In erblast/oetteR: Collection of personal R functions
knitr::opts_chunk$set(echo = F, warning = F, message = F, eval = T)
library(knitr)
library(ISLR)
library(tabplot)
library(tidyverse)
library(tabplot)
library(randomForest)
library(stringr)
library(GGally)
library(forcats)
library(caret)
library(ggcorrplot)
library(Amelia)
library(e1071)
{.tabset}
Data{.tabset}
Load Data
wellPanel(
selectInput('sql_or_lib'
, label = 'Select Data Source'
, choices = c('SQL', 'Sample_Data')
, selected = 'Sample_Data')
, selectInput('sample_data'
, label = 'Select Sample Data'
, choices = c('ISLR::OJ'
, 'ISLR::Caravan'
, 'ISLR::Auto'
, 'ISLR::Carseats'
, 'ISLR::Wage'
, 'ISLR::College'
, 'ISLR::Hitters'
, 'ISLR::Default'
, 'ISLR::Weekly'
, 'ILSR::Wage categoricals'
, 'ISLR::OJ numericals')
, selected = 'ISLR::Wage')
# , textInput('server'
# , label = 'Enter Server'
# , width = 'auto')
#
# , textInput('query'
# , label = 'Paste SQL'
# , width = '100%')
, textInput('path'
, label = 'Save Analysis to this directory'
, width = '100%'
, value = "C:/Users/erbla/Documents")
)
inputPanel(
actionButton('load_data'
, label = 'Load Data')
, checkboxInput('save'
, label = 'Save Results'
, value = F)
)
rea_load = eventReactive(input$load_data,{
if(input$sql_or_lib == 'SQL'){
require(RODBCext)
con = odbcDriverConnect(
str_c('Driver=SQL Server;Trusted_Connection=Yes;Server=', input$server)
)
query = input$query
data = sqlQuery(con, query = query)
close(con)
}
else{
if(input$sample_data == 'ILSR::Wage categoricals'){
data = ISLR::Wage
bool = ! summarise_all(data, is.numeric)
data= data[,bool]
}else if(input$sample_data == 'ISLR::OJ numericals'){
data = ISLR::OJ %>%
as.data.frame()
bool = summarise_all(data, is.numeric)
data= data[,as.logical(bool[1,])]
}else{
exec_str = input$sample_data
data = eval(parse(text = exec_str) )
}
}
return(data)
})
save_plot = function(plot
, path
, name
, yes
, scale = 1
, excel = T){
require(tidyverse)
if(yes == F) return()
folder_name = lubridate::today() %>%
as.character() %>%
stringr::str_replace_all('-','')%>%
stringr::str_c('_multiview')
path = path %>%
stringr::str_c('/',folder_name)
dir.create(path)
file_name_plot = path %>%
stringr::str_c('/',name,'.png')
file_name_excel = path %>%
stringr::str_c('/',name,'.xls')
# tabplot object should save with the same function
safe_ggsave = safely(ggsave)
safe_tableSafe = safely(tabplot::tableSave)
s = safe_ggsave(file_name_plot
, plot = plot
, scale = scale)
if( !purrr::is_empty(s$error) ){
print(s$error)
s = safe_tableSafe(tab = plot
,filename = file_name_plot
, scale = scale)
}
if( !purrr::is_empty(s$error) ){
print(s$error)
plot
dev.copy(png, filename = file_name_plot
)
dev.off()
}
if(excel == T){
coords = ggplot_build(plot)
if(file.exists(file_name_excel)) {
file.remove(file_name_excel)
}
purrr::pwalk(list( x = coords$data
, sheetName = as.character(1:length(coords$data) )
)
, xlsx::write.xlsx
, file = file_name_excel
, col.names = T
, row.names = T
, append = T
)
}
}
Sample View
DT::renderDataTable( options = list( pageLength = 5)
,{
d_clean = rea_clean()
data = d_clean$data
all_variables = d_clean$all_variables
return( data[ ,all_variables] )
})
Summary
Numericals
renderPrint({
d_clean = rea_clean()
data = d_clean$data
numericals = d_clean$numericals
print(summary( data[, numericals]))
})
Categoricals
renderPrint({
d_clean = rea_clean()
data = d_clean$data
categoricals = d_clean$categoricals
print(summary( data[, categoricals]))
})
Missing Values
renderPlot({
d_clean = rea_clean()
data = d_clean$data
all_variables = d_clean$all_variables
p = Amelia::missmap( as_data_frame(data)[, all_variables])
return(p)
})
Clean Data
inputPanel(
numericInput('max_no_lvls_fctr'
, label = 'Max number of levels for categorical variables'
, value = 10
, min = 1
, step = 1)
, numericInput('min_no_vals_num'
, label = 'Minimum number of distinct values for numerical variables'
, value = 6
, min = 1
, step = 1)
, checkboxInput('missing'
, label = 'Exclude Missing Values'
, value = F)
)
renderUI({
data = rea_load()
checkboxGroupInput('deselect_cols'
, label = 'Select Variables to exclude'
, choices = names(data)
)
})
rea_clean = reactive({
data = rea_load()
if(! purrr::is_empty(input$deselect_cols) ) {
data = data %>%
select( - one_of(input$deselect_cols) )
}
d_clean = f_clean_data(data
, as.numeric(input$max_no_lvls_fctr)
, as.numeric(input$min_no_vals_num)
)
return(d_clean)
})
f_clean_data = function(data
, max_number_of_levels_factors = 10
, min_number_of_levels_nums = 6){
require(tidyverse)
require(stringr)
require(caret)
require(forcats)
data = data %>%
as_tibble()
numericals = names(data)[ map_lgl( data, is.numeric)]
categoricals = names(data)[ ! map_lgl( data, is.numeric)]
all_variables = names(data)
# add numericals with less than x distinc values to factors
no_unique_vals = map_dbl(data[, numericals], function(x) length(unique(x)) )
categoricals = c(categoricals, numericals[no_unique_vals < min_number_of_levels_nums] )
numericals = numericals[ !numericals %in% categoricals ]
# convert all non numericals and grouping variables to factors
for (var in categoricals ){
data[[var]] = as.factor(data[[var]])
}
# collapse smallest levels into one if factor levels exceed size x
no_levels = map_dbl( data[, categoricals], function(x) length( levels(x) ) )
for (var in names( no_levels[no_levels > max_number_of_levels_factors] )) {
data[[var]] = fct_lump(data[[var]], n = max_number_of_levels_factors)
}
# Boxcox transform numericals
if(!is_empty(numericals)){
data_box_cox = data[, numericals] %>%
mutate_all( function(x) x + min(x, na.rm = T) + 0.00001 )
trans = preProcess( as.data.frame(data_box_cox), c('BoxCox'), na.remove = T)
pred = predict(trans, as.data.frame(data_box_cox) )
names_transformed = names(pred) %>%
str_c('_boxcox')
names(pred) = names_transformed
data = data %>%
bind_cols(pred)
if(!ncol(data[,numericals])
== ncol(data[,names_transformed])) {
stop( 'boxcox transformation unsucessfull')
}
} else {
names_transformed = NULL
}
# drop categoricals with only one level
if( !is_empty(categoricals)){
data = data %>%
mutate_at( vars(one_of(categoricals) )
, fct_drop )
no_lvl = data[,categoricals] %>%
summarise_all( function(x) list(levels(x)) ) %>%
summarise_all( function(x) length(x[[1]]) )
only_one_lvl = categoricals[no_lvl == 1]
if(length(only_one_lvl) > 0 ) {
data = data %>%
select( - one_of(only_one_lvl) )
all_variables = all_variables[ !all_variables %in% only_one_lvl ]
categoricals = categoricals[ no_lvl > 1]
}
}
# drop observations with missing Values
if(input$missing == T) {
data = data[stats::complete.cases(data),]
print('missing cases exluded')
}
# return statement
return( list( data = data
, numericals = numericals
, categoricals = categoricals
, all_variables = all_variables
, boxcox = names_transformed
)
)
}
Analysis{.tabset}
Group Statistics{.tabset}
renderUI({
d_clean = rea_clean()
categoricals = d_clean$categoricals
inputPanel(
selectInput("group_stats", label = "select grouping variable",
choices = categoricals, selected = categoricals[1])
)
})
f_bring_to_pos_range = function(x){
if( min(x)< 0) x = x + abs(min(x))
return(x)
}
f_diff_of_means_medians = function(df, group, variable){
data = df %>%
select( group = one_of(group), variable = one_of(variable) )%>%
mutate ( variable = f_bring_to_pos_range(variable) ) %>%
group_by( group ) %>%
summarise( means = mean(variable, na.rm = T)
, medians = median(variable, na.rm = T) ) %>%
ungroup() %>%
summarise( diff_of_means = max(means) - min(means)
, diff_of_means_perc = ( ( max(means) - min(means) ) /max(means) ) *100
, diff_of_medians = max(medians) - min(medians)
, diff_of_medians_perc = ( ( max(medians) - min(medians) ) /max(means) ) *100 )
return(data)
}
f_max_diff_of_freq = function(df, var1, var2){
t = table( df[[var1]], df[[var2]] ) %>%
as_tibble() %>%
group_by( Var1 ) %>%
mutate ( diff_var1 = ( max(n)-min(n) )
, diff_var1_perc = ( ( max(n)-min(n))/ max(n) *100)
) %>%
group_by( Var2 ) %>%
mutate ( diff_var2 = ( max(n)-min(n) )
, diff_var2_perc = ( ( max(n)-min(n))/ max(n) *100)
) %>%
ungroup()%>%
summarise( max_diff_freq = max( c(diff_var1, diff_var2) )
, max_diff_freq_perc = max( c(diff_var1_perc, diff_var2_perc) )
)
}
f_anova_stats = function(df, group, variables) {
# returns a dataframe with anova stats
# df : dataframe
# group : grouping variable as character vector, must indicate factor variable
# variables : numerical variables to be analyzed as character vector, must indicate numerical variable
data = df
formula = stringr::str_c('value~',group) %>%
as.formula()
df_anova = data %>%
as_tibble() %>%
select( one_of( c(group, variables) ) ) %>%
gather(key = 'variable', value = 'value', one_of( variables ) ) %>%
group_by( variable ) %>%
nest( one_of(group), value) %>%
mutate( model_anova = purrr::map( data, ~aov( formula, data = .))
, summary_anova = purrr::map( model_anova, summary)
, summary_anova = purrr::map( summary_anova
, function(x) x[[1]])
, anova_pval = purrr::map(summary_anova, 'Pr(>F)')
, anova_pval = purrr::map_dbl(anova_pval
, function(x) x[1])
, model_kruskal = purrr::map( data, ~kruskal.test(formula, data = .) )
, kruskal_pval = purrr::map_dbl(model_kruskal, 'p.value')
, model_shapiro = purrr::map(data
, function(x) shapiro.test(sample(x$value, 5000, replace = T) ) )
, shapiro_stat = purrr::map_dbl(model_shapiro,'statistic')
, shapiro_pval = purrr::map_dbl(model_shapiro,'p.value')
, diff_df = purrr::map( data, f_diff_of_means_medians, group = group, variable = 'value') ) %>%
unnest(diff_df) %>%
select(variable
, shapiro_stat
, shapiro_pval
, anova_pval
, kruskal_pval
, diff_of_means
, diff_of_means_perc
, diff_of_medians
, diff_of_medians_perc
)
return(df_anova)
}
f_chi_square = function(df, group, variables) {
# returns a dataframe with anova stats
# df : dataframe
# group : grouping variable as character vector, must indicate factor variable
# variables : numerical variables to be analyzed as character vector, must indicate factor variable
data = df
variables = variables[!variables == group]
if(purrr::is_empty(variables)) return()
df_chi = data %>%
as_tibble() %>%
select( one_of( c(group, variables) ) ) %>%
gather(key = 'variable', value = 'value', one_of( variables ) ) %>%
group_by( variable ) %>%
nest( one_of( group ), value) %>%
mutate( model_chi = purrr::map( data, ~chisq.test(x = .[[group]]
, y = .[['value']]
) )
,chi_pval = purrr::map_dbl(model_chi, 'p.value')
,diff_df = purrr::map(data, f_max_diff_of_freq, group, 'value') ) %>%
unnest(diff_df) %>%
select(variable, chi_pval, max_diff_freq, max_diff_freq_perc)
return(df_chi)
}
rea_group_stats = reactive({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
numericals = d_clean$numericals
categoricals = d_clean$categoricals
if(! is_empty(numericals)) {
df_num = f_anova_stats(df = data
,group = input$group_stats
,variables = numericals
)
df_boxcox = f_anova_stats(df = data
,group = input$group_stats
,variables = boxcox
)
sig_num = df_num %>%
mutate( p_val = ifelse(shapiro_stat >= 0.9
, anova_pval
, kruskal_pval)
) %>%
filter(p_val <= 0.001) %>%
.[['variable']]
sig_boxcox = df_boxcox %>%
mutate( p_val = ifelse(shapiro_stat >= 0.9
, anova_pval
, kruskal_pval)
) %>%
filter(p_val <= 0.001) %>%
.[['variable']]
} else {
df_num = NULL
df_boxcox = NULL
sig_num = NULL
sig_boxcox = NULL
}
# only works with minimum two categoricals vars
if( length(categoricals) >= 2 ){
df_chi = f_chi_square(df = data
,group = input$group_stats
,variables = categoricals
)
sig_chi = df_chi %>%
filter(chi_pval <= 0.001) %>%
.[['variable']]
} else{
df_chi = NULL
sig_chi = NULL
}
return( list( df = list(df_num = df_num
,df_boxcox = df_boxcox
,df_chi = df_chi
)
, sig = list(sig_num = sig_num
,sig_boxcox = sig_boxcox
,sig_chi = sig_chi
)
)
)
})
Numericals (ANOVA)
render_table_num_group_stat = function( boxcox = F) {
DT::renderDataTable(
extensions = 'Buttons'
, options = list( dom = 'Bftrip'
, buttons = c('copy', 'excel')
, pageLength = 1
)
,{
group = input$group_stats
if( boxcox == F ){
df = rea_group_stats()$df$df_num
}else{
df = rea_group_stats()$df$df_boxcox
}
df = DT::datatable(df
, extensions = 'Buttons'
, options = list( dom = 'Bftrip'
, buttons = c('copy', 'excel')
)
) %>%
DT::formatStyle('shapiro_stat'
, color = DT::styleInterval(0.9, c('red','green') )
) %>%
DT::formatStyle(3:5
, color = DT::styleInterval(0.01, c('green','red') )
) %>%
DT::formatRound(2:5, 6) %>%
DT::formatRound(c(6,8), 3) %>%
DT::formatRound(c(7,9), 2)
})
}
render_table_num_group_stat( boxcox = F )
Numericals boxcox (ANOVA)
render_table_num_group_stat( boxcox = T )
Categoricals (Chi-Square)
DT::renderDataTable({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
numericals = d_clean$numericals
categoricals = d_clean$categoricals
group = input$group_stats
df = rea_group_stats()$df$df_chi
df = DT::datatable(df
, extensions = 'Buttons'
, options = list( dom = 'Bftrip'
, buttons = c('copy', 'excel')
)
) %>%
DT::formatStyle('chi_pval'
, color = DT::styleInterval(0.01, c('green','red') )
) %>%
DT::formatRound( 2, 6) %>%
DT::formatRound( 4, 2)
return(df)
})
Predictive Capacity
renderUI({
d_clean = rea_clean()
categoricals = d_clean$categoricals
numericals = d_clean$numericals
inputPanel(
selectInput("group_imp"
, label = "select predicted variable"
, choices = c(categoricals, numericals)
, selected = categoricals[1])
)
})
rea_imp = reactive({
d_clean = rea_clean()
data = d_clean$data
all_variables = d_clean$all_variables
form = as.formula( stringr::str_c(input$group_imp,'~.') )
m = randomForest::randomForest(form, data[, all_variables] )
imp = tibble( mean_decrease_gini = m$importance[,1]
,variable = names(m$importance[,1])
,group = input$group_imp) %>%
arrange(desc(mean_decrease_gini))
return(imp)
})
renderPlot({
imp = rea_imp()
p = ggplot(imp) +
geom_bar(aes(x = fct_reorder(variable
, mean_decrease_gini)
, y = mean_decrease_gini
, fill = fct_reorder(variable
, mean_decrease_gini
, .desc = T)
)
, stat = 'identity'
, show.legend = F) +
coord_flip() +
labs( x = 'Variable'
, y = 'Predictive capacity (Mean decrease in Gini)')
name = stringr::str_c( input$sql_or_lib,'_predictive_capacity_', imp[[1,'group']] )
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
)
return(p)
})
Decision Tree
renderUI({
d_clean = rea_clean()
categoricals = d_clean$categoricals
numericals = d_clean$numericals
inputPanel(
selectInput("group_tree"
, label = "select predicted variable"
, choices = c(categoricals, numericals)
, selected = categoricals[1]
)
, numericInput('min_split'
,label = 'minimum node size to attempt split'
, min = 2
, max = 10000
, step = 1
, value = 20
)
, numericInput('max_depth'
, label = 'maximum tree depth'
, min = 2
, max = 30
, step = 1
, value = 30
)
, numericInput('cp'
,label = 'minimum complexity reduction to attempt split'
, min = 0.0001
, max = 0.99
, step = 0.001
, value = 0.01
)
, numericInput('xval'
,label = 'k-fold cross validation'
, min = 0
, max = 100
, step = 1
, value = 10
)
)
})
rea_tree = reactive({
d_clean = rea_clean()
data = d_clean$data
pred = input$group_tree
form = stringr::str_c(pred, '~.') %>%
as.formula()
m = rpart::rpart(form
, data
, minsplit = input$min_split
, cp = input$cp
#, maxcompete = 4
#, maxsurrogate = 5
#, usesurrogate = 2
#, xval = 10
#, surrogatestyle = 0
, maxdepth = input$max_depth
, xval = input$xval)
return(m)
})
renderPlot({
m = rea_tree()
rpart.plot::prp(m
, branch.type = 5
, box.palette ="RdYlGn"
, faclen = 0
, extra = 6
, fallen.leaves = input$fallen_leaves
, tweak = input$tweak
, gap = input$gap
, space = input$space
)
name = stringr::str_c( input$sql_or_lib,'_tree1_', input$group_tree )
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
, excel = F
)
})
renderPlot({
m = rea_tree()
rpart.plot::rpart.plot(m
, fallen.leaves = T
, tweak = input$tweak
, gap = input$gap
, space = input$space
)
name = stringr::str_c( input$sql_or_lib,'_tree2_', input$group_tree )
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
, excel = F
)
})
inputPanel(
checkboxInput('fallen_leaves'
, label = 'fallen_leaves'
, value = T)
, numericInput('tweak'
,label = 'increase_text_size'
, min = 0.1
, max = 10
, step = 0.1
, value = 1.2
)
, numericInput('gap'
, label = 'gap'
, min = 0
, max = 100
, step = 1
, value = 2
)
, numericInput('space'
, label = 'space'
, min = 0
, max = 100
, step = 1
, value = 2
)
)
Principle Component Analysis
Plot Principle Components
Berechnent die ersten beiden Hauptkomponenten der numerisch- kontinuierlichen Variablen.
rea_pca = reactive({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
if(is_empty(numericals) | is_empty(boxcox)) return()
if(input$boxcox_pca == T) {
vars = boxcox
}
else{
vars = numericals
}
pca = prcomp(x = select(data, one_of(vars) )
, scale. = input$scale_pca
, center = input$center_pca)
pca$cos2 = pca$rotation^2
pca$contrib = lmap( as_tibble(pca$cos2 ), function(y) y/ sum(y) *100 )
row.names(pca$contrib) = row.names(pca$cos2)
pca$vae = as_tibble (t( pca$sdev / sum(pca$sdev) *100 ) )
colnames(pca$vae) = colnames(pca$contrib)
pca$contrib_abs_perc = t( t( apply( pca$contrib/100, 1, function(x,y) x*pca$vae ) ) )
pca$contrib_abs_perc = unnest( as.data.frame( pca$contrib_abs_perc) )
row.names(pca$contrib_abs_perc) = row.names(pca$contrib)
pca$contrib_abs_perc = as.data.frame( t(pca$contrib_abs_perc) )
pca$contrib_abs_perc$var = row.names(pca$contrib_abs_perc)
pca$contrib_abs_perc = pca$contrib_abs_perc %>%
gather(key = 'key', value = 'value', everything(), -var)
#group variables with less than 2.5% contribution
pca$contrib_abs_perc_reduced = pca$contrib_abs_perc %>%
mutate(key = ifelse(value < 2.5
, ' sum contrib < 2.5%'
, key)) %>%
group_by( var, key) %>%
summarise( value = sum(value) )
# filter principle components that explain less than
# 2.5% of the variance
pca$x = pca$x[, pca$vae > 2.5]
pca$contrib_abs_perc = pca$contrib_abs_perc %>%
filter(var %in% colnames(pca$x))
pca$contrib_abs_perc_reduced = pca$contrib_abs_perc_reduced %>%
filter(var %in% colnames(pca$x))
data = data %>%
cbind(pca$x)
return(list(data = data, pca = pca) )
})
renderUI({
inputPanel(
checkboxInput('center_pca', label = 'center', value = T)
, checkboxInput('scale_pca', label = 'scale', value = T)
, checkboxInput('boxcox_pca', label = 'boxcox', value = T)
)
})
Plot Principle Components
# rotation plot
renderPlot({
data = rea_pca()$data
pca = rea_pca()$pca
group = input$group_pca
x_axis = input$x_axis_pca
y_axis = input$y_axis_pca
if(input$group_pca == 'None') group = NULL
p = ggplot(data) +
geom_point( aes_string(x = x_axis, y = y_axis, color = group)
,alpha=0.4 ) +
labs(title = 'plot1')
name = stringr::str_c( input$sql_or_lib,'_principle_component_')%>%
stringr::str_c( input$group_pca ) %>%
stringr::str_c('_boxcox', input$boxcox_pca) %>%
stringr::str_c('_scale', input$scale_pca) %>%
stringr::str_c('_center', input$center_pca)
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
)
return( p )
})
renderUI({
d_clean = rea_clean()
d_pca = rea_pca()
data = d_pca$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
prin_comp = select(data, starts_with('PC', ignore.case = F)) %>%
names()
inputPanel(
selectInput("x_axis_pca"
, label = "select x_axis"
, choices = prin_comp
, selected = prin_comp[2]
)
, selectInput("y_axis_pca"
, label = "select y_axis"
, choices = prin_comp
, selected = prin_comp[1]
)
, selectInput("group_pca"
, label = "select grouping variable"
, choices = c(categoricals, 'None')
, selected = categoricals[1]
)
)
})
Plot Variance Explained
# variance explained
renderPlot({
pca = rea_pca()$pca
vae = tibble( value = pca$sdev
, pca_n = str_c('pca', 1:length(pca$sdev))
)
p = ggplot(pca$contrib_abs_perc_reduced) +
geom_bar(aes(x = fct_reorder(var, value, sum, .desc = T )
, y = value, fill = key)
, stat = 'identity'
, position='stack') +
scale_fill_brewer(palette = 'Paired')
name = stringr::str_c( input$sql_or_lib,'_variance_explained_')%>%
stringr::str_c( input$group_pca ) %>%
stringr::str_c('_boxcox', input$boxcox_pca) %>%
stringr::str_c('_scale', input$scale_pca) %>%
stringr::str_c('_center', input$center_pca)
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
)
return(p)
})
Correlation
rea_corr = reactive({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
if(is_empty(boxcox)) return()
corr = round( cor(data[, boxcox]), 1)
p_val_corr = ggcorrplot::cor_pmat( data[, boxcox] )
return( list(corr = corr,
p_val_corr = p_val_corr) )
})
renderPlot(width = 1024, height = 1024, {
corr = rea_corr()$corr
p_val_corr = rea_corr()$p_val_corr
p = ggcorrplot::ggcorrplot( corr
,hc.order = TRUE
, type = "lower"
, lab = TRUE
, p.mat = p_val_corr
)
name = stringr::str_c( input$sql_or_lib,'_correlation')
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
)
return(p)
})
Data Visualisation{.tabset}
Tabplot
preselect = c('all'
,'none'
,'categoricals'
,'numericals'
,'top_10_importance'
,'boxcox'
,'none_boxcox'
,'pc1+pc2'
,'pc1+pc2 contrib >2.5%'
,'correlation'
,'group_stat_P<0.001'
,'2fac_2num'
)
renderUI({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
inputPanel(
sliderInput('n_breaks_tab'
, label = "Number of bins:"
, min = 1
, max = 500
, step = 1
, value = 30)
, selectInput("sort_col_tab"
, label = "Sort_by"
, choices = names(data)
, selected = numericals[1])
, checkboxInput('decreasing_tab'
, label = 'decreasing'
, value = T)
, sliderInput('max_levels_tab'
, label = "Maximum number of levels for categorical variables"
, min = 3
, max = 30
, step = 1
, value = 10)
, selectInput("preselect", label = "preselect"
,choices = preselect
,selected = 'top_10_importance')
, selectInput("preselect_correlation", label = "select_correlation"
,choices = boxcox
,selected = boxcox[1])
)
})
renderPlot( {
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
imp = rea_imp()
p = tabplot::tableplot(data
, select_string = input$select_vars_tab
, sortCol = input$sort_col_tab
, decreasing = input$decreasing_tab
, nBins = input$n_breaks_tab
, max_levels = input$max_levels_tab
, scales = 'lin')
index_str = stringr::str_c( which(all_variables %in% input$select_vars_tab)
, collapse = '+' )
name = stringr::str_c( input$sql_or_lib,'_tabplot_')%>%
stringr::str_c('sort_', input$sort_col_tab, '_') %>%
stringr::str_c( index_str)
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
, excel = F
)
return(p)
})
chk_bx_grp_ui = function(x,y, name, label) { renderUI({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
imp = rea_imp()
p_val_corr = rea_corr()$p_val_corr
pca = rea_pca()$pca
if(input[[x]] == 'all') preselect = names(data)
if(input[[x]] == 'none') preselect = NULL
if(input[[x]] == 'top_10_importance') preselect = imp$variable[1:10]
if(input[[x]] == 'boxcox') preselect = boxcox
if(input[[x]] == 'none_boxcox') preselect = names(data)[!names(data) %in% boxcox]
if(input[[x]] == 'numericals') preselect = numericals
if(input[[x]] == 'categoricals') preselect = categoricals
if(input[[x]] == 'group_stat_P<0.001') {
sig = rea_group_stats()$sig
#remove boxcox that are also in numericals
sig_boxcox = sig$sig_boxcox %>%
stringr::str_extract('^[A-Za-z]*') %>%
.[!. %in% sig$sig_num] %>%
stringr::str_c('_boxcox')
preselect = stringr::str_c( c(sig$sig_num, sig_boxcox, sig$sig_chi ) )
}
if(input[[x]] == 'correlation') {
correlating = p_val_corr %>%
as_tibble() %>%
mutate( vars = names(.) ) %>%
select( filter_var = one_of(input[[y]]), vars) %>%
filter( filter_var <= 0.05 ) %>%
select( vars )
preselect = correlating$vars
}
if( startsWith( input[[x]], 'pc1+pc2')) {
if(endsWith( input[[x]], '>2.5%')){
pca_1_2 = pca$contrib_abs_perc_reduced %>%
filter(var %in% c('PC1', 'PC2') )
preselect = pca_1_2$key
}else {
pca_1_2 = pca$contrib_abs_perc %>%
filter(var %in% c('PC1', 'PC2') )
preselect = pca_1_2$key
}
}
if(input[[x]] == '2fac_2num') {
preselect = c(numericals[1], categoricals[1]
,numericals[2], categoricals[2]
,numericals[3], categoricals[3]
,numericals[4], categoricals[4]
)[1:4]
}
checkboxGroupInput( name
, label = label
, choices = names(data)
, selected = preselect
, inline = F )
})
}
chk_bx_grp_ui('preselect', 'preselect_correlation', name = 'select_vars_tab', label = 'Select Variables')
ggduo
renderUI({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
inputPanel(
selectInput("preselect_duo_col"
, label = "Preselect Column"
,choices = preselect
,selected = '2fac_2num')
, selectInput("preselect_duo_row"
, label = "Preselect Row"
,choices = preselect
,selected = '2fac_2num')
)
})
renderUI({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
inputPanel(
selectInput("preselect_corr_duo_col"
, label = "Select Correlation Column"
,choices = boxcox
,selected = boxcox[1])
, selectInput("preselect_corr_duo_row"
, label = "Select Correlation Row"
,choices = boxcox
,selected = boxcox[1])
)
})
rea_duo = eventReactive(input$but_duo
, {
return( list(col = input$select_vars_duo_col
,row = input$select_vars_duo_row
,group = input$group_duo
,corr = input$corr_duo) )
} )
renderPlot({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
ret = rea_duo()
col = ret$col
row = ret$row
group = ret$group
corr = ret$corr
if(group == 'None') group = NULL
lm_with_cor <- function(data, mapping, ..., method = "pearson") {
x <- data[[deparse(mapping$x)]]
y <- data[[deparse(mapping$y)]]
cor <- cor(x, y, method = method)
GGally::ggally_smooth_lm(data, mapping, ...) +
ggplot2::geom_label(
data = data.frame(
x = min(x, na.rm = TRUE),
y = max(y, na.rm = TRUE),
lab = round(cor, digits = 3)
),
mapping = ggplot2::aes(x = x, y = y, label = lab, color = NULL),
hjust = 0, vjust = 1,
size = 5, fontface = "bold"
)
}
if(corr == T) {
p =GGally::ggduo(
data, col, row,
mapping = aes_string(color = group),
types = list(continuous = GGally::wrap(lm_with_cor, alpha = 0.25)),
showStrips = FALSE
) +
theme(legend.position = "bottom")
}
else{
p = GGally::ggduo(
data, col, row,
mapping = aes_string(color = group),
#types = list(continuous = wrap(lm_with_cor, alpha = 0.25)),
showStrips = FALSE
) +
theme(legend.position = "bottom")
}
index_str_row = stringr::str_c( which(all_variables %in% row)
, collapse = '+' )
index_str_col = stringr::str_c( which(all_variables %in% col)
, collapse = '+' )
name = stringr::str_c( input$sql_or_lib,'_ggduo')%>%
stringr::str_c('_sort', input$sort_col_tab ) %>%
stringr::str_c( '_col', index_str_col) %>%
stringr::str_c( '_row', index_str_row)
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
, excel = F
)
return(p)
})
renderUI({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
inputPanel(
selectInput("group_duo"
, label = "select grouping variable"
, choices = c(categoricals, 'None')
, selected = categoricals[length(categoricals)]
)
, checkboxInput('corr_duo'
, label = 'show corr coef'
, value = T)
, actionButton('but_duo', 'Render Plot')
#, cellArgs = list(width = '50%')
)
})
# chk_bx_grp_ui has its own gui rendering function
splitLayout(
chk_bx_grp_ui('preselect_duo_col', 'preselect_corr_duo_col'
, name = 'select_vars_duo_col'
, label = 'Select Variables Column')
, chk_bx_grp_ui('preselect_duo_row', 'preselect_corr_duo_row'
, name = 'select_vars_duo_row'
, label = 'Select Variables Row')
)
Histograms
Histogram A
input_panel_histo = function(suffix) {
renderUI({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
inputPanel(
selectInput(str_c("n_breaks", suffix)
, label = "Number of bins:"
, choices = c(10, 20, 30, 50, 75, 100, 150, 200, 300, 500, 750, 1000)
, selected = 30
)
, selectInput(str_c("variable", suffix)
, label = "Select variable"
, choices = names(data)
, selected = numericals[1]
)
, selectInput(str_c( "group", suffix)
, label = "Select grouping variable"
, choices = c(categoricals , 'None')
, selected = categoricals[1]
)
, selectInput(str_c("graph_type", suffix)
, label = "Graph Type"
, choices = c('bar', 'line', 'violin')
, selected = 'line')
, selectInput(str_c("y_axis", suffix)
, label = "Y-Axis"
, choices = c('density', 'counts')
, selected = 'density')
, checkboxInput(str_c( 'auto_range', suffix )
, label = 'use automatic range'
, value = T)
, textInput(str_c('x_min', suffix)
, label = 'X-min'
, value = '0')
, textInput(str_c('x_max', suffix)
, label = 'X-max'
, value = '1000')
)
})
}
renderPlot_histo = function( suffix ){
renderPlot({
d_clean = rea_clean()
data = d_clean$data
boxcox = d_clean$boxcox
categoricals = d_clean$categoricals
all_variables = d_clean$all_variables
numericals = d_clean$numericals
# get input
n_breaks = input[[ str_c("n_breaks" , suffix) ]]
variable = input[[ str_c("variable" , suffix) ]]
group = input[[ str_c( "group" , suffix) ]]
graph_type = input[[ str_c("graph_type" , suffix) ]]
y_axis = input[[ str_c("y_axis" , suffix) ]]
auto_range = input[[ str_c( 'auto_range', suffix) ]]
x_min = input[[ str_c('x_min' , suffix) ]]
x_max = input[[ str_c('x_max' , suffix) ]]
#y-axis
if(y_axis == 'density') {
y_axis = '..density..'
}
else{
y_axis = '..count..'
}
#group
if(group == 'None') group = NULL
# numericals ----------------------------------------------------------------------------
numericals = c(numericals, boxcox)
#geom_freqpoly
if(variable %in% numericals & graph_type == 'line'){
p = data %>%
ggplot() +
geom_freqpoly( aes_string(x = variable, y = y_axis, color = group)
, bins = as.numeric(n_breaks))
}
#geom_histo
if(variable %in% numericals & graph_type == 'bar' ){
p = data %>%
ggplot() +
geom_histogram( aes_string(x = variable, y = y_axis, fill = group)
, bins = as.numeric(n_breaks), alpha = 0.6
, position = 'identity')
}
#geom_violin
if(variable %in% numericals & graph_type == 'violin'){
medians = data %>%
group_by_(as.symbol(group)) %>%
select( one_of(numericals ) ) %>%
summarise_all( median )
p = data %>%
ggplot() +
geom_violin( aes_string(x = group
, y = variable
, fill = group)
) +
geom_crossbar( data = medians,
mapping = aes_string(x = group
, y = variable
, ymin = variable
, ymax = variable) )
}
# add x range
if(variable %in% numericals & auto_range == F & !graph_type == 'violin'){
p = p +
xlim( c( as.numeric(x_min), as.numeric(x_max)) )
}
# add y range
if(variable %in% numericals & auto_range == F & graph_type == 'violin'){
p = p +
ylim( c( as.numeric(x_min), as.numeric(x_max)) )
}
# categoricals ----------------------------------------------------------------------------
#geom_bar
if(variable %in% categoricals ){
if(y_axis == '..density..') y_axis = '..prop..'
p = data %>%
ggplot() +
geom_bar( aes_string(x = variable, y = y_axis, fill = group, group = group)
, position = 'dodge')
}
y_axis_str = stringr::str_extract_all(y_axis
, '[A-Za-z]') %>%
unlist()%>%
stringr::str_c(collapse = '')
name = stringr::str_c( input$sql_or_lib, '_')%>%
stringr::str_c(suffix ) %>%
stringr::str_c('_', graph_type ) %>%
stringr::str_c('_', y_axis_str) %>%
stringr::str_c( '_group', group) %>%
stringr::str_c( '_var', variable) %>%
stringr::str_c( '_', n_breaks)
save_plot(plot = p
, path = input$path
, name = name
, yes = input$save
)
return(p)
})
}
input_panel_histo('histo_a')
renderPlot_histo('histo_a')
Histogram B
input_panel_histo('histo_b')
renderPlot_histo('histo_b')
Histogram C
input_panel_histo('histo_c')
renderPlot_histo('histo_c')
Histogram D
input_panel_histo('histo_d')
renderPlot_histo('histo_d')
erblast/oetteR documentation built on May 27, 2019, 12:11 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set(echo = F, warning = F, message = F, eval = T)
library(knitr) library(ISLR) library(tabplot) library(tidyverse) library(tabplot) library(randomForest) library(stringr) library(GGally) library(forcats) library(caret) library(ggcorrplot) library(Amelia) library(e1071)
{.tabset}
Data{.tabset}
Load Data
wellPanel( selectInput('sql_or_lib' , label = 'Select Data Source' , choices = c('SQL', 'Sample_Data') , selected = 'Sample_Data') , selectInput('sample_data' , label = 'Select Sample Data' , choices = c('ISLR::OJ' , 'ISLR::Caravan' , 'ISLR::Auto' , 'ISLR::Carseats' , 'ISLR::Wage' , 'ISLR::College' , 'ISLR::Hitters' , 'ISLR::Default' , 'ISLR::Weekly' , 'ILSR::Wage categoricals' , 'ISLR::OJ numericals') , selected = 'ISLR::Wage') # , textInput('server' # , label = 'Enter Server' # , width = 'auto') # # , textInput('query' # , label = 'Paste SQL' # , width = '100%') , textInput('path' , label = 'Save Analysis to this directory' , width = '100%' , value = "C:/Users/erbla/Documents") ) inputPanel( actionButton('load_data' , label = 'Load Data') , checkboxInput('save' , label = 'Save Results' , value = F) ) rea_load = eventReactive(input$load_data,{ if(input$sql_or_lib == 'SQL'){ require(RODBCext) con = odbcDriverConnect( str_c('Driver=SQL Server;Trusted_Connection=Yes;Server=', input$server) ) query = input$query data = sqlQuery(con, query = query) close(con) } else{ if(input$sample_data == 'ILSR::Wage categoricals'){ data = ISLR::Wage bool = ! summarise_all(data, is.numeric) data= data[,bool] }else if(input$sample_data == 'ISLR::OJ numericals'){ data = ISLR::OJ %>% as.data.frame() bool = summarise_all(data, is.numeric) data= data[,as.logical(bool[1,])] }else{ exec_str = input$sample_data data = eval(parse(text = exec_str) ) } } return(data) }) save_plot = function(plot , path , name , yes , scale = 1 , excel = T){ require(tidyverse) if(yes == F) return() folder_name = lubridate::today() %>% as.character() %>% stringr::str_replace_all('-','')%>% stringr::str_c('_multiview') path = path %>% stringr::str_c('/',folder_name) dir.create(path) file_name_plot = path %>% stringr::str_c('/',name,'.png') file_name_excel = path %>% stringr::str_c('/',name,'.xls') # tabplot object should save with the same function safe_ggsave = safely(ggsave) safe_tableSafe = safely(tabplot::tableSave) s = safe_ggsave(file_name_plot , plot = plot , scale = scale) if( !purrr::is_empty(s$error) ){ print(s$error) s = safe_tableSafe(tab = plot ,filename = file_name_plot , scale = scale) } if( !purrr::is_empty(s$error) ){ print(s$error) plot dev.copy(png, filename = file_name_plot ) dev.off() } if(excel == T){ coords = ggplot_build(plot) if(file.exists(file_name_excel)) { file.remove(file_name_excel) } purrr::pwalk(list( x = coords$data , sheetName = as.character(1:length(coords$data) ) ) , xlsx::write.xlsx , file = file_name_excel , col.names = T , row.names = T , append = T ) } }
Sample View
DT::renderDataTable( options = list( pageLength = 5) ,{ d_clean = rea_clean() data = d_clean$data all_variables = d_clean$all_variables return( data[ ,all_variables] ) })
Summary
Numericals
renderPrint({ d_clean = rea_clean() data = d_clean$data numericals = d_clean$numericals print(summary( data[, numericals])) })
Categoricals
renderPrint({ d_clean = rea_clean() data = d_clean$data categoricals = d_clean$categoricals print(summary( data[, categoricals])) })
Missing Values
renderPlot({ d_clean = rea_clean() data = d_clean$data all_variables = d_clean$all_variables p = Amelia::missmap( as_data_frame(data)[, all_variables]) return(p) })
Clean Data
inputPanel( numericInput('max_no_lvls_fctr' , label = 'Max number of levels for categorical variables' , value = 10 , min = 1 , step = 1) , numericInput('min_no_vals_num' , label = 'Minimum number of distinct values for numerical variables' , value = 6 , min = 1 , step = 1) , checkboxInput('missing' , label = 'Exclude Missing Values' , value = F) ) renderUI({ data = rea_load() checkboxGroupInput('deselect_cols' , label = 'Select Variables to exclude' , choices = names(data) ) }) rea_clean = reactive({ data = rea_load() if(! purrr::is_empty(input$deselect_cols) ) { data = data %>% select( - one_of(input$deselect_cols) ) } d_clean = f_clean_data(data , as.numeric(input$max_no_lvls_fctr) , as.numeric(input$min_no_vals_num) ) return(d_clean) }) f_clean_data = function(data , max_number_of_levels_factors = 10 , min_number_of_levels_nums = 6){ require(tidyverse) require(stringr) require(caret) require(forcats) data = data %>% as_tibble() numericals = names(data)[ map_lgl( data, is.numeric)] categoricals = names(data)[ ! map_lgl( data, is.numeric)] all_variables = names(data) # add numericals with less than x distinc values to factors no_unique_vals = map_dbl(data[, numericals], function(x) length(unique(x)) ) categoricals = c(categoricals, numericals[no_unique_vals < min_number_of_levels_nums] ) numericals = numericals[ !numericals %in% categoricals ] # convert all non numericals and grouping variables to factors for (var in categoricals ){ data[[var]] = as.factor(data[[var]]) } # collapse smallest levels into one if factor levels exceed size x no_levels = map_dbl( data[, categoricals], function(x) length( levels(x) ) ) for (var in names( no_levels[no_levels > max_number_of_levels_factors] )) { data[[var]] = fct_lump(data[[var]], n = max_number_of_levels_factors) } # Boxcox transform numericals if(!is_empty(numericals)){ data_box_cox = data[, numericals] %>% mutate_all( function(x) x + min(x, na.rm = T) + 0.00001 ) trans = preProcess( as.data.frame(data_box_cox), c('BoxCox'), na.remove = T) pred = predict(trans, as.data.frame(data_box_cox) ) names_transformed = names(pred) %>% str_c('_boxcox') names(pred) = names_transformed data = data %>% bind_cols(pred) if(!ncol(data[,numericals]) == ncol(data[,names_transformed])) { stop( 'boxcox transformation unsucessfull') } } else { names_transformed = NULL } # drop categoricals with only one level if( !is_empty(categoricals)){ data = data %>% mutate_at( vars(one_of(categoricals) ) , fct_drop ) no_lvl = data[,categoricals] %>% summarise_all( function(x) list(levels(x)) ) %>% summarise_all( function(x) length(x[[1]]) ) only_one_lvl = categoricals[no_lvl == 1] if(length(only_one_lvl) > 0 ) { data = data %>% select( - one_of(only_one_lvl) ) all_variables = all_variables[ !all_variables %in% only_one_lvl ] categoricals = categoricals[ no_lvl > 1] } } # drop observations with missing Values if(input$missing == T) { data = data[stats::complete.cases(data),] print('missing cases exluded') } # return statement return( list( data = data , numericals = numericals , categoricals = categoricals , all_variables = all_variables , boxcox = names_transformed ) ) }
Analysis{.tabset}
Group Statistics{.tabset}
renderUI({ d_clean = rea_clean() categoricals = d_clean$categoricals inputPanel( selectInput("group_stats", label = "select grouping variable", choices = categoricals, selected = categoricals[1]) ) }) f_bring_to_pos_range = function(x){ if( min(x)< 0) x = x + abs(min(x)) return(x) } f_diff_of_means_medians = function(df, group, variable){ data = df %>% select( group = one_of(group), variable = one_of(variable) )%>% mutate ( variable = f_bring_to_pos_range(variable) ) %>% group_by( group ) %>% summarise( means = mean(variable, na.rm = T) , medians = median(variable, na.rm = T) ) %>% ungroup() %>% summarise( diff_of_means = max(means) - min(means) , diff_of_means_perc = ( ( max(means) - min(means) ) /max(means) ) *100 , diff_of_medians = max(medians) - min(medians) , diff_of_medians_perc = ( ( max(medians) - min(medians) ) /max(means) ) *100 ) return(data) } f_max_diff_of_freq = function(df, var1, var2){ t = table( df[[var1]], df[[var2]] ) %>% as_tibble() %>% group_by( Var1 ) %>% mutate ( diff_var1 = ( max(n)-min(n) ) , diff_var1_perc = ( ( max(n)-min(n))/ max(n) *100) ) %>% group_by( Var2 ) %>% mutate ( diff_var2 = ( max(n)-min(n) ) , diff_var2_perc = ( ( max(n)-min(n))/ max(n) *100) ) %>% ungroup()%>% summarise( max_diff_freq = max( c(diff_var1, diff_var2) ) , max_diff_freq_perc = max( c(diff_var1_perc, diff_var2_perc) ) ) } f_anova_stats = function(df, group, variables) { # returns a dataframe with anova stats # df : dataframe # group : grouping variable as character vector, must indicate factor variable # variables : numerical variables to be analyzed as character vector, must indicate numerical variable data = df formula = stringr::str_c('value~',group) %>% as.formula() df_anova = data %>% as_tibble() %>% select( one_of( c(group, variables) ) ) %>% gather(key = 'variable', value = 'value', one_of( variables ) ) %>% group_by( variable ) %>% nest( one_of(group), value) %>% mutate( model_anova = purrr::map( data, ~aov( formula, data = .)) , summary_anova = purrr::map( model_anova, summary) , summary_anova = purrr::map( summary_anova , function(x) x[[1]]) , anova_pval = purrr::map(summary_anova, 'Pr(>F)') , anova_pval = purrr::map_dbl(anova_pval , function(x) x[1]) , model_kruskal = purrr::map( data, ~kruskal.test(formula, data = .) ) , kruskal_pval = purrr::map_dbl(model_kruskal, 'p.value') , model_shapiro = purrr::map(data , function(x) shapiro.test(sample(x$value, 5000, replace = T) ) ) , shapiro_stat = purrr::map_dbl(model_shapiro,'statistic') , shapiro_pval = purrr::map_dbl(model_shapiro,'p.value') , diff_df = purrr::map( data, f_diff_of_means_medians, group = group, variable = 'value') ) %>% unnest(diff_df) %>% select(variable , shapiro_stat , shapiro_pval , anova_pval , kruskal_pval , diff_of_means , diff_of_means_perc , diff_of_medians , diff_of_medians_perc ) return(df_anova) } f_chi_square = function(df, group, variables) { # returns a dataframe with anova stats # df : dataframe # group : grouping variable as character vector, must indicate factor variable # variables : numerical variables to be analyzed as character vector, must indicate factor variable data = df variables = variables[!variables == group] if(purrr::is_empty(variables)) return() df_chi = data %>% as_tibble() %>% select( one_of( c(group, variables) ) ) %>% gather(key = 'variable', value = 'value', one_of( variables ) ) %>% group_by( variable ) %>% nest( one_of( group ), value) %>% mutate( model_chi = purrr::map( data, ~chisq.test(x = .[[group]] , y = .[['value']] ) ) ,chi_pval = purrr::map_dbl(model_chi, 'p.value') ,diff_df = purrr::map(data, f_max_diff_of_freq, group, 'value') ) %>% unnest(diff_df) %>% select(variable, chi_pval, max_diff_freq, max_diff_freq_perc) return(df_chi) } rea_group_stats = reactive({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox numericals = d_clean$numericals categoricals = d_clean$categoricals if(! is_empty(numericals)) { df_num = f_anova_stats(df = data ,group = input$group_stats ,variables = numericals ) df_boxcox = f_anova_stats(df = data ,group = input$group_stats ,variables = boxcox ) sig_num = df_num %>% mutate( p_val = ifelse(shapiro_stat >= 0.9 , anova_pval , kruskal_pval) ) %>% filter(p_val <= 0.001) %>% .[['variable']] sig_boxcox = df_boxcox %>% mutate( p_val = ifelse(shapiro_stat >= 0.9 , anova_pval , kruskal_pval) ) %>% filter(p_val <= 0.001) %>% .[['variable']] } else { df_num = NULL df_boxcox = NULL sig_num = NULL sig_boxcox = NULL } # only works with minimum two categoricals vars if( length(categoricals) >= 2 ){ df_chi = f_chi_square(df = data ,group = input$group_stats ,variables = categoricals ) sig_chi = df_chi %>% filter(chi_pval <= 0.001) %>% .[['variable']] } else{ df_chi = NULL sig_chi = NULL } return( list( df = list(df_num = df_num ,df_boxcox = df_boxcox ,df_chi = df_chi ) , sig = list(sig_num = sig_num ,sig_boxcox = sig_boxcox ,sig_chi = sig_chi ) ) ) })
Numericals (ANOVA)
render_table_num_group_stat = function( boxcox = F) { DT::renderDataTable( extensions = 'Buttons' , options = list( dom = 'Bftrip' , buttons = c('copy', 'excel') , pageLength = 1 ) ,{ group = input$group_stats if( boxcox == F ){ df = rea_group_stats()$df$df_num }else{ df = rea_group_stats()$df$df_boxcox } df = DT::datatable(df , extensions = 'Buttons' , options = list( dom = 'Bftrip' , buttons = c('copy', 'excel') ) ) %>% DT::formatStyle('shapiro_stat' , color = DT::styleInterval(0.9, c('red','green') ) ) %>% DT::formatStyle(3:5 , color = DT::styleInterval(0.01, c('green','red') ) ) %>% DT::formatRound(2:5, 6) %>% DT::formatRound(c(6,8), 3) %>% DT::formatRound(c(7,9), 2) }) } render_table_num_group_stat( boxcox = F )
Numericals boxcox (ANOVA)
render_table_num_group_stat( boxcox = T )
Categoricals (Chi-Square)
DT::renderDataTable({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox numericals = d_clean$numericals categoricals = d_clean$categoricals group = input$group_stats df = rea_group_stats()$df$df_chi df = DT::datatable(df , extensions = 'Buttons' , options = list( dom = 'Bftrip' , buttons = c('copy', 'excel') ) ) %>% DT::formatStyle('chi_pval' , color = DT::styleInterval(0.01, c('green','red') ) ) %>% DT::formatRound( 2, 6) %>% DT::formatRound( 4, 2) return(df) })
Predictive Capacity
renderUI({ d_clean = rea_clean() categoricals = d_clean$categoricals numericals = d_clean$numericals inputPanel( selectInput("group_imp" , label = "select predicted variable" , choices = c(categoricals, numericals) , selected = categoricals[1]) ) }) rea_imp = reactive({ d_clean = rea_clean() data = d_clean$data all_variables = d_clean$all_variables form = as.formula( stringr::str_c(input$group_imp,'~.') ) m = randomForest::randomForest(form, data[, all_variables] ) imp = tibble( mean_decrease_gini = m$importance[,1] ,variable = names(m$importance[,1]) ,group = input$group_imp) %>% arrange(desc(mean_decrease_gini)) return(imp) }) renderPlot({ imp = rea_imp() p = ggplot(imp) + geom_bar(aes(x = fct_reorder(variable , mean_decrease_gini) , y = mean_decrease_gini , fill = fct_reorder(variable , mean_decrease_gini , .desc = T) ) , stat = 'identity' , show.legend = F) + coord_flip() + labs( x = 'Variable' , y = 'Predictive capacity (Mean decrease in Gini)') name = stringr::str_c( input$sql_or_lib,'_predictive_capacity_', imp[[1,'group']] ) save_plot(plot = p , path = input$path , name = name , yes = input$save ) return(p) })
Decision Tree
renderUI({ d_clean = rea_clean() categoricals = d_clean$categoricals numericals = d_clean$numericals inputPanel( selectInput("group_tree" , label = "select predicted variable" , choices = c(categoricals, numericals) , selected = categoricals[1] ) , numericInput('min_split' ,label = 'minimum node size to attempt split' , min = 2 , max = 10000 , step = 1 , value = 20 ) , numericInput('max_depth' , label = 'maximum tree depth' , min = 2 , max = 30 , step = 1 , value = 30 ) , numericInput('cp' ,label = 'minimum complexity reduction to attempt split' , min = 0.0001 , max = 0.99 , step = 0.001 , value = 0.01 ) , numericInput('xval' ,label = 'k-fold cross validation' , min = 0 , max = 100 , step = 1 , value = 10 ) ) }) rea_tree = reactive({ d_clean = rea_clean() data = d_clean$data pred = input$group_tree form = stringr::str_c(pred, '~.') %>% as.formula() m = rpart::rpart(form , data , minsplit = input$min_split , cp = input$cp #, maxcompete = 4 #, maxsurrogate = 5 #, usesurrogate = 2 #, xval = 10 #, surrogatestyle = 0 , maxdepth = input$max_depth , xval = input$xval) return(m) }) renderPlot({ m = rea_tree() rpart.plot::prp(m , branch.type = 5 , box.palette ="RdYlGn" , faclen = 0 , extra = 6 , fallen.leaves = input$fallen_leaves , tweak = input$tweak , gap = input$gap , space = input$space ) name = stringr::str_c( input$sql_or_lib,'_tree1_', input$group_tree ) save_plot(plot = p , path = input$path , name = name , yes = input$save , excel = F ) }) renderPlot({ m = rea_tree() rpart.plot::rpart.plot(m , fallen.leaves = T , tweak = input$tweak , gap = input$gap , space = input$space ) name = stringr::str_c( input$sql_or_lib,'_tree2_', input$group_tree ) save_plot(plot = p , path = input$path , name = name , yes = input$save , excel = F ) }) inputPanel( checkboxInput('fallen_leaves' , label = 'fallen_leaves' , value = T) , numericInput('tweak' ,label = 'increase_text_size' , min = 0.1 , max = 10 , step = 0.1 , value = 1.2 ) , numericInput('gap' , label = 'gap' , min = 0 , max = 100 , step = 1 , value = 2 ) , numericInput('space' , label = 'space' , min = 0 , max = 100 , step = 1 , value = 2 ) )
Principle Component Analysis
Plot Principle Components
Berechnent die ersten beiden Hauptkomponenten der numerisch- kontinuierlichen Variablen.
rea_pca = reactive({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals if(is_empty(numericals) | is_empty(boxcox)) return() if(input$boxcox_pca == T) { vars = boxcox } else{ vars = numericals } pca = prcomp(x = select(data, one_of(vars) ) , scale. = input$scale_pca , center = input$center_pca) pca$cos2 = pca$rotation^2 pca$contrib = lmap( as_tibble(pca$cos2 ), function(y) y/ sum(y) *100 ) row.names(pca$contrib) = row.names(pca$cos2) pca$vae = as_tibble (t( pca$sdev / sum(pca$sdev) *100 ) ) colnames(pca$vae) = colnames(pca$contrib) pca$contrib_abs_perc = t( t( apply( pca$contrib/100, 1, function(x,y) x*pca$vae ) ) ) pca$contrib_abs_perc = unnest( as.data.frame( pca$contrib_abs_perc) ) row.names(pca$contrib_abs_perc) = row.names(pca$contrib) pca$contrib_abs_perc = as.data.frame( t(pca$contrib_abs_perc) ) pca$contrib_abs_perc$var = row.names(pca$contrib_abs_perc) pca$contrib_abs_perc = pca$contrib_abs_perc %>% gather(key = 'key', value = 'value', everything(), -var) #group variables with less than 2.5% contribution pca$contrib_abs_perc_reduced = pca$contrib_abs_perc %>% mutate(key = ifelse(value < 2.5 , ' sum contrib < 2.5%' , key)) %>% group_by( var, key) %>% summarise( value = sum(value) ) # filter principle components that explain less than # 2.5% of the variance pca$x = pca$x[, pca$vae > 2.5] pca$contrib_abs_perc = pca$contrib_abs_perc %>% filter(var %in% colnames(pca$x)) pca$contrib_abs_perc_reduced = pca$contrib_abs_perc_reduced %>% filter(var %in% colnames(pca$x)) data = data %>% cbind(pca$x) return(list(data = data, pca = pca) ) }) renderUI({ inputPanel( checkboxInput('center_pca', label = 'center', value = T) , checkboxInput('scale_pca', label = 'scale', value = T) , checkboxInput('boxcox_pca', label = 'boxcox', value = T) ) })
Plot Principle Components
# rotation plot renderPlot({ data = rea_pca()$data pca = rea_pca()$pca group = input$group_pca x_axis = input$x_axis_pca y_axis = input$y_axis_pca if(input$group_pca == 'None') group = NULL p = ggplot(data) + geom_point( aes_string(x = x_axis, y = y_axis, color = group) ,alpha=0.4 ) + labs(title = 'plot1') name = stringr::str_c( input$sql_or_lib,'_principle_component_')%>% stringr::str_c( input$group_pca ) %>% stringr::str_c('_boxcox', input$boxcox_pca) %>% stringr::str_c('_scale', input$scale_pca) %>% stringr::str_c('_center', input$center_pca) save_plot(plot = p , path = input$path , name = name , yes = input$save ) return( p ) }) renderUI({ d_clean = rea_clean() d_pca = rea_pca() data = d_pca$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals prin_comp = select(data, starts_with('PC', ignore.case = F)) %>% names() inputPanel( selectInput("x_axis_pca" , label = "select x_axis" , choices = prin_comp , selected = prin_comp[2] ) , selectInput("y_axis_pca" , label = "select y_axis" , choices = prin_comp , selected = prin_comp[1] ) , selectInput("group_pca" , label = "select grouping variable" , choices = c(categoricals, 'None') , selected = categoricals[1] ) ) })
Plot Variance Explained
# variance explained renderPlot({ pca = rea_pca()$pca vae = tibble( value = pca$sdev , pca_n = str_c('pca', 1:length(pca$sdev)) ) p = ggplot(pca$contrib_abs_perc_reduced) + geom_bar(aes(x = fct_reorder(var, value, sum, .desc = T ) , y = value, fill = key) , stat = 'identity' , position='stack') + scale_fill_brewer(palette = 'Paired') name = stringr::str_c( input$sql_or_lib,'_variance_explained_')%>% stringr::str_c( input$group_pca ) %>% stringr::str_c('_boxcox', input$boxcox_pca) %>% stringr::str_c('_scale', input$scale_pca) %>% stringr::str_c('_center', input$center_pca) save_plot(plot = p , path = input$path , name = name , yes = input$save ) return(p) })
Correlation
rea_corr = reactive({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals if(is_empty(boxcox)) return() corr = round( cor(data[, boxcox]), 1) p_val_corr = ggcorrplot::cor_pmat( data[, boxcox] ) return( list(corr = corr, p_val_corr = p_val_corr) ) }) renderPlot(width = 1024, height = 1024, { corr = rea_corr()$corr p_val_corr = rea_corr()$p_val_corr p = ggcorrplot::ggcorrplot( corr ,hc.order = TRUE , type = "lower" , lab = TRUE , p.mat = p_val_corr ) name = stringr::str_c( input$sql_or_lib,'_correlation') save_plot(plot = p , path = input$path , name = name , yes = input$save ) return(p) })
Data Visualisation{.tabset}
Tabplot
preselect = c('all' ,'none' ,'categoricals' ,'numericals' ,'top_10_importance' ,'boxcox' ,'none_boxcox' ,'pc1+pc2' ,'pc1+pc2 contrib >2.5%' ,'correlation' ,'group_stat_P<0.001' ,'2fac_2num' ) renderUI({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals inputPanel( sliderInput('n_breaks_tab' , label = "Number of bins:" , min = 1 , max = 500 , step = 1 , value = 30) , selectInput("sort_col_tab" , label = "Sort_by" , choices = names(data) , selected = numericals[1]) , checkboxInput('decreasing_tab' , label = 'decreasing' , value = T) , sliderInput('max_levels_tab' , label = "Maximum number of levels for categorical variables" , min = 3 , max = 30 , step = 1 , value = 10) , selectInput("preselect", label = "preselect" ,choices = preselect ,selected = 'top_10_importance') , selectInput("preselect_correlation", label = "select_correlation" ,choices = boxcox ,selected = boxcox[1]) ) }) renderPlot( { d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals imp = rea_imp() p = tabplot::tableplot(data , select_string = input$select_vars_tab , sortCol = input$sort_col_tab , decreasing = input$decreasing_tab , nBins = input$n_breaks_tab , max_levels = input$max_levels_tab , scales = 'lin') index_str = stringr::str_c( which(all_variables %in% input$select_vars_tab) , collapse = '+' ) name = stringr::str_c( input$sql_or_lib,'_tabplot_')%>% stringr::str_c('sort_', input$sort_col_tab, '_') %>% stringr::str_c( index_str) save_plot(plot = p , path = input$path , name = name , yes = input$save , excel = F ) return(p) }) chk_bx_grp_ui = function(x,y, name, label) { renderUI({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals imp = rea_imp() p_val_corr = rea_corr()$p_val_corr pca = rea_pca()$pca if(input[[x]] == 'all') preselect = names(data) if(input[[x]] == 'none') preselect = NULL if(input[[x]] == 'top_10_importance') preselect = imp$variable[1:10] if(input[[x]] == 'boxcox') preselect = boxcox if(input[[x]] == 'none_boxcox') preselect = names(data)[!names(data) %in% boxcox] if(input[[x]] == 'numericals') preselect = numericals if(input[[x]] == 'categoricals') preselect = categoricals if(input[[x]] == 'group_stat_P<0.001') { sig = rea_group_stats()$sig #remove boxcox that are also in numericals sig_boxcox = sig$sig_boxcox %>% stringr::str_extract('^[A-Za-z]*') %>% .[!. %in% sig$sig_num] %>% stringr::str_c('_boxcox') preselect = stringr::str_c( c(sig$sig_num, sig_boxcox, sig$sig_chi ) ) } if(input[[x]] == 'correlation') { correlating = p_val_corr %>% as_tibble() %>% mutate( vars = names(.) ) %>% select( filter_var = one_of(input[[y]]), vars) %>% filter( filter_var <= 0.05 ) %>% select( vars ) preselect = correlating$vars } if( startsWith( input[[x]], 'pc1+pc2')) { if(endsWith( input[[x]], '>2.5%')){ pca_1_2 = pca$contrib_abs_perc_reduced %>% filter(var %in% c('PC1', 'PC2') ) preselect = pca_1_2$key }else { pca_1_2 = pca$contrib_abs_perc %>% filter(var %in% c('PC1', 'PC2') ) preselect = pca_1_2$key } } if(input[[x]] == '2fac_2num') { preselect = c(numericals[1], categoricals[1] ,numericals[2], categoricals[2] ,numericals[3], categoricals[3] ,numericals[4], categoricals[4] )[1:4] } checkboxGroupInput( name , label = label , choices = names(data) , selected = preselect , inline = F ) }) } chk_bx_grp_ui('preselect', 'preselect_correlation', name = 'select_vars_tab', label = 'Select Variables')
ggduo
renderUI({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals inputPanel( selectInput("preselect_duo_col" , label = "Preselect Column" ,choices = preselect ,selected = '2fac_2num') , selectInput("preselect_duo_row" , label = "Preselect Row" ,choices = preselect ,selected = '2fac_2num') ) }) renderUI({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals inputPanel( selectInput("preselect_corr_duo_col" , label = "Select Correlation Column" ,choices = boxcox ,selected = boxcox[1]) , selectInput("preselect_corr_duo_row" , label = "Select Correlation Row" ,choices = boxcox ,selected = boxcox[1]) ) }) rea_duo = eventReactive(input$but_duo , { return( list(col = input$select_vars_duo_col ,row = input$select_vars_duo_row ,group = input$group_duo ,corr = input$corr_duo) ) } ) renderPlot({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals ret = rea_duo() col = ret$col row = ret$row group = ret$group corr = ret$corr if(group == 'None') group = NULL lm_with_cor <- function(data, mapping, ..., method = "pearson") { x <- data[[deparse(mapping$x)]] y <- data[[deparse(mapping$y)]] cor <- cor(x, y, method = method) GGally::ggally_smooth_lm(data, mapping, ...) + ggplot2::geom_label( data = data.frame( x = min(x, na.rm = TRUE), y = max(y, na.rm = TRUE), lab = round(cor, digits = 3) ), mapping = ggplot2::aes(x = x, y = y, label = lab, color = NULL), hjust = 0, vjust = 1, size = 5, fontface = "bold" ) } if(corr == T) { p =GGally::ggduo( data, col, row, mapping = aes_string(color = group), types = list(continuous = GGally::wrap(lm_with_cor, alpha = 0.25)), showStrips = FALSE ) + theme(legend.position = "bottom") } else{ p = GGally::ggduo( data, col, row, mapping = aes_string(color = group), #types = list(continuous = wrap(lm_with_cor, alpha = 0.25)), showStrips = FALSE ) + theme(legend.position = "bottom") } index_str_row = stringr::str_c( which(all_variables %in% row) , collapse = '+' ) index_str_col = stringr::str_c( which(all_variables %in% col) , collapse = '+' ) name = stringr::str_c( input$sql_or_lib,'_ggduo')%>% stringr::str_c('_sort', input$sort_col_tab ) %>% stringr::str_c( '_col', index_str_col) %>% stringr::str_c( '_row', index_str_row) save_plot(plot = p , path = input$path , name = name , yes = input$save , excel = F ) return(p) }) renderUI({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals inputPanel( selectInput("group_duo" , label = "select grouping variable" , choices = c(categoricals, 'None') , selected = categoricals[length(categoricals)] ) , checkboxInput('corr_duo' , label = 'show corr coef' , value = T) , actionButton('but_duo', 'Render Plot') #, cellArgs = list(width = '50%') ) }) # chk_bx_grp_ui has its own gui rendering function splitLayout( chk_bx_grp_ui('preselect_duo_col', 'preselect_corr_duo_col' , name = 'select_vars_duo_col' , label = 'Select Variables Column') , chk_bx_grp_ui('preselect_duo_row', 'preselect_corr_duo_row' , name = 'select_vars_duo_row' , label = 'Select Variables Row') )
Histograms
Histogram A
input_panel_histo = function(suffix) { renderUI({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals inputPanel( selectInput(str_c("n_breaks", suffix) , label = "Number of bins:" , choices = c(10, 20, 30, 50, 75, 100, 150, 200, 300, 500, 750, 1000) , selected = 30 ) , selectInput(str_c("variable", suffix) , label = "Select variable" , choices = names(data) , selected = numericals[1] ) , selectInput(str_c( "group", suffix) , label = "Select grouping variable" , choices = c(categoricals , 'None') , selected = categoricals[1] ) , selectInput(str_c("graph_type", suffix) , label = "Graph Type" , choices = c('bar', 'line', 'violin') , selected = 'line') , selectInput(str_c("y_axis", suffix) , label = "Y-Axis" , choices = c('density', 'counts') , selected = 'density') , checkboxInput(str_c( 'auto_range', suffix ) , label = 'use automatic range' , value = T) , textInput(str_c('x_min', suffix) , label = 'X-min' , value = '0') , textInput(str_c('x_max', suffix) , label = 'X-max' , value = '1000') ) }) } renderPlot_histo = function( suffix ){ renderPlot({ d_clean = rea_clean() data = d_clean$data boxcox = d_clean$boxcox categoricals = d_clean$categoricals all_variables = d_clean$all_variables numericals = d_clean$numericals # get input n_breaks = input[[ str_c("n_breaks" , suffix) ]] variable = input[[ str_c("variable" , suffix) ]] group = input[[ str_c( "group" , suffix) ]] graph_type = input[[ str_c("graph_type" , suffix) ]] y_axis = input[[ str_c("y_axis" , suffix) ]] auto_range = input[[ str_c( 'auto_range', suffix) ]] x_min = input[[ str_c('x_min' , suffix) ]] x_max = input[[ str_c('x_max' , suffix) ]] #y-axis if(y_axis == 'density') { y_axis = '..density..' } else{ y_axis = '..count..' } #group if(group == 'None') group = NULL # numericals ---------------------------------------------------------------------------- numericals = c(numericals, boxcox) #geom_freqpoly if(variable %in% numericals & graph_type == 'line'){ p = data %>% ggplot() + geom_freqpoly( aes_string(x = variable, y = y_axis, color = group) , bins = as.numeric(n_breaks)) } #geom_histo if(variable %in% numericals & graph_type == 'bar' ){ p = data %>% ggplot() + geom_histogram( aes_string(x = variable, y = y_axis, fill = group) , bins = as.numeric(n_breaks), alpha = 0.6 , position = 'identity') } #geom_violin if(variable %in% numericals & graph_type == 'violin'){ medians = data %>% group_by_(as.symbol(group)) %>% select( one_of(numericals ) ) %>% summarise_all( median ) p = data %>% ggplot() + geom_violin( aes_string(x = group , y = variable , fill = group) ) + geom_crossbar( data = medians, mapping = aes_string(x = group , y = variable , ymin = variable , ymax = variable) ) } # add x range if(variable %in% numericals & auto_range == F & !graph_type == 'violin'){ p = p + xlim( c( as.numeric(x_min), as.numeric(x_max)) ) } # add y range if(variable %in% numericals & auto_range == F & graph_type == 'violin'){ p = p + ylim( c( as.numeric(x_min), as.numeric(x_max)) ) } # categoricals ---------------------------------------------------------------------------- #geom_bar if(variable %in% categoricals ){ if(y_axis == '..density..') y_axis = '..prop..' p = data %>% ggplot() + geom_bar( aes_string(x = variable, y = y_axis, fill = group, group = group) , position = 'dodge') } y_axis_str = stringr::str_extract_all(y_axis , '[A-Za-z]') %>% unlist()%>% stringr::str_c(collapse = '') name = stringr::str_c( input$sql_or_lib, '_')%>% stringr::str_c(suffix ) %>% stringr::str_c('_', graph_type ) %>% stringr::str_c('_', y_axis_str) %>% stringr::str_c( '_group', group) %>% stringr::str_c( '_var', variable) %>% stringr::str_c( '_', n_breaks) save_plot(plot = p , path = input$path , name = name , yes = input$save ) return(p) }) }
input_panel_histo('histo_a') renderPlot_histo('histo_a')
Histogram B
input_panel_histo('histo_b') renderPlot_histo('histo_b')
Histogram C
input_panel_histo('histo_c') renderPlot_histo('histo_c')
Histogram D
input_panel_histo('histo_d') renderPlot_histo('histo_d')
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