### Note: I tried to read_excel. Couldn't do it. I tried install.packages("read_excel"). The message I got was that package couldn't be loaded on this version of R.
### I gave up, converted the Excel file to .csv and tried doing the exercise using the .csv file.
### Warning in install.packages :
### package ‘read_excel’ is not available for this version of R
### A version of this package for your version of R might be available elsewhere,
### see the ideas at
### https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
getwd()
## [1] "C:/Users/Jerome/Documents/0000_Work_Files/0000_Montgomery_College/Data_Science_101/Data_101_Fall_2022/Homework_6_Due_17Oct2022"
library(readr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ dplyr 1.0.10
## ✔ tibble 3.1.8 ✔ stringr 1.4.1
## ✔ tidyr 1.2.1 ✔ forcats 0.5.2
## ✔ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(dplyr)
mnms <- read_csv("mnm.data.csv")
## Rows: 382 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): student_id, color, defect
## dbl (3): id, total, weight_grams
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
str(mnms)
## spec_tbl_df [382 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ student_id : chr [1:382] "AP_LV" "AP_LV" "AP_LV" "AP_LV" ...
## $ id : num [1:382] 1 2 3 4 5 6 7 8 9 10 ...
## $ color : chr [1:382] "r" "r" "r" "r" ...
## $ defect : chr [1:382] "c" "l" "z" "z" ...
## $ total : num [1:382] 27 27 27 27 27 27 27 27 27 27 ...
## $ weight_grams: num [1:382] 40 40 40 40 40 40 40 40 40 40 ...
## - attr(*, "spec")=
## .. cols(
## .. student_id = col_character(),
## .. id = col_double(),
## .. color = col_character(),
## .. defect = col_character(),
## .. total = col_double(),
## .. weight_grams = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
summary(mnms)
## student_id id color defect
## Length:382 Min. : 1.0 Length:382 Length:382
## Class :character 1st Qu.:10.0 Class :character Class :character
## Mode :character Median :20.0 Mode :character Mode :character
## Mean :22.8
## 3rd Qu.:35.0
## Max. :55.0
## total weight_grams
## Min. :18.00 Min. :25.00
## 1st Qu.:27.00 1st Qu.:40.00
## Median :54.00 Median :48.00
## Mean :45.01 Mean :44.25
## 3rd Qu.:55.00 3rd Qu.:50.00
## Max. :56.00 Max. :50.00
summarize(mnms)
## # A tibble: 1 × 0
mean(mnms$weight_grams, na.rm = TRUE)
## [1] 44.24607
sd(mnms$weight_grams, na.rm = T)
## [1] 6.59851
boxplot(mnms$weight_grams)
hist(mnms$weight_grams)
##install.packages("moments") I had to comment this to get knit to run.
library(moments)
skewness(mnms$weight_grams)
## [1] -1.367271
kurtosis(mnms$weight_grams)
## [1] 4.854292
table(mnms$color, mnms$defect)
##
## c l m z
## bl 7 13 4 58
## br 2 3 0 51
## g 5 10 1 35
## o 6 8 2 71
## r 6 4 1 36
## y 7 4 1 47
### Defect Code Z means no defects. Which color has the fewest defects?
table(mnms$color)
##
## bl br g o r y
## 82 56 51 87 47 59
### Calculate percentages
58/82
## [1] 0.7073171
51/56
## [1] 0.9107143
35/51
## [1] 0.6862745
71/87
## [1] 0.816092
36/47
## [1] 0.7659574
47/59
## [1] 0.7966102
### Brown has the highest percentage of non-defect M & Ms. Green has the lowest percentage of non-defect M & Ms.