99 lines
3.4 KiB
Text
99 lines
3.4 KiB
Text
---
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title: "Importing Excel Data with Multiple Header Rows"
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subtitle: |
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A solution for importing Excel Data that contains two header rows.
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date: 06-22-2020
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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```
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```{r, echo=FALSE}
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knitr::read_chunk("excel_headers.R")
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```
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# Problem
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Recently I tried to important some Microsoft Excel data into R, and ran into an issue were the data actually had two different header rows. The top row listed a group, and then the second row listed a category within that group. Searching goggle I couldn't really find a good example of what I was looking for, so I am putting it here in hopes of helping someone else!
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# Example Data
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I have created a small Excel file to demonstrate what I am talking about. Download it [here](https://github.com/mmmmtoasty19/kyleb/tree/master/content/post/2020-06-15-importing-excel-data-with-multiple-headers/example_data.xlsx). This is the data from Excel.
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# Check Data
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First we will read the file in using the package readxl and view the data without doing anything special to it.
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```{r example-data}
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```
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# New Header Names
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### Step 1
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First lets read back the data, this time however with some options. We will set the n_max equal to 2, to only read the first two rows, and set col_names to FALSE so we do not read the first row as headers.
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```{r fix-it-data }
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```
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### Step 2
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Now that we have our headers lets first transpose them to a vertical matrix using the base function t(), then we will turn it back into a tibble to allow us to use tidyr fill function.
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```{r fix-it-names}
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```
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Note that tidyr fill can not work row wise, thus the need to flip the tibble so it is long vs wide.
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### Step 3
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Now we use tidyr fill function to fill the NA's with whatever value it finds above.
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```{r fix-it-names-1}
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```
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### Step 4
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This is where my data differed from many of the examples I could find online. Because the second row is also a header we can not just get rid of them. We can solve this using paste() combined with dplyr mutate to form a new column that combines the first and second column.
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```{r fix-it-names-2}
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```
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### Step 4a
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One more small clean up task, in the example data the first column header Name, did not have a second label, this has created a name with an NA attached. We can use stringr to remove this NA.
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```{r fix-it-names-3}
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```
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### Step 5
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Now that are new name column is the way we want it, we can use dpylrs pull to return a vector of just that column
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```{r fix-it-names-4}
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```
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# Final Data
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Now that we have a vector of column names lets read in the original file using our new names. We set the skip argument to 2, to skip the first two rows, and set col_names equal to our vector of names. Note the last step I used the janitor package to provide names in snake case (the default for the clean names function.)
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```{r fix-it-final}
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```
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# Other Help
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While searching for some solutions to my problem I found two good examples, however neither did exactly what I was trying to do.
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1. This post by Lisa Deburine is pretty close to what I was trying to accomplish and gave me a good starting point. Read it [here](https://debruine.github.io/posts/multi-row-headers/)
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2. This post by Alison Hill solves a simlar but slightly different problem. In her data the 2nd row is actually metadata not a second set of headers. Read it [here](https://alison.rbind.io/post/2018-02-23-read-multiple-header-rows/)
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