When you have to work in a data analysis project the first thing you usually check its for the description of your dataset variables. They are many R packages to work in order to obtain your dataset descriptive information. on this blog, I will try to use some of them as examples. the dataset I will be using its a built-in dataset call “Iris”.
library(psych)
data <- iris
describeBy(data, data$type)## Warning in describeBy(data, data$type): no grouping variable requested## vars n mean sd median trimmed mad min max range skew
## Sepal.Length 1 150 5.84 0.83 5.80 5.81 1.04 4.3 7.9 3.6 0.31
## Sepal.Width 2 150 3.06 0.44 3.00 3.04 0.44 2.0 4.4 2.4 0.31
## Petal.Length 3 150 3.76 1.77 4.35 3.76 1.85 1.0 6.9 5.9 -0.27
## Petal.Width 4 150 1.20 0.76 1.30 1.18 1.04 0.1 2.5 2.4 -0.10
## Species* 5 150 2.00 0.82 2.00 2.00 1.48 1.0 3.0 2.0 0.00
## kurtosis se
## Sepal.Length -0.61 0.07
## Sepal.Width 0.14 0.04
## Petal.Length -1.42 0.14
## Petal.Width -1.36 0.06
## Species* -1.52 0.07library(skimr)
data <- iris
skim(data)| Name | data |
| Number of rows | 150 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Species | 0 | 1 | FALSE | 3 | set: 50, ver: 50, vir: 50 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | 0 | 1 | 5.84 | 0.83 | 4.3 | 5.1 | 5.80 | 6.4 | 7.9 | ▆▇▇▅▂ |
| Sepal.Width | 0 | 1 | 3.06 | 0.44 | 2.0 | 2.8 | 3.00 | 3.3 | 4.4 | ▁▆▇▂▁ |
| Petal.Length | 0 | 1 | 3.76 | 1.77 | 1.0 | 1.6 | 4.35 | 5.1 | 6.9 | ▇▁▆▇▂ |
| Petal.Width | 0 | 1 | 1.20 | 0.76 | 0.1 | 0.3 | 1.30 | 1.8 | 2.5 | ▇▁▇▅▃ |
library(Hmisc)
data3 <- iris
Hmisc::describe(data3)## data3
##
## 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
## --------------------------------------------------------------------------------library(pastecs)
data4 <- iris
stat.desc(data4)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## nbr.val 150.00000000 150.00000000 150.0000000 150.00000000 NA
## nbr.null 0.00000000 0.00000000 0.0000000 0.00000000 NA
## nbr.na 0.00000000 0.00000000 0.0000000 0.00000000 NA
## min 4.30000000 2.00000000 1.0000000 0.10000000 NA
## max 7.90000000 4.40000000 6.9000000 2.50000000 NA
## range 3.60000000 2.40000000 5.9000000 2.40000000 NA
## sum 876.50000000 458.60000000 563.7000000 179.90000000 NA
## median 5.80000000 3.00000000 4.3500000 1.30000000 NA
## mean 5.84333333 3.05733333 3.7580000 1.19933333 NA
## SE.mean 0.06761132 0.03558833 0.1441360 0.06223645 NA
## CI.mean.0.95 0.13360085 0.07032302 0.2848146 0.12298004 NA
## var 0.68569351 0.18997942 3.1162779 0.58100626 NA
## std.dev 0.82806613 0.43586628 1.7652982 0.76223767 NA
## coef.var 0.14171126 0.14256420 0.4697441 0.63555114 NA