R Project for biostatistics – 20210701R

html_document — install.packages(“googlesheets4”) “`{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) # pull in packages we need library(googlesheets4) library(ggpubr) library(tidyverse) # you may need to install them first # install.packages(“googlesheets4”) # install.packages(“ggpubr”) # install.packages(“tidyverse”) # NOTE — you may need additional packages, depending on what is asked of you below 🙂 options(scipen = 999) # call in data data <- read_sheet("https://docs.google.com/spreadsheets/d/1ByIX8wOJKk-CjtJ6XJcg1JyxJN6cJSWbvx6vxNoviDs/edit#gid=0") # regions as defined by the Census Bureau data$Region <- if_else(data$state %in% c("Connecticut", "Maine", "Massachusetts", "New Hampshire", "Rhode Island", "Vermont", "New Jersey", "New York", "Pennsylvania"), "Northeast", if_else(data$state %in% c("Illinois", "Indiana", "Michigan", "Ohio", "Wisconsin", "Iowa", "Kansas", "Minnseota", "Missouri", "Nebraska", "North Dakota", "South Dakota"), "Midwest", if_else(data$state %in% c("Delaware", "Florida", "Georgia", "Maryland", "North Carolina", "South Carolina", "Virginia", "District of Columbia", "West Virginia", "Alabama", "Kentucky", "Mississippi", "Tennessee", "Arkansas", "Louisiana", "Oklahoma", "Texas"), "South", "West"))) # filter to only South region data_south <- data %>% filter(Region == “South”) # filter to only West region data_west <- data %>% filter(Region == “West”) # filter to South and West regions data_south_west <- data %>% filter(Region %in% c(“South”, “West”)) “` #### 1. Describe the data. “`{r} summaries <- data_south_west %>% group_by(Region) %>% summarize(mean00 = mean(`2000`), sd00 = sd(`2000`), median00 = median(`2000`), iqr00 = IQR(`2000`)) # NOTE — you must edit the code in this chunk to get the summary statistics for the other years (copy, paste, and edit 🙂 ) “` | | | 2000 | 2005 | 2010 | 2015 | |——-|—————————|——|——|——|——| | South | Mean (Standard Deviation) | `r round(summaries$mean00[1],2)` (`r round(summaries$sd00[1],2)`) | INSERT FOR 2005 | INSERT FOR 2010 | INSERT FOR 2015 | | | Median (IQR) | `r round(summaries$median00[1],2)` (`r round(summaries$iqr00[1],2)`) | INSERT FOR 2005 | INSERT FOR 2010 | INSERT FOR 2015 | | West | Mean (Standard Deviation) | `r round(summaries$mean00[2],2)` (`r round(summaries$sd00[2],2)`) | INSERT FOR 2005 | INSERT FOR 2010 | INSERT FOR 2015 | | | Median (IQR) | `r round(summaries$median00[2],2)` (`r round(summaries$iqr00[2],2)`) | INSERT FOR 2005 | INSERT FOR 2010 | INSERT FOR 2015 | #### 2. Construct side-by-side boxplots comparing the South and West regions for 2000, 2005, 2010, and 2015. “`{r} bp00 <- ggplot(data=data_south_west, aes(y= `2000`, x=Region, fill=Region)) + geom_boxplot() + theme_minimal() + labs(y = "Spending", x = "Region") + ylim(min=0, max=1500000) + ggtitle("2000") # NOTE -- you must edit the code to be able to construct the final image (below) -- copy, paste, and change values to get the boxplots together, below bp <- ggarrange(bp00, bp05, bp10, bp15, ncol=2, nrow=2) bp ``` #### 3. Describe the distributions. Are they skewed? Do you think there's a difference between the two regions? #### 4. Use the appropriate *t* test to determine if there is a difference in library spending between the South and West regions in 2005. Test at the α = 0.05 level. Include the formal test results (including the formal hypotheses), but in your interpretation, make sure that you explain the results such that a non-statistician can understand. #### 5. Assess the variance assumption. Test at the α = 0.05 level. Include the formal test results (including the formal hypotheses), but in your interpretation, make sure that you explain the results such that a non-statistician can understand. Include whether or not the assumption is broken. #### 6. Assess the normality assumption. In your interpretation, make sure that you explain the results such that a non-statistician can understand. Include whether or not the assumption is broken. #### 7. Use the appropriate nonparametric test to determine if there is a difference in library spending between the South and West regions in 2005. Test at the α = 0.05 level. Include the formal test results (including the formal hypotheses), but in your interpretation, make sure that you explain the results such that a non-statistician can understand. #### 8. Based on your answers to 5 and 6, which test should be reported? Explain why you're choosing the test you're choosing. (Hint: you will either choose the *t* test or the nonparametric test.) #### 9. Use the appropriate *t* test to determine if there is a difference in library spending between 2005 and 2010 for all states. Test at the α = 0.05 level. Include the formal test results (including the formal hypotheses), but in your interpretation, make sure that you explain the results such that a non-statistician can understand. #### 10. Assess the normality assumption. In your interpretation, make sure that you explain the results such that a non-statistician can understand. Include whether or not the assumption is broken. #### 11. Use the appropriate nonparametric test to determine if there is a difference in library spending between 2005 and 2010 for all states. Test at the α = 0.05 level. Include the formal test results (including the formal hypotheses), but in your interpretation, make sure that you explain the results such that a non-statistician can understand. 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