1 Groups Statistics

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1.1 Aggrgate Groups only Unique Group and Count

There are two variables that are numeric, we want to find all the unique groups of these two variables in a dataset and count how many times each unique group occurs

  • r unique occurrence of numeric groups
  • How to add count of unique values by group to R data.frame
# Numeric value combinations unique Groups
vars.group <- c('hgt0', 'wgt0')

# dataset subsetting
df_use <- df_hgt_wgt %>% select(!!!syms(c(vars.group))) %>%
            mutate(hgt0 = round(hgt0/5)*5, wgt0 = round(wgt0/2000)*2000) %>%
            drop_na()

# Group, count and generate means for each numeric variables
# mutate_at(vars.group, funs(as.factor(.))) %>%
df.group.count <- df_use %>% group_by(!!!syms(vars.group)) %>%
                    arrange(!!!syms(vars.group)) %>%
                    summarise(n_obs_group=n())

# Show results Head 10
df.group.count %>% kable() %>% kable_styling_fc()
hgt0 wgt0 n_obs_group
40 2000 122
45 2000 4586
45 4000 470
50 2000 9691
50 4000 13106
55 2000 126
55 4000 1900
60 6000 18

1.2 Aggrgate Groups only Unique Group Show up With Means

Several variables that are grouping identifiers. Several variables that are values which mean be unique for each group members. For example, a Panel of income for N households over T years with also household education information that is invariant over time. Want to generate a dataset where the unit of observation are households, rather than household years. Take average of all numeric variables that are household and year specific.

A complicating factor potentially is that the number of observations differ within group, for example, income might be observed for all years for some households but not for other households.

# In the df_hgt_wgt from R4Econ, there is a country id, village id,
# and individual id, and various other statistics
vars.group <- c('S.country', 'vil.id', 'indi.id')
vars.values <- c('hgt', 'momEdu')

# dataset subsetting
df_use <- df_hgt_wgt %>% select(!!!syms(c(vars.group, vars.values)))

# Group, count and generate means for each numeric variables
df.group <- df_use %>% group_by(!!!syms(vars.group)) %>%
            arrange(!!!syms(vars.group)) %>%
            summarise_if(is.numeric,
                         funs(mean = mean(., na.rm = TRUE),
                              sd = sd(., na.rm = TRUE),
                              n = sum(is.na(.)==0)))

# Show results Head 10
df.group %>% head(10) %>%
  kable() %>%
  kable_styling_fc_wide()
S.country vil.id indi.id hgt_mean momEdu_mean hgt_sd momEdu_sd hgt_n momEdu_n
Cebu 1 1 61.80000 5.3 9.520504 0 7 18
Cebu 1 2 68.86154 7.1 9.058931 0 13 18
Cebu 1 3 80.45882 9.4 29.894231 0 17 18
Cebu 1 4 88.10000 13.9 35.533166 0 18 18
Cebu 1 5 97.70556 11.3 41.090366 0 18 18
Cebu 1 6 87.49444 7.3 35.586439 0 18 18
Cebu 1 7 90.79412 10.4 38.722385 0 17 18
Cebu 1 8 68.45385 13.5 10.011961 0 13 18
Cebu 1 9 86.21111 10.4 35.126057 0 18 18
Cebu 1 10 87.67222 10.5 36.508127 0 18 18 
# Show results Head 10
df.group %>% tail(10) %>%
  kable() %>%
  kable_styling_fc_wide()
S.country vil.id indi.id hgt_mean momEdu_mean hgt_sd momEdu_sd hgt_n momEdu_n
Guatemala 14 2014 66.97000 NaN 8.967974 NaN 10 0
Guatemala 14 2015 71.71818 NaN 11.399984 NaN 11 0
Guatemala 14 2016 66.33000 NaN 9.490352 NaN 10 0
Guatemala 14 2017 76.40769 NaN 14.827871 NaN 13 0
Guatemala 14 2018 74.55385 NaN 12.707846 NaN 13 0
Guatemala 14 2019 70.47500 NaN 11.797390 NaN 12 0
Guatemala 14 2020 60.28750 NaN 7.060036 NaN 8 0
Guatemala 14 2021 84.96000 NaN 15.446193 NaN 10 0
Guatemala 14 2022 79.38667 NaN 15.824749 NaN 15 0
Guatemala 14 2023 66.50000 NaN 8.613113 NaN 8 0