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A correlation analysis of clinical variables of TCGA-KIRC patients

This project contains a pipeline of clinical analysis of the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data of patients, from Genomic Data Commons Data Portal and cBioPortal.

Previously, we presented an exploratory preprocessing analysis. In this section, Chi-squared test is applied to compare two or more proportions of categorical variables and T-student test to compare the means of numeric ones regardind the levels of ‘Overall_Survival_Status’. The Hypoteis test is performed and p-value indicates the strength of evidence in supportting the null hypothesis.

# Set the packages of interest
packages = c("tidyverse","skimr","finalfit","rstatix", "ggpubr","GGally", "plotly")

# if a package is installed, it will be loaded
# otherwise, the missing package(s) will be installed and loaded
package.check <- lapply(packages, FUN = function(x) {
  if (!require(x, character.only = TRUE)) {
    install.packages(x, dependencies = TRUE)
    library(x, character.only = TRUE)
  }
})

suppressMessages(library("tidyverse"))
rm(packages)
setwd(".")
  1. Importing data
kirc_clin <- read_csv("data/kirc_clin.csv")
  1. Taming data
kirc_clin <- kirc_clin %>%
  mutate_if(is.character, as.factor) %>%
  mutate(patient_id = as.character(patient_id),
         age = as.integer(age),
         year_diagnose = as.integer(year_diagnose))

# check 
glimpse(kirc_clin) 

## Rows: 537
## Columns: 29
## $ patient_id          <chr> "TCGA-3Z-A93Z", "TCGA-6D-AA2E", "TCGA-A3-3306", "…
## $ age                 <int> 69, 68, 67, 66, 77, 57, 59, 57, 67, 70, 52, 51, 5…
## $ metastasis_stg      <fct> M0, MX, M0, M0, M0, M0, M0, M0, M0, M0, M0, M0, M…
## $ lymph_stg           <fct> N0, NX, N0, N0, N0, NX, N0, NX, N0, NX, NX, NX, N…
## $ neoplasm_stg        <fct> Stage I, Stage I, Stage I, Stage III, Stage III, …
## $ tumor_stg           <fct> T1, T1, T1, T3, T3, T1, T1, T2, T2, T1, T1, T1, T…
## $ disease_free_mth    <dbl> 12.65, 11.89, 36.79, 47.17, 0.53, NA, NA, 49.05, …
## $ disease_free_stt    <fct> DiseaseFree, DiseaseFree, DiseaseFree, DiseaseFre…
## $ ethnicity           <fct> not hispanic/latino, not hispanic/latino, not his…
## $ frac_genome_alter   <dbl> 0.2240, 0.0000, 0.2316, 0.0494, 0.0844, 0.0883, 0…
## $ histology_grd       <fct> G2, G2, G3, G3, G2, G2, G3, G3, G2, G2, G1, G2, G…
## $ hemoglobin          <fct> Normal, NA, NA, NA, Normal, Low, Low, NA, NA, NA,…
## $ neoadj_therapy      <fct> No, No, No, No, No, No, No, No, No, No, No, No, N…
## $ prior_cancer        <fct> No, No, No, Yes, No, No, No, No, No, No, No, No, …
## $ year_diagnose       <int> 2013, 2013, 2005, 2005, 2006, 2005, 2005, 2005, 2…
## $ tumor_lateral       <fct> Right, Right, Left, Right, Right, Right, Right, L…
## $ long_dim            <dbl> NA, NA, 0.9, 1.0, 1.2, 1.7, 2.0, 1.7, 1.5, 1.5, 1…
## $ mutation_cnt        <dbl> NA, NA, NA, NA, 514, 56, 656, 577, 62, 537, 477, …
## $ over_surv_mth       <dbl> 12.65, 11.89, 36.79, 47.17, 0.53, 39.13, 24.15, 4…
## $ over_surv_stt       <fct> LIVING, LIVING, LIVING, LIVING, LIVING, DECEASED,…
## $ platelet            <fct> Normal, NA, NA, NA, Normal, NA, NA, NA, NA, NA, N…
## $ race                <fct> Black/African.american, Black/African.american, W…
## $ serum_ca            <fct> Normal, NA, NA, NA, Normal, NA, NA, NA, NA, NA, N…
## $ gender              <fct> Male, Female, Male, Male, Female, Male, Male, Mal…
## $ short_dim           <dbl> NA, NA, 0.4, 0.6, 0.4, 0.4, 0.8, 0.8, 0.3, 0.4, 0…
## $ second_long_dim     <dbl> NA, NA, 0.7, 0.8, 1.0, 1.0, 0.9, 0.8, 1.0, 1.1, 0…
## $ tissue_site         <fct> OTHERS, OTHERS, A, A, A, A, A, A, A, A, A, A, A, …
## $ person_neoplasm_stt <fct> Tumor.Free, Tumor.Free, NA, With.Tumor, Tumor.Fre…
## $ wbc                 <fct> Normal, NA, NA, NA, Normal, NA, NA, NA, NA, NA, N…

3.The dependent variable

# Check the number of levels. If greater than 2, it thas to run a simple logistic regression presents only two levels (otherwise, it has to run a ordinal logistic regression)
table(kirc_clin$over_surv_stt, exclude = NULL) 

## 
## DECEASED   LIVING 
##      177      360
  1. Numeric variables vs. over_surv_stt

Correlation matrix - graphic visualization

cols_numeric <- kirc_clin %>% 
  select_if(is.numeric) %>%
  names

kirc_clin_numeric <- kirc_clin %>%
                      select(one_of(c(cols_numeric, "over_surv_stt")))  

levels(kirc_clin_numeric$over_surv_stt) <- c("D","L")

ggpairs(kirc_clin_numeric, columns = cols_numeric, 
        title="Correlation matrix",               
        mapping= aes(colour = over_surv_stt), 
        upper = list(combo = wrap("box_no_facet", alpha=0.1), 
                     continuous = wrap("cor", size = 2, alignPercent = 0.8)),
        lower = list(continuous = wrap("smooth", alpha = 0.3, size=0.2) )) +
        theme(panel.background = element_rect(color = "black", size=0.5, fill="white"),
          panel.grid.major = element_blank())

Run multiple T-tests on over_surv_stt

Transform the data into long format

# Put all variables in the same column except `over_surv_stt`, the grouping variable

levels(kirc_clin_numeric$over_surv_stt) <- c("DECEASED","LIVING")

# Convert to Tidyverse
kirc_clin_numeric.long <- kirc_clin_numeric %>%
  pivot_longer(-over_surv_stt, names_to = "variables", values_to = "value")
kirc_clin_numeric.long <- kirc_clin_numeric.long[!is.na(kirc_clin_numeric.long$value), ]
kirc_clin_numeric.long$value.log <- log2(kirc_clin_numeric.long$value+1)

# OR
# kirc_clin_numeric.long <- kirc_clin_numeric %>% 
#   gather(key = 'variables', value = 'value', -over_surv_stt, na.rm = TRUE) %>%
#     mutate(value.log = log2(kirc_clin_numeric.long$value+1))

kirc_clin_numeric.long %>% sample_n(6) %>% knitr::kable(.)
over_surv_stt variables value value.log
DECEASED long_dim 1.7 1.4329594
LIVING year_diagnose 2007.0 10.9715436
DECEASED second_long_dim 1.5 1.3219281
DECEASED year_diagnose 2002.0 10.9679467
LIVING short_dim 0.3 0.3785116
LIVING mutation_cnt 17.0 4.1699250

Group the data by variables and compare over_surv_stt groups

Adjust the p-values and add significance levels

stat.test <- kirc_clin_numeric.long %>%
  group_by(variables) %>%
  t_test(value ~ over_surv_stt) %>%
  adjust_pvalue(method = "BH") %>%
  add_significance()
stat.test %>% knitr::kable(.)
variables .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
age value DECEASED LIVING 177 360 4.8869626 348.1670 1.60e-06 0.0000035 ****
disease_free_mth value DECEASED LIVING 78 360 -10.9719698 191.6452 0.00e+00 0.0000000 ****
frac_genome_alter value DECEASED LIVING 175 353 1.1960301 343.6013 2.33e-01 0.2621250 ns
long_dim value DECEASED LIVING 173 329 4.1297313 363.4507 4.51e-05 0.0000812 ****
mutation_cnt value DECEASED LIVING 153 298 -1.8305701 428.8339 6.79e-02 0.0873000 ns
over_surv_mth value DECEASED LIVING 177 360 -7.3171956 450.9227 0.00e+00 0.0000000 ****
second_long_dim value DECEASED LIVING 173 329 4.0638742 287.9183 6.23e-05 0.0000934 ****
short_dim value DECEASED LIVING 173 329 0.4784069 344.6781 6.33e-01 0.6330000 ns
year_diagnose value DECEASED LIVING 177 360 -8.8979681 377.0943 0.00e+00 0.0000000 **** 
# Create the plot on logscale
myplot <- ggboxplot(
  kirc_clin_numeric.long, x = "over_surv_stt", y = "value.log",
  fill = "over_surv_stt", palette = "npg", legend = "none", 
  ggtheme = theme_pubr(border = TRUE)
  ) +
  facet_wrap(~variables)

# Add statistical test p-values
# OBS: different p-values over vaule vs. log.value!! 
stat.test <- stat.test %>% add_xy_position(x = "over_surv_stt")
myplot + stat_pvalue_manual(stat.test, label = "p.adj.signif")


# Group the data by variables and do a graph for each variable
graphs <- kirc_clin_numeric.long %>%
  group_by(variables) %>%
  doo(
    ~ggboxplot(
      data =., x = "over_surv_stt", y = "value",
      fill = "over_surv_stt", palette = "npg", legend = "none",
      ggtheme = theme_pubr()
      )  +
      geom_jitter(width = 0.05, alpha = 0.2, color = "orange"), 
    result = "plots"
  )
graphs %>% knitr::kable(.)
variables plots
age 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 69, 68, 67, 66, 77, 57, 59, 57, 67, 70, 52, 51, 53, 51, 52, 47, 79, 75, 86, 41, 75, 79, 68, 76, 34, 42, 74, 62, 57, 82, 60, 50, 46, 72, 48, 64, 54, 51, 51, 60, 54, 69, 52, 46, 49, 47, 57, 49, 78, 59, 74, 75, 37, 65, 68, 37, 65, 62, 54, 61, 62, 48, 72, 40, 58, 45, 80, 69, 83, 85, 48, 58, 84, 71, 48, 48, 58, 72, 71, 63, 35, 46, 65, 55, 72, 72, 58, 46, 75, 74, 60, 66, 51, 61, 63, 75, 76, 76, 81, 57, 47, 48, 68, 58, 65, 49, 81, 68, 60, 68, 78, 88, 49, 77, 79, 82, 49, 61, 63, 69, 80, 63, 73, 57, 60, 70, 52, 60, 54, 51, 78, 75, 77, 77, 63, 79, 63, 33, 76, 53, 53, 62, 81, 72, 59, 53, 88, 72, 74, 90, 64, 65, 54, 69, 71, 69, 43, 52, 40, 61, 72, 56, 46, 59, 64, 53, 66, 66, 47, 71, 61, 69, 50, 77, 53, 77, 65, 71, 73, 65, 45, 39, 62, 57, 50, 68, 75, 53, 59, 73, 72, 83, 52, 61, 56, 74, 79, 46, 79, 61, 68, 62, 65, 59, 64, 61, 52, 45, 61, 66, 41, 63, 51, 74, 73, 58, 70, 63, 57, 56, 61, 62, 63, 65, 43, 42, 38, 53, 71, 65, 41, 67, 69, 62, 49, 50, 69, 48, 60, 61, 54, 60, 69, 70, 67, 74, 65, 60, 51, 64, 69, 59, 76, 72, 47, 49, 64, 65, 64, 53, 75, 75, 60, 52, 36, 56, 65, 76, 43, 70, 67, 79, 64, 75, 62, 57, 74, 68, 51, 74, 61, 40, 59, 62, 40, 50, 69, 57, 42, 79, 43, 43, 72, 63, 73, 62, 57, 55, 52, 46, 78, 55, 67, 59, 48, 76, 74, 34, 74, 70, 57, 79, 59, 42, 49, 46, 47, 58, 63, 54, 46, 76, 40, 63, 44, 40, 43, 47, 58, 40, 77, 57, 75, 42, 67, 72, 75, 41, 72, 58, 54, 66, 58, 54, 68, 49, 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6.247928, 5.392317, 6.189825, 5.882643, 5.78136, 6.066089, 5.882643, 5.78136, 6.108524, 5.643856, 5.83289, 5.357552, 5.459432, 5.754888, 5.954196, 5.523562, 5.554589, 5.72792, 6, 6.247928, 6.149747, 5.807355, 6.247928, 5.523562, 5.930737, 6.303781, 5.554589, 6.169925, 5.754888, 5.882643, 5.83289, 5.857981, 5.78136, 5.83289, 5.672425, 5.78136, 5.930737, 5.954196, 6.321928, 5.906891, 5.321928, 6.247928, 5.754888, 6.189825, 5.882643, 5.491853, 6, 6.247928, 5.930737, 5.61471, 5.70044, 5.72792, 5.554589, 5.643856, 5.643856, 5.807355, 5.584963, 6.087463, 5.61471, 5.426265, 5.643856, 5.882643, 6, 5.70044, 6.426265, 6.209453, 6.087463, 5.857981, 6.169925, 5.392317, 5.857981, 6.189825, 6.022368, 5.426265, 5.61471, 5.906891, 6, 6.189825, 5.857981, 6.044394, 6.266787, 5.882643, 5.97728, 6.321928, 5.426265, 6.129283, 5.584963, 5.954196, 5.672425, 5.930737, 5.97728, 5.882643, 5.285402, 5.954196, 5.523562, 6.129283, 6.022368, 6.022368, 6, 5.70044, 6.409391, 6.149747, 5.584963, 5.78136, 5.97728, 6.209453, 6.044394, 5.491853, 5.930737, 6.303781, 5.954196, 5.906891, 6.507795, 6.285402, 5.882643, 6.044394, 5.78136, 6, 5.78136, 6.209453, 5.491853, 5.70044, 6.228819, 5.70044, 5.97728, 6.303781, 5.72792, 5.70044, 5.83289, 5.954196, 5.930737, 6.108524, 6.209453, 5.491853, 5.044394, 6.375039, 6.108524, 5.97728, 5.83289, 5.321928, 5.906891, 5.930737, 6, 5.554589, 5.70044, 6.442943, 6.149747, 6.321928, 6.228819, 6.129283, 6.087463, 6, 6, 5.857981, 5.97728, 5.459432, 6, 5.807355, 5.72792, 6.392317, 6.266787, 6.129283, 6.266787, 6.087463, 6.442943, 6.409391, 5.392317, 6.189825, 5.906891, 5.70044, 5.882643, 5.954196, 5.930737, 5.285402, 5.930737, 5.906891, 6.087463, 5.357552, 4.754888, 4.906891, 5.392317, 5.247928, 5.72792, 5.807355, 5.906891, 5.754888, 5.807355, 5.584963, 6.087463, 5.807355, 6.357552, 5.78136, 5.807355, 6.357552, 5.97728, 6.266787, 5.392317, 6.108524, 5.882643, 6.189825, 5.584963, <environment: 0x5648fd3bd4a0>, <environment: 0x56490b9db2d8>, <environment: 0x56490bd25de0>, ~over_surv_stt, ~value, black, 0.5454545, 1, butt, FALSE, TRUE, white, black, 0.5454545, 1, TRUE, , plain, black, 12, 0.5, 0.5, 0, 0.9, 0, 0, 0, 0, FALSE, TRUE, 1, 3, 0, 0, 0, TRUE, 0, 0, 0, 3, 0, TRUE, 1, 90, 0, 3, 0, 0, TRUE, 0, -90, 0, 0, 0, 3, TRUE, black, TRUE, 1, 2.4, 0, 0, 0, FALSE, 0, 0, 0, 2.4, 0, TRUE, 1, 0, 2.4, 0, 0, FALSE, 0, 0, 0, 0, 2.4, TRUE, black, FALSE, FALSE, 3, black, 0.5, FALSE, TRUE, NA, TRUE, 6, 6, 6, 6, 12, 1.2, 0.8, TRUE, 0, TRUE, none, center, 0, 0, 0, 0, 12, white, NA, TRUE, 6, grey92, FALSE, TRUE, FALSE, white, TRUE, 1.2, 0, 1, 0, 0, 6, 0, TRUE, panel, 0, 1, 0, 0, 6, 0, TRUE, 0.8, 1, 1, 6, 0, 0, 0, TRUE, panel, 1.2, 0.5, 0.5, TRUE, topleft, 6, 6, 6, 6, #F2F2F2, black, 0.7, TRUE, inside, grey10, 0.8, 4.8, 4.8, 4.8, 4.8, TRUE, -90, TRUE, 3, 3, 90, TRUE, <environment: 0x5648fd9700f0>, <environment: 0x5648fd96d2d8>, <environment: 0x5648fd9ca6c8>, over_surv_stt, value, over_surv_stt
disease_free_mth 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 12.65, 11.89, 36.79, 47.17, 0.53, 49.05, 31.27, 37.12, 49.54, 48.55, 36.33, 38.96, 37.35, 45.5, 53.35, 48.78, 36.07, 34.26, 31.04, 3.75, 20.04, 45.5, 29.89, 88.3, 42.94, 82.26, 51.22, 10.48, 28.68, 74.57, 74.7, 24.15, 53.25, 43.17, 20.7, 18.63, 16.89, 28.29, 65.47, 20.27, 33.44, 15.37, 22.63, 0.1, 0.1, 0, 11.17, 10.61, 0, 109.82, 8.41, 117.71, 122.47, 109.33, 111.99, 53.88, 39.06, 94.12, 46.75, 48.32, 78.58, 39.98, 58.44, 94.22, 83.15, 28.71, 37.55, 38.37, 82.39, 72.83, 12.12, 40.67, 1.41, 26.08, 57.65, 25.23, 6.73, 15.05, 4.17, 21.94, 39.68, 3.55, 8.21, 2.04, 24.57, 5.35, 16.23, 0.13, 28.25, 3.38, 7, 2.14, 2.46, 11.79, 46.85, 89.82, 18.04, 58.54, 2.3, 66, 38.6, 2.46, 37.09, 52.83, 50.99, 38.4, 48.78, 46.35, 56.93, 14.75, 111.43, 106.77, 129.57, 133.84, 9.46, 70.63, 56.73, 86.4, 119.28, 3.09, 58.8, 7.36, 71.35, 73.78, 27.66, 105.29, 123, 130.55, 47.54, 123.72, 23.82, 91.39, 125.95, 32.59, 35.87, 31.93, 21.29, 31.27, 31.64, 24.8, 30.19, 27.96, 21.55, 8.74, 11.99, 5.75, 5.09, 1.25, 0.53, 4.63, 5.45, 0.36, 0.23, 16.79, 49.93, 42.67, 7.33, 45.34, 17.18, 19.84, 25.89, 40.31, 40.01, 23.72, 1.18, 27, 0.85, 24.21, 50.1, 16.59, 6.37, 22.8, 0.53, 34.36, 14.29, 27.27, 29.86, 17.05, 1.74, 8.41, 4.93, 17.35, 15.41, 16.26, 14.16, 110.94, 77.27, 94.65, 88.21, 100.99, 93.27, 60.38, 0.43, 89.29, 2.56, 62.19, 61.73, 64.22, 54.86, 97.37, 31.47, 23, 62.02, 37.22, 14.52, 14.91, 84.49, 7.88, 61.63, 54.73, 49.8, 44.91, 12.22, 28.68, 8.71, 22.5, 12.29, 72.54, 77.92, 47.86, 5.98, 71.75, 48.03, 13.14, 61.63, 6.83, 3.61, 61.93, 60.55, 13.5, 56.87, 68.33, 11.63, 60.91, 14.49, 48.92, 20.37, 36.37, 11.56, 36.93, 6.7, 41.72, 6.93, 87.39, 71.35, 63.57, 58.64, 60.25, 61.17, 61.47, 6.73, 57.36, 58.94, 14.22, 48.85, 49.34, 45.47, 1.15, 47.08, 53.61, 14.91, 33.31, 46.42, 21.58, 25.79, 36.93, 3.88, 46.42, 16.46, 5.81, 42.97, 45.04, 30.62, 41.59, 18.5, 38.67, 36.99, 27.6, 37.45, 35.18, 16.16, 25.66, 6.34, 79.24, 74.15, 30.62, 9.63, 56.14, 74.34, 49.11, 38.27, 15.93, 37.22, 24.18, 22.77, 13.34, 25.66, 33.21, 31.77, 23.46, 13.4, 24.61, 33.44, 19.81, 44.51, 34.92, 0.62, 0.95, 114.91, 46.52, 63.21, 59.82, 2.3, 106.08, 114.32, 11.79, 105.29, 58.9, 3.06, 1.22, 57.62, 49.21, 5.55, 47.14, 74.21, 75, 17.44, 64.22, 71.81, 61.79, 61.86, 57.79, 67.05, 64.13, 10.58, 19.97, 63.93, 48.52, 49.97, 24.64, 5.19, 2, 46.75, 50.2, 47.63, 49.93, 51.25, 9.13, 49.15, 49.24, 50.3, 54.43, 17.67, 45.11, 1.08, 0.82, 130.98, 29.57, 72.9, 9.46, 13.11, 19.55, 5.03, 0.56, 1.81, 62.06, 73.29, 66.95, 4.5, 62.61, 119.55, 4.73, 91.95, 81.77, 3.35, 85.71, 65.8, 66.26, 78.12, 22.08, 74.61, 105.85, 91.33, 103.35, 88.73, 15.01, 25.43, 0.59, 32.52, 62.81, 58.71, 107.46, 63.34, 107.33, 63.37, 58.77, 13.9, 31.24, 5.72, 91.62, 55.29, 94.38, 1.58, 21.75, 69.91, 22.5, 2.04, 3.61, 12.68, 80.12, 67.9, 65.6, 12.25, 12.39, 22.77, 62.58, 43.66, 45.93, 65.83, 12.16, 11.66, 37.12, 11.73, 56.8, 11.99, 65.97, 18.1, 3.91, 6.77, 4.17, 9.99, 70.07, 2, 19.42, 19.94, 23, 16.36, 11.7, 3.770829, 3.68818, 5.239933, 5.590063, 0.6135317, 5.645298, 5.012122, 5.252476, 5.659354, 5.630813, 5.222264, 5.320485, 5.261155, 5.539159, 5.764208, 5.637494, 5.21218, 5.139961, 5.001802, 2.247928, 4.395063, 5.539159, 4.949068, 6.480588, 5.457463, 6.379552, 5.706531, 3.521051, 4.891419, 6.239742, 6.242221, 4.652486, 5.761551, 5.464995, 4.439623, 4.294988, 4.161081, 4.872336, 6.054631, 4.410748, 5.106013, 4.032982, 4.562548, 0.1375035, 0.1375035, 0, 3.605257, 3.537296, 0, 6.792074, 3.234195, 6.891298, 6.948017, 6.785681, 6.820051, 5.778209, 5.324091, 6.571677, 5.577429, 5.624101, 6.314334, 5.356848, 5.893362, 6.573193, 6.394891, 4.892877, 5.268659, 5.299025, 6.381802, 6.206135, 3.713696, 5.380937, 1.269033, 4.759156, 5.874059, 4.713146, 2.950468, 4.004501, 2.370164, 4.519793, 5.346248, 2.185867, 3.203201, 1.604071, 4.67638, 2.666757, 4.106851, 0.1763228, 4.870365, 2.130931, 3, 1.650765, 1.790772, 3.676944, 5.580447, 6.504938, 4.250962, 5.895787, 1.722466, 6.066089, 5.307429, 1.790772, 5.25134, 5.750339, 5.700162, 5.300124, 5.637494, 5.565293, 5.856239, 3.97728, 6.812883, 6.751812, 7.02868, 7.075105, 3.386811, 6.162492, 5.851249, 6.449561, 6.910253, 2.032101, 5.902074, 3.063503, 6.176921, 6.224581, 4.840967, 6.731862, 6.954196, 7.039467, 5.601102, 6.962549, 4.633431, 6.529665, 6.988117, 5.06996, 5.204376, 5.041331, 4.478325, 5.012122, 5.028569, 4.689299, 4.963012, 4.85599, 4.495056, 3.283922, 3.69933, 2.754888, 2.606442, 1.169925, 0.6135317, 2.493135, 2.689299, 0.4436067, 0.2986583, 4.152995, 5.670444, 5.448571, 3.058316, 5.534186, 4.18428, 4.381283, 4.748998, 5.368419, 5.357904, 4.627607, 1.124328, 4.807355, 0.8875253, 4.655924, 5.675251, 4.136684, 2.881665, 4.57289, 0.6135317, 5.144046, 3.934517, 4.8212, 4.947666, 4.173927, 1.454176, 3.234195, 2.568032, 4.197708, 4.036503, 4.109361, 3.922198, 6.806582, 6.290388, 6.579693, 6.479134, 6.672284, 6.558727, 5.939697, 0.5160151, 6.496494, 1.831877, 5.981624, 5.971084, 6.027243, 5.803744, 6.620146, 5.021035, 4.584963, 5.977738, 5.256256, 3.956057, 3.991862, 6.417684, 3.15056, 5.968782, 5.800382, 5.666757, 5.520737, 3.72465, 4.891419, 3.279471, 4.554589, 3.732269, 6.200457, 6.302319, 5.610582, 2.803227, 6.184875, 5.615593, 3.82171, 5.968782, 2.969012, 2.204767, 5.975676, 5.943687, 3.857981, 5.854744, 6.115408, 3.658783, 5.952101, 3.953265, 5.641546, 4.417515, 5.223809, 3.650765, 5.245267, 2.944858, 5.41684, 2.987321, 6.465811, 6.176921, 6.012792, 5.898208, 5.936638, 5.958147, 5.965092, 2.950468, 5.866908, 5.905447, 3.927896, 5.639522, 5.653633, 5.538228, 1.104337, 5.587365, 5.771093, 3.991862, 5.100557, 5.567424, 4.496974, 4.743623, 5.245267, 2.286881, 5.567424, 4.125982, 2.767655, 5.458448, 5.524816, 4.982765, 5.412443, 4.285402, 5.309976, 5.247548, 4.837943, 5.264912, 5.177121, 4.100978, 4.736605, 2.87578, 6.32625, 6.231701, 4.982765, 3.41007, 5.836429, 6.235344, 5.647027, 5.295356, 4.08151, 5.256256, 4.654206, 4.57107, 3.841973, 4.736605, 5.096346, 5.034304, 4.612352, 3.847997, 4.678635, 5.106013, 4.379205, 5.508112, 5.166715, 0.6959938, 0.9634741, 6.856861, 5.570463, 6.004726, 5.926474, 1.722466, 6.742545, 6.849499, 3.676944, 6.731862, 5.904484, 2.02148, 1.15056, 5.873321, 5.649903, 2.711495, 5.589164, 6.232853, 6.247928, 4.204767, 6.027243, 6.186065, 5.972463, 5.97407, 5.877499, 6.088523, 6.02525, 3.533563, 4.390255, 6.020813, 5.629939, 5.671576, 4.680324, 2.629939, 1.584963, 5.577429, 5.678072, 5.603775, 5.670444, 5.707359, 3.340562, 5.648178, 5.650765, 5.680887, 5.792595, 4.22265, 5.527008, 1.056584, 0.8639385, 7.044176, 4.934045, 6.207502, 3.386811, 3.818646, 4.361066, 2.592158, 0.641546, 1.49057, 5.978653, 6.215096, 6.086402, 2.459432, 5.991182, 6.913488, 2.518535, 6.538383, 6.371036, 2.121015, 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long_dim 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 0.9, 1, 1.2, 1.7, 2, 1.7, 1.5, 1.5, 1.5, 2, 1.5, 1.9, 2, 1.3, 1.5, 2, 1, 1.1, 0.8, 1.3, 1.7, 1.2, 1.3, 1.6, 0.7, 1.2, 1, 1, 1.2, 1, 1.7, 1, 1, 1.1, 0.6, 1.2, 1.4, 1.3, 1, 1.1, 1.4, 1.4, 0.9, 1.4, 1, 1, 1.2, 1, 1.2, 0.9, 1, 1, 1, 1.2, 1, 0.8, 1.2, 0.8, 0.9, 1, 1, 1, 0.8, 1, 1, 1, 1, 1.2, 0.8, 0.5, 1.7, 1.6, 1.8, 1.6, 1.4, 2.5, 2, 1.5, 2.7, 3.3, 2.2, 2.4, 3, 2.2, 2, 2, 1.4, 1.5, 1.2, 1.5, 1.7, 2.5, 2.5, 1.6, 2.7, 2.6, 1.6, 1.8, 1.5, 2.5, 2, 1.7, 2.2, 1.2, 1.5, 2.5, 1.7, 1.5, 2, 1.9, 0.7, 2, 1.5, 1.9, 1.5, 1.5, 1.6, 1.4, 1, 2, 2, 2, 1.1, 1.3, 1.2, 1.4, 1.3, 1.5, 1.6, 2, 1.3, 1.7, 1.5, 1.9, 2, 2.5, 1.2, 1.5, 1.8, 2, 1.5, 2, 1.7, 1.6, 2.3, 1.2, 1.3, 1, 1.2, 0.7, 1.2, 2.1, 1.5, 1.6, 1.5, 1.4, 1.2, 2.3, 2.5, 2.5, 1.2, 1.6, 1.8, 1.2, 1.7, 1.4, 0.8, 1.8, 2.1, 1, 2, 1.6, 1.3, 2.5, 1.7, 2.5, 1, 2, 2.9, 2.3, 1.7, 2, 1.7, 1.3, 1.6, 1.3, 2, 1, 1.3, 1.3, 1.3, 1.2, 1.4, 1.7, 1, 1, 1.1, 1, 1.1, 0.8, 1.1, 1, 1, 1, 0.8, 1.1, 1.5, 1.4, 1, 1.2, 1.3, 1.5, 1.2, 0.9, 1.3, 0.9, 0.7, 1, 1.2, 1.1, 1.6, 1.4, 2, 2.1, 2, 1.5, 1.3, 1.1, 1.6, 1.3, 2.5, 1.7, 1, 1.4, 1.2, 1.2, 1.2, 2, 1.8, 1.2, 1.3, 1.3, 1.9, 1.6, 1.6, 1.9, 1.5, 2.1, 1.6, 1.9, 1.6, 1.3, 1.5, 2, 1.5, 1.5, 1.5, 1.6, 1.2, 2.3, 2.2, 1.5, 1.9, 1.1, 1.3, 1.3, 1.4, 1, 1.6, 1.5, 1, 1.4, 1.5, 1.3, 2, 0.8, 1.5, 1, 1.5, 1.1, 1.1, 2.4, 0.9, 1.3, 0.9, 1.2, 1.1, 1, 2, 1, 1.6, 1.3, 1.7, 1.4, 1.6, 1.9, 1.2, 1.6, 1, 1.7, 1.5, 1.5, 1.2, 1.5, 1.6, 1.5, 2, 1.6, 1.1, 1.5, 1.9, 1.3, 2, 1.2, 1.1, 2, 1.5, 1.8, 1.3, 1.5, 1.6, 1.5, 1.3, 1.6, 1, 0.9, 1.5, 1.4, 0.8, 1.7, 1.1, 1.1, 1.5, 1.9, 1.8, 1.8, 0.6, 1, 1.6, 1.6, 1.3, 1.5, 1.6, 1.8, 1.2, 1.2, 0.9, 1.8, 1, 1.8, 1.5, 1.3, 1, 1, 1.9, 1.4, 2.1, 2.5, 1.5, 2.2, 1.6, 0.9, 3.2, 4, 3, 2, 2, 3.2, 2.7, 2.5, 3, 2.5, 2.8, 3, 3, 3, 3.3, 2.2, 3.2, 3.2, 3.2, 3, 2.5, 3.1, 3.5, 3.4, 3.5, 2.2, 2.3, 2.8, 1.2, 2.6, 3.2, 3.1, 3.2, 2.8, 3.5, 3.4, 2.3, 3, 3.2, 3.4, 3, 3, 3.1, 2.2, 2.6, 3.1, 3.2, 2.6, 3.2, 2.5, 3, 1.7, 3, 2.6, 1.8, 2.5, 2.5, 2.6, 3.1, 2.8, 2, 2.5, 3, 1.5, 2.8, 1, 2, 3.5, 3.6, 3.2, 3, 1.3, 1, 1.8, 1.3, 1.8, 2.2, 1.2, 1.1, 1.2, 1.2, 0.8, 1.4, 0.8, 1.3, 1, 1, 2, 1.7, 1.5, 1.1, 1.5, 1.5, 1.3, 0.8, 1.5, 1, 1.2, 2, 1.9, 1.5, 2, 1.4, 2, 1.4, 1.6, 1.5, 2.1, 0.4, 1.1, 1.1, 1.7, 1.4, 1.5, 1.8, 1.6, 1.7, 1.7, 1.8, 1.6, 1.2, 1.4, 1.8, 1.5, 1.5, 1.5, 1.9, 1.7, 1, 0.6, 1, 1.1, 1.4, 1.5, 2, 1.3, 1.5, 2.4, 1.3, 2, 0.9259994, 1, 1.137504, 1.432959, 1.584963, 1.432959, 1.321928, 1.321928, 1.321928, 1.584963, 1.321928, 1.536053, 1.584963, 1.201634, 1.321928, 1.584963, 1, 1.070389, 0.8479969, 1.201634, 1.432959, 1.137504, 1.201634, 1.378512, 0.7655347, 1.137504, 1, 1, 1.137504, 1, 1.432959, 1, 1, 1.070389, 0.6780719, 1.137504, 1.263034, 1.201634, 1, 1.070389, 1.263034, 1.263034, 0.9259994, 1.263034, 1, 1, 1.137504, 1, 1.137504, 0.9259994, 1, 1, 1, 1.137504, 1, 0.8479969, 1.137504, 0.8479969, 0.9259994, 1, 1, 1, 0.8479969, 1, 1, 1, 1, 1.137504, 0.8479969, 0.5849625, 1.432959, 1.378512, 1.485427, 1.378512, 1.263034, 1.807355, 1.584963, 1.321928, 1.887525, 2.104337, 1.678072, 1.765535, 2, 1.678072, 1.584963, 1.584963, 1.263034, 1.321928, 1.137504, 1.321928, 1.432959, 1.807355, 1.807355, 1.378512, 1.887525, 1.847997, 1.378512, 1.485427, 1.321928, 1.807355, 1.584963, 1.432959, 1.678072, 1.137504, 1.321928, 1.807355, 1.432959, 1.321928, 1.584963, 1.536053, 0.7655347, 1.584963, 1.321928, 1.536053, 1.321928, 1.321928, 1.378512, 1.263034, 1, 1.584963, 1.584963, 1.584963, 1.070389, 1.201634, 1.137504, 1.263034, 1.201634, 1.321928, 1.378512, 1.584963, 1.201634, 1.432959, 1.321928, 1.536053, 1.584963, 1.807355, 1.137504, 1.321928, 1.485427, 1.584963, 1.321928, 1.584963, 1.432959, 1.378512, 1.722466, 1.137504, 1.201634, 1, 1.137504, 0.7655347, 1.137504, 1.632268, 1.321928, 1.378512, 1.321928, 1.263034, 1.137504, 1.722466, 1.807355, 1.807355, 1.137504, 1.378512, 1.485427, 1.137504, 1.432959, 1.263034, 0.8479969, 1.485427, 1.632268, 1, 1.584963, 1.378512, 1.201634, 1.807355, 1.432959, 1.807355, 1, 1.584963, 1.963474, 1.722466, 1.432959, 1.584963, 1.432959, 1.201634, 1.378512, 1.201634, 1.584963, 1, 1.201634, 1.201634, 1.201634, 1.137504, 1.263034, 1.432959, 1, 1, 1.070389, 1, 1.070389, 0.8479969, 1.070389, 1, 1, 1, 0.8479969, 1.070389, 1.321928, 1.263034, 1, 1.137504, 1.201634, 1.321928, 1.137504, 0.9259994, 1.201634, 0.9259994, 0.7655347, 1, 1.137504, 1.070389, 1.378512, 1.263034, 1.584963, 1.632268, 1.584963, 1.321928, 1.201634, 1.070389, 1.378512, 1.201634, 1.807355, 1.432959, 1, 1.263034, 1.137504, 1.137504, 1.137504, 1.584963, 1.485427, 1.137504, 1.201634, 1.201634, 1.536053, 1.378512, 1.378512, 1.536053, 1.321928, 1.632268, 1.378512, 1.536053, 1.378512, 1.201634, 1.321928, 1.584963, 1.321928, 1.321928, 1.321928, 1.378512, 1.137504, 1.722466, 1.678072, 1.321928, 1.536053, 1.070389, 1.201634, 1.201634, 1.263034, 1, 1.378512, 1.321928, 1, 1.263034, 1.321928, 1.201634, 1.584963, 0.8479969, 1.321928, 1, 1.321928, 1.070389, 1.070389, 1.765535, 0.9259994, 1.201634, 0.9259994, 1.137504, 1.070389, 1, 1.584963, 1, 1.378512, 1.201634, 1.432959, 1.263034, 1.378512, 1.536053, 1.137504, 1.378512, 1, 1.432959, 1.321928, 1.321928, 1.137504, 1.321928, 1.378512, 1.321928, 1.584963, 1.378512, 1.070389, 1.321928, 1.536053, 1.201634, 1.584963, 1.137504, 1.070389, 1.584963, 1.321928, 1.485427, 1.201634, 1.321928, 1.378512, 1.321928, 1.201634, 1.378512, 1, 0.9259994, 1.321928, 1.263034, 0.8479969, 1.432959, 1.070389, 1.070389, 1.321928, 1.536053, 1.485427, 1.485427, 0.6780719, 1, 1.378512, 1.378512, 1.201634, 1.321928, 1.378512, 1.485427, 1.137504, 1.137504, 0.9259994, 1.485427, 1, 1.485427, 1.321928, 1.201634, 1, 1, 1.536053, 1.263034, 1.632268, 1.807355, 1.321928, 1.678072, 1.378512, 0.9259994, 2.070389, 2.321928, 2, 1.584963, 1.584963, 2.070389, 1.887525, 1.807355, 2, 1.807355, 1.925999, 2, 2, 2, 2.104337, 1.678072, 2.070389, 2.070389, 2.070389, 2, 1.807355, 2.035624, 2.169925, 2.137504, 2.169925, 1.678072, 1.722466, 1.925999, 1.137504, 1.847997, 2.070389, 2.035624, 2.070389, 1.925999, 2.169925, 2.137504, 1.722466, 2, 2.070389, 2.137504, 2, 2, 2.035624, 1.678072, 1.847997, 2.035624, 2.070389, 1.847997, 2.070389, 1.807355, 2, 1.432959, 2, 1.847997, 1.485427, 1.807355, 1.807355, 1.847997, 2.035624, 1.925999, 1.584963, 1.807355, 2, 1.321928, 1.925999, 1, 1.584963, 2.169925, 2.201634, 2.070389, 2, 1.201634, 1, 1.485427, 1.201634, 1.485427, 1.678072, 1.137504, 1.070389, 1.137504, 1.137504, 0.8479969, 1.263034, 0.8479969, 1.201634, 1, 1, 1.584963, 1.432959, 1.321928, 1.070389, 1.321928, 1.321928, 1.201634, 0.8479969, 1.321928, 1, 1.137504, 1.584963, 1.536053, 1.321928, 1.584963, 1.263034, 1.584963, 1.263034, 1.378512, 1.321928, 1.632268, 0.4854268, 1.070389, 1.070389, 1.432959, 1.263034, 1.321928, 1.485427, 1.378512, 1.432959, 1.432959, 1.485427, 1.378512, 1.137504, 1.263034, 1.485427, 1.321928, 1.321928, 1.321928, 1.536053, 1.432959, 1, 0.6780719, 1, 1.070389, 1.263034, 1.321928, 1.584963, 1.201634, 1.321928, 1.765535, 1.201634, 1.584963, <environment: 0x564902b83e88>, <environment: 0x56490b4b63d8>, <environment: 0x56490b7f3118>, ~over_surv_stt, ~value, black, 0.5454545, 1, butt, FALSE, TRUE, white, black, 0.5454545, 1, TRUE, , plain, black, 12, 0.5, 0.5, 0, 0.9, 0, 0, 0, 0, FALSE, TRUE, 1, 3, 0, 0, 0, TRUE, 0, 0, 0, 3, 0, TRUE, 1, 90, 0, 3, 0, 0, TRUE, 0, -90, 0, 0, 0, 3, TRUE, black, TRUE, 1, 2.4, 0, 0, 0, FALSE, 0, 0, 0, 2.4, 0, TRUE, 1, 0, 2.4, 0, 0, FALSE, 0, 0, 0, 0, 2.4, TRUE, black, FALSE, FALSE, 3, black, 0.5, FALSE, TRUE, NA, TRUE, 6, 6, 6, 6, 12, 1.2, 0.8, TRUE, 0, TRUE, none, center, 0, 0, 0, 0, 12, white, NA, TRUE, 6, grey92, FALSE, TRUE, FALSE, white, TRUE, 1.2, 0, 1, 0, 0, 6, 0, TRUE, panel, 0, 1, 0, 0, 6, 0, TRUE, 0.8, 1, 1, 6, 0, 0, 0, TRUE, panel, 1.2, 0.5, 0.5, TRUE, topleft, 6, 6, 6, 6, #F2F2F2, black, 0.7, TRUE, inside, grey10, 0.8, 4.8, 4.8, 4.8, 4.8, TRUE, -90, TRUE, 3, 3, 90, TRUE, <environment: 0x56490ae82a70>, <environment: 0x56490ae846a8>, <environment: 0x56490c731f08>, over_surv_stt, value, over_surv_stt
mutation_cnt 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 514, 56, 656, 577, 62, 537, 477, 150, 46, 40, 137, 708, 25, 42, 80, 55, 42, 1392, 460, 60, 37, 327, 53, 934, 45, 409, 383, 804, 319, 58, 70, 62, 524, 58, 29, 70, 62, 58, 51, 426, 227, 553, 1, 400, 1, 81, 350, 51, 57, 38, 33, 62, 40, 410, 430, 708, 1, 79, 77, 54, 24, 21, 1, 43, 1, 84, 94, 1, 1, 61, 59, 98, 49, 52, 57, 56, 47, 60, 33, 1, 94, 1, 78, 80, 1, 19, 37, 43, 52, 70, 53, 58, 36, 1, 30, 41, 1, 20, 65, 39, 85, 48, 52, 36, 49, 48, 63, 54, 61, 72, 67, 75, 52, 649, 67, 30, 48, 50, 74, 53, 44, 46, 62, 31, 57, 52, 34, 82, 43, 41, 41, 30, 56, 55, 60, 37, 59, 57, 53, 52, 64, 41, 94, 55, 74, 68, 49, 33, 60, 33, 32, 99, 93, 37, 19, 79, 71, 68, 34, 41, 47, 57, 39, 71, 25, 77, 81, 68, 52, 72, 68, 36, 40, 64, 55, 48, 23, 68, 85, 32, 51, 52, 90, 62, 75, 16, 20, 15, 57, 76, 43, 31, 41, 35, 56, 48, 62, 41, 46, 70, 2, 1, 54, 40, 41, 11, 31, 85, 17, 49, 45, 34, 38, 32, 53, 1, 41, 1, 20, 17, 34, 34, 1, 52, 82, 22, 1, 36, 1, 1, 27, 64, 1, 1, 1, 37, 45, 58, 19, 37, 37, 12, 30, 77, 46, 1, 21, 25, 17, 1, 19, 57, 1, 22, 48, 62, 32, 33, 68, 77, 50, 58, 45, 30, 36, 36, 34, 25, 17, 85, 38, 42, 33, 59, 75, 38, 19, 35, 66, 38, 35, 65, 22, 37, 32, 43, 33, 31, 42, 38, 32, 36, 43, 53, 103, 52, 34, 68, 37, 38, 109, 34, 49, 41, 61, 51, 43, 23, 69, 33, 54, 42, 40, 47, 46, 18, 52, 48, 68, 70, 36, 37, 36, 74, 42, 54, 31, 64, 58, 46, 42, 96, 82, 19, 76, 38, 38, 1, 54, 46, 81, 40, 27, 37, 53, 1, 1, 19, 36, 9, 20, 17, 8, 1, 27, 55, 38, 52, 30, 72, 50, 57, 45, 35, 47, 54, 26, 68, 46, 40, 70, 126, 38, 53, 77, 60, 54, 67, 43, 89, 40, 33, 71, 58, 46, 55, 66, 46, 99, 52, 42, 66, 72, 64, 34, 41, 65, 58, 38, 46, 83, 98, 30, 90, 70, 49, 80, 2, 94, 80, 56, 34, 65, 68, 71, 26, 73, 20, 25, 66, 75, 28, 88, 52, 68, 60, 48, 116, 60, 47, 115, 43, 43, 40, 36, 52, 50, 47, 34, 32, 47, 29, 11, 23, 31, 39, 35, 37, 56, 51, 43, 9.008429, 5.83289, 9.35975, 9.174926, 5.97728, 9.071462, 8.900867, 7.238405, 5.554589, 5.357552, 7.108524, 9.469642, 4.70044, 5.426265, 6.33985, 5.807355, 5.426265, 10.44398, 8.848623, 5.930737, 5.247928, 8.357552, 5.754888, 9.868823, 5.523562, 8.67948, 8.584963, 9.652845, 8.321928, 5.882643, 6.149747, 5.97728, 9.036174, 5.882643, 4.906891, 6.149747, 5.97728, 5.882643, 5.70044, 8.738092, 7.83289, 9.113742, 1, 8.647458, 1, 6.357552, 8.455327, 5.70044, 5.857981, 5.285402, 5.087463, 5.97728, 5.357552, 8.682995, 8.751544, 9.469642, 1, 6.321928, 6.285402, 5.78136, 4.643856, 4.459432, 1, 5.459432, 1, 6.409391, 6.569856, 1, 1, 5.954196, 5.906891, 6.629357, 5.643856, 5.72792, 5.857981, 5.83289, 5.584963, 5.930737, 5.087463, 1, 6.569856, 1, 6.303781, 6.33985, 1, 4.321928, 5.247928, 5.459432, 5.72792, 6.149747, 5.754888, 5.882643, 5.209453, 1, 4.954196, 5.392317, 1, 4.392317, 6.044394, 5.321928, 6.426265, 5.61471, 5.72792, 5.209453, 5.643856, 5.61471, 6, 5.78136, 5.954196, 6.189825, 6.087463, 6.247928, 5.72792, 9.344296, 6.087463, 4.954196, 5.61471, 5.672425, 6.228819, 5.754888, 5.491853, 5.554589, 5.97728, 5, 5.857981, 5.72792, 5.129283, 6.375039, 5.459432, 5.392317, 5.392317, 4.954196, 5.83289, 5.807355, 5.930737, 5.247928, 5.906891, 5.857981, 5.754888, 5.72792, 6.022368, 5.392317, 6.569856, 5.807355, 6.228819, 6.108524, 5.643856, 5.087463, 5.930737, 5.087463, 5.044394, 6.643856, 6.554589, 5.247928, 4.321928, 6.321928, 6.169925, 6.108524, 5.129283, 5.392317, 5.584963, 5.857981, 5.321928, 6.169925, 4.70044, 6.285402, 6.357552, 6.108524, 5.72792, 6.189825, 6.108524, 5.209453, 5.357552, 6.022368, 5.807355, 5.61471, 4.584963, 6.108524, 6.426265, 5.044394, 5.70044, 5.72792, 6.507795, 5.97728, 6.247928, 4.087463, 4.392317, 4, 5.857981, 6.266787, 5.459432, 5, 5.392317, 5.169925, 5.83289, 5.61471, 5.97728, 5.392317, 5.554589, 6.149747, 1.584963, 1, 5.78136, 5.357552, 5.392317, 3.584963, 5, 6.426265, 4.169925, 5.643856, 5.523562, 5.129283, 5.285402, 5.044394, 5.754888, 1, 5.392317, 1, 4.392317, 4.169925, 5.129283, 5.129283, 1, 5.72792, 6.375039, 4.523562, 1, 5.209453, 1, 1, 4.807355, 6.022368, 1, 1, 1, 5.247928, 5.523562, 5.882643, 4.321928, 5.247928, 5.247928, 3.70044, 4.954196, 6.285402, 5.554589, 1, 4.459432, 4.70044, 4.169925, 1, 4.321928, 5.857981, 1, 4.523562, 5.61471, 5.97728, 5.044394, 5.087463, 6.108524, 6.285402, 5.672425, 5.882643, 5.523562, 4.954196, 5.209453, 5.209453, 5.129283, 4.70044, 4.169925, 6.426265, 5.285402, 5.426265, 5.087463, 5.906891, 6.247928, 5.285402, 4.321928, 5.169925, 6.066089, 5.285402, 5.169925, 6.044394, 4.523562, 5.247928, 5.044394, 5.459432, 5.087463, 5, 5.426265, 5.285402, 5.044394, 5.209453, 5.459432, 5.754888, 6.70044, 5.72792, 5.129283, 6.108524, 5.247928, 5.285402, 6.78136, 5.129283, 5.643856, 5.392317, 5.954196, 5.70044, 5.459432, 4.584963, 6.129283, 5.087463, 5.78136, 5.426265, 5.357552, 5.584963, 5.554589, 4.247928, 5.72792, 5.61471, 6.108524, 6.149747, 5.209453, 5.247928, 5.209453, 6.228819, 5.426265, 5.78136, 5, 6.022368, 5.882643, 5.554589, 5.426265, 6.599913, 6.375039, 4.321928, 6.266787, 5.285402, 5.285402, 1, 5.78136, 5.554589, 6.357552, 5.357552, 4.807355, 5.247928, 5.754888, 1, 1, 4.321928, 5.209453, 3.321928, 4.392317, 4.169925, 3.169925, 1, 4.807355, 5.807355, 5.285402, 5.72792, 4.954196, 6.189825, 5.672425, 5.857981, 5.523562, 5.169925, 5.584963, 5.78136, 4.754888, 6.108524, 5.554589, 5.357552, 6.149747, 6.988685, 5.285402, 5.754888, 6.285402, 5.930737, 5.78136, 6.087463, 5.459432, 6.491853, 5.357552, 5.087463, 6.169925, 5.882643, 5.554589, 5.807355, 6.066089, 5.554589, 6.643856, 5.72792, 5.426265, 6.066089, 6.189825, 6.022368, 5.129283, 5.392317, 6.044394, 5.882643, 5.285402, 5.554589, 6.392317, 6.629357, 4.954196, 6.507795, 6.149747, 5.643856, 6.33985, 1.584963, 6.569856, 6.33985, 5.83289, 5.129283, 6.044394, 6.108524, 6.169925, 4.754888, 6.209453, 4.392317, 4.70044, 6.066089, 6.247928, 4.857981, 6.475733, 5.72792, 6.108524, 5.930737, 5.61471, 6.870365, 5.930737, 5.584963, 6.857981, 5.459432, 5.459432, 5.357552, 5.209453, 5.72792, 5.672425, 5.584963, 5.129283, 5.044394, 5.584963, 4.906891, 3.584963, 4.584963, 5, 5.321928, 5.169925, 5.247928, 5.83289, 5.70044, 5.459432, <environment: 0x5649095d52b0>, <environment: 0x56490748f230>, <environment: 0x5649073e5d88>, ~over_surv_stt, ~value, black, 0.5454545, 1, butt, FALSE, TRUE, white, black, 0.5454545, 1, TRUE, , plain, black, 12, 0.5, 0.5, 0, 0.9, 0, 0, 0, 0, FALSE, TRUE, 1, 3, 0, 0, 0, TRUE, 0, 0, 0, 3, 0, TRUE, 1, 90, 0, 3, 0, 0, TRUE, 0, -90, 0, 0, 0, 3, TRUE, black, TRUE, 1, 2.4, 0, 0, 0, FALSE, 0, 0, 0, 2.4, 0, TRUE, 1, 0, 2.4, 0, 0, FALSE, 0, 0, 0, 0, 2.4, TRUE, black, FALSE, FALSE, 3, black, 0.5, FALSE, TRUE, NA, TRUE, 6, 6, 6, 6, 12, 1.2, 0.8, TRUE, 0, TRUE, none, center, 0, 0, 0, 0, 12, white, NA, TRUE, 6, grey92, FALSE, TRUE, FALSE, white, TRUE, 1.2, 0, 1, 0, 0, 6, 0, TRUE, panel, 0, 1, 0, 0, 6, 0, TRUE, 0.8, 1, 1, 6, 0, 0, 0, TRUE, panel, 1.2, 0.5, 0.5, TRUE, topleft, 6, 6, 6, 6, #F2F2F2, black, 0.7, TRUE, inside, grey10, 0.8, 4.8, 4.8, 4.8, 4.8, TRUE, -90, TRUE, 3, 3, 90, TRUE, <environment: 0x5649098618a0>, <environment: 0x564909854f70>, <environment: 0x56490992c218>, over_surv_stt, value, over_surv_stt
over_surv_mth 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 12.65, 11.89, 36.79, 47.17, 0.53, 39.13, 24.15, 49.05, 48.98, 37.12, 49.54, 48.55, 36.33, 38.96, 38.44, 37.35, 45.5, 53.35, 48.78, 61.96, 34.26, 31.04, 4.5, 52.89, 45.5, 29.89, 18.43, 88.3, 42.94, 82.26, 51.22, 10.48, 28.68, 74.57, 74.7, 24.15, 53.25, 43.17, 55.72, 20.7, 18.63, 18.86, 28.29, 65.47, 20.27, 33.44, 15.37, 22.63, 0.1, 0.1, 0, 11.17, 10.61, 0, 109.82, 29.07, 117.71, 122.47, 109.33, 15.77, 73.62, 111.99, 68.56, 109.43, 94.12, 46.75, 48.32, 78.58, 39.98, 58.44, 94.22, 83.15, 28.71, 22.44, 37.55, 38.37, 82.39, 72.83, 12.12, 40.67, 1.41, 3.32, 1.41, 4.57, 2.53, 3.48, 28.45, 18.99, 1.38, 3.61, 65.05, 7.82, 5.98, 2.14, 19.71, 57.65, 43.92, 6.64, 3.25, 58.41, 15.7, 46.55, 0.59, 5.52, 52.17, 45.04, 33.48, 16.75, 6.01, 40.41, 36.5, 14.91, 54.43, 29.07, 10.09, 78.38, 68.66, 40.67, 45.27, 27.4, 53.84, 6.7, 56.64, 10.51, 10.28, 65.24, 39.42, 26.05, 29.01, 2.27, 36.83, 70.47, 20.93, 43.27, 11.24, 11.89, 34.33, 7.29, 25.3, 18.5, 15.08, 10.94, 8.05, 2.23, 21.85, 52.04, 15.93, 62.84, 90.8, 90.41, 52.5, 30.45, 58.54, 19.28, 66, 38.6, 52.69, 41.85, 52.83, 50.99, 38.4, 48.78, 46.35, 56.93, 42.38, 111.43, 112.71, 129.57, 133.84, 15.77, 70.63, 85.71, 86.4, 119.28, 126.18, 58.8, 80.85, 71.35, 73.78, 149.05, 105.29, 123, 130.55, 79.83, 131.04, 89.42, 91.39, 125.95, 32.59, 35.87, 1.68, 31.93, 21.29, 31.27, 31.64, 24.8, 30.19, 32.95, 21.55, 16.66, 11.99, 5.75, 5.09, 1.25, 0.53, 4.63, 5.45, 0.36, 0.23, 23.29, 16.79, 49.93, 42.67, 25.03, 45.34, 17.18, 25.53, 25.89, 50.1, 40.01, 23.72, 1.18, 27, 0.85, 24.21, 50.1, 16.59, 6.37, 48.49, 0.53, 34.36, 14.29, 27.27, 29.86, 17.05, 1.74, 8.41, 4.93, 17.35, 15.41, 16.26, 14.16, 110.94, 85.45, 94.65, 90.21, 100.99, 93.27, 32.59, 99.77, 0.43, 89.29, 23.03, 76.97, 62.19, 61.73, 64.22, 54.86, 97.37, 53.38, 3.58, 27.76, 62.02, 80.62, 37.22, 21.19, 15.6, 0.07, 93.92, 18.46, 52.2, 74.11, 62.81, 54.73, 49.8, 49.05, 44.91, 12.22, 31.87, 11.3, 12.32, 33.97, 31.31, 12.29, 72.54, 77.92, 77.56, 5.98, 44.12, 41.72, 71.75, 48.03, 13.14, 61.63, 10.81, 5.32, 61.93, 60.55, 13.5, 56.87, 68.33, 11.63, 60.91, 15.77, 48.92, 36.5, 20.37, 36.37, 10.97, 37.22, 36.93, 6.7, 47.93, 6.93, 87.39, 71.35, 63.57, 58.64, 60.25, 61.17, 61.47, 6.73, 57.36, 58.94, 14.22, 48.85, 49.34, 45.47, 6.93, 47.08, 53.61, 14.91, 36.04, 33.31, 46.42, 31.27, 25.79, 36.93, 27.2, 3.88, 46.42, 16.46, 5.81, 42.97, 45.04, 30.62, 41.59, 18.5, 38.67, 36.99, 27.6, 37.45, 35.18, 35.87, 28.84, 48.06, 6.34, 79.24, 2.04, 74.15, 30.62, 52.23, 9.63, 62.81, 74.34, 49.11, 38.27, 42.41, 37.22, 37.19, 22.77, 13.34, 27, 33.21, 31.77, 23.46, 13.4, 24.61, 33.44, 19.81, 44.51, 34.92, 31.24, 0.95, 114.91, 46.52, 63.21, 73.16, 14.16, 106.08, 114.32, 54.57, 105.29, 58.9, 11.04, 21.22, 83.9, 49.21, 79.6, 47.14, 74.21, 75, 116.75, 64.22, 71.81, 66.16, 61.86, 57.79, 113.37, 64.13, 30.62, 51.48, 63.93, 115.6, 26.91, 49.97, 24.64, 27.63, 39.42, 109.76, 50.2, 56.31, 47.63, 49.93, 51.25, 108.48, 49.15, 49.24, 50.3, 54.43, 38.53, 45.11, 3.06, 4.57, 18.79, 130.98, 71.94, 129.3, 133.61, 25.69, 18.86, 22.31, 25.23, 18.13, 122.73, 62.06, 73.29, 66.95, 53.22, 118.76, 53.38, 75.53, 62.61, 119.55, 7.36, 64.52, 91.95, 81.77, 5.39, 85.71, 73.13, 66.26, 78.12, 35.32, 74.61, 1.35, 105.85, 83.84, 103.35, 88.73, 18.76, 25.43, 46.12, 0.59, 47.04, 69.15, 58.71, 6.77, 14.65, 107.46, 63.34, 92.97, 5.45, 107.33, 63.37, 58.77, 79.47, 23.72, 18.43, 79.57, 90.47, 91.62, 55.29, 94.38, 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10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.9737, 10.97441, 10.97441, 10.97513, 10.97513, 10.97513, 10.97513, 10.97513, 10.97513, 10.97585, 10.97585, 10.96723, 10.96795, 10.96795, 10.96795, 10.96795, 10.96795, 10.96795, 10.96795, 10.96867, 10.96867, 10.96867, 10.96867, 10.96939, 10.96939, 10.97011, 10.97011, 10.96723, 10.96795, 10.96795, 10.96795, 10.96795, 10.96795, 10.96795, 10.96867, 10.96867, 10.96867, 10.96867, 10.96867, 10.96867, 10.96867, 10.96867, 10.97082, 10.97082, 10.97082, 10.97082, 10.97082, 10.97082, 10.97154, 10.97154, 10.97082, 10.97226, 10.96867, 10.96939, 10.96939, 10.96939, 10.96939, 10.96939, 10.96939, 10.96939, 10.96939, 10.97011, 10.97011, 10.97011, 10.97011, 10.97011, 10.97011, 10.97011, 10.97011, 10.97011, 10.97011, 10.97011, 10.97154, 10.97154, 10.97154, 10.97154, 10.97154, 10.97154, 10.97154, 10.97154, 10.97226, 10.97226, 10.97226, 10.96867, 10.96939, 10.97011, 10.97011, 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10.97154, 10.97226, 10.97226, 10.97154, 10.97226, 10.97298, 10.9737, 10.9737, 10.9737, 10.97513, 10.97585, 10.97226, 10.97226, 10.97513, 10.97513, 10.97513, 10.97585, 10.97513, 10.97585, <environment: 0x5649063b3010>, <environment: 0x56490b490710>, <environment: 0x56490acd63b0>, ~over_surv_stt, ~value, black, 0.5454545, 1, butt, FALSE, TRUE, white, black, 0.5454545, 1, TRUE, , plain, black, 12, 0.5, 0.5, 0, 0.9, 0, 0, 0, 0, FALSE, TRUE, 1, 3, 0, 0, 0, TRUE, 0, 0, 0, 3, 0, TRUE, 1, 90, 0, 3, 0, 0, TRUE, 0, -90, 0, 0, 0, 3, TRUE, black, TRUE, 1, 2.4, 0, 0, 0, FALSE, 0, 0, 0, 2.4, 0, TRUE, 1, 0, 2.4, 0, 0, FALSE, 0, 0, 0, 0, 2.4, TRUE, black, FALSE, FALSE, 3, black, 0.5, FALSE, TRUE, NA, TRUE, 6, 6, 6, 6, 12, 1.2, 0.8, TRUE, 0, TRUE, none, center, 0, 0, 0, 0, 12, white, NA, TRUE, 6, grey92, FALSE, TRUE, FALSE, white, TRUE, 1.2, 0, 1, 0, 0, 6, 0, TRUE, panel, 0, 1, 0, 0, 6, 0, TRUE, 0.8, 1, 1, 6, 0, 0, 0, TRUE, panel, 1.2, 0.5, 0.5, TRUE, topleft, 6, 6, 6, 6, #F2F2F2, black, 0.7, TRUE, inside, grey10, 0.8, 4.8, 4.8, 4.8, 4.8, TRUE, -90, TRUE, 3, 3, 90, TRUE, <environment: 0x56490c254820>, <environment: 0x564909f834e0>, <environment: 0x56490e3888e0>, over_surv_stt, value, over_surv_stt 
# Add statitistical tests to each corresponding plot
# variables <- graphs$variables
# for(i in 1:length(variables)){
#   graph.i <- graphs$plots[[i]] + 
#     labs(title = variables[i]) +
#     #stat_pvalue_manual(stat.test[i, ], label = "p.adj.signif")
#   print(graph.i)
# }
# Error in print(graph.i) : objeto 'graph.i' não encontrado

# ggplot(kirc_clin, aes(age, fill= over_surv_stt)) +
#   geom_histogram(bins = 15, position = "dodge")
# t.test(kirc_clin$age ~ kirc_clin$over_surv_stt) 
# 
# ggplot(kirc_clin, aes(year_diagnose, fill= over_surv_stt)) +
#   geom_histogram(bins = 15, position = "dodge")
# t.test(kirc_clin$year_diagnose ~ kirc_clin$over_surv_stt) 
# 
# ggplot(kirc_clin, aes(x=over_surv_stt, y=disease_free_mth)) +
#   geom_boxplot(width = .5) +
#   geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
# t.test(kirc_clin$disease_free_mth ~ kirc_clin$over_surv_stt)
# 
# ggplot(kirc_clin, aes(x=over_surv_stt, y=frac_genome_alter)) +
#   geom_boxplot(width = .5) +
#   geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
# t.test(kirc_clin$frac_genome_alter ~ kirc_clin$over_surv_stt)
# 
# ggplot(kirc_clin, aes(x=over_surv_stt, y=long_dim)) +
#   geom_boxplot(width = .5) +
#   geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
# t.test(kirc_clin$long_dim ~ kirc_clin$over_surv_stt)
# 
# ggplot(kirc_clin, aes(x=over_surv_stt, y=mutation_cnt)) +
#   geom_boxplot(width = .5) +
#   geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
# t.test(kirc_clin$mutation_cnt ~ kirc_clin$over_surv_stt)
# 
# ggplot(kirc_clin, aes(x=over_surv_stt, y=over_surv_mth)) +
#   geom_boxplot(width = .5) +
#   geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
# t.test(kirc_clin$over_surv_mth ~ kirc_clin$over_surv_stt)
# 
# ggplot(kirc_clin, aes(x=over_surv_stt, y=short_dim)) +
#   geom_boxplot(width = .5) +
#   geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
# t.test(kirc_clin$short_dim ~ kirc_clin$over_surv_stt)
# 
# ggplot(kirc_clin, aes(x=over_surv_stt, y=second_long_dim)) +
#   geom_boxplot(width = .5) +
#   geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
# t.test(kirc_clin$second_long_dim ~ kirc_clin$over_surv_stt)

Summary for continuous explanatory variables use a parametric or non-parametric test??

explanatory_num <- kirc_clin %>%
  select(-over_surv_stt) %>%
  select_if(is.numeric) %>%
  names
dependent <- 'over_surv_stt'

table_num <- kirc_clin %>%
  summary_factorlist(dependent, explanatory_num, p=TRUE, 
                     add_dependent_label=TRUE,  na_include = TRUE)

knitr::kable(table_num, row.names=FALSE, align=c("l", "l", "r", "r", "r"))
Dependent: over_surv_stt DECEASED LIVING p
age Mean (SD) 64.2 (12.0) 58.8 (11.9) <0.001
disease_free_mth Mean (SD) 16.4 (18.2) 45.5 (31.5) <0.001
frac_genome_alter Mean (SD) 0.2 (0.2) 0.2 (0.2) 0.230
year_diagnose Mean (SD) 2004.6 (2.4) 2006.7 (2.7) <0.001
long_dim Mean (SD) 1.8 (0.6) 1.6 (0.7) <0.001
mutation_cnt Mean (SD) 60.5 (90.9) 80.7 (142.7) 0.111
over_surv_mth Mean (SD) 31.6 (25.1) 50.5 (33.6) <0.001
short_dim Mean (SD) 0.4 (0.2) 0.4 (0.2) 0.631
second_long_dim Mean (SD) 1.0 (0.3) 0.9 (0.3) <0.001 
# Correlation Matrix
# Pearson's (normal distribution) or Spearman (not-normal) correlations
corr_num <- kirc_clin %>%
     select_if(is.numeric) %>%
     drop_na()

# Check the correlation between variables to exclude the higly correlated
cor_matrix <- cor(corr_num, method = "spearman")
cor_matrix <- round(cor_matrix, 2)

cor_matrix 

##                     age disease_free_mth frac_genome_alter year_diagnose
## age                1.00            -0.06              0.17         -0.03
## disease_free_mth  -0.06             1.00             -0.05         -0.32
## frac_genome_alter  0.17            -0.05              1.00         -0.04
## year_diagnose     -0.03            -0.32             -0.04          1.00
## long_dim           0.03             0.02              0.05         -0.29
## mutation_cnt       0.26            -0.03              0.23          0.04
## over_surv_mth     -0.02             0.83             -0.02         -0.45
## short_dim          0.06             0.06             -0.03         -0.19
## second_long_dim    0.02            -0.02             -0.02         -0.06
##                   long_dim mutation_cnt over_surv_mth short_dim second_long_dim
## age                   0.03         0.26         -0.02      0.06            0.02
## disease_free_mth      0.02        -0.03          0.83      0.06           -0.02
## frac_genome_alter     0.05         0.23         -0.02     -0.03           -0.02
## year_diagnose        -0.29         0.04         -0.45     -0.19           -0.06
## long_dim              1.00        -0.05          0.14      0.37            0.34
## mutation_cnt         -0.05         1.00         -0.02      0.11           -0.10
## over_surv_mth         0.14        -0.02          1.00      0.14           -0.01
## short_dim             0.37         0.11          0.14      1.00           -0.07
## second_long_dim       0.34        -0.10         -0.01     -0.07            1.00
  1. Categorical variables vs. over_surv_stt

Tabulation and chi-square test

# t_metas_stg <- table(kirc_clin$metastasis_stg, kirc_clin$over_surv_stt, exclude = NULL)
# t_metas_stg <- addmargins(round(100*prop.table(t_metas_stg)))
# t_metas_stg
# chisq.test(x = kirc_clin$metastasis_stg, y = kirc_clin$over_surv_stt)

Summary for chategorical explanatory variables Chi-squared warnings will be generated when the expected count in any cell is less than 5.

explanatory_char <- kirc_clin %>%
  select(-over_surv_stt) %>%
  select_if(is.factor) %>%
  names

dependent <- 'over_surv_stt'

table_char <- kirc_clin %>%
  summary_factorlist(dependent, explanatory_char, p=TRUE, 
                     add_dependent_label=TRUE,  na_include = TRUE)

## Warning in chisq.test(tumor_stg, over_surv_stt): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(histology_grd, over_surv_stt): Chi-squared approximation
## may be incorrect

## Warning in chisq.test(hemoglobin, over_surv_stt): Chi-squared approximation may
## be incorrect

## Warning in chisq.test(tumor_lateral, over_surv_stt): Chi-squared approximation
## may be incorrect

## Warning in chisq.test(race, over_surv_stt): Chi-squared approximation may be
## incorrect

## Warning in chisq.test(serum_ca, over_surv_stt): Chi-squared approximation may be
## incorrect

## Warning in chisq.test(wbc, over_surv_stt): Chi-squared approximation may be
## incorrect

knitr::kable(table_char, row.names=FALSE, align=c("l", "l", "r", "r", "r"))
Dependent: over_surv_stt DECEASED LIVING p
metastasis_stg M0 110 (62.1) 316 (87.8) <0.001
M1 64 (36.2) 15 (4.2)
MX 3 (1.7) 27 (7.5)
(Missing) 0 (0.0) 2 (0.6)
lymph_stg N0 85 (48.0) 155 (43.1) 0.001
N1 12 (6.8) 5 (1.4)
NX 80 (45.2) 200 (55.6)
neoplasm_stg Stage I 45 (25.4) 224 (62.2) <0.001
Stage II 13 (7.3) 44 (12.2)
Stage III 51 (28.8) 74 (20.6)
Stage IV 67 (37.9) 16 (4.4)
(Missing) 1 (0.6) 2 (0.6)
tumor_stg T1 50 (28.2) 225 (62.5) <0.001
T2 21 (11.9) 48 (13.3)
T3 96 (54.2) 86 (23.9)
T4 10 (5.6) 1 (0.3)
disease_free_stt DiseaseFree 0 (0.0) 311 (86.4) <0.001
Recurred/Progressed 78 (44.1) 49 (13.6)
(Missing) 99 (55.9) 0 (0.0)
ethnicity hispanic/latino 3 (1.7) 23 (6.4) 0.017
not hispanic/latino 132 (74.6) 227 (63.1)
(Missing) 42 (23.7) 110 (30.6)
histology_grd G1 0 (0.0) 14 (3.9) <0.001
G2 45 (25.4) 185 (51.4)
G3 74 (41.8) 133 (36.9)
G4 57 (32.2) 21 (5.8)
GX 1 (0.6) 4 (1.1)
(Missing) 0 (0.0) 3 (0.8)
hemoglobin Elevated 3 (1.7) 2 (0.6) <0.001
Low 118 (66.7) 145 (40.3)
Normal 43 (24.3) 143 (39.7)
(Missing) 13 (7.3) 70 (19.4)
neoadj_therapy No 167 (94.4) 352 (97.8) 0.069
Yes 10 (5.6) 8 (2.2)
prior_cancer No 153 (86.4) 306 (85.0) 0.753
Yes 24 (13.6) 54 (15.0)
tumor_lateral Bilateral 0 (0.0) 1 (0.3) 0.023
Left 98 (55.4) 155 (43.1)
Right 79 (44.6) 204 (56.7)
platelet Elevated 30 (16.9) 8 (2.2) <0.001
Low 20 (11.3) 26 (7.2)
Normal 108 (61.0) 252 (70.0)
(Missing) 19 (10.7) 74 (20.6)
race Asian 1 (0.6) 7 (1.9) 0.032
Black/African.american 11 (6.2) 45 (12.5)
White 163 (92.1) 303 (84.2)
(Missing) 2 (1.1) 5 (1.4)
serum_ca Elevated 9 (5.1) 1 (0.3) 0.001
Low 66 (37.3) 138 (38.3)
Normal 62 (35.0) 89 (24.7)
(Missing) 40 (22.6) 132 (36.7)
gender Female 65 (36.7) 126 (35.0) 0.767
Male 112 (63.3) 234 (65.0)
tissue_site A 11 (6.2) 68 (18.9) <0.001
B 112 (63.3) 191 (53.1)
C 49 (27.7) 78 (21.7)
OTHERS 5 (2.8) 23 (6.4)
person_neoplasm_stt Tumor.Free 57 (32.2) 304 (84.4) <0.001
With.Tumor 105 (59.3) 36 (10.0)
(Missing) 15 (8.5) 20 (5.6)
wbc Elevated 46 (26.0) 118 (32.8) 0.011
Low 6 (3.4) 3 (0.8)
Normal 104 (58.8) 164 (45.6)
(Missing) 21 (11.9) 75 (20.8) 
# Droping levels with narrow distributions -> check warnings ()
# Group some levels or drop one (NULL = 'level') when grouping is not possible 

kirc_clin2 <- kirc_clin %>%
     mutate(neoplasm_stg = fct_collapse(neoplasm_stg, 'Stage I-II' = c('Stage I','Stage II'), 'Stage III-IV' = c('Stage III','Stage IV')),
            tumor_stg = fct_collapse(tumor_stg, 'T1-T2' = c('T1','T2'), 'T3-T4' = c('T3','T4')),
            histology_grd = fct_collapse(histology_grd, 'G1-G2' = c('G1','G2'), 'G3-G4' = c('G3','G4')),
            hemoglobin = fct_collapse(hemoglobin, 'Not.Low' = c('Normal', 'Elevated')),
            serum_ca = fct_collapse(serum_ca, 'Not.Low' = c('Normal', 'Elevated')),
            wbc = fct_collapse(wbc, 'Not.Elevated' = c('Low', 'Normal')))
                                                       
kirc_clin2 <- kirc_clin2 %>%
     mutate(histology_grd = fct_recode(histology_grd,  NULL = 'GX'),
            tumor_lateral = fct_recode(tumor_lateral, NULL = 'Bilateral'),
            race = fct_recode(race, NULL = 'Asian'))


table_char2 <- kirc_clin2 %>%
  summary_factorlist(dependent, explanatory_char, p=TRUE, 
                     add_dependent_label=TRUE,  na_include = TRUE)

knitr::kable(table_char2, row.names=FALSE, align=c("l", "l", "r", "r", "r"))
Dependent: over_surv_stt DECEASED LIVING p
metastasis_stg M0 110 (62.1) 316 (87.8) <0.001
M1 64 (36.2) 15 (4.2)
MX 3 (1.7) 27 (7.5)
(Missing) 0 (0.0) 2 (0.6)
lymph_stg N0 85 (48.0) 155 (43.1) 0.001
N1 12 (6.8) 5 (1.4)
NX 80 (45.2) 200 (55.6)
neoplasm_stg Stage I-II 58 (32.8) 268 (74.4) <0.001
Stage III-IV 118 (66.7) 90 (25.0)
(Missing) 1 (0.6) 2 (0.6)
tumor_stg T1-T2 71 (40.1) 273 (75.8) <0.001
T3-T4 106 (59.9) 87 (24.2)
disease_free_stt DiseaseFree 0 (0.0) 311 (86.4) <0.001
Recurred/Progressed 78 (44.1) 49 (13.6)
(Missing) 99 (55.9) 0 (0.0)
ethnicity hispanic/latino 3 (1.7) 23 (6.4) 0.017
not hispanic/latino 132 (74.6) 227 (63.1)
(Missing) 42 (23.7) 110 (30.6)
histology_grd G1-G2 45 (25.4) 199 (55.3) <0.001
G3-G4 131 (74.0) 154 (42.8)
(Missing) 1 (0.6) 7 (1.9)
hemoglobin Not.Low 46 (26.0) 145 (40.3) <0.001
Low 118 (66.7) 145 (40.3)
(Missing) 13 (7.3) 70 (19.4)
neoadj_therapy No 167 (94.4) 352 (97.8) 0.069
Yes 10 (5.6) 8 (2.2)
prior_cancer No 153 (86.4) 306 (85.0) 0.753
Yes 24 (13.6) 54 (15.0)
tumor_lateral Left 98 (55.4) 155 (43.1) 0.010
Right 79 (44.6) 204 (56.7)
(Missing) 0 (0.0) 1 (0.3)
platelet Elevated 30 (16.9) 8 (2.2) <0.001
Low 20 (11.3) 26 (7.2)
Normal 108 (61.0) 252 (70.0)
(Missing) 19 (10.7) 74 (20.6)
race Black/African.american 11 (6.2) 45 (12.5) 0.032
White 163 (92.1) 303 (84.2)
(Missing) 3 (1.7) 12 (3.3)
serum_ca Not.Low 71 (40.1) 90 (25.0) 0.028
Low 66 (37.3) 138 (38.3)
(Missing) 40 (22.6) 132 (36.7)
gender Female 65 (36.7) 126 (35.0) 0.767
Male 112 (63.3) 234 (65.0)
tissue_site A 11 (6.2) 68 (18.9) <0.001
B 112 (63.3) 191 (53.1)
C 49 (27.7) 78 (21.7)
OTHERS 5 (2.8) 23 (6.4)
person_neoplasm_stt Tumor.Free 57 (32.2) 304 (84.4) <0.001
With.Tumor 105 (59.3) 36 (10.0)
(Missing) 15 (8.5) 20 (5.6)
wbc Elevated 46 (26.0) 118 (32.8) 0.018
Not.Elevated 110 (62.1) 167 (46.4)
(Missing) 21 (11.9) 75 (20.8)
  1. saving dataset for regression model
kirc_glm <- kirc_clin2 
write_csv(kirc_glm, path = "data/kirc_glm.csv")

Further analysis

sessionInfo()

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
## 
## locale:
##  [1] LC_CTYPE=pt_BR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=pt_BR.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=pt_BR.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=pt_BR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=pt_BR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] plotly_4.9.2.1  GGally_2.0.0    ggpubr_0.4.0    rstatix_0.6.0  
##  [5] finalfit_1.0.2  skimr_2.1.2     forcats_0.5.0   stringr_1.4.0  
##  [9] dplyr_1.0.2     purrr_0.3.4     readr_1.4.0     tidyr_1.1.2    
## [13] tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-149       fs_1.5.0           lubridate_1.7.9    RColorBrewer_1.1-2
##  [5] httr_1.4.2         ggsci_2.9          repr_1.1.0         tools_4.0.3       
##  [9] backports_1.1.10   utf8_1.1.4         R6_2.4.1           mgcv_1.8-33       
## [13] DBI_1.1.0          lazyeval_0.2.2     colorspace_1.4-1   withr_2.3.0       
## [17] tidyselect_1.1.0   curl_4.3           compiler_4.0.3     cli_2.0.2         
## [21] rvest_0.3.6        mice_3.11.0        xml2_1.3.2         labeling_0.3      
## [25] scales_1.1.1       digest_0.6.25      foreign_0.8-79     rmarkdown_2.4     
## [29] rio_0.5.16         base64enc_0.1-3    pkgconfig_2.0.3    htmltools_0.5.0   
## [33] highr_0.8          dbplyr_1.4.4       htmlwidgets_1.5.2  rlang_0.4.7       
## [37] readxl_1.3.1       rstudioapi_0.11    farver_2.0.3       generics_0.0.2    
## [41] jsonlite_1.7.1     zip_2.1.1          car_3.0-10         magrittr_1.5      
## [45] Matrix_1.2-18      Rcpp_1.0.5         munsell_0.5.0      fansi_0.4.1       
## [49] abind_1.4-5        lifecycle_0.2.0    stringi_1.5.3      yaml_2.2.1        
## [53] carData_3.0-4      plyr_1.8.6         grid_4.0.3         blob_1.2.1        
## [57] crayon_1.3.4       lattice_0.20-41    haven_2.3.1        splines_4.0.3     
## [61] hms_0.5.3          knitr_1.30         pillar_1.4.6       boot_1.3-25       
## [65] ggsignif_0.6.0     reprex_0.3.0       glue_1.4.2         evaluate_0.14     
## [69] data.table_1.13.0  modelr_0.1.8       vctrs_0.3.4        cellranger_1.1.0  
## [73] gtable_0.3.0       reshape_0.8.8      assertthat_0.2.1   xfun_0.18         
## [77] openxlsx_4.2.2     broom_0.7.1        survival_3.2-7     viridisLite_0.3.0 
## [81] ellipsis_0.3.1