Week_4_Bolisetti_Lakshmi_Mounika
2023-09-19
Load CSV file
Loading the csv file to garment_prod variable.
garment_prod <-read.csv("/Users/lakshmimounikab/Desktop/Stats with R/R practice/garment_prod.csv")
garment_prod$team <- as.character(garment_prod$team)
View(garment_prod)
summary(garment_prod)## date quarter department day
## Length:1197 Length:1197 Length:1197 Length:1197
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## team targeted_productivity smv wip
## Length:1197 Min. :0.0700 Min. : 2.90 Min. : 7.0
## Class :character 1st Qu.:0.7000 1st Qu.: 3.94 1st Qu.: 774.5
## Mode :character Median :0.7500 Median :15.26 Median : 1039.0
## Mean :0.7296 Mean :15.06 Mean : 1190.5
## 3rd Qu.:0.8000 3rd Qu.:24.26 3rd Qu.: 1252.5
## Max. :0.8000 Max. :54.56 Max. :23122.0
## NA's :506
## over_time incentive idle_time idle_men
## Min. : 0 Min. : 0.00 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 1440 1st Qu.: 0.00 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 3960 Median : 0.00 Median : 0.0000 Median : 0.0000
## Mean : 4567 Mean : 38.21 Mean : 0.7302 Mean : 0.3693
## 3rd Qu.: 6960 3rd Qu.: 50.00 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :25920 Max. :3600.00 Max. :300.0000 Max. :45.0000
##
## no_of_style_change no_of_workers actual_productivity
## Min. :0.0000 Min. : 2.00 Min. :0.2337
## 1st Qu.:0.0000 1st Qu.: 9.00 1st Qu.:0.6503
## Median :0.0000 Median :34.00 Median :0.7733
## Mean :0.1504 Mean :34.61 Mean :0.7351
## 3rd Qu.:0.0000 3rd Qu.:57.00 3rd Qu.:0.8503
## Max. :2.0000 Max. :89.00 Max. :1.1204
## Load required libraries
The required libraries are dplyr, purrr and ggplot2.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(purrr)
library(ggplot2)Static random sampling with aggregate values
In this method, we can select/pluck a particular column and perform aggregate functions on the sampled data.
sample_1 <- garment_prod |> sample_frac(0.5,replace = TRUE)
View(sample_1)
sample_1 |> pluck("targeted_productivity") |> mean()## [1] 0.727592sample_1 |> pluck("wip") |> max()## [1] NAsample_1 |> pluck("actual_productivity") |> mean()## [1] 0.7260001Random sampling of garment_prod dataset
I’ve randomly generated samples taking 7 columns into consideration : quarter, day, targeted_productivity, actual_productivity, smv, over_time, and no_of_workers.
num <-sample(5:10,1)
columns <- c("quarter", "day", "targeted_productivity", "actual_productivity", "smv", "over_time", "no_of_workers")
subsample_list <- list()
for (i in 1:num) {
s_size <- round(0.5 * nrow(garment_prod))
s_index <- sample(1:nrow(garment_prod), size = s_size, replace = TRUE)
subsample <- garment_prod[s_index, columns]
subsample_list[[i]] <- subsample
}Summaries of the sample data
Printing summary statistics for each sub sample created above using for-loop.
summary_table <- lapply(subsample_list, function(subsample){
summary_df <-summary(subsample)
knitr::kable(summary_df, caption = "Summary Statistics")
})
for (i in 1: num){
cat("### Subsample", i, "summary statisics \n")
print(summary_table[[i]])
}## ### Subsample 1 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.3500 |Min. :0.2337 |Min. : 2.90 |Min. : 0 |Min. : 2.00 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6542 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 9.00 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7852 |Median :11.61 |Median : 3780 |Median :33.00 |
## | |NA |NA |Mean :0.7304 |Mean :0.7405 |Mean :14.60 |Mean : 4472 |Mean :33.69 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8505 |3rd Qu.:22.94 |3rd Qu.: 6960 |3rd Qu.:57.00 |
## | |NA |NA |Max. :0.8000 |Max. :1.1081 |Max. :54.56 |Max. :15000 |Max. :60.00 |
## ### Subsample 2 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.0700 |Min. :0.2358 |Min. : 2.90 |Min. : 0 |Min. : 2.00 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6548 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 9.00 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7708 |Median :12.52 |Median : 3900 |Median :34.00 |
## | |NA |NA |Mean :0.7248 |Mean :0.7375 |Mean :14.69 |Mean : 4530 |Mean :34.52 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8503 |3rd Qu.:22.94 |3rd Qu.: 6900 |3rd Qu.:57.00 |
## | |NA |NA |Max. :0.8000 |Max. :1.0966 |Max. :51.02 |Max. :25920 |Max. :60.00 |
## ### Subsample 3 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.3500 |Min. :0.2358 |Min. : 2.90 |Min. : 0 |Min. : 2.00 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6497 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 9.00 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7600 |Median :11.61 |Median : 3900 |Median :33.75 |
## | |NA |NA |Mean :0.7244 |Mean :0.7302 |Mean :14.40 |Mean : 4505 |Mean :33.67 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8505 |3rd Qu.:22.52 |3rd Qu.: 6900 |3rd Qu.:57.00 |
## | |NA |NA |Max. :0.8000 |Max. :1.1204 |Max. :54.56 |Max. :15120 |Max. :89.00 |
## ### Subsample 4 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.3500 |Min. :0.2473 |Min. : 2.90 |Min. : 0 |Min. : 2.00 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6501 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 9.00 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7555 |Median :15.26 |Median : 4200 |Median :34.50 |
## | |NA |NA |Mean :0.7215 |Mean :0.7288 |Mean :15.23 |Mean : 4654 |Mean :35.34 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8489 |3rd Qu.:24.26 |3rd Qu.: 6960 |3rd Qu.:57.00 |
## | |NA |NA |Max. :0.8000 |Max. :1.1204 |Max. :50.48 |Max. :15000 |Max. :60.00 |
## ### Subsample 5 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.0700 |Min. :0.2473 |Min. : 2.90 |Min. : 0 |Min. : 2.0 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6629 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 9.0 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7962 |Median :11.61 |Median : 3840 |Median :34.0 |
## | |NA |NA |Mean :0.7277 |Mean :0.7403 |Mean :14.71 |Mean : 4512 |Mean :33.7 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8505 |3rd Qu.:23.29 |3rd Qu.: 6840 |3rd Qu.:57.0 |
## | |NA |NA |Max. :0.8000 |Max. :1.0966 |Max. :54.56 |Max. :15120 |Max. :60.0 |
## ### Subsample 6 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.0700 |Min. :0.2337 |Min. : 2.90 |Min. : 0 |Min. : 2.0 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6501 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 9.0 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7981 |Median :15.09 |Median : 3960 |Median :34.0 |
## | |NA |NA |Mean :0.7295 |Mean :0.7348 |Mean :14.93 |Mean : 4587 |Mean :34.2 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8504 |3rd Qu.:24.26 |3rd Qu.: 6960 |3rd Qu.:57.0 |
## | |NA |NA |Max. :0.8000 |Max. :1.1204 |Max. :50.89 |Max. :25920 |Max. :60.0 |
## ### Subsample 7 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.0700 |Min. :0.2337 |Min. : 2.90 |Min. : 0 |Min. : 2.00 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6507 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.:10.00 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7961 |Median :15.26 |Median : 4080 |Median :34.00 |
## | |NA |NA |Mean :0.7239 |Mean :0.7409 |Mean :14.81 |Mean : 4641 |Mean :34.29 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8505 |3rd Qu.:22.94 |3rd Qu.: 6960 |3rd Qu.:57.00 |
## | |NA |NA |Max. :0.8000 |Max. :1.1005 |Max. :51.02 |Max. :15120 |Max. :60.00 |
## ### Subsample 8 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.3500 |Min. :0.2494 |Min. : 2.90 |Min. : 0 |Min. : 4.00 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6513 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 8.00 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.7688 |Median :15.26 |Median : 3780 |Median :34.00 |
## | |NA |NA |Mean :0.7303 |Mean :0.7336 |Mean :15.09 |Mean : 4357 |Mean :34.29 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8500 |3rd Qu.:25.75 |3rd Qu.: 6840 |3rd Qu.:57.00 |
## | |NA |NA |Max. :0.8000 |Max. :1.1204 |Max. :54.56 |Max. :15120 |Max. :60.00 |
## ### Subsample 9 summary statisics
##
##
## Table: Summary Statistics
##
## | | quarter | day |targeted_productivity |actual_productivity | smv | over_time |no_of_workers |
## |:--|:----------------|:----------------|:---------------------|:-------------------|:-------------|:-------------|:-------------|
## | |Length:598 |Length:598 |Min. :0.3500 |Min. :0.2337 |Min. : 2.90 |Min. : 0 |Min. : 2.00 |
## | |Class :character |Class :character |1st Qu.:0.7000 |1st Qu.:0.6571 |1st Qu.: 3.94 |1st Qu.: 1440 |1st Qu.: 8.00 |
## | |Mode :character |Mode :character |Median :0.7500 |Median :0.8000 |Median :14.75 |Median : 3540 |Median :34.00 |
## | |NA |NA |Mean :0.7301 |Mean :0.7414 |Mean :14.73 |Mean : 4412 |Mean :33.81 |
## | |NA |NA |3rd Qu.:0.8000 |3rd Qu.:0.8506 |3rd Qu.:22.94 |3rd Qu.: 6960 |3rd Qu.:57.00 |
## | |NA |NA |Max. :0.8000 |Max. :1.1204 |Max. :54.56 |Max. :25920 |Max. :60.00 |Scrutinizing/Analysing subsamples
To understand the variation within the sampled data set, I’ve chosen one column named “no_of_workers”. To examine the sub sample, I’ve chosen to use histograms.
# summary statistics for each subsamples
summary_stats <-lapply(subsample_list, summary)
# plot histogram
histograms <- lapply(subsample_list, function(subsample) {
ggplot(subsample, aes(x = no_of_workers)) +
geom_histogram(binwidth = 1, fill = 'blue', color = 'black') +
labs(title = "Histogram for SMV", x = 'Value', y = 'Frequency')
})
# display histograms and summary
for (i in 1:num) {
cat("Subsample", i, "summary statistics:\n")
print(summary_stats[[i]])
print(histograms[[i]])
}## Subsample 1 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.3500
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7304
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2337 Min. : 2.90 Min. : 0 Min. : 2.00
## 1st Qu.:0.6542 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 9.00
## Median :0.7852 Median :11.61 Median : 3780 Median :33.00
## Mean :0.7405 Mean :14.60 Mean : 4472 Mean :33.69
## 3rd Qu.:0.8505 3rd Qu.:22.94 3rd Qu.: 6960 3rd Qu.:57.00
## Max. :1.1081 Max. :54.56 Max. :15000 Max. :60.00
## Subsample 2 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.0700
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7248
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2358 Min. : 2.90 Min. : 0 Min. : 2.00
## 1st Qu.:0.6548 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 9.00
## Median :0.7708 Median :12.52 Median : 3900 Median :34.00
## Mean :0.7375 Mean :14.69 Mean : 4530 Mean :34.52
## 3rd Qu.:0.8503 3rd Qu.:22.94 3rd Qu.: 6900 3rd Qu.:57.00
## Max. :1.0966 Max. :51.02 Max. :25920 Max. :60.00
## Subsample 3 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.3500
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7244
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2358 Min. : 2.90 Min. : 0 Min. : 2.00
## 1st Qu.:0.6497 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 9.00
## Median :0.7600 Median :11.61 Median : 3900 Median :33.75
## Mean :0.7302 Mean :14.40 Mean : 4505 Mean :33.67
## 3rd Qu.:0.8505 3rd Qu.:22.52 3rd Qu.: 6900 3rd Qu.:57.00
## Max. :1.1204 Max. :54.56 Max. :15120 Max. :89.00
## Subsample 4 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.3500
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7215
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2473 Min. : 2.90 Min. : 0 Min. : 2.00
## 1st Qu.:0.6501 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 9.00
## Median :0.7555 Median :15.26 Median : 4200 Median :34.50
## Mean :0.7288 Mean :15.23 Mean : 4654 Mean :35.34
## 3rd Qu.:0.8489 3rd Qu.:24.26 3rd Qu.: 6960 3rd Qu.:57.00
## Max. :1.1204 Max. :50.48 Max. :15000 Max. :60.00
## Subsample 5 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.0700
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7277
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2473 Min. : 2.90 Min. : 0 Min. : 2.0
## 1st Qu.:0.6629 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 9.0
## Median :0.7962 Median :11.61 Median : 3840 Median :34.0
## Mean :0.7403 Mean :14.71 Mean : 4512 Mean :33.7
## 3rd Qu.:0.8505 3rd Qu.:23.29 3rd Qu.: 6840 3rd Qu.:57.0
## Max. :1.0966 Max. :54.56 Max. :15120 Max. :60.0
## Subsample 6 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.0700
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7295
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2337 Min. : 2.90 Min. : 0 Min. : 2.0
## 1st Qu.:0.6501 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 9.0
## Median :0.7981 Median :15.09 Median : 3960 Median :34.0
## Mean :0.7348 Mean :14.93 Mean : 4587 Mean :34.2
## 3rd Qu.:0.8504 3rd Qu.:24.26 3rd Qu.: 6960 3rd Qu.:57.0
## Max. :1.1204 Max. :50.89 Max. :25920 Max. :60.0
## Subsample 7 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.0700
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7239
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2337 Min. : 2.90 Min. : 0 Min. : 2.00
## 1st Qu.:0.6507 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.:10.00
## Median :0.7961 Median :15.26 Median : 4080 Median :34.00
## Mean :0.7409 Mean :14.81 Mean : 4641 Mean :34.29
## 3rd Qu.:0.8505 3rd Qu.:22.94 3rd Qu.: 6960 3rd Qu.:57.00
## Max. :1.1005 Max. :51.02 Max. :15120 Max. :60.00
## Subsample 8 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.3500
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7303
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2494 Min. : 2.90 Min. : 0 Min. : 4.00
## 1st Qu.:0.6513 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 8.00
## Median :0.7688 Median :15.26 Median : 3780 Median :34.00
## Mean :0.7336 Mean :15.09 Mean : 4357 Mean :34.29
## 3rd Qu.:0.8500 3rd Qu.:25.75 3rd Qu.: 6840 3rd Qu.:57.00
## Max. :1.1204 Max. :54.56 Max. :15120 Max. :60.00
## Subsample 9 summary statistics:
## quarter day targeted_productivity
## Length:598 Length:598 Min. :0.3500
## Class :character Class :character 1st Qu.:0.7000
## Mode :character Mode :character Median :0.7500
## Mean :0.7301
## 3rd Qu.:0.8000
## Max. :0.8000
## actual_productivity smv over_time no_of_workers
## Min. :0.2337 Min. : 2.90 Min. : 0 Min. : 2.00
## 1st Qu.:0.6571 1st Qu.: 3.94 1st Qu.: 1440 1st Qu.: 8.00
## Median :0.8000 Median :14.75 Median : 3540 Median :34.00
## Mean :0.7414 Mean :14.73 Mean : 4412 Mean :33.81
## 3rd Qu.:0.8506 3rd Qu.:22.94 3rd Qu.: 6960 3rd Qu.:57.00
## Max. :1.1204 Max. :54.56 Max. :25920 Max. :60.00
On observing the histograms, I’ve observed that: - The frequency of each sub sample varies from a range of 50- 150 or 200. - Overall variation of all the sub samples are not that different. In other words, there is no huge difference in variation for the sub samples. - The peaks points might seem same but are different with respect the frequency.
Anomalies and consistency
To understand anomaly and consistency, I’ve chosen to go with mean and standard deviation for “no_of_workers” columns for each sub sample.
means <- lapply(subsample_list, function(subsample) {
mean(subsample$no_of_workers)
})
sds <- lapply(subsample_list, function(subsample) {
sd(subsample$no_of_workers)
})for (i in 1:num) {
cat("Subsample", i, "Mean of Number of workers:", means[[i]], "\n")
cat("Subsample", i, "SD of Number of workers:", sds[[i]], "\n")
}## Subsample 1 Mean of Number of workers: 33.69398
## Subsample 1 SD of Number of workers: 22.10367
## Subsample 2 Mean of Number of workers: 34.51839
## Subsample 2 SD of Number of workers: 22.21953
## Subsample 3 Mean of Number of workers: 33.66555
## Subsample 3 SD of Number of workers: 22.46121
## Subsample 4 Mean of Number of workers: 35.33612
## Subsample 4 SD of Number of workers: 22.00519
## Subsample 5 Mean of Number of workers: 33.70485
## Subsample 5 SD of Number of workers: 22.26616
## Subsample 6 Mean of Number of workers: 34.199
## Subsample 6 SD of Number of workers: 22.18205
## Subsample 7 Mean of Number of workers: 34.29097
## Subsample 7 SD of Number of workers: 21.93785
## Subsample 8 Mean of Number of workers: 34.29264
## Subsample 8 SD of Number of workers: 22.35408
## Subsample 9 Mean of Number of workers: 33.80602
## Subsample 9 SD of Number of workers: 22.51149For targeted_productivity, I’ve chosen to go with sum and variance aggregate function.
sums<- lapply(subsample_list, function(subsample) {
sum(subsample$targeted_productivity)
})
vars <- lapply(subsample_list, function(subsample) {
var(subsample$targeted_productivity)
})for (i in 1:num) {
cat("Subsample", i, "Sum of targeted productivity:", sums[[i]], "\n")
cat("Subsample", i, "Variance of targeted productivity:", vars[[i]], "\n")
}## Subsample 1 Sum of targeted productivity: 436.8
## Subsample 1 Variance of targeted productivity: 0.008645037
## Subsample 2 Sum of targeted productivity: 433.44
## Subsample 2 Variance of targeted productivity: 0.01122367
## Subsample 3 Sum of targeted productivity: 433.2
## Subsample 3 Variance of targeted productivity: 0.00988031
## Subsample 4 Sum of targeted productivity: 431.45
## Subsample 4 Variance of targeted productivity: 0.01210448
## Subsample 5 Sum of targeted productivity: 435.19
## Subsample 5 Variance of targeted productivity: 0.01171031
## Subsample 6 Sum of targeted productivity: 436.27
## Subsample 6 Variance of targeted productivity: 0.009756747
## Subsample 7 Sum of targeted productivity: 432.92
## Subsample 7 Variance of targeted productivity: 0.01136765
## Subsample 8 Sum of targeted productivity: 436.7
## Subsample 8 Variance of targeted productivity: 0.009693731
## Subsample 9 Sum of targeted productivity: 436.6
## Subsample 9 Variance of targeted productivity: 0.01030686Conclusion
On observing the data, it is quite obvious that, the mean of each sub sample, has no prominent variation. It ranges from 30 to 40. Similarly, coming to standard deviation, there is no significant change in the values. The value ranges from 21 to 23. This range and minimal deviation proves that the data for no_of_workers column within the sub samples is consistent. For targeted_productivity, sum function ranges from 435 to 441 for this iteration. Variance function varies from 0.007 to 0.015, which is relatively high compared to the other functions. Overall, I would say that based on the aggregate functions performed above and analyzing them, the data is comfortably consistent.