forcats包简介
forcats
安装
# install.packages("devtools")
devtools::install_github("hadley/forcats")## Skipping install of 'forcats' from a github remote, the SHA1 (004279de) has not changed since last install.
## Use `force = TRUE` to force installation使用
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(forcats)
head(gss_cat)## # A tibble: 6 × 9
## year marital age race rincome partyid
## <int> <fctr> <int> <fctr> <fctr> <fctr>
## 1 2000 Never married 26 White $8000 to 9999 Ind,near rep
## 2 2000 Divorced 48 White $8000 to 9999 Not str republican
## 3 2000 Widowed 67 White Not applicable Independent
## 4 2000 Never married 39 White Not applicable Ind,near rep
## 5 2000 Divorced 25 White Not applicable Not str democrat
## 6 2000 Married 25 White $20000 - 24999 Strong democrat
## # ... with 3 more variables: relig <fctr>, denom <fctr>, tvhours <int>改变因子的值(Change level values)
gss_cat %>% count(partyid)## # A tibble: 10 × 2
## partyid n
## <fctr> <int>
## 1 No answer 154
## 2 Don't know 1
## 3 Other party 393
## 4 Strong republican 2314
## 5 Not str republican 3032
## 6 Ind,near rep 1791
## 7 Independent 4119
## 8 Ind,near dem 2499
## 9 Not str democrat 3690
## 10 Strong democrat 3490gss_cat %>%
mutate(partyid = fct_recode(partyid,
"Republican, strong" = "Strong republican",
"Republican, weak" = "Not str republican",
"Independent, near rep" = "Ind,near rep",
"Independent, near dem" = "Ind,near dem",
"Democrat, weak" = "Not str democrat",
"Democrat, strong" = "Strong democrat"
)) %>%
count(partyid)## # A tibble: 10 × 2
## partyid n
## <fctr> <int>
## 1 No answer 154
## 2 Don't know 1
## 3 Other party 393
## 4 Republican, strong 2314
## 5 Republican, weak 3032
## 6 Independent, near rep 1791
## 7 Independent 4119
## 8 Independent, near dem 2499
## 9 Democrat, weak 3690
## 10 Democrat, strong 3490gss_cat %>%
mutate(partyid = fct_collapse(partyid,
other = c("No answer", "Don't know", "Other party"),
rep = c("Strong republican", "Not str republican"),
ind = c("Ind,near rep", "Independent", "Ind,near dem"),
dem = c("Not str democrat", "Strong democrat")
)) %>%
count(partyid)## # A tibble: 4 × 2
## partyid n
## <fctr> <int>
## 1 other 548
## 2 rep 5346
## 3 ind 8409
## 4 dem 7180合并因子,保留最多的那n个因子
gss_cat %>% count(relig)## # A tibble: 15 × 2
## relig n
## <fctr> <int>
## 1 No answer 93
## 2 Don't know 15
## 3 Inter-nondenominational 109
## 4 Native american 23
## 5 Christian 689
## 6 Orthodox-christian 95
## 7 Moslem/islam 104
## 8 Other eastern 32
## 9 Hinduism 71
## 10 Buddhism 147
## 11 Other 224
## 12 None 3523
## 13 Jewish 388
## 14 Catholic 5124
## 15 Protestant 10846#默认为1
gss_cat %>%
mutate(relig = fct_lump(relig)) %>%
count(relig)## # A tibble: 2 × 2
## relig n
## <fctr> <int>
## 1 Protestant 10846
## 2 Other 10637gss_cat %>%
mutate(relig = fct_lump(relig, n = 5)) %>%
count(relig)## # A tibble: 6 × 2
## relig n
## <fctr> <int>
## 1 Christian 689
## 2 None 3523
## 3 Jewish 388
## 4 Catholic 5124
## 5 Protestant 10846
## 6 Other 913#负数为反方向保留，prop为比例
gss_cat %>%
mutate(relig = fct_lump(relig, prop = -0.10)) %>%
count(relig)## # A tibble: 12 × 2
## relig n
## <fctr> <int>
## 1 No answer 93
## 2 Don't know 15
## 3 Inter-nondenominational 109
## 4 Native american 23
## 5 Christian 689
## 6 Orthodox-christian 95
## 7 Moslem/islam 104
## 8 Other eastern 32
## 9 Hinduism 71
## 10 Buddhism 147
## 11 Jewish 388
## 12 Other 19717Change order of levels:
fct_relevel(): move specified level up front.
fct_inorder(): order by first appearance of each level.
fct_reorder(): order by summary of another value (same as stats::reorder()).
fct_infreq():
order by frequency.
fct_shuffle(): randomly shuffle order of levels.
fct_rev(): reverse order of levels.
fct_shift(): shift levels to the left/right.
Change value of levels:
fct_anon(): anonymise factor levels.
fct_lump(): lump rarest (or most common) levels into “other”.
fct_recode(): manually recode levels.
Add new levels:
fct_expand(): add new levels to a factor.
fct_explicit_na(): turn missing values into an explicit factor.
A few other helpers:
fct_c(): concatenate factors using union of levels.
fct_count():
count occurences of levels, optionally sorting by frequency.
fct_unify(): ensure list of factors share the same levels.
fct_unique(): compute from levels of factor.
fct_drop(): drop levels without data (same as base::droplevels()).
lvls_union(): finds union of levels from list of factors.
relig <- gss_cat %>%
group_by(relig) %>%
summarise(
age = mean(age, na.rm = TRUE),
tvhours = mean(tvhours, na.rm = TRUE),
n = n()
)
ggplot(relig, aes(tvhours, relig)) + geom_point()
ggplot(relig, aes(tvhours, fct_reorder(relig, tvhours))) +
geom_point()
by_age <- gss_cat %>%
filter(!is.na(age)) %>%
group_by(age, marital) %>%
count() %>%
mutate(prop = n / sum(n))
ggplot(by_age, aes(age, prop)) +
geom_line(aes(colour = marital))
ggplot(by_age, aes(age, prop)) +
geom_line(aes(colour = fct_reorder2(marital, age, prop))) +
labs(colour = "marital")
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