R: Manually entering data

You can use the data frame edit() function to manually enter / edit data in R.

Start by creating a data frame.

Note I am initializing each of the columns to datatype(0). This tells R that I while I want the name column to be a character and the age column to be numeric, I am leaving the size dynamic. You can set size limits during the initialization phase if you so choose.

 dfe <- data.frame(name=character(0), age = numeric(0), jobTitle = character(0))

Now, let’s use the edit() function to add some data to our data frame

dfe<- edit(dfe)

When the new window pops up, fill in the data and simply click the X when you are done

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You may get warning messages when you close out your edit window. These particular messages I got simply informed me that name and jobTitle were set as factors by R. Remember in R, warnings just want you to be aware of something, they are not errors.

Now if you run dfe, you can see your data frame

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By running the edit() function again, you can edit the values that currently exist in the data frame.  In this example I am going to change Philip’s age from 28 to 29

If you want to add a column to the data frame, just add data to the next empty column

2018-05-27_16-37-59.png

You can just close out now and rename the column in R, or just click on the column header and you will be able to rename it there.

2018-05-27_16-38-24.png

Now we have a new column listing pets

  name   age jobTitle pet
1 Ben    42  Data Sc  cat
2 Philip 29  Data Ana dog
3 Julia  36  Manager  frog

You can use the edit() function to manually edit existing data sets or data imported from other sources.

Below, I am editing the ChickWeight data set

2018-05-27_16-43-00.png

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R: Connecting to SQL Server Database

You can query data from a SQL Server database directly from R using the RODBC package.

install.packages("RODBC")

First you need to form a connection

library(RODBC)
##connection string
cn <- odbcDriverConnect(connection="Driver={SQL Server Native Client 11.0};server=localhost; database=SSRSTraining;trusted_connection=yes;")

We use the odbcDriverConnect() function. Inside we pass a connection = value

Driver = {SQL Server Native Client 11.0};  — this is based on the version of SQL Server you have

server=localhost;  — I used localhost because the SQL Server was on the same computer I was working with. Otherwise, pass the server name

database=SSRSTraining; — name of database I want to work with

trusted_connection=yes; — this means I am able to pass my Windows credentials.

If you don’t have a trusted connect pass the user Id and password like this

uid = userName; pwd = Password;

Note each parameter is separated by a semicolon

Query the database

> ##passes query to SQL Server
> df <- sqlQuery(cn, "select * FROM [SSRSTraining].[dbo].[JobDataSet]")
> head(df)

    Name              Job Hours Complete
1  Sally Predictive Model     1        n
2 Philip      Maintanence    10        n
3    Tom    Ad-hoc Report    12        y
4    Bob             SSRS     3        y
5 Philip         Tableau      7        n
6    Tom         Tableau      9        n

using sqlQuery() – pass through the connection string (cn) and enclose your query in ” ”

 

 

 

R: Importing Excel Files

To work with Excel files in R, you can use the readxl library

install.packages(“readxl”)

Use the read_excel() function to read and Excel workbook

> library(readxl)
>
> ## read excel file
> df1 <- read_excel("r_excel.xlsx")
> head(df1)
# A tibble: 6 x 4
  Name   Job              Hours Complete
  <chr>  <chr>            <dbl> <chr>  
1 Sally  Predictive Model    1. n      
2 Philip Maintanence        10. n      
3 Tom    Ad-hoc Report      12. y      
4 Bob    SSRS                3. y      
5 Philip Tableau             7. n      
6 Tom    Tableau             9. n

By default, read_excel() reads only the first sheet in the Excel file. To read other sheets using the sheet key word.

> ## read  sheet 2
> df2 <- read_excel("r_excel.xlsx", sheet =2)
> head(df2)

# A tibble: 6 x 2
  Animal Num_Legs
  <chr>     <dbl>
1 Dog          4.
2 Duck         2.
3 Snake        0.
4 Horse        4.
5 Spider       8.
6 Human        2.

The next example, we are reading range B2 – C6 on sheet 1 (same as Excel’s range function)

> ## read sheet 1, range B2 - C6
> df3 <- read_excel("r_excel.xlsx", sheet =1, range = "B2:C6")
> df3
# A tibble: 4 x 2
  `Predictive Model`   `1`
  <chr>              <dbl>
1 Maintanence          10.
2 Ad-hoc Report        12.
3 SSRS                  3.
4 Tableau               7.

In this last example, we are importing only the first 4 rows on sheet 2

> ## read sheet 2, first 4 rows only
> df4 <- read_excel("r_excel.xlsx", sheet = 2, n_max = 4)
> df4
# A tibble: 4 x 2
  Animal Num_Legs
  <chr>     <dbl>
1 Dog          4.
2 Duck         2.
3 Snake        0.
4 Horse        4.