我有一个看起来像这样的数据框（我有更多的年份和变量）：
Name State2014 State2015 State2016 Tuition2014 Tuition2015 Tuition2016 StateGrants2014
Jared CA CA MA 22430 23060 40650 5000
Beth CA CA CA 36400 37050 37180 4200
Steven MA MA MA 18010 18250 18720 NA
Lary MA CA MA 24080 30800 24600 6600
Tom MA OR OR 40450 15800 16040 NA
Alfred OR OR OR 23570 23680 23750 3500
Cathy OR OR OR 32070 32070 33040 4700
我的目标（在这个例子中）是获得每个州的平均学费，以及每个州的州补助金总额。我的想法是按年分配数据：
State2014 Tuition2014 StateGrants2014
CA 22430 5000
CA 36400 4200
MA 18010 NA
MA 24080 6600
MA 40450 NA
OR 23570 3500
OR 32070 4700
State2015 Tuition2015
CA 23060
CA 37050
MA 18250
CA 30800
OR 15800
OR 23680
OR 32070
State2016 Tuition2016
MA 40650
CA 37180
MA 18720
MA 24600
OR 16040
OR 23750
OR 33040
然后我会group_by陈述summarize（并将每个作为单独的df保存）以获得以下内容：
State2014 Tuition2014 StateGrants2014
CA 29415 9200
MA 27513 6600
OR 27820 6600
State2015 Tuition2015
CA 30303
MA 18250
OR 23850
State2016 Tuition2016
CA 37180
MA 27990
OR 24277
然后我会合并by by状态。这是我的代码：
years = c(2014,2015,2016)
for (i in seq_along(years){
#grab the variables from a certain year and save as a new df.
df_year <- df[, grep(paste(years[[i]],"$",sep=""), colnames(df))]
#Take off the year from each variable name (to make it easier to summarize)
names(df_year) <- gsub(years[[i]], "", names(df_year), fixed = TRUE)
df_year <- df_year %>%
group_by(state) %>%
summarize(Tuition = mean(Tuition, na.rm = TRUE),
#this part of the code does not work. In this example, I only want to have this part if the year is 2016.
if (years[[i]]=='2016')
{Stategrant = mean(Stategrant, na.rm = TRUE)})
#rename df_year to df####
assign(paste("df",years[[i]],sep=''),df_year)
}
我有大约50年的数据和大量的变量，所以我想使用一个循环。所以我的问题是，如何在group_by() / summarize()函数中添加一个条件语句（总结一年中的某些变量）？谢谢！
*编辑：我意识到我可以从函数中取出if{}，然后执行以下操作：
if (years[[i]]==2016){
df_year <- df_year %>%
group_by(state) %>%
summarize(Tuition = mean(Tuition, na.rm = TRUE),
Stategrant = mean(Stategrant, na.rm = TRUE))
#rename df_year to df####
assign(paste("df",years[[i]],sep=''),df_year)
}
else{
df_year <- df_year %>%
group_by(state) %>%
summarize(Tuition = mean(Tuition, na.rm = TRUE))
#rename df_year to df####
assign(paste("df",years[[i]],sep=''),df_year)
{
}
但是有很多变量组合，使用for循环不会非常有效或有用。
答案 0 :(得分：4)
使用tidy数据可以轻松实现这一点，因此，让我向您展示如何整理数据。请参阅http://r4ds.had.co.nz/tidy-data.html。
library(tidyr)
library(dplyr)
df <- gather(df, key, value, -Name) %>%
# separate years from the variables
separate(key, c("var", "year"), sep = -5) %>%
# the above line splits up e.g. State2014 into State and 2014.
# It does so by splitting at the fifth element from the end of the
# entry. Please check that this works for your other variables
# in case your naming conventions are inconsistent.
spread(var, value) %>%
# turn numbers back to numeric
mutate_at(.cols = c("Tuition", "StateGrants"), as.numeric) %>%
gather(var, val, -Name, -year, -State) %>%
# group by the variables of interest. Note that `var` here
# refers to Tuition and StateGrants. If you have more variables,
# they will be included here as well. If you want to exclude more
# variables from being included here in `var`, add more "-colName"
# entries in the `gather` statement above
group_by(year, State, var) %>%
# summarize:
summarise(mean_values = mean(val))
这会给你：
Source: local data frame [18 x 4]
Groups: year, State [?]
year State var mean_values
<chr> <chr> <chr> <dbl>
1 2014 CA StateGrants 4600.00
2 2014 CA Tuition 29415.00
3 2014 MA StateGrants NA
4 2014 MA Tuition 27513.33
5 2014 OR StateGrants 4100.00
6 2014 OR Tuition 27820.00
7 2015 CA StateGrants NA
8 2015 CA Tuition 30303.33
9 2015 MA StateGrants NA
10 2015 MA Tuition 18250.00
11 2015 OR StateGrants NA
12 2015 OR Tuition 23850.00
13 2016 CA StateGrants NA
14 2016 CA Tuition 37180.00
15 2016 MA StateGrants NA
16 2016 MA Tuition 27990.00
17 2016 OR StateGrants NA
18 2016 OR Tuition 24276.67
如果你不喜欢这种形状，你可以例如在%>% spread(var, mean_values)语句后面添加summarise，以便在不同的列中使用学费和StateGrants。
如果您想为学费和助学金计算不同的职能（例如学费的平均值和助学金总额，您可以执行以下操作：
df <- gather(df, key, value, -Name) %>%
separate(key, c("var", "year"), sep = -5) %>%
spread(var, value) %>%
mutate_at(.cols = c("Tuition", "StateGrants"), as.numeric) %>%
group_by(year, State) %>%
summarise(Grant_Sum = sum(StateGrants, na.rm=T), Tuition_Mean = mean(Tuition) )
这会给你：
Source: local data frame [9 x 4]
Groups: year [?]
year State Grant_Sum Tuition_Mean
<chr> <chr> <dbl> <dbl>
1 2014 CA 9200 29415.00
2 2014 MA 6600 27513.33
3 2014 OR 8200 27820.00
4 2015 CA 0 30303.33
5 2015 MA 0 18250.00
6 2015 OR 0 23850.00
7 2016 CA 0 37180.00
8 2016 MA 0 27990.00
9 2016 OR 0 24276.67
请注意，我在sum处使用了na.rm = T，如果所有元素都是NA，则返回0。确保这在您的用例中有用。
此外，仅举一步说明，为了获得您要求的个人data.frames，您可以使用filter(year == 2014)等，就像df_2014 <- filter(df, year == 2014)一样。