#ifelse

[arshikasingh]

Types of Java if-else Statement

Let's see the types of Java if-else Statement:

[bhtyu]

axtloniqvua netlabfui

InkScape possible totema "svg poddibilities & vectorized & tensorized personalized data" + TinkerCad

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embed [ badanie właściwości mechano-fizycznych obiektów "wpławnych" ]

Możliwości zastosowań układów scalonych. Irracjonalnu, gnu & sistem ant.

Arduino IDE, netfabb;;

"snucie przestrzeni""

[tpointtech12]

Top 5 Java Conditional Statement Tricks You Should Know

Learn how to use Java conditional statements like if, else, and switch to control the flow of your program. Master decision-making in Java with easy-to-follow examples.

[pro-patria-mori-if]

Me sowing: choice is good! branching is good! add in variety, weave in all of the variables from the prologue!!

Me reaping: Now I must write a 31 line if else statement... to determine... which passage someone is sent to next... to get dressed...

[no-empathy-culture-is]

Would conditional be better named as circumstantial?

it depends on the person tbh - i prefer conditional empathy for myself because it feels like an ifelse statement in coding where its just setting out a rule of if x then y, else z. but someone else might prefer circumstantial, because conditional might feel to them like it's implying that they're choosing to give or take empathy by having conditions for it, and that might not be something they want to define their empathy by

[catchascatchcn]

remembered i could turn my horrible time consuming for loops into beautiful efficient ifelse statements. i am a coding god

[tpointtechedu]

The Complete R Programming Tutorial for Aspiring Data Scientists

In the world of data science, the right programming language can make all the difference. Among the top contenders, R programming stands out for its powerful statistical capabilities, robust data analysis tools, and a rich ecosystem of packages. If you're an aspiring data scientist, mastering R can open the door to a wide range of opportunities in research, business intelligence, machine learning, and online R compiler.

In this complete R programming tutorial, we’ll walk you through the essentials you need to start coding with R—from installation to basic syntax, data manipulation, and even simple visualizations.

Why Learn R for Data Science?

R is a language built specifically for statistical computing and data analysis. It is widely used in academia, finance, healthcare, and tech industries. Some key reasons to learn R include:

Open Source & Free: R is completely free to use and has a vast community contributing packages and resources.

Built for Data: Unlike general-purpose languages, R was designed with statistics in mind.

Visualization Power: With packages like ggplot2, R makes data visualization intuitive and beautiful.

Data Analysis-Friendly: Data frames, tidyverse, and built-in functions make data wrangling a breeze.

Step 1: Installing R and RStudio

Before you can dive into coding, you’ll need two essential tools:

R: Download and install R from CRAN.

RStudio: A user-friendly IDE (Integrated Development Environment) that makes writing R code easier. Download it from rstudio.com.

Once installed, open RStudio. You'll see a scripting window, console, environment panel, and files/plots/packages/help panel—everything you need to code efficiently.

Step 2: Writing Your First R Script

Let’s start with a simple script.# This is a comment print("Hello, Data Science World!")

Hit Ctrl + Enter (Windows) or Cmd + Enter (Mac) to run the line. You’ll see the output in the console.

Step 3: Understanding Data Types and Variables

R has several basic data types:# Numeric num <- 42 # Character name <- "Data Scientist" # Logical is_learning <- TRUE # Vector scores <- c(90, 85, 88, 92) # Data Frame students <- data.frame(Name = c("John", "Sara"), Score = c(90, 85))

Use the str() function to explore objects:str(students)

Step 4: Importing and Exploring Data

R can read multiple file formats like CSV, Excel, and JSON. To read a CSV:data <- read.csv("yourfile.csv") head(data) summary(data)

If you're working with large datasets, packages like data.table or readr can offer better performance.

Step 5: Data Manipulation with dplyr

Part of the tidyverse, dplyr is essential for transforming data.library(dplyr) # Select columns data %>% select(Name, Score) # Filter rows data %>% filter(Score > 85) # Add new column data %>% mutate(Grade = ifelse(Score > 90, "A", "B"))

Step 6: Data Visualization with ggplot2

ggplot2 is one of the most powerful visualization tools in R.library(ggplot2) ggplot(data, aes(x = Name, y = Score)) + geom_bar(stat = "identity") + theme_minimal()

You can customize charts with titles, colors, and themes to make your data presentation-ready.

Step 7: Writing Functions

Functions help you reuse code and keep things clean.calculate_grade <- function(score) { if(score > 90) { return("A") } else { return("B") } } calculate_grade(95)

Step 8: Exploring Machine Learning Basics

R offers packages like caret, randomForest, and e1071 for machine learning.

Example using linear regression:model <- lm(Score ~ Age + StudyHours, data = students) summary(model)

This builds a model to predict score based on age and study hours.

Final Thoughts

Learning R is a valuable skill for anyone diving into data science. With its statistical power, ease of use, and strong community support, R continues to be a go-to tool for data scientists around the globe.

Key Takeaways:

Start by installing R and RStudio.

Understand basic syntax, variables, and data structures.

Learn data manipulation with dplyr and visualizations with ggplot2.

Begin exploring models using built-in functions and machine learning packages.

Whether you're analyzing research data, building reports, or preparing for a data science career, this R programming tutorial gives you the solid foundation you need.

For Interview Related Q&A :

Happy coding!

[toastsharkgames]

6.02.2025

- finally admitted to myself I'm VERY stuck with writing dialogue/story beats

-craved and watched a video that's been in my Watch Later List about a guy who had the exact same problem for a year

-went and asked the question: What's the theme of this game? (It was friendship and learning to move on and other vague stuff, now it's concrete: Fear of the Future)

-wrote down the specific theme of every character and the entirety of Magenta's day 6 + special 7 dialogue in one go at like 3am (thank you video!)

7.02.2025

-implemented the code cutscenes and bug fixes for Magenta day 6

-including a second level of their minigame

-also figured out making a new scene + actors for special events is a lot easier than trying to shove hordes of ifelse statements into the main scene

-started working on a savefile function

9.02.2025

-continue working on savefile function (= choosing which slot to save ingame, selecting which one at the home menu, showing progress of your chosen file)

(it's the last one making problems

-make a new "project" to test specific features that are closed in itself (like saves) without having to wait mutliple minutes for it to compile every time

-FOUND OUT ABOUT DIALOGUE COMMANDS. GAMECHANGER

-realized i got no clue how to make certain values stick in home even after deleting the saves and decided it wasnt worth the hustle

-reassigned the old day 7 mage cutscene to be his text for reds route

-started making cs for mage d7

[nj-stone]

IFELSE- How A Sci-Fi Classic Killed A Franchise https://youtu.be/S5Z0bKa1-84?si=7INCdjZKb4-NMqfK via @YouTube

[codigonautas]

9 Ejemplos de Condicionales if, if else, if elif en Python - https://codigonautas.com/9-ejemplos-condicionales-if-else-python/?feed_id=313&_unique_id=674d083760c29 #Condicionales #IfElse #Python

[usfvisualstudent]

Module 10

setwd("E:/College/Intro to Data Science/Rstuido") data <- read.csv(file = "Hot Dog.csv") (library("ggplot2")) colors <- ifelse(data$Beaten == 1, "darkblue", "black") barplot(data$Eaten, names.arg = data$Year, col=colors, border=NA, main = "Time Series Example", xlab="Year", ylab="Hot dogs consumed")

[arshikasingh]

Example of Java if Statement

Let's see the example of Java if Statement:

[thetexvn]

Decision-making statement in Dart Programming

Decision-making statements help to identify which statement/block of statements should be executed to support an expression at runtime. The choice-making statements are called conditional statements or Selection statements...

Read more: https://www.thetexvn.com/blogs/@zia/decision-making-statement-in-dart-programming-139

#dart #programming #statement #ifelse

[usfstatstudent]

Module 11 Assignment

#Importing Data

library(ISwR)

ashina$subject <- factor(1:16)

attach(ashina)

act <- data.frame(vas=vas.active, subject, treat=1, period=grp)

plac <- data.frame(vas=vas.plac, subject, treat=0, period=ifelse(grp==1,2,1))

ashina.long <- rbind(act, plac) >

ashina.long$treat <- factor(ashina.long$treat)

ashina.long$period <- factor(ashina.long$period)

fit.ashina <- lm(vas ~ subject + period + treat, data = ashina.long) drop1(fit.ashina)

anova(fit.ashina)

dd <- vas.active - vas.plac

t.test(dd[grp==1], -dd[grp==2],var.eq=T)

t.test(dd[grp==1], dd[grp==2], var.eq=T)

a <- g1(2, 2, 8) b <- g1(2, 4, 8) x <-- 1:8 y <- c(1:4, 8:5) z <- rnorm (8)

model.matrix(~ a:x); lm (z ~ a:x) model.matrix(~ ax); lm (z ~ aX) model.matrix(~b(x+y)); lm (z ~ b(x +y))

R will reduce the set of the design variables for an interaction term between the categorical variables when a main event is present but it will not detect the singularity caused by the interception. There is no singularities in the first two cases, however the last example has a coincidental singularity.

[ansuf]

alternative if-else php syntax

[pandabunnytech]

JavaScript else if Statement : Programming Foundations #09

JavaScript else if Statement : Programming Foundations #09

JavaScript else if and if statements are used when making programming decisions.  By now, you already know about the JS while loop and using conditionals in looping. Similar to the while loop, the if/else if statement also uses a conditional to determine a decision.

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