A formal statistical test called a hypothesis test is used to confirm or disprove a statistical hypothesis. The following R hypothesis tests are demonstrated in this course. T-test with one sample T-Test of two samples T-test for paired samples Each type of test can be run using the R function t.test().The function comes with the following arguments; t.test(x, y = NULL, alternative = c("two-sided", "less", "greater"), mu = 0, paired = FALSE, var.

Introduction In this post, We learn how we can make publication quality inset maps in R using ggplot2 package (Wickham 2016). When publishing scientific research in journals or presenting research work at a conference, showing maps of data collection sites or experimental locations is one of the key visualization elements. Maps of study sites or sampling locations can help the audience and readers to fathom the data in a better way.

The ggplot2 package doesn’t provide a function to arrange multiple plots in a single figure (Wickham 2016). Still, some packages allow combining multiple plots into a single figure with custom layouts, width, and height, such as cowplot (Wilke 2018), gridExtra, and patchwork (Pedersen 2020). In this post we are going to use several packages, let’us load them in our session require(tidyverse) require(patchwork) require(cowplot) Sample datasets # tuna = tibble( # tl = runif(n = 120, min = 30, max = 120), # seasons = rep(c("Northeast", "Southeast", "Inter"), each = 40) # ) tuna = tibble( tl = c(rnorm(n = 40, mean = 80, sd = 30), rnorm(n = 40, mean = 61,10), rnorm(n = 40, mean = 96, 25)), seasons = rep(c("Northeast", "Southeast", "Inter"), each = 40) ) ridges = tuna %>% ggplot() + ggridges::geom_density_ridges(aes(x = tl, y = seasons, fill = seasons), position = "identity", alpha = .

On April, 2022, the government of the United Republic of Tanzania approved permission of TAMISEMI to recruit and employ 9,800 primary and secondary school teachers, and 7,612 health experts.

Data science has become an essential element for companies that want to gain a competitive advantage. The role of data science is to put the data analytics process into a strategic context so that companies can harness the power of their data while working on their data science project. In this post I will take you through some key concepts that a keen data scientists should carefully and with intent consider and explore before take a data related consutation.