Ram-N

rm(list=ls())
library(ggplot2)

df <- read.csv("~/RStats/data/ResearchGrants2013.csv", stringsAsFactors=F, strip.white=T)
df <- df[,-6] #get rid of the trailing blank
dim(df)

#quick check
unique(df$Research.Category)
table(df$Research.Category)

Ram-N

i <- 1:50
b <- 1:5
ColNames <- paste0("X_", i, "_", rep(b, each=length(i)))

Ram-N

i <- 1:50
b <- 1:5
ColNames <- paste0("X_", i, "_", rep(b, each=length(i)))

Ram-N

df[, !(colnames(df) %in% c("x","bar","foo"))]

Ram-N

import pandas as pd

df = pd.DataFrame([
    ['2018-03-11', '11:03',  86],
    ['2018-03-14', '09:27',  43],
    ['2018-03-14', '12:08',  22],
    ['2018-03-15', '03:13',  140],
    ['2018-03-17', '18:06',  10]],
                    columns=['Date','Time','Duration'])
                    

Ram-N

rm(list=ls())
library(lpSolveAPI)

#utility functions
getColnum <- function(cell, color, edge) {
#  return(4*(cell-1)+edge)
  celloffset <- (cell-1)*4*num.colors
  coloroffset <- (color-1)*4
  return(celloffset+coloroffset+edge)
}

Ram-N

mtcars[, c("cyl", "am", "gear")] <- lapply(mtcars[, c("cyl", "am", "gear")], as.factor)

p <- ggplot(mtcars, aes(mpg, wt, group = cyl)) + 
  geom_line(aes(color=cyl)) +
  geom_point(aes(shape=cyl)) + 
  facet_grid(gear ~ am) +
  theme_bw()                                                                      
p                                                                     

Ram-N

library(tidyr)

df <- data.frame(
  MKT=c("ABIORD", "ABIBOS", "GCFPQR", "ABIORD", "ABIBOS", "GCFPQR", "ABIORD", "ABIBOS", "GCFPQR"), 
  VERSION=c(1,2,3,4,1,2,3,4,1), 
  REVERSALS=c(7,6,7,7,7,6,7,7, 9), 
  NEIGH=c(1,2.3,3.3,4,3.3,2,1,3.3,4.3),
  DIFFMETRIC=c(1.1,2.1,3.1,4.1,3.1,2.1,1.1,3.1,4.1))

Ram-N

def common_elements(list1, list2):
    return list(set(list1) & set(list2))

import itertools
fileidx = 1
for x,y in itertools.combinations(cards, 2):
    match = common_elements(x, y)
    print(len(match), match)
    if len(match):

Ram-N

library(dplyr)

df1 <- mtcars
df2 <- filter(mtcars, mpg >19)

names(df1)
col1 <- 'qsec'
val1 <- 15.5

col2 <- 'qsec'