FthrsAnalytics

let
    Source = Excel.CurrentWorkbook(){[Name=Table]}[Content],
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Person", type text}, {"City", type text}, {"Index", type text}, {"Percentage", Int64.Type}, {"Application Date", type date}}),
    #"Pivoted Column" = Table.Pivot(#"Changed Type", List.Distinct(#"Changed Type"[Index]), "Index", "Percentage", List.Sum),
    #"Demoted Headers" = Table.DemoteHeaders(#"Pivoted Column"),
    #"Kept First Rows" = Table.FirstN(#"Demoted Headers",1),
    #"Transposed Table" = Table.Transpose(#"Kept First Rows"),
    #"Changed Type1" = Table.TransformColumnTypes(#"Transposed Table",{{"Column1", Int64.Type}}),
    #"Removed Errors" = Table.RemoveRowsWithErrors(#"Changed Type1", {"Column1"}),
    #"Transposed Table1" = Table.Transpose(#"Removed Errors"),

FthrsAnalytics

let
    Source = Excel.CurrentWorkbook(){[Name=Table]}[Content],
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Person", type text}, {"City", type text}, {"Index", type text}, {"Percentage", Int64.Type}, {"Application Date", type date}}),
    #"Pivoted Column" = Table.Pivot(#"Changed Type", List.Distinct(#"Changed Type"[Index]), "Index", "Percentage", List.Sum),
    #"Changed Type1" = Table.TransformColumnTypes(#"Pivoted Column",{{"1", type text}, {"2", type text}, {"3", type text}}),
    #"Merged Columns" = Table.CombineColumns(#"Changed Type1", {"1", "2", "3", "4"},Combiner.CombineTextByDelimiter(",", QuoteStyle.None),"Merged"),
    #"Trim Text" = Table.AddColumn(#"Merged Columns", "Percentages", each Text.TrimEnd([Merged], ","), type text),
    #"Removed Columns" = Table.RemoveColumns(#"Trim Text",{"Merged"})
in
    #"Removed Columns"

FthrsAnalytics

##Load Packages
library(tidyverse)
library(rpart)
library(rpart.plot)

##Manipulate
model_data <- dataset

##Create Categorical
pp90_bin <- cut(model_data$points_per_90,

FthrsAnalytics

model_data <- dataset

fantasy_lm <- lm(points_per_90 ~ now_cost + goals_scored + assists + clean_sheets, data = model_data)

model_data$score_lm <- predict(fantasy_lm, model_data)

FthrsAnalytics

##Load Packages needed to run the R code
library(readxl)
library(tidyverse)

##Import Data
file_path <- ##"file path of the source Excel file/Predictive Analytics with R in Power BI.xlsx"

data_set <- read_excel(file_path) %>% 
  select(id, total_points, now_cost, selected_by_percent, minutes, goals_scored, assists, clean_sheets, pos)

FthrsAnalytics

library(readxl)
library(dplyr)
library(broom)

data <- read_excel("C:/Users/jfthr/Google Drive/Feathers Analytics/R Visuals Presentation/R Visuals Power BI Fantasy.xlsx")

model_data <- data %>%
  select(Player, TeamGroup, Price, Selected, Points, MP)

top_teams <- model_data %>%

FthrsAnalytics

Feathers <- 7

Analytics <- 4

Feathers + Analytics

feathers_analytics <- 'new object'

feathers_analytics

FthrsAnalytics

{
"name":"Feathers Analytics Theme",
"background":"#E7E7E7",
"foreground":"#44546A",
"tableAccent":"#2EB2FF",
"dataColors":["#44546A","#2EB2FF","#FF2EA4"]
}

FthrsAnalytics

{
"name":"Feathers Analytics Theme",
"background":"#E7E7E7",
"foreground":"#44546A",
"tableAccent":"#2EB2FF",
"dataColors":["#44546A","#2EB2FF","#FF2EA4"],
"visualStyles":{
	"*":{
		"*":{
			"*":[{