dgkeyes

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####               Letter case - your turn               ####
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# Import the Marine Protected Areas dataset (MPAS-your.csv)
# Summarize the number of Marine Protected Areas by country (Country full).

dgkeyes

third_grade_math_proficiency_18_19 <- read_excel(path = here("data", "math-scores-18-19.xlsx")) %>% 
  filter(student_group == "Total Population (All Students)") %>% 
  filter(grade_level == "Grade 3") %>% 
  select(school_id, contains("number")) %>% 
  pivot_longer(cols = contains("number"),
               names_to = "proficiency_level",
               values_to = "number_proficient") %>% 
  mutate(number_proficient = na_if(number_proficient, "*")) %>%
  mutate(number_proficient = na_if(number_proficient, "--")) %>%
  mutate(number_proficient = as.numeric(number_proficient)) %>% 

dgkeyes

Hi Expensify,

Don’t hire me. I don’t have the background you’re expecting. At all. 

I’ve never done “customer service.” I’ve never worked in a business. I’ve never used Expensify. 

My career stops have included: second grade teacher, PhD in anthropology, college professor, and researcher at a foundation. 

So I’m seriously probably the worst candidate you could ever imagine. 

dgkeyes

iris %>% 
  group_by(Species) %>% 
  summarize(mean_petal_width = mean(Petal.Width)) %>% 
  ungroup() %>% 
  ggplot(aes(Species, mean_petal_width,
             fill = Species)) +
  geom_col(show.legend = FALSE) +
  theme_ipsum() +
  scale_fill_manual(values = c("setosa" = "lightgray",
                               "versicolor" = "lightgray",

dgkeyes

iris %>% 
  group_by(Species) %>% 
  summarize(mean_petal_width = mean(Petal.Width)) %>% 
  ungroup() %>% 
  ggplot(aes(Species, mean_petal_width)) +
  geom_col() +
  theme_ipsum() +
  labs(x = NULL,
       y = NULL,
       title = "Average petal width of irises by species")

dgkeyes

dgkeyes

outputs <- read_excel(path = "data-raw/outputs_profservcices.xlsx") %>% 
  mutate(hours_counted = case_when(
    activity == "Output" ~ hours,
    activity == "A la Carte Referral" & prosper_approved == "Yes" ~ hours,
    activity == "SBLC Referral" & accepted == "Yes" ~ hours,
    activity == "MFS Referral" & accepted == "Yes" ~ hours,
    activity == "MarketLink Referral" & accepted == "Yes" ~ hours
  )) %>% 
  mutate(activity_categorized = case_when(
    activity == "Output" ~ "Output",

dgkeyes

library(tidyverse)

diamonds_summary <- diamonds %>% 
  count(cut) %>% 
  mutate(cut = factor(cut, levels = c("Fair",
                                      "Good",
                                      "Very Good",
                                      "Premium",
                                      "Ideal"))) %>% 
  mutate(cut = fct_rev(cut))

dgkeyes

dk_summarize_with_totals <- function(.data, group_by_var, mean_var){

     groups_summary <- .data %>%
          dplyr::group_by({{ group_by_var }}) %>%
          dplyr::summarize(mean = mean({{ mean_var }})) %>%
          dplyr::rename("group" = {{ group_by_var }} )

     overall_summary <-.data %>%
          dplyr::summarize(mean = mean({{ mean_var }})) %>%
          dplyr::mutate(group = "Total")

dgkeyes

ggplot(sleep_by_gender, 
       aes(gender, 
           avg_sleep,
           fill = gender)) +
    geom_col()