R-Assignment4-Florido

What is a model?

A scientific activity that explains interesting parts of the dataset. These parts are relevant to the industry or discipline the data is from.

What are the five groups of tasks of modeling in data mining?

Exploratory analysis
Dependency modeling
Clustering
Anomaly detection
Predictive analytics

Typically, what does a data miner do?

A data miner searches to understand, define, quantify, visualize and simulate the dataset.

Most data mining techniques can be bifurcated into groups. What are those techniques?

Searching for relationships in the columns (features)
Searching for relationships among the rows (observations)

What is the main goal of exploratory data analysis?

The main goal of exploratory data analysis is to provide a summary of dataset focusing on relevant or interesting characteristics. The two types of summaries are textual and visual.

Most datasets have a dimensionality that makes it very difficult for a standard user to inspect the full data and find interesting properties of these data. TRUE or FALSE?

TRUE

What are data summaries?

Data summaries is what it sounds like an overview of specific charactertistics or key properties.

The summarise() function is a function of which package?

The function summarise() is a function of the dplyr package.

1. Run the following code (below). Study the data. Explain the dataset.

library(DMwR2)

## Warning: package 'DMwR2' was built under R version 4.2.1

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

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, union

data(algae)
algae

## # A tibble: 200 × 18
##    season size  speed   mxPH  mnO2    Cl    NO3   NH4  oPO4   PO4  Chla    a1
##    <fct>  <fct> <fct>  <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 winter small medium  8      9.8  60.8  6.24  578   105   170   50      0  
##  2 spring small medium  8.35   8    57.8  1.29  370   429.  559.   1.3    1.4
##  3 autumn small medium  8.1   11.4  40.0  5.33  347.  126.  187.  15.6    3.3
##  4 spring small medium  8.07   4.8  77.4  2.30   98.2  61.2 139.   1.4    3.1
##  5 autumn small medium  8.06   9    55.4 10.4   234.   58.2  97.6 10.5    9.2
##  6 winter small high    8.25  13.1  65.8  9.25  430    18.2  56.7 28.4   15.1
##  7 summer small high    8.15  10.3  73.2  1.54  110    61.2 112.   3.2    2.4
##  8 autumn small high    8.05  10.6  59.1  4.99  206.   44.7  77.4  6.9   18.2
##  9 winter small medium  8.7    3.4  22.0  0.886 103.   36.3  71    5.54  25.4
## 10 winter small high    7.93   9.9   8    1.39    5.8  27.2  46.6  0.8   17  
## # … with 190 more rows, and 6 more variables: a2 <dbl>, a3 <dbl>, a4 <dbl>,
## #   a5 <dbl>, a6 <dbl>, a7 <dbl>

data(iris)
iris

##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1            5.1         3.5          1.4         0.2     setosa
## 2            4.9         3.0          1.4         0.2     setosa
## 3            4.7         3.2          1.3         0.2     setosa
## 4            4.6         3.1          1.5         0.2     setosa
## 5            5.0         3.6          1.4         0.2     setosa
## 6            5.4         3.9          1.7         0.4     setosa
## 7            4.6         3.4          1.4         0.3     setosa
## 8            5.0         3.4          1.5         0.2     setosa
## 9            4.4         2.9          1.4         0.2     setosa
## 10           4.9         3.1          1.5         0.1     setosa
## 11           5.4         3.7          1.5         0.2     setosa
## 12           4.8         3.4          1.6         0.2     setosa
## 13           4.8         3.0          1.4         0.1     setosa
## 14           4.3         3.0          1.1         0.1     setosa
## 15           5.8         4.0          1.2         0.2     setosa
## 16           5.7         4.4          1.5         0.4     setosa
## 17           5.4         3.9          1.3         0.4     setosa
## 18           5.1         3.5          1.4         0.3     setosa
## 19           5.7         3.8          1.7         0.3     setosa
## 20           5.1         3.8          1.5         0.3     setosa
## 21           5.4         3.4          1.7         0.2     setosa
## 22           5.1         3.7          1.5         0.4     setosa
## 23           4.6         3.6          1.0         0.2     setosa
## 24           5.1         3.3          1.7         0.5     setosa
## 25           4.8         3.4          1.9         0.2     setosa
## 26           5.0         3.0          1.6         0.2     setosa
## 27           5.0         3.4          1.6         0.4     setosa
## 28           5.2         3.5          1.5         0.2     setosa
## 29           5.2         3.4          1.4         0.2     setosa
## 30           4.7         3.2          1.6         0.2     setosa
## 31           4.8         3.1          1.6         0.2     setosa
## 32           5.4         3.4          1.5         0.4     setosa
## 33           5.2         4.1          1.5         0.1     setosa
## 34           5.5         4.2          1.4         0.2     setosa
## 35           4.9         3.1          1.5         0.2     setosa
## 36           5.0         3.2          1.2         0.2     setosa
## 37           5.5         3.5          1.3         0.2     setosa
## 38           4.9         3.6          1.4         0.1     setosa
## 39           4.4         3.0          1.3         0.2     setosa
## 40           5.1         3.4          1.5         0.2     setosa
## 41           5.0         3.5          1.3         0.3     setosa
## 42           4.5         2.3          1.3         0.3     setosa
## 43           4.4         3.2          1.3         0.2     setosa
## 44           5.0         3.5          1.6         0.6     setosa
## 45           5.1         3.8          1.9         0.4     setosa
## 46           4.8         3.0          1.4         0.3     setosa
## 47           5.1         3.8          1.6         0.2     setosa
## 48           4.6         3.2          1.4         0.2     setosa
## 49           5.3         3.7          1.5         0.2     setosa
## 50           5.0         3.3          1.4         0.2     setosa
## 51           7.0         3.2          4.7         1.4 versicolor
## 52           6.4         3.2          4.5         1.5 versicolor
## 53           6.9         3.1          4.9         1.5 versicolor
## 54           5.5         2.3          4.0         1.3 versicolor
## 55           6.5         2.8          4.6         1.5 versicolor
## 56           5.7         2.8          4.5         1.3 versicolor
## 57           6.3         3.3          4.7         1.6 versicolor
## 58           4.9         2.4          3.3         1.0 versicolor
## 59           6.6         2.9          4.6         1.3 versicolor
## 60           5.2         2.7          3.9         1.4 versicolor
## 61           5.0         2.0          3.5         1.0 versicolor
## 62           5.9         3.0          4.2         1.5 versicolor
## 63           6.0         2.2          4.0         1.0 versicolor
## 64           6.1         2.9          4.7         1.4 versicolor
## 65           5.6         2.9          3.6         1.3 versicolor
## 66           6.7         3.1          4.4         1.4 versicolor
## 67           5.6         3.0          4.5         1.5 versicolor
## 68           5.8         2.7          4.1         1.0 versicolor
## 69           6.2         2.2          4.5         1.5 versicolor
## 70           5.6         2.5          3.9         1.1 versicolor
## 71           5.9         3.2          4.8         1.8 versicolor
## 72           6.1         2.8          4.0         1.3 versicolor
## 73           6.3         2.5          4.9         1.5 versicolor
## 74           6.1         2.8          4.7         1.2 versicolor
## 75           6.4         2.9          4.3         1.3 versicolor
## 76           6.6         3.0          4.4         1.4 versicolor
## 77           6.8         2.8          4.8         1.4 versicolor
## 78           6.7         3.0          5.0         1.7 versicolor
## 79           6.0         2.9          4.5         1.5 versicolor
## 80           5.7         2.6          3.5         1.0 versicolor
## 81           5.5         2.4          3.8         1.1 versicolor
## 82           5.5         2.4          3.7         1.0 versicolor
## 83           5.8         2.7          3.9         1.2 versicolor
## 84           6.0         2.7          5.1         1.6 versicolor
## 85           5.4         3.0          4.5         1.5 versicolor
## 86           6.0         3.4          4.5         1.6 versicolor
## 87           6.7         3.1          4.7         1.5 versicolor
## 88           6.3         2.3          4.4         1.3 versicolor
## 89           5.6         3.0          4.1         1.3 versicolor
## 90           5.5         2.5          4.0         1.3 versicolor
## 91           5.5         2.6          4.4         1.2 versicolor
## 92           6.1         3.0          4.6         1.4 versicolor
## 93           5.8         2.6          4.0         1.2 versicolor
## 94           5.0         2.3          3.3         1.0 versicolor
## 95           5.6         2.7          4.2         1.3 versicolor
## 96           5.7         3.0          4.2         1.2 versicolor
## 97           5.7         2.9          4.2         1.3 versicolor
## 98           6.2         2.9          4.3         1.3 versicolor
## 99           5.1         2.5          3.0         1.1 versicolor
## 100          5.7         2.8          4.1         1.3 versicolor
## 101          6.3         3.3          6.0         2.5  virginica
## 102          5.8         2.7          5.1         1.9  virginica
## 103          7.1         3.0          5.9         2.1  virginica
## 104          6.3         2.9          5.6         1.8  virginica
## 105          6.5         3.0          5.8         2.2  virginica
## 106          7.6         3.0          6.6         2.1  virginica
## 107          4.9         2.5          4.5         1.7  virginica
## 108          7.3         2.9          6.3         1.8  virginica
## 109          6.7         2.5          5.8         1.8  virginica
## 110          7.2         3.6          6.1         2.5  virginica
## 111          6.5         3.2          5.1         2.0  virginica
## 112          6.4         2.7          5.3         1.9  virginica
## 113          6.8         3.0          5.5         2.1  virginica
## 114          5.7         2.5          5.0         2.0  virginica
## 115          5.8         2.8          5.1         2.4  virginica
## 116          6.4         3.2          5.3         2.3  virginica
## 117          6.5         3.0          5.5         1.8  virginica
## 118          7.7         3.8          6.7         2.2  virginica
## 119          7.7         2.6          6.9         2.3  virginica
## 120          6.0         2.2          5.0         1.5  virginica
## 121          6.9         3.2          5.7         2.3  virginica
## 122          5.6         2.8          4.9         2.0  virginica
## 123          7.7         2.8          6.7         2.0  virginica
## 124          6.3         2.7          4.9         1.8  virginica
## 125          6.7         3.3          5.7         2.1  virginica
## 126          7.2         3.2          6.0         1.8  virginica
## 127          6.2         2.8          4.8         1.8  virginica
## 128          6.1         3.0          4.9         1.8  virginica
## 129          6.4         2.8          5.6         2.1  virginica
## 130          7.2         3.0          5.8         1.6  virginica
## 131          7.4         2.8          6.1         1.9  virginica
## 132          7.9         3.8          6.4         2.0  virginica
## 133          6.4         2.8          5.6         2.2  virginica
## 134          6.3         2.8          5.1         1.5  virginica
## 135          6.1         2.6          5.6         1.4  virginica
## 136          7.7         3.0          6.1         2.3  virginica
## 137          6.3         3.4          5.6         2.4  virginica
## 138          6.4         3.1          5.5         1.8  virginica
## 139          6.0         3.0          4.8         1.8  virginica
## 140          6.9         3.1          5.4         2.1  virginica
## 141          6.7         3.1          5.6         2.4  virginica
## 142          6.9         3.1          5.1         2.3  virginica
## 143          5.8         2.7          5.1         1.9  virginica
## 144          6.8         3.2          5.9         2.3  virginica
## 145          6.7         3.3          5.7         2.5  virginica
## 146          6.7         3.0          5.2         2.3  virginica
## 147          6.3         2.5          5.0         1.9  virginica
## 148          6.5         3.0          5.2         2.0  virginica
## 149          6.2         3.4          5.4         2.3  virginica
## 150          5.9         3.0          5.1         1.8  virginica

summary(algae)

##     season       size       speed         mxPH            mnO2       
##  autumn:40   large :45   high  :84   Min.   :5.600   Min.   : 1.500  
##  spring:53   medium:84   low   :33   1st Qu.:7.700   1st Qu.: 7.725  
##  summer:45   small :71   medium:83   Median :8.060   Median : 9.800  
##  winter:62                           Mean   :8.012   Mean   : 9.118  
##                                      3rd Qu.:8.400   3rd Qu.:10.800  
##                                      Max.   :9.700   Max.   :13.400  
##                                      NA's   :1       NA's   :2       
##        Cl               NO3              NH4                oPO4       
##  Min.   :  0.222   Min.   : 0.050   Min.   :    5.00   Min.   :  1.00  
##  1st Qu.: 10.981   1st Qu.: 1.296   1st Qu.:   38.33   1st Qu.: 15.70  
##  Median : 32.730   Median : 2.675   Median :  103.17   Median : 40.15  
##  Mean   : 43.636   Mean   : 3.282   Mean   :  501.30   Mean   : 73.59  
##  3rd Qu.: 57.824   3rd Qu.: 4.446   3rd Qu.:  226.95   3rd Qu.: 99.33  
##  Max.   :391.500   Max.   :45.650   Max.   :24064.00   Max.   :564.60  
##  NA's   :10        NA's   :2        NA's   :2          NA's   :2       
##       PO4              Chla               a1              a2        
##  Min.   :  1.00   Min.   :  0.200   Min.   : 0.00   Min.   : 0.000  
##  1st Qu.: 41.38   1st Qu.:  2.000   1st Qu.: 1.50   1st Qu.: 0.000  
##  Median :103.29   Median :  5.475   Median : 6.95   Median : 3.000  
##  Mean   :137.88   Mean   : 13.971   Mean   :16.92   Mean   : 7.458  
##  3rd Qu.:213.75   3rd Qu.: 18.308   3rd Qu.:24.80   3rd Qu.:11.375  
##  Max.   :771.60   Max.   :110.456   Max.   :89.80   Max.   :72.600  
##  NA's   :2        NA's   :12                                        
##        a3               a4               a5               a6        
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
##  Median : 1.550   Median : 0.000   Median : 1.900   Median : 0.000  
##  Mean   : 4.309   Mean   : 1.992   Mean   : 5.064   Mean   : 5.964  
##  3rd Qu.: 4.925   3rd Qu.: 2.400   3rd Qu.: 7.500   3rd Qu.: 6.925  
##  Max.   :42.800   Max.   :44.600   Max.   :44.400   Max.   :77.600  
##                                                                     
##        a7        
##  Min.   : 0.000  
##  1st Qu.: 0.000  
##  Median : 1.000  
##  Mean   : 2.495  
##  3rd Qu.: 2.400  
##  Max.   :31.600  
##

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

What is the algae dataset about?

The algae dataset has three nominal variables.
- Season
- Size of river
- Speed of river
The algae dataset shows the quartile plots of the following numerical variables
- Max PH (mxPH)
- Min Oxygen Level (mnO2)
- Mean value of Chlorine (CL)
- Mean value of Nitrates (NO3)
- Mean value of Ammonium (NH4)
- Mean of Phosphate (oP04 and P04)
- Mean of Chlorophyll (Chla)
- Harmful algae frequencies (a1-a7)

Explain the characteristics of the iris dataset.

We have three different species of iris: setosa, versicolor, and virginica.
We are given the five number summary or the quartile summary for the following characteristics.
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width

Did you discover any correlation between any pair of features in the iris dataset?

I did not I am sorry. Maybe if you provide an example I might be able to find a correlation on my own.

What does the summarise() function do?

The function summarise() can use different functions that produce a scalar value to columns in the data frame table.

We can use the functions, summarise_each() and funs() to perform what kind of task?

The function summarise_each() applies one or more functions to one or more columns.
The function funs() makes it convenient to list functions that you want to apply on columns specified.

What is the task of the group_by() function? This function is included in which package?

The function group_by() is in the package dplyr and it organizes the data by specified nominal variables creating a sub-group.

Which function will you use if you want to study potential differences among sub-groups?

The function summarize can be used after finding sub-groups with group_by to study potential differences among sub-groups.

The top algorithm/code chuck on page 90(Code 4) gives us a way to create a function to obtain the mode of a variable. Go through this algorithm. Now replace

“algae$mxPh” with “iris$Sepal.Length” and “algae$season” with “iris$Petal.Length”

Copy and/or take a screenshot your results for both and include them in this assignment (I only need the first 20 to 40 rows of each sub-group).

Mode <- function(x, na.rm=FALSE){
  if(na.rm) x <- x[!is.na(x)]
  ux <- unique(x)
  return(ux[which.max(tabulate(match(x,ux)))])
}
Mode(iris$Sepal.Length)

## [1] 5

Mode(iris$Petal.Length)

## [1] 1.4

Explain the centralValue() function. What does it do?

The function centralValue() is a life saver if you are looking for the median and mode. If your variables are numeric then it will output the median and if your variables are nominal then it wil output the mode. Yay for shortcuts!

1. Explain the inter-quartile range (IQR)

The data is organized into four quartiles by finding 25 percentile, 50 percentile and 75 percentile. 25 percentile is a value such that 25% of the data is below it and 75% of the data is above it. So inter-quartile range takes the Q3 which is the 75 percentile and subtracts it by Q1 which is the 25 percentile. 50% of the data values are in between the inter-quartile range in other words in between Q1 and Q3.

Explain x-quartile

The function x-quartile can be used to find any quantiles of a continuous variable. Maybe you are not interested in Q1 or Q3 maybe you want to find the 18th percentile, you can with this function.

What does a large value of the IQR mean?

If IQR is a large value that means that the values are spread out over a large range.

What does a small value of the IQR mean?

If IQR is a small value this means that the values are closer together meaning less variation.

Which measure of spread, or variability, is more susceptible to outliers?

The range is more susceptible to ourliers.

1. Using the Iris dataset, obtain the quantiles of the variable (or feature), Length, by Species.

#Sepal Length by Species
aggregate(iris$Sepal.Length, list(Species=iris$Species), quantile)

##      Species  x.0% x.25% x.50% x.75% x.100%
## 1     setosa 4.300 4.800 5.000 5.200  5.800
## 2 versicolor 4.900 5.600 5.900 6.300  7.000
## 3  virginica 4.900 6.225 6.500 6.900  7.900

#Petal Length by Species
aggregate(Petal.Length ~ Species, data=iris, quantile)

##      Species Petal.Length.0% Petal.Length.25% Petal.Length.50% Petal.Length.75%
## 1     setosa           1.000            1.400            1.500            1.575
## 2 versicolor           3.000            4.000            4.350            4.600
## 3  virginica           4.500            5.100            5.550            5.875
##   Petal.Length.100%
## 1             1.900
## 2             5.100
## 3             6.900

Which package provides the better grouping facilities, baseR or dplyr? Which function is best?

It depends on the operations and what type of data we have. If we want to find quantiles by species then baseR is best.

Find the mode of the subgroup “iris$Species.”

Mode(iris$Species)

## [1] setosa
## Levels: setosa versicolor virginica

1. What are “pipes?”

Pipes are used to perform sequential tasks. They look like %>%.

What is “piping syntax?”

Piping syntax is the way the code is written using pipes. It is clean and easy to understand because it focuses on actions. Get up %>% brush teeth %>% get dressed %>% go to work.

What is the “pipe operator” (% > %)? Pipe operator is a special operational function available under the packages magrittr and dplyr.

In Code 9, the second chuck of code from the top of page 92, interpret “Species = iris$Species,” which is the second argument of the aggregate() function. What does it all mean?

This piece of code is telling the computer to focus on species when forming the sub-groups.

In Code 10, the third chuck of code from the top of P.92, interpret all three arguments of the aggregate() What do they all mean?

The chuck of code is telling the computer to calculate the quartiles for Sepal.Length for each species in the iris dataset.

In some datasets a column (or feature, or variable) may contain symbols such as “?” in some of its rows (Look at Section 3.3.1.4 on Pp 60 and 61). If we use the class() function on that column, we are sure to get the column labeled as “factor.” However, assume we want this column to be labeled “integer.” Which function can we use to parse a column, or a vector of values, from “factors” to integers?”

We can use the function parse_integer to parse a column from “factors” to “integers.”

1. What is the following code used for?

data(algae, package="DMwR2")
nasRow <- apply(algae,1,function(r) sum(is.na(r)))
cat("The algae dataset contains ", sum(nasRow)," NA values. \n")

## The algae dataset contains  33  NA values.

The code above is used to find the number of missing values in the Algae dataset. (b) What results are we looking for? We are looking through the rows to find the number of spots that have NA values.

1. What method is used to detect a univariate outlier?
- A method that can be used to detect a univariate outlier is the boxplot rule.
1. What does that method state?
- An value is considered an outlier if it is outside of the interval

What sort of results does the summary () function yield when applied to a dataset? - The function summary() displays the five number summary (descriptive statistics) of the dataset. If the variable is nominal then it displays the sample size (number of occurrences).

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

For what is the function, describe() used?

The function describe() is used to find summary statistics. It is similar to summary() but with more information like missing values, unique values, 5 percentile, 10 percentile, lowest and highest values.

library(Hmisc)

## Warning: package 'Hmisc' was built under R version 4.2.1

## Loading required package: lattice

## Loading required package: survival

## Loading required package: Formula

## Loading required package: ggplot2

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:dplyr':
## 
##     src, summarize

## The following objects are masked from 'package:base':
## 
##     format.pval, units

describe(iris)

## iris 
## 
##  5  Variables      150  Observations
## --------------------------------------------------------------------------------
## Sepal.Length 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##      150        0       35    0.998    5.843   0.9462    4.600    4.800 
##      .25      .50      .75      .90      .95 
##    5.100    5.800    6.400    6.900    7.255 
## 
## lowest : 4.3 4.4 4.5 4.6 4.7, highest: 7.3 7.4 7.6 7.7 7.9
## --------------------------------------------------------------------------------
## Sepal.Width 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##      150        0       23    0.992    3.057   0.4872    2.345    2.500 
##      .25      .50      .75      .90      .95 
##    2.800    3.000    3.300    3.610    3.800 
## 
## lowest : 2.0 2.2 2.3 2.4 2.5, highest: 3.9 4.0 4.1 4.2 4.4
## --------------------------------------------------------------------------------
## Petal.Length 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##      150        0       43    0.998    3.758    1.979     1.30     1.40 
##      .25      .50      .75      .90      .95 
##     1.60     4.35     5.10     5.80     6.10 
## 
## lowest : 1.0 1.1 1.2 1.3 1.4, highest: 6.3 6.4 6.6 6.7 6.9
## --------------------------------------------------------------------------------
## Petal.Width 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##      150        0       22     0.99    1.199   0.8676      0.2      0.2 
##      .25      .50      .75      .90      .95 
##      0.3      1.3      1.8      2.2      2.3 
## 
## lowest : 0.1 0.2 0.3 0.4 0.5, highest: 2.1 2.2 2.3 2.4 2.5
## --------------------------------------------------------------------------------
## Species 
##        n  missing distinct 
##      150        0        3 
##                                            
## Value          setosa versicolor  virginica
## Frequency          50         50         50
## Proportion      0.333      0.333      0.333
## --------------------------------------------------------------------------------

Which package contains the function, describe()?

The function describe() is from the package Hmisc.

Give a definition of the term, parse.

The parse function changes data into text that R can read so it can then be manipulated. For example, changing variables from strings to integers.

R-Assignment4-Florido

Gabriela Florido

2022-07-22