1 Загрузка библиотек

# При отсутствии пакетов раскомментируйте установку
# install.packages(c("readxl","dplyr","tidyr","ggplot2","ggpubr","broom",
#                    "knitr","kableExtra","purrr","tibble","rlang","scales"))

library(readxl)
library(dplyr)
library(tidyr)
library(tibble)
library(purrr)
library(rlang)
library(ggplot2)
library(ggpubr)
library(broom)
library(knitr)
library(kableExtra)
library(scales)

theme_set(theme_bw(base_size = 12))

2 Загрузка и подготовка данных

Данные из листа total_data файла 2026-06-30_Manual_vs_auto.xlsx. В каждой строке — один объект (комета), измеренный либо вручную (manual), либо автоматически (auto); поле Comet_Image служит идентификатором, парная нумерация совпадает между методами.

# Путь к файлу — в той же папке, что и .Rmd
data_file <- "Data/2026-06-30_Manual_vs_auto.xlsx"

raw <- read_excel(data_file, sheet = "total_data")

# Приводим типы, чистим служебные пустые колонки
dat <- raw %>%
  select(-any_of("...20")) %>%                # если есть пустой хвост
  mutate(
    Method = factor(Method, levels = c("manual", "auto")),
    Comet_Image = as.integer(Comet_Image)
  )

# Список числовых параметров для анализа
params <- c(
  "Comet_Length_px", "Comet_Height_px", "Comet_Area",
  "Comet_Intensity", "Comet_Mean_Intensity",
  "Head_Diameter_px", "Head_Area_px", "Head_Intensity",
  "Head_Mean_Intensity", "Head_DNA_%",
  "Tail_Length_px", "Tail_Area_px", "Tail_Intensity",
  "Tail_Mean_Intensity", "Tail_DNA_%", "Tail_Moment"
)
params <- intersect(params, names(dat))

glimpse(dat)

## Rows: 24
## Columns: 19
## $ Method               <fct> manual, manual, manual, manual, manual, manual, m…
## $ Comet_Image          <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4…
## $ Comet_Probability    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0…
## $ Comet_Length_px      <dbl> 41.700, 41.900, 38.100, 39.900, 40.600, 44.500, 2…
## $ Comet_Height_px      <dbl> 25.984, 27.625, 21.065, 27.098, 20.998, 28.102, 1…
## $ Comet_Area           <dbl> 769, 798, 582, 762, 557, 891, 370, 321, 292, 365,…
## $ Comet_Intensity      <dbl> 73469, 77511, 50446, 74203, 50000, 82989, 13072, …
## $ Comet_Mean_Intensity <dbl> 95.53800, 97.13200, 86.67700, 97.37900, 89.76700,…
## $ Head_Diameter_px     <dbl> 5.863, 6.513, 3.908, 6.014, 4.863, 4.689, 12.230,…
## $ Head_Area_px         <dbl> 32, 34, 16, 21, 12, 32, 156, 192, 124, 151, 166, …
## $ Head_Intensity       <dbl> 2562, 3013, 1111, 1956, 666, 2326, 6561, 13840, 8…
## $ Head_Mean_Intensity  <dbl> 80.06200, 88.61800, 69.43800, 93.14300, 55.50000,…
## $ `Head_DNA_%`         <dbl> 3.487185, 3.887190, 2.202355, 2.636012, 1.332000,…
## $ Tail_Length_px       <dbl> 34.573, 35.213, 32.738, 36.275, 35.826, 39.099, 1…
## $ Tail_Area_px         <dbl> 695, 748, 486, 715, 517, 821, 170, 152, 149, 181,…
## $ Tail_Intensity       <dbl> 68165, 73049, 45485, 70564, 47534, 78764, 5367, 6…
## $ Tail_Mean_Intensity  <dbl> 98.07900, 97.65900, 93.59100, 98.69100, 91.94200,…
## $ `Tail_DNA_%`         <dbl> 92.78063, 94.24340, 90.16572, 95.09589, 95.06800,…
## $ Tail_Moment          <dbl> 32.077047, 33.185928, 29.518454, 34.496033, 34.05…

Формируем длинный и широкий (парный) формат:

dat_long <- dat %>%
  select(Method, Comet_Image, all_of(params)) %>%
  pivot_longer(cols = all_of(params),
               names_to = "Parameter",
               values_to = "Value") %>%
  mutate(Parameter = factor(Parameter, levels = params))

dat_wide <- dat %>%
  select(Method, Comet_Image, all_of(params)) %>%
  pivot_longer(cols = all_of(params),
               names_to = "Parameter",
               values_to = "Value") %>%
  pivot_wider(names_from = Method, values_from = Value) %>%
  mutate(Parameter = factor(Parameter, levels = params),
         diff = auto - manual)

3 Описательная статистика

Сводка по методам (n, среднее, SD, медиана, IQR, min–max):

desc_stats <- dat_long %>%
  group_by(Parameter, Method) %>%
  summarise(
    n      = sum(!is.na(Value)),
    mean   = mean(Value, na.rm = TRUE),
    sd     = sd(Value,   na.rm = TRUE),
    median = median(Value, na.rm = TRUE),
    Q1     = quantile(Value, 0.25, na.rm = TRUE),
    Q3     = quantile(Value, 0.75, na.rm = TRUE),
    min    = min(Value, na.rm = TRUE),
    max    = max(Value, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  mutate(across(where(is.numeric), ~ round(.x, 3)))

desc_stats %>%
  kable(caption = "Описательная статистика по методам") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Описательная статистика по методам
Parameter	Method	n	mean	sd	median	Q1	Q3	min	max
Comet_Length_px	manual	12	33.506	8.253	33.344	26.949	40.875	22.001	44.500
Comet_Length_px	auto	12	34.583	9.765	34.000	26.750	43.250	21.000	47.000
Comet_Height_px	manual	12	19.535	6.303	18.500	13.502	26.262	13.010	28.102
Comet_Height_px	auto	12	22.667	7.328	21.000	16.000	30.000	15.000	32.000
Comet_Area	manual	12	516.583	239.094	463.500	313.750	763.750	245.000	891.000
Comet_Area	auto	12	587.083	339.903	494.000	278.750	917.500	238.000	1022.000
Comet_Intensity	manual	12	42060.083	28877.152	34543.500	16067.750	73652.500	13072.000	82989.000
Comet_Intensity	auto	12	104911.333	79669.668	77906.500	33150.250	187054.000	23724.000	200671.000
Comet_Mean_Intensity	manual	12	73.570	22.305	76.661	58.105	93.740	35.330	97.379
Comet_Mean_Intensity	auto	12	156.322	45.050	159.027	117.575	198.039	90.924	204.878
Head_Diameter_px	manual	12	9.856	4.866	9.372	5.613	14.756	3.908	15.572
Head_Diameter_px	auto	12	10.333	3.393	10.000	8.000	14.000	4.000	14.000
Head_Area_px	manual	12	92.750	73.284	79.000	29.250	158.500	12.000	192.000
Head_Area_px	auto	12	98.000	59.115	86.000	48.000	153.000	18.000	184.000
Head_Intensity	manual	12	6312.750	5000.883	4787.000	2233.500	10016.750	666.000	13840.000
Head_Intensity	auto	12	13284.833	10121.434	9867.000	4826.000	22473.000	808.000	27794.000
Head_Mean_Intensity	manual	12	71.327	13.910	72.158	67.178	78.452	42.058	93.143
Head_Mean_Intensity	auto	12	117.990	35.988	117.085	97.860	151.741	44.889	168.938
Head_DNA_%	manual	12	34.377	34.242	27.039	2.761	60.702	1.332	81.076
Head_DNA_%	auto	12	36.846	36.627	32.339	2.553	69.698	0.687	83.238
Tail_Length_px	manual	12	24.142	12.297	24.244	13.986	35.366	7.766	39.099
Tail_Length_px	auto	12	24.250	12.779	26.000	13.750	35.250	7.000	39.000
Tail_Area_px	manual	12	397.333	294.412	333.500	151.250	700.000	48.000	821.000
Tail_Area_px	auto	12	489.083	393.185	407.000	135.250	870.000	67.000	970.000
Tail_Intensity	manual	12	34370.000	32299.009	26133.500	5753.250	68764.750	2092.000	78764.000
Tail_Intensity	auto	12	91626.500	88629.144	65499.500	8684.750	182228.000	5597.000	196143.000
Tail_Mean_Intensity	manual	12	66.724	30.868	68.280	37.818	96.367	31.571	98.691
Tail_Mean_Intensity	auto	12	139.967	67.232	145.995	80.440	204.109	46.594	210.668
Tail_DNA_%	manual	12	61.971	34.111	65.611	36.463	94.410	12.812	95.096
Tail_DNA_%	auto	12	63.154	36.627	67.661	30.302	97.447	16.762	99.313
Tail_Moment	manual	12	18.779	15.452	17.992	5.100	33.404	1.143	37.108
Tail_Moment	auto	12	19.537	16.607	19.792	4.530	34.821	1.173	38.120

4 Статистика парного сравнения методов

Для каждого параметра проводим:

проверку нормальности разностей (Shapiro–Wilk);
парный t-тест Стьюдента;
парный тест Уилкоксона (знаково-ранговый).

Итоговый выбор p-значения делается по нормальности разностей (если p_Shapiro < 0.05 — используется Уилкоксон, иначе — t-тест).

safe_shapiro <- function(x) {
  x <- x[is.finite(x)]
  if (length(x) < 3 || length(unique(x)) < 2) return(NA_real_)
  tryCatch(shapiro.test(x)$p.value, error = function(e) NA_real_)
}

safe_test <- function(d, method = c("t","wilcox")) {
  method <- match.arg(method)
  d <- d[is.finite(d)]
  if (length(d) < 3 || length(unique(d)) < 2) return(NA_real_)
  tryCatch({
    if (method == "t") t.test(d)$p.value
    else wilcox.test(d, exact = FALSE)$p.value
  }, error = function(e) NA_real_)
}

paired_tbl <- dat_wide %>%
  group_by(Parameter) %>%
  summarise(
    n_pairs      = sum(is.finite(manual) & is.finite(auto)),
    mean_manual  = mean(manual, na.rm = TRUE),
    mean_auto    = mean(auto,   na.rm = TRUE),
    mean_diff    = mean(diff,   na.rm = TRUE),
    sd_diff      = sd(diff,     na.rm = TRUE),
    p_Shapiro    = safe_shapiro(diff),
    p_ttest      = safe_test(diff, "t"),
    p_wilcoxon   = safe_test(diff, "wilcox"),
    .groups      = "drop"
  ) %>%
  mutate(
    test_choice = ifelse(is.na(p_Shapiro) | p_Shapiro < 0.05,
                         "Wilcoxon (парный)", "t-test (парный)"),
    p_value     = ifelse(is.na(p_Shapiro) | p_Shapiro < 0.05,
                         p_wilcoxon, p_ttest),
    signif = cut(p_value,
                 breaks = c(-Inf, 0.001, 0.01, 0.05, Inf),
                 labels = c("***","**","*","ns"))
  ) %>%
  mutate(across(where(is.numeric), ~ round(.x, 4)))

paired_tbl %>%
  kable(caption = "Парное сравнение методов (manual vs. auto)") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Парное сравнение методов (manual vs. auto)
Parameter	n_pairs	mean_manual	mean_auto	mean_diff	sd_diff	p_Shapiro	p_ttest	p_wilcoxon	test_choice	p_value	signif
Comet_Length_px	12	33.5062	34.5833	1.0771	1.7795	0.1050	0.0599	0.0774	t-test (парный)	0.0599	ns
Comet_Height_px	12	19.5348	22.6667	3.1318	2.0916	0.0045	0.0003	0.0025	Wilcoxon (парный)	0.0025	**
Comet_Area	12	516.5833	587.0833	70.5000	131.5305	0.5299	0.0903	0.1261	t-test (парный)	0.0903	ns
Comet_Intensity	12	42060.0833	104911.3333	62851.2500	52157.7249	0.0080	0.0016	0.0025	Wilcoxon (парный)	0.0025	**
Comet_Mean_Intensity	12	73.5705	156.3223	82.7518	23.6072	0.2152	0.0000	0.0025	t-test (парный)	0.0000	***
Head_Diameter_px	12	9.8562	10.3333	0.4771	1.8434	0.1992	0.3892	0.4561	t-test (парный)	0.3892	ns
Head_Area_px	12	92.7500	98.0000	5.2500	23.0498	0.1598	0.4468	0.3462	t-test (парный)	0.4468	ns
Head_Intensity	12	6312.7500	13284.8333	6972.0833	5340.1544	0.1634	0.0009	0.0033	t-test (парный)	0.0009	***
Head_Mean_Intensity	12	71.3273	117.9902	46.6629	39.9580	0.2114	0.0019	0.0068	t-test (парный)	0.0019	**
Head_DNA_%	12	34.3771	36.8463	2.4692	7.8685	0.1993	0.3003	0.6661	t-test (парный)	0.3003	ns
Tail_Length_px	12	24.1417	24.2500	0.1083	1.6878	0.3187	0.8281	0.9063	t-test (парный)	0.8281	ns
Tail_Area_px	12	397.3333	489.0833	91.7500	120.3360	0.1029	0.0229	0.0544	t-test (парный)	0.0229
Tail_Intensity	12	34370.0000	91626.5000	57256.5000	57363.6179	0.0043	0.0054	0.0025	Wilcoxon (парный)	0.0025	**
Tail_Mean_Intensity	12	66.7238	139.9674	73.2435	38.3387	0.0843	0.0000	0.0025	t-test (парный)	0.0000	***
Tail_DNA_%	12	61.9714	63.1537	1.1824	5.5875	0.0548	0.4789	0.3670	t-test (парный)	0.4789	ns
Tail_Moment	12	18.7794	19.5368	0.7574	2.0190	0.2097	0.2203	0.2896	t-test (парный)	0.2203	ns

5 Визуализация: boxplot + точки (парные сравнения)

Для каждого параметра — boxplot по методам с наложенными точками и линиями, соединяющими парные наблюдения одной и той же кометы. p-значение приведено по итоговому выбранному тесту.

make_pair_plot <- function(param) {

  df <- dat_wide %>% filter(Parameter == param)
  df_long <- df %>%
    pivot_longer(cols = c(manual, auto),
                 names_to = "Method", values_to = "Value") %>%
    mutate(Method = factor(Method, levels = c("manual","auto")))

  info <- paired_tbl %>% filter(Parameter == param)
  pval <- info$p_value
  test <- info$test_choice
  p_lbl <- if (is.na(pval)) "p = н/д" else {
    if (pval < 0.001) "p < 0.001" else paste0("p = ", format(round(pval, 3), nsmall = 3))
  }

  ggplot(df_long, aes(x = Method, y = Value)) +
    geom_boxplot(aes(fill = Method), width = 0.5,
                 outlier.shape = NA, alpha = 0.35) +
    geom_line(aes(group = Comet_Image),
              colour = "grey55", linewidth = 0.3, alpha = 0.7) +
    geom_point(aes(colour = Method), size = 2.2, alpha = 0.9) +
    scale_fill_manual(values = c(manual = "#1f78b4", auto = "#e31a1c")) +
    scale_colour_manual(values = c(manual = "#1f78b4", auto = "#e31a1c")) +
    labs(title = param,
         subtitle = paste0(test, ": ", p_lbl),
         x = NULL, y = param) +
    theme(legend.position = "none",
          plot.title = element_text(face = "bold"))
}

for (p in params) {
  cat("\n\n## ", p, "\n\n", sep = "")
  print(make_pair_plot(p))
}

5.1 Comet_Length_px

5.2 Comet_Height_px

5.3 Comet_Area

5.4 Comet_Intensity

5.5 Comet_Mean_Intensity

5.6 Head_Diameter_px

5.7 Head_Area_px

5.8 Head_Intensity

5.9 Head_Mean_Intensity

5.10 Head_DNA_%

5.11 Tail_Length_px

5.12 Tail_Area_px

5.13 Tail_Intensity

5.14 Tail_Mean_Intensity

5.15 Tail_DNA_%

5.16 Tail_Moment

6 Регрессионный анализ (auto ~ manual)

Для каждого параметра оценивается линейная регрессия auto = β0 + β1 · manual. На графике приводятся уравнение регрессии и коэффициент детерминации R² (а также p-значение для наклона). Пунктирная линия — идентичность y = x.

fit_one <- function(param) {
  df <- dat_wide %>% filter(Parameter == param) %>%
    filter(is.finite(manual), is.finite(auto))
  if (nrow(df) < 3) return(NULL)
  fit <- lm(auto ~ manual, data = df)
  gl  <- broom::glance(fit)
  td  <- broom::tidy(fit)
  tibble(
    Parameter = param,
    n         = nrow(df),
    intercept = td$estimate[td$term == "(Intercept)"],
    slope     = td$estimate[td$term == "manual"],
    R2        = gl$r.squared,
    adj_R2    = gl$adj.r.squared,
    p_slope   = td$p.value[td$term == "manual"],
    RSE       = gl$sigma
  )
}

reg_tbl <- map_dfr(params, fit_one) %>%
  mutate(across(where(is.numeric), ~ round(.x, 4)))

reg_tbl %>%
  kable(caption = "Параметры линейной регрессии auto ~ manual") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Параметры линейной регрессии auto ~ manual
Parameter	n	intercept	slope	R2	adj_R2	p_slope	RSE
Comet_Length_px	12	-4.8449	1.1767	0.9891	0.9880	0.0000	1.0690
Comet_Height_px	12	0.7739	1.1207	0.9293	0.9222	0.0000	2.0434
Comet_Area	12	-115.0550	1.3592	0.9141	0.9055	0.0000	104.4846
Comet_Intensity	12	-7584.4267	2.6746	0.9398	0.9338	0.0000	20494.4758
Comet_Mean_Intensity	12	10.6826	1.9796	0.9606	0.9567	0.0000	9.3744
Head_Diameter_px	12	3.7158	0.6714	0.9269	0.9196	0.0000	0.9622
Head_Area_px	12	26.0373	0.7759	0.9252	0.9177	0.0000	16.9612
Head_Intensity	12	798.2043	1.9780	0.9551	0.9506	0.0000	2248.6202
Head_Mean_Intensity	12	137.9026	-0.2792	0.0116	-0.0872	0.7385	37.5241
Head_DNA_%	12	0.8989	1.0457	0.9557	0.9512	0.0000	8.0879
Tail_Length_px	12	-0.6293	1.0306	0.9834	0.9818	0.0000	1.7258
Tail_Area_px	12	-30.7233	1.3082	0.9596	0.9556	0.0000	82.8850
Tail_Intensity	12	-749.9035	2.6877	0.9594	0.9553	0.0000	18735.7175
Tail_Mean_Intensity	12	-0.1910	2.1006	0.9302	0.9232	0.0000	18.6350
Tail_DNA_%	12	-2.7257	1.0631	0.9802	0.9782	0.0000	5.4085
Tail_Moment	12	-0.5383	1.0690	0.9893	0.9883	0.0000	1.7983

make_reg_plot <- function(param) {

  df <- dat_wide %>% filter(Parameter == param) %>%
    filter(is.finite(manual), is.finite(auto))
  if (nrow(df) < 3) return(NULL)

  fit <- lm(auto ~ manual, data = df)
  cf  <- coef(fit)
  r2  <- summary(fit)$r.squared
  pv  <- summary(fit)$coefficients["manual", "Pr(>|t|)"]
  sgn <- ifelse(cf[2] >= 0, " + ", " - ")
  eq  <- sprintf("y = %.3f%s%.3f · x",
                 cf[1], sgn, abs(cf[2]))
  r2s <- sprintf("R² = %.3f", r2)
  ps  <- if (pv < 0.001) "p < 0.001" else sprintf("p = %.3f", pv)

  rng <- range(c(df$manual, df$auto), na.rm = TRUE)

  ggplot(df, aes(x = manual, y = auto)) +
    geom_abline(slope = 1, intercept = 0,
                linetype = "dashed", colour = "grey55") +
    geom_smooth(method = "lm", se = TRUE,
                colour = "#e31a1c", fill = "#fbb4ae", alpha = 0.4) +
    geom_point(size = 2.4, colour = "#1f78b4", alpha = 0.9) +
    coord_equal(xlim = rng, ylim = rng) +
    labs(title = param,
         subtitle = paste(eq, "  |  ", r2s, "  |  ", ps),
         x = paste0(param, " (manual)"),
         y = paste0(param, " (auto)")) +
    theme(plot.title = element_text(face = "bold"))
}

for (p in params) {
  plt <- make_reg_plot(p)
  if (!is.null(plt)) {
    cat("\n\n## ", p, "\n\n", sep = "")
    print(plt)
  }
}

6.1 Comet_Length_px

6.2 Comet_Height_px

6.3 Comet_Area

6.4 Comet_Intensity

6.5 Comet_Mean_Intensity

6.6 Head_Diameter_px

6.7 Head_Area_px

6.8 Head_Intensity

6.9 Head_Mean_Intensity

6.10 Head_DNA_%

6.11 Tail_Length_px

6.12 Tail_Area_px

6.13 Tail_Intensity

6.14 Tail_Mean_Intensity

6.15 Tail_DNA_%

6.16 Tail_Moment

7 Сводный вывод

summary_out <- paired_tbl %>%
  select(Parameter, n_pairs, mean_manual, mean_auto,
         mean_diff, test_choice, p_value, signif) %>%
  left_join(reg_tbl %>% select(Parameter, slope, intercept, R2, p_slope),
            by = "Parameter")

summary_out %>%
  kable(caption = "Итог: сравнение методов и линейная связь auto~manual") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE)

Итог: сравнение методов и линейная связь auto~manual
Parameter	n_pairs	mean_manual	mean_auto	mean_diff	test_choice	p_value	signif	slope	intercept	R2	p_slope
Comet_Length_px	12	33.5062	34.5833	1.0771	t-test (парный)	0.0599	ns	1.1767	-4.8449	0.9891	0.0000
Comet_Height_px	12	19.5348	22.6667	3.1318	Wilcoxon (парный)	0.0025	**	1.1207	0.7739	0.9293	0.0000
Comet_Area	12	516.5833	587.0833	70.5000	t-test (парный)	0.0903	ns	1.3592	-115.0550	0.9141	0.0000
Comet_Intensity	12	42060.0833	104911.3333	62851.2500	Wilcoxon (парный)	0.0025	**	2.6746	-7584.4267	0.9398	0.0000
Comet_Mean_Intensity	12	73.5705	156.3223	82.7518	t-test (парный)	0.0000	***	1.9796	10.6826	0.9606	0.0000
Head_Diameter_px	12	9.8562	10.3333	0.4771	t-test (парный)	0.3892	ns	0.6714	3.7158	0.9269	0.0000
Head_Area_px	12	92.7500	98.0000	5.2500	t-test (парный)	0.4468	ns	0.7759	26.0373	0.9252	0.0000
Head_Intensity	12	6312.7500	13284.8333	6972.0833	t-test (парный)	0.0009	***	1.9780	798.2043	0.9551	0.0000
Head_Mean_Intensity	12	71.3273	117.9902	46.6629	t-test (парный)	0.0019	**	-0.2792	137.9026	0.0116	0.7385
Head_DNA_%	12	34.3771	36.8463	2.4692	t-test (парный)	0.3003	ns	1.0457	0.8989	0.9557	0.0000
Tail_Length_px	12	24.1417	24.2500	0.1083	t-test (парный)	0.8281	ns	1.0306	-0.6293	0.9834	0.0000
Tail_Area_px	12	397.3333	489.0833	91.7500	t-test (парный)	0.0229		1.3082	-30.7233	0.9596	0.0000
Tail_Intensity	12	34370.0000	91626.5000	57256.5000	Wilcoxon (парный)	0.0025	**	2.6877	-749.9035	0.9594	0.0000
Tail_Mean_Intensity	12	66.7238	139.9674	73.2435	t-test (парный)	0.0000	***	2.1006	-0.1910	0.9302	0.0000
Tail_DNA_%	12	61.9714	63.1537	1.1824	t-test (парный)	0.4789	ns	1.0631	-2.7257	0.9802	0.0000
Tail_Moment	12	18.7794	19.5368	0.7574	t-test (парный)	0.2203	ns	1.0690	-0.5383	0.9893	0.0000

8 Информация о сессии

sessionInfo()

## R version 4.5.2 (2025-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=Russian_Russia.utf8  LC_CTYPE=Russian_Russia.utf8   
## [3] LC_MONETARY=Russian_Russia.utf8 LC_NUMERIC=C                   
## [5] LC_TIME=Russian_Russia.utf8    
## 
## time zone: Europe/Moscow
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] scales_1.4.0     kableExtra_1.4.0 knitr_1.50       broom_1.0.9     
##  [5] ggpubr_0.6.1     ggplot2_4.0.2    rlang_1.1.7      purrr_1.2.1     
##  [9] tibble_3.3.0     tidyr_1.3.2      dplyr_1.2.0      readxl_1.4.5    
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.10        generics_0.1.4     xml2_1.4.0         rstatix_0.7.2     
##  [5] lattice_0.22-7     stringi_1.8.7      digest_0.6.37      magrittr_2.0.4    
##  [9] evaluate_1.0.5     grid_4.5.2         RColorBrewer_1.1-3 fastmap_1.2.0     
## [13] Matrix_1.7-4       cellranger_1.1.0   jsonlite_2.0.0     backports_1.5.0   
## [17] Formula_1.2-5      mgcv_1.9-3         viridisLite_0.4.2  textshaping_1.0.1 
## [21] jquerylib_0.1.4    abind_1.4-8        cli_3.6.5          splines_4.5.2     
## [25] withr_3.0.2        cachem_1.1.0       yaml_2.3.10        tools_4.5.2       
## [29] ggsignif_0.6.4     vctrs_0.7.1        R6_2.6.1           lifecycle_1.0.5   
## [33] stringr_1.5.1      car_3.1-3          pkgconfig_2.0.3    pillar_1.11.0     
## [37] bslib_0.9.0        gtable_0.3.6       glue_1.8.0         systemfonts_1.2.3 
## [41] xfun_0.52          tidyselect_1.2.1   rstudioapi_0.17.1  farver_2.1.2      
## [45] nlme_3.1-168       htmltools_0.5.8.1  labeling_0.4.3     rmarkdown_2.29    
## [49] carData_3.0-5      svglite_2.2.1      compiler_4.5.2     S7_0.2.1

Сравнение ручной и автоматической обработки ДНК-комет

Verlov N.

22.07.2026