Basics R
Lam Fui Ching
15-5-2023
Get Started
Log a ticket with ICT Support Portal to download and install the
following:-
RStudio Layout
Four RStudio Panels
1. Source
- Write, edit and save the R script
- The script will be evaluated when ‘run’
2. Console
- Display all the evaluations by R
- non-graphic output
- errors
3.
Environment/History/Connections/Tutorial
To view:
- environment - overview of objects (dataset, parameters, lists,
functions etc)
- history
- connection - for example to GIS, GitHub, GitLab etc
- tutorial
4. Files/Plots/Packages/Help
To view:
- the files directories
- graphics or plots
- installed packages
- help
Set up a working directory
Two options:-
Option 1: Script
setwd("Path R scripts and/or data file are stored")
setwd("~/Powerbi Reports/ASCOF/data")
- R uses forward slash (‘/’) within the path name.
Option 2:
Running script
- Each line in the script is a statement, a command to be run.
- Two options to run each statement one-at-a-time.
Firstly, move the cursor anywhere in the start of the line,
then:-
- press key Ctrl-Enter or
- click on the Run button at the top of the source
panel.
This not only runs the current command, but also moves the cursor to
the next command, making it easy to walk step-by-step through the
script.
- To run more than one statement at a time.
It is highlight all the code you want to run and then key
Ctrl-Enter or click Run. (Be careful:
you can highlight less than a full command, and R will try to execute
only what been highlighted!)
Import Data
Import a CSV and MS Excel file.
For CSV
To begin, in the sources, type the following command in R in order to
import a CSV file:
read_csv("Path where your Excel file is stored/File Name.csv")
?! Where is the dataset
<- is “Assigning”
df_csv <- read_csv("Path where your Excel file is stored/File Name.csv")
In the environment, there will be df_csv.
Example
Measures from the Adult Social Care Outcomes Framework - England
2021-22: Open data csv https://digital.nhs.uk/data-and-information/publications/statistical/adult-social-care-outcomes-framework-ascof/2021-22
(accessed in May 2022).
df_csv <- read_csv("meas-from-asc-of-eng-2022-open-data-csv-v3.csv")
Example of df_csv
Import Data cont
For MS Excel
The full steps to import an MS Excel file using the
readxl package.
In the sources, type the following command to install the
readxl package:
install.packages("readxl")
Then, in the sources, type the following command to load the
readxl package:
Import an MS Excel file into R:
df_excel <- read_excel("File Name.xlsx")
And if you want to import a specific sheet within an MS Excel
file:
df_excel <- read_excel("File Name.xlsx", sheet = "Your sheet name")
In the environment, there will be df_excel.
Example
Measures from the Adult Social Care Outcomes Framework - England
2020-21: Open data https://digital.nhs.uk/data-and-information/publications/statistical/adult-social-care-outcomes-framework-ascof/england-2020-21
(accessed in May 2022).
df_excel <- read_excel("meas-from-asc-of-eng-2021-open-data-csv_v2.xlsx")
Example of df_excel
Data management
The objective is to combine the CSV and MS Excel into one
dataframe.
Add column(s)
Add year variable:
df_csv$year <- "2021-22"
df_excel$year <- "2020-21"
Example of df_csv with year variable
Example of df_excel with year variable
Data management cont
Rename column
Standardise the column(s) name:
colnames(df_csv)[8] <- "Measure Value"
Data management cont
Standardise the column(s) strings by converting all column(s) to
characters.
In the environment panel, data detail can be found at drop down of
the dataframe.
Install and load tidyverse package. For more
information https://www.tidyverse.org/.
install.packages("tidyverse")
library(tidyverse)
df_excel <- df_excel %>%
mutate(across(everything(), as.character))
Data management cont
Combine the dataframe
The bind_rows() function is to combines the rows that
were present in the dataframe.
all <- bind_rows(df_csv,df_excel)
Save or export data
Save as CSV
The write.csv() function is to create a CSV file from
the data.
write.csv(the dataframe name,"file name.xlsx", row.names=FALSE)
write_csv(all, "ascof_demographics.csv", row.names=FALSE)
row.names=FALSE is export data without rows names.
Save as MS Excel
Install and load the writexl package.
install.packages("writexl")
library("writexl")
The write_xlsx() function is to create an MS Excel file
from the data.
write_xlsx(the dataframe name,"path to store the Excel file/file name.xlsx")
write_xlsx(all, "ascof_demographics.xlsx")
Full script
Save as CSV
# Set up a working directory
setwd("~/Powerbi Reports/ASCOF/data")
# Install packages
install.packages("readxl")
install.packages("tidyverse")
library(readxl)
library(tidyverse)
#Import data
df_csv <- read_csv("meas-from-asc-of-eng-2022-open-data-csv-v3.csv")
df_excel <- read_excel("meas-from-asc-of-eng-2021-open-data-csv_v2.xlsx")
#Add on column
df_csv$year <- "2021-22"
df_excel$year <- "2020-21"
#Rename column
colnames(df_csv)[8] <- "Measure Value"
#convert all columns to characters
df_excel <- df_excel %>%
mutate(across(everything(), as.character))
# Combine the dataframe
all <- bind_rows(df_csv,df_excel)
# Saved data
write_csv(all, "ascof_demographics.csv", row.names=FALSE)
Save as MS Excel
# Set up a working directory
setwd("~/Powerbi Reports/ASCOF/data")
# Install packages
install.packages("readxl")
install.packages("tidyverse")
install.packages("writexl")
library(readxl)
library(tidyverse)
library(writexl)
#Import data
df_csv <- read_csv("meas-from-asc-of-eng-2022-open-data-csv-v3.csv")
df_excel <- read_excel("meas-from-asc-of-eng-2021-open-data-csv_v2.xlsx")
#Add on column
df_csv$year <- "2021-22"
df_excel$year <- "2020-21"
#Rename column
colnames(df_csv)[8] <- "Measure Value"
#convert all columns to characters
df_excel <- df_excel %>%
mutate(across(everything(), as.character))
# Combine the dataframe
all <- bind_rows(df_csv,df_excel)
# Saved data
write_xlsx(all, "ascof_demographics.xlsx")
The End