require(tidyverse)
require(patchwork)
require(httr)
require(glue)
require(broom)

old_theme <- theme_set(theme_minimal())

• M1 MIDS/MFA
• Université Paris Cité
• Année 2023-2024
• Course Homepage
  
• Moodle

Swiss fertility data

Dataset swiss from datasets::swiss connect fertility and social, economic data within 47 French-speaking districts in Switzerland.

• Fertility : fertility index
• Agriculture : jobs in agricultural sector
• Examination : literacy index (military examination)
• Education : proportion of people with successful secondary education
• Catholic : proportion of Catholics
• Infant.Mortality : mortality quotient at age 0

Fertility index (Fertility) is considered as the response variable

The social and economic variables are covariates (explanatory variables).

See European Fertility Project for more on this dataset.

PCA (Principal Component Analysis) is concerned with covariates.

data("swiss")

swiss %>% 
  glimpse(50)

Rows: 47
Columns: 6
$ Fertility        <dbl> 80.2, 83.1, 92.5, 85.8,…
$ Agriculture      <dbl> 17.0, 45.1, 39.7, 36.5,…
$ Examination      <int> 15, 6, 5, 12, 17, 9, 16…
$ Education        <int> 12, 9, 5, 7, 15, 7, 7, …
$ Catholic         <dbl> 9.96, 84.84, 93.40, 33.…
$ Infant.Mortality <dbl> 22.2, 22.2, 20.2, 20.3,…

Have a look at the documentation of the dataset

Describe the dataset

• Compute summary for each variable

solution

It is enough to call summary() on each column of swiss. This can be done in a functional programming style using package purrr. The collections of summaries can be rearranged so as to build a dataframe that is fit for reporting.

tt <- map_dfr(swiss, summary, .id = "var") %>% 
  mutate(across(where(is.numeric), ~ round(.x, digits=1))) 

tt %>% 
  DT::datatable()

Function skim from skimr delivers all univariate summaries in proper form.

foo <- swiss %>% 
  select(-Fertility) %>% 
  skim()  

foobar <- foo %>%  
  filter(skim_type=="numeric") %>% 
  rename(variable=skim_variable)  %>% 
    mutate(across(where(is.numeric), ~ round(.x, digits=1))) 

foobar %>% 
  DT::datatable(extensions = c('Buttons', 'ColReorder', 'FixedColumns', 'Responsive'),
                options = list( dom = 'Bfrtip',
                buttons = c('csv', 'pdf', 'print'),
                colReorder = TRUE, 
                dom = 't',
                scrollX = TRUE,
                fixedColumns = list(leftColumns = 3, rightColumns = 1))
  ) 

• Display graphic summary for each variable.

solution

We have to pick some graphical summary of the data. Boxplots and violine plots could be used if we look for concision.

We use histograms to get more details about each column.

Not that covariates have different meanings: Agriculture, Catholic, Examination, and Education are percentages with values between \(0\) and \(100\).

We have no details about the standardized fertility index Fertility

Infant.Mortality is also a rate:

Infant mortality is the death of an infant before his or her first birthday. The infant mortality rate is the number of infant deaths for every 1,000 live births. In addition to giving us key information about maternal and infant health, the infant mortality rate is an important marker of the overall health of a society.

see Center for Desease Control

We reuse the function we have already developped during previous sessions.

make_biotifoul(swiss, .f = is.numeric)

Histograms reveal that our covariates have very different distributions.

Religious affiliation (Catholic) tells us that there two types of districts, which is reminiscent of the old principle Cujus regio, ejus religio , see Old Swiss Confederacy.

Agriculture shows that in most districts, agriculture was still a very important activity.

Education reveals that in all but a few districts, most children did not receive secondary education. Examination shows that some districts lag behind the bulk of districts. Even less exhibit a superior performance.

The two demographic variables Fertility and Infant.Mortality look roughly unimodal with a few extreme districts.

Investigate correlations

Compute, display and comment the sample correlation matrix.

Display jointplots for each pair of variables.

solution

Package corrr, functions correlate and rplot provide a conveniemt tool.

Note that rplot() creates a graphical object of class ggplot. We can endow it with more layers.

corrr::correlate(swiss) %>% 
  corrr::rplot() %>% +
  ggtitle("Correlation plot for Swiss Fertility data")

Correlation computed with
• Method: 'pearson'
• Missing treated using: 'pairwise.complete.obs'

The high positive linear correlation between Education and Examination is moderately surprising. The negative correlation between the proportion of people involved in Agriculture and Education and Examinationis also not too surprising. Secondary schooling required pupils from rural areas to move to cities.

A more intriguing observation concerns the pairs Catholic and Examination (negative correlation) and Catholic and Education (little correlation).

The response variable Fertility looks negatively correlated with Examination an Education. These correlations are worth being further explored. In Demography, the decline of Fertility is often associated with the the rise of women education. Note that Examination is about males, and that Education does not give details about the way women complete primary education.

Perform PCA on covariates

Pairwise analysis did not provide us with a clear and simple picture of the French-speaking districts.

Play with centering and scaling

We first call prcomp() with the default arguments for centering and scaling, that is, we center columns and do not attempt to standardize columns.

solution

pco <- swiss %>% 
  select(-Fertility) %>% 
  prcomp()

The result

solution

Hand-made centering of the dataframe

X <- select(swiss, -Fertility)

n <- nrow(X)

Y <-  (X - matrix(1, nrow = n, ncol=1) %*%  rep(1/n,n) %*% as.matrix(X)) 

Y <- as.matrix(Y)

tibble(var=names(X), mX=colMeans(X), mY=colMeans(Y))  %>% 
  mutate(across(where(is.numeric), ~ round(.x, digits=2))) %>% 
  DT::datatable()

Function scale(X, scale=F) from base R does the job.

solution

svd_Y <-  svd(Y)

svd_Y %$% 
  (as.matrix(Y) - u %*% diag(d) %*% t(v)) %>% 
  norm(type = "F")   # <1> checking the factorization

[1] 2.054251e-13

norm( diag(1, ncol(Y)) - 
  (svd_Y %$% (t(v) %*% v)), 'F')  # <2> checking that colomns of `v` frm an orthonormal family. 

[1] 1.261261e-15

Note that we used the exposing pipe %$% from magrittr to unpack svd_Y which is a list with class svd and members named u, d and v.

We could have used with(,) from base R.

solution

The matrix \(1/n Y^T \times Y\) is the covariance matrix of the covariates. The spectral decomposition of the symmetric Semi Definite Positive (SDP) matrix \(1/n Y^T \times Y\) is related with the SVD factorization of \(Y\).

The spectral decomposition of \(Y^T \times Y\) can be obtained using eigen.

(t(eigen(t(Y) %*% Y )$vectors) %*% svd_Y$v ) %>% 
  round(digits=2)

     [,1] [,2] [,3] [,4] [,5]
[1,]    1    0    0    0    0
[2,]    0   -1    0    0    0
[3,]    0    0    1    0    0
[4,]    0    0    0    1    0
[5,]    0    0    0    0    1

Here, the eigenvectors of \(Y^T \times Y\) coincide with the right singular vectors of \(Y\) corresponding to non-zero singular values. Up to sign changes, it is always true when the non-zero singular values are pairwise distinct.

Now we check that prcomp is indeed a wrapper for svd.

(Y - pco$x %*% t(pco$rotation )) %>% 
  round(digits = 2)  %>% 
  head()

             Agriculture Examination Education Catholic Infant.Mortality
Courtelary             0           0         0        0                0
Delemont               0           0         0        0                0
Franches-Mnt           0           0         0        0                0
Moutier                0           0         0        0                0
Neuveville             0           0         0        0                0
Porrentruy             0           0         0        0                0

(svd_Y$v %*% t(pco$rotation )) %>% 
  round(2) 

     Agriculture Examination Education Catholic Infant.Mortality
[1,]           1           0         0        0                0
[2,]           0           1         0        0                0
[3,]           0           0         1        0                0
[4,]           0           0         0        1                0
[5,]           0           0         0        0                1

(t(pco$x) %*% pco$x) %>% 
  round(2)  

         PC1      PC2     PC3    PC4    PC5
PC1 86484.49     0.00    0.00   0.00   0.00
PC2     0.00 21127.44    0.00   0.00   0.00
PC3     0.00     0.00 2706.14   0.00   0.00
PC4     0.00     0.00    0.00 639.22   0.00
PC5     0.00     0.00    0.00   0.00 348.01

solution

Objects of class pca can be handled by generic functions like plot.

plot(pco)

The displayed plot is the so-called screeplot, it provides information about the approximation of the decomposedmatrix by its truncated SVDs.

solution

p_screeplot <- . %>%
  tidy(matrix="pcs") %>% { 
  ggplot(.) +
  aes(x=PC, y=percent, label=format(1.-cumulative,2)) +
  geom_text(angle=45, vjust=-1, hjust=-0.1) + 
  geom_col(fill=NA, colour="black")
  }

1

Define a pipeline for building a screeplot

2

Mind the braces on the right side of the first pipe

3

1- percent tell the reader about the relative Frobenious error achieved by keeping the first components of the SVD expansion.

pco %>% 
  p_screeplot() +
  labs(title="Screeplot for swiss fertility data",
  caption="Keeping the first two components is enough to achieve relative Froebenius relative error 3.3%")

Project the dataset on the first two principal components (perform dimension reduction) and build a scatterplot. Colour the points according to the value of original covariates.

solution

p <-  pco %>%
  augment(swiss) %>% 
  ggplot() +
  aes(x=.fittedPC1, y=.fittedPC2, label=.rownames) +
  geom_point() +
  coord_fixed() +
  ggrepel::geom_text_repel() 

(p + 
  aes(color=Infant.Mortality)) +
(p + 
   aes(color=Education)) +
(p + 
   aes(color=Examination)) +
(p + 
   aes(color=Catholic)) +
(p + 
   aes(color=Agriculture)) +
(p + 
   aes(color=Fertility)) +  
plot_layout(ncol = 2) +
plot_annotation(title="Swiss data on first two PCs" , 
                subtitle = "centered, unscaled")

solution

We can extract factor \(V\) from the SVD factorization using generic function tidy from package broom

pco %>% 
  tidy(matrix="v") %>% 
  glimpse()

Rows: 25
Columns: 3
$ column <chr> "Agriculture", "Agriculture", "Agriculture", "Agriculture", "Ag…
$ PC     <dbl> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, …
$ value  <dbl> 0.28151505, -0.88377692, -0.36961938, -0.02652821, -0.04863543,…

The result is a tibble in long form. It is worth pivoting the dataframe

pco %>% 
  tidy(matrix="v") %>% 
  pivot_wider(id_cols =column, 
              names_from = PC, 
              values_from = value)

# A tibble: 5 × 6
  column               `1`     `2`      `3`     `4`     `5`
  <chr>              <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
1 Agriculture       0.282  -0.884  -0.370   -0.0265 -0.0486
2 Examination      -0.121   0.174  -0.450   -0.867   0.0332
3 Education        -0.0584  0.311  -0.807    0.485  -0.117 
4 Catholic          0.950   0.303   0.00166 -0.0715  0.0223
5 Infant.Mortality  0.0105  0.0193  0.0985  -0.0867 -0.991 

Think of the rows of swiss as vectors. Then matrix v In wide form, we readily access to the decomposition of the or

solution

prep_co_circle <-  . %>%  
  tidy(matrix="v") %>%  
  pivot_wider(id_cols =column, 
              names_from = PC, 
              values_from = value)

co_circle_ppl <-  (
    pco %>% 
    prep_co_circle() %>% 
    filter(F)
    ) %>% 
  ggplot() +
  aes(x=`1`, y=`2`, label=column) +
  geom_segment(aes(xend=0, yend=0), arrow = grid::arrow(ends = "first")) +
  ggrepel::geom_text_repel() +
  coord_fixed() +
  xlim(c(-1.1, 1.1)) + ylim(c(-1.1, 1.1))  +
  annotate("path",
   x=0+1*cos(seq(0,2*pi,length.out=100)),
   y=0+1*sin(seq(0,2*pi,length.out=100)), linetype="dashed") 

co_circle_ppl %+% (
  pco %>% 
  prep_co_circle()
  )  +
  ggtitle("Swiss, correlation circle", 
          subtitle = "centered, unscaled")

# pco %$% {
#   ifelse(!is.null(center), "centered", "not centered") ;
#   ifelse(!is.null(scale), "scaled", "not scaled")
# }

solution

pco2 <- select(swiss, -Fertility) %>% 
 prcomp(scale. = T)

co_circle_ppl %+% (
  pco2 %>% 
  prep_co_circle()
  )  +
  ggtitle("Swiss, correlation circle", 
          subtitle = "centered, scaled")

Sanity checks

• \(X\) : data matrix after column centering (use scale(., center=T, scale-F))

\[X\]

solution

X <-  as.matrix(select(swiss, -Fertility)) |> 
  scale(center = T, scale=F)

# check centering, spot the difference in variances 
X |>  
  as_tibble() |> 
  summarise(across(everything(), c(var, mean)))

# A tibble: 1 × 10
  Agriculture_1 Agriculture_2 Examination_1 Examination_2 Education_1
          <dbl>         <dbl>         <dbl>         <dbl>       <dbl>
1          516.      2.64e-15          63.6     -1.51e-16        92.5
# ℹ 5 more variables: Education_2 <dbl>, Catholic_1 <dbl>, Catholic_2 <dbl>,
#   Infant.Mortality_1 <dbl>, Infant.Mortality_2 <dbl>

# should be 0
norm(X  %*% pco$rotation - pco$x)

[1] 0

# check the left singular vectors
pco$x %*% diag((pco$sdev)^(-1)) |> 
  as_tibble() |> 
  summarise(across(everything(), c(mean,var)))

Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
`.name_repair` is omitted as of tibble 2.0.0.
ℹ Using compatibility `.name_repair`.

# A tibble: 1 × 10
      V1_1  V1_2      V2_1  V2_2      V3_1  V3_2      V4_1  V4_2     V5_1  V5_2
     <dbl> <dbl>     <dbl> <dbl>     <dbl> <dbl>     <dbl> <dbl>    <dbl> <dbl>
1 7.44e-17     1 -1.05e-16     1 -8.24e-17  1.00 -6.84e-17     1 5.56e-16     1

# 
pco$rotation %*% (diag((pco$sdev)^(-2)) %*% t(pco$x) %*% X)

                  Agriculture   Examination     Education      Catholic
Agriculture      4.600000e+01  6.994405e-15  9.325873e-15 -2.192690e-14
Examination      1.346007e-13  4.600000e+01  3.042011e-14  3.273354e-13
Education        1.090239e-13  2.825518e-14  4.600000e+01 -3.185507e-13
Catholic         1.054712e-15 -2.102485e-15 -4.982126e-15  4.600000e+01
Infant.Mortality 1.172396e-13 -2.442491e-14 -7.194245e-14 -1.971756e-13
                 Infant.Mortality
Agriculture         -5.329071e-15
Examination          4.440892e-16
Education           -1.598721e-14
Catholic             4.440892e-16
Infant.Mortality     4.600000e+01

solution

pco |> 
  tidy(matrix="v") |> 
  pivot_wider(id_cols =column, 
              names_from = PC, 
              values_from = value) |> 
  rowwise() |> 
  summarise(column, l2=sum((c_across(where(is.numeric)))^2))

# A tibble: 5 × 2
  column              l2
  <chr>            <dbl>
1 Agriculture       1.00
2 Examination       1.00
3 Education         1   
4 Catholic          1   
5 Infant.Mortality  1.00

Checking Orthogonality of \(V\)

solution

# checking that pco$rotation is an orthogonal matrix 
t(pco$rotation) %*% pco$rotation

              PC1           PC2           PC3           PC4           PC5
PC1  1.000000e+00 -4.341417e-16 -7.220786e-17  2.710505e-18  3.469447e-18
PC2 -4.341417e-16  1.000000e+00  3.649425e-16 -8.001412e-17  6.938894e-17
PC3 -7.220786e-17  3.649425e-16  1.000000e+00  3.642919e-17 -1.387779e-17
PC4  2.710505e-18 -8.001412e-17  3.642919e-17  1.000000e+00  2.498002e-16
PC5  3.469447e-18  6.938894e-17 -1.387779e-17  2.498002e-16  1.000000e+00

pco$rotation %*% t(pco$rotation)

                  Agriculture   Examination     Education      Catholic
Agriculture      1.000000e+00  6.223320e-17  2.177078e-16  3.248270e-16
Examination      6.223320e-17  1.000000e+00 -5.316927e-16  1.517883e-17
Education        2.177078e-16 -5.316927e-16  1.000000e+00 -2.059984e-16
Catholic         3.248270e-16  1.517883e-17 -2.059984e-16  1.000000e+00
Infant.Mortality 6.245005e-17  2.983724e-16 -1.249001e-16 -1.734723e-17
                 Infant.Mortality
Agriculture          6.245005e-17
Examination          2.983724e-16
Education           -1.249001e-16
Catholic            -1.734723e-17
Infant.Mortality     1.000000e+00

Compare standardized and non-standardized PCA

Pay attention to the correlation circles.

1. How well are variables represented?
2. Which variables contribute to the first axis?

solution

pco_c <- swiss %>% 
  select(-Fertility) %>% 
  prcomp()

pco_cs <- swiss %>% 
  select(-Fertility) %>% 
  prcomp(scale.=T, center=T)

(co_circle_ppl %+% (pco_c %>% 
  prep_co_circle())  +
  ggtitle("Swiss, correlation circle", 
          subtitle = "centered, unscaled"))

(
  co_circle_ppl %+% (pco_cs %>% 
  prep_co_circle())  +
  ggtitle("Swiss, correlation circle", 
          subtitle = "centered, scaled")
)

Explain the contrast between the two correlation circles.

In the sequel we focus on standardized PCA.

solution

q <-  p %+% (pco_cs %>% 
  augment(swiss)) +
  ggtitle("Swiss data on first two PCs", subtitle = "centered, scaled")

(q + 
  aes(color=Infant.Mortality)) +
(q + 
   aes(color=Education)) +
(q + 
   aes(color=Examination)) +
(q + 
   aes(color=Catholic)) +
(q + 
   aes(color=Agriculture)) +
(q + 
   aes(color=Fertility)) +  
plot_layout(ncol = 2)

Warning: ggrepel: 45 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ggrepel: 45 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ggrepel: 45 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ggrepel: 45 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ggrepel: 45 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ggrepel: 45 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Investigate eigenvalues of covariance matrix

How many axes should we keep?

solution

p_screeplot %+% (pco_cs%>% 
  tidy(matrix="pcs")) 

plot(pco_cs)

Elbow rule: keep the first three PCs

Provide an interpretation of the first two principal axes

1. Which variables contribute to the two first principal axes?

solution

This comes from the correlation circle. We rely on function prep_co_circle and on the graphical pipeline co_circle_ppl.

(
  co_circle_ppl %+% 
    prep_co_circle(pco_cs) +
    ggtitle("Swiss, correlation circle", 
            subtitle = "centered, scaled")
)

2. Analyze the signs of correlations between variables and axes?

solution

swiss |> 
  select(-Fertility) |> 
  corrr::correlate() |> 
  corrr::shave() |> 
  corrr::rplot(print_cor = T) +
  theme_minimal()

Correlation computed with
• Method: 'pearson'
• Missing treated using: 'pairwise.complete.obs'

Add the Fertility variable

Plot again the correlation circle using the same principal axes as before, but add the Fertility variable. How does Fertility relate with covariates? with principal axes?

solution

U <-  pco_cs %$%    # exposition pipe
  (1/sqrt(nrow(x)-1) *x %*% diag((sdev)^(-1)))

Uprime <- with(pco_cs, 
  1/sqrt(nrow(x)-1) *x %*% diag((sdev)^(-1)))

t(U) %*% U

              [,1]          [,2]         [,3]          [,4]          [,5]
[1,]  1.000000e+00 -1.717376e-16 1.110223e-16 -3.008119e-16  6.210310e-16
[2,] -1.717376e-16  1.000000e+00 2.498002e-16 -1.970266e-16  3.816392e-17
[3,]  1.110223e-16  2.498002e-16 1.000000e+00  4.523508e-15  5.828671e-16
[4,] -3.008119e-16 -1.970266e-16 4.523508e-15  1.000000e+00 -6.432029e-16
[5,]  6.210310e-16  3.816392e-17 5.828671e-16 -6.432029e-16  1.000000e+00

t(Uprime) %*% Uprime

              [,1]          [,2]         [,3]          [,4]          [,5]
[1,]  1.000000e+00 -1.717376e-16 1.110223e-16 -3.008119e-16  6.210310e-16
[2,] -1.717376e-16  1.000000e+00 2.498002e-16 -1.970266e-16  3.816392e-17
[3,]  1.110223e-16  2.498002e-16 1.000000e+00  4.523508e-15  5.828671e-16
[4,] -3.008119e-16 -1.970266e-16 4.523508e-15  1.000000e+00 -6.432029e-16
[5,]  6.210310e-16  3.816392e-17 5.828671e-16 -6.432029e-16  1.000000e+00

(norm(U,type = "F"))^2

[1] 5

Display individuals (districts)

Comment

Biplot

solution

pco <- swiss %>% 
  select(-Fertility) %>% 
  prcomp(scale.=T)

df_cocirc <- pco %>% 
  tidy(matrix="v") %>% 
  pivot_wider(id_cols =column, 
              names_from = PC, 
              values_from = value) 

augment(pco, data=swiss) %>% 
  ggplot() + 
  geom_point(aes(x=.fittedPC1, 
                 y=.fittedPC2, 
                 color=Fertility, label=.rownames)) +
  coord_fixed() + 
  ggrepel::geom_text_repel(aes(x=.fittedPC1, 
                               y=.fittedPC2,
                               color=Infant.Mortality,
                               label=.rownames)) + 
  geom_segment(data=df_cocirc,  
               mapping=aes(x= 4* `1`, 
                           y= 4 * `2`, 
                           linetype=factor(column),
                           label=column,
                           xend=0, 
                           yend=0), 
               arrow = grid::arrow(ends = "first",
                                    unit(.1, "inches")
                                  )) + 
  scale_color_viridis_c() +
  xlim(c(-5,5)) + 
  ylim(c(-5,5)) #+

Warning in geom_point(aes(x = .fittedPC1, y = .fittedPC2, color = Fertility, :
Ignoring unknown aesthetics: label

Warning in geom_segment(data = df_cocirc, mapping = aes(x = 4 * `1`, y = 4 * :
Ignoring unknown aesthetics: label

Warning: ggrepel: 19 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

  # ggrepel::geom_text_repel(data=df_cocirc, 
  #                          aes(x= 4* `1`,
  #                              y= 4 * `2`, 
  #                              label=column), 
  #                          color="red")

solution

autoplot(pco, 
         data=swiss, 
         color="Agriculture", 
         loadings = TRUE, 
         loadings.colour = 'blue',
         loadings.label = TRUE)

References

https://scholar.google.com/citations?user=xbCKOYMAAAAJ&hl=fr&oi=ao