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library(ggplot2)
library(shiny)
library(gganimate)
library(XML)
library(lubridate)
library(tidyverse)
library(shinydashboard)
library(shinythemes)

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sub_4<- train %>% 
  filter(.,no_of_trainings==2) %>% 
  select(.,department,education,gender,is_promoted) 

head(sub_4)

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library(tidyverse)
sub_3 <- train %>% 
  select(.,department,education,gender,is_promoted,no_of_trainings) %>% 
  filter(.,no_of_trainings==2)

head(sub_3)

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sub_2 <- train[which(train$no_of_trainings == 2),names(train) %in% 
                       c("department","education","gender","is_promoted")]
head(sub_2)

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train <- read.csv("~/Analytics Vidhya/HR analytics/train_LZdllcl.csv", header=T)
head(train)

#subset first few rows and columns
sub <- train[1:5000,1:6]
head(sub)

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# assign column names
colnames(x) <- c("A", "B", "C")
x

#subset using the names 
x[,c('A')]

#using the subset function
subset(x,select=c('A'))

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x <- matrix(1:6,nrow=2,ncol=3)
x

#selecting an element
x[1,1]

#selecting just one row
x[2,]

# selecting one or more columns

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x <-c(10,12,23,45,84,25,15,56,09)

#selecting one element
x[1]   

#selecting multiple elements
x[c(3,9)]

#dropping certain elements
x[-c(1,2)]

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data<- data.frame(variable1 = rep(LETTERS[1:3], each = 3),
                 variable2 = rep(paste0("factor", c(1, 2, 3)), 3),
                 num = 1:9) 
head(data)
spread(data,variable2,num)

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x <- "AnaLytics VidHya 001"
str_to_lower(x)
str_to_upper(x)