create 100% stacked bar chart in ggplot

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Mon Jul 31 2023 13:41:14 GMT+0000 (Coordinated Universal Time)

Saved by @vs #r

data <- data.frame(
  Category = c("Category 1", "Category 2", "Category 3", "Category 1", "Category 2", "Category 3"),
  Group = c("Group A", "Group A", "Group A", "Group B", "Group B", "Group B"),
  Count = c(10, 15, 5, 8, 12, 7)
)

ggplot2::ggplot(data, ggplot2::aes(x = Group, y = Count, fill = Category)) +
  ggplot2::geom_bar(position = "fill", stat = "identity") +
  ggplot2::geom_text(aes(label = scales::percent(Count / sum(Count))), position = ggplot2::position_fill(vjust = 0.5)) +
  ggplot2::labs(title = "100% Stacked Bar Chart",
       x = "Groups",
       y = "Proportion") +
  ggplot2::scale_y_continuous(labels = scales::percent_format(scale = 100)) +
  ggplot2::theme_minimal()

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R/ggplot

@vs

stacked bar chart in ggplot change order of categories for ggplot create 100% stacked bar chart in ggplot set colors manually in ggplot default colors with hexa codes and names in ggplot by number of categories change legend title in ggplot multiple subplots with flexible scales modify x axis labels in ggplot change font/text size ggplot vertical axis labels ggplot remove selected legend from plot change legend position

How to Combine Multiple GGPlots into a Figure - Datanovia

library(ggplot2)
library("ggpubr")
theme_set(
  theme_bw() +
    theme(legend.position = "top")
  )

Dynamically select dataframe column in R

var <- "mpg"
#Doesn't work
mtcars$var
#These both work, but note that what they return is different
# the first is a vector, the second is a data.frame
mtcars[[var]]
mtcars[var]

multiple correspondence analysis in R

# load packages
require(FactoMineR)
require(ggplot2)
# load data tea
data(tea)
# select these columns
newtea = tea[, c("Tea", "How", "how", "sugar", "where", "always")]
# take a look
head(newtea)


# number of categories per variable
cats = apply(newtea, 2, function(x) nlevels(as.factor(x)))
cats

# apply MCA
mca1 = MCA(newtea, graph = FALSE)

# table of eigenvalues
mca1$eig


# data frames for ggplot
mca1_vars_df = data.frame(mca1$var$coord, Variable = rep(names(cats), 
                                                         cats))
mca1_obs_df = data.frame(mca1$ind$coord)

# plot of variable categories
ggplot(data = mca1_vars_df,
       aes(x = Dim.1, y = Dim.2, label = rownames(mca1_vars_df))) + 
  geom_hline(yintercept = 0, colour = "gray70") + geom_vline(xintercept = 0, 
                                              colour = "gray70") +
  geom_text(aes(colour = Variable)) + 
  ggtitle("MCA plot of variables using R package FactoMineR")


# XXX ---------------------------------------------------------------------

Base_acm <- Base %>% select(P1_1, P3_1, P3_2, P3_3)
Base_acm$P1_1 <- as.factor(Base_acm$P1_1)
Base_acm$P3_1 <- as.factor(Base_acm$P3_1)
Base_acm$P3_2 <- as.factor(Base_acm$P3_2)
Base_acm$P3_3 <- as.factor(Base_acm$P3_3)

cats=apply(Base_acm, 2, function(x) nlevels(as.factor(x)))

mca2 = MCA(Base_acm, graph = FALSE)

# data frames for ggplot
mca2_vars_df = data.frame(mca2$var$coord, Variable = rep(names(cats), 
                                                         cats))
mca2_obs_df = data.frame(mca2$ind$coord)

# plot of variable categories
ggplot(data = mca2_vars_df,
       aes(x = Dim.1, y = Dim.2, label = rownames(mca2_vars_df))) + 
  geom_hline(yintercept = 0, colour = "gray70") + geom_vline(xintercept = 0, 
                                                        colour = "gray70") +
  geom_text(aes(colour = Variable)) + 
  ggtitle("MCA plot of variables using R package FactoMineR")

factoextra::fviz_screeplot(mca2, addlabels = TRUE, ylim = c(0, 45))

Basic Scatter Plot

x <- data$variable_xaxis
y <- data$variable_yaxis

plot(x, y, main = "Main title",
     xlab = "X axis title", ylab = "Y axis title",
     pch = 19, frame = FALSE)
abline(lm(y ~ x, data = mtcars), col = "blue")

Basic Plot with Geom

qplot(x-coordinate, y-coordinate, data=data_frame, geom = c("point", "smooth")

Basic Plot with Point Layer (ggplot2)

g <- ggplot(data_set, aes(x-variable, y-variable) 

g + geom_point()

basic scatter plot

x <- data$variable_xaxis
y <- data$variable_yaxis

plot(x, y, main = "Main title",
     xlab = "X axis title", ylab = "Y axis title",
     pch = 19, frame = FALSE)
abline(lm(y ~ x, data = mtcars), col = "blue")

Fix Misspellings in a Data frame

library(tidyverse)

df$column <- df$column str_to_title(df$column) #Standardize caps

#Two options 
df$column <- str_replace(df$column, "old", "new") #Option 1, uses str_replace
df$column <- gsub("old", "new", df$column) #Option 2, uses str_replace

Select Rows in a Data Frame that do not match

# make data frame 
a <- data.frame( x =  c(1,2,3,4)) 
b <- data.frame( y =  c(1,2,3,4,5,6))
subset(b, !(y %in% a$x)) #Pulls values from frame b that are not in frame a

Importa vários arquivos iguais da mesma pasta

dir <- "directory of archives"

directories <- as.list(file.path(dir, list.files(dir, pattern = ".*.csv")))

dataframes <- map(directories , rio::import)

dataset <- plyr::ldply(dataframes, data.frame)

Order or sort a character vector


str_order(
  x,
  decreasing = FALSE,
  na_last = TRUE,
  locale = "en",
  numeric = FALSE,
  ...
)
  
# Examples 
  
  str_order(letters)
#>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26
str_sort(letters)
#>  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
#> [20] "t" "u" "v" "w" "x" "y" "z"

str_order(letters, locale = "haw")
#>  [1]  1  5  9 15 21  2  3  4  6  7  8 10 11 12 13 14 16 17 18 19 20 22 23 24 25
#> [26] 26
str_sort(letters, locale = "haw")
#>  [1] "a" "e" "i" "o" "u" "b" "c" "d" "f" "g" "h" "j" "k" "l" "m" "n" "p" "q" "r"
#> [20] "s" "t" "v" "w" "x" "y" "z"

x <- c("100a10", "100a5", "2b", "2a")
str_sort(x)
#> [1] "100a10" "100a5"  "2a"     "2b"    
str_sort(x, numeric = TRUE)
#> [1] "2a"     "2b"     "100a5"  "100a10"

Turns Swedbank Estonia financial statement csv file to cashflow dataframe

# Account
statement <- read.csv("statement.csv", stringsAsFactors = F, sep = ";")

# Get rid of irrelevant columns
statement <- statement[2:13]

# Statement money flows
rahavoog <- statement[statement$Reatüüp == 20,]
rahavoog$Summa <- as.numeric(gsub(",", ".", rahavoog$Summa))

Removes outliers in dataframe column by looking at quartiles

#Outlier Removal Function
remove_outliers <- function(dataframe, column, na.rm = TRUE) {
  Q1 <- quantile(column, .25)
  Q3 <- quantile(column, .75)
  IQR <- IQR(column)
  dataframe <- subset(dataframe, column> (Q1 - 1.5*IQR) & column< (Q3 + 1.5*IQR))
}

#Not looping
df_1 <-remove_outliers(df_1,df_1$Column1)

#Loop through a list of column names
for(i in Column_List){
  df_1 <- remove_outliers(df_1, df_1[[i]])
}

Stopping an R Script running upon a certain condition

if (1 != 2)
  stop("something is wrong")

Try function & Continue the execution

try(log("not a number"), silent = TRUE)
print("errors can't stop me")

create 100% stacked bar chart in ggplot

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create 100% stacked bar chart in ggplot

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