Data Describe

Herramientas para la descriptiva de los datos

Author

Dante Conti, Sergi Ramirez, (c) IDEAI

Published

September 30, 2025

Modified

September 30, 2025

1 Descripción del problema

Este conjunto de datos contiene registros de transacciones de cafeterías, incluyendo detalles sobre ventas, tipo de pago, hora de compra y preferencias del cliente.

Con atributos que abarcan la hora del día, los días de la semana, los meses, los tipos de café y los ingresos, este conjunto de datos proporciona una base sólida para analizar el comportamiento del cliente, los patrones de ventas y las tendencias de rendimiento empresarial.

Estructura del conjunto de datos:

hour_of_day: Hora de compra (0–23)
cash_type: Forma de pago (efectivo/tarjeta)
money: Importe de la transacción (en moneda local)
coffee_name: Tipo de café comprado (p. ej., Latte, Americano, Chocolate caliente)
Time_of_Day: Hora de compra (mañana, tarde, noche)
Weekday: Día de la semana (p. ej., lun., mar., etc.)
Month_name: Mes de compra (p. ej., ene., feb., mar.)
Weekdaysort: Representación numérica para ordenar por día de la semana (1 = lun., 7 = dom.)
Monthsort: Representación numérica para ordenar por mes (1 = ene., 12 = dic.)
Date: Fecha de la transacción (AAAA-MM-DD)
Time: Hora exacta de la transacción (HH:MM:SS)

Para hacer la descriptiva, podréis utilizar la siguiente base de datos.

  hour_of_day cash_type money         coffee_name Time_of_Day Weekday
1          10      card  38.7               Latte     Morning     Fri
2          12      card  38.7       Hot Chocolate   Afternoon     Fri
3          12      card  38.7       Hot Chocolate   Afternoon     Fri
4          13      card  28.9           Americano   Afternoon     Fri
5          13      card  38.7               Latte   Afternoon     Fri
6          15      card  33.8 Americano with Milk   Afternoon     Fri
  Month_name Weekdaysort Monthsort       Date            Time
1        Mar           5         3 2024-03-01 10:15:50.520000
2        Mar           5         3 2024-03-01 12:19:22.539000
3        Mar           5         3 2024-03-01 12:20:18.089000
4        Mar           5         3 2024-03-01 13:46:33.006000
5        Mar           5         3 2024-03-01 13:48:14.626000
6        Mar           5         3 2024-03-01 15:39:47.726000

A continuación vamos a detectar de que clase es cada una de las variables

Code

clases <- sapply(datos, class)
varNum <- names(clases)[which(clases %in% c("numeric", "integer"))]
varCat <- names(clases)[which(clases %in% c("character", "factor"))]

Para poder realizar una descriptiva correcta, descartaremos las variables Time y Date.

2 Análisis exploratorio

2.1 Análisis exploratorio de una variable

2.1.1 Numerical

2.1.1.1 Description

Code

library(psych)
psych::describe(datos[, varNum])

            vars    n  mean   sd median trimmed  mad   min  max range  skew
hour_of_day    1 3547 14.19 4.23  14.00   14.11 5.93  6.00 22.0 16.00  0.12
money          2 3547 31.65 4.88  32.82   31.98 4.36 18.12 38.7 20.58 -0.54
Weekdaysort    3 3547  3.85 1.97   4.00    3.81 2.97  1.00  7.0  6.00  0.08
Monthsort      4 3547  6.45 3.50   7.00    6.42 4.45  1.00 12.0 11.00  0.00
            kurtosis   se
hour_of_day    -1.13 0.07
money          -0.67 0.08
Weekdaysort    -1.23 0.03
Monthsort      -1.38 0.06

Code

par(mfrow = c(2, 4))  
for (var in varNum) {
  hist(datos[, var], main = paste0("Histograma variable ", var))
  boxplot(datos[, var], main = paste0("Boxplot variable ", var))
}

Code

par(mfrow = c(1, 1))

Code

library(ggplot2)
library(patchwork)

plots <- list()

for (var in varNum) {
  
  histo <- ggplot(datos, aes(x = .data[[var]])) + 
    geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
    geom_density(alpha = .2, fill = "#FF6666") +
    geom_vline(aes(xintercept = mean(.data[[var]], na.rm = TRUE)),
               color = "blue", linetype = "dashed", linewidth = 1) +
    ggtitle(paste("Histograma de", var))
  
  boxp <- ggplot(datos, aes(x = .data[[var]])) + 
    geom_boxplot(outlier.colour = "red", outlier.shape = 8,
                 outlier.size = 4) +
    ggtitle(paste("Boxplot de", var))
  
  plots <- append(plots, list(histo, boxp))
}

# Combinar en un grid automático con 2 columnas
final_plot <- Reduce(`+`, plots) + plot_layout(ncol = 2)
final_plot

2.1.2 Categorical

2.1.2.1 Description

Code

for (var in varCat) {
  tablaAbs <- data.frame(table(datos[, var]))
  tablaFreq <- data.frame(table(datos[, var])/sum(table(datos[, var])))
  m <- match(tablaAbs$Var1, tablaFreq$Var1)
  tablaAbs[, "FreqRel"] <- tablaFreq[m, "Freq"]
  colnames(tablaAbs) <- c("Categoria", "FreqAbs", "FreqRel")
  
  cat("===============", var, "===================================\n")
  print(tablaAbs)
  cat("==================================================\n")
}

=============== cash_type ===================================
  Categoria FreqAbs FreqRel
1      card    3547       1
==================================================
=============== coffee_name ===================================
            Categoria FreqAbs    FreqRel
1           Americano     564 0.15900761
2 Americano with Milk     809 0.22808007
3          Cappuccino     486 0.13701720
4               Cocoa     239 0.06738089
5             Cortado     287 0.08091345
6            Espresso     129 0.03636876
7       Hot Chocolate     276 0.07781224
8               Latte     757 0.21341979
==================================================
=============== Time_of_Day ===================================
  Categoria FreqAbs   FreqRel
1 Afternoon    1205 0.3397237
2   Morning    1181 0.3329574
3     Night    1161 0.3273189
==================================================
=============== Weekday ===================================
  Categoria FreqAbs   FreqRel
1       Fri     532 0.1499859
2       Mon     544 0.1533690
3       Sat     470 0.1325063
4       Sun     419 0.1181280
5       Thu     510 0.1437835
6       Tue     572 0.1612630
7       Wed     500 0.1409642
==================================================
=============== Month_name ===================================
   Categoria FreqAbs    FreqRel
1        Apr     168 0.04736397
2        Aug     272 0.07668452
3        Dec     259 0.07301945
4        Feb     423 0.11925571
5        Jan     201 0.05666761
6        Jul     237 0.06681703
7        Jun     223 0.06287003
8        Mar     494 0.13927262
9        May     241 0.06794474
10       Nov     259 0.07301945
11       Oct     426 0.12010149
12       Sep     344 0.09698337
==================================================

2.1.2.2 Graphic

base
ggplot2

Code

par(mfrow = c(2, 3))  
for (var in varCat) {
  barplot(table(datos[, var]))
}
par(mfrow = c(1, 1))

Code

library(ggplot2)
library(gridExtra)

plots <- list()  # lista vacía
i <- 1           # índice

for (var in varCat) {
  tabla <- data.frame(table(datos[, var]) / sum(table(datos[, var])))
  
  p <- ggplot(data = tabla, aes(x = Var1, y = Freq)) +
        geom_bar(stat = "identity", fill = "steelblue") +
        geom_text(aes(label = paste0(round(Freq * 100, 2), "%")),
                  vjust = 1.6, color = "white", size = 3.5) +
        theme_minimal() +
        labs(title = paste("Distribución de", var), x = var, y = "Proporción")
  
  plots[[i]] <- p
  i <- i + 1
}

# Mostrar todos los gráficos en un grid (ejemplo con 2 columnas)
grid.arrange(grobs = plots, ncol = 2)

2.2 Bivariant analysis

2.2.1 Numerical vs. numerical

2.2.1.1 Description

Code

cor(datos[, varNum])

             hour_of_day       money  Weekdaysort    Monthsort
hour_of_day  1.000000000  0.20274794 -0.002613959  0.008292999
money        0.202747935  1.00000000 -0.017264091 -0.050043191
Weekdaysort -0.002613959 -0.01726409  1.000000000  0.044140930
Monthsort    0.008292999 -0.05004319  0.044140930  1.000000000

2.2.1.2 Graphic

base
ggplot2

Code

library(PerformanceAnalytics)
chart.Correlation(as.matrix(datos[, varNum]),histogram = TRUE,pch=12)

Code

library(ggcorrplot)
corr <- round(cor(datos[, varNum]), 1)
ggcorrplot(corr, lab = T)

2.2.2 Numerical vs. categorical

2.2.2.1 Description

Code

for (varN in varNum) {
  for (varC in varCat) {
   print(psych::describeBy(datos[, varN], group = datos[, varC])) 
  }
}


 Descriptive statistics by group 
group: card
   vars    n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 3547 14.19 4.23     14   14.11 5.93   6  22    16 0.12    -1.13 0.07

 Descriptive statistics by group 
group: Americano
   vars   n  mean  sd median trimmed  mad min max range skew kurtosis   se
X1    1 564 13.19 3.7     13      13 4.45   6  22    16 0.42    -0.53 0.16
------------------------------------------------------------ 
group: Americano with Milk
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 809 13.62 4.31     13   13.39 4.45   6  22    16 0.37    -1.09 0.15
------------------------------------------------------------ 
group: Cappuccino
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 486 14.92 4.23     15   15.05 5.93   6  22    16 -0.22    -1.05 0.19
------------------------------------------------------------ 
group: Cocoa
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 239 15.26 4.26     16    15.4 5.93   7  22    15 -0.28    -1.14 0.28
------------------------------------------------------------ 
group: Cortado
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 287 12.65 3.99     12   12.26 4.45   7  22    15 0.69     -0.6 0.24
------------------------------------------------------------ 
group: Espresso
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 129 13.6 3.57     14   13.49 4.45   7  22    15 0.24    -0.78 0.31
------------------------------------------------------------ 
group: Hot Chocolate
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 276 16.32 3.89     17   16.51 4.45   8  22    14 -0.41    -0.89 0.23
------------------------------------------------------------ 
group: Latte
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 757 14.63 4.31     15   14.67 5.93   7  22    15 -0.08    -1.11 0.16

 Descriptive statistics by group 
group: Afternoon
   vars    n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 1205 14.07 1.45     14   14.09 1.48  12  16     4 -0.06    -1.36 0.04
------------------------------------------------------------ 
group: Morning
   vars    n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 1181  9.4 1.27     10    9.47 1.48   6  11     5 -0.34    -0.93 0.04
------------------------------------------------------------ 
group: Night
   vars    n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 1161 19.18 1.63     19    19.1 1.48  17  22     5  0.2    -1.17 0.05

 Descriptive statistics by group 
group: Fri
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 532 13.83 4.46     14   13.64 5.93   6  22    16 0.23    -1.09 0.19
------------------------------------------------------------ 
group: Mon
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 544 14.01 4.29     14   13.94 5.93   6  22    16 0.06    -1.17 0.18
------------------------------------------------------------ 
group: Sat
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 470 13.93 3.89   13.5   13.71 3.71   7  22    15  0.4    -0.79 0.18
------------------------------------------------------------ 
group: Sun
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 419 14.33 3.96     14   14.21 4.45   7  22    15 0.23    -1.02 0.19
------------------------------------------------------------ 
group: Thu
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 510 14.74 4.27     15   14.76 5.93   7  22    15 -0.05     -1.2 0.19
------------------------------------------------------------ 
group: Tue
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 572 14.27 4.39     14   14.24 5.93   7  22    15 0.04    -1.31 0.18
------------------------------------------------------------ 
group: Wed
   vars   n  mean  sd median trimmed  mad min max range skew kurtosis   se
X1    1 500 14.23 4.2     14    14.2 5.93   7  22    15 0.08    -1.16 0.19

 Descriptive statistics by group 
group: Apr
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 168 14.4 3.13     14   14.38 4.45  10  20    10  0.1    -1.37 0.24
------------------------------------------------------------ 
group: Aug
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 272 13.51 4.51     12   13.26 4.45   7  22    15  0.4    -1.18 0.27
------------------------------------------------------------ 
group: Dec
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 259 14.33 4.39     15   14.25 5.93   7  22    15  0.1    -1.23 0.27
------------------------------------------------------------ 
group: Feb
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 423 14.06 3.66     15   14.17 4.45   6  21    15 -0.23    -0.93 0.18
------------------------------------------------------------ 
group: Jan
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 201 14.34 4.36     14   14.25 5.93   7  22    15 0.09     -1.1 0.31
------------------------------------------------------------ 
group: Jul
   vars   n  mean  sd median trimmed  mad min max range skew kurtosis   se
X1    1 237 14.18 4.8     13   14.02 5.93   7  22    15  0.3    -1.35 0.31
------------------------------------------------------------ 
group: Jun
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 223 15.23 4.59     16   15.38 5.93   7  22    15 -0.15    -1.47 0.31
------------------------------------------------------------ 
group: Mar
   vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 494 13.51 3.37     14   13.53 4.45   6  21    15 -0.03    -1.02 0.15
------------------------------------------------------------ 
group: May
   vars   n mean  sd median trimmed  mad min max range  skew kurtosis   se
X1    1 241 15.2 4.1     15    15.3 5.93   7  22    15 -0.17    -1.12 0.26
------------------------------------------------------------ 
group: Nov
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 259 14.1 4.27     14   14.06 4.45   7  22    15 0.08    -1.03 0.27
------------------------------------------------------------ 
group: Oct
   vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 426 14.04 4.59     14   13.91 5.93   7  22    15  0.2    -1.21 0.22
------------------------------------------------------------ 
group: Sep
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 344 14.4 4.73   13.5   14.34 6.67   7  22    15 0.14    -1.43 0.26

 Descriptive statistics by group 
group: card
   vars    n  mean   sd median trimmed  mad   min  max range  skew kurtosis
X1    1 3547 31.65 4.88  32.82   31.98 4.36 18.12 38.7 20.58 -0.54    -0.67
     se
X1 0.08

 Descriptive statistics by group 
group: Americano
   vars   n  mean   sd median trimmed mad   min  max range  skew kurtosis   se
X1    1 564 25.98 1.68  25.96   25.99   0 23.02 28.9  5.88 -0.25    -0.22 0.07
------------------------------------------------------------ 
group: Americano with Milk
   vars   n  mean   sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 809 30.59 1.88  30.86   30.57 2.91 27.92 33.8  5.88 -0.17       -1 0.07
------------------------------------------------------------ 
group: Cappuccino
   vars   n  mean   sd median trimmed  mad   min  max range skew kurtosis   se
X1    1 486 35.88 1.82  35.76   35.94 2.91 32.82 38.7  5.88 -0.4     -0.7 0.08
------------------------------------------------------------ 
group: Cocoa
   vars   n  mean   sd median trimmed mad   min  max range  skew kurtosis   se
X1    1 239 35.65 1.23  35.76    35.7   0 32.82 38.7  5.88 -0.53        2 0.08
------------------------------------------------------------ 
group: Cortado
   vars   n  mean   sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 287 25.73 2.09  25.96   25.68 2.91 23.02 28.9  5.88 -0.03    -1.23 0.12
------------------------------------------------------------ 
group: Espresso
   vars   n  mean   sd median trimmed  mad   min max range  skew kurtosis   se
X1    1 129 20.85 1.97  21.06   20.81 2.91 18.12  24  5.88 -0.12    -1.09 0.17
------------------------------------------------------------ 
group: Hot Chocolate
   vars   n  mean   sd median trimmed mad   min  max range  skew kurtosis   se
X1    1 276 35.99 1.44  35.76   36.03   0 32.82 38.7  5.88 -0.13     0.77 0.09
------------------------------------------------------------ 
group: Latte
   vars   n mean   sd median trimmed mad   min  max range  skew kurtosis   se
X1    1 757 35.5 1.82  35.76   35.48   0 32.82 38.7  5.88 -0.18    -0.82 0.07

 Descriptive statistics by group 
group: Afternoon
   vars    n  mean   sd median trimmed  mad   min  max range  skew kurtosis
X1    1 1205 31.64 4.92  32.82   31.95 4.36 18.12 38.7 20.58 -0.53    -0.75
     se
X1 0.14
------------------------------------------------------------ 
group: Morning
   vars    n  mean   sd median trimmed  mad   min  max range  skew kurtosis
X1    1 1181 30.42 4.94  30.86    30.6 7.26 18.12 38.7 20.58 -0.25     -0.8
     se
X1 0.14
------------------------------------------------------------ 
group: Night
   vars    n  mean   sd median trimmed  mad   min  max range  skew kurtosis
X1    1 1161 32.89 4.43   33.8   33.38 2.91 18.12 38.7 20.58 -0.91    -0.02
     se
X1 0.13

 Descriptive statistics by group 
group: Fri
   vars   n  mean   sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 532 31.58 4.91  32.82   31.87 4.36 18.12 38.7 20.58 -0.51    -0.77 0.21
------------------------------------------------------------ 
group: Mon
   vars   n  mean   sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 544 31.92 4.51  32.82   32.17 4.36 18.12 38.7 20.58 -0.53    -0.72 0.19
------------------------------------------------------------ 
group: Sat
   vars   n  mean sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 470 31.35  5  32.82   31.66 4.36 18.12 38.7 20.58 -0.52    -0.58 0.23
------------------------------------------------------------ 
group: Sun
   vars   n  mean   sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 419 31.83 4.88  32.82   32.22 4.36 18.12 38.7 20.58 -0.59    -0.64 0.24
------------------------------------------------------------ 
group: Thu
   vars   n  mean   sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 510 31.55 5.19  32.82   31.95 4.36 18.12 38.7 20.58 -0.57    -0.76 0.23
------------------------------------------------------------ 
group: Tue
   vars   n  mean   sd median trimmed  mad   min  max range  skew kurtosis  se
X1    1 572 31.76 4.74  32.82   32.06 4.36 18.12 38.7 20.58 -0.53    -0.62 0.2
------------------------------------------------------------ 
group: Wed
   vars   n mean   sd median trimmed  mad   min  max range  skew kurtosis   se
X1    1 500 31.5 4.94  32.82   31.85 4.36 18.12 38.7 20.58 -0.51     -0.8 0.22

 Descriptive statistics by group 
group: Apr
   vars   n  mean   sd median trimmed  mad min  max range  skew kurtosis   se
X1    1 168 34.05 4.49   33.8   34.33 7.26  24 38.7  14.7 -0.38    -1.31 0.35
------------------------------------------------------------ 
group: Aug
   vars   n  mean   sd median trimmed  mad   min   max range skew kurtosis   se
X1    1 272 27.99 4.63  27.92   28.32 7.26 18.12 32.82  14.7 -0.4    -1.09 0.28
------------------------------------------------------------ 
group: Dec
   vars   n  mean   sd median trimmed mad   min   max range  skew kurtosis   se
X1    1 259 31.81 4.61  35.76   32.31   0 21.06 35.76  14.7 -0.72    -0.78 0.29
------------------------------------------------------------ 
group: Feb
   vars   n  mean   sd median trimmed  mad   min   max range  skew kurtosis
X1    1 423 31.24 4.69  30.86   31.58 7.26 21.06 35.76  14.7 -0.43    -1.23
     se
X1 0.23
------------------------------------------------------------ 
group: Jan
   vars   n  mean   sd median trimmed  mad   min   max range  skew kurtosis
X1    1 201 31.84 4.33  30.86   32.23 7.26 21.06 35.76  14.7 -0.61    -0.92
     se
X1 0.31
------------------------------------------------------------ 
group: Jul
   vars   n  mean   sd median trimmed  mad   min   max range skew kurtosis   se
X1    1 237 29.18 4.77  27.92   29.46 7.26 18.12 37.72  19.6 -0.5    -0.58 0.31
------------------------------------------------------------ 
group: Jun
   vars   n  mean   sd median trimmed mad   min   max range  skew kurtosis   se
X1    1 223 34.16 4.29  37.72   34.76   0 23.02 37.72  14.7 -0.96    -0.06 0.29
------------------------------------------------------------ 
group: Mar
   vars   n  mean   sd median trimmed  mad   min  max range skew kurtosis   se
X1    1 494 32.17 4.91   33.8    32.3 7.26 21.06 38.7 17.64 -0.3    -1.19 0.22
------------------------------------------------------------ 
group: May
   vars   n  mean   sd median trimmed mad   min   max range  skew kurtosis   se
X1    1 241 33.88 4.44  37.72   34.32   0 23.02 37.72  14.7 -0.67     -0.9 0.29
------------------------------------------------------------ 
group: Nov
   vars   n  mean   sd median trimmed mad   min   max range  skew kurtosis   se
X1    1 259 33.17 3.84  35.76   33.79   0 21.06 35.76  14.7 -1.18     0.12 0.24
------------------------------------------------------------ 
group: Oct
   vars   n  mean   sd median trimmed mad   min   max range  skew kurtosis   se
X1    1 426 32.61 4.29  35.76   33.21   0 21.06 35.76  14.7 -1.01    -0.29 0.21
------------------------------------------------------------ 
group: Sep
   vars   n  mean   sd median trimmed  mad   min   max range  skew kurtosis
X1    1 344 29.04 4.46  27.92   29.33 7.26 18.12 35.76 17.64 -0.57    -0.62
     se
X1 0.24

 Descriptive statistics by group 
group: card
   vars    n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 3547 3.85 1.97      4    3.81 2.97   1   7     6 0.08    -1.23 0.03

 Descriptive statistics by group 
group: Americano
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 564 3.74 1.89      4    3.69 2.97   1   7     6 0.06    -1.14 0.08
------------------------------------------------------------ 
group: Americano with Milk
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 809 3.82 2.02      4    3.78 2.97   1   7     6 0.12     -1.3 0.07
------------------------------------------------------------ 
group: Cappuccino
   vars   n mean sd median trimmed  mad min max range skew kurtosis   se
X1    1 486 3.99  2      4    3.99 2.97   1   7     6 0.01    -1.23 0.09
------------------------------------------------------------ 
group: Cocoa
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 239  3.7 1.94      4    3.63 2.97   1   7     6 0.17    -1.26 0.13
------------------------------------------------------------ 
group: Cortado
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 287 4.18 2.01      4    4.22 2.97   1   7     6 -0.14     -1.3 0.12
------------------------------------------------------------ 
group: Espresso
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 129 4.08 1.77      4    4.08 1.48   1   7     6 0.07    -0.93 0.16
------------------------------------------------------------ 
group: Hot Chocolate
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 276 3.91 1.95      4    3.89 2.97   1   7     6 0.11    -1.15 0.12
------------------------------------------------------------ 
group: Latte
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 757 3.71 1.99      4    3.64 2.97   1   7     6 0.15    -1.23 0.07

 Descriptive statistics by group 
group: Afternoon
   vars    n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 1205 4.04 2.01      4    4.05 2.97   1   7     6 -0.06    -1.27 0.06
------------------------------------------------------------ 
group: Morning
   vars    n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 1181 3.75 1.97      4    3.69 2.97   1   7     6 0.12    -1.25 0.06
------------------------------------------------------------ 
group: Night
   vars    n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 1161 3.74 1.92      4    3.67 2.97   1   7     6 0.19    -1.11 0.06

 Descriptive statistics by group 
group: Fri
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 532    5  0      5       5   0   5   5     0  NaN      NaN  0
------------------------------------------------------------ 
group: Mon
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 544    1  0      1       1   0   1   1     0  NaN      NaN  0
------------------------------------------------------------ 
group: Sat
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 470    6  0      6       6   0   6   6     0  NaN      NaN  0
------------------------------------------------------------ 
group: Sun
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 419    7  0      7       7   0   7   7     0  NaN      NaN  0
------------------------------------------------------------ 
group: Thu
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 510    4  0      4       4   0   4   4     0  NaN      NaN  0
------------------------------------------------------------ 
group: Tue
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 572    2  0      2       2   0   2   2     0  NaN      NaN  0
------------------------------------------------------------ 
group: Wed
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 500    3  0      3       3   0   3   3     0  NaN      NaN  0

 Descriptive statistics by group 
group: Apr
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 168 3.89 2.06      4    3.87 2.97   1   7     6 0.03    -1.33 0.16
------------------------------------------------------------ 
group: Aug
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 272 4.04 2.01      4    4.06 2.97   1   7     6 -0.07    -1.27 0.12
------------------------------------------------------------ 
group: Dec
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 259 3.97 2.12      4    3.96 2.97   1   7     6 0.05    -1.42 0.13
------------------------------------------------------------ 
group: Feb
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 423 3.47 1.87      3    3.37 2.97   1   7     6 0.25       -1 0.09
------------------------------------------------------------ 
group: Jan
   vars   n mean  sd median trimmed  mad min max range  skew kurtosis   se
X1    1 201 3.83 1.8      4    3.85 1.48   1   7     6 -0.11    -1.13 0.13
------------------------------------------------------------ 
group: Jul
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 237 3.78 1.85      3    3.73 1.48   1   7     6 0.23    -1.12 0.12
------------------------------------------------------------ 
group: Jun
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 223 4.17 2.02      4    4.21 2.97   1   7     6 -0.06     -1.3 0.14
------------------------------------------------------------ 
group: Mar
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 494 3.82 1.87      4     3.8 2.97   1   7     6 0.03    -1.17 0.08
------------------------------------------------------------ 
group: May
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 241 3.84 1.97      4     3.8 2.97   1   7     6 0.15    -1.12 0.13
------------------------------------------------------------ 
group: Nov
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 259 4.08 1.99      4    4.11 2.97   1   7     6 -0.16    -1.33 0.12
------------------------------------------------------------ 
group: Oct
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 426  3.7 1.94      4    3.63 2.97   1   7     6 0.23    -1.11 0.09
------------------------------------------------------------ 
group: Sep
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 344 3.91 2.15      4    3.88 2.97   1   7     6 0.07    -1.44 0.12

 Descriptive statistics by group 
group: card
   vars    n mean  sd median trimmed  mad min max range skew kurtosis   se
X1    1 3547 6.45 3.5      7    6.42 4.45   1  12    11    0    -1.38 0.06

 Descriptive statistics by group 
group: Americano
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 564 5.23 3.35      4    4.92 2.97   1  12    11 0.62    -1.03 0.14
------------------------------------------------------------ 
group: Americano with Milk
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 809 6.59 3.39      7     6.6 4.45   1  12    11 -0.11    -1.24 0.12
------------------------------------------------------------ 
group: Cappuccino
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 486 6.24 3.35      6    6.15 4.45   1  12    11 0.17    -1.19 0.15
------------------------------------------------------------ 
group: Cocoa
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 239 6.29 4.02      6     6.2 5.93   1  12    11  0.1    -1.71 0.26
------------------------------------------------------------ 
group: Cortado
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis  se
X1    1 287  7.1 3.37      8    7.22 2.97   1  12    11 -0.32    -1.07 0.2
------------------------------------------------------------ 
group: Espresso
   vars   n mean   sd median trimmed  mad min max range skew kurtosis  se
X1    1 129 6.37 3.37      7    6.28 4.45   1  12    11 0.06    -1.24 0.3
------------------------------------------------------------ 
group: Hot Chocolate
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 276 6.96 3.81    7.5    7.02 5.19   1  12    11 -0.17     -1.6 0.23
------------------------------------------------------------ 
group: Latte
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 757    7 3.37      8    7.11 4.45   1  12    11 -0.29    -1.24 0.12

 Descriptive statistics by group 
group: Afternoon
   vars    n mean   sd median trimmed  mad min max range skew kurtosis  se
X1    1 1205 6.02 3.61      5     5.9 4.45   1  12    11 0.23    -1.45 0.1
------------------------------------------------------------ 
group: Morning
   vars    n mean   sd median trimmed  mad min max range  skew kurtosis  se
X1    1 1181 6.74 3.44      7    6.77 4.45   1  12    11 -0.15    -1.32 0.1
------------------------------------------------------------ 
group: Night
   vars    n mean   sd median trimmed  mad min max range  skew kurtosis  se
X1    1 1161 6.61 3.41      7    6.61 4.45   1  12    11 -0.06    -1.27 0.1

 Descriptive statistics by group 
group: Fri
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 532 6.08 3.57      6       6 4.45   1  12    11 0.14    -1.44 0.15
------------------------------------------------------------ 
group: Mon
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 544  6.3 3.58      6    6.22 4.45   1  12    11 0.06    -1.46 0.15
------------------------------------------------------------ 
group: Sat
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 470 6.75 3.53      7     6.8 4.45   1  12    11 -0.11    -1.35 0.16
------------------------------------------------------------ 
group: Sun
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 419 7.18 3.25      8    7.25 4.45   1  12    11 -0.2    -1.17 0.16
------------------------------------------------------------ 
group: Thu
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 510 6.21 3.47      6    6.15 4.45   1  12    11 0.07    -1.37 0.15
------------------------------------------------------------ 
group: Tue
   vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 572 6.79 3.47      7    6.82 4.45   1  12    11 -0.14    -1.36 0.15
------------------------------------------------------------ 
group: Wed
   vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 500 6.01 3.44      6    5.86 4.45   1  12    11  0.2    -1.33 0.15

 Descriptive statistics by group 
group: Apr
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 168    4  0      4       4   0   4   4     0  NaN      NaN  0
------------------------------------------------------------ 
group: Aug
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 272    8  0      8       8   0   8   8     0  NaN      NaN  0
------------------------------------------------------------ 
group: Dec
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 259   12  0     12      12   0  12  12     0  NaN      NaN  0
------------------------------------------------------------ 
group: Feb
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 423    2  0      2       2   0   2   2     0  NaN      NaN  0
------------------------------------------------------------ 
group: Jan
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 201    1  0      1       1   0   1   1     0  NaN      NaN  0
------------------------------------------------------------ 
group: Jul
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 237    7  0      7       7   0   7   7     0  NaN      NaN  0
------------------------------------------------------------ 
group: Jun
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 223    6  0      6       6   0   6   6     0  NaN      NaN  0
------------------------------------------------------------ 
group: Mar
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 494    3  0      3       3   0   3   3     0  NaN      NaN  0
------------------------------------------------------------ 
group: May
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 241    5  0      5       5   0   5   5     0  NaN      NaN  0
------------------------------------------------------------ 
group: Nov
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 259   11  0     11      11   0  11  11     0  NaN      NaN  0
------------------------------------------------------------ 
group: Oct
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 426   10  0     10      10   0  10  10     0  NaN      NaN  0
------------------------------------------------------------ 
group: Sep
   vars   n mean sd median trimmed mad min max range skew kurtosis se
X1    1 344    9  0      9       9   0   9   9     0  NaN      NaN  0

2.2.2.2 Graphic

Code

library(ggplot2)
library(gridExtra)

plots <- list()
i <- 1

for (varC in varCat) {
  for (varN in varNum) {
    
    grafico <- ggplot(datos, aes(x = .data[[varN]], fill = .data[[varC]])) + 
      geom_histogram(colour = "black",
                     lwd = 0.75,
                     linetype = 1,
                     position = "identity",
                     alpha = 0.5) +
      labs(title = paste("Histograma de", varN, "por", varC),
           x = varN, y = "Frecuencia", fill = varC) +
      theme_minimal()
    
    plots[[i]] <- grafico
    i <- i + 1
  }
}

# Mostrar todos en un grid (2 columnas)
grid.arrange(grobs = plots, ncol = 2)

2.2.3 Categorical vs. categorical

2.2.3.1 Description

Code

for (varc1 in varCat) {
  for (varc2 in varCat) {
    if (varc1 != varc2) {
      prop_table <- prop.table(table(datos[, varc1], datos[, varc2]))
      cat("=============", varc1, " vs. ", varc2, "=========================\n")
      print(prop_table)
    }
  }
}

============= cash_type  vs.  coffee_name =========================
      
        Americano Americano with Milk Cappuccino      Cocoa    Cortado
  card 0.15900761          0.22808007 0.13701720 0.06738089 0.08091345
      
         Espresso Hot Chocolate      Latte
  card 0.03636876    0.07781224 0.21341979
============= cash_type  vs.  Time_of_Day =========================
      
       Afternoon   Morning     Night
  card 0.3397237 0.3329574 0.3273189
============= cash_type  vs.  Weekday =========================
      
             Fri       Mon       Sat       Sun       Thu       Tue       Wed
  card 0.1499859 0.1533690 0.1325063 0.1181280 0.1437835 0.1612630 0.1409642
============= cash_type  vs.  Month_name =========================
      
              Apr        Aug        Dec        Feb        Jan        Jul
  card 0.04736397 0.07668452 0.07301945 0.11925571 0.05666761 0.06681703
      
              Jun        Mar        May        Nov        Oct        Sep
  card 0.06287003 0.13927262 0.06794474 0.07301945 0.12010149 0.09698337
============= coffee_name  vs.  cash_type =========================
                     
                            card
  Americano           0.15900761
  Americano with Milk 0.22808007
  Cappuccino          0.13701720
  Cocoa               0.06738089
  Cortado             0.08091345
  Espresso            0.03636876
  Hot Chocolate       0.07781224
  Latte               0.21341979
============= coffee_name  vs.  Time_of_Day =========================
                     
                        Afternoon     Morning       Night
  Americano           0.065689315 0.061742317 0.031575980
  Americano with Milk 0.067380885 0.093318297 0.067380885
  Cappuccino          0.046236256 0.034395264 0.056385678
  Cocoa               0.021144629 0.016351847 0.029884409
  Cortado             0.024809698 0.040315760 0.015787990
  Espresso            0.015787990 0.012404849 0.008175923
  Hot Chocolate       0.022554271 0.013814491 0.041443473
  Latte               0.076120665 0.060614604 0.076684522
============= coffee_name  vs.  Weekday =========================
                     
                              Fri         Mon         Sat         Sun
  Americano           0.029602481 0.026219340 0.019453059 0.012968706
  Americano with Milk 0.029038624 0.036086834 0.033831407 0.027910911
  Cappuccino          0.017479560 0.020016916 0.019453059 0.019734987
  Cocoa               0.014660276 0.009585565 0.006484353 0.006766281
  Cortado             0.010431350 0.009867494 0.015787990 0.011840992
  Espresso            0.005920496 0.002819284 0.003946997 0.004510854
  Hot Chocolate       0.012686778 0.009867494 0.006766281 0.011277136
  Latte               0.030166338 0.038906118 0.026783197 0.023118128
                     
                              Thu         Tue         Wed
  Americano           0.023118128 0.022836200 0.024809698
  Americano with Milk 0.029038624 0.040315760 0.031857908
  Cappuccino          0.021708486 0.017761489 0.020862701
  Cocoa               0.006484353 0.016069918 0.007330138
  Cortado             0.011840992 0.012404849 0.008739780
  Espresso            0.007612067 0.004510854 0.007048210
  Hot Chocolate       0.013532563 0.013814491 0.009867494
  Latte               0.030448266 0.033549478 0.030448266
============= coffee_name  vs.  Month_name =========================
                     
                               Apr          Aug          Dec          Feb
  Americano           0.0093036369 0.0104313504 0.0076120665 0.0329856217
  Americano with Milk 0.0107132788 0.0202988441 0.0160699182 0.0239639132
  Cappuccino          0.0101494220 0.0095855653 0.0107132788 0.0146602763
  Cocoa               0.0011277136 0.0031012123 0.0059204962 0.0157879899
  Cortado             0.0045108542 0.0112771356 0.0087397801 0.0028192839
  Espresso            0.0011277136 0.0039469975 0.0033831407 0.0047927826
  Hot Chocolate       0.0028192839 0.0016915703 0.0073301381 0.0090217085
  Latte               0.0076120665 0.0163518466 0.0132506343 0.0152241331
                     
                               Jan          Jul          Jun          Mar
  Americano           0.0070482098 0.0101494220 0.0039469975 0.0377784043
  Americano with Milk 0.0146602763 0.0183253454 0.0186072738 0.0231181280
  Cappuccino          0.0076120665 0.0090217085 0.0129687059 0.0163518466
  Cocoa               0.0039469975 0.0025373555 0.0011277136 0.0101494220
  Cortado             0.0062024246 0.0039469975 0.0053566394 0.0084578517
  Espresso            0.0014096420 0.0039469975 0.0028192839 0.0053566394
  Hot Chocolate       0.0042289259 0.0031012123 0.0039469975 0.0121229208
  Latte               0.0115590640 0.0157879899 0.0140964195 0.0259374119
                     
                               May          Nov          Oct          Sep
  Americano           0.0112771356 0.0070482098 0.0124048492 0.0090217085
  Americano with Milk 0.0152241331 0.0146602763 0.0231181280 0.0293205526
  Cappuccino          0.0146602763 0.0073301381 0.0124048492 0.0115590640
  Cocoa               0.0022554271 0.0098674937 0.0090217085 0.0025373555
  Cortado             0.0047927826 0.0036650691 0.0095855653 0.0115590640
  Espresso            0.0019734987 0.0008457852 0.0033831407 0.0033831407
  Hot Chocolate       0.0036650691 0.0104313504 0.0163518466 0.0031012123
  Latte               0.0140964195 0.0191711305 0.0338314068 0.0265012687
============= Time_of_Day  vs.  cash_type =========================
           
                 card
  Afternoon 0.3397237
  Morning   0.3329574
  Night     0.3273189
============= Time_of_Day  vs.  coffee_name =========================
           
              Americano Americano with Milk  Cappuccino       Cocoa     Cortado
  Afternoon 0.065689315         0.067380885 0.046236256 0.021144629 0.024809698
  Morning   0.061742317         0.093318297 0.034395264 0.016351847 0.040315760
  Night     0.031575980         0.067380885 0.056385678 0.029884409 0.015787990
           
               Espresso Hot Chocolate       Latte
  Afternoon 0.015787990   0.022554271 0.076120665
  Morning   0.012404849   0.013814491 0.060614604
  Night     0.008175923   0.041443473 0.076684522
============= Time_of_Day  vs.  Weekday =========================
           
                   Fri        Mon        Sat        Sun        Thu        Tue
  Afternoon 0.04849168 0.04990133 0.05469411 0.04736397 0.04764590 0.04510854
  Morning   0.05441218 0.05441218 0.04426276 0.03383141 0.04116154 0.05835918
  Night     0.04708204 0.04905554 0.03354948 0.03693262 0.05497604 0.05779532
           
                   Wed
  Afternoon 0.04651818
  Morning   0.04651818
  Night     0.04792783
============= Time_of_Day  vs.  Month_name =========================
           
                   Apr        Aug        Dec        Feb        Jan        Jul
  Afternoon 0.02058077 0.02001692 0.02199041 0.04961940 0.02114463 0.01606992
  Morning   0.01127714 0.03411334 0.02537356 0.03326755 0.01719763 0.02762898
  Night     0.01550606 0.02255427 0.02565548 0.03636876 0.01832535 0.02311813
           
                   Jun        Mar        May        Nov        Oct        Sep
  Afternoon 0.01522413 0.06146039 0.02199041 0.02847477 0.03834226 0.02480970
  Morning   0.01888920 0.04623626 0.01691570 0.02368198 0.04341697 0.03495912
  Night     0.02875670 0.03157598 0.02903862 0.02086270 0.03834226 0.03721455
============= Weekday  vs.  cash_type =========================
     
           card
  Fri 0.1499859
  Mon 0.1533690
  Sat 0.1325063
  Sun 0.1181280
  Thu 0.1437835
  Tue 0.1612630
  Wed 0.1409642
============= Weekday  vs.  coffee_name =========================
     
        Americano Americano with Milk  Cappuccino       Cocoa     Cortado
  Fri 0.029602481         0.029038624 0.017479560 0.014660276 0.010431350
  Mon 0.026219340         0.036086834 0.020016916 0.009585565 0.009867494
  Sat 0.019453059         0.033831407 0.019453059 0.006484353 0.015787990
  Sun 0.012968706         0.027910911 0.019734987 0.006766281 0.011840992
  Thu 0.023118128         0.029038624 0.021708486 0.006484353 0.011840992
  Tue 0.022836200         0.040315760 0.017761489 0.016069918 0.012404849
  Wed 0.024809698         0.031857908 0.020862701 0.007330138 0.008739780
     
         Espresso Hot Chocolate       Latte
  Fri 0.005920496   0.012686778 0.030166338
  Mon 0.002819284   0.009867494 0.038906118
  Sat 0.003946997   0.006766281 0.026783197
  Sun 0.004510854   0.011277136 0.023118128
  Thu 0.007612067   0.013532563 0.030448266
  Tue 0.004510854   0.013814491 0.033549478
  Wed 0.007048210   0.009867494 0.030448266
============= Weekday  vs.  Time_of_Day =========================
     
       Afternoon    Morning      Night
  Fri 0.04849168 0.05441218 0.04708204
  Mon 0.04990133 0.05441218 0.04905554
  Sat 0.05469411 0.04426276 0.03354948
  Sun 0.04736397 0.03383141 0.03693262
  Thu 0.04764590 0.04116154 0.05497604
  Tue 0.04510854 0.05835918 0.05779532
  Wed 0.04651818 0.04651818 0.04792783
============= Weekday  vs.  Month_name =========================
     
              Apr         Aug         Dec         Feb         Jan         Jul
  Fri 0.007048210 0.009585565 0.008175923 0.020016916 0.011277136 0.010995207
  Mon 0.008175923 0.011559064 0.011559064 0.024809698 0.008175923 0.006766281
  Sat 0.006484353 0.013250634 0.011277136 0.009303637 0.009021708 0.007612067
  Sun 0.006484353 0.010431350 0.012122921 0.009021708 0.002819284 0.006766281
  Thu 0.006484353 0.012404849 0.007893995 0.017761489 0.010431350 0.007612067
  Tue 0.007330138 0.009867494 0.012686778 0.015787990 0.007893995 0.013814491
  Wed 0.005356639 0.009585565 0.009303637 0.022554271 0.007048210 0.013250634
     
              Jun         Mar         May         Nov         Oct         Sep
  Fri 0.007330138 0.025091627 0.009021708 0.012122921 0.018325345 0.010995207
  Mon 0.007612067 0.019453059 0.010431350 0.009585565 0.018325345 0.016915703
  Sat 0.009867494 0.020298844 0.005638568 0.014942205 0.010713279 0.014096420
  Sun 0.010995207 0.011277136 0.009867494 0.008175923 0.014096420 0.016069918
  Thu 0.009303637 0.018889202 0.013532563 0.009867494 0.018043417 0.011559064
  Tue 0.008739780 0.021426558 0.010149422 0.013250634 0.021990414 0.018325345
  Wed 0.009021708 0.022836200 0.009303637 0.005074711 0.018607274 0.009021708
============= Month_name  vs.  cash_type =========================
     
            card
  Apr 0.04736397
  Aug 0.07668452
  Dec 0.07301945
  Feb 0.11925571
  Jan 0.05666761
  Jul 0.06681703
  Jun 0.06287003
  Mar 0.13927262
  May 0.06794474
  Nov 0.07301945
  Oct 0.12010149
  Sep 0.09698337
============= Month_name  vs.  coffee_name =========================
     
         Americano Americano with Milk   Cappuccino        Cocoa      Cortado
  Apr 0.0093036369        0.0107132788 0.0101494220 0.0011277136 0.0045108542
  Aug 0.0104313504        0.0202988441 0.0095855653 0.0031012123 0.0112771356
  Dec 0.0076120665        0.0160699182 0.0107132788 0.0059204962 0.0087397801
  Feb 0.0329856217        0.0239639132 0.0146602763 0.0157879899 0.0028192839
  Jan 0.0070482098        0.0146602763 0.0076120665 0.0039469975 0.0062024246
  Jul 0.0101494220        0.0183253454 0.0090217085 0.0025373555 0.0039469975
  Jun 0.0039469975        0.0186072738 0.0129687059 0.0011277136 0.0053566394
  Mar 0.0377784043        0.0231181280 0.0163518466 0.0101494220 0.0084578517
  May 0.0112771356        0.0152241331 0.0146602763 0.0022554271 0.0047927826
  Nov 0.0070482098        0.0146602763 0.0073301381 0.0098674937 0.0036650691
  Oct 0.0124048492        0.0231181280 0.0124048492 0.0090217085 0.0095855653
  Sep 0.0090217085        0.0293205526 0.0115590640 0.0025373555 0.0115590640
     
          Espresso Hot Chocolate        Latte
  Apr 0.0011277136  0.0028192839 0.0076120665
  Aug 0.0039469975  0.0016915703 0.0163518466
  Dec 0.0033831407  0.0073301381 0.0132506343
  Feb 0.0047927826  0.0090217085 0.0152241331
  Jan 0.0014096420  0.0042289259 0.0115590640
  Jul 0.0039469975  0.0031012123 0.0157879899
  Jun 0.0028192839  0.0039469975 0.0140964195
  Mar 0.0053566394  0.0121229208 0.0259374119
  May 0.0019734987  0.0036650691 0.0140964195
  Nov 0.0008457852  0.0104313504 0.0191711305
  Oct 0.0033831407  0.0163518466 0.0338314068
  Sep 0.0033831407  0.0031012123 0.0265012687
============= Month_name  vs.  Time_of_Day =========================
     
       Afternoon    Morning      Night
  Apr 0.02058077 0.01127714 0.01550606
  Aug 0.02001692 0.03411334 0.02255427
  Dec 0.02199041 0.02537356 0.02565548
  Feb 0.04961940 0.03326755 0.03636876
  Jan 0.02114463 0.01719763 0.01832535
  Jul 0.01606992 0.02762898 0.02311813
  Jun 0.01522413 0.01888920 0.02875670
  Mar 0.06146039 0.04623626 0.03157598
  May 0.02199041 0.01691570 0.02903862
  Nov 0.02847477 0.02368198 0.02086270
  Oct 0.03834226 0.04341697 0.03834226
  Sep 0.02480970 0.03495912 0.03721455
============= Month_name  vs.  Weekday =========================
     
              Fri         Mon         Sat         Sun         Thu         Tue
  Apr 0.007048210 0.008175923 0.006484353 0.006484353 0.006484353 0.007330138
  Aug 0.009585565 0.011559064 0.013250634 0.010431350 0.012404849 0.009867494
  Dec 0.008175923 0.011559064 0.011277136 0.012122921 0.007893995 0.012686778
  Feb 0.020016916 0.024809698 0.009303637 0.009021708 0.017761489 0.015787990
  Jan 0.011277136 0.008175923 0.009021708 0.002819284 0.010431350 0.007893995
  Jul 0.010995207 0.006766281 0.007612067 0.006766281 0.007612067 0.013814491
  Jun 0.007330138 0.007612067 0.009867494 0.010995207 0.009303637 0.008739780
  Mar 0.025091627 0.019453059 0.020298844 0.011277136 0.018889202 0.021426558
  May 0.009021708 0.010431350 0.005638568 0.009867494 0.013532563 0.010149422
  Nov 0.012122921 0.009585565 0.014942205 0.008175923 0.009867494 0.013250634
  Oct 0.018325345 0.018325345 0.010713279 0.014096420 0.018043417 0.021990414
  Sep 0.010995207 0.016915703 0.014096420 0.016069918 0.011559064 0.018325345
     
              Wed
  Apr 0.005356639
  Aug 0.009585565
  Dec 0.009303637
  Feb 0.022554271
  Jan 0.007048210
  Jul 0.013250634
  Jun 0.009021708
  Mar 0.022836200
  May 0.009303637
  Nov 0.005074711
  Oct 0.018607274
  Sep 0.009021708

2.2.3.2 Graphic

Code

par(mfrow = c(3, 3))  
for (varc1 in varCat) {
  for (varc2 in varCat) {
    if (varc1 != varc2) {
      prop_table <- prop.table(table(datos[, varc1], datos[, varc2]))
      barplot(prop_table, beside = TRUE)
    }
  }
}

Code

par(mfrow = c(1, 1))

3 Automatic Descriptive Analysis (EDA)

Existen muchas herramientas que realizan la descriptiva de manera automática sin necesidad de la programación. Este apartado permite recoger algunas de ellas para su facilidad.

3.1 `Skim`

Code

library(skimr)
library(tidyverse)

## Podem visualitzar un descriptiu de les dades 
skim(datos)

Data summary
Name	datos
Number of rows	3547
Number of columns	9
_______________________
Column type frequency:
character	5
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
cash_type	1	4	4	1
coffee_name	1	5	19	8
Time_of_Day	1	5	9	3
Weekday	1	3	3	7
Month_name	1	3	3	12

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
hour_of_day	1	14.19	4.23	6.00	10.00	14.00	18.00	22.0	▆▇▆▇▆
money	1	31.65	4.88	18.12	27.92	32.82	35.76	38.7	▁▃▂▅▇
Weekdaysort	1	3.85	1.97	1.00	2.00	4.00	6.00	7.0	▇▃▃▃▆
Monthsort	1	6.45	3.50	1.00	3.00	7.00	10.00	12.0	▇▃▃▅▇

Code

# Visualitzem exclusivament les variables numériques
skim(datos) %>% yank("numeric")

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
hour_of_day	1	14.19	4.23	6.00	10.00	14.00	18.00	22.0	▆▇▆▇▆
money	1	31.65	4.88	18.12	27.92	32.82	35.76	38.7	▁▃▂▅▇
Weekdaysort	1	3.85	1.97	1.00	2.00	4.00	6.00	7.0	▇▃▃▃▆
Monthsort	1	6.45	3.50	1.00	3.00	7.00	10.00	12.0	▇▃▃▅▇

Code

skim(datos) %>% yank("character")

Variable type: character

skim_variable	complete_rate	min	max	n_unique
cash_type	1	4	4	1
coffee_name	1	5	19	8
Time_of_Day	1	5	9	3
Weekday	1	3	3	7
Month_name	1	3	3	12

3.2 `Vis`

Code

library(visdat)

## Busquem per a variables numériques o categóriques si hi ha NA's
vis_dat(datos)

Code

## Visualitzem percentatges de NA's en les variables
vis_miss(datos)

Code

## Generem la matriu de correlacions
datos %>% select(where(is.numeric)) %>% 
  vis_cor()

Code

## Podem visualitzar condicionants de les dades. En aquest cas, mirem si tenim mes de 
## 2 clases
vis_expect(datos, ~ .x > 2)

3.3 Inspectdf

Code

library(inspectdf)

## Tipus de dades
inspect_types(datos) %>% show_plot()

Code

## Utilització de la memoria
inspect_mem(datos) %>% show_plot()

Code

## Comprovem NA's
data_price_dummy <- datos %>% 
  mutate(price_dummy = if_else(money > 35, "High", "Low"))

inspect_na(data_price_dummy %>% filter(price_dummy == "High"),
           data_price_dummy %>% filter(price_dummy == "Low")) %>%
  show_plot()

Code

## Comprovem la distribució de les variables 
inspect_num(datos) %>% show_plot()

Code

## check categorical variable distribution
inspect_imb(datos) %>% show_plot()

Code

## check two categorical
inspect_imb(data_price_dummy %>% filter(price_dummy == "High"),
            data_price_dummy %>% filter(price_dummy == "Low")) %>%
  show_plot() + theme(legend.position = "none")

Code

## similiar to inspect_imb, but for all levels
inspect_cat(datos) %>% show_plot()

Code

inspect_cor(datos) %>% show_plot()

3.4 dataReporter (antiguo dataMaid)

Code

library("dataReporter")
dataReporter::makeDataReport(datos, output = "html", file = "/Users/ramitjans/Downloads/Preprocessing/report.Rmd")
dataReporter::makeCodebook(data = datos, file = "/Users/ramitjans/Downloads/Preprocessing/codebook.Rmd")

3.5 DataExplorer

Code

library(DataExplorer)
plot_str(datos)
introduce(datos)

  rows columns discrete_columns continuous_columns all_missing_columns
1 3547       9                5                  4                   0
  total_missing_values complete_rows total_observations memory_usage
1                    0          3547              31923       217536

Code

plot_intro(datos)

Code

plot_missing(datos)

Code

plot_bar(datos)

Code

plot_bar(datos, with = "money")

Code

plot_bar(datos, by = "cash_type")

Code

plot_histogram(datos)

Code

plot_correlation(na.omit(datos), maxcat = 5L)

3.6 SmartEDA

Code

library("SmartEDA")
## Overview of the data
ExpData(data = datos,type = 1)

## structure of the data    
ExpData(data = datos,type = 2)

3.6.1 Frequency or custom tables for categorical variables

Code

SmartEDA::ExpCTable(datos,Target=NULL,margin=1,clim=10,nlim=5,round=2,bin=NULL,per=T)

      Variable               Valid Frequency Percent CumPercent
1  coffee_name           Americano       564   15.90      15.90
2  coffee_name Americano with Milk       809   22.81      38.71
3  coffee_name          Cappuccino       486   13.70      52.41
4  coffee_name               Cocoa       239    6.74      59.15
5  coffee_name             Cortado       287    8.09      67.24
6  coffee_name            Espresso       129    3.64      70.88
7  coffee_name       Hot Chocolate       276    7.78      78.66
8  coffee_name               Latte       757   21.34     100.00
9  coffee_name               TOTAL      3547      NA         NA
10 Time_of_Day           Afternoon      1205   33.97      33.97
11 Time_of_Day             Morning      1181   33.30      67.27
12 Time_of_Day               Night      1161   32.73     100.00
13 Time_of_Day               TOTAL      3547      NA         NA
14     Weekday                 Fri       532   15.00      15.00
15     Weekday                 Mon       544   15.34      30.34
16     Weekday                 Sat       470   13.25      43.59
17     Weekday                 Sun       419   11.81      55.40
18     Weekday                 Thu       510   14.38      69.78
19     Weekday                 Tue       572   16.13      85.91
20     Weekday                 Wed       500   14.10     100.01
21     Weekday               TOTAL      3547      NA         NA

3.7 `Esquisse`

Este paquete nos permite abrir un Shiny con los controles para realizar mediante menús.

Code

esquisse::esquisser(datos)

4 Bibliografia

https://www.analyticsvidhya.com/blog/2022/10/three-r-libraries-for-automated-eda/
https://cran.r-project.org/web/packages/dlookr/vignettes/EDA.html
https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html
https://daya6489.github.io/SmartEDA/

1 Descripción del problema

2 Análisis exploratorio

2.1 Análisis exploratorio de una variable

2.1.1 Numerical

2.1.1.1 Description

2.1.1.2 Graphic

2.1.2 Categorical

2.1.2.1 Description

2.1.2.2 Graphic

2.2 Bivariant analysis

2.2.1 Numerical vs. numerical

2.2.1.1 Description

2.2.1.2 Graphic

2.2.2 Numerical vs. categorical

2.2.2.1 Description

2.2.2.2 Graphic

2.2.3 Categorical vs. categorical

2.2.3.1 Description

2.2.3.2 Graphic

3 Automatic Descriptive Analysis (EDA)

3.1 Skim

3.2 Vis

3.3 Inspectdf

3.4 dataReporter (antiguo dataMaid)

3.5 DataExplorer

3.6 SmartEDA

3.6.1 Frequency or custom tables for categorical variables

3.7 Esquisse

4 Bibliografia

3.1 `Skim`

3.2 `Vis`

3.7 `Esquisse`