I am working on a data set which is large and having many columns. I am using data.table to speed up the calculations. However at certain points I am not sure how to go about and convert my data.table back to data.frame and do the calculation. This slows up the process. It would help a lot to have suggestions on how I can write the below in data.table. Below is a snap of my code on a dummy data -
library(data.table)
#### set the seed value
set.seed(9901)
#### create the sample variables for creating the data
p01 <- sample(1:100,1000,replace = T)
p02 <- sample(1:100,1000,replace = T)
p03 <- sample(1:100,1000,replace = T)
p04 <- sample(1:100,1000,replace = T)
p05 <- sample(1:100,1000,replace = T)
p06 <- sample(1:100,1000,replace = T)
p07 <- sample(1:100,1000,replace = T)
#### create the data.table
data <- data.table(cbind(p01,p02,p03,p04,p05,p06,p07))
###user input for last column
lcol <- 6
###calculate start column as last - 3
scol <- lcol-3
###calculate average for scol:lcol
data <- data[,avg:= apply(.SD,1,mean,na.rm=T),.SDcols=scol:lcol]
###converting to data.frame since do not know the solution in data.table
data <- as.data.frame(data)
###calculate the trend in percentage
data$t01 <- data[,lcol-00]/data[,"avg"]-1
data$t02 <- data[,lcol-01]/data[,"avg"]-1
data$t03 <- data[,lcol-02]/data[,"avg"]-1
data$t04 <- data[,lcol-03]/data[,"avg"]-1
data$t05 <- data[,lcol-04]/data[,"avg"]-1
###converting back to data.table
data <- as.data.table(data)
###calculate the min and max for the trend
data1 <- data[,`:=` (trend_min = apply(.SD,1,min,na.rm=T),
trend_max = apply(.SD,1,max,na.rm=T)),.SDcols=c(scol:lcol)]
###calculate flag if any of t04 OR t05 is an outlier for min and max values. This would be many columns in actual data
data1$flag1 <- ifelse(data1$t04 < data1$trend_min | data1$t04 > data1$trend_max,1,0)
data1$flag2 <- ifelse(data1$t05 < data1$trend_min | data1$t05 > data1$trend_max,1,0)
data1$flag <- ifelse(data1$flag1 == 1 | data1$flag2 == 1,1,0)
So basically, how can I -
calculate the percentages based on user input of column index. Note it is not simple divide but percentage
How can I create the flag variable....I think I need to use any function but not sure how....
Some steps can be made more efficient, i.e. instead of using the apply with MARGIN = 1, the mean, min, max can be replaced with rowMeans, pmin, pmax
library(data.table)
data[ , avg:= rowMeans(.SD, na.rm = TRUE) ,.SDcols=scol:lcol]
data[, sprintf('t%02d', 1:5) := lapply(.SD, function(x) x/avg -1),
.SDcol = patterns("^p0[1-5]")]
data[,`:=` (trend_min = do.call(pmin, c(.SD,na.rm=TRUE)),
trend_max = do.call(pmax, c(.SD,na.rm=TRUE)) ),.SDcols=c(scol:lcol)]
data
# p01 p02 p03 p04 p05 p06 p07 avg t01 t02 t03 t04 t05 trend_min trend_max
# 1: 35 53 22 82 100 59 69 65.75 -0.46768061 -0.19391635 -0.6653992 0.24714829 0.5209125 22 100
# 2: 78 75 15 65 70 69 66 54.75 0.42465753 0.36986301 -0.7260274 0.18721461 0.2785388 15 70
# 3: 15 45 27 61 63 75 99 56.50 -0.73451327 -0.20353982 -0.5221239 0.07964602 0.1150442 27 75
# 4: 41 80 13 22 63 84 17 45.50 -0.09890110 0.75824176 -0.7142857 -0.51648352 0.3846154 13 84
# 5: 53 9 75 47 25 75 66 55.50 -0.04504505 -0.83783784 0.3513514 -0.15315315 -0.5495495 25 75
# ---
# 996: 33 75 9 61 74 55 57 49.75 -0.33668342 0.50753769 -0.8190955 0.22613065 0.4874372 9 74
# 997: 24 68 74 11 43 75 37 50.75 -0.52709360 0.33990148 0.4581281 -0.78325123 -0.1527094 11 75
# 998: 62 78 82 97 56 50 74 71.25 -0.12982456 0.09473684 0.1508772 0.36140351 -0.2140351 50 97
# 999: 70 88 93 4 39 75 93 52.75 0.32701422 0.66824645 0.7630332 -0.92417062 -0.2606635 4 93
#1000: 20 50 99 94 62 66 98 80.25 -0.75077882 -0.37694704 0.2336449 0.17133956 -0.2274143 62 99
and then create the 'flag'
data[, flag := +(Reduce(`|`, lapply(.SD, function(x)
x < trend_min| x > trend_max))), .SDcols = t04:t05]