Added R Introduction 4 with plots and across (new)

Tutorial_Scripts/Optimization_DecisionSupport/BIA_T13_Optimization_exercise.Rmd

+---
+title:  'Tutorial 13: Optimization'
+output: html_notebook
+editor_options: 
+  chunk_output_type: inline
+---
+
+This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M), Information Systems and Energy Efficient Systems, University of Bamberg.
+
+
+```{r}
+library(lpSolve)
+library(ggplot2)
+```
+
+
+```{r lpSolve default example}
+#
+# Set up problem: maximize
+#   x1 + 9 x2 +   x3 subject to
+#   x1 + 2 x2 + 3 x3  <= 9
+# 3 x1 + 2 x2 + 2 x3  <= 15
+#
+objective.in <- c(1, 9, 1)
+const.mat <- matrix(c(1, 2, 3, 3, 2, 2), nrow=2, byrow=TRUE)
+f.dir <- c("<=", "<=")
+const.rhs <- c(9, 15)
+direction <- "max"
+lp(direction, objective.in, const.mat, f.dir, const.rhs)
+lp(direction, objective.in, const.mat, f.dir, const.rhs)$solution
+```

Tutorial_Scripts/RIntro/BIA_T04_RIntro4.Rmd

+---
+title:  'Tutorial 4: R Introduction 4'
+output: html_notebook
+editor_options: 
+  chunk_output_type: inline
+---
+
+This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M), Information Systems and Energy Efficient Systems, University of Bamberg.
+
+
+```{r Using dplyr: summarise}
+summarise(fruits, mean(weight))
+
+fruits_grouped_color <- group_by(fruits, color)
+summarise(fruits_grouped_color, avg_weight = mean(weight), num_fruits = n())
+
+# Task 8
+summarise(fruits_grouped_color, avg_weight = mean(weight), num_fruits = n(), sd_weigth = sd(weight), var_weight = var(weight), min_weight = min(weight),  max_weight = max(weight))
+
+# Task 9
+summarize(fruits_grouped_color, fist_fruit = first(fruit), last = last(fruit), n_distinct_fruits = n_distinct(fruit))
+
+# Task 10
+?n_distinct
+```
+
+```{r Using dplyr: the piping operator}
+fruits %>% 
+  group_by(color) %>%
+  summarise(avg_weight = mean(weight), num_fruits = n()) %>%
+  arrange(avg_weight)
+
+# Task 11
+fruits %>% filter(between(weight, 8, 100))
+
+# Task 12
+fruits_weight %>% arrange(desc(fruits_per_pound)) %>% select(fruit, fruits_per_pound)
+
+# Task 13
+fruits %>% group_by(color) %>% summarise(avg_weight = mean(weight), num_fruits = n(), sd_weigth = sd(weight), var_weight = var(weight), min_weight = min(weight),  max_weight = max(weight))
+```
+
+
+
+```{r Using dplyr: Joins}
+recipe <- data.frame(recipeNo=c(1,1,1,1,2,2,2),
+                     fruit=c("banana", "lemon", "raspberry", "shugar", "cantaloupe", "plum", "lemon"))
+full_join(fruits, recipe)
+left_join(fruits, recipe)
+```
+
+```{r Using dplyr: Across}
+random_numbers <- 
+  data.frame(group = c("a", "a", "a", "b", "b", "b"),
+             a_1 = runif(3, 0, 1),
+             a_2 = runif(3, 0, 1),
+             a_3 = runif(3, 0, 1),
+             b_1 = runif(3, 0, 1))
+
+# Goal: Want to select only columns starting with "a_"
+random_numbers %>% select(a_1, a_2, a_3)
+# better:
+random_numbers %>% select(contains("a_"))
+
+# Goal: Want to round all numerics
+random_numbers %>%
+mutate(a_1 = round(a_1,2),
+       a_2 = round(a_2,2),
+       a_3 = round(a_3,2),
+       b_1 = round(b_1,2))
+# better:
+random_numbers %>%
+mutate(across(is.numeric, ~round(.,2)))
+
+
+# Goal: Want to calculate grouped mean for all numerics
+random_numbers %>%
+  group_by(group) %>%
+  summarise(
+    a_1 = mean(a_1),
+    a_2 = mean(a_2),
+    a_3 = mean(a_3),
+    b_1 = mean(b_1)
+  )
+
+# better:
+random_numbers %>%
+group_by(group) %>% summarise(across(is.numeric,~mean(.)))
+```
+
+
+
+```{r Illustrating analytical results by plotting techniques}
+# Load and prepare data
+Shower <- read.csv2("../../data/Shower_data.csv")
+Shower$group <- as.factor(Shower$group)
+levels(Shower$group) <- c("First group", "Second group", "Fourth group", 
+                          "Third group", "Fifth group", "Sixth group")
+Shower$AvgTemperature <- as.numeric(Shower$AvgTemperature)
+
+# Scatterplot
+plot(x = Shower$AvgTemperature, y = Shower$Volume)
+# Scatterplot with formular
+plot(Shower$Volume ~ Shower$AvgTemperature)
+
+# Barplot mean_volume per group
+Shower_groups <- Shower %>% group_by(group) %>% summarize(mean_volume = mean(Volume, na.rm = T))
+barplot(Shower_groups$mean_volume, names.arg = Shower_groups$group)
+
+# pie chart
+pie(Shower_groups$mean_volume)
+
+
+# boxplot
+selected_showers <- Shower[Shower$Hh_ID %in% c(7890, 4624),]
+boxplot(selected_showers$Volume ~ selected_showers$Hh_ID)
+
+
+# line plot
+plot(selected_showers[selected_showers$Hh_ID == 7890,]$Volume ~ selected_showers[selected_showers$Hh_ID == 7890,]$Shower, type="l", col="blue")
+lines(selected_showers[selected_showers$Hh_ID == 4624,]$Volume ~ selected_showers[selected_showers$Hh_ID == 4624,]$Shower, col="red")
+legend(60, 120, legend=c("Hh_ID = 7890", "Hh_ID = 4624"),
+       col=c("blue", "red"), lty=1:2, cex=0.8)
+
+
+# histogram
+
+hist(Shower$Volume, breaks = 20)
+
+# histogram and density line
+hist(Shower$Volume, probability = T, breaks = 20)
+lines(density(na.omit(Shower$Volume)), col="blue", lwd=2)
+
+
+# QQPlot
+qqnorm(Shower$Volume)
+
+# logarithmized QQplot
+qqnorm(log(Shower$Volume))
+
+```
+

Tutorial_Scripts/RIntro/BIA_T04_RIntro4_exercise.Rmd

+---
+title:  'Tutorial 4: R Introduction 4'
+output: html_notebook
+editor_options: 
+  chunk_output_type: inline
+---
+
+This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M), Information Systems and Energy Efficient Systems, University of Bamberg.
+
+
+```{r Using dplyr: summarise}
+summarise(fruits, mean(weight))
+
+fruits_grouped_color <- group_by(fruits, color)
+summarise(fruits_grouped_color, avg_weight = mean(weight), num_fruits = n())
+
+# Task 8
+
+# Task 9
+
+# Task 10
+
+```
+
+```{r Using dplyr: the piping operator}
+fruits %>% 
+  group_by(color) %>%
+  summarise(avg_weight = mean(weight), num_fruits = n()) %>%
+  arrange(avg_weight)
+
+# Task 11
+
+
+# Task 12
+
+
+# Task 13
+
+```
+
+
+
+```{r Using dplyr: Joins}
+recipe <- data.frame(recipeNo=c(1,1,1,1,2,2,2),
+                     fruit=c("banana", "lemon", "raspberry", "shugar", "cantaloupe", "plum", "lemon"))
+full_join(fruits, recipe)
+left_join(fruits, recipe)
+```
+
+```{r Using dplyr: Across}
+random_numbers <- 
+  data.frame(group = c("a", "a", "a", "b", "b", "b"),
+             a_1 = runif(3, 0, 1),
+             a_2 = runif(3, 0, 1),
+             a_3 = runif(3, 0, 1),
+             b_1 = runif(3, 0, 1))
+
+# Goal: Want to select only columns starting with "a_"
+random_numbers %>% select(a_1, a_2, a_3)
+# better:
+random_numbers %>% select(contains("a_"))
+
+# Goal: Want to round all numerics
+random_numbers %>%
+mutate(a_1 = round(a_1,2),
+       a_2 = round(a_2,2),
+       a_3 = round(a_3,2),
+       b_1 = round(b_1,2))
+# better:
+random_numbers %>%
+mutate(across(is.numeric, ~round(.,2)))
+
+
+# Goal: Want to calculate grouped mean for all numerics
+random_numbers %>%
+  group_by(group) %>%
+  summarise(
+    a_1 = mean(a_1),
+    a_2 = mean(a_2),
+    a_3 = mean(a_3),
+    b_1 = mean(b_1)
+  )
+
+# better:
+random_numbers %>%
+group_by(group) %>% summarise(across(is.numeric,~mean(.)))
+```
+
+
+
+```{r Illustrating analytical results by plotting techniques}
+# Load and prepare data
+Shower <- read.csv2("../../data/Shower_data.csv")
+Shower$group <- as.factor(Shower$group)
+levels(Shower$group) <- c("First group", "Second group", "Fourth group", 
+                          "Third group", "Fifth group", "Sixth group")
+Shower$AvgTemperature <- as.numeric(Shower$AvgTemperature)
+
+# Scatterplot
+plot()
+# Scatterplot with formular
+plot( ~ )
+
+# Barplot mean_volume per group
+Shower_groups <- Shower %>% group_by(group) %>% summarize(mean_volume = mean(Volume, na.rm = T))
+
+barplot()
+
+# pie chart
+pie()
+
+
+# boxplot
+selected_showers <- Shower[Shower$Hh_ID %in% c(7890, 4624),]
+
+boxplot()
+
+
+# line plot
+plot()
+lines()
+
+
+
+# histogram
+
+hist()
+
+# histogram and density line
+hist()
+lines()
+
+
+# QQPlot
+qqnorm()
+
+# logarithmized QQplot
+qqnorm()
+
+```
+