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In the following data frame, how can I group by the first two columns and check if all the values in the fourth column are identical? If they are identical I would like to replace them with ''.
In this example, the group combinations 'embryonated + protein' and 'Hatching + Lipid' are the only two groups whose letters are not all a.
df
Stage variable Temperature letters Mean
30 Embryonated Moisture 30 a 808.70882
31 Embryonated NFE 20 a 53.28806
32 Embryonated NFE 25 a 45.38572
33 Embryonated NFE 30 a 84.56113
34 Embryonated Protein 20 ab 118.53608
35 Embryonated Protein 25 b 127.29849
36 Embryonated Protein 30 a 84.55175
37 Hatching Ash 20 a 16.95345
38 Hatching Ash 25 a 14.54980
39 Hatching Ash 30 a 13.38510
40 Hatching Energy 20 a 4931.18857
41 Hatching Energy 25 a 4187.27213
42 Hatching Energy 30 a 4314.61171
43 Hatching Lipid 20 b 26.44363
44 Hatching Lipid 25 a 19.90928
45 Hatching Lipid 30 ab 22.27561
46 Hatching Moisture 20 a 785.63062
47 Hatching Moisture 25 a 818.69860
48 Hatching Moisture 30 a 815.32070
49 Hatching NFE 20 a 60.34359
50 Hatching NFE 25 a 43.02979
I have tried using dplyr to no avail.
grp_cols <- names(df)[c(1,2)] #group by stage and variable
# Convert character vector to list of symbols
dots <- lapply(grp_cols3, as.symbol)
res = df %>% group_by(.dots=dots) %>%
do(k=all(letters=='a')) #(returns all groups as `FALSE`)
Data:
dput(df)
structure(list(Stage = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Developing",
"Embryonated", "Hatching", "Laid"), class = "factor"), variable = structure(c(1L,
5L, 5L, 5L, 2L, 2L, 2L, 4L, 4L, 4L, 6L, 6L, 6L, 3L, 3L, 3L, 1L,
1L, 1L, 5L, 5L), .Label = c("Moisture", "Protein", "Lipid", "Ash",
"NFE", "Energy"), class = "factor"), Temperature = c("30", "20",
"25", "30", "20", "25", "30", "20", "25", "30", "20", "25", "30",
"20", "25", "30", "20", "25", "30", "20", "25"), letters = c("a",
"a", "a", "a", "ab", "b", "a", "a", "a", "a", "a", "a", "a",
"b", "a", "ab", "a", "a", "a", "a", "a"), Mean = c(808.708818349727,
53.2880626188374, 45.3857220182952, 84.5611267892406, 118.536080769588,
127.298486932385, 84.5517498179938, 16.9534468121571, 14.5497954869813,
13.3850951354759, 4931.18857123979, 4187.27213494545, 4314.61171127083,
26.4436265667305, 19.9092762683653, 22.2756088142943, 785.630624024365,
818.698598619779, 815.320702070777, 60.3435858953567, 43.0297881562102
)), .Names = c("Stage", "variable", "Temperature", "letters",
"Mean"), row.names = 30:50, class = "data.frame")
224
To check for equality of three columns by row, we can use logical comparison of equality with double equal sign (==) and & operator.
Group By Count in R using dplyr You can use group_by() function along with the summarise() from dplyr package to find the group by count in R DataFrame, group_by() returns the grouped_df ( A grouped Data Frame) and use summarise() on grouped df to get the group by count.
The group_by() function in R is from dplyr package that is used to group rows by column values in the DataFrame, It is similar to GROUP BY clause in SQL. R dplyr groupby is used to collect identical data into groups on DataFrame and perform aggregate functions on the grouped data.
Similarly to readr , dplyr and tidyr are also part of the tidyverse. These packages were loaded in R's memory when we called library(tidyverse) earlier.
Split the data by each group, look for the n_distinct values, then replace with '' where this is the case:
df %>%
group_by(Stage,variable) %>%
mutate(letters = replace(letters, n_distinct(letters)==1, '') )
Similar logic works in data.table too:
library(data.table)
setDT(df)
df[, letters := if(uniqueN(letters)==1) '' else letters, by=.(Stage,variable)]
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