praveenkumarpgiindia

if(!require(ggplot2)){install.packages('ggplot2')}
library(ggplot2)

n=20 #set sample size
nSims<-100000 #set number of simulations

x<-rnorm(n = n, mean = 100, sd = 15) #create sample from normal distribution

#95%CI
CIU<-mean(x)+qt(0.975, df = n-1)*sd(x)*sqrt(1/n)

praveenkumarpgiindia

require(ggplot2)

#Save downloaded Scopus data in your working directory
scopusdata<-read.csv("scopusPS2010_2015.csv")

plot1<-ggplot(scopusdata, aes(x=Cited.by))  + 
  geom_histogram(colour="#535353", fill="#84D5F0", binwidth=2) +
  xlab("Number of Citations") + ylab("Number of Articles")  + 
  ggtitle("Citation Data for Psychological Science 2011-2015") +
  coord_cartesian(xlim = c(-5, 250))

praveenkumarpgiindia

#Script based on Carter & McCullough (2014) doi: 10.3389/fpsyg.2014.00823

#Load Libraries
library(meta)
library(metafor)

#Insert effect sizes and sample sizes
es.d<-c(0.38,0.41,-0.14,0.63,0.22)
n1<-c(75,48,22,18,60)
n2<-c(75,52,21,20,55)

praveenkumarpgiindia

if(!require(meta)){install.packages('meta')}
library(meta)

nSims <- 1000000 #number of simulated experiments
numberstudies<-4 # nSim/numberofstudies should be whole number
p <-numeric(nSims) #set up empty container for all simulated p-values
metapran <-numeric(nSims/numberstudies) #set up empty container for all simulated p-values for random effects MA
metapfix <-numeric(nSims/numberstudies) #set up empty container for all simulated p-values for fixed effects MA
heterog.p<-numeric(nSims/numberstudies) #set up empty container for test for heterogeneity
d <-numeric(nSims) #set up empty container for all simulated d's

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options(scipen=999) #disable scientific notation for numbers

#Figure 1 & 2 (set to N <- 5000 for Figure 2)
# Set x-axis upper and lower scalepoint (to do: automate)
low_x<--1
high_x<-1
y_max<-2

#Set sample size per group and effect size d (assumes equal sample sizes per group)
N<-50 #sample size per group for indepndent t-test

praveenkumarpgiindia

#Load packages
library(readxl)
library(mailR)

#Read student data
info <- read_excel("student_names_email.xls",
                   sheet = 1,
                   col_names = TRUE)

#Loop from 1 to the number of email addresses in the spreadsheet

praveenkumarpgiindia

## meta analysis, sample size based
metaZn =  function(zdisc,zrepl,ndisc,nrepl)
  {
  ## calculate meta analysis Zscore
  ## zdisc and zrepl are zscores in the discovery and replication sets respectively
  ## ndisc and nrepl are sample sizes in the discovery and replication sets
    wdisc = sqrt(ndisc)
    wrepl = sqrt(nrepl)
    ( zdisc * wdisc  + zrepl * wrepl )/sqrt( wdisc^2 + wrepl^2 )
}

praveenkumarpgiindia

# Load the raw training data and replace missing values with NA
training.data.raw <- read.csv('train.csv',header=T,na.strings=c(""))

# Output the number of missing values for each column
sapply(training.data.raw,function(x) sum(is.na(x)))

# Quick check for how many different values for each feature
sapply(training.data.raw, function(x) length(unique(x)))

# A visual way to check for missing data

praveenkumarpgiindia

C# For Python Programmers

Syntax and core concepts

Basic Syntax

• Single-line comments are started with //. Multi-line comments are started with /* and ended with */.

• C# uses braces ({ and }) instead of indentation to organize code into blocks. If a block is a single line, the braces can be omitted. For example,