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2020-08-14 13:36:36 +02:00
# Created by Miha 20.2.2020
#------------------------------------ CUSTOM FUNCTIONS ----------------------------------#
# Description #
# We create custom functions which will enable us to create
# frequency tables (flextable), ggplots and average tables (flextable)
# And also modify this table (color, width, height)
#-------------------- PREPARE DATA FOR FREQUENCY TABLE ---------------------#
# Function which will create frequency and proportion tables
# for single dimension of quality (dimenzija kakovosti)
# and therefore prepare data for flextable input
# The pripose of frequency tables (generated from Q2 variable)
# is the for comparison of each UE to all UE
# FUnction is intended ONLY for the first chapter
#' of @UE_single_report!!!!!
#' See @chapter @I. @Ključni @podatki
#' @param dfS data frame for single UE (upravna enota)
#' @param dfA data frame for all UE
#' @param item columns for which we wish to prepare
#' @param likert if TRUE, we gave grid questions
#' with likert scale on scale 1-5. Therefore we
#' onnly calculate proportion of values from 3-5
#' (% delež strinjanja)
#' @return A data.frame.
#' @author MM (mat.miha@@gmail.com)
prepFreq <- function (dfS, dfA, item, likert = FALSE) {
if(!missing(dfS)) {
# Our client would like to have tables with
# the proportion of values/asnwers
# larger than 2, i.e.,
# means:
## 3 - se strinja
## 4 - se zelo strinja
## 5 - se popolnoma strinja
# Therefore we will recode
# values 1-2 in into 1 and
# 3-5 into 2, in order to
# get the right proportion
# PREPARE DATA FOR TABLES #
#=========================#
# Check if Frequency table will
# consist of single or
# multiple variable
# like in case of Q2
# i.e. first chapter
# in report
# Rbind data frame columns for
# frequency table purposes
df1 <- subset(dfS, select = item)
if (ncol(df1) > 1 | likert == TRUE) {
# Data for single UE -----------
# concatenates multiple vectors into a single vector
# Or cbinds two columns into single
# for frequency table purposes
df1 <- stack(df1)[1]
# In order to prevent errors we need to check
# if some value on scale from 1-5 is missing
# IF it is we need to generate it and
# label it as 0
l.scale <- as.character(rep(1:5))
# Find possible difference - missing values
dif <- setdiff(l.scale, rownames(df1))
# Check if tehre was any missing
if (length(dif) > 0) {
df1 <- rbind(df1, dif)
# Missing categories should have 0
df1[rownames(df1) %in% dif, "values"] <- NA
}
# Recode data
df1 <- memisc::recode(df1$values,
"1:2=1;2:5=2")
# Data for ALL UE --------------
df2 <- subset(dfA, select = item)
# Rbind data frame columns for
# frequency table purposes
df2 <- stack(df2)[1]
# Recode data
df2 <- memisc::recode(df2$values,
"1:2=1;2:5=2")
} else {
# If we have single variable
# for frequency table then
# Rbind data frame columns for
# frequency table purposes
# Data for single UE -----------
df1 <- subset(dfS, select = item)
df1 <- stack(df1)[, 1]
# Data for ALL UE --------------
df2 <- subset(dfA, select = item)
# Rbind data frame columns for
# frequency table purposes
df2 <- stack(df2)[, 1]
}
# FREQUENCY TABLES #
#==================#
#' Using @janitor and
#' @tabyl
# Freq table for single UE
# It frequency table consist
# of multiple columns the df1
# will be data frame otherwise
# a vector.
if (ncol(subset(dfS, select = item)) > 1 | likert == TRUE) {
#' @IMPORTANT!!! zanima nas samo druga vrstica
#' Torej podatki za delež strinjanja
#' ker smo "2" rekodirali kot strinjanje
#' in 1 kot nestrinjanje
# Pogledao še, če imamo kakšno manjkajolo vrednost oz NA
# V tem primeru moramo vzeti prvo vrstico
df1.freq <- tabyl(df1, show_na = FALSE)
df1.freq <- df1.freq %>% filter(df1 == 2)
# Freq table for ALL UE
df2.freq <- tabyl(df2, show_na = FALSE)[2,]
if(all(is.na(df2.freq))) {
df2.freq <- tabyl(df2, show_na = FALSE)
}
} else {
# Druge vrstice ni, ker imamo samo eno spremenljivko
# oz. postavko
df1.freq <- tabyl(df1, show_na = FALSE)
# Freq table for ALL UE
df2.freq <- tabyl(df2, show_na = FALSE)
}
# Tidy and finalize data
# Merge data by UE type column (single and all)
df.freq <-
merge(df1.freq,
df2.freq,
by.x = "df1",
by.y = "df2",
all = TRUE,
sort = FALSE)
# If there are missing values (NA)
# replace them with 0
# For frequency table
df.freq[is.na(df.freq)] <- 0
# Get % for single UE
df.freq$percent.x <-
formatC(as.numeric(df.freq$percent.x) * 100 ,
format = "f",
digits = 1)
# Get % for ALL UE
df.freq$percent.y <-
formatC(as.numeric(df.freq$percent.y) * 100 ,
format = "f",
digits = 1)
return(df.freq)
} else {
warning("Argument si missing. Please check which arguments
does the function require!")
}
}
#------------------ //PREPARE DATA FOR FREQUENCY TABLE// -------------------#
#---------------------------- WORD FLEXTABLE ------------------------------#
# Function which will create, color and modify
# flextable which will be exported to word document
# In report there are three type of tables.
# Table with two columns, three columns and four columns.
# Therefore function will be created so it will automatically
# detect the number of columns and add name and columns
#' @param df data frame with UE data (seperate and all)
#' @param footnote If flextable sohlda have footnote
#' @param col.width width of table columns (table size)
#' @param all IF TRUE we are doing table for all UE
#' SKUPNO POROČILO, therefore the extracolumn for comparison
#' of single against all UE is not needed
#' @return A flextable.
#' @author MM (mat.miha@@gmail.com)
wordTable <- function(df, footnote = FALSE, col.width, all = FALSE) {
# Create Flextable
tab <- flextable(df)
# Check whixh type of table we are doing -----------
if (ncol(df) == 5 & all == FALSE) {
# Table has five column which means
# we are doing table for "n" and "%"
# Rename table headers
tab <- set_header_labels(
tab,
stolpci = " ",
n.x = "UE",
percent.x = "UE",
n.y = "RS",
percent.y = "RS"
)
} else if (ncol(df) == 3 & all == FALSE) {
# Table has three column which means
# we are doing table mean
tab <- set_header_labels(tab,
stolpci = " ",
m.UE = "UE",
m.UE.all = "RS")
}
#---------------------------------
# MODIFY TABLE -------------------
# Add another row below header
# Again check the type of table
if (ncol(df) == 5 & all == FALSE) {
# with frequency and proportion
tab <- add_header_row(tab,
values = c("", "n", "%", "n", "%"),
top = FALSE)
} else if (ncol(df) == 3 & all == FALSE) {
tab <- add_header_row(tab,
values = c("", "Povp.", "Povp."),
top = FALSE)
}
# Add another row below header
# Again check the type of table
if (ncol(df) == 5 & all == FALSE) {
# Merge table header as Custumer wants
# In first row merge second and third cell
tab <- merge_at(tab,
i = 1,
j = 2:3,
part = "header")
# In first row merge fourth and fifth cell
tab <- merge_at(tab,
i = 1,
j = 4:5,
part = "header")
}
# Apply theme box to a flextable: borders around table
tab <- theme_box(tab)
# Centre Table values
tab <- align_text_col(tab, align = "center")
# And align table header to center
tab <- align_nottext_col(tab, align = "center", header = TRUE)
# But keep row names or row labels justified left
tab <- align(
tab,
i = 1:nrow(df),
j = 1,
align = "left",
part = "body"
)
# Check if table needs additional explanation
if (footnote == TRUE & ncol(df) == 5 & all == FALSE) {
tab <- tab %>% add_footer_lines(
paste0(
"% predstavlja delež strinjanja ",
"(3 - Niti se strinjam niti se ne strinjam,
4 - Se strinjam, 5 - Povsem se strinjam."
)
)
} else if (footnote == TRUE & ncol(df) == 3 & all == FALSE) {
tab <- tab %>% add_footer_lines(
paste0(
"Stolpca 'Povp.' predstavljata poprečno oceno vseh ",
"odgovorov na lestvici od 1- Sploh se ne strinjam ",
"do 5 - Povsem se strinjam."
)
)
} else if (all == TRUE & footnote != FALSE) {
# VELJA LE ZA TABELO V SKUPNEM POROČILU!!!!
# Nadalje preverimo ali je footnote za povprečja ali deleže
if (!any(grepl("%" , colnames(df)))) {
tab <- tab %>% add_footer_lines(
paste0(
"Stolpec 'Povp.' predstavlja poprečno oceno vseh ",
"odgovorov na lestvici od 1 - Sploh se ne strinjam ",
"do 5 - Povsem se strinjam."
)
)
} else {
tab <- tab %>% add_footer_lines(
paste0(
"% predstavlja delež strinjanja ",
"3 - Niti se strinjam niti se ne strinjam,
4 - Se strinjam, ",
"5 - Povsem se strinjam)."
)
)
}
}
# Modify table size, i.e., add width
tab <- width(tab, j = 1, width = col.width)
# Finally add background color for header
tab <- bg(tab, bg = "#81d4FA", part = "header")
# And Also backgorund color for the last row.
# BUT color last row only
# If the last row presents (has row) TOTAL (Skupaj")
if (colSums("Skupaj" == df)[[1]] > 0 ) {
tab <- bg(
tab,
i = nrow(df),
j = 1:ncol(df),
bg = "#81d4FA",
part = "body"
)
}
# Finally change the default fontsize Arial 11 to 10
tab <- fontsize(tab, size = 10, part = "all")
# Return flextable
return(tab)
}
#-------------------------- //WORD FLEXTABLE// ----------------------------#
#------------------------------- GGPLOT: BARPLOT --------------------------------#
# Function which will create vertical barplot for each field (sklop)
# Plot will display proportion or means for each UE compared to ALL UE
# according to values in TABLE 1, 2, ...
#' @param data data frame with UE data (seperate and all)
#' @param field numeric value indicating field (row number) for wich
#' we would like to creat a plot
#' @param xime Label of ggplot x-axis
#' @param yime Label of ggplot y-axis
#' @param col.width Dodging width, when different to the width of
#' the individual elements. This is useful when you want
#' to align narrow geoms with wider geoms
#' @param FieldALL Default is set to FALSE
#' If TRUE, the barplot will be created
#' for all six fields (sklopov) and compared to ALL UE
#' so in total 12 bars will be displayed in plot
#' @param stat Default value is "pct" which means
#' ggplot will plot percentages (0-100). If set to
#' "mena", plto will display mean of likert scale frm
#' 1-5
#' @return ggplot (list).
#' @author MM (mat.miha@@gmail.com)
graf <- function(data,
field,
xime,
yime,
col.width,
FieldALL = FALSE,
stat = "pct") {
# Prepare data frame for ggplot--------------------------------
# Each row represents unique field (sklop)
# End for each field we will create plot
# Check if we are doing for single or all fields
if(FieldALL == FALSE) {
data <- data[field,]
}
# For ploting purposes (plot numeric to x-axis)
# We need to change class from string to numeric
data[2:length(data)] <-
data[2:length(data)] %>% mutate_if(is.character, as.numeric)
# Now we need to reshape data: GGPLOT requirements for ploting
# more than one data for same field (sklop)
# Reshape data according to column stolpci
data <- melt(data, id.vars = 1)
# We need to change factor to character in order
# for us to be able to recode values in that column
data$variable <- as.character(as.factor(data$variable))
# Check if we plot percentages or mean
# as data preparation differs
if (stat == "pct") {
# We only need rows in which percentages are
# Due to the fact we will plot percentages
data <- data[grep("percent", data$variable),]
# Now we recode column "variable" as we want
# so label bars according to UE
data$variable <-
memisc::recode(data$variable, "'percent.x'='UE';'percent.y'='RS'")
} else {
# Now we recode column "variable" as we want
# so label bars according to UE
data$variable <-
memisc::recode(data$variable, "'m.UE'='UE';'m.UE.all'='RS'")
}
# Before starting with the ggplot first check
# if we plot percentages or means
# Plot PErcentages
if (stat == "pct") {
scale <- function(x) {
x <- scale_y_continuous(
labels = function(x)
paste0(x, "%"),
expand = c(0, 0),
limits = c(0, 105),
oob = rescale_none
)
return(x)
}
} else {
# Plot means
scale <- function(x) {
x <-
scale_y_continuous(expand = c(0, 0),
limits = c(1, 5.4),
oob = rescale_none)
return(x)
}
}
# Start with ggplot---------------------------------
p <-
ggplot(data, aes(
# Check if we are doing for single or all fields
if (FieldALL == FALSE) {
x = variable
} else {
x = stolpci
},
y = value,
fill = as.factor(variable)
)) +
# a theme with a white background
theme_bw() +
# Barplot z širino stolpcev 0.4 in position dodge pomeni primerjava (glej graf)
geom_bar(stat = "identity", position = "dodge") +
# Horizontalna postavitev grafa
coord_flip()
# Y-axis should start from 0-105 with % as labels
#' or as 1 - 5 as means. According to @stat parameter
p <- p + scale(x) +
# barva stolpcev (name = "") ker ne želimo še poimenovati "legendo" grafa
# # Define colors for bars:
scale_fill_manual(values = c("#0288D1", "#81d4FA")) +
# Ime x in y osi
labs(x = xime, y = yime, "fill" = "") +
# Legenda naj se prikaže pod grafom
theme(legend.position = "bottom") +
# V posameznih stolpcih bodo prikazana povprečja posameznih kategorij
# Pozicija prikazanih povprečjih pa bo vedno tam, kjer je maksimalna vrednost
geom_text(
aes(
# Check if we are ploting single quality dimension or
# all quality dimensions
if (FieldALL == FALSE) {
x = variable
} else {
x = stolpci
},
# Check if we are adding labels and the top ob each
# bar for percentages or means
if (stat == "pct") {
# When labelingf percentages at the top
# of the each bar we add 1.5 when
# we plot percentages. otherwise
# value/labels is too near the bar border
y = max(value + 1.5, na.rm = TRUE)
} else {
y = max(value + 0.2, na.rm = TRUE)
},
label = ifelse(is.nan(value), "", sprintf("%1.1f", value))
),
angle = 0,
position = position_dodge(width = 0.6),
size = 3.2
)
return(p)
}
#----------------------------- //GGPLOT: BARPLOT// ------------------------------#
#-------------------- CALCULATE MEAN FOR FLEXTABLE ---------------------#
# Function which will create frequency and proportion tables
# for single dimension of quality (dimenzija kakovosti)
# and therefore prepare data for flextable input
# The pripose of frequency tables (generated from Q2 variable)
# is the for comparison of each UE to all UE
# FUnction is intended ONLY for the first chapter
#' of @UE_single_report!!!!!
#' See @chapter @I. @Ključni @podatki
#' @param df1 data frame for selected UE (upravna enota)
#' @param df2 data frame for all UE
#' @param item columns for which we wish to prepare
#' @return A data.frame.
#' @author MM (mat.miha@@gmail.com)
prepMean <- function (df1, df2, item) {
if(!missing(df1)) {
# PREPARE DATA FOR TABLES #
#=========================#
# Data for single UE -----------
df1 <- subset(df1, select = item)
# Rbind data frame columns for
# frequency table purposes
df1 <- stack(df1)[1]
# Data for ALL UE --------------
df2 <- subset(df2, select = item)
# Rbind data frame columns for
# frequency table purposes
df2 <- stack(df2)[1]
# MEANS #
#=======#
# Single UE
m.UE <-
formatC(mean(as.numeric(df1[, 1]), na.rm = TRUE), format = "f", digits = 1)
# All UE
m.UE.all <-
formatC(mean(as.numeric(df2[, 1]), na.rm = TRUE), format = "f", digits = 1)
# Combine to data frame
UE.m <-
as.data.frame(cbind(m.UE, m.UE.all), stringsAsFactors = FALSE)
return(UE.m)
} else {
warning("Argument si missing.
Please check which arguments function require!")
}
}
#------------------ //CALCULATE MEAN FOR FLEXTABLE// -------------------#
#------------------- CONTINGENCY TABLE (CROSSTABS) ---------------------#
#' # Function which will create, contingency flextable (crosstable)
#' For each dimmension items and display proportion for
#' single UE and all UE
#' @param dfS data frame for single UE (upravna enota)
#' @param dfA data frame for all UE
#' @param item columns for which we wish to create crosstabs
#' @param dim.nam Names of flextable rows (dimension item names)
#' @UPDATE MARCH 2020: client would like to have stacked bar plot
#' besides every contingency table. But this true only for
#' single UE report so we add this as @pl argument
#' @param
#' #' @return A flextable or data frame. It dependes on @pl argument
#' @author MM (mat.miha@@gmail.com)
# Contingency flextable
contingTable <- function(dfS, dfA, item, dim.nam, pl = FALSE) {
# GET UE DATA -----------------------------------
# Single UE
df1 <- subset(dfS, select = item)
# All UE
df2 <- subset(dfA, select = item)
# PREPARE DATA FOR SINGLE UE----------------------
# Data for each dimenzion of single UE
df1.dim <- apply(df1, 2, function(x)
tabyl(x, show_missing_levels = TRUE))
# Get %
df1.dim <- lapply(df1.dim, function(x) {
x[3] <- round(x[3] * 100, 1)
x
})
# Loop and prepare data for all items of dimension
df1.tb <- lapply(seq_along(df1.dim), function(x) {
# Transfose data for contigency table purposes
# and select only dirst and third row, as
# we will add totala row with total n
df1.dim[[x]] <-
as.data.frame(t(df1.dim[[x]])[c(1, 3), ]) %>%
# first row as column names ofr table purposes
row_to_names(., row_number = 1) %>%
# Add total N of answers, also for table
# pruposes
cbind("n" = colSums(df1.dim[[x]][2]), .)
})
# Rbind data for flextable
# Rbind data for flextable
# V primeru ko delamo kontingenčno tabelo:
# se lahko zgodi, da se kategorije ne ujemajo in vrne error.
# Torej je potrebno preveriti, ali pri posamezni UE manjka
# kakšna kategorija glede na vse UE, ter to kategorijo
# ustvarimo in dodamo frekvenco 0
df1.tb <- suppressWarnings(do.call(dplyr::bind_rows, df1.tb))
df1.tb <- df1.tb[, !is.na(colnames(df1.tb))]
df1.tb <- df1.tb %>%mutate_if(is.factor, as.character)
# Id there is NA it means that the categori was missing
# So there is 0% of that answers
df1.tb[is.na(df1.tb)] <- 0
# PREPARE DATA FOR ALL UE----------------------
# Data for each dimenzion of single UE
df2.dim <- apply(df2, 2, function(x)
tabyl(x, show_missing_levels = TRUE))
# Get %
df2.dim <- lapply(df2.dim, function(x) {
x[3] <- round(x[3] * 100, 2)
x
})
# Loop and prepare data for all items of dimension
df2.tb <- lapply(seq_along(df2.dim), function(x) {
# Transfose data for contigency table purposes
# and select only dirst and third row, as
# we will add totala row with total n
df2.dim[[x]] <-
as.data.frame(t(df2.dim[[x]])[c(1, 3), ]) %>%
# first row as column names ofr table purposes
row_to_names(., row_number = 1) %>%
# Add total N of answers, also for table
# pruposes
cbind("n" = colSums(df2.dim[[x]][2]), .)
})
# Rbind data for flextable
# V primeru ko delamo kontingenčno tabelo:
# se lahko zgodi, da se kategorije ne ujemajo in vrne error.
# Torej je potrebno preveriti, ali pri posamezni UE manjka
# kakšna kategorija glede na vse UE, ter to kategorijo
# ustvarimo in dodamo frekvenco 0
df2.tb <- suppressWarnings(do.call(dplyr::bind_rows, df2.tb))
df2.tb <- df2.tb[, !is.na(colnames(df2.tb))]
df2.tb <- df2.tb %>%mutate_if(is.factor, as.character)
# Id there is NA it means that the category was missing
# So there is 0% of that answers
df2.tb[is.na(df2.tb)] <- 0
# CBIND SINGLE AND ALL UE DATA AND CREATE FLEXTABLE ----
# Finally add dimension item names
# And label UE
# Single UE
df1.tb <- cbind(UE="UE", dim.nam, df1.tb)
# All UE
df2.tb <- cbind(UE = "RS", dim.nam, df2.tb)
# Rbind data for table priposes
# USE bind_rows if walue on a likert scale is
# missing in single UE and is present in all UE
# If value is missing (e.g. 1) we still want to
# display 0 frequency for that value in table
# Use suppressWarnings to surpress warningas
# from Unequal factor levels: coercing to character
# Different nubmer of columns of two df
df <- suppressWarnings(bind_rows(df1.tb, df2.tb))
# NA to 0
df[is.na(df)] <- 0
# First check is some colnames are missing
# which means there is no frequency or no data
# for that. Therefore we create dummy column
# We want to display all labels in tables
col.nam.likert <- c("1", "2", "3", "4", "5")
# Find names of missing columns
Missing <- setdiff(col.nam.likert, colnames(df[, 4:ncol(df)]))
# Add them, filled with '0's
df[Missing] <- 0
# Reorder for table purposes
df <-
df %>% dplyr::select("UE", "dim.nam", "n", "1", "2", "3", "4", "5")
# Add colnames which will be displayed in
# flextable
colnames(df) <-
c(
"UE",
"Postavke",
"n",
"Sploh se ne strinjam (%)",
"Se ne strinjam (%)",
"Niti se strinjam niti se ne strinjam (%)",
"Se strinjam (%)",
"Povsem se strinjam (%)"
)
# Finally round N for table purposes
df$n <- format(df$n, digits = 0)
# Check if we need flextable or data frame for
# plot purposes
if (pl == FALSE) {
# CREATE FLEXTABLE ------------------------------
df.tab <- flextable(df, cwidth = .5)
# Merge cel in first columns so only name
# of UE is displayed: first si single UE
df.tab <- merge_at(df.tab, i = 1:nrow(df1.tb), j = 1:1)
# Merge cells in first columns so only name
# of RS is displayed: All UE
df.tab <-
merge_at(df.tab, i = (nrow(df1.tb) + 1):nrow(df), j = 1:1)
# Add border to seperate UE from RS
df.tab <- hline(
df.tab,
i = nrow(df1.tb),
border = fp_border(color = "black") ,
part = "body"
)
# Change fontsize so the large table will fit to one page
df.tab <- fontsize(df.tab, size = 8, part = "all")
# Apply theme box to a flextable: borders around table
df.tab <- theme_box(df.tab)
# And align table header to center
df.tab <-
align_nottext_col(df.tab, align = "center", header = TRUE)
# Return Felxtable---------------------
return(df.tab)
} else {
# We will plot data for single UE so we are only
# interested in UE data
df <- df[df == "UE",]
# Prepare data for ggplot
df <-
# Gather multiple columns and collapses into key-value pairs
df %>% gather(UE, Delez, `Sploh se ne strinjam (%)`:`Povsem se strinjam (%)`)
# For Plot purposes percentage needs to be numeric
df$Delez <- as.numeric(df$Delez)
return(df)
}
}
#----------------- //CONTINGENCY TABLE (CROSSTABS)// -------------------#
#--------------------- GGPLOT FOR CONTINGENCY TABLE DATA -----------------------#
#' @UPDATE MARCH 2020: After clients wishes
#' Function which will create,plot for each contingency table
#' for each of the dimension presented in report for seperate UE
#' @param data data frame prepared for ggplot (see function
#' # @contingTable)
#' @IMPORTANT: this function can be be used only after @contg.pl
#' was used. THEY ARE CONNECTED
#' @param x value for x axis
#' @param y Value for y axis
#' @param dFill The colors of filled objects, like bars
#' @param ynam y ayis lable
#' @return ggplot
#' @author MM (mat.miha@@gmail.com)
# Contingency flextable
cont.pl <- function(data, x, y, dFill, ynam) {
p <- ggplot(data, aes(
fill = dFill,
y = y,
# package forcats, fct_rev() is very handy for
# reversing factor levels while plotting
x = fct_rev(x),
group = fct_rev(dFill)
)) +
geom_bar(stat = "identity") +
# Color bars
scale_fill_brewer(guide = guide_legend(reverse = FALSE), palette = "Blues") +
# Add values/percentages in each bar category
geom_text(aes(label = paste0(y, "%")),
position = position_stack(vjust = 0.5),
size = 3) +
# remove space between axis & area-plot in ggplot2
scale_y_continuous(limits = c(0,101), expand = c(0, 0)) +
# Vertical plot
coord_flip() +
# Add new label names
xlab(ynam) + ylab("Delež") +
# Remove legrnd title andd add legend to two rows
guides(fill = guide_legend(nrow = 3, byrow = TRUE, title="")) +
# a theme with a white background
theme_bw() +
# Add legend to bottom
theme(legend.position="bottom")
return(p)
}
#------------------- //GGPLOT FOR CONTINGENCY TABLE DATA// ---------------------#
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