6 Columns, with headers as shown below. As many rows as you have pairs of data.

{“smallUrl”:“https://www. wikihow. com/images/7/76/Mean_742. jpg”,“bigUrl”:"/images/thumb/7/76/Mean_742. jpg/183px-Mean_742. jpg",“smallWidth”:460,“smallHeight”:764,“bigWidth”:183,“bigHeight”:304,“licensing”:"<div class="mw-parser-output">

Image by: Uploader
\nLicense: <a target="_blank" rel="nofollow noreferrer noopener" class="external text" href="https://creativecommons. org/licenses/by/3. 0/">Creative Commons</a>\n</p></div>"}If two (or more) pieces of data in one column are the same, find the mean of the ranks as if those pieces of data had been ranked normally, then rank the data with this mean. In the example at right, there are two 5s that would otherwise have ranks of 2 and 3. Since there are two 5s, take the mean of their ranks. The mean of 2 and 3 is 2. 5, so assign the rank 2. 5 to both 5s.

If there was no tie in previous steps, insert this value into the simplified Spearman’s Rank Correlation Coefficient formula{“smallUrl”:“https://www. wikihow. com/images/f/f4/Step8_271. jpg”,“bigUrl”:"/images/thumb/f/f4/Step8_271. jpg/314px-Step8_271. jpg",“smallWidth”:460,“smallHeight”:119,“bigWidth”:314,“bigHeight”:81,“licensing”:"<div class="mw-parser-output">

Image by: Uploader
\nLicense: <a target="_blank" rel="nofollow noreferrer noopener" class="external text" href="https://creativecommons. org/licenses/by/3. 0/">Creative Commons</a>\n</p></div>"} and replace the “n” with the number of pairs of data you have to calculate the answer. [3] X Research source {“smallUrl”:“https://www. wikihow. com/images/7/7d/Step9_402. jpg”,“bigUrl”:"/images/thumb/7/7d/Step9_402. jpg/211px-Step9_402. jpg",“smallWidth”:460,“smallHeight”:155,“bigWidth”:211,“bigHeight”:71,“licensing”:"<div class="mw-parser-output">

Image by: Uploader
\nLicense: <a target="_blank" rel="nofollow noreferrer noopener" class="external text" href="https://creativecommons. org/licenses/by/3. 0/">Creative Commons</a>\n</p></div>"} If there were ties in any of previous steps, use the standard Spearman’s Rank Correlation Coefficient formula instead:{“smallUrl”:“https://www. wikihow. com/images/a/ad/Spearman. png”,“bigUrl”:"/images/thumb/a/ad/Spearman. png/254px-Spearman. png",“smallWidth”:460,“smallHeight”:105,“bigWidth”:254,“bigHeight”:58,“licensing”:"<div class="mw-parser-output">

License: <a target="_blank" rel="nofollow noreferrer noopener" class="external text" href="https://creativecommons. org/licenses/by-sa/3. 0/">Creative Commons</a>
\n</p>

Obtained from <a target="_blank" rel="nofollow noreferrer noopener" class="external free" href="https://en. wikipedia. org/wiki/Spearman's_rank_correlation_coefficient">https://en. wikipedia. org/wiki/Spearman’s_rank_correlation_coefficient</a>. \n</p></div>"}

Close to -1 - Negative correlation. Close to 0 - No linear correlation. Close to 1 - Positive correlation.

d <- read. csv(“NAME_OF_YOUR_CSV. csv”) and hit enter cor(rank(d[,1]),rank(d[,2]))[5] X Research source