Analyses for M.1. without P8 (Autobiographical Memories)

Focus on Seasons - individual stimuli

1 Read, Clean, Recode, Merge

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Read
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Read files
folder <- "E:/Cinetic idei noi/Cinetic elevi"
file <- "M1 de introdus anotimpuri.xlsx"

setwd(folder)
Data <- rio::import(file.path(folder, file))

## Recode NA
Data <- 
  Data %>%             # sum(is.na(Data)) = 5873
  na_if("na")          # sum(is.na(Data)) = 6199


## Variable names
nume <- c("Stim", "Varsta_amin", "Ano", "Val", "Viv", "Relv")
toate <- paste(nume, rep(1:15, each = length(nume)), sep = "_")

## Check that all variable names are consistent with column headers
if(toate %in% names(Data)){
  cat("All column names are consistent.")
} else {
  cat("Column names are NOT consistent. \n")
  cat("Missmatches: \n ")
  setdiff(toate, names(Data))
}

2 Sample descriptives

cat("## Number of subjects")

## Number of subjects

Data %>% 
 dplyr::summarise(count = dplyr::n_distinct(ID))

cat("## Number of subjects per Protocol")

## Number of subjects per Protocol

Data %>%
 group_by(Protocol) %>%
 dplyr::summarise(count = dplyr::n_distinct(ID))

3 Season Memories and Valence

3.1 Make data frames

## Exclude P6 & P7
Data_Season <- 
  Data %>%
  dplyr::filter(!Protocol %in% c(6, 7, 8))

## Melt to Long

# Data_Vara <-                          # pivot_longer() only in development version of tidyr... dont use now
#   Data_Season %>%                     # devtools::install_github("tidyverse/tidyr")
#   tidyr::pivot_longer(
#     -c(1:5),
#     #cols = starts_with("Ano"), 
#     names_to = c(".value", "var"), 
#     names_sep = "_", 
#     values_drop_na = TRUE
#   )

Data_Season_melt <-                         
  Data_Season %>%
  gather(variable, value, -c(1:5)) %>%
  mutate(group = readr::parse_number(variable)) %>%
  mutate(variable = gsub("\\d","",x = variable)) %>%
  spread(variable, value) %>%
  rename_all(~stringr::str_replace_all(., "_", "")) %>%           # delete the "_" at end
  mutate(Ano = factor(Ano, levels = c("Vara", "Primavara", "Toamna", "Iarna"))) %>%
  mutate_at(vars("Relv", "Val", "Varstaamin", "Viv"), funs(as.numeric(as.character(.))))
  
## Season data frames
# Data_Vara <-
#   Data_Season_melt %>%
#   filter(!is.na(Ano)) %>%                # delete rows were there is no Ano
#   filter(Ano == "Vara")
# 
# Data_Primavara <-
#   Data_Season_melt %>%
#   filter(!is.na(Ano)) %>%                # delete rows were there is no Ano
#   filter(Ano == "Primavara")
# 
# Data_Toamna <-
#   Data_Season_melt %>%
#   filter(!is.na(Ano)) %>%                # delete rows were there is no Ano
#   filter(Ano == "Toamna")
# 
# Data_Iarna <-
#   Data_Season_melt %>%
#   filter(!is.na(Ano)) %>%                # delete rows were there is no Ano
#   filter(Ano == "Iarna")
# 
# 
# ## Excel downloadable DT tables
# Data_Vara %>%                              
#   select(-Nume) %>%
#     DT::datatable(                                  
#       extensions = 'Buttons',
#       options = list(pageLength = 10,
#                      scrollX='500px', 
#                      dom = 'Bfrtip', 
#                      buttons = c('excel', "csv")))
# 
# Data_Primavara %>%                              
#   select(-Nume) %>%
#     DT::datatable(                                  
#       extensions = 'Buttons',
#       options = list(pageLength = 10,
#                      scrollX='500px', 
#                      dom = 'Bfrtip', 
#                      buttons = c('excel', "csv")))
# 
# Data_Toamna %>%                              
#   select(-Nume) %>%
#     DT::datatable(                                  
#       extensions = 'Buttons',
#       options = list(pageLength = 10,
#                      scrollX='500px', 
#                      dom = 'Bfrtip', 
#                      buttons = c('excel', "csv")))
# 
# Data_Iarna %>%                              
#   select(-Nume) %>%
#     DT::datatable(                                  
#       extensions = 'Buttons',
#       options = list(pageLength = 10,
#                      scrollX='500px', 
#                      dom = 'Bfrtip', 
#                      buttons = c('excel', "csv")))


cat("### Melt to Long Format")

3.1.1 Melt to Long Format

Data_Season_melt %>%
  dplyr::select(-Nume) %>%
    DT::datatable(
      extensions = 'Buttons',
      options = list(pageLength = 10,
                     scrollX='500px',
                     dom = 'Bfrtip',
                     buttons = c('excel', "csv")))

cat("### Wide Format")

3.1.2 Wide Format

Data_Season %>%
  dplyr::select(-Nume) %>%
    DT::datatable(
      extensions = 'Buttons',
      options = list(pageLength = 10,
                     scrollX='500px',
                     dom = 'Bfrtip',
                     buttons = c('excel', "csv")))

## Data Frame for Plots
Data_Season_melt_nona <-
  Data_Season_melt %>%
  filter(!is.na(Ano))


cat("### Wide Format for Ano ~ Valence")

3.1.3 Wide Format for Ano ~ Valence

Data_Season_melt_nona %>%
  dplyr::select(ID, Ano, Val) %>%
  rownames_to_column() %>%
  spread(key = Ano, value = Val) %>%
  arrange(ID) %>%
    DT::datatable(
      extensions = 'Buttons',
      options = list(pageLength = 10,
                     scrollX='500px',
                     dom = 'Bfrtip',
                     buttons = c('excel', "csv")))

3.2 Define Function for Plots

## Function for Ano Bar Plot
my_comparisons <- 
  gtools::combinations(n = length(unique(Data_Season_melt_nona$Ano)), r = 2, v = as.character(Data_Season_melt_nona$Ano), repeats.allowed = FALSE) %>%
  as.data.frame() %>% 
  mutate_if(is.factor, as.character) %>%
  purrr::pmap(list) %>% 
  lapply(unlist)

func_plot_ano <- function(df, y_var, y_var_lab, label.y_set = 7, yticks.by_set = 1, facet = FALSE){
  if(facet){
    facet <- "Protocol"
  }else{
    facet <- NULL
  }
  p <-
    df  %>%
    ggpubr::ggbarplot(x = "Ano", y = y_var, 
                      add = "mean_se",
                      color = "black", fill = "lightgray",
                      xlab = "Anotimp", ylab = y_var_lab,
                      label = TRUE, lab.nb.digits = 2, lab.pos= "in",
                      facet.by = facet) +
    stat_compare_means(method = "anova",
                       label.x = 0.9, label.y = label.y_set) +
    stat_compare_means(comparisons = my_comparisons,
                       label = "p.signif", method = "t.test", paired = FALSE, na.rm = TRUE) 
  ggpar(p, yticks.by = yticks.by_set)                                     # the rating scale is 1-7
}


## Dodged 
func_dodged_ano <- function(df, y_var, y_var_lab, facet = FALSE){
  y_var<- sym(y_var)
  
  if(facet) {
    df <- 
      df %>% 
      mutate(Protocol = paste0("Protocol ", Protocol)) %>%
      group_by(Protocol)  
  }
  
  p <-
    df  %>%
    dplyr::count(Ano, !!y_var) %>%                        # Group by, then count number in each group
    mutate(pct = prop.table(n)) %>%                     # Calculate percent within each var
    mutate(Val_fac = as.factor(!!y_var)) %>%
    ggplot(aes(x = Ano, y = pct, fill = Val_fac, label = scales::percent(pct))) + 
      geom_col(position = 'dodge') + 
      geom_text(position = position_dodge(width = .9),    # move to center of bars
                vjust = -0.5,                             # nudge above top of bar
                size = 3) + 
      scale_y_continuous(labels = scales::percent) +
      {if(facet) facet_wrap(~Protocol, scales = "free", ncol = 1, nrow = 8)} +
      ggtitle(y_var_lab) +
      xlab("Anotimp") + ylab("Percentage %") + 
      guides(fill = guide_legend(title = "Value", nrow = 1)) + 
      scale_fill_grey(start = 0.8, end = 0.2, na.value = "red", aesthetics = "fill") +
      theme(legend.position = "bottom", legend.direction = "horizontal", 
            legend.justification = c(0, 1), panel.border = element_rect(fill = NA, colour = "black"))
  p
}  

3.3 Plots of Seasons

## Test for Val -- works well
# Data_Season_melt_nona  %>%
#   ggpubr::ggbarplot(x = "Ano", y = "Val", 
#                     add = "mean_se",
#                     color = "black", fill = "lightgray",
#                     xlab = "Anotimp", ylab = "Valenta",
#                     label = TRUE, lab.nb.digits = 2, lab.pos= "in") +
#   stat_compare_means(method = "anova",
#                      label.x = 0.9, label.y = 7) +
#   stat_compare_means(comparisons = my_comparisons,
#                      label = "p.signif", method = "t.test", paired = FALSE, na.rm = TRUE) 


func_plot_ano(Data_Season_melt_nona, "Val", "Valenta")

func_plot_ano(Data_Season_melt_nona, "Relv", "Relevanta personala")

func_plot_ano(Data_Season_melt_nona, "Viv", "Vivid")

func_plot_ano(Data_Season_melt_nona, "Varstaamin", "Varsta amintire", label.y_set = 50, yticks.by_set = 5)

3.4 Plots of Seasons by Protocol

func_plot_ano(Data_Season_melt_nona, "Val", "Valenta", facet = TRUE) 

func_plot_ano(Data_Season_melt_nona, "Relv", "Relevanta personala", facet = TRUE)

func_plot_ano(Data_Season_melt_nona, "Viv", "Vivid", facet = TRUE)

func_plot_ano(Data_Season_melt_nona, "Varstaamin", "Varsta amintire", label.y_set = 50, yticks.by_set = 5, facet = TRUE)

3.5 Plots with proportion of values

# Stacked - Test for Val -- works well
cat("### Stacked: Val")

3.5.1 Stacked: Val

Data_Season_melt_nona %>%
  dplyr::count(Ano, Val) %>%                   # Group by, then count number in each group
  mutate(pct = n/sum(n)) %>%                   # Calculate percent within each var; could use prop.table(n)
  mutate(Val_fac = as.factor(Val)) %>%
ggplot(aes(Ano, n, fill = Val_fac)) +
  geom_bar(stat = "identity") +  
  geom_text(aes(label = paste0(sprintf("%1.1f", pct*100), "%"), size = scales::rescale(pct, to=c(2, 5))),
            position = position_stack(vjust=0.5), show.legend = FALSE) +
  guides(fill = guide_legend(title = "Value", nrow = 1)) + 
  theme(legend.position = "bottom", legend.direction = "horizontal", legend.justification = c(0, 1))

# # Dodged - Test for Val -- works well
# Data_Season_melt_nona %>% 
#   dplyr::count(Ano, Val) %>%                        # Group by, then count number in each group
#   mutate(pct = prop.table(n)) %>%                   # Calculate percent within each var
#   mutate(Val_fac = as.factor(Val)) %>%
#   ggplot(aes(x = Ano, y = pct, fill = Val_fac, label = scales::percent(pct))) + 
#     geom_col(position = 'dodge') + 
#     geom_text(position = position_dodge(width = .9),    # move to center of bars
#               vjust = -0.5,                             # nudge above top of bar
#               size = 3) + 
#     scale_y_continuous(labels = scales::percent) +
#     xlab("Anotimp") + ylab("Percentage %") + 
#     guides(fill = guide_legend(title = "Value", nrow = 1)) + 
#     scale_fill_grey(start = 0.8, end = 0.2, na.value = "red", aesthetics = "fill") +
#     theme(legend.position = "bottom", legend.direction = "horizontal", legend.justification = c(0, 1)) 
  
cat("### Dodged: Val, Relev, Vivid")

3.5.2 Dodged: Val, Relev, Vivid

func_dodged_ano(Data_Season_melt_nona, "Val", "Valenta")

func_dodged_ano(Data_Season_melt_nona, "Relv", "Relevanta personala")

func_dodged_ano(Data_Season_melt_nona, "Viv", "Vivid")

3.6 Plots with proportion of values by Protocol

func_dodged_ano(Data_Season_melt_nona, "Val", "Valenta", facet = TRUE)

func_dodged_ano(Data_Season_melt_nona, "Relv", "Relevanta personala", facet = TRUE)

func_dodged_ano(Data_Season_melt_nona, "Viv", "Vivid", facet = TRUE)

3.7 Likert Plots for Season

# Proportions and z-scores
Prop_val <- 
  Data_Season_melt_nona %>%
    dplyr::select(ID, Protocol, Ano, Val) %>%
    group_by(Ano) %>%
    mutate(
      Val = as.factor(Val),
      Val = forcats::fct_collapse(Val, low = c("1", "2", "3"), neutral = "4", high = c("5", "6", "7"))
      ) %>%
    dplyr::count(Val) %>% 
    mutate(total = sum(n),
           perc = 100*n/total)

cat("### Proportions - compared to 0.5 probability")

3.7.1 Proportions - compared to 0.5 probability

Prop_val %>%
filter(Val == "high") %>%
  rowwise %>%
  mutate(tst = list(broom::tidy(prop.test(n, total, conf.level = 0.95)))) %>%
  tidyr::unnest(tst)

Prop_val %>%
filter(Val == "low") %>%
  rowwise %>%
  mutate(tst = list(broom::tidy(prop.test(n, total, conf.level = 0.95)))) %>% 
  tidyr::unnest(tst)

cat("### Proportions - Multiple comparisons")

3.7.2 Proportions - Multiple comparisons

Pair_Comp_prop_high <-       # compaire all proportions pairwise
  Prop_val %>%
  filter(Val == "high") %>%
  dplyr::select(-perc) %>%
  unite("Categ", c("Ano", "Val"), sep = "-") %>%
  column_to_rownames("Categ") 

Pair_Comp_prop_high_mat <-
  Pair_Comp_prop_high %>%
    rownames_to_column("rowname") %>%
    dplyr::rename(success = n) %>%
    mutate(failure = total - success) %>%
    dplyr::select(-total) %>%
    column_to_rownames("rowname") %>%
    as.matrix() 

Pair_Comp_prop_low <-       # compaire all proportions pairwise
  Prop_val %>%
  filter(Val == "low") %>%
  dplyr::select(-perc) %>%
  unite("Categ", c("Ano", "Val"), sep = "-") %>%
  column_to_rownames("Categ") 

Pair_Comp_prop_low_mat <-
  Pair_Comp_prop_low %>%
  rownames_to_column("rowname") %>%
  dplyr::rename(success = n) %>%
  mutate(failure = total - success) %>%
  dplyr::select(-total) %>%
  column_to_rownames("rowname") %>%
  as.matrix()

cat("#### Pairwise comparisons using Pairwise comparison of proportions")

3.7.2.1 Pairwise comparisons using Pairwise comparison of proportions

pairwise.prop.test(x = Pair_Comp_prop_high_mat, p.adjust.method = "none") %>% 
  tidy()

pairwise.prop.test(x = Pair_Comp_prop_low_mat, p.adjust.method = "none") %>% 
  tidy()

cat("#### Pairwise comparisons using Pairwise comparison of proportions (Fisher exact)")    # library(fmsb)

3.7.2.2 Pairwise comparisons using Pairwise comparison of proportions (Fisher exact)

fmsb::pairwise.fisher.test(x = Pair_Comp_prop_high_mat, p.adjust.method = "none") %>% 
  tidy()

fmsb::pairwise.fisher.test(x = Pair_Comp_prop_low_mat, p.adjust.method = "none") %>% 
  tidy()

# library(paircompviz)
# paircompviz::paircomp(Pair_Comp_prop_high$n, Pair_Comp_prop_high$total, correct = FALSE,
#                       test = "prop", result = TRUE, p.adjust.method = "none") 

# Data for Plot
cat("### Proportions - Plot of Low-Neutral-High")

3.7.3 Proportions - Plot of Low-Neutral-High

Likert_val <- 
  Data_Season_melt_nona %>%
  dplyr::select(ID, Protocol, group, Ano, Val) %>%
  spread(key = Ano, value = Val) %>%
  mutate_at(vars("Vara", "Primavara", "Toamna", "Iarna"), ~as.factor(.))

# Plots  # library(likert)
Likertobj_Val <- likert(Likert_val[, c("Vara", "Primavara", "Toamna", "Iarna")], nlevels = 7)   # here are percentages
Likertobj_Val_perc <- Likertobj_Val$results
# check if same with Prop dataframe above; or prop.table(table(Likert_val$Vara))

plot(Likertobj_Val, type = "bar", 
     centered = TRUE, center = 4, include.center = TRUE,              # "4" is neutral
     wrap = 30, low.color = 'burlywood', high.color = 'maroon') +
  guides(fill = guide_legend(nrow = 1))

3.8 Relationship: Frequency of remembering - Valence

Anofreq_Val <- 
  Data_Season_melt_nona %>%
    dplyr::select(ID, Protocol, Ano, Val) %>%
    group_by(ID, Ano) %>%
    dplyr::summarize(Mean_Val = mean(Val, na.rm=TRUE),
                     Freq_Ano = n()) 

cat("### Scatter plot with correlation coefficient for all Seasons")

3.8.1 Scatter plot with correlation coefficient for all Seasons

ggpubr::ggscatter(Anofreq_Val, x = "Freq_Ano", y = "Mean_Val",
            add = "reg.line",  
            add.params = list(color = "blue", fill = "lightgray"), 
            conf.int = TRUE ) +
stat_cor(method = "pearson", label.x = 7, label.y = 10)

cat("### Scatter plot with correlation coefficient for each Season")

3.8.2 Scatter plot with correlation coefficient for each Season

ggpubr::ggscatter(Anofreq_Val, x = "Freq_Ano", y = "Mean_Val",
   color = "Ano", palette = "jco",
   add = "reg.line", conf.int = TRUE,
   xlim = c(0, 15), ylim = c(0, 8)) + 
stat_cor(aes(color = Ano), method = "pearson", label.x = 11)

3.9 Most negative Valence (ones)

# Proportions and z-scores
Prop_val2 <- 
  Data_Season_melt_nona %>%
    dplyr::select(ID, Protocol, Ano, Val) %>%
    group_by(Ano) %>%
    dplyr::count(Val) %>% 
    mutate(total = sum(n),
           perc = 100*n/total)

cat("### Proportions - compared to 0.5 probability")

3.9.1 Proportions - compared to 0.5 probability

Prop_val2 %>%
filter(Val == 1) %>%
  rowwise %>%
  mutate(tst = list(broom::tidy(prop.test(n, total, conf.level = 0.95)))) %>%
  tidyr::unnest(tst)

cat("### Proportions - Multiple comparisons")

3.9.2 Proportions - Multiple comparisons

Pair_Comp_prop_ones <-       # compaire all proportions pairwise
  Prop_val2 %>%
  filter(Val == 1) %>%
  dplyr::select(-perc) %>%
  unite("Categ", c("Ano", "Val"), sep = "-") %>%
  column_to_rownames("Categ") 

Pair_Comp_prop_ones_mat <-
  Pair_Comp_prop_ones %>%
    rownames_to_column("rowname") %>%
    dplyr::rename(success = n) %>%
    mutate(failure = total - success) %>%
    dplyr::select(-total) %>%
    column_to_rownames("rowname") %>%
    as.matrix() 

cat("#### Pairwise comparisons using Pairwise comparison of proportions")

3.9.2.1 Pairwise comparisons using Pairwise comparison of proportions

pairwise.prop.test(x = Pair_Comp_prop_ones_mat, p.adjust.method = "none") %>% 
  tidy()

cat("#### Pairwise comparisons using Pairwise comparison of proportions (Fisher exact)")    # library(fmsb)

3.9.2.2 Pairwise comparisons using Pairwise comparison of proportions (Fisher exact)

fmsb::pairwise.fisher.test(x = Pair_Comp_prop_ones_mat, p.adjust.method = "none") %>% 
  tidy()

# Data for Plot
cat("### Proportions - Plot of Low-Neutral-High")

3.9.3 Proportions - Plot of Low-Neutral-High

Likert_val_all <- 
  Data_Season_melt_nona %>%
  dplyr::select(ID, Protocol, group, Ano, Val) %>%
  spread(key = Ano, value = Val) %>%
  mutate_at(vars("Vara", "Primavara", "Toamna", "Iarna"), ~as.factor(.))

# Plots  # library(likert)
Likertobj_Val_all <- likert(Likert_val[, c("Vara", "Primavara", "Toamna", "Iarna")], nlevels = 7)   # here are percentages
Likertobj_Val_perc_all <- Likertobj_Val$results

plot(Likertobj_Val_all, type = "heat") + 
  ggtitle("Mean (SD) vs Percetages of Valence Ratings") + 
  theme(legend.position = 'none')

4 Session Info

R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)

Matrix products: default

locale:
[1] LC_COLLATE=Romanian_Romania.1250  LC_CTYPE=Romanian_Romania.1250    LC_MONETARY=Romanian_Romania.1250 LC_NUMERIC=C                     
[5] LC_TIME=Romanian_Romania.1250    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] likert_1.3.5               xtable_1.8-4               fmsb_0.6.3                 rio_0.5.16                 scales_1.0.0              
 [6] ggpubr_0.2                 magrittr_1.5               tadaatoolbox_0.16.1        summarytools_0.8.8         rstatix_0.2.0             
[11] broom_0.5.2                PerformanceAnalytics_1.5.2 xts_0.11-2                 zoo_1.8-4                  psych_1.8.12              
[16] plyr_1.8.4                 forcats_0.4.0              stringr_1.4.0              dplyr_0.8.3                purrr_0.3.2               
[21] readr_1.3.1                tidyr_1.0.0                tibble_2.1.3               ggplot2_3.2.1              tidyverse_1.2.1           
[26] papaja_0.1.0.9842          pacman_0.5.1              

loaded via a namespace (and not attached):
 [1] colorspace_1.4-1   ggsignif_0.4.0     pryr_0.1.4         ellipsis_0.3.0     rstudioapi_0.8     DT_0.5             mvtnorm_1.0-11    
 [8] lubridate_1.7.4    xml2_1.2.0         codetools_0.2-16   mnormt_1.5-5       knitr_1.25         zeallot_0.1.0      pixiedust_0.8.6   
[15] jsonlite_1.6       shiny_1.2.0        compiler_3.6.1     httr_1.4.0         backports_1.1.4    assertthat_0.2.1   Matrix_1.2-17     
[22] lazyeval_0.2.2     cli_1.1.0          later_0.7.5        htmltools_0.3.6    tools_3.6.1        gtable_0.3.0       glue_1.3.1        
[29] reshape2_1.4.3     Rcpp_1.0.2         carData_3.0-2      cellranger_1.1.0   vctrs_0.2.0        nlme_3.1-140       crosstalk_1.0.0   
[36] xfun_0.9           openxlsx_4.1.0     rvest_0.3.2        mime_0.7           lifecycle_0.1.0    gtools_3.8.1       MASS_7.3-51.4     
[43] hms_0.5.1          promises_1.0.1     parallel_3.6.1     expm_0.999-3       pwr_1.2-2          yaml_2.2.0         curl_3.2          
[50] gridExtra_2.3      pander_0.6.3       stringi_1.4.3      nortest_1.0-4      boot_1.3-22        zip_1.0.0          rlang_0.4.0       
[57] pkgconfig_2.0.3    matrixStats_0.54.0 bitops_1.0-6       lattice_0.20-38    labeling_0.3       rapportools_1.0    htmlwidgets_1.3   
[64] tidyselect_0.2.5   ggsci_2.9          R6_2.4.0           DescTools_0.99.29  generics_0.0.2     pillar_1.4.2       haven_2.1.1       
[71] foreign_0.8-71     withr_2.1.2        abind_1.4-5        RCurl_1.95-4.11    modelr_0.1.5       crayon_1.3.4       car_3.0-2         
[78] viridis_0.5.1      grid_3.6.1         readxl_1.1.0       data.table_1.11.8  digest_0.6.21      httpuv_1.4.5       munsell_0.5.0     
[85] viridisLite_0.3.0  quadprog_1.5-5    

 

---

A work by Claudiu Papasteri

claudiu.papasteri@gmail.com