Data World Blog

In some cases there are missing data or the missing data has been coded in a way that we didn't expected, we have to be very careful with these situacions. In this example we work with the dataset airquality require(datasets) data("airquality") ?airquality #gives us important info about the dataset A data frame with 154 observations on 6 variables. [,1] Ozone numeric Ozone (ppb) [,2] Solar.R numeric Solar R (lang) [,3] Wind numeric Wind (mph) [,4] Temp numeric Temperature (degrees F) [,5] Month numeric Month (1–12) [ ,6] Day numeric Day of month (1–31) head(airquality) #shows the first rows in the dataset ## Ozone Solar.R Wind Temp Month Day ## 1 41 190 7.4 67 5 1 ## 2 36 118 8.0 72 5 2 ## 3 12 149 12.6 74 5 3 ## 4 18 313 11.5 62 5 4 ## 5 NA NA 14.3 56 5 5 ## 6 28 NA 14.9 66 5 6 str(airqua...

Initial analysis is a very important step that should always be performed prior to analysing the data we are working with. The data we receive most of the time is messy and may contain mistakes that can lead us to wrong conclusions. Here we will use the dataset infert , that is already present in R. To get to know the data is very important to know the background and the meaning of each variable present in the dataset. Since infert is a dataset in R we can get information about the data using the following code: require(datasets) ?infert #gives us important info about the dataset inf <- infert #renamed dataset as 'inf' This gives us the following information: Format 1.Education: 0 = 0-5 years, 1 = 6-11 years, 2 = 12+ years 2.Age: Age in years of case 3.Parity: Count 4.Number of prior induced abortions: 0 = 0, 1 = 1, 2 = 2 or more 5.Case status: 1 = case 0 = control 6.Number of prior spontaneous abortions: 0 = 0, 1 = 1, 2...