- How do i upload my excel file into sendblaster 4 how to#
- How do i upload my excel file into sendblaster 4 install#
- How do i upload my excel file into sendblaster 4 code#
Here is an example with the same file than in the user-friendly method: dat <- read.csv( The command to import a CSV file is read.csv() (or read.csv2() which is equivalent but with other default import options). Saving your import options in your script (thanks to a line of code) allows you to quickly import your dataset the exact same way without having to repeat all the necessary steps everytime you import your dataset. Similarily to setting the working directory, I also recommend using the text editor instead of the user-friendly method for the simple reason that you can save your import options when using the text editor (and not when using the user-friendly method). However, the main drawback is that your import options will not be saved for a future usage so you will need to import your dataset manually each time you open RStudio.
How do i upload my excel file into sendblaster 4 code#
This user-friendly method has the advantage that you do not need to remember the code (see the next section for the entire code). You should now see your dataset in a new window and from there you can start analyzing your data. Change this option if missing values in your raw data are coded as “NA” or “0” (tip: do not code yourself missing values as “0”, otherwise you will not be able to distinguish the true zero values and the missing values)Īfter changing the import options corresponding to your data, click on “Import”. From our raw data above, you can see that missing values are simply empty cells, so leave NA to default or change it to “empty”. NA: how missing values are specified (default is empty cells).Change it to semicolon if your values are separated by “ ” From our raw data above, you can see that the delimiter is a comma (“,”). Delimiter: the character which separate the values.First Row as Names: specify whether the variables names are present or not (default is that variables names are present).However, if your file contains some blank rows at the top (or information you want to disregard), set the number of rows to skip Skip: specify the number of top rows you want to skip (default is 0).However, the main drawback with using specific names for datasets is that if, for instance, you want to reuse the code you created while analysing tennis data on other datasets, you will need to edit your code by replacing all occurences of “tennis_data” by the name of your new dataset You could use more explicit names such as “tennis_data” if you are using data on tennis matches for example. I personnaly rename my datasets with a generic name such as “dat”, others use “df” (for dataframe), “data”, or even “my_data”. Avoid special characters and long names (as you will have to type the name of your dataset several times). Name: set the name of your data set (default is the name of the file).Below, the import options you will most likely use: If this is not the case, you can change the import options at the bottom of the window (below the data preview) corresponding to the information you gathered when looking at the raw data. If your data have been correctly imported, you can click on “Import”. No matter what type of file and how you import it, there is one gold standard regarding how datasets are structured: columns correspond to variables, rows correspond to observations (in the broad sense of the term) and each value must have its own cell:įrom this window, you can have a preview of your data, and more importantly, check whether your data seems to have been imported correctly.There are several other ways to import an Excel file (probably even some I am not aware of), but I present the two most simple yet robust ways to import such files.I focus here only on Excel files as it is the most common type of file for a dataset
How do i upload my excel file into sendblaster 4 how to#
How do i upload my excel file into sendblaster 4 install#
As we have seen in this article on how to install R and RStudio, R is useful for many kind of computational tasks and statistical analyses.