavo) periodicals with less frequent periodicity (monthly, quarterly, annual) (on the right). The latter referred to earlier, pioneering publications (1829-1903), whereas the former included the most recently established periodicals (1928-1935). We can see a gradient from the most to the least frequent and recent as we move along the x axis from left to right. The second dimension further separates foreign commercial publishers (newspaper, newsgroup, private entreprises) above and Chinese publishers (organization, academic, publishing house) below. As we move downward, the same dimension delineates a gradient from less (10-50 pages) to more substantial periodicals (100-500 pages). The former group was associated with weeklies, the latter with monthlies.

Just run the following lines of code to obtain the summary of results and dimensions:

summary(res.MCA1)
dim(res.MCA1)

We can launch explor to interact with the graph:

explor(res.MCA1)


Each variable is represented by a distinct color. Larger circles reflect stronger contribution:

res <- explor::prepare_results(res.MCA1)
explor::MCA_var_plot(res, xax = 1, yax = 2, var_sup = FALSE, var_sup_choice = ,
    var_lab_min_contrib = 0, col_var = "Variable", symbol_var = NULL, size_var = "Contrib",
    size_range = c(52.5, 700), labels_size = 10, point_size = 56, transitions = TRUE,
    labels_positions = "auto", labels_prepend_var = FALSE, xlim = c(-3.28, 4.81),
    ylim = c(-2.94, 5.14))


Distribution of periodicals according to their language:

explor::MCA_ind_plot(res, xax = 1, yax = 2, ind_sup = FALSE, lab_var = NULL,
    ind_lab_min_contrib = 0, col_var = "Language", labels_size = 9, point_opacity = 0.5,
    opacity_var = NULL, point_size = 64, ellipses = TRUE, transitions = TRUE,
    labels_positions = NULL, xlim = c(-2.12, 3.31), ylim = c(-2.15, 3.28))


Distribution of periodicals according to their periodicity (stronger opacity reflects lower quality of projection):

explor::MCA_ind_plot(res, xax = 1, yax = 2, ind_sup = FALSE, lab_var = NULL,
    ind_lab_min_contrib = 0, col_var = "Periodicity", labels_size = 9, point_opacity = 0.5,
    opacity_var = "Cos2", point_size = 64, ellipses = TRUE, transitions = TRUE,
    labels_positions = NULL, xlim = c(-2.12, 3.31), ylim = c(-2.15, 3.28))


Finally, we apply hierarchical clustering on all 48 dimensions:

res.MCA<-MCA(crowmca31,ncp=48,graph=FALSE)
res.HCPC<-HCPC(res.MCA,nb.clust=4,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='tree',title='Newspaper Directory (1931): Hierarchical Tree')