Loading and Piping Data

Can load data into R using read.csv() to get data from a comma separated variable (CSV) file. DataFrameName <- read.csv(MyDataFile.csv) Tidyverse package provides a lot of useful functions for manipulating data. head(DataFrameName) – Shows first 6 rows of the data tail(DataFrameName) – Shows last 6 rows of data view(DataFrameName) – Shows all rows in tabularContinue reading “Loading and Piping Data”

Date Formats – Lessons Not Learned

An article from BATIMES popped up in my inbox this morning, the author raises the issue of varying date formats causing problems of misinterpretation. Different countries, different companies within the same country, different departments in the same company and different systems within the same department use differing formats. Even different reports from the same systemContinue reading “Date Formats – Lessons Not Learned”

Data Cleansing m/d/yyyy Dates

A common issue I’ve found with data cleansing when pulling into Excel is the date format wil often default to US format. If it’s a 2 digit month and day (mm/dd/yyyy) then that’s not much of a problem but where it can be one or two digit (m/d/yyyy so 1/1/2020 and 10/10/2020 are both possiblilities)Continue reading “Data Cleansing m/d/yyyy Dates”

The oldest apprentice in town (probably)

My employer, keen to get some value out of the apprenticeship levy, has decided to put a number of us on appreticeship programmes (I suppose it’s cheaper than regular training). In my case, a few months short of turning 50, that means a Level 4 Data Analysis diploma because I know a bit about Excel.Continue reading “The oldest apprentice in town (probably)”