Move values in a data frame from one column to another based on matching criteria - json

Question

I am receiving output from a JSON object,however the JSON returns three fields sometimes two somtimes one, depending in the input. As a result I have a dataframe which looks like this:
mixed score type
1 1 0.0183232 positive
2 neutral <NA> <NA>
3 -0.566558 negative <NA>
4 0.473484 positive <NA>
5 0.856743 positive <NA>
6 -0.422655 negative <NA>
Mixed can take values of 1 or 0
Score can take a positive or negative value between -1 and +1
Type can take a value of either positive, negative or neutral
I'm wondering how I can rearrange the values in the data.frame so that they are in the correct column i.e.
mixed score type
1 1 0.018323 positive
2 <NA> <NA> neutral
3 <NA> -0.566558 negative
4 <NA> 0.473484 positive
5 <NA> 0.856743 positive
6 <NA> -0.422655 negative

Answer 1

Not an elegant solution at all, but the best I could come up with.
### Seeds initial Dataframe
mixed = c("1", "neutral", "0.473484", "-0.566558", "0.856743", "-0.422655", "-0.692675")
score = c("0.0183232", "0", "positive", "negative", "positive", "negative", "negative")
type = c("positive", "0", "0", "0", "0", "0", "0")
df = data.frame(mixed, score, type)
# Create a new DF (3 cols by nrow ize) for output
df <- as.data.frame(matrix(0, ncol = 3, nrow = i))
setnames(df, old=c("V1","V2", "V3"), new=c("mixed", "score", "type"))
df
# Create a 2nd new DF (3 cols by nrow ize) for output
df.2 <- as.data.frame(matrix(0, ncol = 3, nrow = i))
setnames(df.2, old=c("V1","V2", "V3"), new=c("mixed", "score", "type"))
df.2
#Check each column cell by cell if it does copy it do the shadow dataframe
# Set all <NA> values to Null
df[is.na(df)] <- 0
# Set interation length to column length
l <- 51
# Checked the mixed column for '1' and then copy it to the new frame
for(l in 1:l)
if (df$mixed[l] == '1')
{
df.2$mixed[l] <-df$mixed[l]
}
# Checked the mixed column for a value which is less than 1 and then copy it to the score column in the new frame
for(l in 1:l)
if (df$mixed[l] < '1')
{
df.2$score[l] <-df$mixed[l]
}
# Checked the mixed column for positive/negative/neutral and then copy it to the type column in the new frame
for(l in 1:l)
if (df$mixed[l] == "positive" | df$mixed[l] == "negative" | df$mixed[l] == "neutral")
{
df.2$type[l] <-df$mixed[l]
}
# Checked the score column for '1' and then copy it to mixed column in the new frame
for(l in 1:l)
if (df$score[l] == '1')
{
df.2$mixed[l] <-df$score[l]
}
# Checked the score column for a value which is less than 1 and then copy it to the score column in the new frame
for(l in 1:l)
if (df$score[l] < '1')
{
df.2$score[l] <-df$score[l]
}
# Checked the score column for positive/negative/neutral and then copy it to the type column in the new frame
for(l in 1:l)
if (df$score[l] == "positive" | df$score[l] == "negative" | df$score[l] == "neutral")
{
df.2$type[l] <-df$score[l]
}
# Checked the type column for '1' and then copy it to mixed column in the new frame **This one works***
for(l in 1:l)
if (df$type[l] == '1')
{
df.2$mixed[l] <-df$type[l]
}
# Checked the type column for a value which is less than 1 and then copy it to the score column in the new frame ** this one is erasing data in the new frame**
for(l in 1:l)
if (df$type[l] < '1')
{
df.2$score[l] <-df$type[l]
}
# Checked the type column for positive/negative/neutral and then copy it to the type column in the new frame **This one works***
for(l in 1:l)
if (df$type[l] == "positive" | df$type[l] == "negative" | df$type[l] == "neutral")
{
df.2$type[l] <-df$type[l]
}