Publications by Alexis Marcolini
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Import your data data <- read_excel("../00_data/MyData.xlsx") %>% mutate(rev_men = as.numeric(rev_men)) ## Warning: There was 1 warning in `mutate()`. ## ℹ In argument: `rev_men = as.numeric(rev_men)`. ## Caused by warning: ## ! NAs introduced by coercion data <- as_tibble(data) data("mtcars") mtcars <- as_tibble(mtcars) Repeat the sa...
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Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_u...
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Import your data data("mtcars") Repeat the same operation over different columns of a data frame Case of numeric variables Create your own function Repeat the same operation over different elements of a list When you have a grouping variable (factor) Create your own Choose either one of the two cases above and apply it to your data...
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Ch20 Vectors Introduction Vector basics Important types of automatic vector Using atomic vectors sample(10) + 10 ## [1] 13 17 14 15 16 11 12 19 20 18 1:10 + 1:2 ## [1] 2 4 4 6 6 8 8 10 10 12 1:10 + 1:3 ## Warning in 1:10 + 1:3: longer object length is not a multiple of shorter object ## length ## [1] 2 4 6 5 7 9 8 10 12 11 dat...
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Import your data data <- read_excel("../00_data/MyData.xlsx") Chapter 15 Create a factor Modify factor order Make two bar charts here - one before ordering another after # Transform data: calculate average male athletes by state State_cd_by_ef_male_count <- data %>% group_by(state_cd) %>% summarise( avg_ef_male_count = mea...
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Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_u...
644 sym 7 tbl
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Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_u...
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Introduction When should you write a function? df <- tibble::tibble( a = rnorm(10), b = rnorm(10), c = rnorm(10), d = rnorm(10) ) # Rescale each column df$a <- (df$a - min(df$a, na.rm = TRUE)) / (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE)) df$b <- (df$b - min(df$b, na.rm = TRUE)) / (max(df$b, na.rm = TRUE) - min(df$b...
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Import your data # excel file data <- read_excel("../00_data/MyData.xlsx") %>% rename(X = institution_name) stressors <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-11/stressor.csv') ## Rows: 7332 Columns: 5 ## ── Column specification ──────────────...
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Chapter 15 Factors Creating factors General social survey gss_cat ## # A tibble: 21,483 × 9 ## year marital age race rincome partyid relig denom tvhours ## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int> ## 1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… ...
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