Week4 data dive

library(tidyverse)

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library(ggplot2)
library(dbplyr)

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## Attaching package: 'dbplyr'
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##     ident, sql

week3_dataset <- read.csv("baseball.csv",header = TRUE)

This is random sample of RS column.

In this case, I think the tenth index is very low. Since the 50 percentile of this column is 711. That means that the random variable that is picked mostly under 50%

df_sample_rs <- list()
num_iterations <- 10  
for (i in 1:num_iterations) {
  sample_mean <- week3_dataset %>%
    sample_frac(0.5, replace = TRUE) %>%
    pluck("RS") %>%
    mean(na.rm = TRUE)
  df_sample_rs[[i]] <- sample_mean
}
df_sample_rs

## [[1]]
## [1] 719.5763
## 
## [[2]]
## [1] 723.1055
## 
## [[3]]
## [1] 719.336
## 
## [[4]]
## [1] 716.3458
## 
## [[5]]
## [1] 716.2549
## 
## [[6]]
## [1] 720.1981
## 
## [[7]]
## [1] 722.3003
## 
## [[8]]
## [1] 721.3864
## 
## [[9]]
## [1] 714.9578
## 
## [[10]]
## [1] 708.6315

This is random sample of RA column.

I think every random sample is very close to the mean of this column. I don’t think their is any anomaly in it.

df_sample_OOBP <- list()
for (i in 1:num_iterations) {
  sample_mean <- week3_dataset %>%
    sample_frac(0.5, replace = TRUE) %>%
    pluck("OOBP") %>%
    mean(na.rm = TRUE)
  df_sample_OOBP[[i]] <- sample_mean
}
df_sample_OOBP

## [[1]]
## [1] 0.3307489
## 
## [[2]]
## [1] 0.3340443
## 
## [[3]]
## [1] 0.3328515
## 
## [[4]]
## [1] 0.3312212
## 
## [[5]]
## [1] 0.3330188
## 
## [[6]]
## [1] 0.3327309
## 
## [[7]]
## [1] 0.3321549
## 
## [[8]]
## [1] 0.3333316
## 
## [[9]]
## [1] 0.332724
## 
## [[10]]
## [1] 0.3317095

This is random sample of W column

I think their is not a single season that has very low W and the mean of this column is also 80. So their is not a anomaly.

df_sample_w <- list()
for (i in 1:num_iterations) {
  sample_mean <- week3_dataset %>%
    sample_frac(0.5, replace = TRUE) %>%
    pluck("W") %>%
    mean(na.rm = TRUE)
  df_sample_w[[i]] <- sample_mean
}
df_sample_w

## [[1]]
## [1] 81.30195
## 
## [[2]]
## [1] 81.31981
## 
## [[3]]
## [1] 81.125
## 
## [[4]]
## [1] 81.44805
## 
## [[5]]
## [1] 81.26948
## 
## [[6]]
## [1] 80.3263
## 
## [[7]]
## [1] 80.39935
## 
## [[8]]
## [1] 81.40097
## 
## [[9]]
## [1] 81.61364
## 
## [[10]]
## [1] 81.5

This is random sample of OBP column

I think this one is very awkward. A lot of the random sample is below the mean of this column. But, it is not very significant, so I won’t count is as anomaly.

df_sample_OBP <- list()
for (i in 1:num_iterations) {
  sample_mean <- week3_dataset %>%
    sample_frac(0.5, replace = TRUE) %>%
    pluck("OBP") %>%
    mean(na.rm = TRUE)
  df_sample_OBP[[i]] <- sample_mean
}
df_sample_OBP

## [[1]]
## [1] 0.3269399
## 
## [[2]]
## [1] 0.3268036
## 
## [[3]]
## [1] 0.3257565
## 
## [[4]]
## [1] 0.3257776
## 
## [[5]]
## [1] 0.3258295
## 
## [[6]]
## [1] 0.3261558
## 
## [[7]]
## [1] 0.3265016
## 
## [[8]]
## [1] 0.3258523
## 
## [[9]]
## [1] 0.3264529
## 
## [[10]]
## [1] 0.3255162

This is random sample of SLG column

I think every random sample is very close to the mean of this column. I don’t think their is any anomaly in it.

df_sample_SLG <- list()
for (i in 1:num_iterations) {
  sample_mean <- week3_dataset %>%
    sample_frac(0.5, replace = TRUE) %>%
    pluck("SLG") %>%
    mean(na.rm = TRUE)
  df_sample_SLG[[i]] <- sample_mean
}
df_sample_SLG

## [[1]]
## [1] 0.3975097
## 
## [[2]]
## [1] 0.397237
## 
## [[3]]
## [1] 0.3968458
## 
## [[4]]
## [1] 0.3959334
## 
## [[5]]
## [1] 0.3970244
## 
## [[6]]
## [1] 0.3968295
## 
## [[7]]
## [1] 0.3962159
## 
## [[8]]
## [1] 0.3960081
## 
## [[9]]
## [1] 0.3965244
## 
## [[10]]
## [1] 0.3981218

This is random sample of BA column

I think every random sample is basically the same as the mean of this column. I don’t think their is any anomaly in it.

df_sample_BA <- list()
for (i in 1:num_iterations) {
  sample_mean <- week3_dataset %>%
    sample_frac(0.5, replace = TRUE) %>%
    pluck("BA") %>%
    mean(na.rm = TRUE)
  df_sample_BA[[i]] <- sample_mean
}
df_sample_BA

## [[1]]
## [1] 0.2590487
## 
## [[2]]
## [1] 0.2597305
## 
## [[3]]
## [1] 0.2595617
## 
## [[4]]
## [1] 0.2585341
## 
## [[5]]
## [1] 0.2597646
## 
## [[6]]
## [1] 0.2598718
## 
## [[7]]
## [1] 0.2583263
## 
## [[8]]
## [1] 0.2587873
## 
## [[9]]
## [1] 0.2598718
## 
## [[10]]
## [1] 0.2597419