R - data analysis - descriptive

Packages

• data manipulation: dplyr
• data visualization: ggplot

Getting started

• load package: library(package_name)
• load data: data(dataframe_name)

Overview

• data dimension: dim(dataframe_name)
• no. of rows: norws(dataframe_name)
• column names: names(dataframe_name)
• data structure (dimension + data types): str(dataframe_name)

dplyr - Data manipulation

• filter(condition): filter data based on conditions

• general syntax: dataframe_name %>% filter(col_1 == value1, col_2 != value2)

• summarise(): numerical summaries of data

• general syntax: dataframe_name %>% summarise(new_var_name = stats_function(col_name))

• summary stats functions:

  • mean()
  • median()
  • std()
  • var()
  • range()
  • IQR()
  • min()
  • max()
  • sum()

• mutate(): create a new column

• general syntax: dataframe_name(with added column) <- dataframe_name %>% mutate(new_col_name = ifelse(old_col == value, , "value_if_true", "value_if_false" ))

• group_by(): useful for summarise data based on group

• arrange(): ordering data arrange(desc(col_names))
• select(): like in SQL, selecting only columns of interest
• distinct()
• sample_n()

ggplot - Data visualization

• general syntax: ggplot(data = dataframe_name, aes(x = col_name)) + geom_histogram()
• histogram: geom_histogram(binwidth = )
• bar: geom_bar()
• scatterplot: geom_point