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Stefano Sofia

2016-Sep-07 11:09 UTC

[R] how to manage missing values correctly when importing a data frame

Dear R users,
I have a data frame with 22 columns, called Storia_RM_RT. Here the first 4 rows:

Station_RM Sensor_RM Place_RM Y_init_RM M_init_RM D_init_RM Long_cent_RM
Lat_cent_RM Height_RM Continues Station_RT Sensor_RT Place_RT Name1_RT Name2_RT
Long_cent_RT Lat_cent_RT Height_RT Actual_net Notes Test_20141231 Test_20151231
1400 2701 Novafeltria 1959 1 1 12.289552 43.890057 293 NO NA NA NA NA NA NA NA
NA CAE NA NO NO
1460 2702 Carpegna 1963 1 1 12.332614 43.778107 748 SI 702 2954 Carpegna
Carpegna Carpegna 12.340618 43.780575 715 RT NA NO NO
1500 2703 Pesaro 1957 1 1 12.909822 43.910889 11 SI 112 1229 Pesaro
Villa_Fastiggi Villa_Fastiggi 12.86939 43.890610 22 RT NA YES YES
1520 2704 Fano 1957 1 1 13.017591 43.840054 4 SI 152 2671 Fano Foce_Metauro
Metaurilia 13.053796 43.826328 7.12 RT NA YES YES

I load it with
Storia_RM_RT <- read.table(file="Storia_RM_RT.txt", header = TRUE,
sep=" ", dec = ".", stringsAsFactors = FALSE)

print(Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RM == 1500]) gives
[1] "YES"

while
print(Storia_RM_RT$Omogenea_20151231[Storia_RM_RT$Station_RT == 112]) gives
[1] NA   "YES"


print(lapply(Storia_RM_RT, class)) gives

$Station_RM
[1] "integer"

$Sensor_RM
[1] "integer"

$Place_RM
[1] "character"

$Y_init_RM
[1] "integer"

$M_init_RM
[1] "integer"

$D_init_RM
[1] "integer"

$Long_cent_RM
[1] "numeric"

$Lat_cent_RM
[1] "numeric"

$Height_RM
[1] "integer"

$Continues
[1] "character"

$Station_RT
[1] "integer"

$Sensor_RT
[1] "integer"

$Place_RT
[1] "character"

$Name1_RT
[1] "character"

$Name2_RT
[1] "character"

$Long_cent_RT
[1] "numeric"

$Lat_cent_RT
[1] "numeric"
$Quota_RT
[1] "numeric"

$Actual_net
[1] "character"

$Notes
[1] "logical"

$Test_20141231
[1] "character"

$Test_20151231
[1] "character"

I am struggling to understand why the query through the field Station_RT does
not work.
Could please somebody help me to manage correctly the missing values? Is the
mistake somewhere else?

Thank you
Stefano Sofia


________________________________

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Sarah Goslee

2016-Sep-07 13:11 UTC

head link

[R] how to manage missing values correctly when importing a data frame

R is refusing to make unwarranted assumptions about your data.

See inline.


# it's nicer to use dput() instead of pasting raw data

Storia_RM_RT <- structure(list(Station_RM = c(1400L, 1460L, 1500L,
1520L), Sensor_RM = 2701:2704,
    Place_RM = c("Novafeltria", "Carpegna",
"Pesaro", "Fano"),
    Y_init_RM = c(1959L, 1963L, 1957L, 1957L), M_init_RM = c(1L,
    1L, 1L, 1L), D_init_RM = c(1L, 1L, 1L, 1L), Long_cent_RM = c(12.289552,
    12.332614, 12.909822, 13.017591), Lat_cent_RM = c(43.890057,
    43.778107, 43.910889, 43.840054), Height_RM = c(293L, 748L,
    11L, 4L), Continues = c("NO", "SI", "SI",
"SI"), Station_RT = c(NA,
    702L, 112L, 152L), Sensor_RT = c(NA, 2954L, 1229L, 2671L),
    Place_RT = c(NA, "Carpegna", "Pesaro",
"Fano"), Name1_RT = c(NA,
    "Carpegna", "Villa_Fastiggi", "Foce_Metauro"),
Name2_RT = c(NA,
    "Carpegna", "Villa_Fastiggi", "Metaurilia"),
Long_cent_RT = c(NA,
    12.340618, 12.86939, 13.053796), Lat_cent_RT = c(NA, 43.780575,
    43.89061, 43.826328), Height_RT = c(NA, 715, 22, 7.12), Actual_net
= c("CAE",
    "RT", "RT", "RT"), Notes = c(NA, NA, NA, NA),
Test_20141231 = c("NO",
    "NO", "YES", "YES"), Test_20151231 =
c("NO", "NO", "YES",
    "YES")), .Names = c("Station_RM", "Sensor_RM",
"Place_RM",
"Y_init_RM", "M_init_RM", "D_init_RM",
"Long_cent_RM", "Lat_cent_RM",
"Height_RM", "Continues", "Station_RT",
"Sensor_RT", "Place_RT",
"Name1_RT", "Name2_RT", "Long_cent_RT",
"Lat_cent_RT", "Height_RT",
"Actual_net", "Notes", "Test_20141231",
"Test_20151231"), class "data.frame", row.names = c(NA,
-4L))

> Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RM == 1500][1] "YES"

# Storia_RM_RT$Omogenea_20151231[Storia_RM_RT$Station_RT == 112]
# there's no such column; you probably mean Test_20151231
> Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RT == 112][1] NA    "YES"

# What do you expect to have happen when Station_RT is NA? R has no idea
# whether it is 112 or not, so R returns an "I don't know" value
that
# lets the user decide how to handle the missing data, rather than making
# assumptions.

# But you probably want one of these constructions:

Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RT == 112 &
!is.na(Storia_RM_RT$Station_RT)]

# subset automatically handles NAs, making the assumption I'm assuming you
want.
subset(Storia_RM_RT, Station_RT == 112 )$Test_20151231

# This is the first form, somewhat more elegantly
with(Storia_RM_RT, Test_20151231[Station_RT == 112 & !is.na(Station_RT)])

On Wed, Sep 7, 2016 at 7:09 AM, Stefano Sofia
<stefano.sofia at regione.marche.it> wrote:> Dear R users,
> I have a data frame with 22 columns, called Storia_RM_RT. Here the first 4
rows:
>
> Station_RM Sensor_RM Place_RM Y_init_RM M_init_RM D_init_RM Long_cent_RM
Lat_cent_RM Height_RM Continues Station_RT Sensor_RT Place_RT Name1_RT Name2_RT
Long_cent_RT Lat_cent_RT Height_RT Actual_net Notes Test_20141231 Test_20151231
> 1400 2701 Novafeltria 1959 1 1 12.289552 43.890057 293 NO NA NA NA NA NA NA
NA NA CAE NA NO NO
> 1460 2702 Carpegna 1963 1 1 12.332614 43.778107 748 SI 702 2954 Carpegna
Carpegna Carpegna 12.340618 43.780575 715 RT NA NO NO
> 1500 2703 Pesaro 1957 1 1 12.909822 43.910889 11 SI 112 1229 Pesaro
Villa_Fastiggi Villa_Fastiggi 12.86939 43.890610 22 RT NA YES YES
> 1520 2704 Fano 1957 1 1 13.017591 43.840054 4 SI 152 2671 Fano Foce_Metauro
Metaurilia 13.053796 43.826328 7.12 RT NA YES YES
>
> I load it with
> Storia_RM_RT <- read.table(file="Storia_RM_RT.txt", header =
TRUE, sep=" ", dec = ".", stringsAsFactors = FALSE)
>
> print(Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RM == 1500]) gives
> [1] "YES"
>
> while
> print(Storia_RM_RT$Omogenea_20151231[Storia_RM_RT$Station_RT == 112]) gives
> [1] NA   "YES"
>
>
> print(lapply(Storia_RM_RT, class)) gives
>
> $Station_RM
> [1] "integer"
>
> $Sensor_RM
> [1] "integer"
>
> $Place_RM
> [1] "character"
>
> $Y_init_RM
> [1] "integer"
>
> $M_init_RM
> [1] "integer"
>
> $D_init_RM
> [1] "integer"
>
> $Long_cent_RM
> [1] "numeric"
>
> $Lat_cent_RM
> [1] "numeric"
>
> $Height_RM
> [1] "integer"
>
> $Continues
> [1] "character"
>
> $Station_RT
> [1] "integer"
>
> $Sensor_RT
> [1] "integer"
>
> $Place_RT
> [1] "character"
>
> $Name1_RT
> [1] "character"
>
> $Name2_RT
> [1] "character"
>
> $Long_cent_RT
> [1] "numeric"
>
> $Lat_cent_RT
> [1] "numeric"
> $Quota_RT
> [1] "numeric"
>
> $Actual_net
> [1] "character"
>
> $Notes
> [1] "logical"
>
> $Test_20141231
> [1] "character"
>
> $Test_20151231
> [1] "character"
>
> I am struggling to understand why the query through the field Station_RT
does not work.
> Could please somebody help me to manage correctly the missing values? Is
the mistake somewhere else?
>
> Thank you
> Stefano Sofia
>
>
-- 
Sarah Goslee
http://www.functionaldiversity.org

PIKAL Petr

2016-Sep-07 13:49 UTC

head link

[R] how to manage missing values correctly when importing a data frame

Hi

Although you did not present your data in suitable format I do not see any
problem.

See in line
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Stefano
> Sofia
> Sent: Wednesday, September 7, 2016 1:09 PM
> To: r-help at r-project.org
> Subject: [R] how to manage missing values correctly when importing a data
> frame
>
> Dear R users,
> I have a data frame with 22 columns, called Storia_RM_RT. Here the first 4
> rows:
>
> Station_RM Sensor_RM Place_RM Y_init_RM M_init_RM D_init_RM
> Long_cent_RM Lat_cent_RM Height_RM Continues Station_RT Sensor_RT
> Place_RT Name1_RT Name2_RT Long_cent_RT Lat_cent_RT Height_RT
> Actual_net Notes Test_20141231 Test_20151231
> 1400 2701 Novafeltria 1959 1 1 12.289552 43.890057 293 NO NA NA NA NA NA
> NA NA NA CAE NA NO NO
> 1460 2702 Carpegna 1963 1 1 12.332614 43.778107 748 SI 702 2954 Carpegna
> Carpegna Carpegna 12.340618 43.780575 715 RT NA NO NO
> 1500 2703 Pesaro 1957 1 1 12.909822 43.910889 11 SI 112 1229 Pesaro
> Villa_Fastiggi Villa_Fastiggi 12.86939 43.890610 22 RT NA YES YES
> 1520 2704 Fano 1957 1 1 13.017591 43.840054 4 SI 152 2671 Fano
> Foce_Metauro Metaurilia 13.053796 43.826328 7.12 RT NA YES YES
>
> I load it with
> Storia_RM_RT <- read.table(file="Storia_RM_RT.txt", header =
TRUE, sep="
> ", dec = ".", stringsAsFactors = FALSE)
>
> print(Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RM == 1500])
> gives [1] "YES"
So you have unique value here
>
> while
> print(Storia_RM_RT$Omogenea_20151231[Storia_RM_RT$Station_RT => 112])
gives
> [1] NA   "YES"
and two values here, one is NA

see

dat<-data.frame(x1=c("a", "a", "b", NA),
x2=c(1,2,3,3))
dat$x1<-as.character(dat$x1)
> dat$x1[dat$x2==1]
[1] "a"> dat$x1[dat$x2==2]
[1] "a"> dat$x1[dat$x2==3]
[1] "b" NA>
You should consult R intro to understand basic object types, their distinctions
and language basics.

Cheers
Petr

>
>
> print(lapply(Storia_RM_RT, class)) gives
>
> $Station_RM
> [1] "integer"
>
> $Sensor_RM
> [1] "integer"
>
> $Place_RM
> [1] "character"
>
> $Y_init_RM
> [1] "integer"
>
> $M_init_RM
> [1] "integer"
>
> $D_init_RM
> [1] "integer"
>
> $Long_cent_RM
> [1] "numeric"
>
> $Lat_cent_RM
> [1] "numeric"
>
> $Height_RM
> [1] "integer"
>
> $Continues
> [1] "character"
>
> $Station_RT
> [1] "integer"
>
> $Sensor_RT
> [1] "integer"
>
> $Place_RT
> [1] "character"
>
> $Name1_RT
> [1] "character"
>
> $Name2_RT
> [1] "character"
>
> $Long_cent_RT
> [1] "numeric"
>
> $Lat_cent_RT
> [1] "numeric"
> $Quota_RT
> [1] "numeric"
>
> $Actual_net
> [1] "character"
>
> $Notes
> [1] "logical"
>
> $Test_20141231
> [1] "character"
>
> $Test_20151231
> [1] "character"
>
> I am struggling to understand why the query through the field Station_RT
> does not work.
> Could please somebody help me to manage correctly the missing values? Is
> the mistake somewhere else?
>
> Thank you
> Stefano Sofia
>
>
> ________________________________
>
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> informazioni confidenziali, pertanto ? destinato solo a persone autorizzate
> alla ricezione. I messaggi di posta elettronica per i client di Regione
Marche
> possono contenere informazioni confidenziali e con privilegi legali. Se non
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> il destinatario specificato, non leggere, copiare, inoltrare o archiviare
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> mittente ed eliminarlo completamente dal sistema del proprio computer. Ai
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Stefano Sofia

2016-Sep-07 14:26 UTC

head link

[R] how to manage missing values correctly when importing a data frame

Thank you for your explanations, and your patience.
With all the humbleness that I can have, I am not a beginner in R. Said that I
am really sorry if my question shows a big lack in understanding some basic
object types and their distinctions.

I still find difficult to understand your comments (which are obviously
correct), and I beg your pardon if I keep asking you the same question.
In my query to the data frame, Station_RT is exactly 112, and there is only one
row where Station_RT is equal to 112. I would expect a unique value for
Test_20151231.
Why R should expect to handle the possibility of having Station_RT = NA?

# > Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RT == 112]
# What do you expect to have happen when Station_RT is NA? R has no idea
# whether it is 112 or not, so R returns an "I don't know" value
that
# lets the user decide how to handle the missing data, rather than making
# assumptions.

Again, sorry for my question
Stefano

________________________________________
Da: Sarah Goslee [sarah.goslee at gmail.com]
Inviato: mercoled? 7 settembre 2016 15.11
A: Stefano Sofia
Cc: r-help at r-project.org
Oggetto: Re: [R] how to manage missing values correctly when importing a data
frame

R is refusing to make unwarranted assumptions about your data.

See inline.

# it's nicer to use dput() instead of pasting raw data

Storia_RM_RT <- structure(list(Station_RM = c(1400L, 1460L, 1500L,
1520L), Sensor_RM = 2701:2704,
    Place_RM = c("Novafeltria", "Carpegna",
"Pesaro", "Fano"),
    Y_init_RM = c(1959L, 1963L, 1957L, 1957L), M_init_RM = c(1L,
    1L, 1L, 1L), D_init_RM = c(1L, 1L, 1L, 1L), Long_cent_RM = c(12.289552,
    12.332614, 12.909822, 13.017591), Lat_cent_RM = c(43.890057,
    43.778107, 43.910889, 43.840054), Height_RM = c(293L, 748L,
    11L, 4L), Continues = c("NO", "SI", "SI",
"SI"), Station_RT = c(NA,
    702L, 112L, 152L), Sensor_RT = c(NA, 2954L, 1229L, 2671L),
    Place_RT = c(NA, "Carpegna", "Pesaro",
"Fano"), Name1_RT = c(NA,
    "Carpegna", "Villa_Fastiggi", "Foce_Metauro"),
Name2_RT = c(NA,
    "Carpegna", "Villa_Fastiggi", "Metaurilia"),
Long_cent_RT = c(NA,
    12.340618, 12.86939, 13.053796), Lat_cent_RT = c(NA, 43.780575,
    43.89061, 43.826328), Height_RT = c(NA, 715, 22, 7.12), Actual_net
= c("CAE",
    "RT", "RT", "RT"), Notes = c(NA, NA, NA, NA),
Test_20141231 = c("NO",
    "NO", "YES", "YES"), Test_20151231 =
c("NO", "NO", "YES",
    "YES")), .Names = c("Station_RM", "Sensor_RM",
"Place_RM",
"Y_init_RM", "M_init_RM", "D_init_RM",
"Long_cent_RM", "Lat_cent_RM",
"Height_RM", "Continues", "Station_RT",
"Sensor_RT", "Place_RT",
"Name1_RT", "Name2_RT", "Long_cent_RT",
"Lat_cent_RT", "Height_RT",
"Actual_net", "Notes", "Test_20141231",
"Test_20151231"), class "data.frame", row.names = c(NA,
-4L))

> Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RM == 1500][1] "YES"

# Storia_RM_RT$Omogenea_20151231[Storia_RM_RT$Station_RT == 112]
# there's no such column; you probably mean Test_20151231
> Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RT == 112][1] NA    "YES"

# What do you expect to have happen when Station_RT is NA? R has no idea
# whether it is 112 or not, so R returns an "I don't know" value
that
# lets the user decide how to handle the missing data, rather than making
# assumptions.

# But you probably want one of these constructions:

Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RT == 112 &
!is.na(Storia_RM_RT$Station_RT)]

# subset automatically handles NAs, making the assumption I'm assuming you
want.
subset(Storia_RM_RT, Station_RT == 112 )$Test_20151231

# This is the first form, somewhat more elegantly
with(Storia_RM_RT, Test_20151231[Station_RT == 112 & !is.na(Station_RT)])

On Wed, Sep 7, 2016 at 7:09 AM, Stefano Sofia
<stefano.sofia at regione.marche.it> wrote:> Dear R users,
> I have a data frame with 22 columns, called Storia_RM_RT. Here the first 4
rows:
>
> Station_RM Sensor_RM Place_RM Y_init_RM M_init_RM D_init_RM Long_cent_RM
Lat_cent_RM Height_RM Continues Station_RT Sensor_RT Place_RT Name1_RT Name2_RT
Long_cent_RT Lat_cent_RT Height_RT Actual_net Notes Test_20141231 Test_20151231
> 1400 2701 Novafeltria 1959 1 1 12.289552 43.890057 293 NO NA NA NA NA NA NA
NA NA CAE NA NO NO
> 1460 2702 Carpegna 1963 1 1 12.332614 43.778107 748 SI 702 2954 Carpegna
Carpegna Carpegna 12.340618 43.780575 715 RT NA NO NO
> 1500 2703 Pesaro 1957 1 1 12.909822 43.910889 11 SI 112 1229 Pesaro
Villa_Fastiggi Villa_Fastiggi 12.86939 43.890610 22 RT NA YES YES
> 1520 2704 Fano 1957 1 1 13.017591 43.840054 4 SI 152 2671 Fano Foce_Metauro
Metaurilia 13.053796 43.826328 7.12 RT NA YES YES
>
> I load it with
> Storia_RM_RT <- read.table(file="Storia_RM_RT.txt", header =
TRUE, sep=" ", dec = ".", stringsAsFactors = FALSE)
>
> print(Storia_RM_RT$Test_20151231[Storia_RM_RT$Station_RM == 1500]) gives
> [1] "YES"
>
> while
> print(Storia_RM_RT$Omogenea_20151231[Storia_RM_RT$Station_RT == 112]) gives
> [1] NA   "YES"
>
>
> print(lapply(Storia_RM_RT, class)) gives
>
> $Station_RM
> [1] "integer"
>
> $Sensor_RM
> [1] "integer"
>
> $Place_RM
> [1] "character"
>
> $Y_init_RM
> [1] "integer"
>
> $M_init_RM
> [1] "integer"
>
> $D_init_RM
> [1] "integer"
>
> $Long_cent_RM
> [1] "numeric"
>
> $Lat_cent_RM
> [1] "numeric"
>
> $Height_RM
> [1] "integer"
>
> $Continues
> [1] "character"
>
> $Station_RT
> [1] "integer"
>
> $Sensor_RT
> [1] "integer"
>
> $Place_RT
> [1] "character"
>
> $Name1_RT
> [1] "character"
>
> $Name2_RT
> [1] "character"
>
> $Long_cent_RT
> [1] "numeric"
>
> $Lat_cent_RT
> [1] "numeric"
> $Quota_RT
> [1] "numeric"
>
> $Actual_net
> [1] "character"
>
> $Notes
> [1] "logical"
>
> $Test_20141231
> [1] "character"
>
> $Test_20151231
> [1] "character"
>
> I am struggling to understand why the query through the field Station_RT
does not work.
> Could please somebody help me to manage correctly the missing values? Is
the mistake somewhere else?
>
> Thank you
> Stefano Sofia
>
>
--
Sarah Goslee
http://www.functionaldiversity.org

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R help - Sep 2016 - how to manage missing values correctly when importing a data frame

[R] how to manage missing values correctly when importing a data frame

[R] how to manage missing values correctly when importing a data frame

[R] how to manage missing values correctly when importing a data frame

[R] how to manage missing values correctly when importing a data frame