使用基于第三列的第二列中的值替换一列中的值

问题描述 投票：-1回答：2

所以我有一个2500个地方的列表，我尝试过地理编码。大约97不会地理编码。不久前，有人手工完成并对这些进行了地理编码。我已经用最旧的手工编码表离开了最新的地理编码地点列表。我想用手动地理编码的记录替换丢失/坏的地理编码。数据看起来像这样

NewLat OldLat Flag
29.019 39.213 1
41.23  41.23  0
NA     38.13  1
0.00   41.29  1

我喜欢它以便发生以下情况：

如果NewLat为NA或0.00，请将该值替换为OldLat。如果记录的标志为1，则替换为OldLat。

希望的结果是

  NewLat OldLat Flag
    39.213 39.213 1
    41.23  41.23  0
    38.13  38.13  1
    41.29  41.29  1

到目前为止我有

df$NewLat[is.na(df$NewLat)]<-df$OldLat

但第二部分是难倒我。我试过了

if("1"%in%df$Flag){df$NewLat=df$OldLat}

和elseif (df$Flag =1) {df$NewLat=df$OldLat}

和mutate(df, df$NewLat = ifelse(df$NewLat<1.0,df$OldLat,df$NewLat))

但似乎没有任何工作。

有什么建议？

编辑：在获得帮助之后，我主要使用它，除了经度仍然没有未突变的初始记录。这是代码

# Set the working directory
setwd("C:/Users/bwhite/Desktop/Geocode")

# read in the Newest CDOE data that was geocoded in MapMarker; change missing to NA, 2521 records
MM <-read.csv("CDOE_Schools_021919_GEOCODED.csv", stringsAsFactors = FALSE,na.strings = c("", "NA"))

# see how many rows are missing out_county; 97 this time around
sum(is.na(MM$Out_County))

# see how many rows have a "0" for lat and long. Should match the out_county
sum(MM$NewLat<1.000)
sum(is.na(MM$NewLat))
sum(MM$NewLong <1.000 & MM$NewLong >-99.00)
sum(is.na(MM$NewLong))

# see how many bad geocode flags there are but don't include NA's, there are 150
sum(MM$Bad_Geo,na.rm=TRUE)

# Create unique ID in MM
MM$Key<-paste(MM$SCHOOL_NAME,MM$PHYSICAL_ADDRESS)

# read in the previous CDOE OpenData CSV, 2481 records
OD <-read.csv("CDPHE_CDOE_School_Locations_and_District_Office_Locations.csv", stringsAsFactors = FALSE)

# Create unique ID
OD$Key<-paste(OD$School_Name,OD$Address)

# Inner Join to see how many records match, 2189 records match
InnerTest<-merge(x=MM,y=OD[,c("COUNTY","OldLong","OldLat","Key")], by="Key")

# Left Outer Join the OD County/Lat/Long columns to the MM by School Code
Joined<-merge(x=MM,y=OD[,c("COUNTY","OldLong","OldLat","Key")], by="Key", all.x = TRUE)

################################################
############# COUNTY CALCULATIONS ##############
################################################

#Fill in the county values from the MM table with Out_County field
Joined$County = Joined$Out_County

# Fill in the remaining missing county values from the OD table County field
Joined$County[is.na(Joined$County)] <- Joined$COUNTY[is.na(Joined$County)]

# See how many schools are now missing County variable. #18 new schools remain to be geocoded by hand
sum(is.na(Joined$County))


################################################
############# Lat/LONG CALCULATIONS ##############
################################################

#For all bad geocodes, or for 0.00 lats, or for NA lats, fill in lat with the previous OpenData Lat 
library(dplyr)
Joined %>% 
  mutate(NewLat=ifelse(is.na(NewLat)|NewLat<1.0 &Bad_Geo==1,OldLat,NewLat))


#For all bad geocodes, or for 0.00 longs, or for NA longs, fill in long with the previous OpenData long
Joined %>% 
  mutate(NewLong=ifelse(is.na(NewLong)|NewLong=0.00 &Bad_Geo==1,OldLong,NewLong))

# See how many lats are missing now ; now there are zero!
sum(is.na(Joined$NewLat))
sum(MM$Latitude==0)

# See how many longs are missing now; There are still 97
sum(is.na(Joined$NewLong))
sum(MM$NewLong==0)

# Remove extraneous columns
Joined$Key<-NULL


# Export to Excel
?write.csv
write.csv(Joined,file="CDOE_Schools_GEOCODED_CLEANED.csv")

这是dput(head(MM,15))的输出

structure(list(SCHOOL_CODE = c(2572L, 5828L, 5972L, 7296L, 8762L, 
10L, 11L, 12L, 14L, 15L, 16L, 17L, 18L, 19L, 20L), SCHOOL_NAME = c("LEGACY ACADEMY", 
"MESA VALLEY COMMUNITY SCHOOL", "MOLHOLM ELEMENTARY SCHOOL", 
"RED SANDSTONE ELEMENTARY SCHOOL", "CHRISTIAN COMMUNITY SCHOOLS", 
"ABRAHAM LINCOLN HIGH SCHOOL", "ACADEMY CHARTER SCHOOL", "ACRES GREEN ELEMENTARY SCHOOL", 
"GLACIER PEAK ELEMENTARY SCHOOL", "ACADEMY OF CHARTER SCHOOLS", 
"FOX HOLLOW ELEMENTARY SCHOOL", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL", 
"LIBERTY MIDDLE SCHOOL", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL", 
"ADAMS CITY MIDDLE SCHOOL"), PHYSICAL_ADDRESS = c("1975 LEGACY CIRCLE", 
"2387 PATTERSON RD", "6000 WEST 9TH AVENUE", "551 NORTH FRONTAGE ROAD", 
"3099 F ROAD", "2285 SOUTH FEDERAL BOULEVARD", "1551 PRAIRIE HAWK DRIVE", 
"13524 NORTH ACRES GREEN DRIVE", "12060 JASMINE STREET", "11800 LOWELL BLVD", 
"6363 SOUTH WACO STREET", "3475 HAMPTON PARK DRIVE", "21500 EAST DRY CREEK ROAD", 
"8550 CHARITY DRIVE", "4451 EAST 72ND AVENUE"), PHYSICAL_CITY = c("ELIZABETH", 
"GRAND JUNCTION", "LAKEWOOD", "VAIL", "GRAND JUNCTION", "DENVER", 
"CASTLE ROCK", "LITTLETON", "BRIGHTON", "WESTMINSTER", "AURORA", 
"COLORADO SPRINGS", "AURORA", "COLORADO SPRINGS", "COMMERCE CITY"
), PHISICAL_STATE = c("CO", "CO", "CO", "CO", "CO", "CO", "CO", 
"CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO"), PHYSICAL_ZIPCODE = c(80107L, 
81505L, 80214L, 81657L, 81504L, 80219L, 80104L, 80124L, 80605L, 
80031L, 80116L, 80920L, 80016L, 80920L, 80022L), PHYSICAL_ZIPCODE_4 = c(8330L, 
1219L, 2301L, 4062L, NA, 5433L, 7900L, 2701L, 4625L, 5097L, 1098L, 
4611L, 2086L, 7360L, 1405L), PHONE = c(3036462636, 9702547202, 
3039826207, 9703282910, 9704344619, 7204235000, 3036604881, 3033877125, 
7209725940, 3032898088, 7208868700, 7192345600, 7208862400, 7192344000, 
3032895881), LOWEST_GRADE = c("Kindergarten", "Kindergarten", 
"Preschool", "Preschool", "Preschool", "9th Grade", "Preschool", 
"Preschool", "Kindergarten", "Preschool", "Preschool", "Kindergarten", 
"6th Grade", "Preschool", "6th Grade"), HIGHEST_GRADE = c("8th Grade", 
"12th Grade", "6th Grade", "5th Grade", "Preschool", "12th Grade", 
"8th Grade", "6th Grade", "5th Grade", "12th Grade", "5th Grade", 
"5th Grade", "8th Grade", "5th Grade", "8th Grade"), ORGANIZATION_CODE = c(920L, 
2000L, 1420L, 910L, 2000L, 880L, 900L, 900L, 20L, 8001L, 130L, 
1040L, 130L, 1040L, 30L), District_Name = c("ELIZABETH SCHOOL DISTRICT", 
"MESA COUNTY VALLEY 51", "JEFFERSON COUNTY R-1", "EAGLE COUNTY RE 50", 
"MESA COUNTY VALLEY 51", "DENVER COUNTY 1", "DOUGLAS COUNTY RE 1", 
"DOUGLAS COUNTY RE 1", "ADAMS 12 FIVE STAR SCHOOLS", "CHARTER SCHOOL INSTITUTE", 
"CHERRY CREEK 5", "ACADEMY 20", "CHERRY CREEK 5", "ACADEMY 20", 
"ADAMS COUNTY 14"), District_Setting = c("Remote", "Urban-Suburban", 
"Denver Metro", "Outlying Town", NA, "Denver Metro", "Denver Metro", 
"Denver Metro", "Denver Metro", "Urban-Suburban", "Denver Metro", 
"Urban-Suburban", "Denver Metro", "Urban-Suburban", "Denver Metro"
), CHARTER = c("Y", "Y", "N", "N", NA, "N", "Y", "N", "N", "Y", 
"N", "N", "N", "N", "N"), Type = c("Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Non-Public School Mailing Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address "
), County = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), Out_County = c("ELBERT", "MESA", "JEFFERSON", "EAGLE", 
"MESA", "DENVER", "DOUGLAS", "DOUGLAS", "ADAMS", "ADAMS", "ARAPAHOE", 
"EL PASO", "ARAPAHOE", "EL PASO", "ADAMS"), Organization_Size = c(NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_), 
    MatchCode = c("S80", "S80", "S80", "S80", "S80", "S80", "S90", 
    "S82", "S90", "S80", "S90", "S80", "S80", "S80", "S80"), 
    LocationCode = c("AI0", "AI0", "AI0", "AI0", "AI0", "AS0", 
    "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0"
    ), NewLong = c(-104.627296, -108.537918, -105.11515, -106.389023, 
    -108.47805, -105.025124, -104.87014, -104.896454, -104.917328, 
    -105.034142, -104.780891, -104.761169, -104.735603, -104.764404, 
    -104.935112), NewLat = c(39.359467, 39.09177, 39.731579, 
    39.645741, 39.091736, 39.676849, 39.384583, 39.557961, 39.915554, 
    39.911575, 39.601196, 38.952129, 39.579823, 38.961929, 39.827293
    ), Bad_Geo = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_), Key = c("LEGACY ACADEMY 1975 LEGACY CIRCLE", 
    "MESA VALLEY COMMUNITY SCHOOL 2387 PATTERSON RD", "MOLHOLM ELEMENTARY SCHOOL 6000 WEST 9TH AVENUE", 
    "RED SANDSTONE ELEMENTARY SCHOOL 551 NORTH FRONTAGE ROAD", 
    "CHRISTIAN COMMUNITY SCHOOLS 3099 F ROAD", "ABRAHAM LINCOLN HIGH SCHOOL 2285 SOUTH FEDERAL BOULEVARD", 
    "ACADEMY CHARTER SCHOOL 1551 PRAIRIE HAWK DRIVE", "ACRES GREEN ELEMENTARY SCHOOL 13524 NORTH ACRES GREEN DRIVE", 
    "GLACIER PEAK ELEMENTARY SCHOOL 12060 JASMINE STREET", "ACADEMY OF CHARTER SCHOOLS 11800 LOWELL BLVD", 
    "FOX HOLLOW ELEMENTARY SCHOOL 6363 SOUTH WACO STREET", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL 3475 HAMPTON PARK DRIVE", 
    "LIBERTY MIDDLE SCHOOL 21500 EAST DRY CREEK ROAD", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL 8550 CHARITY DRIVE", 
    "ADAMS CITY MIDDLE SCHOOL 4451 EAST 72ND AVENUE")), .Names = c("SCHOOL_CODE", 
"SCHOOL_NAME", "PHYSICAL_ADDRESS", "PHYSICAL_CITY", "PHISICAL_STATE", 
"PHYSICAL_ZIPCODE", "PHYSICAL_ZIPCODE_4", "PHONE", "LOWEST_GRADE", 
"HIGHEST_GRADE", "ORGANIZATION_CODE", "District_Name", "District_Setting", 
"CHARTER", "Type", "County", "Out_County", "Organization_Size", 
"MatchCode", "LocationCode", "NewLong", "NewLat", "Bad_Geo", 
"Key"), row.names = c(NA, 15L), class = "data.frame")
> dput(head(MM,15))
structure(list(SCHOOL_CODE = c(2572L, 5828L, 5972L, 7296L, 8762L, 
10L, 11L, 12L, 14L, 15L, 16L, 17L, 18L, 19L, 20L), SCHOOL_NAME = c("LEGACY ACADEMY", 
"MESA VALLEY COMMUNITY SCHOOL", "MOLHOLM ELEMENTARY SCHOOL", 
"RED SANDSTONE ELEMENTARY SCHOOL", "CHRISTIAN COMMUNITY SCHOOLS", 
"ABRAHAM LINCOLN HIGH SCHOOL", "ACADEMY CHARTER SCHOOL", "ACRES GREEN ELEMENTARY SCHOOL", 
"GLACIER PEAK ELEMENTARY SCHOOL", "ACADEMY OF CHARTER SCHOOLS", 
"FOX HOLLOW ELEMENTARY SCHOOL", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL", 
"LIBERTY MIDDLE SCHOOL", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL", 
"ADAMS CITY MIDDLE SCHOOL"), PHYSICAL_ADDRESS = c("1975 LEGACY CIRCLE", 
"2387 PATTERSON RD", "6000 WEST 9TH AVENUE", "551 NORTH FRONTAGE ROAD", 
"3099 F ROAD", "2285 SOUTH FEDERAL BOULEVARD", "1551 PRAIRIE HAWK DRIVE", 
"13524 NORTH ACRES GREEN DRIVE", "12060 JASMINE STREET", "11800 LOWELL BLVD", 
"6363 SOUTH WACO STREET", "3475 HAMPTON PARK DRIVE", "21500 EAST DRY CREEK ROAD", 
"8550 CHARITY DRIVE", "4451 EAST 72ND AVENUE"), PHYSICAL_CITY = c("ELIZABETH", 
"GRAND JUNCTION", "LAKEWOOD", "VAIL", "GRAND JUNCTION", "DENVER", 
"CASTLE ROCK", "LITTLETON", "BRIGHTON", "WESTMINSTER", "AURORA", 
"COLORADO SPRINGS", "AURORA", "COLORADO SPRINGS", "COMMERCE CITY"
), PHISICAL_STATE = c("CO", "CO", "CO", "CO", "CO", "CO", "CO", 
"CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO"), PHYSICAL_ZIPCODE = c(80107L, 
81505L, 80214L, 81657L, 81504L, 80219L, 80104L, 80124L, 80605L, 
80031L, 80116L, 80920L, 80016L, 80920L, 80022L), PHYSICAL_ZIPCODE_4 = c(8330L, 
1219L, 2301L, 4062L, NA, 5433L, 7900L, 2701L, 4625L, 5097L, 1098L, 
4611L, 2086L, 7360L, 1405L), PHONE = c(3036462636, 9702547202, 
3039826207, 9703282910, 9704344619, 7204235000, 3036604881, 3033877125, 
7209725940, 3032898088, 7208868700, 7192345600, 7208862400, 7192344000, 
3032895881), LOWEST_GRADE = c("Kindergarten", "Kindergarten", 
"Preschool", "Preschool", "Preschool", "9th Grade", "Preschool", 
"Preschool", "Kindergarten", "Preschool", "Preschool", "Kindergarten", 
"6th Grade", "Preschool", "6th Grade"), HIGHEST_GRADE = c("8th Grade", 
"12th Grade", "6th Grade", "5th Grade", "Preschool", "12th Grade", 
"8th Grade", "6th Grade", "5th Grade", "12th Grade", "5th Grade", 
"5th Grade", "8th Grade", "5th Grade", "8th Grade"), ORGANIZATION_CODE = c(920L, 
2000L, 1420L, 910L, 2000L, 880L, 900L, 900L, 20L, 8001L, 130L, 
1040L, 130L, 1040L, 30L), District_Name = c("ELIZABETH SCHOOL DISTRICT", 
"MESA COUNTY VALLEY 51", "JEFFERSON COUNTY R-1", "EAGLE COUNTY RE 50", 
"MESA COUNTY VALLEY 51", "DENVER COUNTY 1", "DOUGLAS COUNTY RE 1", 
"DOUGLAS COUNTY RE 1", "ADAMS 12 FIVE STAR SCHOOLS", "CHARTER SCHOOL INSTITUTE", 
"CHERRY CREEK 5", "ACADEMY 20", "CHERRY CREEK 5", "ACADEMY 20", 
"ADAMS COUNTY 14"), District_Setting = c("Remote", "Urban-Suburban", 
"Denver Metro", "Outlying Town", NA, "Denver Metro", "Denver Metro", 
"Denver Metro", "Denver Metro", "Urban-Suburban", "Denver Metro", 
"Urban-Suburban", "Denver Metro", "Urban-Suburban", "Denver Metro"
), CHARTER = c("Y", "Y", "N", "N", NA, "N", "Y", "N", "N", "Y", 
"N", "N", "N", "N", "N"), Type = c("Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Non-Public School Mailing Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address "
), County = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), Out_County = c("ELBERT", "MESA", "JEFFERSON", "EAGLE", 
"MESA", "DENVER", "DOUGLAS", "DOUGLAS", "ADAMS", "ADAMS", "ARAPAHOE", 
"EL PASO", "ARAPAHOE", "EL PASO", "ADAMS"), Organization_Size = c(NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_), 
    MatchCode = c("S80", "S80", "S80", "S80", "S80", "S80", "S90", 
    "S82", "S90", "S80", "S90", "S80", "S80", "S80", "S80"), 
    LocationCode = c("AI0", "AI0", "AI0", "AI0", "AI0", "AS0", 
    "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0"
    ), NewLong = c(-104.627296, -108.537918, -105.11515, -106.389023, 
    -108.47805, -105.025124, -104.87014, -104.896454, -104.917328, 
    -105.034142, -104.780891, -104.761169, -104.735603, -104.764404, 
    -104.935112), NewLat = c(39.359467, 39.09177, 39.731579, 
    39.645741, 39.091736, 39.676849, 39.384583, 39.557961, 39.915554, 
    39.911575, 39.601196, 38.952129, 39.579823, 38.961929, 39.827293
    ), Bad_Geo = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_), Key = c("LEGACY ACADEMY 1975 LEGACY CIRCLE", 
    "MESA VALLEY COMMUNITY SCHOOL 2387 PATTERSON RD", "MOLHOLM ELEMENTARY SCHOOL 6000 WEST 9TH AVENUE", 
    "RED SANDSTONE ELEMENTARY SCHOOL 551 NORTH FRONTAGE ROAD", 
    "CHRISTIAN COMMUNITY SCHOOLS 3099 F ROAD", "ABRAHAM LINCOLN HIGH SCHOOL 2285 SOUTH FEDERAL BOULEVARD", 
    "ACADEMY CHARTER SCHOOL 1551 PRAIRIE HAWK DRIVE", "ACRES GREEN ELEMENTARY SCHOOL 13524 NORTH ACRES GREEN DRIVE", 
    "GLACIER PEAK ELEMENTARY SCHOOL 12060 JASMINE STREET", "ACADEMY OF CHARTER SCHOOLS 11800 LOWELL BLVD", 
    "FOX HOLLOW ELEMENTARY SCHOOL 6363 SOUTH WACO STREET", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL 3475 HAMPTON PARK DRIVE", 
    "LIBERTY MIDDLE SCHOOL 21500 EAST DRY CREEK ROAD", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL 8550 CHARITY DRIVE", 
    "ADAMS CITY MIDDLE SCHOOL 4451 EAST 72ND AVENUE")), .Names = c("SCHOOL_CODE", 
"SCHOOL_NAME", "PHYSICAL_ADDRESS", "PHYSICAL_CITY", "PHISICAL_STATE", 
"PHYSICAL_ZIPCODE", "PHYSICAL_ZIPCODE_4", "PHONE", "LOWEST_GRADE", 
"HIGHEST_GRADE", "ORGANIZATION_CODE", "District_Name", "District_Setting", 
"CHARTER", "Type", "County", "Out_County", "Organization_Size", 
"MatchCode", "LocationCode", "NewLong", "NewLat", "Bad_Geo", 
"Key"), row.names = c(NA, 15L), class = "data.frame")
r if-statement conditional
2个回答
0
投票

编辑在预期输出添加到前面后移动答案：

 df %>% 
      mutate(NewLat=ifelse(is.na(NewLat)|NewLat==0 |Flag==1,OldLat,NewLat))

      NewLat OldLat Flag
    1 39.213 39.213    1
    2 41.230 41.230    0
    3 38.130 38.130    1
    4 41.290 41.290    0

原版的：：

希望我理解逻辑：尝试：

library(dplyr)
df %>% 
  mutate(NewLat=ifelse(is.na(NewLat)|NewLat==0 &Flag==1,OldLat,NewLat))

结果：

   NewLat OldLat Flag
1 29.019 39.213    1
2 41.230 41.230    0
3 38.130 38.130    1
4  0.000 41.290    0

也许这个？

df %>% 
  mutate(NewLat=ifelse(is.na(NewLat)|NewLat==0 |Flag==1,OldLat,NewLat))

  NewLat OldLat Flag
1 39.213 39.213    1
2 41.230 41.230    0
3 38.130 38.130    1
4 41.290 41.290    0
0
投票

您没有提供预期的输出，因此不确定这是否正确，但我的理解是您希望替换三行。您还可以使用dplyr::if_else而不是base::ifelse来获得更快，类型稳定的输出。

library(tidyverse)
tbl <- read_table2(
"NewLat OldLat Flag
29.019 39.213 1
41.23  41.23  0
NA     38.13  1
0.00   41.29  0"
)
tbl %>%
  mutate(NewLat = if_else(is.na(NewLat) | NewLat == 0 | Flag == 1, OldLat, NewLat))
#> # A tibble: 4 x 3
#>   NewLat OldLat  Flag
#>    <dbl>  <dbl> <dbl>
#> 1   39.2   39.2     1
#> 2   41.2   41.2     0
#> 3   38.1   38.1     1
#> 4   41.3   41.3     0

由reprex package创建于2019-02-20（v0.2.1）

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