statar
Tools Inspired by 'Stata' to Manipulate Tabular Data
A set of tools inspired by 'Stata' to explore data.frames ('summarize', 'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
This package contains R functions corresponding to useful Stata commands.
The package includes:
- panel data functions (monthly/quarterly dates, lead/lag, fillin)
- data.frame functions (tabulate, merge)
- vector functions (xtile, pctile, winsorize)
- graph functions (binscatter)
Elapsed dates
The classes "monthly" and "quarterly" print as dates and are compatible with usual time extraction (ie month, year, etc). Yet, they are stored as integers representing the number of elapsed periods since 1970/01/0 (resp in week, months, quarters). This is particularly handy for simple algebra:
# elapsed dates library(lubridate) date <- mdy(c("04/03/1992", "01/04/1992", "03/15/1992")) datem <- as.monthly(date) # displays as a period datem #> [1] "1992m04" "1992m01" "1992m03" # behaves as an integer for numerical operations: datem + 1 #> [1] "1992m05" "1992m02" "1992m04" # behaves as a date for period extractions: year(datem) #> [1] 1992 1992 1992lag / lead
tlag/tlead a vector with respect to a number of periods, not with respect to the number of rows
year <- c(1989, 1991, 1992)value <- c(4.1, 4.5, 3.3)tlag(value, 1, time = year)library(lubridate)date <- mdy(c("01/04/1992", "03/15/1992", "04/03/1992"))datem <- as.monthly(date)value <- c(4.1, 4.5, 3.3)tlag(value, time = datem) In constrast to comparable functions in zoo and xts, these functions can be applied to any vector and be used within a dplyr chain:
df <- data_frame( id = c(1, 1, 1, 2, 2), year = c(1989, 1991, 1992, 1991, 1992), value = c(4.1, 4.5, 3.3, 3.2, 5.2))df %>% group_by(id) %>% mutate(value_l = tlag(value, time = year))is.panel
is.panel checks whether a dataset is a panel i.e. the time variable is never missing and the combinations (id, time) are unique.
df <- data_frame( id1 = c(1, 1, 1, 2, 2), id2 = 1:5, year = c(1991, 1993, NA, 1992, 1992), value = c(4.1, 4.5, 3.3, 3.2, 5.2))df %>% group_by(id1) %>% is.panel(year)df1 <- df %>% filter(!is.na(year))df1 %>% is.panel(year)df1 %>% group_by(id1) %>% is.panel(year)df1 %>% group_by(id1, id2) %>% is.panel(year)fill_gap
fill_gap transforms a unbalanced panel into a balanced panel. It corresponds to the stata command tsfill. Missing observations are added as rows with missing values.
df <- data_frame( id = c(1, 1, 1, 2), datem = as.monthly(mdy(c("04/03/1992", "01/04/1992", "03/15/1992", "05/11/1992"))), value = c(4.1, 4.5, 3.3, 3.2))df %>% group_by(id) %>% fill_gap(datem)df %>% group_by(id) %>% fill_gap(datem, full = TRUE)df %>% group_by(id) %>% fill_gap(datem, roll = "nearest")Data Frame Functions
tab = tabulate
tab prints distinct rows with their count. Compared to the dplyr function count, this command adds frequency, percent, and cumulative percent.
N <- 1e2 ; K = 10df <- data_frame( id = sample(c(NA,1:5), N/K, TRUE), v1 = sample(1:5, N/K, TRUE) )tab(df, id)tab(df, id, na.rm = TRUE)tab(df, id, v1)join = merge
join is a wrapper for dplyr merge functionalities, with two added functions
-
The option
checkchecks there are no duplicates in the master or using data.tables (as in Stata).# merge m:1 v1join(x, y, kind = "full", check = m~1) -
The option
genspecifies the name of a new variable that identifies non matched and matched rows (as in Stata).# merge m:1 v1, gen(_merge)join(x, y, kind = "full", gen = "_merge") -
The option
updateallows to update missing values of the master dataset by the value in the using dataset
Vector Functions
# sample_mode returns the statistical modesample_mode(c(1, 2, 2))sample_mode(c(1, 2))sample_mode(c(NA, NA, 1))sample_mode(c(NA, NA, 1), na.rm = TRUE) # pctile computes quantile and weighted quantile of type 2 (similarly to Stata _pctile)v <- c(NA, 1:10) pctile(v, probs = c(0.3, 0.7), na.rm = TRUE) # xtile creates integer variable for quantile categories (corresponds to Stata xtile)v <- c(NA, 1:10) xtile(v, n_quantiles = 3) # 3 groups based on tercilesxtile(v, probs = c(0.3, 0.7)) # 3 groups based on two quantilesxtile(v, cutpoints = c(2, 3)) # 3 groups based on two cutpoints # winsorize (default based on 5 x interquartile range)v <- c(1:4, 99)winsorize(v)winsorize(v, replace = NA)winsorize(v, probs = c(0.01, 0.99))winsorize(v, cutpoints = c(1, 50))Graph Functions
stat_binmean
stat_binmean() is a stat for ggplot2. It returns the mean of y and x within bins of x. It's a bareborne version of the Stata command binscatter
ggplot(iris, aes(x = Sepal.Width , y = Sepal.Length)) + stat_binmean()ggplot(iris, aes(x = Sepal.Width , y = Sepal.Length, color = Species)) + stat_binmean(n=10) ggplot(iris, aes(x = Sepal.Width , y = Sepal.Length, color = Species)) + stat_binmean(n=10) + stat_smooth(method = "lm", se = FALSE)Installation
You can install