The full code is available on my GITHUB page<\/a><\/strong><\/span>.<\/p>\n Loading Packages & Simulating Data<\/strong><\/span><\/p>\n Create a test number<\/strong><\/span><\/p>\n First, let’s normalize the Dates so that they are numeric. Basically, instead of dates we want a value indicating whether the test was test 1, or test 5, or test N. We will do this by creating a row_number() <\/strong>id\/counter for each individual athlete.<\/p>\n Calculating the time between tests<\/strong><\/span><\/p>\n Alternatively, we may not just want to know the test number of each test but we may want to determine the amount of days between each test.<\/p>\n The code to do this is a bit ugly looking so let’s unpack it.<\/p>\n Notice that Tom and Bob have 1 day between all of their tests while Franklin’s second test was 11 days after his first and his third test was 27 days after his second.<\/p>\n Calculate the difference in jump height from one test to the next<\/strong><\/span><\/p>\n Here, we use the lag()<\/strong> function to calculate the difference in one value from the value before it within in the same column. Since we grouped by athlete, which is what we want, their first test will always have an NA, in this new column, since there was no test preceding it.<\/p>\n Calculating the difference in jump height from the baseline test<\/strong><\/span><\/p>\n Finally, we might also be interested to evaluate the performance on each test relative to the athlete’s baseline test. To do this we simply subtract jump_height<\/strong> from the jump_height<\/strong> indexed in row one for each athlete.<\/p>\n\r\n## load packages ----------------------------------------------\r\nlibrary(tidyverse)\r\nlibrary(lubridate)\r\n\r\n## Simulate data ----------------------------------------------\r\nset.seed(78)\r\ndat <- tibble(\r\n \r\n athlete = rep(c("Tom", "Bob", "Franklin"), times = c(5, 10, 3)),\r\n test_dates = c(\r\n seq(as.Date("2023-01-01"), as.Date("2023-01-5"), by = "days"),\r\n seq(as.Date("2023-02-15"), as.Date("2023-02-24"), by = "days"),\r\n as.Date(c("2023-01-19", "2023-01-30", "2023-02-26"))\r\n ),\r\n jump_height = round(rnorm(n = 18, mean = 28, sd = 2.5), 1)\r\n \r\n)\r\n\r\ndat\r\n<\/pre>\n![\"\"](\"https:\/\/optimumsportsperformance.com\/blog\/wp-content\/uploads\/2023\/06\/Screenshot-2023-05-31-at-7.33.38-PM.png\")
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\nWe can see that Tom has 5 tests, Bob has 10, and Franklin has only 3. Additionally, Tom and Bob tested every day, consecutively, while Franklin was less compliant and has larger time frames between his tests.<\/p>\n\r\n### Create a test number ------------------------------------------\r\ndat <- dat %>%\r\n group_by(athlete) %>%\r\n mutate(test_day = row_number())\r\n\r\ndat\r\n<\/pre>\n
![\"\"](\"https:\/\/optimumsportsperformance.com\/blog\/wp-content\/uploads\/2023\/06\/Screenshot-2023-05-31-at-7.36.12-PM.png\")
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\r\n### Calculate the time between tests -------------------------------\r\ndat <- dat %>%\r\n group_by(athlete) %>%\r\n mutate(time_btw_tests = coalesce(as.numeric(as.character(difftime(test_dates, lag(test_dates)))), 0))\r\n\r\ndat\r\n<\/pre>\n
![\"\"](\"https:\/\/optimumsportsperformance.com\/blog\/wp-content\/uploads\/2023\/06\/Screenshot-2023-05-31-at-7.39.27-PM.png\")
<\/a><\/p>\n\r\n### Calculate difference in jump height from one day to the next -------------------\r\ndat <- dat %>%\r\n group_by(athlete) %>%\r\n mutate(test_to_test_diff = jump_height - lag(jump_height))\r\n\r\ndat\r\n<\/pre>\n
![\"\"](\"https:\/\/optimumsportsperformance.com\/blog\/wp-content\/uploads\/2023\/06\/Screenshot-2023-05-31-at-7.45.39-PM-1024x696.png\")
<\/a><\/p>\n\r\n### Calculate difference in jump height from each test to the baseline test -------------\r\n\r\ndat <- dat %>%\r\n group_by(athlete) %>%\r\n mutate(test_to_baseline_diff = jump_height - jump_height[1])\r\n\r\ndat\r\n<\/pre>\n
![\"\"](\"https:\/\/optimumsportsperformance.com\/blog\/wp-content\/uploads\/2023\/06\/Screenshot-2023-05-31-at-7.49.35-PM-1024x544.png\")
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