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:
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 1992
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
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 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")
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
is a wrapper for dplyr merge functionalities, with two added functions
The option check
checks 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 gen
specifies 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 update
allows to update missing values of the master dataset by the value in the using dataset
# 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))
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)
You can install