Sample Dataset
J. Kavanagh
5/30/2020
First Steps
Set the directory for your RStudio session using either the Session pull down menu or manually inputting the exact folder where your sources files are situated. Note the example below is a sample
setwd("/Users/yourname/Dropbox/foldername")Load the packages the following packages we’re going to be using, ‘tidyverse’ is a collection of data manipulation packages and ‘lubridate’ is designed for working with dates and time data.
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────── tidyverse 1.2.1 ──## ✓ ggplot2 3.2.1 ✓ purrr 0.3.4
## ✓ tibble 2.1.3 ✓ dplyr 0.8.3
## ✓ tidyr 1.0.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── Conflicts ─────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(lubridate)##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## dateImport the file - this imports the file into the software and will allow for data manipulation
read.csv('CavanBarracksSurvey-IrishArmyCensus1922.csv', stringsAsFactors = F) -> cavanExplore the dataset - the head() function gives the first few entries of the dataset
head(cavan)## Date.Created Date.Modified Forename Surname Age
## 1 2015-06-18T16:43:39 2015-11-08T00:43:37 HUGH McGOVERN 22
## 2 2015-07-15T11:25:11 2015-11-08T00:53:09 MICHAEL ANDERSON 28
## 3 2015-02-22T08:17:57 2015-11-07T23:46:04 THOMAS MURPHY 19
## 4 2015-02-22T08:17:27 2015-11-07T23:02:25 MARTIN NOONE 22
## 5 2015-07-15T11:25:25 2015-11-08T01:07:11 LAURENCE GERAGHTY 17
## 6 2015-02-22T08:17:56 2015-11-07T23:45:20 JOHN CASSIDY 20
## Barracks...Post Date.of.Attestation Rank Corps
## 1 Dowra 1922-03-10 Private West Cavan Brigade
## 2 Virginia Lodge 1922-05-05 Private Infantry
## 3 Cavan 1922-10-16 Private Infantry
## 4 Belturbet 1922-04-15 Staff / Sergeant
## 5 Virginia Lodge 1922-05-06 Private Infantry
## 6 Cavan 1922-08-05 Private Infantry
## Division Command
## 1 Barracks 1st Midland
## 2 Co. Cavan 1st Eastern
## 3 5th Northern Division Eastern Command
## 4 1st Midland Division Mid Western Command
## 5 Co. Cavan 1st Eastern
## 6 5th Northern Division Eastern Command
## Home.Address No..on.Pay.Book
## 1 Culleagh, Co. Cavan 8153
## 2 Suncroft, Curragh Camp, Co. Kildare 17746
## 3 97 McDonald Street, Belfast, Co. Antrim 25239
## 4 Inver, Barnatra, Ballina, Co. Mayo 7164
## 5 Maddenstown, Curragh Camp, Co. Kildare 17752
## 6 Newington Avenue, Belfast, Co. Antrim 18157
## Place.of.Attestation Next.of.Kin
## 1 Swanlinbar
## 2 Naas Mother
## 3 Cavan Mother
## 4 Longford Mother
## 5 Trim Father
## 6 Clones Mother
## Name...Address.of.Next.of.Kin Marital.Status
## 1 MRS. MARY McGOVERN - Culleagh, Co. Cavan Single
## 2 MRS. KATE ANDERSON - Suncroft, Curragh Camp, Co. Kildare Single
## 3 ELLEN MURPHY - 97 McDonald Street, Belfast, Co. Antrim Single
## 4 MRS. ELLEN NOONE - Inver, Barnatra, Ballina, Co. Mayo Single
## 5 PATRICK GERAGHTY - Maddenstown, Curragh Camp, Co. Kildare Single
## 6 MARY CASSIDY - Newington Avenue, Belfast, Co. Antrim Single
## Religion Additional.Notes Regimental.Number
## 1 Roman Catholic 802 (23257)
## 2 Roman Catholic Check address 13592 R.
## 3 Roman Catholic 15679 V.R.
## 4 Roman Catholic 2389 (23325)
## 5 Roman Catholic 13547 R.
## 6 Roman Catholic 15673 V.R.Lets explore some counting and selecting processes - note the %>% is a pipeline which allows you to call up specific columns within your dataset
# This method uses the %>% as a pipeline to count the number of soldiers per division - using cavan$ will bring up all the columns within the dataset as shown in head(cavan)
cavan %>% count(Division)## # A tibble: 4 x 2
## Division n
## <chr> <int>
## 1 1st Midland Division 108
## 2 5th Northern Division 169
## 3 Barracks 21
## 4 Co. Cavan 65# If you were interested in specific columns within the dataset, the following command allows for a selection of specific fields in this example: Division, Command and Date.of.Attestation
cavan %>% select(Division, Command, Date.of.Attestation) -> cavan_date_comm_div
head(cavan_date_comm_div)## Division Command Date.of.Attestation
## 1 Barracks 1st Midland 1922-03-10
## 2 Co. Cavan 1st Eastern 1922-05-05
## 3 5th Northern Division Eastern Command 1922-10-16
## 4 1st Midland Division Mid Western Command 1922-04-15
## 5 Co. Cavan 1st Eastern 1922-05-06
## 6 5th Northern Division Eastern Command 1922-08-05# This converts the class of the field from a factor i.e. text to a specific date class of object. The class() allows you to check that the change occurred successfully.
class(cavan$Date.of.Attestation)## [1] "character"cavan$Date.of.Attestation <- as.Date(cavan$Date.of.Attestation)
class(cavan$Date.of.Attestation)## [1] "Date"As this dataset contains a series of specific dates, it is possible to subdivide the dataset by very specific date ranges
# This creates a specific date range between July-October 1922 as the dates when soldiers were recruited.
subset(cavan, as.Date(Date.of.Attestation) >= '1922-01-01' & as.Date(Date.of.Attestation) <= '1922-10-01') -> jan_oct# For example if you wanted to count the number of men who were attested into the military and their age between January - October 1922, the following code would create discrete statistics
jan_oct %>% count(Date.of.Attestation, Age) -> jan_oct_date_age# You can change the column names using colnames()
colnames(jan_oct_date_age) <- c("Date", "Age", "No_of_Soldiers")
head(jan_oct_date_age)## # A tibble: 6 x 3
## Date Age No_of_Soldiers
## <date> <int> <int>
## 1 1922-02-01 21 1
## 2 1922-02-02 22 2
## 3 1922-02-05 19 1
## 4 1922-02-07 20 1
## 5 1922-02-07 21 2
## 6 1922-02-07 23 1# Graphs - One of the most important ways to help readers understand the data that has been collated is by visual representations such as graphs.
# The following code will create a chart of the above dataset showing the distribution of the soldiers' age across the period July - October 1922.
ggplot(jan_oct_date_age, aes(Date)) + geom_line(aes(y=Age)) + xlab("Months") + ylab("Age") + theme_minimal() + theme(axis.text.x = element_text(colour = "darkslategrey", size = 20), axis.text.y = element_text(colour = "darkslategrey", size = 20), text = element_text(family = "Georgia")) 